diff --git a/.asf.yaml b/.asf.yaml
index 8f1d49fbb6f7..b3301eee6b5f 100644
--- a/.asf.yaml
+++ b/.asf.yaml
@@ -51,6 +51,10 @@ github:
 
   protected_branches:
     master: {}
+    release-2.68.0-postrelease: {}
+    release-2.68: {}
+    release-2.67.0-postrelease: {}
+    release-2.67: {}
     release-2.66.0-postrelease: {}
     release-2.66: {}
     release-2.65.0-postrelease: {}
diff --git a/.editorconfig b/.editorconfig
new file mode 100644
index 000000000000..7ee8fffb1ba8
--- /dev/null
+++ b/.editorconfig
@@ -0,0 +1,30 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# EditorConfig is awesome: https://EditorConfig.org
+
+# top-most EditorConfig file
+root = true
+
+[*]
+indent_style = space
+indent_size = 2
+end_of_line = lf
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = true
+
+[*.{go,mod,sum}]
+indent_style = tab
+indent_size = unset
+
+[Dockerfile]
+indent_size = 4
diff --git a/.gemini/config.yaml b/.gemini/config.yaml
index 70e33f3d77cd..9fe8d66315e2 100644
--- a/.gemini/config.yaml
+++ b/.gemini/config.yaml
@@ -46,7 +46,7 @@ code_review:
 
     # Post code review on PR open.
     # Type boolean, default: true.
-    code_review: true
+    code_review: false
 
 # List of glob patterns to ignore (files and directories).
 # Type: array of string, default: [].
diff --git a/.github/REVIEWERS.yml b/.github/REVIEWERS.yml
index bfeea361910f..4513391ff881 100644
--- a/.github/REVIEWERS.yml
+++ b/.github/REVIEWERS.yml
@@ -23,6 +23,7 @@ labels:
       - jrmccluskey
       - lostluck
       - shunping
+      - liferoad
     exclusionList: []
   - name: Python
     reviewers:
@@ -49,9 +50,6 @@ labels:
     reviewers:
       - igorbernstein2
       - mutianf
-      - djyau
-      - andre-sampaio
-      - meeral-k
     exclusionList: []
   - name: kafka
     reviewers:
@@ -63,6 +61,7 @@ labels:
     reviewers:
       - Abacn
       - damccorm
+      - liferoad
     exclusionList: []
   - name: website
     reviewers:
diff --git a/.github/actions/build-push-docker-action/action.yml b/.github/actions/build-push-docker-action/action.yml
new file mode 100644
index 000000000000..84c331f09a69
--- /dev/null
+++ b/.github/actions/build-push-docker-action/action.yml
@@ -0,0 +1,45 @@
+# This is a composite action to build and push a Docker image.
+name: 'Docker Build and Push'
+description: 'Builds and pushes a Docker image to a container registry.'
+
+inputs:
+  dockerfile_path:
+    description: 'Path to the Dockerfile'
+    required: true
+  image_name:
+    description: 'Base name for the Docker image (e.g., gcr.io/my-project/my-app)'
+    required: true
+  image_tag:
+    description: 'Tag for the Docker image (e.g., latest, or a git sha)'
+    required: true
+  build_context:
+    description: 'The build context for the Docker build command'
+    required: false
+    default: '.'
+
+outputs:
+  image_url:
+    description: "The full URL of the pushed image, including the tag"
+    value: ${{ steps.build-push.outputs.image_url }} # the value is set from a step's output
+
+runs:
+  using: "composite"
+  steps:
+    - name: Configure Docker to use Google Cloud credentials
+      shell: bash
+      run: gcloud auth configure-docker --quiet
+
+    - name: Build and Push Docker Image
+      id: build-push # give the step an ID to reference its output
+      shell: bash
+      run: |
+        # Construct the full image URL from the inputs
+        FULL_IMAGE_URL="${{ inputs.image_name }}:${{ inputs.image_tag }}"
+        echo "Building image: $FULL_IMAGE_URL"
+        
+        # Build the image
+        docker build -t $FULL_IMAGE_URL -f ${{ inputs.dockerfile_path }} ${{ inputs.build_context }}
+        # Push the image
+        docker push $FULL_IMAGE_URL
+        # Set the output value for this action
+        echo "image_url=$FULL_IMAGE_URL" >> $GITHUB_OUTPUT
\ No newline at end of file
diff --git a/.github/actions/dind-up-action/action.yml b/.github/actions/dind-up-action/action.yml
new file mode 100644
index 000000000000..23cc8613bb67
--- /dev/null
+++ b/.github/actions/dind-up-action/action.yml
@@ -0,0 +1,275 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+name: "Start and Prepare DinD"
+description: "Launch, verify, and prepare a Docker-in-Docker environment."
+inputs:
+  # --- Core DinD Config ---
+  container-name:
+    description: "Name for the DinD container."
+    default: dind-daemon
+  bind-address:
+    default: 127.0.0.1
+  port:
+    default: "2375"
+  storage-volume:
+    default: dind-storage
+  execroot-volume:
+    default: dind-execroot
+  ephemeral-volumes:
+    description: "Generate unique per-run volume names (recommended)."
+    default: "true"
+  auto-prune-dangling:
+    description: "Prune dangling ephemeral DinD volumes from previous runs."
+    default: "true"
+  tmpfs-run-size:
+    default: 1g
+  tmpfs-varrun-size:
+    default: 1g
+  storage-driver:
+    default: overlay2
+  additional-dockerd-args:
+    default: ""
+  use-host-network:
+    description: "Run DinD with --network host instead of publishing a TCP port."
+    default: "false"
+
+  # --- Health & Wait Config ---
+  health-interval:
+    default: 2s
+  health-retries:
+    default: "60"
+  health-start-period:
+    default: 10s
+  wait-timeout:
+    default: "180"
+
+  # --- NEW: Optional Setup & Verification Steps ---
+  cleanup-dind-on-start:
+    description: "Run 'docker system prune' inside DinD immediately after it starts."
+    default: "true"
+  smoke-test-port-mapping:
+    description: "Run a quick test to ensure port mapping from DinD is working."
+    default: "true"
+  prime-testcontainers:
+    description: "Start and stop a small container via the testcontainers library to prime Ryuk."
+    default: "false"
+
+  # --- Output Config ---
+  export-gh-env:
+    description: "Also write DOCKER_HOST and DIND_IP to $GITHUB_ENV for the rest of the job."
+    default: "false"
+
+outputs:
+  docker-host:
+    description: "The TCP address for the DinD daemon (e.g., tcp://127.0.0.1:2375)."
+    value: ${{ steps.set-output.outputs.docker-host }}
+  dind-ip:
+    description: "The discovered bridge IP address of the DinD container."
+    value: ${{ steps.discover-ip.outputs.dind-ip }}
+  container-name:
+    description: "The name of the running DinD container."
+    value: ${{ inputs.container-name || 'dind-daemon' }}
+  storage-volume:
+    value: ${{ steps.set-output.outputs.storage_volume }}
+  execroot-volume:
+    value: ${{ steps.set-output.outputs.execroot_volume }}
+
+runs:
+  using: "composite"
+  steps:
+    - name: Prune old dangling ephemeral DinD volumes
+      if: ${{ inputs.auto-prune-dangling == 'true' }}
+      shell: bash
+      run: |
+        docker volume ls -q \
+          --filter "label=com.github.dind=1" \
+          --filter "label=com.github.repo=${GITHUB_REPOSITORY}" \
+          --filter "dangling=true" | xargs -r docker volume rm || true
+
+    - name: Start docker:dind
+      shell: bash
+      run: |
+        # (Your original 'Start docker:dind' script is perfect here - no changes needed)
+        set -euo pipefail
+        NAME="${{ inputs.container-name || 'dind-daemon' }}"
+        BIND="${{ inputs.bind-address || '127.0.0.1' }}"
+        PORT="${{ inputs.port || '2375' }}"
+        SD="${{ inputs.storage-driver || 'overlay2' }}"
+        TRS="${{ inputs.tmpfs-run-size || '1g' }}"
+        TVRS="${{ inputs.tmpfs-varrun-size || '1g' }}"
+        HI="${{ inputs.health-interval || '2s' }}"
+        HR="${{ inputs.health-retries || '60' }}"
+        HSP="${{ inputs.health-start-period || '10s' }}"
+        EXTRA="${{ inputs.additional-dockerd-args }}"
+        USE_HOST_NET="${{ inputs.use-host-network || 'false' }}"
+
+        if [[ "${{ inputs.ephemeral-volumes }}" == "true" ]]; then
+          SUFFIX="${GITHUB_RUN_ID:-0}-${GITHUB_RUN_ATTEMPT:-0}-${GITHUB_JOB:-job}"
+          STORAGE_VOL="dind-storage-${SUFFIX}"
+          EXECROOT_VOL="dind-execroot-${SUFFIX}"
+        else
+          STORAGE_VOL="${{ inputs.storage-volume || 'dind-storage' }}"
+          EXECROOT_VOL="${{ inputs.execroot-volume || 'dind-execroot' }}"
+        fi
+
+        docker volume create --name "${STORAGE_VOL}" --label "com.github.dind=1" --label "com.github.repo=${GITHUB_REPOSITORY}" >/dev/null
+        docker volume create --name "${EXECROOT_VOL}" --label "com.github.dind=1" --label "com.github.repo=${GITHUB_REPOSITORY}" >/dev/null
+        docker rm -f -v "$NAME" 2>/dev/null || true
+
+        NET_ARGS=""
+        PUBLISH_ARGS="-p ${BIND}:${PORT}:${PORT}"
+        if [[ "${USE_HOST_NET}" == "true" ]]; then
+          NET_ARGS="--network host"
+          PUBLISH_ARGS=""
+        fi
+
+        docker run -d --privileged --name "$NAME" \
+          --cgroupns=host \
+          -e DOCKER_TLS_CERTDIR= \
+          ${NET_ARGS} \
+          ${PUBLISH_ARGS} \
+          -v "${STORAGE_VOL}:/var/lib/docker" \
+          -v "${EXECROOT_VOL}:/execroot" \
+          --tmpfs /run:rw,exec,size=${TRS} \
+          --tmpfs /var/run:rw,exec,size=${TVRS} \
+          --label "com.github.dind=1" \
+          --health-cmd='docker info > /dev/null' \
+          --health-interval=${HI} \
+          --health-retries=${HR} \
+          --health-start-period=${HSP} \
+          docker:dind \
+            --host=tcp://0.0.0.0:${PORT} \
+            --host=unix:///var/run/docker.sock \
+            --storage-driver=${SD} \
+            --exec-root=/execroot ${EXTRA}
+
+        {
+          echo "STORAGE_VOL=${STORAGE_VOL}"
+          echo "EXECROOT_VOL=${EXECROOT_VOL}"
+        } >> "$GITHUB_ENV"
+
+    - name: Wait for DinD daemon
+      shell: bash
+      run: |
+        set -euo pipefail
+        NAME="${{ inputs.container-name || 'dind-daemon' }}"
+        HOST="${{ inputs.bind-address || '127.0.0.1' }}"
+        PORT="${{ inputs.port || '2375' }}"
+        TIMEOUT="${{ inputs.wait-timeout || '180' }}"
+        echo "Waiting for Docker-in-Docker to be ready..."
+        if ! timeout ${TIMEOUT}s bash -c 'until docker -H "tcp://'"${HOST}"':'"${PORT}"'" info >/dev/null 2>&1; do sleep 2; done'; then
+          echo "::error::DinD failed to start within ${TIMEOUT}s."
+          docker logs "$NAME" || true
+          exit 1
+        fi
+        echo "DinD is ready."
+        docker -H "tcp://${HOST}:${PORT}" info --format 'Daemon OK → OS={{.OperatingSystem}} Version={{.ServerVersion}}'
+
+    - id: set-output
+      shell: bash
+      run: |
+        HOST="${{ inputs.bind-address || '127.0.0.1' }}"
+        PORT="${{ inputs.port || '2375' }}"
+        echo "docker-host=tcp://${HOST}:${PORT}" >> "$GITHUB_OUTPUT"
+        echo "storage_volume=${STORAGE_VOL:-}" >> "$GITHUB_OUTPUT"
+        echo "execroot_volume=${EXECROOT_VOL:-}" >> "$GITHUB_OUTPUT"
+
+    # --- NEW: Integrated Setup & Verification Steps ---
+
+    - name: Cleanup DinD Environment
+      if: ${{ inputs.cleanup-dind-on-start == 'true' }}
+      shell: bash
+      run: |
+        echo "Performing initial cleanup of DinD environment..."
+        DIND_HOST="${{ steps.set-output.outputs.docker-host }}"
+        docker -H "${DIND_HOST}" system prune -af --volumes
+        docker -H "${DIND_HOST}" image prune -af
+
+    - id: discover-ip
+      name: Discover DinD Container IP
+      shell: bash
+      run: |
+        set -euo pipefail
+        NAME="${{ inputs.container-name || 'dind-daemon' }}"
+        
+        # Use host daemon to inspect the DinD container
+        nm=$(docker inspect -f '{{.HostConfig.NetworkMode}}' "$NAME")
+        echo "DinD NetworkMode=${nm}"
+
+        # Try to find the bridge network IP
+        ip=$(docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' "$NAME" || true)
+        
+        # If still empty, likely host networking -> use loopback
+        if [[ -z "${ip}" || "${nm}" == "host" ]]; then
+          echo "No bridge IP found or using host network. Falling back to 127.0.0.1."
+          ip="127.0.0.1"
+        fi
+        
+        echo "Discovered DinD IP: ${ip}"
+        echo "dind-ip=${ip}" >> "$GITHUB_OUTPUT"
+
+    - name: Smoke Test Port Mapping
+      if: ${{ inputs.smoke-test-port-mapping == 'true' }}
+      env:
+        DOCKER_HOST: ${{ steps.set-output.outputs.docker-host }}
+        DIND_IP: ${{ steps.discover-ip.outputs.dind-ip }}
+      shell: bash
+      run: |
+        set -euo pipefail
+        echo "Running port mapping smoke test..."
+        docker pull redis:7.2-alpine
+        cid=$(docker run -d -p 0:6379 --name redis-smoke redis:7-alpine)
+        hostport=$(docker port redis-smoke 6379/tcp | sed 's/.*://')
+        echo "Redis container started, mapped to host port ${hostport}"
+        echo "Probing connection to ${DIND_IP}:${hostport} ..."
+        
+        timeout 5 bash -c 'exec 3<>/dev/tcp/$DIND_IP/'"$hostport"
+        if [[ $? -eq 0 ]]; then
+          echo "TCP connection successful. Port mapping is working."
+        else
+          echo "::error::Failed to connect to mapped port on ${DIND_IP}:${hostport}"
+          docker logs redis-smoke
+          exit 1
+        fi
+        docker rm -f "$cid"
+
+    - name: Prime Testcontainers (Ryuk)
+      if: ${{ inputs.prime-testcontainers == 'true' }}
+      env:
+        DOCKER_HOST: ${{ steps.set-output.outputs.docker-host }}
+        TESTCONTAINERS_HOST_OVERRIDE: ${{ steps.discover-ip.outputs.dind-ip }}
+      shell: bash
+      run: |
+        echo "Priming Testcontainers/Ryuk..."
+        python -m pip install -q --upgrade pip testcontainers
+        # Use a tiny image for a fast and stable prime
+        docker pull alpine:3.19
+        python - <<'PY'
+        from testcontainers.core.container import DockerContainer
+        c = DockerContainer("alpine:3.19").with_command("true")
+        c.start()
+        c.stop()
+        print("Ryuk primed and ready.")
+        PY
+
+    - name: Export Environment Variables
+      if: ${{ inputs.export-gh-env == 'true' }}
+      shell: bash
+      run: |
+        echo "DOCKER_HOST=${{ steps.set-output.outputs.docker-host }}" >> "$GITHUB_ENV"
+        echo "DIND_IP=${{ steps.discover-ip.outputs.dind-ip }}" >> "$GITHUB_ENV"
\ No newline at end of file
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index 248e8d6a69bf..e7a40726ed9b 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -46,7 +46,3 @@ updates:
     directory: "/"
     schedule:
       interval: "daily"
-    allow:
-      # Allow only automatic updates for official github actions
-      # Other github-actions require approval from INFRA
-      - dependency-name: "actions/*"
diff --git a/.github/trigger_files/IO_Iceberg_Integration_Tests_Dataflow.json b/.github/trigger_files/IO_Iceberg_Integration_Tests_Dataflow.json
index 8fab48cc672a..5abe02fc09c7 100644
--- a/.github/trigger_files/IO_Iceberg_Integration_Tests_Dataflow.json
+++ b/.github/trigger_files/IO_Iceberg_Integration_Tests_Dataflow.json
@@ -1,4 +1,4 @@
 {
   "comment": "Modify this file in a trivial way to cause this test suite to run.",
-  "modification": 5
+  "modification": 1
 }
diff --git a/.github/trigger_files/IO_Iceberg_Managed_Integration_Tests_Dataflow.json b/.github/trigger_files/IO_Iceberg_Managed_Integration_Tests_Dataflow.json
index 12481ae0dbc8..5abe02fc09c7 100644
--- a/.github/trigger_files/IO_Iceberg_Managed_Integration_Tests_Dataflow.json
+++ b/.github/trigger_files/IO_Iceberg_Managed_Integration_Tests_Dataflow.json
@@ -1,4 +1,4 @@
 {
   "comment": "Modify this file in a trivial way to cause this test suite to run.",
-  "modification": 4
+  "modification": 1
 }
diff --git a/.github/trigger_files/beam_PostCommit_Java_DataflowV1.json b/.github/trigger_files/beam_PostCommit_Java_DataflowV1.json
index ca1b701693f8..bba1872a33e8 100644
--- a/.github/trigger_files/beam_PostCommit_Java_DataflowV1.json
+++ b/.github/trigger_files/beam_PostCommit_Java_DataflowV1.json
@@ -1,4 +1,6 @@
 {
+  "https://github.com/apache/beam/pull/36138": "Cleanly separating v1 worker and v2 sdk harness container image handling",
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "https://github.com/apache/beam/pull/35177": "Introducing WindowedValueReceiver to runners",
     "comment": "Modify this file in a trivial way to cause this test suite to run",
     "modification": 1,
diff --git a/.github/trigger_files/beam_PostCommit_Java_DataflowV2.json b/.github/trigger_files/beam_PostCommit_Java_DataflowV2.json
index 3f4759213f78..78b2bdb93e2b 100644
--- a/.github/trigger_files/beam_PostCommit_Java_DataflowV2.json
+++ b/.github/trigger_files/beam_PostCommit_Java_DataflowV2.json
@@ -1,4 +1,6 @@
 {
+  "https://github.com/apache/beam/pull/36138": "Cleanly separating v1 worker and v2 sdk harness container image handling",
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "https://github.com/apache/beam/pull/35177": "Introducing WindowedValueReceiver to runners",
     "comment": "Modify this file in a trivial way to cause this test suite to run",
     "modification": 3,
diff --git a/.github/trigger_files/beam_PostCommit_Java_Examples_Dataflow_Java.json b/.github/trigger_files/beam_PostCommit_Java_Examples_Dataflow_Java.json
index 77f68d215005..cdc04bcd331a 100644
--- a/.github/trigger_files/beam_PostCommit_Java_Examples_Dataflow_Java.json
+++ b/.github/trigger_files/beam_PostCommit_Java_Examples_Dataflow_Java.json
@@ -1 +1,5 @@
-{"revision":  1}
\ No newline at end of file
+{
+  "https://github.com/apache/beam/pull/36138": "Cleanly separating v1 worker and v2 sdk harness container image handling",
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
+  "revision":  1
+}
diff --git a/.github/trigger_files/beam_PostCommit_Java_Examples_Dataflow_V2.json b/.github/trigger_files/beam_PostCommit_Java_Examples_Dataflow_V2.json
index b26833333238..ffdd1b908f46 100644
--- a/.github/trigger_files/beam_PostCommit_Java_Examples_Dataflow_V2.json
+++ b/.github/trigger_files/beam_PostCommit_Java_Examples_Dataflow_V2.json
@@ -1,4 +1,6 @@
 {
+  "https://github.com/apache/beam/pull/36138": "Cleanly separating v1 worker and v2 sdk harness container image handling",
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "comment": "Modify this file in a trivial way to cause this test suite to run",
   "modification": 2
 }
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow.json
index 85482285d1ae..2d05fc1b5d19 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow.json
@@ -1,4 +1,5 @@
 {
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "comment": "Modify this file in a trivial way to cause this test suite to run",
   "modification": 2,
   "https://github.com/apache/beam/pull/34294": "noting that PR #34294 should run this test",
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_JavaVersions.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_JavaVersions.json
index b970762c8397..e3d6056a5de9 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_JavaVersions.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_JavaVersions.json
@@ -1,4 +1,4 @@
 {
   "comment": "Modify this file in a trivial way to cause this test suite to run",
-  "https://github.com/apache/beam/pull/31156": "noting that PR #31156 should run this test"
+  "modification": 1
 }
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_Streaming.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_Streaming.json
index c695f7cb67b7..24fc17d4c74a 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_Streaming.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_Streaming.json
@@ -1,4 +1,5 @@
 {
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "comment": "Modify this file in a trivial way to cause this test suite to run",
   "https://github.com/apache/beam/pull/31156": "noting that PR #31156 should run this test",
   "https://github.com/apache/beam/pull/31268": "noting that PR #31268 should run this test",
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_V2.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_V2.json
index c695f7cb67b7..24fc17d4c74a 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_V2.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_V2.json
@@ -1,4 +1,5 @@
 {
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "comment": "Modify this file in a trivial way to cause this test suite to run",
   "https://github.com/apache/beam/pull/31156": "noting that PR #31156 should run this test",
   "https://github.com/apache/beam/pull/31268": "noting that PR #31268 should run this test",
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_V2_Streaming.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_V2_Streaming.json
index 96e098eb7f97..7dab8be7160a 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_V2_Streaming.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Dataflow_V2_Streaming.json
@@ -1,4 +1,5 @@
 {
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "comment": "Modify this file in a trivial way to cause this test suite to run",
   "https://github.com/apache/beam/pull/31156": "noting that PR #31156 should run this test",
   "https://github.com/apache/beam/pull/31268": "noting that PR #31268 should run this test",
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Direct.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Direct.json
index 42959ad85255..7e7462c0b059 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Direct.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Direct.json
@@ -1,4 +1,5 @@
 {
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "https://github.com/apache/beam/pull/35213": "Eliminating getPane() in favor of getPaneInfo()",
   "https://github.com/apache/beam/pull/35177": "Introducing WindowedValueReceiver to runners",
   "comment": "Modify this file in a trivial way to cause this test suite to run",
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Flink.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Flink.json
index 3ce625b167aa..afda4087adf8 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Flink.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Flink.json
@@ -1,4 +1,5 @@
 {
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "https://github.com/apache/beam/pull/35213": "Eliminating getPane() in favor of getPaneInfo()",
   "https://github.com/apache/beam/pull/35177": "Introducing WindowedValueReceiver to runners",
   "comment": "Modify this file in a trivial way to cause this test suite to run",
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Samza.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Samza.json
index 1fd497f4748d..db03186ab405 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Samza.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Samza.json
@@ -1,4 +1,5 @@
 {
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "https://github.com/apache/beam/pull/35177": "Introducing WindowedValueReceiver to runners",
   "comment": "Modify this file in a trivial way to cause this test suite to run",
   "https://github.com/apache/beam/pull/31156": "noting that PR #31156 should run this test",
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Spark.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Spark.json
index 09dc40d75c2b..f0c7c2ae3cfd 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Spark.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Spark.json
@@ -1,4 +1,5 @@
 {
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "https://github.com/apache/beam/pull/35213": "Eliminating getPane() in favor of getPaneInfo()",
   "https://github.com/apache/beam/pull/35177": "Introducing WindowedValueReceiver to runners",
   "comment": "Modify this file in a trivial way to cause this test suite to run",
@@ -11,5 +12,6 @@
   "https://github.com/apache/beam/pull/34080": "noting that PR #34080 should run this test",
   "https://github.com/apache/beam/pull/34155": "noting that PR #34155 should run this test",
   "https://github.com/apache/beam/pull/34560": "noting that PR #34560 should run this test",
-  "https://github.com/apache/beam/pull/35159": "moving WindowedValue and making an interface"
+  "https://github.com/apache/beam/pull/35159": "moving WindowedValue and making an interface",
+  "https://github.com/apache/beam/pull/35316": "noting that PR #35316 should run this test"
 }
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Spark_Java11.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Spark_Java11.json
index 3a01c4921572..4c0f8e51eca0 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Spark_Java11.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Spark_Java11.json
@@ -7,5 +7,6 @@
   "https://github.com/apache/beam/pull/33322": "noting that PR #33322 should run this test",
   "https://github.com/apache/beam/pull/34080": "noting that PR #34080 should run this test",
   "https://github.com/apache/beam/pull/34155": "noting that PR #34155 should run this test",
-  "https://github.com/apache/beam/pull/34560": "noting that PR #34560 should run this test"
+  "https://github.com/apache/beam/pull/34560": "noting that PR #34560 should run this test",
+  "https://github.com/apache/beam/pull/35316": "noting that PR #35316 should run this test"
 }
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Twister2.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Twister2.json
index b970762c8397..2ec5e41ecf4a 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Twister2.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_Twister2.json
@@ -1,4 +1,5 @@
 {
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "comment": "Modify this file in a trivial way to cause this test suite to run",
   "https://github.com/apache/beam/pull/31156": "noting that PR #31156 should run this test"
 }
diff --git a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_ULR.json b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_ULR.json
index 26d472693709..6e2f429dd24e 100644
--- a/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_ULR.json
+++ b/.github/trigger_files/beam_PostCommit_Java_ValidatesRunner_ULR.json
@@ -1,4 +1,5 @@
 {
+  "https://github.com/apache/beam/pull/34902": "Introducing OutputBuilder",
   "comment": "Modify this file in a trivial way to cause this test suite to run",
   "https://github.com/apache/beam/pull/31156": "noting that PR #31156 should run this test",
   "https://github.com/apache/beam/pull/35159": "moving WindowedValue and making an interface"
diff --git a/.github/trigger_files/beam_PostCommit_Python.json b/.github/trigger_files/beam_PostCommit_Python.json
index 2934a91b84b1..815b511b8988 100644
--- a/.github/trigger_files/beam_PostCommit_Python.json
+++ b/.github/trigger_files/beam_PostCommit_Python.json
@@ -1,5 +1,5 @@
 {
   "comment": "Modify this file in a trivial way to cause this test suite to run.",
-  "modification": 1
+  "modification": 30
 }
 
diff --git a/.github/trigger_files/beam_PostCommit_Python_Dependency.json b/.github/trigger_files/beam_PostCommit_Python_Dependency.json
index a7fc54b3e4bb..5b57011b2c2b 100644
--- a/.github/trigger_files/beam_PostCommit_Python_Dependency.json
+++ b/.github/trigger_files/beam_PostCommit_Python_Dependency.json
@@ -1,4 +1,4 @@
 {
     "comment": "Modify this file in a trivial way to cause this test suite to run",
-    "modification": 1
+    "modification": 2
   }
\ No newline at end of file
diff --git a/.github/trigger_files/beam_PostCommit_Python_Portable_Flink.json b/.github/trigger_files/beam_PostCommit_Python_Portable_Flink.json
new file mode 100644
index 000000000000..7fdef31c64c5
--- /dev/null
+++ b/.github/trigger_files/beam_PostCommit_Python_Portable_Flink.json
@@ -0,0 +1,3 @@
+{
+  "comment": "Trigger file for PostCommit Python Portable Flink tests"
+}
\ No newline at end of file
diff --git a/.github/trigger_files/beam_PostCommit_Python_ValidatesRunner_Dataflow.json b/.github/trigger_files/beam_PostCommit_Python_ValidatesRunner_Dataflow.json
new file mode 100644
index 000000000000..b26833333238
--- /dev/null
+++ b/.github/trigger_files/beam_PostCommit_Python_ValidatesRunner_Dataflow.json
@@ -0,0 +1,4 @@
+{
+  "comment": "Modify this file in a trivial way to cause this test suite to run",
+  "modification": 2
+}
diff --git a/.github/trigger_files/beam_PostCommit_Python_Xlang_Gcp_Dataflow.json b/.github/trigger_files/beam_PostCommit_Python_Xlang_Gcp_Dataflow.json
index 2504db607e46..95fef3e26ca2 100644
--- a/.github/trigger_files/beam_PostCommit_Python_Xlang_Gcp_Dataflow.json
+++ b/.github/trigger_files/beam_PostCommit_Python_Xlang_Gcp_Dataflow.json
@@ -1,4 +1,4 @@
 {
   "comment": "Modify this file in a trivial way to cause this test suite to run",
-  "modification": 12
+  "modification": 13
 }
diff --git a/.github/trigger_files/beam_PostCommit_Python_Xlang_Gcp_Direct.json b/.github/trigger_files/beam_PostCommit_Python_Xlang_Gcp_Direct.json
index 38ae1cf68222..2504db607e46 100644
--- a/.github/trigger_files/beam_PostCommit_Python_Xlang_Gcp_Direct.json
+++ b/.github/trigger_files/beam_PostCommit_Python_Xlang_Gcp_Direct.json
@@ -1,4 +1,4 @@
 {
   "comment": "Modify this file in a trivial way to cause this test suite to run",
-  "modification": 8
+  "modification": 12
 }
diff --git a/.github/trigger_files/beam_PostCommit_Python_Xlang_IO_Dataflow.json b/.github/trigger_files/beam_PostCommit_Python_Xlang_IO_Dataflow.json
index c537844dc84a..e0266d62f2e0 100644
--- a/.github/trigger_files/beam_PostCommit_Python_Xlang_IO_Dataflow.json
+++ b/.github/trigger_files/beam_PostCommit_Python_Xlang_IO_Dataflow.json
@@ -1,4 +1,4 @@
 {
   "comment": "Modify this file in a trivial way to cause this test suite to run",
-  "modification": 3
+  "modification": 4
 }
diff --git a/.github/trigger_files/beam_PostCommit_Python_Xlang_IO_Direct.json b/.github/trigger_files/beam_PostCommit_Python_Xlang_IO_Direct.json
index f1ba03a243ee..b26833333238 100644
--- a/.github/trigger_files/beam_PostCommit_Python_Xlang_IO_Direct.json
+++ b/.github/trigger_files/beam_PostCommit_Python_Xlang_IO_Direct.json
@@ -1,4 +1,4 @@
 {
   "comment": "Modify this file in a trivial way to cause this test suite to run",
-  "modification": 5
+  "modification": 2
 }
diff --git a/.github/trigger_files/beam_PostCommit_SQL.json b/.github/trigger_files/beam_PostCommit_SQL.json
index 833fd9b0d174..6cc79a7a0325 100644
--- a/.github/trigger_files/beam_PostCommit_SQL.json
+++ b/.github/trigger_files/beam_PostCommit_SQL.json
@@ -1,4 +1,4 @@
 {
   "comment": "Modify this file in a trivial way to cause this test suite to run ",
-  "modification": 2
+  "modification": 1
 }
diff --git a/.github/trigger_files/beam_PostCommit_XVR_Direct.json b/.github/trigger_files/beam_PostCommit_XVR_Direct.json
index bcb86e6ab5e7..73867c483554 100644
--- a/.github/trigger_files/beam_PostCommit_XVR_Direct.json
+++ b/.github/trigger_files/beam_PostCommit_XVR_Direct.json
@@ -1,4 +1,3 @@
 {
-  "https://github.com/apache/beam/pull/32648": "testing Flink 1.19 support",
-  "modification": 2
+  "modification": 5
 }
diff --git a/.github/trigger_files/beam_PostCommit_XVR_Flink.json b/.github/trigger_files/beam_PostCommit_XVR_Flink.json
index bb1b9f4c25e9..2d8ad3760b4b 100644
--- a/.github/trigger_files/beam_PostCommit_XVR_Flink.json
+++ b/.github/trigger_files/beam_PostCommit_XVR_Flink.json
@@ -1,4 +1,3 @@
 {
-  "https://github.com/apache/beam/pull/32440": "testing datastream optimizations",
-  "https://github.com/apache/beam/pull/32648": "testing addition of Flink 1.19 support"
+  "modification": 2
 }
diff --git a/.github/trigger_files/beam_PostCommit_XVR_GoUsingJava_Dataflow.json b/.github/trigger_files/beam_PostCommit_XVR_GoUsingJava_Dataflow.json
new file mode 100644
index 000000000000..920c8d132e4a
--- /dev/null
+++ b/.github/trigger_files/beam_PostCommit_XVR_GoUsingJava_Dataflow.json
@@ -0,0 +1,4 @@
+{
+  "comment": "Modify this file in a trivial way to cause this test suite to run",
+  "modification": 1
+}
\ No newline at end of file
diff --git a/.github/trigger_files/beam_PostCommit_Yaml_Xlang_Direct.json b/.github/trigger_files/beam_PostCommit_Yaml_Xlang_Direct.json
index 8b2c8c445c1f..b5704c67ef1c 100644
--- a/.github/trigger_files/beam_PostCommit_Yaml_Xlang_Direct.json
+++ b/.github/trigger_files/beam_PostCommit_Yaml_Xlang_Direct.json
@@ -1,4 +1,4 @@
 {
   "comment": "Modify this file in a trivial way to cause this test suite to run",
-  "revision": 5
+  "revision": 6
 }
diff --git a/.github/trigger_files/beam_PreCommit_SQL.json b/.github/trigger_files/beam_PreCommit_SQL.json
new file mode 100644
index 000000000000..5abe02fc09c7
--- /dev/null
+++ b/.github/trigger_files/beam_PreCommit_SQL.json
@@ -0,0 +1,4 @@
+{
+  "comment": "Modify this file in a trivial way to cause this test suite to run.",
+  "modification": 1
+}
diff --git a/.github/workflows/assign_milestone.yml b/.github/workflows/assign_milestone.yml
index b6d47bd1ac69..1f4ce3073ec2 100644
--- a/.github/workflows/assign_milestone.yml
+++ b/.github/workflows/assign_milestone.yml
@@ -35,7 +35,7 @@ jobs:
         with:
           fetch-depth: 2
 
-      - uses: actions/github-script@v7
+      - uses: actions/github-script@v8
         with:
           script: |
             const fs = require('fs')
diff --git a/.github/workflows/beam_CleanUpGCPResources.yml b/.github/workflows/beam_CleanUpGCPResources.yml
index 71ed805504c4..84c44451bae9 100644
--- a/.github/workflows/beam_CleanUpGCPResources.yml
+++ b/.github/workflows/beam_CleanUpGCPResources.yml
@@ -74,7 +74,7 @@ jobs:
         with:
           disable-cache: true
       - name: Setup gcloud
-        uses: google-github-actions/setup-gcloud@v2
+        uses: google-github-actions/setup-gcloud@v3
       - name: Install gcloud bigtable cli
         run: gcloud components install cbt
       - name: run cleanup GCP resources
diff --git a/.github/workflows/beam_IODatastoresCredentialsRotation.yml b/.github/workflows/beam_IODatastoresCredentialsRotation.yml
index d6b04afdebe3..ee6dcc123a91 100644
--- a/.github/workflows/beam_IODatastoresCredentialsRotation.yml
+++ b/.github/workflows/beam_IODatastoresCredentialsRotation.yml
@@ -82,17 +82,17 @@ jobs:
         run: |
           date=$(date -u +"%Y-%m-%d")
           echo "date=$date" >> $GITHUB_ENV
-      - name: Send email
-        uses: dawidd6/action-send-mail@v3
-        if: failure()
-        with:
-          server_address: smtp.gmail.com
-          server_port: 465
-          secure: true
-          username: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_ADDRESS }}
-          password: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_PASSWORD }}
-          subject: Credentials Rotation Failure on IO-Datastores cluster (${{ env.date }})
-          to: dev@beam.apache.org
-          from: gactions@beam.apache.org
-          body: |
-            Something went wrong during the automatic credentials rotation for IO-Datastores Cluster, performed at ${{ env.date }}. It may be necessary to check the state of the cluster certificates. For further details refer to the following links:\n * Failing job: https://github.com/apache/beam/actions/workflows/beam_IODatastoresCredentialsRotation.yml \n * Job configuration: https://github.com/apache/beam/blob/master/.github/workflows/beam_IODatastoresCredentialsRotation.yml \n * Cluster URL: https://pantheon.corp.google.com/kubernetes/clusters/details/us-central1-a/io-datastores/details?mods=dataflow_dev&project=apache-beam-testing
\ No newline at end of file
+#      - name: Send email
+#        uses: dawidd6/action-send-mail@v3
+#        if: failure()
+#        with:
+#          server_address: smtp.gmail.com
+#          server_port: 465
+#          secure: true
+#          username: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_ADDRESS }}
+#          password: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_PASSWORD }}
+#          subject: Credentials Rotation Failure on IO-Datastores cluster (${{ env.date }})
+#          to: dev@beam.apache.org
+#          from: gactions@beam.apache.org
+#          body: |
+#            Something went wrong during the automatic credentials rotation for IO-Datastores Cluster, performed at ${{ env.date }}. It may be necessary to check the state of the cluster certificates. For further details refer to the following links:\n * Failing job: https://github.com/apache/beam/actions/workflows/beam_IODatastoresCredentialsRotation.yml \n * Job configuration: https://github.com/apache/beam/blob/master/.github/workflows/beam_IODatastoresCredentialsRotation.yml \n * Cluster URL: https://pantheon.corp.google.com/kubernetes/clusters/details/us-central1-a/io-datastores/details?mods=dataflow_dev&project=apache-beam-testing
diff --git a/.github/workflows/beam_Inference_Python_Benchmarks_Dataflow.yml b/.github/workflows/beam_Inference_Python_Benchmarks_Dataflow.yml
index 6b60517a1899..ff7480c320af 100644
--- a/.github/workflows/beam_Inference_Python_Benchmarks_Dataflow.yml
+++ b/.github/workflows/beam_Inference_Python_Benchmarks_Dataflow.yml
@@ -55,7 +55,7 @@ jobs:
       (github.event_name == 'schedule' && github.repository == 'apache/beam') ||
       github.event.comment.body == 'Run Inference Benchmarks'
     runs-on: [self-hosted, ubuntu-20.04, main]
-    timeout-minutes: 900
+    timeout-minutes: 1000
     name: ${{ matrix.job_name }} (${{ matrix.job_phrase }})
     strategy:
       matrix:
@@ -72,7 +72,12 @@ jobs:
       - name: Setup Python environment
         uses: ./.github/actions/setup-environment-action
         with:
+          java-version: default
           python-version: '3.10'
+      - name: Package Python SDK using Gradle
+        run: ./gradlew :sdks:python:sdist -PpythonVersion=3.10
+      - name: Configure Docker for Artifact Registry
+        run: gcloud auth configure-docker us-docker.pkg.dev
       - name: Prepare test arguments
         uses: ./.github/actions/test-arguments-action
         with:
@@ -86,9 +91,28 @@ jobs:
             ${{ github.workspace }}/.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_Pytorch_Imagenet_Classification_Resnet_152_Tesla_T4_GPU.txt
             ${{ github.workspace }}/.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_Pytorch_Sentiment_Streaming_DistilBert_Base_Uncased.txt
             ${{ github.workspace }}/.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_Pytorch_Sentiment_Batch_DistilBert_Base_Uncased.txt
+            ${{ github.workspace }}/.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_VLLM_Gemma_Batch.txt
       # The env variables are created and populated in the test-arguments-action as "<github.job>_test_arguments_<argument_file_paths_index>"
       - name: get current time
         run: echo "NOW_UTC=$(date '+%m%d%H%M%S' --utc)" >> $GITHUB_ENV
+      - name: Build VLLM Development Image
+        id: build_vllm_image
+        uses: ./.github/actions/build-push-docker-action
+        with:
+          dockerfile_path: 'sdks/python/apache_beam/ml/inference/test_resources/vllm.dockerfile'
+          image_name: 'us-docker.pkg.dev/apache-beam-testing/beam-temp/beam-vllm-gpu-base'
+          image_tag: ${{ github.sha }}
+      - name: Run VLLM Gemma Batch Test
+        uses: ./.github/actions/gradle-command-self-hosted-action
+        timeout-minutes: 180
+        with:
+          gradle-command: :sdks:python:apache_beam:testing:load_tests:run
+          arguments: |
+            -PloadTest.mainClass=apache_beam.testing.benchmarks.inference.vllm_gemma_benchmarks \
+            -Prunner=DataflowRunner \
+            -PsdkLocationOverride=false \
+            -PpythonVersion=3.10 \
+            -PloadTest.requirementsTxtFile=apache_beam/ml/inference/vllm_tests_requirements.txt '-PloadTest.args=${{ env.beam_Inference_Python_Benchmarks_Dataflow_test_arguments_8 }} --mode=batch --job_name=benchmark-tests-vllm-with-gemma-2b-it-batch-${{env.NOW_UTC}} --sdk_container_image=${{ steps.build_vllm_image.outputs.image_url }}'
       - name: run Pytorch Sentiment Streaming using Hugging Face distilbert-base-uncased model
         uses: ./.github/actions/gradle-command-self-hosted-action
         timeout-minutes: 180
diff --git a/.github/workflows/beam_Infrastructure_PolicyEnforcer.yml b/.github/workflows/beam_Infrastructure_PolicyEnforcer.yml
new file mode 100644
index 000000000000..82ab2c0fb609
--- /dev/null
+++ b/.github/workflows/beam_Infrastructure_PolicyEnforcer.yml
@@ -0,0 +1,83 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# This workflow works with the infrastructure policy enforcer to
+# generate a report of IAM and Service Account Policies violations
+
+name: Infrastructure Policy Enforcer
+
+on:
+  workflow_dispatch:
+  schedule:
+    # Once a week at 9:00 AM on Monday
+    - cron: '0 9 * * 1'
+
+# This allows a subsequently queued workflow run to interrupt previous runs
+concurrency:
+  group: '${{ github.workflow }} @ ${{ github.event.issue.number || github.sha || github.head_ref || github.ref }}-${{ github.event.schedule || github.event.comment.id || github.event.sender.login }}'
+  cancel-in-progress: true
+
+#Setting explicit permissions for the action to avoid the default permissions which are `write-all` in case of pull_request_target event
+permissions:
+  contents: read
+  issues: write
+
+jobs:
+  beam_Infrastructure_PolicyEnforcer:
+    name: Check and Report Infrastructure Policies Violations
+    runs-on: [self-hosted, ubuntu-20.04, main]
+    timeout-minutes: 30
+    steps:
+      - uses: actions/checkout@v4
+      
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.13'
+          
+      - name: Install Python dependencies
+        working-directory: ./infra/enforcement
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          
+      - name: Setup gcloud
+        uses: google-github-actions/setup-gcloud@v3
+          
+      - name: Run IAM Policy Enforcement
+        working-directory: ./infra/enforcement
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          GITHUB_REPOSITORY: ${{ github.repository }}
+          SMTP_SERVER: smtp.gmail.com
+          SMTP_PORT: 465
+          EMAIL_ADDRESS: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_ADDRESS }}
+          EMAIL_PASSWORD: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_PASSWORD }}
+          EMAIL_RECIPIENT: "dev@beam.apache.org"
+        run: python iam.py --action print
+
+      - name: Run Account Keys Policy Enforcement
+        working-directory: ./infra/enforcement
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          GITHUB_REPOSITORY: ${{ github.repository }}
+          SMTP_SERVER: smtp.gmail.com
+          SMTP_PORT: 465
+          EMAIL_ADDRESS: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_ADDRESS }}
+          EMAIL_PASSWORD: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_PASSWORD }}
+          EMAIL_RECIPIENT: "dev@beam.apache.org"
+        run: python account_keys.py --action print
diff --git a/.github/workflows/beam_Infrastructure_SecurityLogging.yml b/.github/workflows/beam_Infrastructure_SecurityLogging.yml
new file mode 100644
index 000000000000..106e0cf6d547
--- /dev/null
+++ b/.github/workflows/beam_Infrastructure_SecurityLogging.yml
@@ -0,0 +1,77 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# This workflow works with the GCP security log analyzer to
+# generate weekly security reports and initialize log sinks
+
+name: GCP Security Log Analyzer
+
+on:
+  workflow_dispatch:
+  schedule:
+    # Once a week at 9:00 AM on Monday
+    - cron: '0 9 * * 1'
+  push:
+    paths:
+      - 'infra/security/config.yml'
+
+# This allows a subsequently queued workflow run to interrupt previous runs
+concurrency:
+  group: '${{ github.workflow }} @ ${{ github.sha || github.head_ref || github.ref }}-${{ github.event.schedule || github.event.sender.login }}'
+  cancel-in-progress: true
+
+#Setting explicit permissions for the action to avoid the default permissions which are `write-all` in case of pull_request_target event
+permissions:
+  contents: read
+
+jobs:
+  beam_GCP_Security_LogAnalyzer:
+    name: GCP Security Log Analysis
+    runs-on: [self-hosted, ubuntu-20.04, main]
+    timeout-minutes: 30
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.13'
+
+      - name: Install Python dependencies
+        working-directory: ./infra/security
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+          
+      - name: Setup gcloud
+        uses: google-github-actions/setup-gcloud@v3
+
+      - name: Initialize Log Sinks
+        if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
+        working-directory: ./infra/security
+        run: python log_analyzer.py --config config.yml initialize
+
+      - name: Generate Weekly Security Report
+        if: github.event_name == 'schedule' || github.event_name == 'workflow_dispatch'
+        working-directory: ./infra/security
+        env:
+          SMTP_SERVER: smtp.gmail.com
+          SMTP_PORT: 465
+          EMAIL_ADDRESS: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_ADDRESS }}
+          EMAIL_PASSWORD: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_PASSWORD }}
+          EMAIL_RECIPIENT: "dev@beam.apache.org"
+        run: python log_analyzer.py --config config.yml generate-report --dry-run
diff --git a/.github/workflows/beam_Infrastructure_ServiceAccountKeys.yml b/.github/workflows/beam_Infrastructure_ServiceAccountKeys.yml
new file mode 100644
index 000000000000..d84f41d158ba
--- /dev/null
+++ b/.github/workflows/beam_Infrastructure_ServiceAccountKeys.yml
@@ -0,0 +1,68 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# This workflow modifies the GCP Service Account keys and manages the
+# storage, saving them onto Google Cloud Secret Manager. It also handles
+# the rotation of the keys.
+
+name: Service Account Keys Management
+
+on:
+  workflow_dispatch:
+  # Trigger when the keys.yaml file is modified on the main branch
+  push:
+    branches:
+      - main
+    paths:
+      - 'infra/keys/keys.yaml'
+  schedule:
+    # Once a week at 9:00 AM on Monday
+    - cron: '0 9 * * 1'
+
+# This ensures that only one workflow run is running at a time, and others are queued.
+concurrency:
+  group: ${{ github.workflow }}
+  cancel-in-progress: false
+
+#Setting explicit permissions for the action to avoid the default permissions which are `write-all` in case of pull_request_target event
+permissions:
+  contents: read
+
+jobs:
+  beam_UserRoles:
+    name: Apply user roles changes
+    runs-on: [self-hosted, ubuntu-20.04, main]
+    timeout-minutes: 30
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup gcloud
+        uses: google-github-actions/setup-gcloud@v3
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.13'
+
+      - name: Install Python dependencies
+        working-directory: ./infra/keys
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+      
+      - name: Run Service Account Key Management
+        working-directory: ./infra/keys
+        run: python keys.py --cron-dry-run
diff --git a/.github/workflows/beam_Infrastructure_UsersPermissions.yml b/.github/workflows/beam_Infrastructure_UsersPermissions.yml
new file mode 100644
index 000000000000..07f7c6fa2406
--- /dev/null
+++ b/.github/workflows/beam_Infrastructure_UsersPermissions.yml
@@ -0,0 +1,62 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# This workflow modifies the GCP User Roles when the infra/users.yml file is updated.
+# It applies the changes using Terraform to manage the IAM roles for users defined in the users.yml
+
+name: Modify the GCP User Roles according to the infra/users.yml file
+
+on:
+  workflow_dispatch:
+  # Trigger when the users.yml file is modified on the main branch
+  push:
+    branches:
+      - main
+    paths:
+      - 'infra/iam/users.yml'
+
+# This allows a subsequently queued workflow run to interrupt previous runs
+concurrency:
+  group: '${{ github.workflow }} @ ${{ github.event.issue.number || github.sha || github.head_ref || github.ref }}-${{ github.event.schedule || github.event.comment.id || github.event.sender.login }}'
+  cancel-in-progress: true
+
+#Setting explicit permissions for the action to avoid the default permissions which are `write-all` in case of pull_request_target event
+permissions:
+  contents: read
+
+jobs:
+  beam_UserRoles:
+    name: Apply user roles changes
+    runs-on: [self-hosted, ubuntu-20.04, main]
+    timeout-minutes: 30
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup gcloud
+        uses: google-github-actions/setup-gcloud@v3
+      - name: Install Terraform
+        uses: hashicorp/setup-terraform@v3
+        with:
+          terraform_version: 1.12.2
+      - name: Initialize Terraform
+        working-directory: ./infra/iam
+        run: terraform init
+      - name: Terraform Plan
+        working-directory: ./infra/iam
+        run: terraform plan -out=tfplan
+      - name: Terraform Apply
+        working-directory: ./infra/iam
+        run: terraform apply -auto-approve tfplan
diff --git a/.github/workflows/beam_MetricsCredentialsRotation.yml b/.github/workflows/beam_MetricsCredentialsRotation.yml
index 0138b9e35571..0eac22a04072 100644
--- a/.github/workflows/beam_MetricsCredentialsRotation.yml
+++ b/.github/workflows/beam_MetricsCredentialsRotation.yml
@@ -82,17 +82,17 @@ jobs:
         run: |
           date=$(date -u +"%Y-%m-%d")
           echo "date=$date" >> $GITHUB_ENV
-      - name: Send email
-        uses: dawidd6/action-send-mail@v3
-        if: failure()
-        with:
-          server_address: smtp.gmail.com
-          server_port: 465
-          secure: true
-          username: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_ADDRESS }}
-          password: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_PASSWORD }}
-          subject: Credentials Rotation Failure on Metrics cluster (${{ env.date }})
-          to: dev@beam.apache.org
-          from: gactions@beam.apache.org
-          body: |
-            Something went wrong during the automatic credentials rotation for Metrics Cluster, performed at ${{ env.date }}. It may be necessary to check the state of the cluster certificates. For further details refer to the following links:\n * Failing job: https://github.com/apache/beam/actions/workflows/beam_MetricsCredentialsRotation.yml \n * Job configuration: https://github.com/apache/beam/blob/master/.github/workflows/beam_MetricsCredentialsRotation.yml \n * Cluster URL: https://pantheon.corp.google.com/kubernetes/clusters/details/us-central1-a/metrics/details?mods=dataflow_dev&project=apache-beam-testing
+#      - name: Send email
+#        uses: dawidd6/action-send-mail@v3
+#        if: failure()
+#        with:
+#          server_address: smtp.gmail.com
+#          server_port: 465
+#          secure: true
+#          username: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_ADDRESS }}
+#          password: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_PASSWORD }}
+#          subject: Credentials Rotation Failure on Metrics cluster (${{ env.date }})
+#          to: dev@beam.apache.org
+#          from: gactions@beam.apache.org
+#          body: |
+#            Something went wrong during the automatic credentials rotation for Metrics Cluster, performed at ${{ env.date }}. It may be necessary to check the state of the cluster certificates. For further details refer to the following links:\n * Failing job: https://github.com/apache/beam/actions/workflows/beam_MetricsCredentialsRotation.yml \n * Job configuration: https://github.com/apache/beam/blob/master/.github/workflows/beam_MetricsCredentialsRotation.yml \n * Cluster URL: https://pantheon.corp.google.com/kubernetes/clusters/details/us-central1-a/metrics/details?mods=dataflow_dev&project=apache-beam-testing
diff --git a/.github/workflows/beam_Metrics_Report.yml b/.github/workflows/beam_Metrics_Report.yml
index 1d20bd64b7e6..70ed354958b8 100644
--- a/.github/workflows/beam_Metrics_Report.yml
+++ b/.github/workflows/beam_Metrics_Report.yml
@@ -58,7 +58,7 @@ jobs:
         (github.event_name == 'schedule' && github.repository == 'apache/beam')
         || github.event_name == 'workflow_dispatch'
       )
-     
+
     steps:
       - uses: actions/checkout@v4
       - name: Setup environment
@@ -82,15 +82,15 @@ jobs:
         run: |
           date=$(date -u +"%Y-%m-%d")
           echo "date=$date" >> $GITHUB_ENV
-      - name: Send mail
-        uses: dawidd6/action-send-mail@v3
-        with:
-          server_address: smtp.gmail.com
-          server_port: 465
-          secure: true
-          username: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_ADDRESS }}
-          password: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_PASSWORD }}
-          subject: Beam Metrics Report ${{ env.date }}
-          to: dev@beam.apache.org
-          from: beamactions@gmail.com
-          html_body: file://${{ github.workspace }}/.test-infra/jenkins/metrics_report/beam-metrics_report.html
+#      - name: Send mail
+#        uses: dawidd6/action-send-mail@v6
+#        with:
+#          server_address: smtp.gmail.com
+#          server_port: 465
+#          secure: true
+#          username: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_ADDRESS }}
+#          password: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_PASSWORD }}
+#          subject: Beam Metrics Report ${{ env.date }}
+#          to: dev@beam.apache.org
+#          from: beamactions@gmail.com
+#          html_body: file://${{ github.workspace }}/.test-infra/jenkins/metrics_report/beam-metrics_report.html
diff --git a/.github/workflows/beam_Playground_Precommit.yml b/.github/workflows/beam_Playground_Precommit.yml
index 8f03a1c37d25..a0fbe7881fe1 100644
--- a/.github/workflows/beam_Playground_Precommit.yml
+++ b/.github/workflows/beam_Playground_Precommit.yml
@@ -75,7 +75,7 @@ jobs:
           sudo apt-get install sbt --yes
           sudo wget https://codeload.github.com/spotify/scio.g8/zip/7c1ba7c1651dfd70976028842e721da4107c0d6d -O scio.g8.zip && unzip scio.g8.zip && sudo mv scio.g8-7c1ba7c1651dfd70976028842e721da4107c0d6d /opt/scio.g8
       - name: Set up Cloud SDK and its components
-        uses: google-github-actions/setup-gcloud@v2
+        uses: google-github-actions/setup-gcloud@v3
         with:
           install_components: 'beta,cloud-datastore-emulator'
           version: '${{ env.DATASTORE_EMULATOR_VERSION }}'
diff --git a/.github/workflows/beam_PostCommit_Java_BigQueryEarlyRollout.yml b/.github/workflows/beam_PostCommit_Java_BigQueryEarlyRollout.yml
index 8d780ba46b33..a76c48b8968f 100644
--- a/.github/workflows/beam_PostCommit_Java_BigQueryEarlyRollout.yml
+++ b/.github/workflows/beam_PostCommit_Java_BigQueryEarlyRollout.yml
@@ -83,20 +83,20 @@ jobs:
         run: |
           date=$(date -u +"%Y-%m-%d")
           echo "date=$date" >> $GITHUB_ENV
-      - name: Send email
-        uses: dawidd6/action-send-mail@v3
-        if: failure()
-        with:
-          server_address: smtp.gmail.com
-          server_port: 465
-          secure: true
-          username: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_ADDRESS }}
-          password: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_PASSWORD }}
-          subject: BigQueryEarlyRollout Beam Failure (${{ env.date }}) - investigate and escalate quickly
-          to: datapls-plat-team@google.com # Team at Google responsible for escalating BQ failures
-          from: gactions@beam.apache.org
-          body: |
-            PostCommit Java BigQueryEarlyRollout failed on ${{ env.date }}. This test monitors BigQuery rollouts impacting Beam and should be escalated immediately if a real issue is encountered to pause further rollouts. For further details refer to the following links:\n * Failing job: https://github.com/apache/beam/actions/workflows/beam_PostCommit_Java_BigQueryEarlyRollout.yml \n * Job configuration: https://github.com/apache/beam/blob/master/.github/workflows/beam_PostCommit_Java_BigQueryEarlyRollout.yml
+#      - name: Send email
+#        uses: dawidd6/action-send-mail@v3
+#        if: failure()
+#        with:
+#          server_address: smtp.gmail.com
+#          server_port: 465
+#          secure: true
+#          username: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_ADDRESS }}
+#          password: ${{ secrets.ISSUE_REPORT_SENDER_EMAIL_PASSWORD }}
+#          subject: BigQueryEarlyRollout Beam Failure (${{ env.date }}) - investigate and escalate quickly
+#          to: datapls-plat-team@google.com # Team at Google responsible for escalating BQ failures
+#          from: gactions@beam.apache.org
+#          body: |
+#            PostCommit Java BigQueryEarlyRollout failed on ${{ env.date }}. This test monitors BigQuery rollouts impacting Beam and should be escalated immediately if a real issue is encountered to pause further rollouts. For further details refer to the following links:\n * Failing job: https://github.com/apache/beam/actions/workflows/beam_PostCommit_Java_BigQueryEarlyRollout.yml \n * Job configuration: https://github.com/apache/beam/blob/master/.github/workflows/beam_PostCommit_Java_BigQueryEarlyRollout.yml
       - name: Archive JUnit Test Results
         uses: actions/upload-artifact@v4
         if: ${{ !success() }}
diff --git a/.github/workflows/beam_PostCommit_Java_Jpms_Direct_Java21.yml b/.github/workflows/beam_PostCommit_Java_Jpms_Direct_Java21.yml
index b4870b9d9fb9..52f7faacad67 100644
--- a/.github/workflows/beam_PostCommit_Java_Jpms_Direct_Java21.yml
+++ b/.github/workflows/beam_PostCommit_Java_Jpms_Direct_Java21.yml
@@ -71,7 +71,7 @@ jobs:
         github_token: ${{ secrets.GITHUB_TOKEN }}
         github_job: ${{ matrix.job_name }} (${{ matrix.job_phrase }})
     - name: Set up Java
-      uses: actions/setup-java@v4
+      uses: actions/setup-java@v5
       with:
         distribution: 'temurin'
         java-version: |
diff --git a/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Dataflow_JavaVersions.yml b/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Dataflow_JavaVersions.yml
index a84485c717fb..c03e2435a83b 100644
--- a/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Dataflow_JavaVersions.yml
+++ b/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Dataflow_JavaVersions.yml
@@ -80,24 +80,13 @@ jobs:
           java-version: |
             ${{ matrix.java_version }}
             11
-      - name: run jar Java${{ matrix.java_version }} script
-        run: |
-          ./gradlew runners:google-cloud-dataflow-java:testJar :runners:google-cloud-dataflow-java:worker:shadowJar \
-          -Dorg.gradle.java.home=$JAVA_HOME_${{ matrix.java_version }}_X64
       - name: run validatesRunner Java${{ matrix.java_version }} script
         uses: ./.github/actions/gradle-command-self-hosted-action
         with:
           gradle-command: :runners:google-cloud-dataflow-java:validatesRunner
           arguments: |
-            -x shadowJar \
-            -x shadowTestJar \
-            -x compileJava \
-            -x compileTestJava \
-            -x jar \
-            -x testJar \
-            -x classes \
-            -x testClasses \
-            -Dorg.gradle.java.home=$JAVA_HOME_${{ matrix.java_version }}_X64 \
+            -PtestJavaVersion=${{ matrix.java_version }} \
+            -Pjava${{ matrix.java_version }}Home=$JAVA_HOME_${{ matrix.java_version }}_X64 \
           max-workers: 12
       - name: Archive JUnit Test Results
         uses: actions/upload-artifact@v4
diff --git a/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Direct_JavaVersions.yml b/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Direct_JavaVersions.yml
index 11b18548249e..365b50e9e350 100644
--- a/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Direct_JavaVersions.yml
+++ b/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Direct_JavaVersions.yml
@@ -80,20 +80,13 @@ jobs:
           java-version: |
             ${{ matrix.java_version }}
             11
-      - name: run jar Java${{ matrix.java_version }} script
-        run: |
-          ./gradlew :runners:direct-java:shadowJar :runners:direct-java:shadowTestJar \
-          -Dorg.gradle.java.home=$JAVA_HOME_${{ matrix.java_version }}_X64
       - name: run validatesRunner Java${{ matrix.java_version }} script
         uses: ./.github/actions/gradle-command-self-hosted-action
         with:
           gradle-command: :runners:direct-java:validatesRunner
           arguments: |
-            -x shadowJar \
-            -x shadowTestJar \
-            -x compileJava \
-            -x compileTestJava \
-            -Dorg.gradle.java.home=$JAVA_HOME_${{ matrix.java_version }}_X64 \
+            -PtestJavaVersion=${{ matrix.java_version }} \
+            -Pjava${{ matrix.java_version }}Home=$JAVA_HOME_${{ matrix.java_version }}_X64 \
       - name: Archive JUnit Test Results
         uses: actions/upload-artifact@v4
         if: ${{ !success() }}
diff --git a/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Flink_Java8.yml b/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Flink_Java8.yml
index 7f7b9c9270d2..9b061028cbce 100644
--- a/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Flink_Java8.yml
+++ b/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Flink_Java8.yml
@@ -78,23 +78,13 @@ jobs:
           java-version: |
             8
             11
-      - name: run jar Java8 script
-        run: |
-          ./gradlew :runners:flink:1.19:jar :runners:flink:1.19:testJar
       - name: run validatesRunner Java8 script
         uses: ./.github/actions/gradle-command-self-hosted-action
         with:
           gradle-command: :runners:flink:1.19:validatesRunner
           arguments: |
-            -x shadowJar \
-            -x shadowTestJar \
-            -x compileJava \
-            -x compileTestJava \
-            -x jar \
-            -x testJar \
-            -x classes \
-            -x testClasses \
-            -Dorg.gradle.java.home=$JAVA_HOME_8_X64 \
+            -PtestJavaVersion=8 \
+            -Pjava8Home=$JAVA_HOME_8_X64 \
           max-workers: 12
       - name: Archive JUnit Test Results
         uses: actions/upload-artifact@v4
diff --git a/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Spark_Java8.yml b/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Spark_Java8.yml
index 0852fcdf4afa..dae408e4346f 100644
--- a/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Spark_Java8.yml
+++ b/.github/workflows/beam_PostCommit_Java_ValidatesRunner_Spark_Java8.yml
@@ -78,23 +78,13 @@ jobs:
           java-version: |
             8
             11
-      - name: run jar Java8 script
-        run: |
-          ./gradlew :runners:spark:3:jar :runners:spark:3:testJar
       - name: run validatesRunner Java8 script
         uses: ./.github/actions/gradle-command-self-hosted-action
         with:
           gradle-command: :runners:spark:3:validatesRunner
           arguments: |
-            -x shadowJar \
-            -x shadowTestJar \
-            -x compileJava \
-            -x compileTestJava \
-            -x jar \
-            -x testJar \
-            -x classes \
-            -x testClasses \
-            -Dorg.gradle.java.home=$JAVA_HOME_8_X64 \
+            -PtestJavaVersion=8 \
+            -Pjava8Home=$JAVA_HOME_8_X64 \
           max-workers: 12
       - name: Archive JUnit Test Results
         uses: actions/upload-artifact@v4
diff --git a/.github/workflows/beam_PostCommit_Python.yml b/.github/workflows/beam_PostCommit_Python.yml
index 2a98ccb0efb0..b96067b498e7 100644
--- a/.github/workflows/beam_PostCommit_Python.yml
+++ b/.github/workflows/beam_PostCommit_Python.yml
@@ -53,15 +53,16 @@ env:
 
 jobs:
   beam_PostCommit_Python:
-    name: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.python_version }})
-    runs-on: [self-hosted, ubuntu-20.04, highmem22]
+    name: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.python_version }}) (${{ join(matrix.os, ', ') }})
+    runs-on: ${{ matrix.os }}
     timeout-minutes: 240
     strategy:
       fail-fast: false
       matrix:
-        job_name: [beam_PostCommit_Python]
-        job_phrase: [Run Python PostCommit]
+        job_name: ['beam_PostCommit_Python']
+        job_phrase: ['Run Python PostCommit']
         python_version: ['3.9', '3.10', '3.11', '3.12']
+        os: [[self-hosted, ubuntu-20.04, highmem22]]
     if: |
       github.event_name == 'workflow_dispatch' ||
       github.event_name == 'pull_request_target' ||
@@ -74,7 +75,7 @@ jobs:
         with:
           comment_phrase: ${{ matrix.job_phrase }} ${{ matrix.python_version }}
           github_token: ${{ secrets.GITHUB_TOKEN }}
-          github_job: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.python_version }})
+          github_job: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.python_version }}) (${{ join(matrix.os, ', ') }})
       - name: Setup environment
         uses: ./.github/actions/setup-environment-action
         with:
@@ -99,6 +100,7 @@ jobs:
           arguments: |
             -Pjava21Home=$JAVA_HOME_21_X64 \
             -PuseWheelDistribution \
+            -Pposargs="-m (not require_docker_in_docker)" \
             -PpythonVersion=${{ matrix.python_version }} \
         env:
           CLOUDSDK_CONFIG: ${{ env.KUBELET_GCLOUD_CONFIG_PATH}}
@@ -106,7 +108,7 @@ jobs:
         uses: actions/upload-artifact@v4
         if: failure()
         with:
-          name: Python ${{ matrix.python_version }} Test Results
+          name: Python ${{ matrix.python_version }} Test Results (${{ join(matrix.os, ', ') }})
           path: '**/pytest*.xml'
       - name: Publish Python Test Results
         uses: EnricoMi/publish-unit-test-result-action@v2
@@ -115,4 +117,5 @@ jobs:
           commit: '${{ env.prsha || env.GITHUB_SHA }}'
           comment_mode: ${{ github.event_name == 'issue_comment'  && 'always' || 'off' }}
           files: '**/pytest*.xml'
-          large_files: true
\ No newline at end of file
+          large_files: true
+          check_name: "Python ${{ matrix.python_version }} Test Results (${{ join(matrix.os, ', ') }})"
diff --git a/.github/workflows/beam_PostCommit_Python_Arm.yml b/.github/workflows/beam_PostCommit_Python_Arm.yml
index 8b990ea01cf5..4f37276779d8 100644
--- a/.github/workflows/beam_PostCommit_Python_Arm.yml
+++ b/.github/workflows/beam_PostCommit_Python_Arm.yml
@@ -85,12 +85,12 @@ jobs:
           sudo curl -L https://github.com/docker/compose/releases/download/1.22.0/docker-compose-$(uname -s)-$(uname -m) -o /usr/local/bin/docker-compose
           sudo chmod +x /usr/local/bin/docker-compose
       - name: Authenticate on GCP
-        uses: google-github-actions/auth@v2
+        uses: google-github-actions/auth@v3
         with:
           service_account: ${{ secrets.GCP_SA_EMAIL }}
           credentials_json: ${{ secrets.GCP_SA_KEY }}
       - name: Set up Cloud SDK
-        uses: google-github-actions/setup-gcloud@v2
+        uses: google-github-actions/setup-gcloud@v3
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
       - name: GCloud Docker credential helper
diff --git a/.github/workflows/beam_PostCommit_Python_Dependency.yml b/.github/workflows/beam_PostCommit_Python_Dependency.yml
index f5ffdcb5ce13..609271cda75d 100644
--- a/.github/workflows/beam_PostCommit_Python_Dependency.yml
+++ b/.github/workflows/beam_PostCommit_Python_Dependency.yml
@@ -60,7 +60,7 @@ jobs:
         job_name: ['beam_PostCommit_Python_Dependency']
         job_phrase: ['Run Python PostCommit Dependency']
         python_version: ['3.9','3.12']
-    timeout-minutes: 120
+    timeout-minutes: 180
     if: |
       github.event_name == 'workflow_dispatch' ||
       github.event_name == 'pull_request_target' ||
diff --git a/.github/workflows/beam_PostCommit_Python_Portable_Flink.yml b/.github/workflows/beam_PostCommit_Python_Portable_Flink.yml
new file mode 100644
index 000000000000..363d4703ef18
--- /dev/null
+++ b/.github/workflows/beam_PostCommit_Python_Portable_Flink.yml
@@ -0,0 +1,102 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+name: PostCommit Python Portable Flink
+
+on:
+  schedule:
+    - cron: '30 5/6 * * *'
+  pull_request_target:
+    paths: ['release/trigger_all_tests.json', '.github/trigger_files/beam_PostCommit_Python_Portable_Flink.json']
+  workflow_dispatch:
+
+#Setting explicit permissions for the action to avoid the default permissions which are `write-all` in case of pull_request_target event
+permissions:
+  actions: write
+  pull-requests: write
+  checks: write
+  contents: read
+  deployments: read
+  id-token: none
+  issues: write
+  discussions: read
+  packages: read
+  pages: read
+  repository-projects: read
+  security-events: read
+  statuses: read
+
+# This allows a subsequently queued workflow run to interrupt previous runs
+concurrency:
+  group: '${{ github.workflow }} @ ${{ github.event.issue.number || github.sha || github.head_ref || github.ref }}-${{ github.event.schedule || github.event.comment.id || github.event.sender.login }}'
+  cancel-in-progress: true
+
+env:
+  DEVELOCITY_ACCESS_KEY: ${{ secrets.DEVELOCITY_ACCESS_KEY }}
+  GRADLE_ENTERPRISE_CACHE_USERNAME: ${{ secrets.GE_CACHE_USERNAME }}
+  GRADLE_ENTERPRISE_CACHE_PASSWORD: ${{ secrets.GE_CACHE_PASSWORD }}
+
+jobs:
+  beam_PostCommit_Python_Portable_Flink:
+    if: |
+      (github.event_name == 'schedule' && github.repository == 'apache/beam') ||
+      github.event_name == 'workflow_dispatch' ||
+      github.event_name == 'pull_request_target'
+    runs-on: [self-hosted, ubuntu-20.04, main]
+    timeout-minutes: 120
+    name: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.environment_type }})
+    strategy:
+      fail-fast: false
+      matrix:
+        job_name: ["beam_PostCommit_Python_Portable_Flink"]
+        job_phrase: ["Run Python Portable Flink"]
+        # TODO: Enable PROCESS https://github.com/apache/beam/issues/35702
+        # environment_type: ['DOCKER', 'LOOPBACK', 'PROCESS']
+        environment_type: ['DOCKER', 'LOOPBACK']
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup repository
+        uses: ./.github/actions/setup-action
+        with:
+          comment_phrase: ${{ matrix.job_phrase }} ${{ matrix.environment_type }}
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          github_job: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.environment_type }})
+      - name: Setup environment
+        uses: ./.github/actions/setup-environment-action
+        with:
+          java-version: default
+          python-version: '3.9'
+      - name: Run flinkCompatibilityMatrix${{ matrix.environment_type }} script
+        env:
+          CLOUDSDK_CONFIG: ${{ env.KUBELET_GCLOUD_CONFIG_PATH}}
+        uses: ./.github/actions/gradle-command-self-hosted-action
+        with:
+          gradle-command: :sdks:python:test-suites:portable:py39:flinkCompatibilityMatrix${{ matrix.environment_type }}
+          arguments: |
+            -PpythonVersion=3.9 \
+      - name: Archive Python Test Results
+        uses: actions/upload-artifact@v4
+        if: failure()
+        with:
+          name: Python Test Results ${{ matrix.environment_type }}
+          path: '**/pytest*.xml'
+      - name: Publish Python Test Results
+        uses: EnricoMi/publish-unit-test-result-action@v2
+        if: always()
+        with:
+          commit: '${{ env.prsha || env.GITHUB_SHA }}'
+          comment_mode: ${{ github.event_name == 'issue_comment'  && 'always' || 'off' }}
+          files: '**/pytest*.xml'
+          large_files: true
diff --git a/.github/workflows/beam_PostCommit_Python_Xlang_Gcp_Dataflow.yml b/.github/workflows/beam_PostCommit_Python_Xlang_Gcp_Dataflow.yml
index 71e032597e6c..ef2768f1efd9 100644
--- a/.github/workflows/beam_PostCommit_Python_Xlang_Gcp_Dataflow.yml
+++ b/.github/workflows/beam_PostCommit_Python_Xlang_Gcp_Dataflow.yml
@@ -57,7 +57,7 @@ jobs:
       (github.event_name == 'schedule' && github.repository == 'apache/beam') ||
       github.event.comment.body == 'Run Python_Xlang_Gcp_Dataflow PostCommit'
     runs-on: [self-hosted, ubuntu-20.04, main]
-    timeout-minutes: 180
+    timeout-minutes: 240
     name: ${{ matrix.job_name }} (${{ matrix.job_phrase }})
     strategy:
       matrix:
@@ -95,4 +95,4 @@ jobs:
           commit: '${{ env.prsha || env.GITHUB_SHA }}'
           comment_mode: ${{ github.event_name == 'issue_comment'  && 'always' || 'off' }}
           files: '**/pytest*.xml'
-          large_files: true
\ No newline at end of file
+          large_files: true
diff --git a/.github/workflows/beam_PostCommit_Python_Xlang_Gcp_Direct.yml b/.github/workflows/beam_PostCommit_Python_Xlang_Gcp_Direct.yml
index d6582cea858a..0ad20571f92c 100644
--- a/.github/workflows/beam_PostCommit_Python_Xlang_Gcp_Direct.yml
+++ b/.github/workflows/beam_PostCommit_Python_Xlang_Gcp_Direct.yml
@@ -57,7 +57,7 @@ jobs:
       (github.event_name == 'schedule' && github.repository == 'apache/beam') ||
       github.event.comment.body == 'Run Python_Xlang_Gcp_Direct PostCommit'
     runs-on: [self-hosted, ubuntu-20.04, highmem]
-    timeout-minutes: 100
+    timeout-minutes: 160
     name: ${{ matrix.job_name }} (${{ matrix.job_phrase }})
     strategy:
       matrix:
@@ -98,4 +98,4 @@ jobs:
           commit: '${{ env.prsha || env.GITHUB_SHA }}'
           comment_mode: ${{ github.event_name == 'issue_comment'  && 'always' || 'off' }}
           files: '**/pytest*.xml'
-          large_files: true
\ No newline at end of file
+          large_files: true
diff --git a/.github/workflows/beam_PostCommit_XVR_GoUsingJava_Dataflow.yml b/.github/workflows/beam_PostCommit_XVR_GoUsingJava_Dataflow.yml
index 5f72507bfc20..1ce6d369c216 100644
--- a/.github/workflows/beam_PostCommit_XVR_GoUsingJava_Dataflow.yml
+++ b/.github/workflows/beam_PostCommit_XVR_GoUsingJava_Dataflow.yml
@@ -16,13 +16,13 @@
 # TODO(https://github.com/apache/beam/issues/32492): re-enable the suite
 # on cron and add release/trigger_all_tests.json to trigger path once fixed.
 
-name: PostCommit XVR GoUsingJava Dataflow (DISABLED)
+name: PostCommit XVR GoUsingJava Dataflow
 
 on:
-  # schedule:
-  #  - cron: '45 5/6 * * *'
+  schedule:
+    - cron: '45 5/6 * * *'
   pull_request_target:
-    paths: ['.github/trigger_files/beam_PostCommit_XVR_GoUsingJava_Dataflow.json']
+    paths: ['.github/trigger_files/beam_PostCommit_XVR_GoUsingJava_Dataflow.json', 'release/trigger_all_tests.json']
   workflow_dispatch:
 
 #Setting explicit permissions for the action to avoid the default permissions which are `write-all` in case of pull_request_target event
diff --git a/.github/workflows/beam_PostCommit_Yaml_Xlang_Direct.yml b/.github/workflows/beam_PostCommit_Yaml_Xlang_Direct.yml
index 9215aba0f1de..1446f5b1dd1f 100644
--- a/.github/workflows/beam_PostCommit_Yaml_Xlang_Direct.yml
+++ b/.github/workflows/beam_PostCommit_Yaml_Xlang_Direct.yml
@@ -76,7 +76,7 @@ jobs:
           python-version: default
           java-version: '11'
       - name: Set up Cloud SDK
-        uses: google-github-actions/setup-gcloud@v2
+        uses: google-github-actions/setup-gcloud@v3
       - name: run PostCommit Yaml Xlang Direct script
         uses: ./.github/actions/gradle-command-self-hosted-action
         with:
diff --git a/.github/workflows/beam_PreCommit_Java_Kafka_IO_Direct.yml b/.github/workflows/beam_PreCommit_Java_Kafka_IO_Direct.yml
index 72dcb3f2bd29..1ba0ade06fd0 100644
--- a/.github/workflows/beam_PreCommit_Java_Kafka_IO_Direct.yml
+++ b/.github/workflows/beam_PreCommit_Java_Kafka_IO_Direct.yml
@@ -100,7 +100,7 @@ jobs:
           arguments: |
             -PdisableSpotlessCheck=true \
             -PdisableCheckStyle=true \
-            --no-parallel \
+          max-workers: 4
       - name: Archive JUnit Test Results
         uses: actions/upload-artifact@v4
         if: ${{ !success() }}
diff --git a/.github/workflows/beam_PreCommit_Java_Pulsar_IO_Direct.yml b/.github/workflows/beam_PreCommit_Java_Pulsar_IO_Direct.yml
index 1a45436cedf7..c22e0dd4cb07 100644
--- a/.github/workflows/beam_PreCommit_Java_Pulsar_IO_Direct.yml
+++ b/.github/workflows/beam_PreCommit_Java_Pulsar_IO_Direct.yml
@@ -21,31 +21,13 @@ on:
     branches: ['master', 'release-*']
     paths:
       - "sdks/java/io/pulsar/**"
-      - "sdks/java/io/common/**"
-      - "sdks/java/core/src/main/**"
-      - "build.gradle"
-      - "buildSrc/**"
-      - "gradle/**"
-      - "gradle.properties"
-      - "gradlew"
-      - "gradle.bat"
-      - "settings.gradle.kts"
       - ".github/workflows/beam_PreCommit_Java_Pulsar_IO_Direct.yml"
   pull_request_target:
     branches: ['master', 'release-*']
     paths:
       - "sdks/java/io/pulsar/**"
-      - "sdks/java/io/common/**"
-      - "sdks/java/core/src/main/**"
+      - ".github/workflows/beam_PreCommit_Java_Pulsar_IO_Direct.yml"
       - 'release/trigger_all_tests.json'
-      - '.github/trigger_files/beam_PreCommit_Java_Pulsar_IO_Direct.json'
-      - "build.gradle"
-      - "buildSrc/**"
-      - "gradle/**"
-      - "gradle.properties"
-      - "gradlew"
-      - "gradle.bat"
-      - "settings.gradle.kts"
   issue_comment:
     types: [created]
   schedule:
@@ -110,6 +92,13 @@ jobs:
           arguments: |
             -PdisableSpotlessCheck=true \
             -PdisableCheckStyle=true \
+      - name: run Pulsar IO IT script
+        uses: ./.github/actions/gradle-command-self-hosted-action
+        with:
+          gradle-command: :sdks:java:io:pulsar:integrationTest
+          arguments: |
+            -PdisableSpotlessCheck=true \
+            -PdisableCheckStyle=true \
       - name: Archive JUnit Test Results
         uses: actions/upload-artifact@v4
         if: ${{ !success() }}
@@ -135,4 +124,4 @@ jobs:
         if: always()
         with:
           name: Publish SpotBugs
-          path: '**/build/reports/spotbugs/*.html'
\ No newline at end of file
+          path: '**/build/reports/spotbugs/*.html'
diff --git a/.github/workflows/beam_PreCommit_Portable_Python.yml b/.github/workflows/beam_PreCommit_Portable_Python.yml
index 883294b1d583..9052a87e012f 100644
--- a/.github/workflows/beam_PreCommit_Portable_Python.yml
+++ b/.github/workflows/beam_PreCommit_Portable_Python.yml
@@ -86,6 +86,7 @@ jobs:
     if: |
       github.event_name == 'push' ||
       github.event_name == 'pull_request_target' ||
+      github.event_name == 'workflow_dispatch' ||
       (github.event_name == 'schedule' && github.repository == 'apache/beam') ||
       startsWith(github.event.comment.body, 'Run Portable_Python PreCommit')
     steps:
diff --git a/.github/workflows/beam_PreCommit_Prism_Python.yml b/.github/workflows/beam_PreCommit_Prism_Python.yml
index ddb822c2ca28..ea1d29ffeb5b 100644
--- a/.github/workflows/beam_PreCommit_Prism_Python.yml
+++ b/.github/workflows/beam_PreCommit_Prism_Python.yml
@@ -80,6 +80,7 @@ jobs:
     if: |
       github.event_name == 'push' ||
       github.event_name == 'pull_request_target' ||
+      github.event_name == 'workflow_dispatch' ||
       (github.event_name == 'schedule' && github.repository == 'apache/beam') ||
       startsWith(github.event.comment.body, 'Run Prism_Python PreCommit')
     steps:
diff --git a/.github/workflows/beam_PreCommit_Python.yml b/.github/workflows/beam_PreCommit_Python.yml
index 3ad9020f17f7..db56f526a02d 100644
--- a/.github/workflows/beam_PreCommit_Python.yml
+++ b/.github/workflows/beam_PreCommit_Python.yml
@@ -53,6 +53,23 @@ env:
   DEVELOCITY_ACCESS_KEY: ${{ secrets.DEVELOCITY_ACCESS_KEY }}
   GRADLE_ENTERPRISE_CACHE_USERNAME: ${{ secrets.GE_CACHE_USERNAME }}
   GRADLE_ENTERPRISE_CACHE_PASSWORD: ${{ secrets.GE_CACHE_PASSWORD }}
+  # Aggressive stability settings for flaky CI environment
+  PYTHONHASHSEED: "0"
+  OMP_NUM_THREADS: "1"
+  OPENBLAS_NUM_THREADS: "1"
+  # gRPC stability - more conservative for unstable networks
+  GRPC_ARG_KEEPALIVE_TIME_MS: "10000"
+  GRPC_ARG_KEEPALIVE_TIMEOUT_MS: "15000"
+  GRPC_ARG_KEEPALIVE_PERMIT_WITHOUT_CALLS: "1"
+  GRPC_ARG_HTTP2_MAX_PINGS_WITHOUT_DATA: "0"
+  GRPC_ARG_MAX_RECONNECT_BACKOFF_MS: "30000"
+  # Beam-specific - very generous timeouts
+  BEAM_RETRY_MAX_ATTEMPTS: "5"
+  BEAM_RETRY_INITIAL_DELAY_MS: "5000"
+  BEAM_RETRY_MAX_DELAY_MS: "120000"
+  # Force stable execution
+  BEAM_TESTING_FORCE_SINGLE_BUNDLE: "true"
+  BEAM_TESTING_DETERMINISTIC_ORDER: "true"
 
 jobs:
   beam_PreCommit_Python:
@@ -91,6 +108,23 @@ jobs:
           PY_VER_CLEAN=${PY_VER//.}
           echo "py_ver_clean=$PY_VER_CLEAN" >> $GITHUB_OUTPUT
       - name: Run pythonPreCommit
+        env:
+          TOX_TESTENV_PASSENV: "DOCKER_*,TESTCONTAINERS_*,TC_*,BEAM_*,GRPC_*,OMP_*,OPENBLAS_*,PYTHONHASHSEED,PYTEST_*"
+          # Aggressive retry and timeout settings for flaky CI
+          PYTEST_ADDOPTS: "-v --tb=short --maxfail=5 --durations=30 --reruns=5 --reruns-delay=15 --timeout=600 --disable-warnings"
+          # Container stability - much more generous timeouts
+          TC_TIMEOUT: "300"
+          TC_MAX_TRIES: "15"
+          TC_SLEEP_TIME: "5"
+          # Additional gRPC stability for flaky environment
+          GRPC_ARG_MAX_CONNECTION_IDLE_MS: "60000"
+          GRPC_ARG_HTTP2_BDP_PROBE: "1"
+          GRPC_ARG_SO_REUSEPORT: "1"
+          # Force sequential execution to reduce load
+          PYTEST_XDIST_WORKER_COUNT: "1"
+          # Additional gRPC settings
+          GRPC_ARG_MAX_RECONNECT_BACKOFF_MS: "120000"
+          GRPC_ARG_INITIAL_RECONNECT_BACKOFF_MS: "2000"
         uses: ./.github/actions/gradle-command-self-hosted-action
         with:
           gradle-command: :sdks:python:test-suites:tox:py${{steps.set_py_ver_clean.outputs.py_ver_clean}}:preCommitPy${{steps.set_py_ver_clean.outputs.py_ver_clean}}
@@ -110,4 +144,14 @@ jobs:
           commit: '${{ env.prsha || env.GITHUB_SHA }}'
           comment_mode: ${{ github.event_name == 'issue_comment'  && 'always' || 'off' }}
           files: '**/pytest*.xml'
-          large_files: true
\ No newline at end of file
+          large_files: true
+      - name: Cleanup
+        if: always()
+        run: |
+          # Kill any remaining processes
+          sudo pkill -f "gradle" || true
+          sudo pkill -f "java" || true
+          sudo pkill -f "python.*pytest" || true
+          # Clean up temp files
+          sudo rm -rf /tmp/beam-* || true
+          sudo rm -rf /tmp/gradle-* || true
\ No newline at end of file
diff --git a/.github/workflows/beam_PreCommit_Python_Coverage.yml b/.github/workflows/beam_PreCommit_Python_Coverage.yml
index 093f7026b13a..7c675c01183b 100644
--- a/.github/workflows/beam_PreCommit_Python_Coverage.yml
+++ b/.github/workflows/beam_PreCommit_Python_Coverage.yml
@@ -58,36 +58,73 @@ env:
 
 jobs:
   beam_PreCommit_Python_Coverage:
-    name: ${{ matrix.job_name }} (${{ matrix.job_phrase }})
-    runs-on: [self-hosted, ubuntu-20.04, main]
+    name: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.python_version }}) (${{ join(matrix.os, ', ') }})
+    runs-on: ${{ matrix.os }}
     strategy:
+      fail-fast: false
       matrix:
         job_name: [beam_PreCommit_Python_Coverage]
         job_phrase: [Run Python_Coverage PreCommit]
+        python_version: ['3.9']
+        # Run on both self-hosted and GitHub-hosted runners.
+        # Some tests (marked require_docker_in_docker) can't run on Beam's
+        # self-hosted runners due to Docker-in-Docker environment constraint.
+        # These tests will only execute on ubuntu-latest (GitHub-hosted).
+        # Context: https://github.com/apache/beam/pull/35585
+        os: [[self-hosted, ubuntu-20.04, highmem], [ubuntu-latest]]
     timeout-minutes: 180
     if: |
       github.event_name == 'push' ||
       github.event_name == 'pull_request_target' ||
       (github.event_name == 'schedule' && github.repository == 'apache/beam') ||
       github.event_name == 'workflow_dispatch' ||
-      github.event.comment.body == 'Run Python_Coverage PreCommit'
+      startswith(github.event.comment.body, 'Run Python_Coverage PreCommit 3.')
     steps:
       - uses: actions/checkout@v4
       - name: Setup repository
         uses: ./.github/actions/setup-action
         with:
-          comment_phrase: ${{ matrix.job_phrase }}
+          comment_phrase: ${{ matrix.job_phrase }} ${{ matrix.python_version }}
           github_token: ${{ secrets.GITHUB_TOKEN }}
-          github_job: ${{ matrix.job_name }} (${{ matrix.job_phrase }})
+          github_job: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.python_version }}) (${{ join(matrix.os, ', ') }})
       - name: Setup environment
         uses: ./.github/actions/setup-environment-action
         with:
           java-version: default
-          python-version: default
+          python-version: ${{ matrix.python_version }}
+      - name: Start DinD
+        uses: ./.github/actions/dind-up-action
+        id: dind
+        if: contains(matrix.os, 'self-hosted')
+        with:
+          # Enable all the new features
+          cleanup-dind-on-start: "true"
+          smoke-test-port-mapping: "true"
+          prime-testcontainers: "true"
+          tmpfs-run-size: 2g
+          tmpfs-varrun-size: 4g
+          export-gh-env: "true"
       - name: Run preCommitPyCoverage
+        env:
+          DOCKER_HOST: ${{ contains(matrix.os, 'self-hosted') && steps.dind.outputs.docker-host || '' }}
+          TOX_TESTENV_PASSENV: "DOCKER_*,TESTCONTAINERS_*,TC_*,BEAM_*,GRPC_*,OMP_*,OPENBLAS_*,PYTHONHASHSEED,PYTEST_*"
+          TESTCONTAINERS_HOST_OVERRIDE: ${{ contains(matrix.os, 'self-hosted') && env.DIND_IP || '' }}
+          TESTCONTAINERS_DOCKER_SOCKET_OVERRIDE: "/var/run/docker.sock"
+          TESTCONTAINERS_RYUK_DISABLED: "false"
+          TESTCONTAINERS_RYUK_CONTAINER_PRIVILEGED: "true"
+          PYTEST_ADDOPTS: "-v --tb=short --maxfail=3 --durations=20 --reruns=2 --reruns-delay=5"
+          TC_TIMEOUT: "120"
+          TC_MAX_TRIES: "120"
+          TC_SLEEP_TIME: "1"
         uses: ./.github/actions/gradle-command-self-hosted-action
         with:
           gradle-command: :sdks:python:test-suites:tox:py39:preCommitPyCoverage
+          arguments: |
+            -Pposargs="${{
+              contains(matrix.os, 'self-hosted') &&
+                '-m (not require_docker_in_docker)' ||
+                '-m require_docker_in_docker'
+            }}"
       - uses: codecov/codecov-action@v3
         with:
           flags: python
@@ -96,13 +133,16 @@ jobs:
         uses: actions/upload-artifact@v4
         if: failure()
         with:
-          name: Python Test Results
+          name: Python ${{ matrix.python_version }} Test Results (${{ join(matrix.os, ', ') }})
           path: '**/pytest*.xml'
       - name: Publish Python Test Results
+        env:
+          DOCKER_HOST: ""  # Unset DOCKER_HOST to run on host Docker daemon
         uses: EnricoMi/publish-unit-test-result-action@v2
         if: always()
         with:
           commit: '${{ env.prsha || env.GITHUB_SHA }}'
           comment_mode: ${{ github.event_name == 'issue_comment'  && 'always' || 'off' }}
           files: '**/pytest*.xml'
-          large_files: true
\ No newline at end of file
+          large_files: true
+          check_name: "Python ${{ matrix.python_version }} Test Results (${{ join(matrix.os, ', ') }})"
diff --git a/.github/workflows/beam_PreCommit_Python_Examples.yml b/.github/workflows/beam_PreCommit_Python_Examples.yml
index c76d140eadeb..68acb72e0d61 100644
--- a/.github/workflows/beam_PreCommit_Python_Examples.yml
+++ b/.github/workflows/beam_PreCommit_Python_Examples.yml
@@ -53,6 +53,7 @@ env:
   DEVELOCITY_ACCESS_KEY: ${{ secrets.DEVELOCITY_ACCESS_KEY }}
   GRADLE_ENTERPRISE_CACHE_USERNAME: ${{ secrets.GE_CACHE_USERNAME }}
   GRADLE_ENTERPRISE_CACHE_PASSWORD: ${{ secrets.GE_CACHE_PASSWORD }}
+  ALLOYDB_PASSWORD: ${{ secrets.ALLOYDB_PASSWORD }}
 
 jobs:
   beam_PreCommit_Python_Examples:
@@ -110,4 +111,4 @@ jobs:
           commit: '${{ env.prsha || env.GITHUB_SHA }}'
           comment_mode: ${{ github.event_name == 'issue_comment'  && 'always' || 'off' }}
           files: '**/pytest*.xml'
-          large_files: true
\ No newline at end of file
+          large_files: true
diff --git a/.github/workflows/beam_PreCommit_Python_ML.yml b/.github/workflows/beam_PreCommit_Python_ML.yml
index 50ae079d3db3..471dcf953be5 100644
--- a/.github/workflows/beam_PreCommit_Python_ML.yml
+++ b/.github/workflows/beam_PreCommit_Python_ML.yml
@@ -57,8 +57,8 @@ env:
 
 jobs:
   beam_PreCommit_Python_ML:
-    name: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.python_version }})
-    runs-on: [self-hosted, ubuntu-20.04, main]
+    name: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.python_version }}) (${{ join(matrix.os, ', ') }})
+    runs-on: ${{ matrix.os }}
     timeout-minutes: 180
     strategy:
       fail-fast: false
@@ -66,6 +66,22 @@ jobs:
         job_name: ['beam_PreCommit_Python_ML']
         job_phrase: ['Run Python_ML PreCommit']
         python_version: ['3.9','3.10','3.11','3.12']
+        # Run on both self-hosted and GitHub-hosted runners.
+        # Some tests (marked require_docker_in_docker) can't run on Beam's
+        # self-hosted runners due to Docker-in-Docker environment constraint.
+        # These tests will only execute on ubuntu-latest (GitHub-hosted).
+        # Context: https://github.com/apache/beam/pull/35585.
+        os: [[self-hosted, ubuntu-20.04, main], [ubuntu-latest]]
+        exclude:
+          # Temporary exclude Python 3.9, 3.10, 3.11 from ubuntu-latest. This
+          # results in pip dependency resolution exceeded maximum depth issue.
+          # Context: https://github.com/apache/beam/pull/35816.
+          - python_version: '3.9'
+            os: [ubuntu-latest]
+          - python_version: '3.10'
+            os: [ubuntu-latest]
+          - python_version: '3.11'
+            os: [ubuntu-latest]
     if: |
       github.event_name == 'push' ||
       github.event_name == 'pull_request_target' ||
@@ -79,7 +95,7 @@ jobs:
         with:
           comment_phrase: ${{ matrix.job_phrase }} ${{ matrix.python_version }}
           github_token: ${{ secrets.GITHUB_TOKEN }}
-          github_job: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.python_version }})
+          github_job: ${{ matrix.job_name }} (${{ matrix.job_phrase }} ${{ matrix.python_version }}) (${{ join(matrix.os, ', ') }})
       - name: Setup environment
         uses: ./.github/actions/setup-environment-action
         with:
@@ -96,13 +112,17 @@ jobs:
         with:
           gradle-command: :sdks:python:test-suites:tox:py${{steps.set_py_ver_clean.outputs.py_ver_clean}}:testPy${{steps.set_py_ver_clean.outputs.py_ver_clean}}ML
           arguments: |
-            -Pposargs=apache_beam/ml/ \
+            -Pposargs="${{
+              contains(matrix.os, 'self-hosted') &&
+              'apache_beam/ml/ -m (not require_docker_in_docker)' ||
+                'apache_beam/ml/ -m require_docker_in_docker'
+            }}" \
             -PpythonVersion=${{ matrix.python_version }}
       - name: Archive Python Test Results
         uses: actions/upload-artifact@v4
         if: failure()
         with:
-          name: Python ${{ matrix.python_version }} Test Results
+          name: Python ${{ matrix.python_version }} Test Results ${{ matrix.os }}
           path: '**/pytest*.xml'
       - name: Publish Python Test Results
         uses: EnricoMi/publish-unit-test-result-action@v2
@@ -111,4 +131,5 @@ jobs:
           commit: '${{ env.prsha || env.GITHUB_SHA }}'
           comment_mode: ${{ github.event_name == 'issue_comment'  && 'always' || 'off' }}
           files: '**/pytest*.xml'
-          large_files: true
\ No newline at end of file
+          large_files: true
+          check_name: "Python ${{ matrix.python_version }} Test Results (${{ join(matrix.os, ', ') }})"
diff --git a/.github/workflows/beam_PreCommit_Python_Transforms.yml b/.github/workflows/beam_PreCommit_Python_Transforms.yml
index 8753777057c6..4982dd2f7263 100644
--- a/.github/workflows/beam_PreCommit_Python_Transforms.yml
+++ b/.github/workflows/beam_PreCommit_Python_Transforms.yml
@@ -111,4 +111,4 @@ jobs:
           commit: '${{ env.prsha || env.GITHUB_SHA }}'
           comment_mode: ${{ github.event_name == 'issue_comment'  && 'always' || 'off' }}
           files: '**/pytest*.xml'
-          large_files: true
\ No newline at end of file
+          large_files: true
diff --git a/.github/workflows/beam_PreCommit_Whitespace.yml b/.github/workflows/beam_PreCommit_Whitespace.yml
index 8e5b3f0200c2..a378991dcfcb 100644
--- a/.github/workflows/beam_PreCommit_Whitespace.yml
+++ b/.github/workflows/beam_PreCommit_Whitespace.yml
@@ -86,3 +86,8 @@ jobs:
         uses: ./.github/actions/gradle-command-self-hosted-action
         with:
           gradle-command: :whitespacePreCommit
+      - name: validate CHANGES.md
+        uses: ./.github/actions/gradle-command-self-hosted-action
+        with:
+          gradle-command: :validateChanges
+
diff --git a/.github/workflows/beam_Publish_Beam_SDK_Snapshots.yml b/.github/workflows/beam_Publish_Beam_SDK_Snapshots.yml
index 49fcff4e91f0..ad75591c3b21 100644
--- a/.github/workflows/beam_Publish_Beam_SDK_Snapshots.yml
+++ b/.github/workflows/beam_Publish_Beam_SDK_Snapshots.yml
@@ -91,15 +91,18 @@ jobs:
         run: |
           BEAM_VERSION_LINE=$(cat gradle.properties | grep "sdk_version")
           echo "BEAM_VERSION=${BEAM_VERSION_LINE#*sdk_version=}" >> $GITHUB_ENV
+      - name: Set latest tag only on master branch
+        if: github.ref == 'refs/heads/master'
+        run: echo "LATEST_TAG=,latest" >> $GITHUB_ENV
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v1
       - name: Authenticate on GCP
-        uses: google-github-actions/auth@v2
+        uses: google-github-actions/auth@v3
         with:
           service_account: ${{ secrets.GCP_SA_EMAIL }}
           credentials_json: ${{ secrets.GCP_SA_KEY }}
       - name: Set up Cloud SDK
-        uses: google-github-actions/setup-gcloud@v2
+        uses: google-github-actions/setup-gcloud@v3
       - name: GCloud Docker credential helper
         run: |
           gcloud auth configure-docker ${{ env.docker_registry }}
@@ -120,6 +123,6 @@ jobs:
           arguments: |
             -Pjava11Home=$JAVA_HOME_11_X64 \
             -Pdocker-repository-root=gcr.io/apache-beam-testing/beam-sdk \
-            -Pdocker-tag-list=${{ github.sha }},${BEAM_VERSION},latest \
+            -Pdocker-tag-list=${{ github.sha }},${BEAM_VERSION}${LATEST_TAG} \
             -Pcontainer-architecture-list=arm64,amd64 \
             -Ppush-containers \
diff --git a/.github/workflows/beam_Publish_Docker_Snapshots.yml b/.github/workflows/beam_Publish_Docker_Snapshots.yml
index 97ad789cec08..ad3f0da22962 100644
--- a/.github/workflows/beam_Publish_Docker_Snapshots.yml
+++ b/.github/workflows/beam_Publish_Docker_Snapshots.yml
@@ -70,6 +70,9 @@ jobs:
           github_job: ${{ matrix.job_name }} (${{ matrix.job_phrase }})
       - name: Setup environment
         uses: ./.github/actions/setup-environment-action
+      - name: Set latest tag only on master branch
+        if: github.ref == 'refs/heads/master'
+        run: echo "LATEST_TAG=,latest" >> $GITHUB_ENV
       - name: GCloud Docker credential helper
         run: |
           gcloud auth configure-docker ${{ env.docker_registry }}
@@ -79,11 +82,11 @@ jobs:
           gradle-command: :runners:spark:3:job-server:container:dockerPush
           arguments: |
             -Pdocker-repository-root=gcr.io/apache-beam-testing/beam_portability \
-            -Pdocker-tag-list=latest \
+            -Pdocker-tag-list=${{ github.sha }}${LATEST_TAG}
       - name: run Publish Docker Snapshots script for Flink
         uses: ./.github/actions/gradle-command-self-hosted-action
         with:
           gradle-command: :runners:flink:1.17:job-server-container:dockerPush
           arguments: |
             -Pdocker-repository-root=gcr.io/apache-beam-testing/beam_portability \
-            -Pdocker-tag-list=latest
\ No newline at end of file
+            -Pdocker-tag-list=${{ github.sha }}${LATEST_TAG}
diff --git a/.github/workflows/beam_Python_ValidatesContainer_Dataflow_ARM.yml b/.github/workflows/beam_Python_ValidatesContainer_Dataflow_ARM.yml
index e70ec88d1abd..bf4764029148 100644
--- a/.github/workflows/beam_Python_ValidatesContainer_Dataflow_ARM.yml
+++ b/.github/workflows/beam_Python_ValidatesContainer_Dataflow_ARM.yml
@@ -75,12 +75,12 @@ jobs:
         with:
           python-version: ${{ matrix.python_version }}
       - name: Authenticate on GCP
-        uses: google-github-actions/auth@v2
+        uses: google-github-actions/auth@v3
         with:
           service_account: ${{ secrets.GCP_SA_EMAIL }}
           credentials_json: ${{ secrets.GCP_SA_KEY }}
       - name: Set up Cloud SDK
-        uses: google-github-actions/setup-gcloud@v2
+        uses: google-github-actions/setup-gcloud@v3
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
       - name: GCloud Docker credential helper
diff --git a/.github/workflows/beam_StressTests_Java_KafkaIO.yml b/.github/workflows/beam_StressTests_Java_KafkaIO.yml
index fc4649eee0b3..1230e81324b5 100644
--- a/.github/workflows/beam_StressTests_Java_KafkaIO.yml
+++ b/.github/workflows/beam_StressTests_Java_KafkaIO.yml
@@ -17,7 +17,7 @@ name: StressTests Java KafkaIO
 
 on:
   schedule:
-    - cron: '0 10 * * 0'
+    - cron: '0 14 * * 0'
   workflow_dispatch:
 
 #Setting explicit permissions for the action to avoid the default permissions which are `write-all` in case of pull_request_target event
diff --git a/.github/workflows/build_release_candidate.yml b/.github/workflows/build_release_candidate.yml
index b9283665f03c..ebad40a5e49a 100644
--- a/.github/workflows/build_release_candidate.yml
+++ b/.github/workflows/build_release_candidate.yml
@@ -40,6 +40,8 @@ on:
           beam_site_pr: create the documentation update PR against apache/beam-site.
           --
           prism: build and upload the artifacts to the release for this tag
+          --
+          managed_io_docs_pr: create the managed-io.md update PR against apache/beam.
         required: true
         default: |
            {java_artifacts: "no",
@@ -47,7 +49,8 @@ on:
             docker_artifacts: "no",
             python_artifacts: "no",
             beam_site_pr: "no",
-            prism: "no"}
+            prism: "no",
+            managed_io_docs_pr: "no"}
 
 env:
   DEVELOCITY_ACCESS_KEY: ${{ secrets.DEVELOCITY_ACCESS_KEY }}
@@ -63,7 +66,7 @@ jobs:
           ref: "v${{ github.event.inputs.RELEASE }}-RC${{ github.event.inputs.RC }}"
           repository: apache/beam
       - name: Install Java 11
-        uses: actions/setup-java@v4
+        uses: actions/setup-java@v5
         with:
           distribution: 'temurin'
           java-version: |
@@ -71,7 +74,7 @@ jobs:
             11
       - name: Import GPG key
         id: import_gpg
-        uses: crazy-max/ghaction-import-gpg@111c56156bcc6918c056dbef52164cfa583dc549
+        uses: crazy-max/ghaction-import-gpg@e89d40939c28e39f97cf32126055eeae86ba74ec
         with:
           gpg_private_key: ${{ secrets.GPG_PRIVATE_KEY }}
       - name: Auth for nexus
@@ -117,13 +120,13 @@ jobs:
             echo "Must provide an apache password to stage artifacts to https://dist.apache.org/repos/dist/dev/beam/"
           fi
       - name: Install Java 11
-        uses: actions/setup-java@v4
+        uses: actions/setup-java@v5
         with:
           distribution: 'temurin'
           java-version: '11'
       - name: Import GPG key
         id: import_gpg
-        uses: crazy-max/ghaction-import-gpg@111c56156bcc6918c056dbef52164cfa583dc549
+        uses: crazy-max/ghaction-import-gpg@e89d40939c28e39f97cf32126055eeae86ba74ec
         with:
           gpg_private_key: ${{ secrets.GPG_PRIVATE_KEY }}
       - name: stage source
@@ -190,7 +193,7 @@ jobs:
           disable-cache: true
       - name: Import GPG key
         id: import_gpg
-        uses: crazy-max/ghaction-import-gpg@111c56156bcc6918c056dbef52164cfa583dc549
+        uses: crazy-max/ghaction-import-gpg@e89d40939c28e39f97cf32126055eeae86ba74ec
         with:
           gpg_private_key: ${{ secrets.GPG_PRIVATE_KEY }}
       - name: Install dependencies
@@ -271,9 +274,9 @@ jobs:
           ref: "v${{ github.event.inputs.RELEASE }}-RC${{ github.event.inputs.RC }}"
           repository: apache/beam
       - name: Free Disk Space (Ubuntu)
-        uses: jlumbroso/free-disk-space@v1.3.0
+        uses: jlumbroso/free-disk-space@v1.3.1
       - name: Install Java 11
-        uses: actions/setup-java@v4
+        uses: actions/setup-java@v5
         with:
           distribution: 'temurin'
           java-version: '11'
@@ -307,7 +310,7 @@ jobs:
       SITE_ROOT_DIR: ${{ github.workspace }}/beam-site
     steps:
       - name: Free Disk Space (Ubuntu)
-        uses: jlumbroso/free-disk-space@v1.3.0
+        uses: jlumbroso/free-disk-space@v1.3.1
         with:
           docker-images: false
       - name: Checkout Beam Repo
@@ -328,11 +331,11 @@ jobs:
         with:
           python-version: '3.9'
       - name: Install node
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v5
         with:
           node-version: '16'
       - name: Install Java 21
-        uses: actions/setup-java@v4
+        uses: actions/setup-java@v5
         with:
           distribution: 'temurin'
           java-version: '21'
@@ -446,12 +449,12 @@ jobs:
           then
             echo "Must provide an apache password to stage artifacts to https://dist.apache.org/repos/dist/dev/beam/"
           fi
-      - uses: actions/setup-go@v5
+      - uses: actions/setup-go@v6
         with:
           go-version: '1.24'
       - name: Import GPG key
         id: import_gpg
-        uses: crazy-max/ghaction-import-gpg@111c56156bcc6918c056dbef52164cfa583dc549
+        uses: crazy-max/ghaction-import-gpg@e89d40939c28e39f97cf32126055eeae86ba74ec
         with:
           gpg_private_key: ${{ secrets.GPG_PRIVATE_KEY }}
       - name: Build prism artifacts
@@ -545,3 +548,69 @@ jobs:
           svn add --force --parents prism
           svn status
           svn commit -m "Staging Prism artifacts for Apache Beam ${RELEASE} RC${RC_NUM}" --non-interactive --username "${{ github.event.inputs.APACHE_ID }}" --password "${{ github.event.inputs.APACHE_PASSWORD }}"
+
+  managed_io_docs_pr:
+    if:  ${{ fromJson(github.event.inputs.STAGE).managed_io_docs_pr == 'yes'}}
+    runs-on: ubuntu-22.04
+    env:
+      BRANCH_NAME: updates_managed_io_docs_${{ github.event.inputs.RELEASE }}_rc${{ github.event.inputs.RC }}
+      BEAM_ROOT_DIR: ${{ github.workspace }}/beam
+      MANAGED_IO_DOCS_PATH: website/www/site/content/en/documentation/io/managed-io.md
+    steps:
+      - name: Checkout Beam Repo
+        uses: actions/checkout@v4
+        with:
+          ref: "v${{ github.event.inputs.RELEASE }}-RC${{ github.event.inputs.RC }}"
+          repository: apache/beam
+          path: beam
+          token: ${{ github.event.inputs.REPO_TOKEN }}
+          persist-credentials: false
+      - name: Install Python 3.9
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.9'
+      - name: Install Java 11
+        uses: actions/setup-java@v5
+        with:
+          distribution: 'temurin'
+          java-version: '11'
+      - name: Remove default github maven configuration
+        # This step is a workaround to avoid a decryption issue of Beam's
+        # net.linguica.gradle.maven.settings plugin and github's provided maven
+        # settings.xml file
+        run: rm ~/.m2/settings.xml || true
+      - name: Install SDK
+        working-directory: beam/sdks/python
+        run: |
+          pip install -e.
+      - name: Build Expansion Service Jar
+        working-directory: beam
+        run: |
+          ./gradlew sdks:java:io:expansion-service:shadowJar
+      - name: Build GCP Expansion Service Jar
+        working-directory: beam
+        run: |
+          ./gradlew sdks:java:io:google-cloud-platform:expansion-service:shadowJar
+      - name: Generate Managed IO Docs
+        working-directory: beam/sdks/python
+        run: |
+          python gen_managed_doc.py --output_location ${{ runner.temp }}/managed-io.md
+      - name: Create commit on beam branch
+        working-directory: beam
+        run: |
+          git fetch origin master
+          git checkout -b $BRANCH_NAME origin/master
+          mv ${{ runner.temp }}/managed-io.md ${{ env.MANAGED_IO_DOCS_PATH }}
+          git config user.name $GITHUB_ACTOR
+          git config user.email actions@"$RUNNER_NAME".local
+          git add ${{ env.MANAGED_IO_DOCS_PATH }}
+          git commit --allow-empty -m "Update managed-io.md for release ${{ github.event.inputs.RELEASE }}-RC${{ github.event.inputs.RC }}."
+          git push -f --set-upstream origin $BRANCH_NAME
+      - name: Create beam PR
+        working-directory: beam
+        env:
+          GH_TOKEN: ${{ github.event.inputs.REPO_TOKEN }}
+          PR_TITLE: "Update managed-io.md for release ${{ github.event.inputs.RELEASE }}-RC${{ github.event.inputs.RC }}"
+          PR_BODY: "Content generated from release ${{ github.event.inputs.RELEASE }}-RC${{ github.event.inputs.RC }}."
+        run: |
+          gh pr create -t "$PR_TITLE" -b "$PR_BODY" --base master --repo apache/beam
diff --git a/.github/workflows/build_runner_image.yml b/.github/workflows/build_runner_image.yml
index 0f17a9073daf..ddd01d7644e4 100644
--- a/.github/workflows/build_runner_image.yml
+++ b/.github/workflows/build_runner_image.yml
@@ -47,7 +47,7 @@ jobs:
     - name: Set up Docker Buildx
       uses: docker/setup-buildx-action@v1
     - name: Build and Load to docker
-      uses: docker/build-push-action@v4
+      uses: docker/build-push-action@v6
       with:
         context: ${{ env.working-directory }}
         load: true
@@ -57,7 +57,7 @@ jobs:
     - name: Push Docker image
       if: github.ref == 'refs/heads/master'
       id: docker_build
-      uses: docker/build-push-action@v4
+      uses: docker/build-push-action@v6
       with:
         context: ${{ env.working-directory }}
         push: true
diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index 51087dadd244..3408d3c32de7 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -202,7 +202,7 @@ jobs:
     if: needs.check_env_variables.outputs.gcp-variables-set == 'true'
     steps:
       - name: Download compressed sources from artifacts
-        uses: actions/download-artifact@v4.1.8
+        uses: actions/download-artifact@v5
         with:
           name: source_zip
           path: source/
@@ -233,13 +233,13 @@ jobs:
         py_version: ["cp39-", "cp310-", "cp311-", "cp312-"]
     steps:
     - name: Download python source distribution from artifacts
-      uses: actions/download-artifact@v4.1.8
+      uses: actions/download-artifact@v5
       with:
         name: source
         path: apache-beam-source
     - name: Download Python SDK RC source distribution from artifacts
       if: ${{ needs.build_source.outputs.is_rc == 1 }}
-      uses: actions/download-artifact@v4.1.8
+      uses: actions/download-artifact@v5
       with:
         name: source_rc${{ needs.build_source.outputs.rc_num }}
         path: apache-beam-source-rc
@@ -247,7 +247,7 @@ jobs:
       uses: actions/setup-python@v5
       with:
         python-version: 3.9
-    - uses: docker/setup-qemu-action@v1
+    - uses: docker/setup-qemu-action@v3
       if: ${{matrix.os_python.arch == 'aarch64'}}
       name: Set up QEMU
     - name: Install cibuildwheel
@@ -316,7 +316,7 @@ jobs:
     if: needs.check_env_variables.outputs.gcp-variables-set == 'true' && github.event_name != 'pull_request'
     steps:
     - name: Download wheels from artifacts
-      uses: actions/download-artifact@v4
+      uses: actions/download-artifact@v5
       with:
         pattern: wheelhouse-*
         merge-multiple: true
diff --git a/.github/workflows/code_completion_plugin_tests.yml b/.github/workflows/code_completion_plugin_tests.yml
index 0c14f4a2ffab..6829272ffdf0 100644
--- a/.github/workflows/code_completion_plugin_tests.yml
+++ b/.github/workflows/code_completion_plugin_tests.yml
@@ -24,6 +24,7 @@
 
 name: Code Completion Plugin Tests
 on:
+  workflow_dispatch:
   push:
     branches-ignore:
       - 'master'
@@ -73,7 +74,7 @@ jobs:
 
       # Setup Java environment for the next steps
       - name: Setup Java
-        uses: actions/setup-java@v4
+        uses: actions/setup-java@v5
         with:
           distribution: 'temurin'
           java-version: '11'
diff --git a/.github/workflows/finalize_release.yml b/.github/workflows/finalize_release.yml
index 5180fa9a4818..b702ad4c8a5c 100644
--- a/.github/workflows/finalize_release.yml
+++ b/.github/workflows/finalize_release.yml
@@ -51,9 +51,9 @@ jobs:
         RC_NUM: "${{ github.event.inputs.RC }}"
         RC_VERSION: "rc${{ github.event.inputs.RC }}"
       run: |
-        
+
         echo "Publish SDK docker images to Docker Hub."
-        
+
         echo "================Pull RC Containers from DockerHub==========="
         IMAGES=$(docker search apache/beam --format "{{.Name}}" --limit 100)
         KNOWN_IMAGES=()
@@ -64,7 +64,7 @@ jobs:
             KNOWN_IMAGES+=( $IMAGE )
           fi
         done < <(echo "${IMAGES}")
-        
+
         echo "================Confirming Release and RC version==========="
         echo "Publishing the following images:"
         # Sort by name for easy examination
@@ -75,7 +75,7 @@ jobs:
         for IMAGE in "${KNOWN_IMAGES[@]}"; do
           # Perform a carbon copy of ${RC_VERSION} to dockerhub with a new tag as ${RELEASE}.
           docker buildx imagetools create --tag "${IMAGE}:${RELEASE}" "${IMAGE}:${RELEASE}${RC_VERSION}"
-      
+
           # Perform a carbon copy of ${RC_VERSION} to dockerhub with a new tag as latest.
           docker buildx imagetools create --tag "${IMAGE}:latest" "${IMAGE}:${RELEASE}"
         done
@@ -93,8 +93,10 @@ jobs:
         echo "::add-mask::$PYPI_PASSWORD"
     - name: Validate PyPi id/password
       run: |
-        echo "::add-mask::${{ github.event.inputs.PYPI_API_TOKEN }}"
-        if [ "${{ github.event.inputs.PYPI_API_TOKEN }}" == "" ]
+        # Workaround for Actions bug - https://github.com/actions/runner/issues/643
+        PYPI_API_TOKEN=$(jq -r '.inputs.PYPI_API_TOKEN' $GITHUB_EVENT_PATH)
+        echo "::add-mask::$PYPI_API_TOKEN"
+        if [ "$PYPI_API_TOKEN" == "" ]
         then
           echo "Must provide a PyPi password to publish artifacts to PyPi"
           exit 1
@@ -131,7 +133,7 @@ jobs:
         git config user.email actions@"$RUNNER_NAME".local
     - name: Import GPG key
       id: import_gpg
-      uses: crazy-max/ghaction-import-gpg@111c56156bcc6918c056dbef52164cfa583dc549
+      uses: crazy-max/ghaction-import-gpg@e89d40939c28e39f97cf32126055eeae86ba74ec
       with:
         gpg_private_key: ${{ secrets.GPG_PRIVATE_KEY }}
     - name: Push tags
@@ -142,14 +144,14 @@ jobs:
       run: |
         # Ensure local tags are in sync. If there's a mismatch, it will tell you.
         git fetch --all --tags --prune
-        
+
         # If the tag exists, a commit number is produced, otherwise there's an error.
         git rev-list $RC_TAG -n 1
-        
+
         # Tag for Go SDK
         git tag "sdks/$VERSION_TAG" "$RC_TAG"^{} -m "Tagging release" --local-user="${{steps.import_gpg.outputs.name}}"
         git push https://github.com/apache/beam "sdks/$VERSION_TAG"
-        
+
         # Tag for repo root.
         git tag "$VERSION_TAG" "$RC_TAG"^{} -m "Tagging release" --local-user="${{steps.import_gpg.outputs.name}}"
         git push https://github.com/apache/beam "$VERSION_TAG"
diff --git a/.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_VLLM_Gemma_Batch.txt b/.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_VLLM_Gemma_Batch.txt
new file mode 100644
index 000000000000..6101fe5da457
--- /dev/null
+++ b/.github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_VLLM_Gemma_Batch.txt
@@ -0,0 +1,36 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+--runner=DataflowRunner
+--region=us-central1
+--temp_location=gs://temp-storage-for-perf-tests/loadtests
+--staging_location=gs://temp-storage-for-perf-tests/loadtests
+--input=gs://apache-beam-ml/testing/inputs/sentences_50k.txt
+--machine_type=n1-standard-8
+--worker_zone=us-central1-b
+--disk_size_gb=50
+--input_options={}
+--num_workers=8
+--max_num_workers=25
+--autoscaling_algorithm=THROUGHPUT_BASED
+--publish_to_big_query=true
+--sdk_location=container
+--output_table=apache-beam-testing.beam_run_inference.result_gemma_vllm_batch
+--metrics_dataset=beam_run_inference
+--metrics_table=gemma_vllm_batch
+--influx_measurement=gemma_vllm_batch
+--model_gcs_path=gs://apache-beam-ml/models/gemma-2b-it
+--dataflow_service_options=worker_accelerator=type:nvidia-tesla-t4;count:1;install-nvidia-driver
+--experiments=use_runner_v2
\ No newline at end of file
diff --git a/.github/workflows/pr-bot-new-prs.yml b/.github/workflows/pr-bot-new-prs.yml
index 0f17d662db9c..ac1a599e8539 100644
--- a/.github/workflows/pr-bot-new-prs.yml
+++ b/.github/workflows/pr-bot-new-prs.yml
@@ -35,7 +35,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Setup Node
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v5
         with:
           node-version: 16
       - name: Install pr-bot npm dependencies
diff --git a/.github/workflows/pr-bot-pr-updates.yml b/.github/workflows/pr-bot-pr-updates.yml
index 02c8a2473ff3..962dc5e2d9a9 100644
--- a/.github/workflows/pr-bot-pr-updates.yml
+++ b/.github/workflows/pr-bot-pr-updates.yml
@@ -40,7 +40,7 @@ jobs:
         with:
           ref: 'master'
       - name: Setup Node
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v5
         with:
           node-version: 16
       - name: Install pr-bot npm dependencies
diff --git a/.github/workflows/pr-bot-prs-needing-attention.yml b/.github/workflows/pr-bot-prs-needing-attention.yml
index 95be91e8dcb4..dba7a25a94f8 100644
--- a/.github/workflows/pr-bot-prs-needing-attention.yml
+++ b/.github/workflows/pr-bot-prs-needing-attention.yml
@@ -35,7 +35,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Setup Node
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v5
         with:
           node-version: 16
       - name: Install pr-bot npm dependencies
diff --git a/.github/workflows/refresh_looker_metrics.yml b/.github/workflows/refresh_looker_metrics.yml
index 17c993f96a02..ff0a1d33593c 100644
--- a/.github/workflows/refresh_looker_metrics.yml
+++ b/.github/workflows/refresh_looker_metrics.yml
@@ -43,10 +43,10 @@ jobs:
           python-version: 3.11
       - run: pip install requests google-cloud-storage looker-sdk
       - name: Authenticate on GCP
-        uses: google-github-actions/auth@v2
+        uses: google-github-actions/auth@v3
         with:
           service_account: ${{ secrets.GCP_SA_EMAIL }}
           credentials_json: ${{ secrets.GCP_SA_KEY }}
       - name: Set up Cloud SDK
-        uses: google-github-actions/setup-gcloud@v2
+        uses: google-github-actions/setup-gcloud@v3
       - run: python .test-infra/tools/refresh_looker_metrics.py
diff --git a/.github/workflows/reportGenerator.yml b/.github/workflows/reportGenerator.yml
index 91890b12ff00..da8c7ca206ac 100644
--- a/.github/workflows/reportGenerator.yml
+++ b/.github/workflows/reportGenerator.yml
@@ -28,7 +28,7 @@ jobs:
     steps:
     - uses: actions/checkout@v4
     - name: Setup Node
-      uses: actions/setup-node@v4
+      uses: actions/setup-node@v5
       with:
         node-version: 16
     - run: |
diff --git a/.github/workflows/republish_released_docker_containers.yml b/.github/workflows/republish_released_docker_containers.yml
index 2a1bda2a6eb3..9172ff9d4296 100644
--- a/.github/workflows/republish_released_docker_containers.yml
+++ b/.github/workflows/republish_released_docker_containers.yml
@@ -32,7 +32,7 @@ on:
   - cron: "0 6 * * 1"
 env:
   docker_registry: gcr.io
-  release: "${{ github.event.inputs.RELEASE || '2.66.0' }}"
+  release: "${{ github.event.inputs.RELEASE || '2.68.0' }}"
   rc: "${{ github.event.inputs.RC || '2' }}"
 
 jobs:
@@ -61,9 +61,9 @@ jobs:
           ref: "release-${{ env.release }}-postrelease"
           repository: apache/beam
       - name: Free Disk Space (Ubuntu)
-        uses: jlumbroso/free-disk-space@v1.3.0
+        uses: jlumbroso/free-disk-space@v1.3.1
       - name: Install Java 11
-        uses: actions/setup-java@v4
+        uses: actions/setup-java@v5
         with:
           distribution: 'temurin'
           java-version: '11'
@@ -72,12 +72,12 @@ jobs:
         with:
           python-version: '3.9'
       - name: Authenticate on GCP
-        uses: google-github-actions/auth@v2
+        uses: google-github-actions/auth@v3
         with:
           service_account: ${{ secrets.GCP_SA_EMAIL }}
           credentials_json: ${{ secrets.GCP_SA_KEY }}
       - name: Set up Cloud SDK
-        uses: google-github-actions/setup-gcloud@v2
+        uses: google-github-actions/setup-gcloud@v3
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
       - name: Remove default github maven configuration
diff --git a/.github/workflows/run_rc_validation_python_mobile_gaming.yml b/.github/workflows/run_rc_validation_python_mobile_gaming.yml
index 847139b36f0c..ea6fe1a44683 100644
--- a/.github/workflows/run_rc_validation_python_mobile_gaming.yml
+++ b/.github/workflows/run_rc_validation_python_mobile_gaming.yml
@@ -115,7 +115,7 @@ jobs:
         shell: bash
 
       - name: Set up Cloud SDK
-        uses: google-github-actions/setup-gcloud@v2
+        uses: google-github-actions/setup-gcloud@v3
 
       - name: Download RC Artifacts
         run: |
diff --git a/.github/workflows/run_rc_validation_python_yaml.yml b/.github/workflows/run_rc_validation_python_yaml.yml
index de534d8ed59e..96a9b8801674 100644
--- a/.github/workflows/run_rc_validation_python_yaml.yml
+++ b/.github/workflows/run_rc_validation_python_yaml.yml
@@ -102,7 +102,7 @@ jobs:
         shell: bash
 
       - name: Set up Cloud SDK
-        uses: google-github-actions/setup-gcloud@v2
+        uses: google-github-actions/setup-gcloud@v3
 
       - name: Download RC Artifacts
         run: |
diff --git a/.github/workflows/self-assign.yml b/.github/workflows/self-assign.yml
index 739b23c78be4..13459bbfa986 100644
--- a/.github/workflows/self-assign.yml
+++ b/.github/workflows/self-assign.yml
@@ -25,7 +25,7 @@ jobs:
     if: ${{ !github.event.issue.pull_request }}
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/github-script@v7
+    - uses: actions/github-script@v8
       with:
         script: |
           const body = context.payload.comment.body.replace( /\r\n/g, " " ).replace( /\n/g, " " ).split(' ');
diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml
index 490d25bf9882..e3d1a4c5cb0a 100644
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -28,7 +28,7 @@ jobs:
       issues: write
       pull-requests: write
     steps:
-    - uses: actions/stale@v9
+    - uses: actions/stale@v10
       with:
         repo-token: ${{ secrets.GITHUB_TOKEN }}
         stale-pr-message: 'This pull request has been marked as stale due to 60 days of inactivity. It will be closed in 1 week if no further activity occurs. If you think that’s incorrect or this pull request requires a review, please simply write any comment. If closed, you can revive the PR at any time and @mention a reviewer or discuss it on the dev@beam.apache.org list. Thank you for your contributions.'
diff --git a/.github/workflows/tour_of_beam_backend.yml b/.github/workflows/tour_of_beam_backend.yml
index e3a016a4b5a7..fb7b61f6b05c 100644
--- a/.github/workflows/tour_of_beam_backend.yml
+++ b/.github/workflows/tour_of_beam_backend.yml
@@ -42,7 +42,7 @@ jobs:
         working-directory: ./learning/tour-of-beam/backend
     steps:
       - uses: actions/checkout@v4
-      - uses: actions/setup-go@v5
+      - uses: actions/setup-go@v6
         with:
           # pin to the biggest Go version supported by Cloud Functions runtime
           go-version: '1.16'
@@ -58,7 +58,7 @@ jobs:
         run: go test -v ./...
 
       - name: golangci-lint
-        uses: golangci/golangci-lint-action@v3
+        uses: golangci/golangci-lint-action@v8
         with:
           version: v1.49.0
           working-directory: learning/tour-of-beam/backend
diff --git a/.github/workflows/typescript_tests.yml b/.github/workflows/typescript_tests.yml
index a3f929817661..d438b4dd93f9 100644
--- a/.github/workflows/typescript_tests.yml
+++ b/.github/workflows/typescript_tests.yml
@@ -42,6 +42,8 @@ on:
 concurrency:
   group: '${{ github.workflow }} @ ${{ github.event.issue.number || github.event.pull_request.head.label || github.sha || github.head_ref || github.ref }}-${{ github.event.schedule || github.event.comment.id || github.event.sender.login}}'
   cancel-in-progress: true
+env:
+  DEVELOCITY_ACCESS_KEY: ${{ secrets.DEVELOCITY_ACCESS_KEY }}
 jobs:
   typescript_unit_tests:
     name: 'TypeScript Unit Tests'
@@ -57,21 +59,32 @@ jobs:
           persist-credentials: false
           submodules: recursive
       - name: Install node
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v5
         with:
-          node-version: '16'
+          node-version: '18'
+      - name: Install Develocity npm Agent
+        run: npm exec -y -- pacote extract @gradle-tech/develocity-agent@2.0.2 ~/.node_libraries/@gradle-tech/develocity-agent
+        working-directory: ./sdks/typescript
       - run: npm ci
         working-directory: ./sdks/typescript
+        env:
+          NODE_OPTIONS: '-r @gradle-tech/develocity-agent/preload'
       - run: npm run build
         working-directory: ./sdks/typescript
+        env:
+          NODE_OPTIONS: '-r @gradle-tech/develocity-agent/preload'
       - run: npm run prettier-check
         working-directory: ./sdks/typescript
+        env:
+          NODE_OPTIONS: '-r @gradle-tech/develocity-agent/preload'
         if: contains(matrix.os, 'ubuntu-20.04')
 #      - run: npm run codecovTest
 #        working-directory: ./sdks/typescript
 #        if: ${{ matrix.os == 'ubuntu-latest' }}
       - run: npm test
         working-directory: ./sdks/typescript
+        env:
+          NODE_OPTIONS: '-r @gradle-tech/develocity-agent/preload'
 #        if: ${{ matrix.os != 'ubuntu-latest' }}
   typescript_xlang_tests:
     name: 'TypeScript xlang Tests'
@@ -88,9 +101,12 @@ jobs:
           persist-credentials: false
           submodules: recursive
       - name: Install Node
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v5
         with:
-          node-version: '16'
+          node-version: '18'
+      - name: Install Develocity npm Agent
+        run: npm exec -y -- pacote extract @gradle-tech/develocity-agent@2.0.2 ~/.node_libraries/@gradle-tech/develocity-agent
+        working-directory: ./sdks/typescript
       - name: Install Python
         uses: actions/setup-python@v5
         with:
@@ -102,12 +118,17 @@ jobs:
           pip install -e .
       - run: npm ci
         working-directory: ./sdks/typescript
+        env:
+          NODE_OPTIONS: '-r @gradle-tech/develocity-agent/preload'
       - run: npm run build
         working-directory: ./sdks/typescript
+        env:
+          NODE_OPTIONS: '-r @gradle-tech/develocity-agent/preload'
       - run: npm test -- --grep "@xlang" --grep "@ulr"
         working-directory: ./sdks/typescript
         env:
           BEAM_SERVICE_OVERRIDES: '{"python:*": "python"}'
+          NODE_OPTIONS: '-r @gradle-tech/develocity-agent/preload'
 
   check_gcp_variables:
     timeout-minutes: 5
@@ -143,9 +164,12 @@ jobs:
           persist-credentials: false
           submodules: recursive
       - name: Install node
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v5
         with:
-          node-version: '16'
+          node-version: '18'
+      - name: Install Develocity npm Agent
+        run: npm exec -y -- pacote extract @gradle-tech/develocity-agent@2.0.2 ~/.node_libraries/@gradle-tech/develocity-agent
+        working-directory: ./sdks/typescript
       - name: Install python
         uses: actions/setup-python@v5
         with:
@@ -157,8 +181,12 @@ jobs:
           pip install -e ".[gcp]"
       - run: npm ci
         working-directory: ./sdks/typescript
+        env:
+          NODE_OPTIONS: '-r @gradle-tech/develocity-agent/preload'
       - run: npm run build
         working-directory: ./sdks/typescript
+        env:
+          NODE_OPTIONS: '-r @gradle-tech/develocity-agent/preload'
       - run: npm test -- --grep "@dataflow"
         working-directory: ./sdks/typescript
         env:
@@ -166,3 +194,4 @@ jobs:
           GCP_PROJECT_ID: ${{ secrets.GCP_PROJECT_ID }}
           GCP_REGION: ${{ secrets.GCP_REGION }}
           GCP_TESTING_BUCKET: 'gs://${{ secrets.GCP_TESTING_BUCKET }}/tmp'
+          NODE_OPTIONS: '-r @gradle-tech/develocity-agent/preload'
diff --git a/.test-infra/metrics/grafana/dashboards/home/getting_started.json b/.test-infra/metrics/grafana/dashboards/home/getting_started.json
index 41280f4311ee..d1ba640457e1 100644
--- a/.test-infra/metrics/grafana/dashboards/home/getting_started.json
+++ b/.test-infra/metrics/grafana/dashboards/home/getting_started.json
@@ -77,19 +77,6 @@
       ],
       "title": "Performance tests metrics",
       "type": "dashlist"
-    },
-    {
-      "datasource": null,
-      "gridPos": {
-        "h": 15,
-        "w": 6,
-        "x": 18,
-        "y": 2
-      },
-      "id": 123124,
-      "links": [],
-      "title": "Useful links",
-      "type": "homelinks"
     }
   ],
   "schemaVersion": 22,
diff --git a/.test-infra/mock-apis/go.mod b/.test-infra/mock-apis/go.mod
index 3bbc5152c975..42161f63e239 100644
--- a/.test-infra/mock-apis/go.mod
+++ b/.test-infra/mock-apis/go.mod
@@ -20,7 +20,8 @@
 // directory.
 module github.com/apache/beam/test-infra/mock-apis
 
-go 1.21
+go 1.23.0
+
 toolchain go1.24.4
 
 require (
@@ -35,8 +36,7 @@ require (
 
 require (
 	cloud.google.com/go v0.110.6 // indirect
-	cloud.google.com/go/compute v1.23.0 // indirect
-	cloud.google.com/go/compute/metadata v0.2.3 // indirect
+	cloud.google.com/go/compute/metadata v0.3.0 // indirect
 	cloud.google.com/go/longrunning v0.5.1 // indirect
 	github.com/cespare/xxhash/v2 v2.2.0 // indirect
 	github.com/dgryski/go-rendezvous v0.0.0-20200823014737-9f7001d12a5f // indirect
@@ -48,7 +48,7 @@ require (
 	go.opencensus.io v0.24.0 // indirect
 	golang.org/x/crypto v0.35.0 // indirect
 	golang.org/x/net v0.23.0 // indirect
-	golang.org/x/oauth2 v0.12.0 // indirect
+	golang.org/x/oauth2 v0.27.0 // indirect
 	golang.org/x/sync v0.11.0 // indirect
 	golang.org/x/sys v0.30.0 // indirect
 	golang.org/x/text v0.22.0 // indirect
diff --git a/.test-infra/mock-apis/go.sum b/.test-infra/mock-apis/go.sum
index 5a2446ddbc46..48e16c656a38 100644
--- a/.test-infra/mock-apis/go.sum
+++ b/.test-infra/mock-apis/go.sum
@@ -2,10 +2,8 @@ cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMT
 cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
 cloud.google.com/go v0.110.6 h1:8uYAkj3YHTP/1iwReuHPxLSbdcyc+dSBbzFMrVwDR6Q=
 cloud.google.com/go v0.110.6/go.mod h1:+EYjdK8e5RME/VY/qLCAtuyALQ9q67dvuum8i+H5xsI=
-cloud.google.com/go/compute v1.23.0 h1:tP41Zoavr8ptEqaW6j+LQOnyBBhO7OkOMAGrgLopTwY=
-cloud.google.com/go/compute v1.23.0/go.mod h1:4tCnrn48xsqlwSAiLf1HXMQk8CONslYbdiEZc9FEIbM=
-cloud.google.com/go/compute/metadata v0.2.3 h1:mg4jlk7mCAj6xXp9UJ4fjI9VUI5rubuGBW5aJ7UnBMY=
-cloud.google.com/go/compute/metadata v0.2.3/go.mod h1:VAV5nSsACxMJvgaAuX6Pk2AawlZn8kiOGuCv6gTkwuA=
+cloud.google.com/go/compute/metadata v0.3.0 h1:Tz+eQXMEqDIKRsmY3cHTL6FVaynIjX2QxYC4trgAKZc=
+cloud.google.com/go/compute/metadata v0.3.0/go.mod h1:zFmK7XCadkQkj6TtorcaGlCW1hT1fIilQDwofLpJ20k=
 cloud.google.com/go/iam v1.1.1 h1:lW7fzj15aVIXYHREOqjRBV9PsH0Z6u8Y46a1YGvQP4Y=
 cloud.google.com/go/iam v1.1.1/go.mod h1:A5avdyVL2tCppe4unb0951eI9jreack+RJ0/d+KUZOU=
 cloud.google.com/go/logging v1.8.1 h1:26skQWPeYhvIasWKm48+Eq7oUqdcdbwsCVwz5Ys0FvU=
@@ -125,8 +123,8 @@ golang.org/x/net v0.23.0 h1:7EYJ93RZ9vYSZAIb2x3lnuvqO5zneoD6IvWjuhfxjTs=
 golang.org/x/net v0.23.0/go.mod h1:JKghWKKOSdJwpW2GEx0Ja7fmaKnMsbu+MWVZTokSYmg=
 golang.org/x/oauth2 v0.0.0-20180821212333-d2e6202438be/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
 golang.org/x/oauth2 v0.0.0-20200107190931-bf48bf16ab8d/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
-golang.org/x/oauth2 v0.12.0 h1:smVPGxink+n1ZI5pkQa8y6fZT0RW0MgCO5bFpepy4B4=
-golang.org/x/oauth2 v0.12.0/go.mod h1:A74bZ3aGXgCY0qaIC9Ahg6Lglin4AMAco8cIv9baba4=
+golang.org/x/oauth2 v0.27.0 h1:da9Vo7/tDv5RH/7nZDz1eMGS/q1Vv1N/7FCrBhI9I3M=
+golang.org/x/oauth2 v0.27.0/go.mod h1:onh5ek6nERTohokkhCD/y2cV4Do3fxFHFuAejCkRWT8=
 golang.org/x/sync v0.0.0-20180314180146-1d60e4601c6f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.0.0-20181108010431-42b317875d0f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.0.0-20181221193216-37e7f081c4d4/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
diff --git a/.test-infra/tools/refresh_looker_metrics.py b/.test-infra/tools/refresh_looker_metrics.py
index 200be34f3fd1..a4c6999be775 100644
--- a/.test-infra/tools/refresh_looker_metrics.py
+++ b/.test-infra/tools/refresh_looker_metrics.py
@@ -42,6 +42,7 @@
     ("80", ["253", "254", "255", "256", "257"]),  # PyTorch Resnet 152 Tesla T4
     ("82", ["263", "264", "265", "266", "267"]),  # PyTorch Sentiment Streaming DistilBERT base uncased
     ("85", ["268", "269", "270", "271", "272"]),  # PyTorch Sentiment Batch DistilBERT base uncased
+    ("86", ["284", "285", "286", "287", "288"]),  # VLLM Batch Gemma
 ]
 
 
diff --git a/CHANGES.md b/CHANGES.md
index 965f40c2204d..fff7e3e89b42 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -32,7 +32,6 @@
 ## I/Os
 
 * Support for X source added (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
-* Add support for streaming writes in IOBase (Python)
 
 ## New Features / Improvements
 
@@ -51,6 +50,7 @@
 * Fixed X (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
 
 ## Security Fixes
+
 * Fixed [CVE-YYYY-NNNN](https://www.cve.org/CVERecord?id=CVE-YYYY-NNNN) (Java/Python/Go) ([#X](https://github.com/apache/beam/issues/X)).
 
 ## Known Issues
@@ -59,35 +59,31 @@
 * ([#X](https://github.com/apache/beam/issues/X)).
 -->
 
-# [2.67.0] - Unreleased
+# [2.69.0] - Unreleased
 
 ## Highlights
 
 * New highly anticipated feature X added to Python SDK ([#X](https://github.com/apache/beam/issues/X)).
 * New highly anticipated feature Y added to Java SDK ([#Y](https://github.com/apache/beam/issues/Y)).
+* (Python) Add YAML Editor and Visualization Panel ([#35772](https://github.com/apache/beam/issues/35772)).
 
 ## I/Os
 
 * Support for X source added (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
-* Debezium IO upgraded to 3.1.1 requires Java 17 (Java) ([#34747](https://github.com/apache/beam/issues/34747)).
+* Upgraded Iceberg dependency to 1.10.0 ([#36123](https://github.com/apache/beam/issues/36123)).
 
 ## New Features / Improvements
 
 * X feature added (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
-* Add pip-based install support for JupyterLab Sidepanel extension ([#35397](https://github.com/apache/beam/issues/#35397)).
-* [IcebergIO] Create tables with a specified table properties ([#35496](https://github.com/apache/beam/pull/35496))
-* Add support for comma-separated options in Python SDK (Python) ([#35580](https://github.com/apache/beam/pull/35580)).
-  Python SDK now supports comma-separated values for experiments and dataflow_service_options,
-  matching Java SDK behavior while maintaining backward compatibility.
-* Milvus enrichment handler added (Python) ([#35216](https://github.com/apache/beam/pull/35216)).
-  Beam now supports Milvus enrichment handler capabilities for vector, keyword,
-  and hybrid search operations.
+* Python examples added for CloudSQL enrichment handler on [Beam website](https://beam.apache.org/documentation/transforms/python/elementwise/enrichment-cloudsql/) (Python) ([#35473](https://github.com/apache/beam/issues/36095)).
+* Support for batch mode execution in WriteToPubSub transform added (Python) ([#35990](https://github.com/apache/beam/issues/35990)).
 
 ## Breaking Changes
 
 * X behavior was changed ([#X](https://github.com/apache/beam/issues/X)).
-* Go: The pubsubio.Read transform now accepts ReadOptions as a value type instead of a pointer, and requires exactly one of Topic or Subscription to be set (they are mutually exclusive). Additionally, the ReadOptions struct now includes a Topic field for specifying the topic directly, replacing the previous topic parameter in the Read function signature ([#35369])(https://github.com/apache/beam/pull/35369).
-* SQL: The `ParquetTable` external table provider has changed its handling of the `LOCATION` property. To read from a directory, the path must now end with a trailing slash (e.g., `LOCATION '/path/to/data/'`). Previously, a trailing slash was not required. This change was made to enable support for glob patterns and single-file paths ([#35582])(https://github.com/apache/beam/pull/35582).
+* (Python) Fixed transform naming conflict when executing DataTransform on a dictionary of PColls ([#30445](https://github.com/apache/beam/issues/30445)).
+  This may break update compatibility if you don't provide a `--transform_name_mapping`.
+* Removed deprecated Hadoop versions (2.10.2 and 3.2.4) that are no longer supported for [Iceberg](https://github.com/apache/iceberg/issues/10940) from IcebergIO ([#36282](https://github.com/apache/beam/issues/36282)).
 
 ## Deprecations
 
@@ -96,23 +92,123 @@
 ## Bugfixes
 
 * Fixed X (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
-* [YAML] Fixed handling of missing optional fields in JSON parsing ([#35179](https://github.com/apache/beam/issues/35179)).
+* PulsarIO has now changed support status from incomplete to experimental. Both read and writes should now minimally
+  function (un-partitioned topics, without schema support, timestamp ordered messages for read) (Java)
+  ([#36141](https://github.com/apache/beam/issues/36141)).
 
 ## Known Issues
 
 * ([#X](https://github.com/apache/beam/issues/X)).
 
+# [2.68.0] - 2025-09-22
+
+## Highlights
+
+* [Python] Prism runner now enabled by default for most Python pipelines using the direct runner ([#34612](https://github.com/apache/beam/pull/34612)). This may break some tests, see https://github.com/apache/beam/pull/34612 for details on how to handle issues.
+
+## I/Os
+
+* Upgraded Iceberg dependency to 1.9.2 ([#35981](https://github.com/apache/beam/pull/35981))
+
+## New Features / Improvements
+
+* BigtableRead Connector for BeamYaml added with new Config Param ([#35696](https://github.com/apache/beam/pull/35696))
+* MongoDB Java driver upgraded from 3.12.11 to 5.5.0 with API refactoring and GridFS implementation updates (Java) ([#35946](https://github.com/apache/beam/pull/35946)).
+* Introduced a dedicated module for JUnit-based testing support: `sdks/java/testing/junit`, which provides `TestPipelineExtension` for JUnit 5 while maintaining backward compatibility with existing JUnit 4 `TestRule`-based tests (Java) ([#18733](https://github.com/apache/beam/issues/18733), [#35688](https://github.com/apache/beam/pull/35688)).
+  - To use JUnit 5 with Beam tests, add a test-scoped dependency on `org.apache.beam:beam-sdks-java-testing-junit`.
+* Google CloudSQL enrichment handler added (Python) ([#34398](https://github.com/apache/beam/pull/34398)).
+  Beam now supports data enrichment capabilities using SQL databases, with built-in support for:
+  - Managed PostgreSQL, MySQL, and Microsoft SQL Server instances on CloudSQL
+  - Unmanaged SQL database instances not hosted on CloudSQL (e.g., self-hosted or on-premises databases)
+* [Python] Added the `ReactiveThrottler` and `ThrottlingSignaler` classes to streamline throttling behavior in DoFns, expose throttling mechanisms for users ([#35984](https://github.com/apache/beam/pull/35984))
+* Added a pipeline option to specify the processing timeout for a single element by any PTransform (Java/Python/Go) ([#35174](https://github.com/apache/beam/issues/35174)).
+  - When specified, the SDK harness automatically restarts if an element takes too long to process. Beam runner may then retry processing of the same work item.
+  - Use the `--element_processing_timeout_minutes` option to reduce the chance of having stalled pipelines due to unexpected cases of slow processing, where slowness might not happen again if processing of the same element is retried.
+* (Python) Adding GCP Spanner Change Stream support for Python (apache_beam.io.gcp.spanner) ([#24103](https://github.com/apache/beam/issues/24103)).
+
+## Breaking Changes
+
+* Previously deprecated Beam ZetaSQL component has been removed ([#34423](https://github.com/apache/beam/issues/34423)).
+  ZetaSQL users could migrate to Calcite SQL with BigQuery dialect enabled.
+* Upgraded Beam vendored Calcite to 1.40.0 for Beam SQL ([#35483](https://github.com/apache/beam/issues/35483)), which
+  improves support for BigQuery and other SQL dialects. Note: Minor behavior changes are observed such as output
+  significant digits related to casting.
+* (Python) The deterministic fallback coder for complex types like NamedTuple, Enum, and dataclasses now uses cloudpickle instead of dill. If your pipeline is affected, you may see a warning like: "Using fallback deterministic coder for type X...". You can revert to the previous behavior by using the pipeline option `--update_compatibility_version=2.67.0` ([35725](https://github.com/apache/beam/pull/35725)). Report any pickling related issues to [#34903](https://github.com/apache/beam/issues/34903)
+* (Python) Prism runner now enabled by default for most Python pipelines using the direct runner ([#34612](https://github.com/apache/beam/pull/34612)). This may break some tests, see https://github.com/apache/beam/pull/34612 for details on how to handle issues.
+* Dropped Java 8 support for [IO expansion-service](https://central.sonatype.com/artifact/org.apache.beam/beam-sdks-java-io-expansion-service). Cross-language pipelines using this expansion service will need a Java11+ runtime ([#35981](https://github.com/apache/beam/pull/35981)).
+
+## Deprecations
+
+* Python SDK native SpannerIO (apache_beam/io/gcp/experimental/spannerio) is deprecated. Use cross-language wrapper
+  (apache_beam/io/gcp/spanner) instead (Python) ([#35860](https://github.com/apache/beam/issues/35860)).
+* Samza runner is deprecated and scheduled for removal in Beam 3.0 ([#35448](https://github.com/apache/beam/issues/35448)).
+* Twister2 runner is deprecated and scheduled for removal in Beam 3.0 ([#35905](https://github.com/apache/beam/issues/35905))).
+
+## Bugfixes
+
+* (Python) Fixed Java YAML provider fails on Windows ([#35617](https://github.com/apache/beam/issues/35617)).
+* Fixed BigQueryIO creating temporary datasets in wrong project when temp_dataset is specified with a different project than the pipeline project. For some jobs, temporary datasets will now be created in the correct project (Python) ([#35813](https://github.com/apache/beam/issues/35813)).
+* (Go) Fix duplicates due to reads after blind writes to Bag State ([#35869](https://github.com/apache/beam/issues/35869)).
+  * Earlier Go SDK versions can avoid the issue by not reading in the same call after a blind write.
+
+# [2.67.0] - 2025-08-12
+
+## Highlights
+
+
+## I/Os
+
+* Debezium IO upgraded to 3.1.1 requires Java 17 (Java) ([#34747](https://github.com/apache/beam/issues/34747)).
+* Add support for streaming writes in IOBase (Python)
+* Add IT test for streaming writes for IOBase (Python)
+* Implement support for streaming writes in FileBasedSink (Python)
+* Expose support for streaming writes in AvroIO (Python)
+* Expose support for streaming writes in ParquetIO (Python)
+* Expose support for streaming writes in TextIO (Python)
+* Expose support for streaming writes in TFRecordsIO (Python)
+
+## New Features / Improvements
+
+* Added support for Processing time Timer in the Spark Classic runner ([#33633](https://github.com/apache/beam/issues/33633)).
+* Add pip-based install support for JupyterLab Sidepanel extension ([#35397](https://github.com/apache/beam/issues/35397)).
+* [IcebergIO] Create tables with a specified table properties ([#35496](https://github.com/apache/beam/pull/35496))
+* Add support for comma-separated options in Python SDK (Python) ([#35580](https://github.com/apache/beam/pull/35580)).
+  Python SDK now supports comma-separated values for experiments and dataflow_service_options,
+  matching Java SDK behavior while maintaining backward compatibility.
+* Milvus enrichment handler added (Python) ([#35216](https://github.com/apache/beam/pull/35216)).
+  Beam now supports Milvus enrichment handler capabilities for vector, keyword,
+  and hybrid search operations.
+* [Beam SQL] Add support for DATABASEs, with an implementation for Iceberg ([#35637](https://github.com/apache/beam/issues/35637))
+* Respect BatchSize and MaxBufferingDuration when using `JdbcIO.WriteWithResults`. Previously, these settings were ignored ([#35669](https://github.com/apache/beam/pull/35669)).
+* BigTableWrite Connector for BeamYaml added with mutation feature ([#35435](https://github.com/apache/beam/pull/35435))
+
+## Breaking Changes
+
+* Go: The pubsubio.Read transform now accepts ReadOptions as a value type instead of a pointer, and requires exactly one of Topic or Subscription to be set (they are mutually exclusive). Additionally, the ReadOptions struct now includes a Topic field for specifying the topic directly, replacing the previous topic parameter in the Read function signature ([#35369](https://github.com/apache/beam/pull/35369)).
+* SQL: The `ParquetTable` external table provider has changed its handling of the `LOCATION` property. To read from a directory, the path must now end with a trailing slash (e.g., `LOCATION '/path/to/data/'`). Previously, a trailing slash was not required. This change was made to enable support for glob patterns and single-file paths ([#35582](https://github.com/apache/beam/pull/35582)).
+
+## Bugfixes
+
+* (YAML) Fixed handling of missing optional fields in JSON parsing ([#35179](https://github.com/apache/beam/issues/35179)).
+* (Python) Fix WriteToBigQuery transform using CopyJob does not work with WRITE_TRUNCATE write disposition ([#34247](https://github.com/apache/beam/issues/34247))
+* (Python) Fixed dicomio tags mismatch in integration tests ([#30760](https://github.com/apache/beam/issues/30760)).
+* (Java) Fixed spammy logging issues that affected versions 2.64.0 to 2.66.0.
+
+## Known Issues
+
+* ([#35666](https://github.com/apache/beam/issues/35666)). YAML Flatten incorrectly drops fields when input PCollections' schema are different. This issue exists for all versions since 2.52.0.
+
 # [2.66.0] - 2025-07-01
 
 ## Beam 3.0.0 Development Highlights
 
-* [Java] Java 8 support is now deprecated. It is still supported until Beam 3.
+* (Java) Java 8 support is now deprecated. It is still supported until Beam 3.
   From now, pipeline submitted by Java 8 client uses Java 11 SDK container for
   remote pipeline execution ([35064](https://github.com/apache/beam/pull/35064)).
 
 ## Highlights
 
-* [Python] Several quality-of-life improvements to the vLLM model handler. If you use Beam RunInference with vLLM model handlers, we strongly recommend updating past this release.
+* (Python) Several quality-of-life improvements to the vLLM model handler. If you use Beam RunInference with vLLM model handlers, we strongly recommend updating past this release.
 
 ## I/Os
 
@@ -123,10 +219,11 @@
 * [IcebergIO] Dynamically create namespaces if needed ([#35228](https://github.com/apache/beam/pull/35228))
 
 ## New Features / Improvements
+
 * [Beam SQL] Introducing Beam Catalogs ([#35223](https://github.com/apache/beam/pull/35223))
 * Adding Google Storage Requests Pays feature (Golang)([#30747](https://github.com/apache/beam/issues/30747)).
-* [Python] Prism runner now auto-enabled for some Python pipelines using the direct runner ([#34921](https://github.com/apache/beam/pull/34921)).
-* [YAML] WriteToTFRecord and ReadFromTFRecord Beam YAML support
+* (Python) Prism runner now auto-enabled for some Python pipelines using the direct runner ([#34921](https://github.com/apache/beam/pull/34921)).
+* (YAML) WriteToTFRecord and ReadFromTFRecord Beam YAML support
 * Python: Added JupyterLab 4.x extension compatibility for enhanced notebook integration ([#34495](https://github.com/apache/beam/pull/34495)).
 
 ## Breaking Changes
@@ -140,13 +237,17 @@
 ## Bugfixes
 
 * (Java) Fixed CassandraIO ReadAll does not let a pipeline handle or retry exceptions ([#34191](https://github.com/apache/beam/pull/34191)).
-* [Python] Fixed vLLM model handlers breaking Beam logging. ([#35053](https://github.com/apache/beam/pull/35053)).
-* [Python] Fixed vLLM connection leaks that caused a throughput bottleneck and underutilization of GPU ([#35053](https://github.com/apache/beam/pull/35053)).
-* [Python] Fixed vLLM server recovery mechanism in the event of a process termination ([#35234](https://github.com/apache/beam/pull/35234)).
+* (Python) Fixed vLLM model handlers breaking Beam logging. ([#35053](https://github.com/apache/beam/pull/35053)).
+* (Python) Fixed vLLM connection leaks that caused a throughput bottleneck and underutilization of GPU ([#35053](https://github.com/apache/beam/pull/35053)).
+* (Python) Fixed vLLM server recovery mechanism in the event of a process termination ([#35234](https://github.com/apache/beam/pull/35234)).
 * (Python) Fixed cloudpickle overwriting class states every time loading a same object of dynamic class ([#35062](https://github.com/apache/beam/issues/35062)).
-* [Python] Fixed pip install apache-beam[interactive] causes crash on google colab ([#35148](https://github.com/apache/beam/pull/35148)).
+* (Python) Fixed pip install apache-beam[interactive] causes crash on google colab ([#35148](https://github.com/apache/beam/pull/35148)).
 * [IcebergIO] Fixed Beam <-> Iceberg conversion logic for arrays of structs and maps of structs ([#35230](https://github.com/apache/beam/pull/35230)).
 
+## Known Issues
+
+* (Java) Using histogram metrics can cause spammy logs. To mitigate this issue, filter worker startup logs, or upgrade to 2.67.0.
+
 # [2.65.0] - 2025-05-12
 
 ## I/Os
@@ -160,24 +261,24 @@
 
 ## Breaking Changes
 
-* [Python] Cloudpickle is set as the default `pickle_library`, where previously
+* (Python) Cloudpickle is set as the default `pickle_library`, where previously
   dill was the default in [#34695](https://github.com/apache/beam/pull/34695).
   For known issues, reporting new issues, and understanding cloudpickle
   behavior refer to [#34903](https://github.com/apache/beam/issues/34903).
-* [Python] Reshuffle now preserves PaneInfo, where previously PaneInfo was lost
+* (Python) Reshuffle now preserves PaneInfo, where previously PaneInfo was lost
   after reshuffle. To opt out of this change, set the
   update_compatibility_version to a previous Beam version e.g. "2.64.0".
 ([#34348](https://github.com/apache/beam/pull/34348))
-* [Python] PaneInfo is encoded by PaneInfoCoder, where previously PaneInfo was
+* (Python) PaneInfo is encoded by PaneInfoCoder, where previously PaneInfo was
   encoded with FastPrimitivesCoder falling back to PickleCoder. This only
   affects cases where PaneInfo is directly stored as an element.
   ([#34824](https://github.com/apache/beam/pull/34824))
-* [Python] BigQueryFileLoads now adds a Reshuffle before triggering load jobs.
+* (Python) BigQueryFileLoads now adds a Reshuffle before triggering load jobs.
   This fixes a bug where there can be data loss in a streaming pipeline if there
   is a pending load job during autoscaling. To opt out of this change, set the
   update_compatibility_version to a previous Beam version e.g. "2.64.0".
 ([#34657](https://github.com/apache/beam/pull/34657))
-* [YAML] Kafka source and sink will be automatically replaced with compatible managed transforms.
+* (YAML) Kafka source and sink will be automatically replaced with compatible managed transforms.
   For older Beam versions, streaming update compatiblity can be maintained by specifying the pipeline
   option `update_compatibility_version` ([#34767](https://github.com/apache/beam/issues/34767)).
 
@@ -190,7 +291,7 @@
 
 * Fixed read Beam rows from cross-lang transform (for example, ReadFromJdbc) involving negative 32-bit integers incorrectly decoded to large integers ([#34089](https://github.com/apache/beam/issues/34089))
 * (Java) Fixed SDF-based KafkaIO (ReadFromKafkaViaSDF) to properly handle custom deserializers that extend Deserializer<Row> interface([#34505](https://github.com/apache/beam/pull/34505))
-* [Python] `TypedDict` typehints are now compatible with `Mapping` and `Dict` type annotations.
+* (Python) `TypedDict` typehints are now compatible with `Mapping` and `Dict` type annotations.
 
 ## Security Fixes
 
@@ -198,39 +299,40 @@
 
 ## Known Issues
 
-* [Python] GroupIntoBatches may fail in streaming pipelines. This is caused by cloudpickle. To mitigate this issue specify `pickle_library=dill` in pipeline options ([#35062](https://github.com/apache/beam/issues/35062))
-* [Python] vLLM breaks dataflow logging. To mitigate this issue, set the `VLLM_CONFIGURE_LOGGING=0` environment variable in your custom container.
-* [Python] vLLM leaks connections causing a throughput bottleneck and underutilization of GPU. To mitigate this issue increase the number of `number_of_worker_harness_threads`.
+* (Python) GroupIntoBatches may fail in streaming pipelines. This is caused by cloudpickle. To mitigate this issue specify `pickle_library=dill` in pipeline options ([#35062](https://github.com/apache/beam/issues/35062))
+* (Python) vLLM breaks dataflow logging. To mitigate this issue, set the `VLLM_CONFIGURE_LOGGING=0` environment variable in your custom container.
+* (Python) vLLM leaks connections causing a throughput bottleneck and underutilization of GPU. To mitigate this issue increase the number of `number_of_worker_harness_threads`.
+* (Java) Using histogram metrics can cause spammy logs. To mitigate this issue, filter worker startup logs, or upgrade to 2.67.0.
 
 # [2.64.0] - 2025-03-31
 
 ## Highlights
 
-* Managed API for [Java](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/managed/Managed.html) and [Python](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.managed.html#module-apache_beam.transforms.managed) supports [key I/O connectors](https://beam.apache.org/documentation/io/connectors/) Iceberg, Kafka, and BigQuery.
+* Managed API for (Java)(https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/managed/Managed.html) and (Python)(https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.managed.html#module-apache_beam.transforms.managed) supports [key I/O connectors](https://beam.apache.org/documentation/io/connectors/) Iceberg, Kafka, and BigQuery.
 
 ## I/Os
 
-* [Java] Use API compatible with both com.google.cloud.bigdataoss:util 2.x and 3.x in BatchLoads ([#34105](https://github.com/apache/beam/pull/34105))
+* (Java) Use API compatible with both com.google.cloud.bigdataoss:util 2.x and 3.x in BatchLoads ([#34105](https://github.com/apache/beam/pull/34105))
 * [IcebergIO] Added new CDC source for batch and streaming, available as `Managed.ICEBERG_CDC` ([#33504](https://github.com/apache/beam/pull/33504))
 * [IcebergIO] Address edge case where bundle retry following a successful data commit results in data duplication ([#34264](https://github.com/apache/beam/pull/34264))
-* [Java&Python] Add explicit schema support to JdbcIO read and xlang transform ([#23029](https://github.com/apache/beam/issues/23029))
+* (Java&Python) Add explicit schema support to JdbcIO read and xlang transform ([#23029](https://github.com/apache/beam/issues/23029))
 
 ## New Features / Improvements
 
-* [Python] Support custom coders in Reshuffle ([#29908](https://github.com/apache/beam/issues/29908), [#33356](https://github.com/apache/beam/issues/33356)).
-* [Java] Upgrade SLF4J to 2.0.16. Update default Spark version to 3.5.0. ([#33574](https://github.com/apache/beam/pull/33574))
-* [Java] Support for `--add-modules` JVM option is added through a new pipeline option `JdkAddRootModules`. This allows extending the module graph with optional modules such as SDK incubator modules. Sample usage: `<pipeline invocation> --jdkAddRootModules=jdk.incubator.vector` ([#30281](https://github.com/apache/beam/issues/30281)).
-* Managed API for [Java](https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/managed/Managed.html) and [Python](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.managed.html#module-apache_beam.transforms.managed) supports [key I/O connectors](https://beam.apache.org/documentation/io/connectors/) Iceberg, Kafka, and BigQuery.
-* [YAML] Beam YAML UDFs (such as those used in MapToFields) can now have declared dependencies
+* (Python) Support custom coders in Reshuffle ([#29908](https://github.com/apache/beam/issues/29908), [#33356](https://github.com/apache/beam/issues/33356)).
+* (Java) Upgrade SLF4J to 2.0.16. Update default Spark version to 3.5.0. ([#33574](https://github.com/apache/beam/pull/33574))
+* (Java) Support for `--add-modules` JVM option is added through a new pipeline option `JdkAddRootModules`. This allows extending the module graph with optional modules such as SDK incubator modules. Sample usage: `<pipeline invocation> --jdkAddRootModules=jdk.incubator.vector` ([#30281](https://github.com/apache/beam/issues/30281)).
+* Managed API for (Java)(https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/managed/Managed.html) and (Python)(https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.managed.html#module-apache_beam.transforms.managed) supports [key I/O connectors](https://beam.apache.org/documentation/io/connectors/) Iceberg, Kafka, and BigQuery.
+* (YAML) Beam YAML UDFs (such as those used in MapToFields) can now have declared dependencies
   (e.g. pypi packages for Python, or extra jars for Java).
 * Prism now supports event time triggers for most common cases. ([#31438](https://github.com/apache/beam/issues/31438))
   * Prism does not yet support triggered side inputs, or triggers on merging windows (such as session windows).
 
 ## Breaking Changes
 
-* [Python] Reshuffle now correctly respects user-specified type hints, fixing a previous bug where it might use FastPrimitivesCoder wrongly. This change could break pipelines with incorrect type hints in Reshuffle. If you have issues after upgrading, temporarily set update_compatibility_version to a previous Beam version to use the old behavior. The recommended solution is to fix the type hints in your code. ([#33932](https://github.com/apache/beam/pull/33932))
-* [Java] SparkReceiver 2 has been moved to SparkReceiver 3 that supports Spark 3.x. ([#33574](https://github.com/apache/beam/pull/33574))
-* [Python] Correct parsing of `collections.abc.Sequence` type hints was added, which can lead to pipelines failing type hint checks that were previously passing erroneously. These issues will be most commonly seen trying to consume a PCollection with a `Sequence` type hint after a GroupByKey or a CoGroupByKey. ([#33999](https://github.com/apache/beam/pull/33999)).
+* (Python) Reshuffle now correctly respects user-specified type hints, fixing a previous bug where it might use FastPrimitivesCoder wrongly. This change could break pipelines with incorrect type hints in Reshuffle. If you have issues after upgrading, temporarily set update_compatibility_version to a previous Beam version to use the old behavior. The recommended solution is to fix the type hints in your code. ([#33932](https://github.com/apache/beam/pull/33932))
+* (Java) SparkReceiver 2 has been moved to SparkReceiver 3 that supports Spark 3.x. ([#33574](https://github.com/apache/beam/pull/33574))
+* (Python) Correct parsing of `collections.abc.Sequence` type hints was added, which can lead to pipelines failing type hint checks that were previously passing erroneously. These issues will be most commonly seen trying to consume a PCollection with a `Sequence` type hint after a GroupByKey or a CoGroupByKey. ([#33999](https://github.com/apache/beam/pull/33999)).
 
 ## Bugfixes
 
@@ -245,6 +347,7 @@
 
 * (Java) Current version of protobuf has a [bug](https://github.com/protocolbuffers/protobuf/issues/20599) leading to incompatibilities with clients using older versions of Protobuf ([example issue](https://github.com/GoogleCloudPlatform/DataflowTemplates/issues/2191)). This issue has been seen in SpannerIO in particular. Tracked in [#34452](https://github.com/GoogleCloudPlatform/DataflowTemplates/issues/34452).
 * (Java) When constructing `SpannerConfig` for `SpannerIO`, calling `withHost` with a null or empty host will now result in a Null Pointer Exception (`java.lang.NullPointerException: Cannot invoke "java.lang.CharSequence.length()" because "this.text" is null`). See https://github.com/GoogleCloudPlatform/DataflowTemplates/issues/34489 for context.
+* (Java) Using histogram metrics can cause spammy logs. To mitigate this issue, filter worker startup logs, or upgrade to 2.67.0.
 
 # [2.63.0] - 2025-02-18
 
@@ -279,6 +382,7 @@
   * With this change user workers will request batched GetWork responses from backend and backend will send multiple WorkItems in the same response proto.
   * The feature can be disabled by passing `--windmillRequestBatchedGetWorkResponse=false`
 * Added supports for staging arbitrary files via `--files_to_stage` flag (Python) ([#34208](https://github.com/apache/beam/pull/34208))
+
 ## Breaking Changes
 
 * AWS V1 I/Os have been removed (Java). As part of this, x-lang Python Kinesis I/O has been updated to consume the V2 IO and it also no longer supports setting producer_properties ([#33430](https://github.com/apache/beam/issues/33430)).
@@ -338,7 +442,7 @@
 ## Known Issues
 
 [comment]: # ( When updating known issues after release, make sure also update website blog in website/www/site/content/blog.)
-* [Python] If you are using the official Apache Beam Python containers for version 2.62.0, be aware that they include NumPy version 1.26.4. It is strongly recommended that you explicitly specify numpy==1.26.4 in your project's dependency list. ([#33639](https://github.com/apache/beam/issues/33639)).
+* (Python) If you are using the official Apache Beam Python containers for version 2.62.0, be aware that they include NumPy version 1.26.4. It is strongly recommended that you explicitly specify numpy==1.26.4 in your project's dependency list. ([#33639](https://github.com/apache/beam/issues/33639)).
 * [Dataflow Streaming Appliance] Commits fail with KeyCommitTooLargeException when a key outputs >180MB of results. Bug affects versions 2.60.0 to 2.62.0,
   * fix will be released with 2.63.0. [#33588](https://github.com/apache/beam/issues/33588).
   * To resolve this issue, downgrade to 2.59.0 or upgrade to 2.63.0 or enable [Streaming Engine](https://cloud.google.com/dataflow/docs/streaming-engine#use).
@@ -347,7 +451,7 @@
 
 ## Highlights
 
-* [Python] Introduce Managed Transforms API ([#31495](https://github.com/apache/beam/pull/31495))
+* (Python) Introduce Managed Transforms API ([#31495](https://github.com/apache/beam/pull/31495))
 * Flink 1.19 support added ([#32648](https://github.com/apache/beam/pull/32648))
 
 ## I/Os
@@ -385,7 +489,7 @@
 [comment]: # ( When updating known issues after release, make sure also update website blog in website/www/site/content/blog.)
 * [Managed Iceberg] DataFile metadata is assigned incorrect partition values ([#33497](https://github.com/apache/beam/issues/33497)).
   * Fixed in 2.62.0
-* [Python] If you are using the official Apache Beam Python containers for version 2.61.0, be aware that they include NumPy version 1.26.4. It is strongly recommended that you explicitly specify numpy==1.26.4 in your project's dependency list. ([#33639](https://github.com/apache/beam/issues/33639)).
+* (Python) If you are using the official Apache Beam Python containers for version 2.61.0, be aware that they include NumPy version 1.26.4. It is strongly recommended that you explicitly specify numpy==1.26.4 in your project's dependency list. ([#33639](https://github.com/apache/beam/issues/33639)).
 * [Dataflow Streaming Appliance] Commits fail with KeyCommitTooLargeException when a key outputs >180MB of results. Bug affects versions 2.60.0 to 2.62.0,
   * fix will be released with 2.63.0. [#33588](https://github.com/apache/beam/issues/33588).
   * To resolve this issue, downgrade to 2.59.0 or upgrade to 2.63.0 or enable [Streaming Engine](https://cloud.google.com/dataflow/docs/streaming-engine#use).
@@ -814,7 +918,6 @@ should handle this. ([#25252](https://github.com/apache/beam/issues/25252)).
 * Introduced a pipeline option `--max_cache_memory_usage_mb` to configure state and side input cache size. The cache has been enabled to a default of 100 MB. Use `--max_cache_memory_usage_mb=X` to provide cache size for the user state API and side inputs. ([#28770](https://github.com/apache/beam/issues/28770)).
 * Beam YAML stable release. Beam pipelines can now be written using YAML and leverage the Beam YAML framework which includes a preliminary set of IO's and turnkey transforms. More information can be found in the YAML root folder and in the [README](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/README.md).
 
-
 ## Breaking Changes
 
 * `org.apache.beam.sdk.io.CountingSource.CounterMark` uses custom `CounterMarkCoder` as a default coder since all Avro-dependent
@@ -832,16 +935,10 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Fixed a memory leak, which affected some long-running Python pipelines: [#28246](https://github.com/apache/beam/issues/28246).
 
 ## Security Fixes
+
 * Fixed [CVE-2023-39325](https://www.cve.org/CVERecord?id=CVE-2023-39325) (Java/Python/Go) ([#29118](https://github.com/apache/beam/issues/29118)).
 * Mitigated [CVE-2023-47248](https://nvd.nist.gov/vuln/detail/CVE-2023-47248)  (Python) [#29392](https://github.com/apache/beam/issues/29392).
 
-## Known issues
-
-* MLTransform drops the identical elements in the output PCollection. For any duplicate elements, a single element will be emitted downstream. ([#29600](https://github.com/apache/beam/issues/29600)).
-* Some Python pipelines that run with 2.52.0-2.54.0 SDKs and use large materialized side inputs might be affected by a performance regression. To restore the prior behavior on these SDK versions, supply the `--max_cache_memory_usage_mb=0` pipeline option. (Python) ([#30360](https://github.com/apache/beam/issues/30360)).
-* Users who lauch Python pipelines in an environment without internet access and use the `--setup_file` pipeline option might experience an increase in pipeline submission time. This has been fixed in 2.56.0 ([#31070](https://github.com/apache/beam/pull/31070)).
-* Transforms which use `SnappyCoder` are update incompatible with previous versions of the same transform (Java) on some runners. This includes PubSubIO's read ([#28655](https://github.com/apache/beam/pull/28655#issuecomment-2407839769)).
-
 # [2.51.0] - 2023-10-03
 
 ## New Features / Improvements
@@ -851,7 +948,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Added support to run `mypy` on user pipelines ([#27906](https://github.com/apache/beam/issues/27906))
 * Python SDK worker start-up logs and crash logs are now captured by a buffer and logged at appropriate levels via Beam logging API. Dataflow Runner users might observe that most `worker-startup` log content is now captured by the `worker` logger. Users who relied on `print()` statements for logging might notice that some logs don't flush before pipeline succeeds - we strongly advise to use `logging` package instead of `print()` statements for logging. ([#28317](https://github.com/apache/beam/pull/28317))
 
-
 ## Breaking Changes
 
 * Removed fastjson library dependency for Beam SQL. Table property is changed to be based on jackson ObjectNode (Java) ([#24154](https://github.com/apache/beam/issues/24154)).
@@ -859,15 +955,14 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Removed the parameter `t reflect.Type` from `parquetio.Write`. The element type is derived from the input PCollection (Go) ([#28490](https://github.com/apache/beam/issues/28490))
 * Refactor BeamSqlSeekableTable.setUp adding a parameter joinSubsetType. [#28283](https://github.com/apache/beam/issues/28283)
 
-
 ## Bugfixes
 
 * Fixed exception chaining issue in GCS connector (Python) ([#26769](https://github.com/apache/beam/issues/26769#issuecomment-1700422615)).
 * Fixed streaming inserts exception handling, GoogleAPICallErrors are now retried according to retry strategy and routed to failed rows where appropriate rather than causing a pipeline error (Python) ([#21080](https://github.com/apache/beam/issues/21080)).
 * Fixed a bug in Python SDK's cross-language Bigtable sink that mishandled records that don't have an explicit timestamp set: [#28632](https://github.com/apache/beam/issues/28632).
 
-
 ## Security Fixes
+
 * Python containers updated, fixing [CVE-2021-30474](https://nvd.nist.gov/vuln/detail/CVE-2021-30474), [CVE-2021-30475](https://nvd.nist.gov/vuln/detail/CVE-2021-30475), [CVE-2021-30473](https://nvd.nist.gov/vuln/detail/CVE-2021-30473), [CVE-2020-36133](https://nvd.nist.gov/vuln/detail/CVE-2020-36133), [CVE-2020-36131](https://nvd.nist.gov/vuln/detail/CVE-2020-36131), [CVE-2020-36130](https://nvd.nist.gov/vuln/detail/CVE-2020-36130), and [CVE-2020-36135](https://nvd.nist.gov/vuln/detail/CVE-2020-36135)
 * Used go 1.21.1 to build, fixing [CVE-2023-39320](https://security-tracker.debian.org/tracker/CVE-2023-39320)
 
@@ -878,7 +973,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
   dependency to 1.8.3 or earlier on some runners that don't use Beam Docker containers: [#28811](https://github.com/apache/beam/issues/28811)
 * MLTransform drops the identical elements in the output PCollection. For any duplicate elements, a single element will be emitted downstream. ([#29600](https://github.com/apache/beam/issues/29600)).
 
-
 # [2.50.0] - 2023-08-30
 
 ## Highlights
@@ -943,7 +1037,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 
 # [2.49.0] - 2023-07-17
 
-
 ## I/Os
 
 * Support for Bigtable Change Streams added in Java `BigtableIO.ReadChangeStream` ([#27183](https://github.com/apache/beam/issues/27183))
@@ -968,7 +1061,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Long-running Python pipelines might experience a memory leak: [#28246](https://github.com/apache/beam/issues/28246).
 * Python pipelines using the `--impersonate_service_account` option with BigQuery IOs might fail on Dataflow ([#32030](https://github.com/apache/beam/issues/32030)). This is fixed in 2.59.0 release.
 
-
 # [2.48.0] - 2023-05-31
 
 ## Highlights
@@ -1014,7 +1106,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Long-running Python pipelines might experience a memory leak: [#28246](https://github.com/apache/beam/issues/28246).
 * Python SDK's cross-language Bigtable sink mishandles records that don't have an explicit timestamp set: [#28632](https://github.com/apache/beam/issues/28632). To avoid this issue, set explicit timestamps for all records before writing to Bigtable.
 
-
 # [2.47.0] - 2023-05-10
 
 ## Highlights
@@ -1192,7 +1283,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Fixed Beam SQL CalciteUtils (Java) and Cross-language JdbcIO (Python) did not support JDBC CHAR/VARCHAR, BINARY/VARBINARY logical types ([#23747](https://github.com/apache/beam/issues/23747), [#23526](https://github.com/apache/beam/issues/23526)).
 * Ensure iterated and emitted types are used with the generic register package are registered with the type and schema registries.(Go) ([#23889](https://github.com/apache/beam/pull/23889))
 
-
 # [2.43.0] - 2022-11-17
 
 ## Highlights
@@ -1286,7 +1376,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Fixed a condition where retrying queries would yield an incorrect cursor in the Java SDK Firestore Connector ([#22089](https://github.com/apache/beam/issues/22089)).
 * Fixed plumbing allowed lateness in Go SDK. It was ignoring the user set value earlier and always used to set to 0. ([#22474](https://github.com/apache/beam/issues/22474)).
 
-
 # [2.40.0] - 2022-06-25
 
 ## Highlights
@@ -1313,6 +1402,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Default coder updated to compress sources used with `BoundedSourceAsSDFWrapperFn` and `UnboundedSourceAsSDFWrapper`.
 
 ## Bugfixes
+
 * Fixed Java expansion service to allow specific files to stage ([BEAM-14160](https://issues.apache.org/jira/browse/BEAM-14160)).
 * Fixed Elasticsearch connection when using both ssl and username/password (Java) ([BEAM-14000](https://issues.apache.org/jira/browse/BEAM-14000))
 
@@ -1334,7 +1424,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
   [BEAM-14283](https://issues.apache.org/jira/browse/BEAM-14283)).
 * Implemented Apache PulsarIO ([BEAM-8218](https://issues.apache.org/jira/browse/BEAM-8218)).
 
-
 ## New Features / Improvements
 
 * Support for flink scala 2.12, because most of the libraries support version 2.12 onwards. ([beam-14386](https://issues.apache.org/jira/browse/BEAM-14386))
@@ -1351,7 +1440,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Upgrade to ZetaSQL 2022.04.1 ([BEAM-14348](https://issues.apache.org/jira/browse/BEAM-14348)).
 * Fixed ReadFromBigQuery cannot be used with the interactive runner ([BEAM-14112](https://issues.apache.org/jira/browse/BEAM-14112)).
 
-
 ## Breaking Changes
 
 * Unused functions `ShallowCloneParDoPayload()`, `ShallowCloneSideInput()`, and `ShallowCloneFunctionSpec()` have been removed from the Go SDK's pipelinex package ([BEAM-13739](https://issues.apache.org/jira/browse/BEAM-13739)).
@@ -1375,10 +1463,10 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Fixed Java Spanner IO NPE when ProjectID not specified in template executions (Java) ([BEAM-14405](https://issues.apache.org/jira/browse/BEAM-14405)).
 * Fixed potential NPE in BigQueryServicesImpl.getErrorInfo (Java) ([BEAM-14133](https://issues.apache.org/jira/browse/BEAM-14133)).
 
-
 # [2.38.0] - 2022-04-20
 
 ## I/Os
+
 * Introduce projection pushdown optimizer to the Java SDK ([BEAM-12976](https://issues.apache.org/jira/browse/BEAM-12976)). The optimizer currently only works on the [BigQuery Storage API](https://beam.apache.org/documentation/io/built-in/google-bigquery/#storage-api), but more I/Os will be added in future releases. If you encounter a bug with the optimizer, please file a JIRA and disable the optimizer using pipeline option `--experiments=disable_projection_pushdown`.
 * A new IO for Neo4j graph databases was added. ([BEAM-1857](https://issues.apache.org/jira/browse/BEAM-1857))  It has the ability to update nodes and relationships using UNWIND statements and to read data using cypher statements with parameters.
 * `amazon-web-services2` has reached feature parity and is finally recommended over the earlier `amazon-web-services` and `kinesis` modules (Java). These will be deprecated in one of the next releases ([BEAM-13174](https://issues.apache.org/jira/browse/BEAM-13174)).
@@ -1420,6 +1508,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 # [2.37.0] - 2022-03-04
 
 ## Highlights
+
 * Java 17 support for Dataflow ([BEAM-12240](https://issues.apache.org/jira/browse/BEAM-12240)).
   * Users using Dataflow Runner V2 may see issues with state cache due to inaccurate object sizes ([BEAM-13695](https://issues.apache.org/jira/browse/BEAM-13695)).
   * ZetaSql is currently unsupported ([issue](https://github.com/google/zetasql/issues/89)).
@@ -1443,10 +1532,13 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 
 ## Breaking Changes
 
+
 ## Deprecations
 
+
 ## Bugfixes
 
+
 ## Known Issues
 
 * On rare occations, Python Datastore source may swallow some exceptions. Users are adviced to upgrade to Beam 2.38.0 or later ([BEAM-14282](https://issues.apache.org/jira/browse/BEAM-14282))
@@ -1567,7 +1659,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 ## Breaking Changes
 
 * SQL Rows are no longer flattened ([BEAM-5505](https://issues.apache.org/jira/browse/BEAM-5505)).
-* [Go SDK] beam.TryCrossLanguage's signature now matches beam.CrossLanguage. Like other Try functions it returns an error instead of panicking. ([BEAM-9918](https://issues.apache.org/jira/browse/BEAM-9918)).
+* (Go SDK) beam.TryCrossLanguage's signature now matches beam.CrossLanguage. Like other Try functions it returns an error instead of panicking. ([BEAM-9918](https://issues.apache.org/jira/browse/BEAM-9918)).
 * [BEAM-12925](https://jira.apache.org/jira/browse/BEAM-12925) was fixed. It used to silently pass incorrect null data read from JdbcIO. Pipelines affected by this will now start throwing failures instead of silently passing incorrect data.
 
 ## Bugfixes
@@ -1597,12 +1689,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
     * Minimum Go version is now Go v1.16
   * See the announcement blogpost for full information once published.
 
-<!--
-## I/Os
-
-* Support for X source added (Java/Python) ([BEAM-X](https://issues.apache.org/jira/browse/BEAM-X)).
--->
-
 ## New Features / Improvements
 
 * Projection pushdown in SchemaIO ([BEAM-12609](https://issues.apache.org/jira/browse/BEAM-12609)).
@@ -1622,7 +1708,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Python GBK will stop supporting unbounded PCollections that have global windowing and a default trigger in Beam 2.34. This can be overriden with `--allow_unsafe_triggers`. ([BEAM-9487](https://issues.apache.org/jira/browse/BEAM-9487)).
 * Python GBK will start requiring safe triggers or the `--allow_unsafe_triggers` flag starting with Beam 2.34. ([BEAM-9487](https://issues.apache.org/jira/browse/BEAM-9487)).
 
-## Bug fixes
+## Bugfixes
 
 * Workaround to not delete orphaned files to avoid missing events when using Python WriteToFiles in streaming pipeline ([BEAM-12950](https://issues.apache.org/jira/browse/BEAM-12950)))
 
@@ -1635,6 +1721,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 # [2.32.0] - 2021-08-25
 
 ## Highlights
+
 * The [Beam DataFrame
   API](https://beam.apache.org/documentation/dsls/dataframes/overview/) is no
   longer experimental! We've spent the time since the [2.26.0 preview
@@ -1654,7 +1741,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
   the API, guided by your
   [feedback](https://beam.apache.org/community/contact-us/).
 
-
 ## I/Os
 
 * New experimental Firestore connector in Java SDK, providing sources and sinks to Google Cloud Firestore ([BEAM-8376](https://issues.apache.org/jira/browse/BEAM-8376)).
@@ -1687,6 +1773,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Fixed race condition in RabbitMqIO causing duplicate acks (Java) ([BEAM-6516](https://issues.apache.org/jira/browse/BEAM-6516)))
 
 ## Known Issues
+
 * On rare occations, Python GCS source may swallow some exceptions. Users are adviced to upgrade to Beam 2.38.0 or later ([BEAM-14282](https://issues.apache.org/jira/browse/BEAM-14282))
 
 # [2.31.0] - 2021-07-08
@@ -1774,6 +1861,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 # [2.28.0] - 2021-02-22
 
 ## Highlights
+
 * Many improvements related to Parquet support ([BEAM-11460](https://issues.apache.org/jira/browse/BEAM-11460), [BEAM-8202](https://issues.apache.org/jira/browse/BEAM-8202), and [BEAM-11526](https://issues.apache.org/jira/browse/BEAM-11526))
 * Hash Functions in BeamSQL ([BEAM-10074](https://issues.apache.org/jira/browse/BEAM-10074))
 * Hash functions in ZetaSQL ([BEAM-11624](https://issues.apache.org/jira/browse/BEAM-11624))
@@ -1821,10 +1909,10 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
   on removed APIs. If affected, ensure to use an appropriate Guava version via `dependencyManagement` in Maven and
   `force` in Gradle.
 
-
 # [2.27.0] - 2021-01-08
 
 ## I/Os
+
 * ReadFromMongoDB can now be used with MongoDB Atlas (Python) ([BEAM-11266](https://issues.apache.org/jira/browse/BEAM-11266).)
 * ReadFromMongoDB/WriteToMongoDB will mask password in display_data (Python) ([BEAM-11444](https://issues.apache.org/jira/browse/BEAM-11444).)
 * Support for X source added (Java/Python) ([BEAM-X](https://issues.apache.org/jira/browse/BEAM-X)).
@@ -1856,6 +1944,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Added support for Contextual Text IO (Java), a version of text IO that provides metadata about the records ([BEAM-10124](https://issues.apache.org/jira/browse/BEAM-10124)). Support for this IO is currently experimental. Specifically, **there are no update-compatibility guarantees** for streaming jobs with this IO between current future verisons of Apache Beam SDK.
 
 ## New Features / Improvements
+
 * Added support for avro payload format in Beam SQL Kafka Table ([BEAM-10885](https://issues.apache.org/jira/browse/BEAM-10885))
 * Added support for json payload format in Beam SQL Kafka Table ([BEAM-10893](https://issues.apache.org/jira/browse/BEAM-10893))
 * Added support for protobuf payload format in Beam SQL Kafka Table ([BEAM-10892](https://issues.apache.org/jira/browse/BEAM-10892))
@@ -1873,7 +1962,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 
 * Non-idempotent combiners built via `CombineFn.from_callable()` or `CombineFn.maybe_from_callable()` can lead to incorrect behavior. ([BEAM-11522](https://issues.apache.org/jira/browse/BEAM-11522)).
 
-
 # [2.25.0] - 2020-10-23
 
 ## Highlights
@@ -1920,7 +2008,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 * Dataflow streaming timers once against not strictly time ordered when set earlier mid-bundle, as the fix for  [BEAM-8543](https://issues.apache.org/jira/browse/BEAM-8543) introduced more severe bugs and has been rolled back.
 * Default compressor change breaks dataflow python streaming job update compatibility. Please use python SDK version <= 2.23.0 or > 2.25.0 if job update is critical.([BEAM-11113](https://issues.apache.org/jira/browse/BEAM-11113))
 
-
 # [2.24.0] - 2020-09-18
 
 ## Highlights
@@ -1956,11 +2043,6 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
   --temp_location, or pass method="STREAMING_INSERTS" to WriteToBigQuery ([BEAM-6928](https://issues.apache.org/jira/browse/BEAM-6928)).
 * Python SDK now understands `typing.FrozenSet` type hints, which are not interchangeable with `typing.Set`. You may need to update your pipelines if type checking fails. ([BEAM-10197](https://issues.apache.org/jira/browse/BEAM-10197))
 
-## Known issues
-
-* When a timer fires but is reset prior to being executed, a watermark hold may be leaked, causing a stuck pipeline [BEAM-10991](https://issues.apache.org/jira/browse/BEAM-10991).
-* Default compressor change breaks dataflow python streaming job update compatibility. Please use python SDK version <= 2.23.0 or > 2.25.0 if job update is critical.([BEAM-11113](https://issues.apache.org/jira/browse/BEAM-11113))
-
 # [2.23.0] - 2020-06-29
 
 ## Highlights
@@ -2007,6 +2089,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 
 ## Highlights
 
+
 ## I/Os
 
 * Basic Kafka read/write support for DataflowRunner (Python) ([BEAM-8019](https://issues.apache.org/jira/browse/BEAM-8019)).
@@ -2035,6 +2118,7 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 
 ## Deprecations
 
+
 ## Known Issues
 
 
@@ -2042,7 +2126,9 @@ as a workaround, a copy of "old" `CountingSource` class should be placed into a
 
 ## Highlights
 
+
 ## I/Os
+
 * Python: Deprecated module `apache_beam.io.gcp.datastore.v1` has been removed
 as the client it uses is out of date and does not support Python 3
 ([BEAM-9529](https://issues.apache.org/jira/browse/BEAM-9529)).
@@ -2054,6 +2140,7 @@ for example usage.
 * Python SDK: Added integration tests and updated batch write functionality for Google Cloud Spanner transform ([BEAM-8949](https://issues.apache.org/jira/browse/BEAM-8949)).
 
 ## New Features / Improvements
+
 * Python SDK will now use Python 3 type annotations as pipeline type hints.
 ([#10717](https://github.com/apache/beam/pull/10717))
 
@@ -2064,7 +2151,7 @@ for example usage.
     for that function.
 
     More details will be in
-    [Ensuring Python Type Safety](https://beam.apache.org/documentation/sdks/python-type-safety/)
+    (Ensuring Python Type Safety)(https://beam.apache.org/documentation/sdks/python-type-safety/)
     and an upcoming
     [blog post](https://beam.apache.org/blog/python-typing/index.html).
 
@@ -2094,7 +2181,6 @@ conversion to beam schema options. *Remark: Schema aware is still experimental.*
   The files are added to `/opt/apache/beam/third_party_licenses/`.
   By default, no licenses/notices are added to the docker images. ([BEAM-9136](https://issues.apache.org/jira/browse/BEAM-9136))
 
-
 ## Breaking Changes
 
 * Dataflow runner now requires the `--region` option to be set, unless a default value is set in the environment ([BEAM-9199](https://issues.apache.org/jira/browse/BEAM-9199)). See [here](https://cloud.google.com/dataflow/docs/concepts/regional-endpoints) for more details.
@@ -2104,6 +2190,7 @@ conversion to beam schema options. *Remark: Schema aware is still experimental.*
 * Go SDK docker images are no longer released until further notice.
 
 ## Deprecations
+
 * Java SDK: Beam Schema FieldType.getMetadata is now deprecated and is replaced by the Beam
 Schema Options, it will be removed in version `2.23.0`. ([BEAM-9704](https://issues.apache.org/jira/browse/BEAM-9704))
 * The `--zone` option in the Dataflow runner is now deprecated. Please use `--worker_zone` instead. ([BEAM-9716](https://issues.apache.org/jira/browse/BEAM-9716))
@@ -2124,7 +2211,6 @@ Schema Options, it will be removed in version `2.23.0`. ([BEAM-9704](https://iss
 * Python SDK: Support for Google Cloud Spanner. This is an experimental module for reading and writing data from Google Cloud Spanner ([BEAM-7246](https://issues.apache.org/jira/browse/BEAM-7246)).
 * Python SDK: Adds support for standard HDFS URLs (with server name). ([#10223](https://github.com/apache/beam/pull/10223)).
 
-
 ## New Features / Improvements
 
 * New AnnotateVideo & AnnotateVideoWithContext PTransform's that integrates GCP Video Intelligence functionality. (Python) ([BEAM-9146](https://issues.apache.org/jira/browse/BEAM-9146))
@@ -2149,6 +2235,7 @@ Schema Options, it will be removed in version `2.23.0`. ([BEAM-9704](https://iss
 
 ## Deprecations
 
+
 ## Bugfixes
 
 * Fixed numpy operators in ApproximateQuantiles (Python) ([BEAM-9579](https://issues.apache.org/jira/browse/BEAM-9579)).
@@ -2165,4 +2252,6 @@ Schema Options, it will be removed in version `2.23.0`. ([BEAM-9704](https://iss
 
 # [2.19.0] - 2020-01-31
 
+## Highlights
+
 - For versions 2.19.0 and older release notes are available on [Apache Beam Blog](https://beam.apache.org/blog/).
diff --git a/CI.md b/CI.md
index f5254e49e4b0..44d7f03f9a0b 100644
--- a/CI.md
+++ b/CI.md
@@ -33,7 +33,7 @@ to use and develop for, so we decided to use it firstly for a few workflows then
 migrated all workflows previously run on Jenkins.
 
 For this reason there are mainly two types of GHA workflows running
-- Self-hosted runner GHAs. These were mifrated from Jenkins with workflow name
+- Self-hosted runner GHAs. These were migrated from Jenkins with workflow name
   prefix (beam_*.yml) as well as new workflows added following the same naming
   convention, including PreCommit, PostCommit, LoadTest, PerformanceTest, and
   several infrastructure jobs. See [.github/workflow/README](.github/workflow/README.md)
diff --git a/build.gradle.kts b/build.gradle.kts
index d1f0740e7c02..f72e12af176e 100644
--- a/build.gradle.kts
+++ b/build.gradle.kts
@@ -1,3 +1,5 @@
+import java.util.TreeMap
+
 /*
  * Licensed to the Apache Software Foundation (ASF) under one
  * or more contributor license agreements.  See the NOTICE file
@@ -253,6 +255,7 @@ tasks.register("javaPreCommit") {
   dependsOn(":examples:java:sql:preCommit")
   dependsOn(":examples:java:twitter:build")
   dependsOn(":examples:java:twitter:preCommit")
+  dependsOn(":examples:java:iceberg:build")
   dependsOn(":examples:multi-language:build")
   dependsOn(":model:fn-execution:build")
   dependsOn(":model:job-management:build")
@@ -320,6 +323,7 @@ tasks.register("javaPreCommit") {
   dependsOn(":sdks:java:managed:build")
   dependsOn(":sdks:java:testing:expansion-service:build")
   dependsOn(":sdks:java:testing:jpms-tests:build")
+  dependsOn(":sdks:java:testing:junit:build")
   dependsOn(":sdks:java:testing:load-tests:build")
   dependsOn(":sdks:java:testing:nexmark:build")
   dependsOn(":sdks:java:testing:test-utils:build")
@@ -354,6 +358,7 @@ tasks.register("javaioPreCommit") {
   dependsOn(":sdks:java:io:mqtt:build")
   dependsOn(":sdks:java:io:neo4j:build")
   dependsOn(":sdks:java:io:parquet:build")
+  dependsOn(":sdks:java:io:pulsar:build")
   dependsOn(":sdks:java:io:rabbitmq:build")
   dependsOn(":sdks:java:io:redis:build")
   dependsOn(":sdks:java:io:rrio:build")
@@ -380,12 +385,12 @@ tasks.register("sqlPreCommit") {
   dependsOn(":sdks:java:extensions:sql:datacatalog:build")
   dependsOn(":sdks:java:extensions:sql:expansion-service:build")
   dependsOn(":sdks:java:extensions:sql:hcatalog:build")
+  dependsOn(":sdks:java:extensions:sql:iceberg:build")
   dependsOn(":sdks:java:extensions:sql:jdbc:build")
   dependsOn(":sdks:java:extensions:sql:jdbc:preCommit")
   dependsOn(":sdks:java:extensions:sql:perf-tests:build")
   dependsOn(":sdks:java:extensions:sql:udf-test-provider:build")
   dependsOn(":sdks:java:extensions:sql:udf:build")
-  dependsOn(":sdks:java:extensions:sql:zetasql:build")
 }
 
 tasks.register("javaPreCommitPortabilityApi") {
@@ -427,6 +432,7 @@ tasks.register("sqlPostCommit") {
   dependsOn(":sdks:java:extensions:sql:postCommit")
   dependsOn(":sdks:java:extensions:sql:jdbc:postCommit")
   dependsOn(":sdks:java:extensions:sql:datacatalog:postCommit")
+  dependsOn(":sdks:java:extensions:sql:iceberg:integrationTest")
   dependsOn(":sdks:java:extensions:sql:hadoopVersionsTest")
 }
 
@@ -511,6 +517,271 @@ tasks.register("pythonFormatterPreCommit") {
   dependsOn("sdks:python:test-suites:tox:pycommon:formatter")
 }
 
+tasks.register("formatChanges") {
+  group = "formatting"
+  description = "Formats CHANGES.md according to the template structure"
+
+  doLast {
+    val changesFile = file("CHANGES.md")
+    if (!changesFile.exists()) {
+      throw GradleException("CHANGES.md file not found")
+    }
+
+    val content = changesFile.readText()
+    val lines = content.lines().toMutableList()
+
+    // Find template end (after --> that follows <!-- Template -->)
+    var templateStartIndex = -1
+    var templateEndIndex = -1
+
+    for (i in lines.indices) {
+      if (lines[i].trim() == "<!-- Template -->") {
+        templateStartIndex = i
+      } else if (templateStartIndex != -1 && lines[i].trim() == "-->") {
+        templateEndIndex = i
+        break
+      }
+    }
+
+    if (templateEndIndex == -1) {
+      throw GradleException("Template end marker not found in CHANGES.md")
+    }
+
+    // Process each release section
+    var i = templateEndIndex + 1
+    val formattedLines = mutableListOf<String>()
+
+    // Keep header and template exactly as-is (lines 0 to templateEndIndex inclusive)
+    formattedLines.addAll(lines.subList(0, templateEndIndex + 1))
+
+    // Always add blank line after template
+    formattedLines.add("")
+
+    while (i < lines.size) {
+      val line = lines[i]
+
+      // Check if this is a release header
+      if (line.startsWith("# [")) {
+        formattedLines.add(line)
+        i++
+
+        // Expected sections in order (following template)
+        val expectedSections = listOf(
+          "## Beam 3.0.0 Development Highlights",
+          "## Highlights",
+          "## I/Os",
+          "## New Features / Improvements",
+          "## Breaking Changes",
+          "## Deprecations",
+          "## Bugfixes",
+          "## Security Fixes",
+          "## Known Issues"
+        )
+
+        val sectionContent = mutableMapOf<String, MutableList<String>>()
+        var currentSection = ""
+
+        // Parse existing sections
+        while (i < lines.size && !lines[i].startsWith("# [")) {
+          val currentLine = lines[i]
+
+          if (currentLine.startsWith("## ")) {
+            currentSection = currentLine
+            if (!sectionContent.containsKey(currentSection)) {
+              sectionContent[currentSection] = mutableListOf()
+            }
+          } else if (currentSection.isNotEmpty()) {
+            sectionContent[currentSection]!!.add(currentLine)
+          }
+          i++
+        }
+
+        // Only add sections that actually exist with content
+        for (section in expectedSections) {
+          if (sectionContent.containsKey(section)) {
+            formattedLines.add("")
+            formattedLines.add(section)
+            formattedLines.add("")
+
+            // Remove empty lines at start and end
+            val content = sectionContent[section]!!
+            while (content.isNotEmpty() && content.first().trim().isEmpty()) {
+              content.removeAt(0)
+            }
+            while (content.isNotEmpty() && content.last().trim().isEmpty()) {
+              content.removeAt(content.size - 1)
+            }
+
+            // Format content according to template rules
+            val formattedContent = content.map { line ->
+              // Convert SDK language references from [Language] to (Language)
+              line.replace(Regex("\\[([^\\]]*(?:Java|Python|Go|Kotlin|TypeScript|YAML)[^\\]]*)\\]")) { matchResult ->
+                val languages = matchResult.groupValues[1]
+                // Only convert if it's clearly a language reference (not a link or other content)
+                if (languages.matches(Regex(".*(?:Java|Python|Go|Kotlin|TypeScript|YAML).*"))) {
+                  "($languages)"
+                } else {
+                  matchResult.value
+                }
+              }
+            }
+
+            formattedLines.addAll(formattedContent)
+          }
+        }
+
+        if (i < lines.size) {
+          formattedLines.add("")
+        }
+      } else {
+        i++
+      }
+    }
+
+    // Write formatted content back
+    changesFile.writeText(formattedLines.joinToString("\n"))
+    println("CHANGES.md has been formatted according to template structure")
+  }
+}
+
+tasks.register("validateChanges") {
+  group = "verification"
+  description = "Validates CHANGES.md follows required formatting rules"
+
+  doLast {
+    val changesFile = file("CHANGES.md")
+    if (!changesFile.exists()) {
+      throw GradleException("CHANGES.md file not found")
+    }
+
+    val content = changesFile.readText()
+    val lines = content.lines()
+    val errors = mutableListOf<String>()
+
+    // Find template section boundaries
+    var templateStartIndex = -1
+    var templateEndIndex = -1
+
+    for (i in lines.indices) {
+      if (lines[i].trim() == "<!-- Template -->") {
+        templateStartIndex = i
+        println("Found template start at line ${i+1}")
+      } else if (templateStartIndex != -1 && lines[i].trim() == "-->") {
+        templateEndIndex = i
+        println("Found template end at line ${i+1}")
+        break
+      }
+    }
+
+    if (templateStartIndex == -1 || templateEndIndex == -1) {
+      throw GradleException("Template section not found in CHANGES.md")
+    }
+
+    println("Template section: lines ${templateStartIndex+1} to ${templateEndIndex+1}")
+
+    // Find unreleased section after the template section
+    var unreleasedSectionStart = -1
+    for (i in (templateEndIndex + 1) until lines.size) {
+      if (lines[i].startsWith("# [") && lines[i].contains("Unreleased")) {
+        unreleasedSectionStart = i
+        println("Found unreleased section at line ${i+1}: ${lines[i]}")
+        break
+      }
+    }
+
+    if (unreleasedSectionStart == -1) {
+      throw GradleException("Unreleased section not found in CHANGES.md")
+    }
+
+    // Check entries in the unreleased section
+    var i = unreleasedSectionStart + 1
+    val items = TreeMap<Int, String>()
+    var lastline = 0
+    var item = ""
+    while (i < lines.size && !lines[i].startsWith("# [")) {
+      val line = lines[i].trim()
+      if (line.isEmpty()) {
+        // skip
+      } else if (line.startsWith("* ")) {
+        items.put(lastline, item)
+        lastline = i
+        item = line
+      } else if (line.startsWith("##")) {
+        items.put(lastline, item)
+        lastline = i
+        item = ""
+      } else {
+        item += line
+      }
+      i++
+    }
+    items.put(lastline, item)
+    println("Starting validation from line ${i+1}")
+
+    items.forEach { (i, line) ->
+      if (line.startsWith("* ")) {
+        println("Checking line ${i+1}: $line")
+
+        // Skip comment lines
+        if (line.startsWith("* [comment]:")) {
+          println("  Skipping comment line")
+        } else {
+          // Rule 1: Check if language references use parentheses instead of brackets
+          val languagePattern = "\\[(Java|Python|Go|Kotlin|TypeScript|YAML)(?:/(?:Java|Python|Go|Kotlin|TypeScript|YAML))*\\]"
+          val languageRegex = Regex(languagePattern)
+
+          // Check if there's a language reference in brackets
+          val matches = languageRegex.findAll(line).toList()
+          if (matches.isNotEmpty()) {
+            for (match in matches) {
+              val matchText = match.value
+              val matchPosition = match.range.first
+              println("  Found language reference: $matchText at position $matchPosition")
+
+              // Check if this is part of an issue link or URL
+              val beforeMatch = if (matchPosition > 0) line.substring(0, matchPosition) else ""
+              val isPartOfLink = beforeMatch.contains("[#") ||
+                                beforeMatch.contains("http") ||
+                                line.contains("CVE-")
+
+              println("  Is part of link: $isPartOfLink")
+
+              if (!isPartOfLink) {
+                val error = "Line ${i+1}: Language references should use parentheses () instead of brackets []: $line"
+                println("  Adding error: $error")
+                errors.add(error)
+              }
+            }
+          } else {
+            println("  No bracketed language reference found")
+          }
+
+          // Rule 2: Check if each entry has an issue link
+          val issueLinkPattern = "\\(\\[#[0-9a-zA-Z]+\\]\\(https://github\\.com/apache/beam/issues/[0-9a-zA-Z]+\\)\\)"
+          val issueLinkRegex = Regex(issueLinkPattern)
+
+          val hasIssueLink = issueLinkRegex.containsMatchIn(line)
+          println("  Has issue link: $hasIssueLink")
+
+          if (!hasIssueLink) {
+            val error = "Line ${i+1}: Missing or malformed issue link. Each entry should end with ([#X](https://github.com/apache/beam/issues/X)): $line"
+            println("  Adding error: $error")
+            errors.add(error)
+          }
+        }
+      }
+    }
+
+    println("Found ${errors.size} errors")
+
+    if (errors.isNotEmpty()) {
+      throw GradleException("CHANGES.md validation failed with the following errors:\n${errors.joinToString("\n")}\n\nYou can run ./gradlew formatChanges to correct some issues.")
+    }
+
+    println("CHANGES.md validation successful")
+  }
+}
+
 tasks.register("python39PostCommit") {
   dependsOn(":sdks:python:test-suites:dataflow:py39:postCommitIT")
   dependsOn(":sdks:python:test-suites:direct:py39:postCommitIT")
diff --git a/buildSrc/build.gradle.kts b/buildSrc/build.gradle.kts
index d053713cab91..9ad1a6a5bf3b 100644
--- a/buildSrc/build.gradle.kts
+++ b/buildSrc/build.gradle.kts
@@ -53,12 +53,12 @@ dependencies {
   runtimeOnly("gradle.plugin.com.dorongold.plugins:task-tree:1.5")                                         // Adds a 'taskTree' task to print task dependency tree
   runtimeOnly("net.linguica.gradle:maven-settings-plugin:0.5")
   runtimeOnly("gradle.plugin.io.pry.gradle.offline_dependencies:gradle-offline-dependencies-plugin:0.5.0") // Enable creating an offline repository
-  runtimeOnly("net.ltgt.gradle:gradle-errorprone-plugin:3.1.0")                                            // Enable errorprone Java static analysis
+  runtimeOnly("net.ltgt.gradle:gradle-errorprone-plugin:4.2.0")                                            // Enable errorprone Java static analysis
   runtimeOnly("org.ajoberstar.grgit:grgit-gradle:5.3.2")                                                   // Enable website git publish to asf-site branch
   runtimeOnly("com.avast.gradle:gradle-docker-compose-plugin:0.16.12")                                     // Enable docker compose tasks
   runtimeOnly("ca.cutterslade.gradle:gradle-dependency-analyze:1.8.3")                                     // Enable dep analysis
   runtimeOnly("gradle.plugin.net.ossindex:ossindex-gradle-plugin:0.4.11")                                  // Enable dep vulnerability analysis
-  runtimeOnly("org.checkerframework:checkerframework-gradle-plugin:0.6.37")                                // Enable enhanced static checking plugin
+  runtimeOnly("org.checkerframework:checkerframework-gradle-plugin:0.6.56")                                // Enable enhanced static checking plugin
 }
 
 // Because buildSrc is built and tested automatically _before_ gradle
@@ -92,9 +92,5 @@ gradlePlugin {
       id = "org.apache.beam.vendor-java"
       implementationClass = "org.apache.beam.gradle.VendorJavaPlugin"
     }
-    create("beamJenkins") {
-      id = "org.apache.beam.jenkins"
-      implementationClass = "org.apache.beam.gradle.BeamJenkinsPlugin"
-    }
   }
 }
diff --git a/buildSrc/src/main/groovy/org/apache/beam/gradle/BeamModulePlugin.groovy b/buildSrc/src/main/groovy/org/apache/beam/gradle/BeamModulePlugin.groovy
index 9fb6b89db768..f192d5301722 100644
--- a/buildSrc/src/main/groovy/org/apache/beam/gradle/BeamModulePlugin.groovy
+++ b/buildSrc/src/main/groovy/org/apache/beam/gradle/BeamModulePlugin.groovy
@@ -604,25 +604,25 @@ class BeamModulePlugin implements Plugin<Project> {
     def checkerframework_version = "3.42.0"
     def classgraph_version = "4.8.162"
     def dbcp2_version = "2.9.0"
-    def errorprone_version = "2.10.0"
+    def errorprone_version = "2.31.0"
     // [bomupgrader] determined by: com.google.api:gax, consistent with: google_cloud_platform_libraries_bom
-    def gax_version = "2.67.0"
+    def gax_version = "2.68.2"
     def google_ads_version = "33.0.0"
     def google_clients_version = "2.0.0"
     def google_cloud_bigdataoss_version = "2.2.26"
-    // [bomupgrader] determined by: com.google.cloud:google-cloud-spanner, consistent with: google_cloud_platform_libraries_bom
+    // [bomupgrader] TODO(#35868): currently pinned, should be determined by: com.google.cloud:google-cloud-spanner, consistent with: google_cloud_platform_libraries_bom
     def google_cloud_spanner_version = "6.95.1"
     def google_code_gson_version = "2.10.1"
     def google_oauth_clients_version = "1.34.1"
     // [bomupgrader] determined by: io.grpc:grpc-netty, consistent with: google_cloud_platform_libraries_bom
     def grpc_version = "1.71.0"
     def guava_version = "33.1.0-jre"
-    def hadoop_version = "3.4.1"
+    def hadoop_version = "3.4.2"
     def hamcrest_version = "2.1"
     def influxdb_version = "2.19"
     def httpclient_version = "4.5.13"
     def httpcore_version = "4.4.14"
-    def iceberg_bqms_catalog_version = "1.6.1-1.0.0"
+    def iceberg_bqms_catalog_version = "1.6.1-1.0.1"
     def jackson_version = "2.15.4"
     def jaxb_api_version = "2.3.3"
     def jsr305_version = "3.0.2"
@@ -648,7 +648,7 @@ class BeamModulePlugin implements Plugin<Project> {
     def spotbugs_version = "4.8.3"
     def testcontainers_version = "1.19.7"
     // [bomupgrader] determined by: org.apache.arrow:arrow-memory-core, consistent with: google_cloud_platform_libraries_bom
-    def arrow_version = "15.0.2"
+    def arrow_version = "17.0.0"
     def jmh_version = "1.34"
     def jupiter_version = "5.7.0"
 
@@ -676,7 +676,6 @@ class BeamModulePlugin implements Plugin<Project> {
         auto_value_annotations                      : "com.google.auto.value:auto-value-annotations:$autovalue_version",
         // TODO: https://github.com/apache/beam/issues/34993 after stopping supporting Java 8
         avro                                        : "org.apache.avro:avro:1.11.4",
-        avro_tests                                  : "org.apache.avro:avro:1.11.3:tests",
         aws_java_sdk2_apache_client                 : "software.amazon.awssdk:apache-client:$aws_java_sdk2_version",
         aws_java_sdk2_netty_client                  : "software.amazon.awssdk:netty-nio-client:$aws_java_sdk2_version",
         aws_java_sdk2_auth                          : "software.amazon.awssdk:auth:$aws_java_sdk2_version",
@@ -718,7 +717,7 @@ class BeamModulePlugin implements Plugin<Project> {
         commons_compress                            : "org.apache.commons:commons-compress:1.26.2",
         commons_csv                                 : "org.apache.commons:commons-csv:1.8",
         commons_io                                  : "commons-io:commons-io:2.16.1",
-        commons_lang3                               : "org.apache.commons:commons-lang3:3.14.0",
+        commons_lang3                               : "org.apache.commons:commons-lang3:3.18.0",
         commons_logging                             : "commons-logging:commons-logging:1.2",
         commons_math3                               : "org.apache.commons:commons-math3:3.6.1",
         dbcp2                                       : "org.apache.commons:commons-dbcp2:$dbcp2_version",
@@ -733,12 +732,12 @@ class BeamModulePlugin implements Plugin<Project> {
         google_api_client_gson                      : "com.google.api-client:google-api-client-gson:$google_clients_version",
         google_api_client_java6                     : "com.google.api-client:google-api-client-java6:$google_clients_version",
         google_api_common                           : "com.google.api:api-common", // google_cloud_platform_libraries_bom sets version
-        google_api_services_bigquery                : "com.google.apis:google-api-services-bigquery:v2-rev20250511-2.0.0",  // [bomupgrader] sets version
+        google_api_services_bigquery                : "com.google.apis:google-api-services-bigquery:v2-rev20250706-2.0.0",  // [bomupgrader] sets version
         google_api_services_cloudresourcemanager    : "com.google.apis:google-api-services-cloudresourcemanager:v1-rev20240310-2.0.0",  // [bomupgrader] sets version
         google_api_services_dataflow                : "com.google.apis:google-api-services-dataflow:v1b3-rev20250519-$google_clients_version",
         google_api_services_healthcare              : "com.google.apis:google-api-services-healthcare:v1-rev20240130-$google_clients_version",
         google_api_services_pubsub                  : "com.google.apis:google-api-services-pubsub:v1-rev20220904-$google_clients_version",
-        google_api_services_storage                 : "com.google.apis:google-api-services-storage:v1-rev20250524-2.0.0",  // [bomupgrader] sets version
+        google_api_services_storage                 : "com.google.apis:google-api-services-storage:v1-rev20250718-2.0.0",  // [bomupgrader] sets version
         google_auth_library_credentials             : "com.google.auth:google-auth-library-credentials", // google_cloud_platform_libraries_bom sets version
         google_auth_library_oauth2_http             : "com.google.auth:google-auth-library-oauth2-http", // google_cloud_platform_libraries_bom sets version
         google_cloud_bigquery                       : "com.google.cloud:google-cloud-bigquery", // google_cloud_platform_libraries_bom sets version
@@ -750,14 +749,16 @@ class BeamModulePlugin implements Plugin<Project> {
         google_cloud_core_grpc                      : "com.google.cloud:google-cloud-core-grpc", // google_cloud_platform_libraries_bom sets version
         google_cloud_datacatalog_v1beta1            : "com.google.cloud:google-cloud-datacatalog", // google_cloud_platform_libraries_bom sets version
         google_cloud_dataflow_java_proto_library_all: "com.google.cloud.dataflow:google-cloud-dataflow-java-proto-library-all:0.5.160304",
-        google_cloud_datastore_v1_proto_client      : "com.google.cloud.datastore:datastore-v1-proto-client:2.29.1",   // [bomupgrader] sets version
+        google_cloud_datastore_v1_proto_client      : "com.google.cloud.datastore:datastore-v1-proto-client:2.31.1",   // [bomupgrader] sets version
         google_cloud_firestore                      : "com.google.cloud:google-cloud-firestore", // google_cloud_platform_libraries_bom sets version
         google_cloud_pubsub                         : "com.google.cloud:google-cloud-pubsub", // google_cloud_platform_libraries_bom sets version
         google_cloud_pubsublite                     : "com.google.cloud:google-cloud-pubsublite",  // google_cloud_platform_libraries_bom sets version
         // [bomupgrader] the BOM version is set by scripts/tools/bomupgrader.py. If update manually, also update
         // libraries-bom version on sdks/java/container/license_scripts/dep_urls_java.yaml
-        google_cloud_platform_libraries_bom         : "com.google.cloud:libraries-bom:26.62.0",
+        google_cloud_platform_libraries_bom         : "com.google.cloud:libraries-bom:26.65.0",
         google_cloud_secret_manager                 : "com.google.cloud:google-cloud-secretmanager", // google_cloud_platform_libraries_bom sets version
+        // TODO(#35868) remove pinned google_cloud_spanner_bom after tests or upstream fixed
+        google_cloud_spanner_bom                    : "com.google.cloud:google-cloud-spanner-bom:$google_cloud_spanner_version",
         google_cloud_spanner                        : "com.google.cloud:google-cloud-spanner", // google_cloud_platform_libraries_bom sets version
         google_cloud_spanner_test                   : "com.google.cloud:google-cloud-spanner:$google_cloud_spanner_version:tests",
         google_cloud_vertexai                       : "com.google.cloud:google-cloud-vertexai", // google_cloud_platform_libraries_bom sets version
@@ -841,7 +842,9 @@ class BeamModulePlugin implements Plugin<Project> {
         log4j2_log4j12_api                          : "org.apache.logging.log4j:log4j-1.2-api:$log4j2_version",
         mockito_core                                : "org.mockito:mockito-core:4.11.0",
         mockito_inline                              : "org.mockito:mockito-inline:4.11.0",
-        mongo_java_driver                           : "org.mongodb:mongo-java-driver:3.12.11",
+        mongo_java_driver                           : "org.mongodb:mongodb-driver-sync:5.5.0",
+        mongo_bson                                  : "org.mongodb:bson:5.5.0",
+        mongodb_driver_core                         : "org.mongodb:mongodb-driver-core:5.5.0",
         nemo_compiler_frontend_beam                 : "org.apache.nemo:nemo-compiler-frontend-beam:$nemo_version",
         netty_all                                   : "io.netty:netty-all:$netty_version",
         netty_handler                               : "io.netty:netty-handler:$netty_version",
@@ -908,7 +911,7 @@ class BeamModulePlugin implements Plugin<Project> {
         threetenbp                                  : "org.threeten:threetenbp:1.6.8",
         vendored_grpc_1_69_0                        : "org.apache.beam:beam-vendor-grpc-1_69_0:0.1",
         vendored_guava_32_1_2_jre                   : "org.apache.beam:beam-vendor-guava-32_1_2-jre:0.1",
-        vendored_calcite_1_28_0                     : "org.apache.beam:beam-vendor-calcite-1_28_0:0.2",
+        vendored_calcite_1_40_0                     : "org.apache.beam:beam-vendor-calcite-1_40_0:0.1",
         woodstox_core_asl                           : "org.codehaus.woodstox:woodstox-core-asl:4.4.1",
         zstd_jni                                    : "com.github.luben:zstd-jni:1.5.6-3",
         quickcheck_core                             : "com.pholser:junit-quickcheck-core:$quickcheck_version",
@@ -1191,6 +1194,7 @@ class BeamModulePlugin implements Plugin<Project> {
 
       List<String> skipDefRegexes = []
       skipDefRegexes << "AutoValue_.*"
+      skipDefRegexes << "AutoBuilder_.*"
       skipDefRegexes << "AutoOneOf_.*"
       skipDefRegexes << ".*\\.jmh_generated\\..*"
       skipDefRegexes += configuration.generatedClassPatterns
@@ -1284,7 +1288,8 @@ class BeamModulePlugin implements Plugin<Project> {
         '**/org/apache/beam/gradle/**',
         '**/org/apache/beam/model/**',
         '**/org/apache/beam/runners/dataflow/worker/windmill/**',
-        '**/AutoValue_*'
+        '**/AutoValue_*',
+        '**/AutoBuilder_*',
       ]
 
       def jacocoEnabled = project.hasProperty('enableJacocoReport')
@@ -1434,6 +1439,8 @@ class BeamModulePlugin implements Plugin<Project> {
             include 'src/*/java/**/*.java'
             exclude '**/DefaultPackageTest.java'
           }
+          // For spotless:off and spotless:on
+          toggleOffOn()
         }
       }
 
@@ -1494,7 +1501,7 @@ class BeamModulePlugin implements Plugin<Project> {
 
         project.dependencies {
           errorprone("com.google.errorprone:error_prone_core:$errorprone_version")
-          errorprone("jp.skypencil.errorprone.slf4j:errorprone-slf4j:0.1.2")
+          errorprone("jp.skypencil.errorprone.slf4j:errorprone-slf4j:0.1.28")
         }
 
         project.configurations.errorprone { resolutionStrategy.force "com.google.errorprone:error_prone_core:$errorprone_version" }
@@ -1511,56 +1518,91 @@ class BeamModulePlugin implements Plugin<Project> {
             options.fork = true
             options.forkOptions.jvmArgs += errorProneAddModuleOpts
           }
-
-          // TODO(https://github.com/apache/beam/issues/20955): Enable errorprone checks
-          options.errorprone.errorproneArgs.add("-Xep:AutoValueImmutableFields:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:AutoValueSubclassLeaked:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:BadImport:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:BadInstanceof:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:BigDecimalEquals:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:ComparableType:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:DoNotMockAutoValue:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:EmptyBlockTag:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:EmptyCatch:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:EqualsGetClass:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:EqualsUnsafeCast:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:EscapedEntity:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:ExtendsAutoValue:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:InlineFormatString:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:InlineMeSuggester:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:InvalidBlockTag:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:InvalidInlineTag:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:InvalidLink:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:InvalidParam:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:InvalidThrows:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:JavaTimeDefaultTimeZone:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:JavaUtilDate:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:JodaConstructors:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:MalformedInlineTag:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:MissingSummary:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:MixedMutabilityReturnType:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:PreferJavaTimeOverload:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:MutablePublicArray:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:NonCanonicalType:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:ProtectedMembersInFinalClass:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:Slf4jFormatShouldBeConst:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:Slf4jSignOnlyFormat:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:StaticAssignmentInConstructor:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:ThreadPriorityCheck:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:TimeUnitConversionChecker:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:UndefinedEquals:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:UnescapedEntity:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:UnnecessaryLambda:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:UnnecessaryMethodReference:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:UnnecessaryParentheses:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:UnrecognisedJavadocTag:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:UnsafeReflectiveConstructionCast:OFF")
-          options.errorprone.errorproneArgs.add("-Xep:UseCorrectAssertInTests:OFF")
-
-          // Sometimes a static logger is preferred, which is the convention
-          // currently used in beam. See docs:
-          // https://github.com/KengoTODA/findbugs-slf4j#slf4j_logger_should_be_non_static
-          options.errorprone.errorproneArgs.add("-Xep:Slf4jLoggerShouldBeNonStatic:OFF")
+          def disabledChecks = [
+            // TODO(https://github.com/apache/beam/issues/20955): Enable errorprone checks
+            "AutoValueImmutableFields",
+            "AutoValueImmutableFields",
+            "AutoValueSubclassLeaked",
+            "BadImport",
+            "BadInstanceof",
+            "BigDecimalEquals",
+            "ComparableType",
+            "DoNotMockAutoValue",
+            "EmptyBlockTag",
+            "EmptyCatch",
+            "EqualsGetClass",
+            "EqualsUnsafeCast",
+            "EscapedEntity",
+            "ExtendsAutoValue",
+            "InlineFormatString",
+            "InlineMeSuggester",
+            "InvalidBlockTag",
+            "InvalidInlineTag",
+            "InvalidLink",
+            "InvalidParam",
+            "InvalidThrows",
+            "JavaTimeDefaultTimeZone",
+            "JavaUtilDate",
+            "JodaConstructors",
+            "MalformedInlineTag",
+            "MissingSummary",
+            "MixedMutabilityReturnType",
+            "PreferJavaTimeOverload",
+            "MutablePublicArray",
+            "NonCanonicalType",
+            "ProtectedMembersInFinalClass",
+            "Slf4jFormatShouldBeConst",
+            "Slf4jSignOnlyFormat",
+            "StaticAssignmentInConstructor",
+            "ThreadPriorityCheck",
+            "TimeUnitConversionChecker",
+            "UndefinedEquals",
+            "UnescapedEntity",
+            "UnnecessaryLambda",
+            "UnnecessaryMethodReference",
+            "UnnecessaryParentheses",
+            "UnrecognisedJavadocTag",
+            "UnsafeReflectiveConstructionCast",
+            "UseCorrectAssertInTests",
+            // errorprone 3.2.0+ checks
+            "DirectInvocationOnMock",
+            "Finalize",
+            "JUnitIncompatibleType",
+            "LongDoubleConversion",
+            "MockNotUsedInProduction",
+            "NarrowCalculation",
+            "NullableTypeParameter",
+            "NullableWildcard",
+            "StringCharset",
+            "SuperCallToObjectMethod",
+            "UnnecessaryLongToIntConversion",
+            "UnusedVariable",
+            // intended suppressions emerged in newer protobuf versions
+            "AutoValueBoxedValues",
+            // For backward compatibility. Public method checked in before this check impl
+            // Possible use in interface subclasses
+            "ClassInitializationDeadlock",
+            // for encoding efficiency and backward compatibility
+            "EnumOrdinal",
+            // widely used in non-public methods
+            "NotJavadoc",
+            // return values used for assignments widely, and for backward compatibility.
+            "NonApiType",
+            // Used to test self equal
+            "SelfAssertion",
+            // Sometimes a static logger is preferred, which is the convention currently used in beam. See docs:
+            // https://github.com/KengoTODA/findbugs-slf4j#slf4j_logger_should_be_non_static
+            "Slf4jLoggerShouldBeNonStatic",
+            // allow implicit Locale.Default
+            "StringCaseLocaleUsage",
+            // DoFn methods are executed reflectively at pipeline runtime
+            "UnusedMethod",
+            // Void is a valid element type of DoFn elements
+            "VoidUsed",
+          ]
+          disabledChecks.each {
+            options.errorprone.errorproneArgs.add("-Xep:${it}:OFF")
+          }
         }
       }
 
@@ -1833,13 +1875,12 @@ class BeamModulePlugin implements Plugin<Project> {
       project.ext.includeInJavaBom = configuration.publish
       project.ext.exportJavadoc = configuration.exportJavadoc
 
-      boolean publishEnabledByCommand = isRelease(project) || project.hasProperty('publishing')
       if (forkJavaVersion == '') {
         // project needs newer version and not served.
         // If not publishing ,disable the project. Otherwise, fail the build
         def msg = "project ${project.name} needs newer Java version to compile. Consider set -Pjava${project.javaVersion}Home"
-        if (publishEnabledByCommand) {
-          throw new GradleException("Publish enabled but " + msg + ".")
+        if (isRelease(project)) {
+          throw new GradleException("Release enabled but " + msg + ".")
         } else {
           logger.config(msg + " if needed.")
           project.tasks.each {
@@ -1847,7 +1888,7 @@ class BeamModulePlugin implements Plugin<Project> {
           }
         }
       }
-      if (publishEnabledByCommand && configuration.publish) {
+      if ((isRelease(project) || project.hasProperty('publishing')) && configuration.publish) {
         project.apply plugin: "maven-publish"
 
         // plugin to support repository authentication via ~/.m2/settings.xml
@@ -1925,15 +1966,25 @@ class BeamModulePlugin implements Plugin<Project> {
 
           publications {
             mavenJava(MavenPublication) {
+              // only publish test and its sources when test folder is non-empty
+              def testFolder = project.file("src/test")
+              boolean testExists = testFolder.exists() && testFolder.list().size() != 0
+
               if (configuration.shadowClosure) {
                 artifact project.shadowJar
-                artifact project.shadowTestJar
+                if (testExists) {
+                  artifact project.shadowTestJar
+                }
               } else {
                 artifact project.jar
-                artifact project.testJar
+                if (testExists) {
+                  artifact project.testJar
+                }
               }
               artifact project.sourcesJar
-              artifact project.testSourcesJar
+              if (testExists) {
+                artifact project.testSourcesJar
+              }
               artifact project.javadocJar
 
               artifactId = project.archivesBaseName
@@ -2896,8 +2947,9 @@ class BeamModulePlugin implements Plugin<Project> {
       // CrossLanguageValidatesRunnerTask is setup under python sdk but also runs tasks not involving
       // python versions. set 'skipNonPythonTask' property to avoid duplicated run of these tasks.
       if (!(project.hasProperty('skipNonPythonTask') && project.skipNonPythonTask == 'true')) {
-        System.err.println 'GoUsingJava tests have been disabled: https://github.com/apache/beam/issues/30517#issuecomment-2341881604.'
-        // mainTask.configure { dependsOn goTask }
+        // Re-enabled GoUsingJava tests after fixing underlying issues
+        // Previous issues: Docker daemon connectivity, SDK worker communication, timeout configurations
+        mainTask.configure { dependsOn goTask }
       }
       cleanupTask.configure { mustRunAfter goTask }
       config.cleanupJobServer.configure { mustRunAfter goTask }
@@ -3032,6 +3084,10 @@ class BeamModulePlugin implements Plugin<Project> {
       project.ext.pythonVersion = project.hasProperty('pythonVersion') ?
           project.pythonVersion : '3.9'
 
+      // Set min/max python versions used for containers and supported versions.
+      project.ext.minPythonVersion = 9
+      project.ext.maxPythonVersion = 13
+
       def setupVirtualenv = project.tasks.register('setupVirtualenv')  {
         doLast {
           def virtualenvCmd = [
diff --git a/buildSrc/src/main/groovy/org/apache/beam/gradle/Repositories.groovy b/buildSrc/src/main/groovy/org/apache/beam/gradle/Repositories.groovy
index 58ec64a0add3..12e48356898a 100644
--- a/buildSrc/src/main/groovy/org/apache/beam/gradle/Repositories.groovy
+++ b/buildSrc/src/main/groovy/org/apache/beam/gradle/Repositories.groovy
@@ -45,9 +45,6 @@ class Repositories {
         content { includeGroup "io.confluent" }
       }
 
-      // Release staging repository
-      maven { url "https://oss.sonatype.org/content/repositories/staging/" }
-
       // Apache nightly snapshots
       maven {
         url "https://repository.apache.org/snapshots"
diff --git a/contributor-docs/code-change-guide.md b/contributor-docs/code-change-guide.md
index 55a1c0beac8e..d21eeb133f99 100644
--- a/contributor-docs/code-change-guide.md
+++ b/contributor-docs/code-change-guide.md
@@ -444,16 +444,35 @@ If you're using Dataflow Runner v2 and `sdks/java/harness` or its dependencies (
     --experiments=use_runner_v2,use_staged_dataflow_worker_jar
     ```
 
+#### SDK container image change
+
+If you have changed codes under `sdks/java/container`, you need to build a custom SDK container to make the change
+effective.
+
+```shell
+    ./gradlew :sdks:java:container:java11:docker # or java17, java21, etc
+    # change version number to the actual tag below
+    docker tag apache/beam_java8_sdk:2.68.0.dev \
+      "us-docker.pkg.dev/apache-beam-testing/beam-temp/beam_java11_sdk:2.68.0-custom"  # change to your artifact registry
+    docker push "us-docker.pkg.dev/apache-beam-testing/beam-temp/beam_java11_sdk:2.68.0-custom"
+```
+
+Then run the pipeline with the following options:
+```
+    --experiments=use_runner_v2 \
+    --sdkContainerImage="us.gcr.io/apache-beam-testing/beam_java11_sdk:2.49.0-custom"
+```
+
 #### Snapshot Version Containers
 
-By default, a Snapshot version for an SDK under development will use the containers published to the [apache-beam-testing project's container registry](https://us.gcr.io/apache-beam-testing/github-actions). For example, the most recent snapshot container for Java 17 can be found [here](https://us.gcr.io/apache-beam-testing/github-actions/beam_java17_sdk).
+By default, a Snapshot version for an SDK under development will use the containers published to the apache-beam-testing project's container registry (`https://gcr.io/apache-beam-testing/beam-sdk/...`). For example, the most recent snapshot container for Java 21 can be found [here](https://gcr.io/apache-beam-testing/beam-sdk/beam_java21_sdk).
 
 When a version is entering the [release candidate stage](https://github.com/apache/beam/blob/master/contributor-docs/release-guide.md), one final SNAPSHOT version will be published.
 This SNAPSHOT version will use the final containers published on [DockerHub](https://hub.docker.com/search?q=apache%2Fbeam).
 
 **NOTE:** During the release process, there may be some downtime where a container is not available for use for a SNAPSHOT version. To avoid this, it is recommended to either switch to the latest SNAPSHOT version available or to use [custom containers](https://beam.apache.org/documentation/runtime/environments/#custom-containers). You should also only rely on snapshot versions for important workloads if absolutely necessary.
 
-Certain runners may override this snapshot behavior; for example, the Dataflow runner overrides all SNAPSHOT containers into a [single registry](https://console.cloud.google.com/gcr/images/cloud-dataflow/GLOBAL/v1beta3). The same downtime will still be incurred, however, when switching to the final container
+Certain runners may override this snapshot behavior; for example, the Dataflow runner overrides all SNAPSHOT containers into a [single registry](gcr.io/cloud-dataflow/). The same downtime will still be incurred, however, when switching to the final container.
 
 ## Python development guide
 
@@ -635,6 +654,19 @@ Tips for using the Dataflow runner:
 
 ## Appendix
 
+### Formatting CHANGES.md
+
+When updating the `CHANGES.md` file with your changes, use the following Gradle command to ensure proper formatting:
+
+```shell
+./gradlew formatChanges
+```
+
+This command:
+* Organizes sections in the correct order according to the template
+* Ensures all required sections are present
+* Preserves existing content while maintaining consistent formatting
+
 ### Common Issues
 
 * If you run into some strange errors such as `java.lang.NoClassDefFoundError` or errors related to proto changes, try these:
diff --git a/contributor-docs/discussion-docs/2025.md b/contributor-docs/discussion-docs/2025.md
index 8081e7e69345..f5ce610690a1 100644
--- a/contributor-docs/discussion-docs/2025.md
+++ b/contributor-docs/discussion-docs/2025.md
@@ -15,18 +15,27 @@ limitations under the License.
 # List Of Documents Submitted To dev@beam.apache.org In 2025
 | No. | Author | Subject | Date (UTC) |
 |---|---|---|---|
-| 1 | Kenneth Knowles | [Apache Beam Release Acceptance Criteria  - Google Sheets](https://docs.google.com/spreadsheets/d/1qk-N5vjXvbcEk68GjbkSZTR8AGqyNUM-oLFo_ZXBpJw) | 2025-01-13 10:54:22 |
+| 1 | Kenneth Knowles | [Apache Beam Release Acceptance Criteria - Google Sheets](https://docs.google.com/spreadsheets/d/1qk-N5vjXvbcEk68GjbkSZTR8AGqyNUM-oLFo_ZXBpJw) | 2025-01-13 10:54:22 |
 | 2 | Danny McCormick | [Apache Beam Vendored Dependencies Release Guide](https://s.apache.org/beam-release-vendored-artifacts) | 2025-01-13 15:00:51 |
 | 3 | Danny McCormick | [Beam Python & ML Dependency Extras](https://docs.google.com/document/d/1c84Gc-cZRCfrU8f7kWGsNR2o8oSRjCM-dGHO9KvPWPw) | 2025-01-27 15:33:36 |
 | 4 | Danny McCormick | [How vLLM Model Handler Works (Plus a Summary of Model Memory Management in Beam ML)](https://docs.google.com/document/d/1UB4umrtnp1Eg45fiUB3iLS7kPK3BE6pcf0YRDkA289Q) | 2025-01-31 11:56:59 |
 | 5 | Shunping Huang | [Improve Logging Dependencies in Beam Java SDK](https://docs.google.com/document/d/1IkbiM4m8D-aB3NYI1aErFZHt6M7BQ-8eCULh284Davs) | 2025-02-04 15:13:14 |
 | 6 | Ahmed Abualsaud | [Iceberg Incremental Source design](https://s.apache.org/beam-iceberg-incremental-source) | 2025-03-03 14:52:42 |
 | 7 | Kenneth Knowles | [[PUBLIC] Timers, Watermark Holds, Loops, Batch and Drain](https://s.apache.org/beam-timers-and-drain) | 2025-03-11 14:00:00 |
-| 8 | Robert Bradshaw  | [Apache Beam YAML testing](https://s.apache.org/beam-yaml-testing) | 2025-03-17 14:00:00 |
-| 9 | Jack McCluskey   | [[Design Doc] Generic Remote Model Handlers for RunInference](https://docs.google.com/document/d/17A_oHJ7s3ol4TGCUpKeYc6iozkcTwN_e4H_J3kRlgPM/edit?usp=sharing) | 2025-03-24 14:00:00 |
-| 10 | Robert Bradshaw   | [Beam YAML Unknown Schemas](https://s.apache.org/beam-yaml-unknown-schema) | 2025-03-26 14:00:00 |
+| 8 | Robert Bradshaw | [Apache Beam YAML testing](https://s.apache.org/beam-yaml-testing) | 2025-03-17 14:00:00 |
+| 9 | Jack McCluskey | [[Design Doc] Generic Remote Model Handlers for RunInference](https://docs.google.com/document/d/17A_oHJ7s3ol4TGCUpKeYc6iozkcTwN_e4H_J3kRlgPM/edit?usp=sharing) | 2025-03-24 14:00:00 |
+| 10 | Robert Bradshaw | [Beam YAML Unknown Schemas](https://s.apache.org/beam-yaml-unknown-schema) | 2025-03-26 14:00:00 |
 | 11 | Derrick Williams | [Beam Yaml Integration Tests Gap Assessment and Proposal](https://s.apache.org/beam-yaml-it) | 2025-04-11 10:08:00 |
-| 12 | Jack McCluskey   | [Beam Python Yapf Version upgrade](https://s.apache.org/beam-python-yapf-upgrade) | 2025-05-01 16:30:00 |
-| 13 | Minbo Bae | [Beam Protobuf Schema (Java)](https://docs.google.com/document/d/1euOq_Uu4sycT-AiN6MQxJmsOgInyABiFSwW-U3kpwlk/edit?tab=t.0#heading=h.xzptrog8pyxf) | 2025-05-27 00:43:08 |
+| 12 | Jack McCluskey | [Beam Python Yapf Version upgrade](https://s.apache.org/beam-python-yapf-upgrade) | 2025-05-01 16:30:00 |
+| 13 | Kenneth Knowles | [Beam Element Extended Metadata - CDC Metadata](https://s.apache.org/beam-cdc-metadata) | 2025-05-02 14:05:13 |
 | 14 | Charles Nguyen | [Beam YAML, Kafka and Iceberg User Accessibility (GSoC 2025)](https://docs.google.com/document/d/1m7AKZYkTf_cuJKU1eCh8oX25lOxwixnVUblEj16aSDU/edit?usp=sharing) | 2025-05-24 11:11:14 |
-| 15 | Kenneth Knowles | [Beam Element Extended Metadata - CDC Metadata](https://s.apache.org/beam-cdc-metadata) | 2025-05-02 14:05:13 |
+| 15 | Minbo Bae | [Beam Protobuf Schema (Java)](https://docs.google.com/document/d/1euOq_Uu4sycT-AiN6MQxJmsOgInyABiFSwW-U3kpwlk/edit?tab=t.0#heading=h.xzptrog8pyxf) | 2025-05-27 00:43:08 |
+| 16 | Mohamed Awnallah | [[Proposal][GSoC 2025] Milvus Vector Enrichment Handler for Beam](https://docs.google.com/document/d/1lzoSGSblrFtf7YK9n5p9BEBw8jRhKeOOKqy0FP1urvg/edit?usp=sharing) | 2025-05-29 17:55:11 |
+| 17 | Ahmed Abualsaud | [Introducing Catalogs to Beam SQL](https://docs.google.com/document/d/16P0JrcJ28KSoMMpLYExWPZaala7CE4Ezen-jC_ly3M4/edit?tab=t.0) | 2025-06-11 08:38:55 |
+| 18 | Enrique Calderon | [[GSoC 2025] Git based Privilege Management System](https://summerofcode.withgoogle.com/programs/2025/projects/QRKMhW67) | 2025-06-11 11:14:54 |
+| 19 | Chen Canyu | [[GSoC 2025] Proposal: Enable pip-based installation for Beam JupyterLab SidePanel](https://summerofcode.withgoogle.com/media/user/a0dca52853b4/proposal/gAAAAABojNSkDnSn0L_3Y8TRLRvPS99439gBLcsrk5beUZHCj3bGBredej4j0i0A3AppWrm6KBCO2THzaNliJ55wJ3ksKFcqto3En2h74H-UQPo8j846W3k=.pdf) | 2025-06-23 11:04:36 |
+| 20 | Jack McCluskey | [The Current State (and Future) of Beam Python Type Hinting](https://s.apache.org/beam-python-type-hinting-overview) | 2025-06-26 10:12:03 |
+| 21 | Danny McCormick | [[Proposal] Beam ML containers](https://docs.google.com/document/d/1JcVFJsPbVvtvaYdGi-DzWy9PIIYJhL7LwWGEXt2NZMk/edit?usp=sharing) | 2025-06-30 15:07:04 |
+| 22 | Mohamed Awnallah | [[Proposal][GSoC 2025] Milvus Vector Sink I/O Connector for Beam](https://docs.google.com/document/d/1agpFq9dy8_7ptMxTET0X7AmGIbDeY0_hGUq-5GNVDqs/edit?usp=sharing) | 2025-07-16 16:18:57 |
+| 23 | Shunping Huang | [Design: Turn-key PTransforms for Time Series Processing in Beam](https://s.apache.org/beam-time-series-processing) | 2025-07-18 20:33:00 |
+| 24 | Jack McCluskey | [Evaluating Third-Party Libraries for Use in Beam Python Type Checking](https://s.apache.org/beam-python-third-party-type-checking) | 2025-07-23 10:03:03 |
diff --git a/contributor-docs/release-guide.md b/contributor-docs/release-guide.md
index 63293442e0d5..c0209d6071b7 100644
--- a/contributor-docs/release-guide.md
+++ b/contributor-docs/release-guide.md
@@ -561,7 +561,8 @@ and update all the JSON configuration fields with "yes" if it is the first time
 5. Build javadoc, pydoc, typedocs for a PR to update beam-site.
     - **NOTE**: Do not merge this PR until after an RC has been approved (see
       "Finalize the Release").
-6. Build Prism binaries for various platforms, and upload them into [dist.apache.org](https://dist.apache.org/repos/dist/dev/beam)
+6. Build and create PR to update beam Managed IO documentation.
+7. Build Prism binaries for various platforms, and upload them into [dist.apache.org](https://dist.apache.org/repos/dist/dev/beam)
    and the Github Release with the matching RC tag.
 
 ### Verify source and artifact distributions
@@ -638,8 +639,9 @@ __Attention:__ Verify that:
 Beam publishes API reference manuals for each release on the website.  For Java
 and Python SDKs, that’s Javadoc and PyDoc, respectively.  The final step of
 building the candidate is to propose website pull requests that update these
-manuals. The first pr will get created by the build_release_candidate action,
-you will need to create the second one manually
+manuals. `beam-site release-docs` PR and `beam managed-io` update PR will get
+created by the build_release_candidate action, you will need to create the
+`beam release-docs` update PR manually.
 
 Merge the pull requests only after finalizing the release.  To avoid invalid
 redirects for the 'current' version, merge these PRs in the order listed.  Once
@@ -657,15 +659,20 @@ created by the `build_release_candidate` workflow (see above).
 
 **PR 2: apache/beam**
 
+This pull request is against the `apache/beam` repo, to update the  `managed-io.md`.
+It is created by the `build_release_candidate` workflow and updates the documentation for Managed IOs.
+
+**PR 3: apache/beam**
+
 This pull request is against the `apache/beam` repo, on the `master` branch
 ([example](https://github.com/apache/beam/pull/17378)).
 
 - Update `CHANGES.md` to update release date and remove template.
 - Update release version in `website/www/site/config.toml`.
 - Add new release in `website/www/site/content/en/get-started/downloads.md`.
+   - For the current release, use `closer.lua` script for download links (e.g., `https://www.apache.org/dyn/closer.lua/beam/{{< param release_latest >}}/apache-beam-{{< param release_latest >}}-source-release.zip`)
    - Download links will not work until the release is finalized.
-- Update links to prior releases to point to https://archive.apache.org (see
-  example PR).
+- Move the previous release to the "Archived releases" section and update its links to point to https://archive.apache.org (see example PR).
 - Create the Blog post:
 
 #### Blog post
@@ -846,6 +853,7 @@ template; please adjust as you see fit.
     * Docker images published to Docker Hub [11].
     * PR to run tests against release branch [12].
     * Github Release pre-release page for v1.2.3-RC3 [13].
+    * pull request to apache/beam updating the managed-io docs[15]
 
     The vote will be open for at least 72 hours. It is adopted by majority approval, with at least 3 PMC affirmative votes.
 
@@ -868,6 +876,7 @@ template; please adjust as you see fit.
     [12] https://github.com/apache/beam/pull/...
     [13] https://github.com/apache/beam/releases/tag/v1.2.3-RC3
     [14] https://github.com/apache/beam/blob/master/contributor-docs/rc-testing-guide.md
+    [15] https://github.com/apache/beam/pull/...
 
 If there are any issues found in the release candidate, reply on the vote
 thread to cancel the vote.  There’s no need to wait 72 hours. Go back to
@@ -1043,7 +1052,7 @@ svn rm $OLD_RELEASE_VERSION   # Delete all artifacts from old releases.
 svn commit -m "Adding artifacts for the ${RELEASE_VERSION} release and removing old artifacts"
 ```
 
-Make sure the last release's artifacts have been copied from `dist.apache.org` to `archive.apache.org`.
+Make sure the old release's artifacts have been copied to [archive.apache.org](https://archive.apache.org/dist/beam/).
 This should happen automatically: [dev@ thread](https://lists.apache.org/thread.html/39c26c57c5125a7ca06c3c9315b4917b86cd0e4567b7174f4bc4d63b%40%3Cdev.beam.apache.org%3E) with context.
 
 #### Recordkeeping with ASF
diff --git a/dev-support/docker/Dockerfile b/dev-support/docker/Dockerfile
index 0803db8887e8..143c3c6decf4 100644
--- a/dev-support/docker/Dockerfile
+++ b/dev-support/docker/Dockerfile
@@ -18,7 +18,7 @@
 # Dockerfile for installing the necessary dependencies for building Hadoop.
 # See BUILDING.txt.
 
-FROM ubuntu:20.04
+FROM ubuntu:24.04
 
 ARG DEBIAN_FRONTEND=noninteractive
 
@@ -62,12 +62,13 @@ ENV LC_ALL en_US.UTF-8
 ###
 # Install grpcio-tools mypy-protobuf for `python3 sdks/python/setup.py sdist` to work
 ###
-RUN pip3 install grpcio-tools mypy-protobuf
+RUN pip3 install --break-system-packages grpcio-tools mypy-protobuf
 
 ###
 # Install useful tools
 # Install distlib to avoid https://github.com/pypa/virtualenv/issues/2006
-RUN pip3 install distlib==0.3.1 yapf==0.29.0 pytest
+# Specify the version of pluggy to fix it to the version installed on the system.
+RUN pip3 install --break-system-packages distlib==0.3.9 yapf==0.43.0 pytest pluggy==1.4.0
 ###
 
 ###
diff --git a/examples/java/build.gradle b/examples/java/build.gradle
index 5a1d5b2e8fdc..cdbcb5ce8bf9 100644
--- a/examples/java/build.gradle
+++ b/examples/java/build.gradle
@@ -36,6 +36,8 @@ ext.summary = """Apache Beam SDK provides a simple, Java-based
 interface for processing virtually any size data. This
 artifact includes all Apache Beam Java SDK examples."""
 
+apply from: "$projectDir/common.gradle"
+
 /** Define the list of runners which execute a precommit test.
  * Some runners are run from separate projects, see the preCommit task below
  * for details.
@@ -71,8 +73,6 @@ dependencies {
   implementation project(":sdks:java:extensions:python")
   implementation project(":sdks:java:io:google-cloud-platform")
   implementation project(":sdks:java:io:kafka")
-  runtimeOnly project(":sdks:java:io:iceberg")
-  implementation project(":sdks:java:managed")
   implementation project(":sdks:java:extensions:ml")
   implementation library.java.avro
   implementation library.java.bigdataoss_util
@@ -176,39 +176,6 @@ task preCommit() {
   }
 }
 
-/*
- * A convenient task to run individual example directly on Beam repo.
- *
- * Usage:
- * ./gradlew :examples:java:execute -PmainClass=org.apache.beam.examples.<ClassName>`\
- *  -Pexec.args="runner=[DataflowRunner|DirectRunner|FlinkRunner|SparkRunner|PrismRunner] \
- * <pipeline options>"
- */
-tasks.create(name:"execute", type:JavaExec) {
-  mainClass = project.hasProperty("mainClass") ? project.getProperty("mainClass") : "NONE"
-  def execArgs =  project.findProperty("exec.args")
-  String runner
-  if (execArgs) {
-    // configure runner dependency from args
-    def runnerPattern = /runner[ =]([A-Za-z]+)/
-    def matcher = execArgs =~ runnerPattern
-    if (matcher) {
-      runner = matcher[0][1]
-      runner = runner.substring(0, 1).toLowerCase() + runner.substring(1);
-      if (!(runner in (preCommitRunners + nonPreCommitRunners))) {
-        throw new GradleException("Unsupported runner: " + runner)
-      }
-    }
-  }
-  if (runner) {
-    classpath = sourceSets.main.runtimeClasspath + configurations."${runner}PreCommit"
-  } else {
-    classpath = sourceSets.main.runtimeClasspath
-  }
-  systemProperties System.getProperties()
-  args execArgs ? execArgs.split() : []
-}
-
 // Run this task to validate the Java environment setup for contributors
 task wordCount(type:JavaExec) {
   description "Run the Java word count example"
diff --git a/examples/java/common.gradle b/examples/java/common.gradle
new file mode 100644
index 000000000000..b8a3ef27f9a8
--- /dev/null
+++ b/examples/java/common.gradle
@@ -0,0 +1,50 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+ /*
+ * A convenient task to run individual example directly on Beam repo.
+ *
+ * Usage:
+ * ./gradlew :examples:java:execute -PmainClass=org.apache.beam.examples.<ClassName>`\
+ *  -Pexec.args="runner=[DataflowRunner|DirectRunner|FlinkRunner|SparkRunner|PrismRunner] \
+ * <pipeline options>"
+ */
+tasks.create(name:"execute", type:JavaExec) {
+  mainClass = project.hasProperty("mainClass") ? project.getProperty("mainClass") : "NONE"
+  def execArgs =  project.findProperty("exec.args")
+  String runner
+  if (execArgs) {
+    // configure runner dependency from args
+    def runnerPattern = /runner[ =]([A-Za-z]+)/
+    def matcher = execArgs =~ runnerPattern
+    if (matcher) {
+      runner = matcher[0][1]
+      runner = runner.substring(0, 1).toLowerCase() + runner.substring(1);
+      if (!(runner in (preCommitRunners + nonPreCommitRunners))) {
+        throw new GradleException("Unsupported runner: " + runner)
+      }
+    }
+  }
+  if (runner) {
+    classpath = sourceSets.main.runtimeClasspath + configurations."${runner}PreCommit"
+  } else {
+    classpath = sourceSets.main.runtimeClasspath
+  }
+  systemProperties System.getProperties()
+  args execArgs ? execArgs.split() : []
+}
diff --git a/examples/java/iceberg/build.gradle b/examples/java/iceberg/build.gradle
new file mode 100644
index 000000000000..4d258e9be5ac
--- /dev/null
+++ b/examples/java/iceberg/build.gradle
@@ -0,0 +1,96 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+plugins {
+  id 'java'
+  id 'org.apache.beam.module'
+  id 'com.gradleup.shadow'
+}
+
+applyJavaNature(
+  exportJavadoc: false,
+  automaticModuleName: 'org.apache.beam.examples.iceberg',
+  // iceberg requires Java11+
+  requireJavaVersion: JavaVersion.VERSION_11
+)
+
+description = "Apache Beam :: Examples :: Java :: Iceberg"
+ext.summary = """Apache Beam Java SDK examples using IcebergIO."""
+
+apply from: "$project.rootDir/examples/java/common.gradle"
+
+/** Define the list of runners which execute a precommit test.
+ * Some runners are run from separate projects, see the preCommit task below
+ * for details.
+ */
+def preCommitRunners = ["directRunner", "flinkRunner", "sparkRunner"]
+// The following runners have configuration created but not added to preCommit
+def nonPreCommitRunners = ["dataflowRunner", "prismRunner"]
+for (String runner : preCommitRunners) {
+  configurations.create(runner + "PreCommit")
+}
+for (String runner: nonPreCommitRunners) {
+  configurations.create(runner + "PreCommit")
+}
+configurations.sparkRunnerPreCommit {
+  // Ban certain dependencies to prevent a StackOverflow within Spark
+  // because JUL -> SLF4J -> JUL, and similarly JDK14 -> SLF4J -> JDK14
+  exclude group: "org.slf4j", module: "jul-to-slf4j"
+  exclude group: "org.slf4j", module: "slf4j-jdk14"
+}
+
+dependencies {
+  implementation enforcedPlatform(library.java.google_cloud_platform_libraries_bom)
+  runtimeOnly project(":sdks:java:io:iceberg")
+  runtimeOnly project(":sdks:java:io:iceberg:bqms")
+  implementation project(path: ":sdks:java:core", configuration: "shadow")
+  implementation project(":sdks:java:extensions:google-cloud-platform-core")
+  implementation project(":sdks:java:io:google-cloud-platform")
+  implementation project(":sdks:java:managed")
+  implementation library.java.google_auth_library_oauth2_http
+  implementation library.java.joda_time
+  runtimeOnly project(path: ":runners:direct-java", configuration: "shadow")
+  implementation library.java.vendored_guava_32_1_2_jre
+  runtimeOnly library.java.hadoop_client
+  runtimeOnly library.java.bigdataoss_gcs_connector
+
+  // Add dependencies for the PreCommit configurations
+  // For each runner a project level dependency on the examples project.
+  for (String runner : preCommitRunners) {
+    delegate.add(runner + "PreCommit", project(path: ":examples:java", configuration: "testRuntimeMigration"))
+  }
+  directRunnerPreCommit project(path: ":runners:direct-java", configuration: "shadow")
+  flinkRunnerPreCommit project(":runners:flink:${project.ext.latestFlinkVersion}")
+  sparkRunnerPreCommit project(":runners:spark:3")
+  sparkRunnerPreCommit project(":sdks:java:io:hadoop-file-system")
+  dataflowRunnerPreCommit project(":runners:google-cloud-dataflow-java")
+  dataflowRunnerPreCommit project(":runners:google-cloud-dataflow-java:worker") // v2 worker
+  dataflowRunnerPreCommit project(":sdks:java:harness") // v2 worker
+  prismRunnerPreCommit project(":runners:prism:java")
+
+  // Add dependency if requested on command line for runner
+  if (project.hasProperty("runnerDependency")) {
+    runtimeOnly project(path: project.getProperty("runnerDependency"))
+  }
+}
+
+configurations.all {
+  // iceberg-core needs avro:1.12.0
+  resolutionStrategy.force 'org.apache.avro:avro:1.12.0'
+}
diff --git a/examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergBatchWriteExample.java b/examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergBatchWriteExample.java
new file mode 100644
index 000000000000..2a5f85e524ed
--- /dev/null
+++ b/examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergBatchWriteExample.java
@@ -0,0 +1,208 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.examples.iceberg;
+
+import java.io.IOException;
+import java.util.Map;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.extensions.gcp.options.GcpOptions;
+import org.apache.beam.sdk.managed.Managed;
+import org.apache.beam.sdk.options.Default;
+import org.apache.beam.sdk.options.Description;
+import org.apache.beam.sdk.options.PipelineOptionsFactory;
+import org.apache.beam.sdk.options.Validation;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.MapElements;
+import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.transforms.Sum;
+import org.apache.beam.sdk.util.Preconditions;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.sdk.values.TypeDescriptors;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+
+/**
+ * This pipeline demonstrates a batch write to an Iceberg table using the BigQuery Metastore
+ * catalog.
+ *
+ * <p>The pipeline reads from a public BigQuery table containing Google Analytics session data,
+ * extracts and aggregates the total number of transactions per web browser, and writes the results
+ * to a new Iceberg table managed by the BigQuery Metastore.
+ *
+ * <p>This example is a demonstration of the Iceberg BigQuery Metastore. For more information, see
+ * the documentation at https://cloud.google.com/bigquery/docs/blms-use-dataproc.
+ */
+public class IcebergBatchWriteExample {
+
+  public static final Schema BQ_SCHEMA =
+      Schema.builder().addStringField("browser").addInt64Field("transactions").build();
+
+  public static final Schema AGGREGATED_SCHEMA =
+      Schema.builder().addStringField("browser").addInt64Field("transaction_count").build();
+
+  public static final String BQ_TABLE =
+      "bigquery-public-data.google_analytics_sample.ga_sessions_20170801";
+
+  private static Row flattenAnalyticsRow(Row row) {
+    Row device = Preconditions.checkStateNotNull(row.getRow("device"));
+    Row totals = Preconditions.checkStateNotNull(row.getRow("totals"));
+    return Row.withSchema(BQ_SCHEMA)
+        .withFieldValue("browser", Preconditions.checkStateNotNull(device.getString("browser")))
+        .withFieldValue(
+            "transactions", Preconditions.checkStateNotNull(totals.getInt64("transactions")))
+        .build();
+  }
+
+  static class ExtractBrowserTransactionsFn extends DoFn<Row, KV<String, Long>> {
+    @ProcessElement
+    public void processElement(ProcessContext c) {
+      Row row = c.element();
+      c.output(
+          KV.of(
+              Preconditions.checkStateNotNull(row.getString("browser")),
+              Preconditions.checkStateNotNull(row.getInt64("transactions"))));
+    }
+  }
+
+  static class FormatCountsFn extends DoFn<KV<String, Long>, Row> {
+    @ProcessElement
+    public void processElement(ProcessContext c) {
+      Row row =
+          Row.withSchema(AGGREGATED_SCHEMA)
+              .withFieldValue("browser", c.element().getKey())
+              .withFieldValue("transaction_count", c.element().getValue())
+              .build();
+      c.output(row);
+    }
+  }
+
+  static class CountTransactions extends PTransform<PCollection<Row>, PCollection<Row>> {
+    @Override
+    public PCollection<Row> expand(PCollection<Row> rows) {
+      PCollection<KV<String, Long>> browserTransactions =
+          rows.apply(ParDo.of(new ExtractBrowserTransactionsFn()));
+      PCollection<KV<String, Long>> browserCounts = browserTransactions.apply(Sum.longsPerKey());
+      return browserCounts.apply(ParDo.of(new FormatCountsFn()));
+    }
+  }
+
+  /** Pipeline options for this example. */
+  public interface IcebergPipelineOptions extends GcpOptions {
+    @Description(
+        "Warehouse location where the table's data will be written to. "
+            + "As of 07/14/25 BigLake only supports Single Region buckets")
+    @Validation.Required
+    @Default.String("gs://analytics_warehouse")
+    String getWarehouse();
+
+    void setWarehouse(String warehouse);
+
+    @Description("The Iceberg table to write to, in the format 'dataset.table'.")
+    @Validation.Required
+    @Default.String("analytics_dataset.transactions_by_browser")
+    String getIcebergTable();
+
+    void setIcebergTable(String value);
+
+    @Description("The name of the catalog to use.")
+    @Validation.Required
+    @Default.String("analytics")
+    String getCatalogName();
+
+    void setCatalogName(String catalogName);
+
+    @Description("The implementation of the Iceberg catalog.")
+    @Default.String("org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog")
+    String getCatalogImpl();
+
+    void setCatalogImpl(String catalogImpl);
+
+    @Description("The GCP location for the BigQuery Metastore.")
+    @Default.String("us-central1")
+    String getGcpLocation();
+
+    void setGcpLocation(String gcpLocation);
+
+    @Description("The implementation of the Iceberg FileIO.")
+    @Default.String("org.apache.iceberg.gcp.gcs.GCSFileIO")
+    String getIoImpl();
+
+    void setIoImpl(String ioImpl);
+  }
+
+  /**
+   * Main entry point for the pipeline.
+   *
+   * @param args Command line arguments
+   * @throws IOException if there's an issue with the pipeline setup
+   */
+  public static void main(String[] args) throws IOException {
+    IcebergPipelineOptions options =
+        PipelineOptionsFactory.fromArgs(args).withValidation().as(IcebergPipelineOptions.class);
+
+    final String tableIdentifier = options.getIcebergTable();
+    final String warehouseLocation = options.getWarehouse();
+    final String catalogName = options.getCatalogName();
+    final String projectName = options.getProject();
+    final String catalogImpl = options.getCatalogImpl();
+    final String gcpLocation = options.getGcpLocation();
+    final String ioImpl = options.getIoImpl();
+
+    Map<String, String> catalogProps =
+        ImmutableMap.<String, String>builder()
+            .put("warehouse", warehouseLocation)
+            .put("catalog-impl", catalogImpl)
+            .put("gcp_project", projectName)
+            .put("gcp_location", gcpLocation)
+            .put("io-impl", ioImpl)
+            .build();
+
+    Map<String, Object> icebergWriteConfig =
+        ImmutableMap.<String, Object>builder()
+            .put("table", tableIdentifier)
+            .put("catalog_properties", catalogProps)
+            .put("catalog_name", catalogName)
+            .build();
+
+    Map<String, Object> bigQueryReadConfig =
+        ImmutableMap.<String, Object>builder()
+            .put("table", BQ_TABLE)
+            .put("fields", ImmutableList.of("device.browser", "totals.transactions"))
+            .put("row_restriction", "totals.transactions is not null")
+            .build();
+
+    Pipeline p = Pipeline.create(options);
+
+    p.apply("ReadFromBigQuery", Managed.read(Managed.BIGQUERY).withConfig(bigQueryReadConfig))
+        .get("output")
+        .apply(
+            "Flatten",
+            MapElements.into(TypeDescriptors.rows())
+                .via(IcebergBatchWriteExample::flattenAnalyticsRow))
+        .setRowSchema(BQ_SCHEMA)
+        .apply("CountTransactions", new CountTransactions())
+        .setRowSchema(AGGREGATED_SCHEMA)
+        .apply("WriteToIceberg", Managed.write(Managed.ICEBERG).withConfig(icebergWriteConfig));
+
+    p.run().waitUntilFinish();
+  }
+}
diff --git a/examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergRestCatalogCDCExample.java b/examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergRestCatalogCDCExample.java
new file mode 100644
index 000000000000..4229e401ab94
--- /dev/null
+++ b/examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergRestCatalogCDCExample.java
@@ -0,0 +1,235 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.examples.iceberg;
+
+import static org.apache.beam.sdk.managed.Managed.ICEBERG_CDC;
+
+import com.google.auth.oauth2.GoogleCredentials;
+import java.io.IOException;
+import java.util.Map;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.RowCoder;
+import org.apache.beam.sdk.extensions.gcp.options.GcpOptions;
+import org.apache.beam.sdk.managed.Managed;
+import org.apache.beam.sdk.options.Default;
+import org.apache.beam.sdk.options.Description;
+import org.apache.beam.sdk.options.PipelineOptionsFactory;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.transforms.Sum;
+import org.apache.beam.sdk.transforms.windowing.FixedWindows;
+import org.apache.beam.sdk.transforms.windowing.Window;
+import org.apache.beam.sdk.util.Preconditions;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+import org.joda.time.DateTime;
+import org.joda.time.Duration;
+
+/**
+ * This pipeline demonstrates how to read a continuous stream of change data capture (CDC) events
+ * from an Apache Iceberg table. It processes these events to calculate the hourly total of
+ * passengers and writes the aggregated results into a new Iceberg table.
+ *
+ * <p>This pipeline can be used to process the output of {@link
+ * IcebergRestCatalogStreamingWriteExample}.
+ *
+ * <p>This pipeline also includes a flag {@code --triggerStreamingWrite} which, when enabled, will
+ * start the {@link IcebergRestCatalogStreamingWriteExample} in a separate thread to populate the
+ * source table. This is useful for execute the end-to-end functionality.
+ *
+ * <p>This example is a demonstration of the Iceberg REST Catalog. For more information, see the
+ * documentation at {@link https://cloud.google.com/bigquery/docs/blms-rest-catalog}.
+ *
+ * <p>For more information on Apache Beam Iceberg Managed-IO features, see the documentation at
+ * {@link https://beam.apache.org/documentation/io/managed-io/}.
+ */
+public class IcebergRestCatalogCDCExample {
+
+  // Schema for the source table containing minute-level aggregated data
+  public static final Schema SOURCE_SCHEMA =
+      Schema.builder().addDateTimeField("ride_minute").addInt64Field("passenger_count").build();
+
+  // Schema for the destination table containing hourly aggregated data
+  public static final Schema HOURLY_PASSENGER_COUNT_SCHEMA =
+      Schema.builder().addDateTimeField("ride_hour").addInt64Field("passenger_count").build();
+
+  public static void main(String[] args) throws IOException {
+    IcebergCdcOptions options =
+        PipelineOptionsFactory.fromArgs(args).withValidation().as(IcebergCdcOptions.class);
+
+    if (options.getTriggerStreamingWrite()) {
+      new Thread(
+              () -> {
+                try {
+                  IcebergRestCatalogStreamingWriteExample.main(
+                      new String[] {
+                        "--runner=" + options.getRunner().getSimpleName(),
+                        "--project=" + options.getProject(),
+                        "--icebergTable=" + options.getSourceTable(),
+                        "--catalogUri=" + options.getCatalogUri(),
+                        "--warehouse=" + options.getWarehouse(),
+                        "--catalogName=" + options.getCatalogName()
+                      });
+                } catch (IOException e) {
+                  throw new RuntimeException(e);
+                }
+              })
+          .start();
+    }
+
+    final String sourceTable = options.getSourceTable();
+    final String destinationTable = options.getDestinationTable();
+    final String catalogUri = options.getCatalogUri();
+    final String warehouseLocation = options.getWarehouse();
+    final String projectName = options.getProject();
+    final String catalogName = options.getCatalogName();
+    final int pollIntervalSeconds = 120;
+    final int triggeringFrequencySeconds = 30;
+
+    // Note: The token expires in 1 hour. Users may need to re-run the pipeline.
+    // Future updates to Iceberg and the BigLake Metastore will support token refreshing.
+    Map<String, String> catalogProps =
+        ImmutableMap.<String, String>builder()
+            .put("type", "rest")
+            .put("uri", catalogUri)
+            .put("warehouse", warehouseLocation)
+            .put("header.x-goog-user-project", projectName)
+            .put("oauth2-server-uri", "https://oauth2.googleapis.com/token")
+            .put(
+                "token",
+                GoogleCredentials.getApplicationDefault().refreshAccessToken().getTokenValue())
+            .put("rest-metrics-reporting-enabled", "false")
+            .build();
+
+    Pipeline p = Pipeline.create(options);
+
+    // Configure the Iceberg CDC read
+    Map<String, Object> icebergReadConfig =
+        ImmutableMap.<String, Object>builder()
+            .put("table", sourceTable)
+            .put("filter", "\"ride_minute\" IS NOT NULL AND \"passenger_count\" IS NOT NULL")
+            .put("keep", ImmutableList.of("ride_minute", "passenger_count"))
+            .put("catalog_name", catalogName)
+            .put("catalog_properties", catalogProps)
+            .put("streaming", true)
+            .put("poll_interval_seconds", pollIntervalSeconds)
+            .build();
+
+    // Read CDC events from the source Iceberg table
+    PCollection<Row> cdcEvents =
+        p.apply("ReadFromIceberg", Managed.read(ICEBERG_CDC).withConfig(icebergReadConfig))
+            .getSinglePCollection()
+            .setRowSchema(SOURCE_SCHEMA);
+
+    // Aggregate passenger counts per hour
+    PCollection<Row> aggregatedRows =
+        cdcEvents
+            .apply(
+                "ApplyHourlyWindow",
+                Window.<Row>into(FixedWindows.of(Duration.standardMinutes(10))))
+            .apply("ExtractHourAndCount", ParDo.of(new ExtractHourAndPassengerCount()))
+            .apply("SumPassengerCountPerHour", Sum.longsPerKey())
+            .apply("FormatToRow", ParDo.of(new FormatAggregatedRow()))
+            .setCoder(RowCoder.of(HOURLY_PASSENGER_COUNT_SCHEMA));
+
+    // Configure the Iceberg write
+    Map<String, Object> icebergWriteConfig =
+        ImmutableMap.<String, Object>builder()
+            .put("table", destinationTable)
+            .put("partition_fields", ImmutableList.of("day(ride_hour)"))
+            .put("catalog_properties", catalogProps)
+            .put("catalog_name", catalogName)
+            .put("triggering_frequency_seconds", triggeringFrequencySeconds)
+            .build();
+
+    // Write the aggregated results to the destination Iceberg table
+    aggregatedRows.apply(
+        "WriteToIceberg", Managed.write(Managed.ICEBERG).withConfig(icebergWriteConfig));
+
+    p.run().waitUntilFinish();
+  }
+
+  private static class FormatAggregatedRow extends DoFn<KV<String, Long>, Row> {
+    @ProcessElement
+    public void processElement(@Element KV<String, Long> kv, OutputReceiver<Row> out) {
+      Row row =
+          Row.withSchema(HOURLY_PASSENGER_COUNT_SCHEMA)
+              .withFieldValue("ride_hour", DateTime.parse(kv.getKey()))
+              .withFieldValue("passenger_count", kv.getValue())
+              .build();
+      out.output(row);
+    }
+  }
+
+  private static class ExtractHourAndPassengerCount extends DoFn<Row, KV<String, Long>> {
+    @ProcessElement
+    public void processElement(@Element Row row, OutputReceiver<KV<String, Long>> out) {
+      DateTime rideHour =
+          ((DateTime) Preconditions.checkStateNotNull(row.getDateTime("ride_minute")))
+              .withSecondOfMinute(0)
+              .withMillisOfSecond(0);
+      out.output(
+          KV.of(
+              rideHour.toString(),
+              Preconditions.checkStateNotNull(row.getInt64("passenger_count"))));
+    }
+  }
+
+  /** Pipeline options for this example. */
+  public interface IcebergCdcOptions extends GcpOptions {
+    @Description("The source Iceberg table to read CDC events from")
+    @Default.String("taxi_dataset.ride_metrics_by_minute")
+    String getSourceTable();
+
+    void setSourceTable(String sourceTable);
+
+    @Description("The destination Iceberg table to write aggregated results to")
+    @Default.String("taxi_dataset.passenger_count_by_hour")
+    String getDestinationTable();
+
+    void setDestinationTable(String destinationTable);
+
+    @Description("Warehouse location for the Iceberg catalog")
+    @Default.String("gs://biglake_taxi_ride_metrics")
+    String getWarehouse();
+
+    void setWarehouse(String warehouse);
+
+    @Description("The URI for the REST catalog")
+    @Default.String("https://biglake.googleapis.com/iceberg/v1beta/restcatalog")
+    String getCatalogUri();
+
+    void setCatalogUri(String value);
+
+    @Description("The name of the Iceberg catalog")
+    @Default.String("taxi_rides")
+    String getCatalogName();
+
+    void setCatalogName(String catalogName);
+
+    @Description("Trigger the streaming write example")
+    @Default.Boolean(false)
+    boolean getTriggerStreamingWrite();
+
+    void setTriggerStreamingWrite(boolean triggerStreamingWrite);
+  }
+}
diff --git a/examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergRestCatalogStreamingWriteExample.java b/examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergRestCatalogStreamingWriteExample.java
new file mode 100644
index 000000000000..0ea73cdf0c87
--- /dev/null
+++ b/examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergRestCatalogStreamingWriteExample.java
@@ -0,0 +1,295 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.examples.iceberg;
+
+import com.google.auth.oauth2.GoogleCredentials;
+import java.io.IOException;
+import java.io.Serializable;
+import java.util.Map;
+import java.util.Objects;
+import javax.annotation.Nullable;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.KvCoder;
+import org.apache.beam.sdk.coders.RowCoder;
+import org.apache.beam.sdk.coders.SerializableCoder;
+import org.apache.beam.sdk.coders.StringUtf8Coder;
+import org.apache.beam.sdk.extensions.gcp.options.GcpOptions;
+import org.apache.beam.sdk.io.gcp.pubsub.PubsubIO;
+import org.apache.beam.sdk.managed.Managed;
+import org.apache.beam.sdk.options.Default;
+import org.apache.beam.sdk.options.Description;
+import org.apache.beam.sdk.options.PipelineOptionsFactory;
+import org.apache.beam.sdk.options.Validation;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.transforms.Combine;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.Filter;
+import org.apache.beam.sdk.transforms.JsonToRow;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.transforms.WithKeys;
+import org.apache.beam.sdk.transforms.windowing.FixedWindows;
+import org.apache.beam.sdk.transforms.windowing.Window;
+import org.apache.beam.sdk.util.Preconditions;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+import org.joda.time.DateTime;
+import org.joda.time.Duration;
+
+/**
+ * Reads real-time NYC taxi ride information from {@code
+ * projects/pubsub-public-data/topics/taxirides-realtime} and writes aggregated metrics data to an
+ * Iceberg table using Beam's {@link Managed} IcebergIO sink.
+ *
+ * <p>This is a streaming pipeline that processes taxi ride events, filters for 'dropoff' status,
+ * aggregates metrics within fixed 10-second windows by minute of the ride, and writes the results
+ * to a single Iceberg table. The Iceberg sink triggers writes every 30 seconds, creating new
+ * snapshots.
+ *
+ * <p>This example is a demonstration of the Iceberg REST Catalog. For more information, see the
+ * documentation at {@link https://cloud.google.com/bigquery/docs/blms-rest-catalog}.
+ */
+public class IcebergRestCatalogStreamingWriteExample {
+
+  // Schema for the incoming taxi ride data from Pub/Sub
+  public static final Schema TAXIRIDES_SCHEMA =
+      Schema.builder()
+          .addStringField("ride_id")
+          .addStringField("ride_status")
+          .addDateTimeField("timestamp")
+          .addInt64Field("passenger_count")
+          .addDoubleField("meter_reading")
+          .build();
+
+  // Schema for the aggregated data to be written to Iceberg
+  public static final Schema AGGREGATED_SCHEMA =
+      Schema.builder()
+          .addDateTimeField("ride_minute")
+          .addInt64Field("total_rides")
+          .addDoubleField("revenue")
+          .addInt64Field("passenger_count")
+          .build();
+
+  public static final String TAXI_RIDES_TOPIC =
+      "projects/pubsub-public-data/topics/taxirides-realtime";
+
+  /**
+   * Checks if a {@link Row} contains all required fields.
+   *
+   * @param row The input Row
+   * @return {@code true} if all fields are present, {@code false} otherwise
+   */
+  private static boolean areAllFieldsPresent(Row row) {
+    return row.getString("ride_id") != null
+        && row.getString("ride_status") != null
+        && row.getDateTime("timestamp") != null
+        && row.getInt64("passenger_count") != null
+        && row.getDouble("meter_reading") != null;
+  }
+
+  /**
+   * The main entry point for the pipeline.
+   *
+   * @param args Command-line arguments
+   * @throws IOException If there is an issue with Google Credentials
+   */
+  public static void main(String[] args) throws IOException {
+    IcebergPipelineOptions options =
+        PipelineOptionsFactory.fromArgs(args).withValidation().as(IcebergPipelineOptions.class);
+
+    final String tableIdentifier = options.getIcebergTable();
+    final String catalogUri = options.getCatalogUri();
+    final String warehouseLocation = options.getWarehouse();
+    final String projectName = options.getProject();
+    final String catalogName = options.getCatalogName();
+    final int triggeringFrequencySeconds = 30;
+
+    // Note: The token expires in 1 hour. Users may need to re-run the pipeline.
+    // Future updates to Iceberg and the BigLake Metastore will support token refreshing.
+    Map<String, String> catalogProps =
+        ImmutableMap.<String, String>builder()
+            .put("type", "rest")
+            .put("uri", catalogUri)
+            .put("warehouse", warehouseLocation)
+            .put("header.x-goog-user-project", projectName)
+            .put("oauth2-server-uri", "https://oauth2.googleapis.com/token")
+            .put(
+                "token",
+                GoogleCredentials.getApplicationDefault().refreshAccessToken().getTokenValue())
+            .put("rest-metrics-reporting-enabled", "false")
+            .build();
+
+    Map<String, Object> icebergWriteConfig =
+        ImmutableMap.<String, Object>builder()
+            .put("table", tableIdentifier)
+            .put("catalog_properties", catalogProps)
+            .put("catalog_name", catalogName)
+            .put("triggering_frequency_seconds", triggeringFrequencySeconds)
+            .build();
+
+    Pipeline p = Pipeline.create(options);
+
+    p.apply("ReadFromPubSub", PubsubIO.readStrings().fromTopic(TAXI_RIDES_TOPIC))
+        .apply("ConvertJsonToRow", JsonToRow.withSchema(TAXIRIDES_SCHEMA))
+        .apply("FilterNullFields", Filter.by(r -> areAllFieldsPresent(r)))
+        .apply("FilterDropoffRides", Filter.by(r -> "dropoff".equals(r.getString("ride_status"))))
+        .apply("ApplyFixedWindow", Window.<Row>into(FixedWindows.of(Duration.standardSeconds(10))))
+        .apply(
+            "ExtractMinuteAsKey",
+            WithKeys.<String, Row>of(
+                (Row row) ->
+                    ((DateTime) Preconditions.checkStateNotNull(row.getDateTime("timestamp")))
+                        .withSecondOfMinute(0)
+                        .withMillisOfSecond(0)
+                        .toString()))
+        .setCoder(KvCoder.of(StringUtf8Coder.of(), RowCoder.of(TAXIRIDES_SCHEMA)))
+        .apply("AggregateMetrics", Combine.<String, Row, Accum>perKey(new AggregateMetricsFn()))
+        .setCoder(KvCoder.of(StringUtf8Coder.of(), SerializableCoder.of(Accum.class)))
+        .apply("FormatForIceberg", ParDo.of(new FormatRowFn()))
+        .setCoder(RowCoder.of(AGGREGATED_SCHEMA))
+        .apply("WriteToIceberg", Managed.write(Managed.ICEBERG).withConfig(icebergWriteConfig));
+
+    p.run().waitUntilFinish();
+  }
+
+  private static class FormatRowFn extends DoFn<KV<String, Accum>, Row> {
+    @ProcessElement
+    public void processElement(@Element KV<String, Accum> element, OutputReceiver<Row> out) {
+      Accum metrics = Preconditions.checkStateNotNull(element.getValue());
+      DateTime rideMinute = DateTime.parse(element.getKey());
+      Row row =
+          Row.withSchema(AGGREGATED_SCHEMA)
+              .withFieldValue("ride_minute", rideMinute)
+              .withFieldValue("total_rides", metrics.getTotalRides())
+              .withFieldValue("revenue", metrics.getRevenue())
+              .withFieldValue("passenger_count", metrics.getPassengerCount())
+              .build();
+      out.output(row);
+    }
+  }
+
+  public static class Accum implements Serializable {
+    private final long totalRides;
+    private final double revenue;
+    private final long passengerCount;
+
+    public Accum(long totalRides, double revenue, long passengerCount) {
+      this.totalRides = totalRides;
+      this.revenue = revenue;
+      this.passengerCount = passengerCount;
+    }
+
+    public long getTotalRides() {
+      return totalRides;
+    }
+
+    public double getRevenue() {
+      return revenue;
+    }
+
+    public long getPassengerCount() {
+      return passengerCount;
+    }
+
+    @Override
+    public boolean equals(@Nullable Object o) {
+      if (this == o) {
+        return true;
+      }
+      if (o == null || getClass() != o.getClass()) {
+        return false;
+      }
+      Accum accum = (Accum) o;
+      return totalRides == accum.totalRides
+          && Double.compare(accum.revenue, revenue) == 0
+          && passengerCount == accum.passengerCount;
+    }
+
+    @Override
+    public int hashCode() {
+      return Objects.hash(totalRides, revenue, passengerCount);
+    }
+  }
+
+  public static class AggregateMetricsFn extends Combine.CombineFn<Row, Accum, Accum> {
+    @Override
+    public Accum createAccumulator() {
+      return new Accum(0, 0, 0);
+    }
+
+    @Override
+    public Accum addInput(Accum mutableAccumulator, Row input) {
+      return new Accum(
+          mutableAccumulator.getTotalRides() + 1,
+          mutableAccumulator.getRevenue()
+              + Preconditions.checkStateNotNull(input.getDouble("meter_reading")),
+          mutableAccumulator.getPassengerCount()
+              + Preconditions.checkStateNotNull(input.getInt64("passenger_count")));
+    }
+
+    @Override
+    public Accum mergeAccumulators(Iterable<Accum> accumulators) {
+      long totalRides = 0;
+      double revenue = 0;
+      long passengerCount = 0;
+      for (Accum accum : accumulators) {
+        totalRides += accum.getTotalRides();
+        revenue += accum.getRevenue();
+        passengerCount += accum.getPassengerCount();
+      }
+      return new Accum(totalRides, revenue, passengerCount);
+    }
+
+    @Override
+    public Accum extractOutput(Accum accumulator) {
+      return accumulator;
+    }
+  }
+
+  /** Pipeline options for the IcebergRestCatalogStreamingWriteExample. */
+  public interface IcebergPipelineOptions extends GcpOptions {
+    @Description(
+        "Warehouse location where the table's data will be written to. "
+            + "As of 07/14/25 BigLake only supports Single Region buckets")
+    @Validation.Required
+    @Default.String("gs://biglake_taxi_ride_metrics")
+    String getWarehouse();
+
+    void setWarehouse(String warehouse);
+
+    @Description("The URI for the REST catalog.")
+    @Validation.Required
+    @Default.String("https://biglake.googleapis.com/iceberg/v1beta/restcatalog")
+    String getCatalogUri();
+
+    void setCatalogUri(String value);
+
+    @Description("The iceberg table to write to.")
+    @Validation.Required
+    @Default.String("taxi_dataset.ride_metrics_by_minute")
+    String getIcebergTable();
+
+    void setIcebergTable(String value);
+
+    @Validation.Required
+    @Default.String("taxi_rides")
+    String getCatalogName();
+
+    void setCatalogName(String catalogName);
+  }
+}
diff --git a/examples/java/src/main/java/org/apache/beam/examples/cookbook/IcebergTaxiExamples.java b/examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergTaxiExamples.java
similarity index 99%
rename from examples/java/src/main/java/org/apache/beam/examples/cookbook/IcebergTaxiExamples.java
rename to examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergTaxiExamples.java
index 446d11d03be4..5b4fe1b9b913 100644
--- a/examples/java/src/main/java/org/apache/beam/examples/cookbook/IcebergTaxiExamples.java
+++ b/examples/java/iceberg/src/main/java/org/apache/beam/examples/iceberg/IcebergTaxiExamples.java
@@ -15,7 +15,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-package org.apache.beam.examples.cookbook;
+package org.apache.beam.examples.iceberg;
 
 import java.util.Arrays;
 import java.util.Map;
diff --git a/examples/java/src/main/java/org/apache/beam/examples/complete/StreamingWordExtract.java b/examples/java/src/main/java/org/apache/beam/examples/complete/StreamingWordExtract.java
index 4cfbcd56b983..e4ce5e3eb17e 100644
--- a/examples/java/src/main/java/org/apache/beam/examples/complete/StreamingWordExtract.java
+++ b/examples/java/src/main/java/org/apache/beam/examples/complete/StreamingWordExtract.java
@@ -123,13 +123,11 @@ public static void main(String[] args) throws IOException {
     Pipeline pipeline = Pipeline.create(options);
 
     String tableSpec =
-        new StringBuilder()
-            .append(options.getProject())
-            .append(":")
-            .append(options.getBigQueryDataset())
-            .append(".")
-            .append(options.getBigQueryTable())
-            .toString();
+        options.getProject()
+            + ":"
+            + options.getBigQueryDataset()
+            + "."
+            + options.getBigQueryTable();
     pipeline
         .apply("ReadLines", TextIO.read().from(options.getInputFile()))
         .apply(ParDo.of(new ExtractWords()))
diff --git a/examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/utils/SchemasUtils.java b/examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/utils/SchemasUtils.java
index 4d1a6cb66add..9d908e8bd6ca 100644
--- a/examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/utils/SchemasUtils.java
+++ b/examples/java/src/main/java/org/apache/beam/examples/complete/datatokenization/utils/SchemasUtils.java
@@ -95,7 +95,7 @@ private void validateSchemaTypes(TableSchema bigQuerySchema) {
     try {
       beamSchema = fromTableSchema(bigQuerySchema);
     } catch (UnsupportedOperationException exception) {
-      LOG.error("Check json schema, {}", exception.getMessage());
+      LOG.error("Check json schema", exception);
     } catch (Exception e) {
       LOG.error("Missing schema keywords, please check what all required fields presented");
     }
@@ -140,14 +140,15 @@ public static String getGcsFileAsString(String filePath) {
       result = FileSystems.match(filePath);
       checkArgument(
           result.status() == MatchResult.Status.OK && !result.metadata().isEmpty(),
-          "Failed to match any files with the pattern: " + filePath);
+          "Failed to match any files with the pattern: %s",
+          filePath);
 
       List<ResourceId> rId =
           result.metadata().stream()
               .map(MatchResult.Metadata::resourceId)
               .collect(Collectors.toList());
 
-      checkArgument(rId.size() == 1, "Expected exactly 1 file, but got " + rId.size() + " files.");
+      checkArgument(rId.size() == 1, "Expected exactly 1 file, but got %s files.", rId.size());
 
       Reader reader =
           Channels.newReader(FileSystems.open(rId.get(0)), StandardCharsets.UTF_8.name());
diff --git a/examples/notebooks/beam-ml/alloydb_product_catalog_embeddings.ipynb b/examples/notebooks/beam-ml/alloydb_product_catalog_embeddings.ipynb
index 4b14f0fea79d..3ff7a606236a 100644
--- a/examples/notebooks/beam-ml/alloydb_product_catalog_embeddings.ipynb
+++ b/examples/notebooks/beam-ml/alloydb_product_catalog_embeddings.ipynb
@@ -151,7 +151,7 @@
       "outputs": [],
       "source": [
         "# Apache Beam with GCP support\n",
-        "!pip install apache_beam[gcp]>=2.66.0\n",
+        "!pip install apache_beam[interactive,gcp]>=2.66.0\n",
         "# Huggingface sentence-transformers for embedding models\n",
         "!pip install sentence-transformers --quiet"
       ]
@@ -238,8 +238,10 @@
     {
       "cell_type": "code",
       "source": [
-        "from google.colab import auth\n",
-        "auth.authenticate_user(project_id=PROJECT_ID)"
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
       ],
       "metadata": {
         "id": "CLM12rbiZHTN"
@@ -1104,8 +1106,10 @@
       },
       "outputs": [],
       "source": [
-        "from google.colab import auth\n",
-        "auth.authenticate_user(project_id=PROJECT_ID)"
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
       ]
     },
     {
@@ -2187,8 +2191,10 @@
         "# Replace <PROJECT_ID> with a valid Google Cloud project ID.\n",
         "PROJECT_ID = '' # @param {type:'string'}\n",
         "\n",
-        "from google.colab import auth\n",
-        "auth.authenticate_user(project_id=PROJECT_ID)"
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
       ]
     },
     {
@@ -2339,8 +2345,10 @@
         "# Replace <PROJECT_ID> with a valid Google Cloud project ID.\n",
         "PROJECT_ID = '' # @param {type:'string'}\n",
         "\n",
-        "from google.colab import auth\n",
-        "auth.authenticate_user(project_id=PROJECT_ID)"
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
       ],
       "metadata": {
         "id": "VCqJmaznt1nS"
diff --git a/examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_iforest.ipynb b/examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_iforest.ipynb
index db2de68f27c9..f91fb71e9217 100644
--- a/examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_iforest.ipynb
+++ b/examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_iforest.ipynb
@@ -121,8 +121,10 @@
     {
       "cell_type": "code",
       "source": [
-        "from google.colab import auth\n",
-        "auth.authenticate_user(project_id=PROJECT_ID)"
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
       ],
       "metadata": {
         "id": "A_49Y2aTQeiH"
@@ -489,7 +491,7 @@
       "cell_type": "code",
       "source": [
         "# For running with dataflow runner\n",
-        "!pip install 'apache_beam[gcp, interactive]=={BEAM_VERSION}' --quiet"
+        "!pip install 'apache_beam[interactive,gcp]=={BEAM_VERSION}' --quiet"
       ],
       "metadata": {
         "id": "0C0qur71DiN3"
diff --git a/examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_timesfm.ipynb b/examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_timesfm.ipynb
new file mode 100644
index 000000000000..e232daf02d3e
--- /dev/null
+++ b/examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_timesfm.ipynb
@@ -0,0 +1,2712 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+        "\n",
+        "# Licensed to the Apache Software Foundation (ASF) under one\n",
+        "# or more contributor license agreements. See the NOTICE file\n",
+        "# distributed with this work for additional information\n",
+        "# regarding copyright ownership. The ASF licenses this file\n",
+        "# to you under the Apache License, Version 2.0 (the\n",
+        "# \"License\"); you may not use this file except in compliance\n",
+        "# with the License. You may obtain a copy of the License at\n",
+        "#\n",
+        "#   http://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing,\n",
+        "# software distributed under the License is distributed on an\n",
+        "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+        "# KIND, either express or implied. See the License for the\n",
+        "# specific language governing permissions and limitations\n",
+        "# under the License"
+      ],
+      "metadata": {
+        "id": "eMMlVe_Gukos"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# TimesFM Anomaly Detection Pipeline Diagram\n",
+        "Time series data is a sequence of data points indexed by time, where each data point is recorded at a specific interval. TimesFM is a foundation model pretrained on a large corpus of time series data. Its architecture is a decoder-only transformer, similar to LLMs, which learns to predict the next part of a time series from previous data. We can use the follow pipeline to detect anomalies in time series data and periodically learn from incoming data to improve our timesfm predictions.\n",
+        "\n",
+        "![Untitled 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+      ],
+      "metadata": {
+        "id": "cAgnGkn3GFVb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": true,
+        "id": "oCgmuQtdrSkG"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install timesfm[torch]\n",
+        "!pip install 'apache_beam[interactive,gcp,test] == 2.67.0'\n",
+        "!pip install google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Ordered Sliding Window![preprocessing.jpg](data:image/jpeg;base64,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+      ],
+      "metadata": {
+        "id": "VgyZHICtuRMz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import logging\n",
+        "\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.coders import BooleanCoder\n",
+        "from apache_beam.coders import PickleCoder\n",
+        "from apache_beam.coders import TimestampCoder\n",
+        "from apache_beam.transforms.timeutil import TimeDomain\n",
+        "from apache_beam.transforms.userstate import OrderedListStateSpec\n",
+        "from apache_beam.transforms.userstate import ReadModifyWriteStateSpec\n",
+        "from apache_beam.transforms.userstate import TimerSpec\n",
+        "from apache_beam.transforms.userstate import on_timer\n",
+        "from apache_beam.utils.timestamp import MAX_TIMESTAMP\n",
+        "from apache_beam.utils.timestamp import Timestamp\n",
+        "\n",
+        "_LOGGER = logging.getLogger(__name__)\n",
+        "logging.basicConfig(level=logging.INFO)\n",
+        "_LOGGER.setLevel(logging.INFO)\n",
+        "\n",
+        "\n",
+        "class OrderedSlidingWindowFn(beam.DoFn):\n",
+        "\n",
+        "  ORDERED_BUFFER_STATE = OrderedListStateSpec('ordered_buffer', PickleCoder())\n",
+        "  WINDOW_TIMER = TimerSpec('window_timer', TimeDomain.WATERMARK)\n",
+        "  TIMER_STATE = ReadModifyWriteStateSpec('timer_state', BooleanCoder())\n",
+        "  EARLIEST_TS_STATE = ReadModifyWriteStateSpec('earliest_ts', TimestampCoder())\n",
+        "\n",
+        "  def __init__(self, window_size, slide_interval):\n",
+        "    self.window_size = window_size\n",
+        "    self.slide_interval = slide_interval\n",
+        "\n",
+        "  def start_bundle(self):\n",
+        "    _LOGGER.debug(\"start bundle\")\n",
+        "\n",
+        "  def finish_bundle(self):\n",
+        "    _LOGGER.debug(\"finish bundle\")\n",
+        "\n",
+        "  def process(\n",
+        "      self,\n",
+        "      element,\n",
+        "      timestamp=beam.DoFn.TimestampParam,\n",
+        "      ordered_buffer=beam.DoFn.StateParam(ORDERED_BUFFER_STATE),\n",
+        "      window_timer=beam.DoFn.TimerParam(WINDOW_TIMER),\n",
+        "      timer_state=beam.DoFn.StateParam(TIMER_STATE),\n",
+        "      earliest_ts_state=beam.DoFn.StateParam(EARLIEST_TS_STATE)):\n",
+        "\n",
+        "    _, value = element\n",
+        "    ordered_buffer.add((timestamp, value))\n",
+        "\n",
+        "    _LOGGER.debug(\"receive %s at %s\", element, timestamp)\n",
+        "    timer_started = timer_state.read()\n",
+        "\n",
+        "    earliest = earliest_ts_state.read()\n",
+        "    if not earliest or earliest > timestamp:\n",
+        "      earliest_ts_state.write(timestamp)\n",
+        "\n",
+        "    if not timer_started:\n",
+        "      earliest_ts_state.write(timestamp)\n",
+        "\n",
+        "      first_slide_start = int(\n",
+        "          timestamp.micros / 1e6 // self.slide_interval) * self.slide_interval\n",
+        "      first_slide_start_ts = Timestamp.of(first_slide_start)\n",
+        "\n",
+        "      first_window_end_ts = first_slide_start_ts + self.window_size\n",
+        "      _LOGGER.debug(\"set timer to %s\", first_window_end_ts)\n",
+        "      window_timer.set(first_window_end_ts)\n",
+        "\n",
+        "      timer_state.write(True)\n",
+        "\n",
+        "    return []\n",
+        "\n",
+        "  @on_timer(WINDOW_TIMER)\n",
+        "  def on_timer(\n",
+        "      self,\n",
+        "      key=beam.DoFn.KeyParam,\n",
+        "      fire_ts=beam.DoFn.TimestampParam,\n",
+        "      ordered_buffer=beam.DoFn.StateParam(ORDERED_BUFFER_STATE),\n",
+        "      window_timer=beam.DoFn.TimerParam(WINDOW_TIMER),\n",
+        "      timer_state=beam.DoFn.StateParam(TIMER_STATE),\n",
+        "      earliest_ts_state=beam.DoFn.StateParam(EARLIEST_TS_STATE)):\n",
+        "    _LOGGER.debug(\"timer fire at %s\", fire_ts)\n",
+        "    window_end_ts = fire_ts\n",
+        "    window_start_ts = window_end_ts - self.window_size\n",
+        "\n",
+        "    window_values = list(\n",
+        "        ordered_buffer.read_range(window_start_ts, window_end_ts))\n",
+        "\n",
+        "    _LOGGER.debug(\n",
+        "        \"window start: %s, window end: %s\", window_start_ts, window_end_ts)\n",
+        "    _LOGGER.debug(\"windowed data in buffer %s\", str(window_values))\n",
+        "    if window_values:\n",
+        "      yield (key, (window_start_ts, window_end_ts, window_values))\n",
+        "\n",
+        "    next_window_end_ts = fire_ts + self.slide_interval\n",
+        "    next_window_start_ts = window_start_ts + self.slide_interval\n",
+        "\n",
+        "    earliest_ts = earliest_ts_state.read()\n",
+        "    ordered_buffer.clear_range(earliest_ts, next_window_start_ts)\n",
+        "\n",
+        "    remaining_data = list(\n",
+        "        ordered_buffer.read_range(next_window_start_ts, MAX_TIMESTAMP))\n",
+        "\n",
+        "    if not remaining_data:\n",
+        "      timer_state.clear()\n",
+        "      earliest_ts_state.write(next_window_start_ts)\n",
+        "      return\n",
+        "\n",
+        "    _LOGGER.debug(\"set timer to %s\", next_window_end_ts)\n",
+        "    window_timer.set(next_window_end_ts)\n",
+        "\n",
+        "\n",
+        "class FillGapsFn(beam.DoFn):\n",
+        "  def __init__(self, expected_interval: float):\n",
+        "    \"\"\"\n",
+        "    Args:\n",
+        "      expected_interval: The expected time delta between elements, in seconds.\n",
+        "    \"\"\"\n",
+        "    self.expected_interval = expected_interval\n",
+        "\n",
+        "  def process(self, element):\n",
+        "    key, (window_start_ts, window_end_ts, window_elements) = element\n",
+        "\n",
+        "    received_data = {\n",
+        "        round(float(ts.micros / 1e6), 5): val\n",
+        "        for ts, val in window_elements\n",
+        "    }\n",
+        "\n",
+        "    start_sec = float(window_start_ts.micros / 1e6)\n",
+        "    end_sec = float(window_end_ts.micros / 1e6)\n",
+        "\n",
+        "    filled_values = []\n",
+        "    current_ts_sec = start_sec\n",
+        "\n",
+        "    while current_ts_sec < end_sec:\n",
+        "      lookup_ts = round(current_ts_sec, 5)\n",
+        "\n",
+        "      if lookup_ts in received_data:\n",
+        "        filled_values.append(float(received_data[lookup_ts]))\n",
+        "      else:\n",
+        "        filled_values.append('NaN')\n",
+        "\n",
+        "      current_ts_sec += self.expected_interval\n",
+        "\n",
+        "    yield (key, (window_start_ts, window_end_ts, filled_values))\n"
+      ],
+      "metadata": {
+        "id": "E1fHKPrkuLFW"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Model 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+      ],
+      "metadata": {
+        "id": "aP8LqLobuViH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import apache_beam as beam\n",
+        "from apache_beam.ml.inference.base import ModelHandler\n",
+        "import timesfm\n",
+        "import logging\n",
+        "import numpy as np\n",
+        "import os\n",
+        "from google.cloud import storage\n",
+        "from apache_beam.io.gcp.gcsio import GcsIO\n",
+        "from apache_beam.utils.timestamp import Timestamp\n",
+        "\n",
+        "class LatestModelCheckpointLoader(beam.PTransform):\n",
+        "    \"\"\"A PTransform that finds the latest model checkpoint in a GCS path.\"\"\"\n",
+        "    def __init__(self, gcs_bucket, gcs_prefix):\n",
+        "        self.gcs_bucket = gcs_bucket\n",
+        "        self.gcs_prefix = gcs_prefix\n",
+        "\n",
+        "    def expand(self, pcoll):\n",
+        "        return pcoll | \"FindLatestModel\" >> beam.Map(self._find_latest_model_path)\n",
+        "\n",
+        "    def _find_latest_model_path(self, _):\n",
+        "        try:\n",
+        "            storage_client = storage.Client()\n",
+        "            blobs = storage_client.list_blobs(self.gcs_bucket, prefix=self.gcs_prefix)\n",
+        "            # Filter for model files and find the most recent one\n",
+        "            model_blobs = [b for b in blobs if b.name.endswith(\".pth\")]\n",
+        "            latest_blob = max(model_blobs, key=lambda b: b.time_created, default=None)\n",
+        "\n",
+        "            if latest_blob:\n",
+        "                path = f\"gs://{self.gcs_bucket}/{latest_blob.name}\"\n",
+        "                logging.info(f\"Found latest finetuned model at: {path}\")\n",
+        "                return path\n",
+        "        except Exception as e:\n",
+        "            logging.error(f\"Error finding latest model in GCS: {e}\")\n",
+        "\n",
+        "        # Return a path to the base model if no finetuned one exists or an error occurs\n",
+        "        base_model = \"google/timesfm-1.0-200m-pytorch\"\n",
+        "        logging.info(f\"No finetuned model found. Using base model: {base_model}\")\n",
+        "        return base_model\n",
+        "\n",
+        "class DynamicTimesFmModelHandler(ModelHandler[np.ndarray, np.ndarray, timesfm.TimesFm]):\n",
+        "    \"\"\"\n",
+        "    A model handler that loads a TimesFM model from a dynamic path (GCS or Hugging Face).\n",
+        "    The model path is provided as a side input to RunInference.\n",
+        "    \"\"\"\n",
+        "    def __init__(self, model_uri: str, hparams):\n",
+        "        self._hparams = hparams\n",
+        "        self._model = None\n",
+        "        self._model_uri = model_uri\n",
+        "        self._context_len = hparams.context_len\n",
+        "        self._horizon_len = hparams.horizon_len\n",
+        "\n",
+        "    def load_model(self) -> timesfm.TimesFm:\n",
+        "        \"\"\"Loads a model from the handler's current model_uri.\"\"\"\n",
+        "        logging.info(f\"Loading TimesFM model from path: {self._model_uri}...\")\n",
+        "\n",
+        "        checkpoint_config = {}\n",
+        "        if self._model_uri.startswith(\"gs://\"):\n",
+        "            try:\n",
+        "                gcs = GcsIO()\n",
+        "                file_name = os.path.basename(self._model_uri)\n",
+        "                local_path = f\"/tmp/{file_name}\"\n",
+        "                with gcs.open(self._model_uri, 'rb') as f_in, open(local_path, 'wb') as f_out:\n",
+        "                    f_out.write(f_in.read())\n",
+        "                checkpoint_config['path'] = local_path\n",
+        "                logging.info(f\"Downloaded model from GCS to {local_path}\")\n",
+        "            except Exception as e:\n",
+        "                logging.error(f\"Failed to download model from GCS: {e}. Check path and permissions.\")\n",
+        "                raise e # Re-raise the exception to fail fast if the model can't be loaded.\n",
+        "        else:\n",
+        "            checkpoint_config['huggingface_repo_id'] = self._model_uri\n",
+        "\n",
+        "        self._model = timesfm.TimesFm(\n",
+        "            hparams=self._hparams,\n",
+        "            checkpoint=timesfm.TimesFmCheckpoint(**checkpoint_config)\n",
+        "        )\n",
+        "        logging.info(\"TimesFM model loaded successfully.\")\n",
+        "        return self._model\n",
+        "\n",
+        "    def update_model_path(self, model_path: str):\n",
+        "        \"\"\"\n",
+        "        This method is called by RunInference when a new model metadata is available\n",
+        "        from the side input. It updates the model URI that `load_model` will use.\n",
+        "        \"\"\"\n",
+        "        if not model_path:\n",
+        "            logging.info(\"Received an empty model path update. No action taken.\")\n",
+        "            return\n",
+        "        logging.info(f\"Received model update. New model URI: {model_path}\")\n",
+        "        self._model_uri = model_path\n",
+        "        self._model = self.load_model()\n",
+        "        logging.info(\"Model has been updated in the handler.\")\n",
+        "\n",
+        "    def run_inference(self, batch, model, inference_args=None):\n",
+        "        \"\"\"\n",
+        "            Runs inference on a batch of data.\n",
+        "\n",
+        "            Note: While this is a standard method for ModelHandler, we will call the\n",
+        "            model's `forecast` method directly in our DoFn for clarity.\n",
+        "            \"\"\"\n",
+        "        # print(\"Running inference on batch:\", batch)\n",
+        "        # logging.info(f\"Running inference on batch:\", batch)\n",
+        "\n",
+        "        anomalies_found = []\n",
+        "\n",
+        "        key, (window_start_ts, _, values_array) = batch[0]\n",
+        "\n",
+        "        # A window must have enough data for both context and horizon.\n",
+        "        # if len(values_array) < self.context_len + self.horizon_len:\n",
+        "        #     return\n",
+        "\n",
+        "        current_context = np.array(values_array[:self._context_len])\n",
+        "        actual_horizon_values = np.array(\n",
+        "            values_array[self._context_len:self._context_len + self._horizon_len])\n",
+        "\n",
+        "        print(\"Current context shape:\", current_context.shape)\n",
+        "        print(\"Actual horizon values shape:\", actual_horizon_values.shape)\n",
+        "        point_forecast, experimental_quantile_forecast = model.forecast(\n",
+        "            [current_context],\n",
+        "            freq=[0],\n",
+        "        )\n",
+        "\n",
+        "        current_predicted_horizon_values = point_forecast[\n",
+        "            0, :, 0] if point_forecast.ndim == 3 else point_forecast[0]\n",
+        "\n",
+        "        current_q20_values = experimental_quantile_forecast[0, :, 2]\n",
+        "        current_q30_values = experimental_quantile_forecast[0, :, 3]\n",
+        "        current_q70_values = experimental_quantile_forecast[0, :, 7]\n",
+        "        current_q80_values = experimental_quantile_forecast[0, :, 8]\n",
+        "\n",
+        "        for j in range(len(actual_horizon_values)):\n",
+        "            current_actual = actual_horizon_values[j]\n",
+        "\n",
+        "            point_Q1 = np.nanmean([current_q20_values[j], current_q30_values[j]])\n",
+        "            point_Q3 = np.nanmean([current_q70_values[j], current_q80_values[j]])\n",
+        "            point_IQR = point_Q3 - point_Q1\n",
+        "\n",
+        "            upper_thresh = point_Q3 + 1.5 * point_IQR\n",
+        "            lower_thresh = point_Q1 - 1.5 * point_IQR\n",
+        "\n",
+        "            if current_actual > upper_thresh or current_actual < lower_thresh:\n",
+        "                score = (current_actual - upper_thresh\n",
+        "                            ) / point_IQR if current_actual > upper_thresh else (\n",
+        "                                lower_thresh - current_actual) / point_IQR\n",
+        "\n",
+        "                anomaly_timestamp_seconds = (window_start_ts.micros / 1e6) + (\n",
+        "                    self._context_len + j)\n",
+        "\n",
+        "                index_in_window = self._context_len + j\n",
+        "\n",
+        "                anomalies_found.append({\n",
+        "                    'key': key,\n",
+        "                    'timestamp': Timestamp(anomaly_timestamp_seconds),\n",
+        "                    'index_in_window': index_in_window,\n",
+        "                    'actual_value': current_actual,\n",
+        "                    'predicted_value': current_predicted_horizon_values[j],\n",
+        "                    'is_anomaly': True,\n",
+        "                    'outlier_score': score,\n",
+        "                    'lower_bound': lower_thresh,\n",
+        "                    'upper_bound': upper_thresh,\n",
+        "                })\n",
+        "        payload = {\n",
+        "            \"start_ts_micros\": window_start_ts.micros,\n",
+        "            \"predicted_values\": current_predicted_horizon_values.tolist(),\n",
+        "            \"q20_values\": current_q20_values.tolist(),\n",
+        "            \"q30_values\": current_q30_values.tolist(),\n",
+        "            \"q70_values\": current_q70_values.tolist(),\n",
+        "            \"q80_values\": current_q80_values.tolist(),\n",
+        "            \"anomalies\": anomalies_found,  # Your original list is now inside the dictionary\n",
+        "            \"actual_horizon_values\": actual_horizon_values.tolist()\n",
+        "        }\n",
+        "        result_with_context = (batch[0], payload)\n",
+        "\n",
+        "        return [result_with_context]"
+      ],
+      "metadata": {
+        "id": "oT9NIaWcuUgb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a26eae71-067a-4314-b49a-3b028ee75903"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " See https://github.com/google-research/timesfm/blob/master/README.md for updated APIs.\n",
+            "Loaded PyTorch TimesFM, likely because python version is 3.11.13 (main, Jun  4 2025, 08:57:29) [GCC 11.4.0].\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# LLM Classifier![classification.jpg](data:image/jpeg;base64,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+      ],
+      "metadata": {
+        "id": "CU9zuwUUu7tX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import apache_beam as beam\n",
+        "import google.generativeai as genai\n",
+        "import logging\n",
+        "import os\n",
+        "import re\n",
+        "import json\n",
+        "import numpy as np\n",
+        "from apache_beam.utils.timestamp import Timestamp\n",
+        "from dotenv import load_dotenv\n",
+        "from apache_beam.transforms.userstate import BagStateSpec\n",
+        "\n",
+        "import apache_beam as beam\n",
+        "import json\n",
+        "import numpy as np\n",
+        "\n",
+        "from apache_beam.coders.coders import PickleCoder\n",
+        "\n",
+        "from apache_beam.transforms.userstate import BagStateSpec, ReadModifyWriteStateSpec, TimerSpec, on_timer\n",
+        "\n",
+        "\n",
+        "class CustomJsonEncoderForLLM(json.JSONEncoder):\n",
+        "    \"\"\"Encodes special types like Timestamp and numpy objects into JSON.\"\"\"\n",
+        "    def default(self, obj):\n",
+        "        if isinstance(obj, Timestamp):\n",
+        "            # Store as a dict with a special key for easy decoding\n",
+        "            return {'__timestamp__': True, 'micros': obj.micros}\n",
+        "        if isinstance(obj, np.integer):\n",
+        "            return int(obj)\n",
+        "        if isinstance(obj, np.floating):\n",
+        "            return float(obj)\n",
+        "        if isinstance(obj, np.ndarray):\n",
+        "            return obj.tolist()\n",
+        "        return super().default(obj)\n",
+        "\n",
+        "def custom_json_decoder(dct):\n",
+        "    \"\"\"Decodes a Timestamp object from our custom dict format.\"\"\"\n",
+        "    if '__timestamp__' in dct:\n",
+        "        return Timestamp(micros=dct['micros'])\n",
+        "    return dct\n",
+        "\n",
+        "class JsonCoderWithNumpyAndTimestamp(beam.coders.Coder):\n",
+        "    \"\"\"A custom Beam Coder that handles JSON serialization for Timestamps and numpy types.\"\"\"\n",
+        "    def encode(self, value):\n",
+        "        return json.dumps(value, cls=CustomJsonEncoderForLLM).encode('utf-8')\n",
+        "\n",
+        "    def decode(self, encoded):\n",
+        "        return json.loads(encoded.decode('utf-8'), object_hook=custom_json_decoder)\n",
+        "\n",
+        "    def is_deterministic(self):\n",
+        "        return True\n",
+        "\n",
+        "\n",
+        "# It's highly recommended to manage API keys via GCP Secret Manager\n",
+        "# and access them as environment variables in your Dataflow job.\n",
+        "# genai.configure(api_key=os.environ[\"GEMINI_API_KEY\"])\n",
+        "\n",
+        "class LLMClassifierFn(beam.DoFn):\n",
+        "    \"\"\"\n",
+        "    Takes an anomaly, formats a detailed prompt with surrounding context,\n",
+        "    calls the Gemini model to classify it, and routes the original data\n",
+        "    based on the model's decision.\n",
+        "\n",
+        "    This DoFn is stateful, deferring anomalies that occur too close to\n",
+        "    the end of a window until a subsequent window provides enough context.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    DEFERRED_ANOMALIES_STATE = BagStateSpec(\n",
+        "        'deferred_anomalies', coder=JsonCoderWithNumpyAndTimestamp())\n",
+        "    YIELD_BUFFER_STATE = ReadModifyWriteStateSpec('yield_buffer', PickleCoder())\n",
+        "\n",
+        "    # <<< CHANGE: Define a timer and a state to track if it's set\n",
+        "    EXPIRY_TIMER = TimerSpec('expiry', beam.TimeDomain.WATERMARK)\n",
+        "    # <<< CHANGE: Add state to track the last yielded timestamp\n",
+        "    LAST_YIELDED_TIMESTAMP_STATE = ReadModifyWriteStateSpec('last_yielded_ts', PickleCoder())\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "    def __init__(self, secret, context_points=25, slide_interval=128, expected_interval_secs=1):\n",
+        "        self.context_points = context_points\n",
+        "        self._model = None\n",
+        "        self.secret = secret\n",
+        "        self.slide_interval = slide_interval\n",
+        "        self.expected_interval_micros = expected_interval_secs * 1_000_000\n",
+        "\n",
+        "        self._last_window_data = None\n",
+        "\n",
+        "\n",
+        "    def setup(self):\n",
+        "        # Configure the generative model\n",
+        "\n",
+        "        genai.configure(api_key=self.secret)\n",
+        "        logging.getLogger().setLevel(logging.INFO)\n",
+        "\n",
+        "\n",
+        "        generation_config = {\n",
+        "            \"temperature\": 0.2,\n",
+        "            \"top_p\": 1,\n",
+        "            \"top_k\": 1,\n",
+        "            \"max_output_tokens\": 256,\n",
+        "            \"response_mime_type\": \"application/json\",\n",
+        "        }\n",
+        "        # For a full list of safety settings, see the Gemini API documentation\n",
+        "        safety_settings = [\n",
+        "            {\"category\": \"HARM_CATEGORY_HARASSMENT\", \"threshold\": \"BLOCK_NONE\"},\n",
+        "            {\"category\": \"HARM_CATEGORY_HATE_SPEECH\", \"threshold\": \"BLOCK_NONE\"},\n",
+        "        ]\n",
+        "        self._model = genai.GenerativeModel(\n",
+        "            model_name=\"gemini-1.5-flash-latest\",\n",
+        "            generation_config=generation_config,\n",
+        "            safety_settings=safety_settings\n",
+        "        )\n",
+        "        logging.info(\"Gemini Model has been successfully initialized.\")\n",
+        "\n",
+        "    def _build_prompt(self, anomaly_data, context_before, context_after):\n",
+        "        mean_before = np.mean(context_before) if context_before.size > 0 else 0\n",
+        "        mean_after = np.mean(context_after) if context_after.size > 0 else 0\n",
+        "        std_before = np.std(context_before) if context_before.size > 0 else 0\n",
+        "        std_after = np.std(context_after) if context_after.size > 0 else 0\n",
+        "\n",
+        "        return f\"\"\"\n",
+        "        You are an expert time-series analyst classifying an outlier from NYC taxi pickup data.\n",
+        "        Normal behavior includes daily and weekly cyclical patterns.\n",
+        "\n",
+        "        **1. Outlier Context:**\n",
+        "        * **--> The Outlier:**\n",
+        "            * **Timestamp:** {Timestamp(micros=anomaly_data['timestamp'].micros)}\n",
+        "            * **Actual Value:** {anomaly_data['actual_value']:.2f}\n",
+        "            * **Predicted Value:** {anomaly_data['predicted_value']:.2f}\n",
+        "            * **Anomaly Upper Bound:** {anomaly_data['upper_bound']:.2f}\n",
+        "            * **Anomaly Lower Bound:** {anomaly_data['lower_bound']:.2f}\n",
+        "\n",
+        "        **2. Data Surrounding the Outlier:**\n",
+        "        * **Data Before ({len(context_before)} points):** {np.round(context_before, 2).tolist()}\n",
+        "        * **Data After ({len(context_after)} points):** {np.round(context_after, 2).tolist()}\n",
+        "\n",
+        "        **3. Statistical Context:**\n",
+        "        * **Mean Before:** {mean_before:.2f}\n",
+        "        * **Mean After:** {mean_after:.2f}\n",
+        "        * **Std. Dev. Before:** {std_before:.2f}\n",
+        "        * **Std. Dev. After:** {std_after:.2f}\n",
+        "\n",
+        "        **4. Your Task:**\n",
+        "\n",
+        "        **Step 1: Analyze the Evidence.** In a few sentences, describe the behavior of the data *after* the outlier. Does it quickly revert to the \"Predicted Value\" or the \"Mean Before\"? Or does it establish a new level, closer to the \"Mean After\"?\n",
+        "\n",
+        "        **Step 2: Make a Decision.** Classify the outlier.\n",
+        "        * **REMOVE:** If it's a transient, one-off event. This is likely if the data after the outlier rapidly returns to the established pattern.\n",
+        "        * **KEEP:** If it signifies a sustained shift in the pattern that the model should learn from. This is likely if the `Mean After` has shifted significantly.\n",
+        "\n",
+        "        **Step 3: Provide Final Output.** Respond with a single JSON object. Do not add any text outside the JSON block.\n",
+        "\n",
+        "        {{\n",
+        "          \"reasoning_steps\": \"Your analysis from Step 1 goes here.\",\n",
+        "          \"decision\": \"KEEP or REMOVE\",\n",
+        "          \"confidence_score\": <A float between 0.0 and 1.0>\n",
+        "        }}\n",
+        "        \"\"\"\n",
+        "\n",
+        "    def process(self, element,\n",
+        "                deferred_anomalies=beam.DoFn.StateParam(DEFERRED_ANOMALIES_STATE),\n",
+        "                yield_buffer=beam.DoFn.StateParam(YIELD_BUFFER_STATE),\n",
+        "                expiry_timer=beam.DoFn.TimerParam(EXPIRY_TIMER)):\n",
+        "\n",
+        "        key, data = element\n",
+        "        window_start_ts = data['window_start_ts']\n",
+        "\n",
+        "        # Set a timer to fire based on the event time of the current element.\n",
+        "        # Each new element will push the timer forward. The timer will only\n",
+        "        # fire when a gap in the input stream occurs, allowing the buffer\n",
+        "        # to contain data from multiple consecutive sliding windows.\n",
+        "        # We set it far enough ahead to allow the next window's data to arrive.\n",
+        "        grace_period_secs = self.slide_interval * 2\n",
+        "        expiry_timer.set(window_start_ts + grace_period_secs)\n",
+        "        anomalies_in_window = data.get('anomalies', [])\n",
+        "        values_in_element = data.get('values_array', [])\n",
+        "\n",
+        "        for anomaly in anomalies_in_window:\n",
+        "             deferred_anomalies.add(anomaly)\n",
+        "\n",
+        "        buffer = yield_buffer.read() or {}\n",
+        "        for i, value in enumerate(values_in_element):\n",
+        "            point_timestamp = Timestamp(micros=window_start_ts.micros + (i * self.expected_interval_micros))\n",
+        "            buffer[point_timestamp] = value\n",
+        "        yield_buffer.write(buffer)\n",
+        "\n",
+        "    @on_timer(EXPIRY_TIMER)\n",
+        "    def on_expiry_timer(\n",
+        "        self,\n",
+        "        deferred_anomalies=beam.DoFn.StateParam(DEFERRED_ANOMALIES_STATE),\n",
+        "        yield_buffer=beam.DoFn.StateParam(YIELD_BUFFER_STATE),\n",
+        "        # <<< CHANGE: Add the new state parameter here\n",
+        "        last_yielded_ts_state=beam.DoFn.StateParam(LAST_YIELDED_TIMESTAMP_STATE)):\n",
+        "\n",
+        "        all_anomalies_to_consider = list(deferred_anomalies.read())\n",
+        "        buffered_points_map = yield_buffer.read() or {}\n",
+        "\n",
+        "        if not buffered_points_map:\n",
+        "            return\n",
+        "\n",
+        "        sorted_points = sorted(buffered_points_map.items())\n",
+        "        all_timestamps = [ts for ts, val in sorted_points]\n",
+        "        all_values = [val for ts, val in sorted_points]\n",
+        "\n",
+        "        anomalies_to_process_now = []\n",
+        "        prompts_to_batch = []\n",
+        "        final_deferred = []\n",
+        "\n",
+        "        for anomaly_data in all_anomalies_to_consider:\n",
+        "            anomaly_ts = anomaly_data['timestamp']\n",
+        "            try:\n",
+        "                idx_in_full_data = all_timestamps.index(anomaly_ts)\n",
+        "\n",
+        "                if (idx_in_full_data + self.context_points) < len(all_values):\n",
+        "                    start_ctx = max(0, idx_in_full_data - self.context_points)\n",
+        "                    end_ctx = idx_in_full_data + self.context_points + 1\n",
+        "\n",
+        "                    context_before = np.array(all_values[start_ctx:idx_in_full_data])\n",
+        "                    context_after = np.array(all_values[idx_in_full_data + 1:end_ctx])\n",
+        "\n",
+        "                    anomaly_data['index_in_window'] = idx_in_full_data\n",
+        "                    prompt = self._build_prompt(anomaly_data, context_before, context_after)\n",
+        "                    prompts_to_batch.append(prompt)\n",
+        "                    anomalies_to_process_now.append(anomaly_data)\n",
+        "                else:\n",
+        "                    final_deferred.append(anomaly_data)\n",
+        "            except ValueError:\n",
+        "                 final_deferred.append(anomaly_data)\n",
+        "\n",
+        "        if prompts_to_batch:\n",
+        "            try:\n",
+        "                logging.info(f\"Sending a batch of {len(prompts_to_batch)} prompts to the LLM.\")\n",
+        "                responses = self._model.generate_content(prompts_to_batch)\n",
+        "                for anomaly_data, response in zip(anomalies_to_process_now, responses):\n",
+        "                    try:\n",
+        "                        response_data = json.loads(response.text)\n",
+        "                        decision = response_data.get('decision', 'KEEP').strip().upper()\n",
+        "                        idx = anomaly_data['index_in_window']\n",
+        "\n",
+        "                        if decision == 'REMOVE':\n",
+        "                            logging.warning(f\"LLM decided to REMOVE anomaly at {anomaly_data['timestamp']}. Imputing value.\")\n",
+        "                            all_values[idx] = anomaly_data['predicted_value']\n",
+        "                    except (json.JSONDecodeError, AttributeError) as e:\n",
+        "                        logging.error(f\"Error processing LLM response for {anomaly_data['timestamp']}: {e}. Defaulting to KEEP.\")\n",
+        "            except Exception as e:\n",
+        "                logging.error(f\"Error calling LLM with a batch: {e}. Defaulting to KEEP for all.\")\n",
+        "\n",
+        "        # <<< CHANGE: New logic to yield only new data\n",
+        "        last_yielded_ts = last_yielded_ts_state.read()\n",
+        "        latest_ts_in_batch = None\n",
+        "\n",
+        "        for i, (ts, original_val) in enumerate(sorted_points):\n",
+        "            # Only yield points that are newer than the last batch we yielded\n",
+        "            if last_yielded_ts is None or ts > last_yielded_ts:\n",
+        "                yield {\n",
+        "                    'timestamp': ts,\n",
+        "                    'value': all_values[i]\n",
+        "                }\n",
+        "                latest_ts_in_batch = ts\n",
+        "\n",
+        "        # After yielding, update the state with the latest timestamp from this batch\n",
+        "        if latest_ts_in_batch:\n",
+        "            last_yielded_ts_state.write(latest_ts_in_batch)\n",
+        "\n",
+        "        # Prune the buffer. We need to keep enough historical data to serve\n",
+        "        # as `context_before` for the anomalies that we are re-deferring.\n",
+        "        if latest_ts_in_batch:\n",
+        "            all_buffered_points = yield_buffer.read() or {}\n",
+        "\n",
+        "            # Find the earliest timestamp we need to keep. This will be\n",
+        "            # `context_points` before the last yielded point, ensuring\n",
+        "            # context is available for the next batch.\n",
+        "            try:\n",
+        "                last_yielded_index = all_timestamps.index(latest_ts_in_batch)\n",
+        "                context_start_index = max(0, last_yielded_index - self.context_points)\n",
+        "                context_start_ts = all_timestamps[context_start_index]\n",
+        "\n",
+        "                pruned_buffer = {\n",
+        "                    ts: val\n",
+        "                    for ts, val in all_buffered_points.items()\n",
+        "                    if ts >= context_start_ts\n",
+        "                }\n",
+        "                yield_buffer.write(pruned_buffer)\n",
+        "            except ValueError:\n",
+        "                # This can happen if the buffer is in an inconsistent state.\n",
+        "                # As a fallback, we clear it if we aren't deferring anything.\n",
+        "                logging.warning(\n",
+        "                    f\"Could not find last yielded timestamp \"\n",
+        "                    f\"{latest_ts_in_batch} in buffer for pruning.\"\n",
+        "                )\n",
+        "                if not final_deferred:\n",
+        "                    yield_buffer.clear()\n",
+        "        elif not final_deferred:\n",
+        "            # If we didn't yield anything and we're not deferring anything,\n",
+        "            # the buffer is fully processed and can be cleared.\n",
+        "            yield_buffer.clear()\n",
+        "\n",
+        "        # Re-add anomalies that couldn't be processed to the state so they can\n",
+        "        # be considered in the next firing.\n",
+        "        deferred_anomalies.clear()\n",
+        "        if final_deferred:\n",
+        "            logging.info(f\"Re-deferring {len(final_deferred)} anomalies due to insufficient context.\")\n",
+        "            for anomaly in final_deferred:\n",
+        "                deferred_anomalies.add(anomaly)\n"
+      ],
+      "metadata": {
+        "id": "c55ou9f5vADf"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Finetuning Component"
+      ],
+      "metadata": {
+        "id": "WSl5lV_9ugQY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\"\"\"\n",
+        "TimesFM Finetuner: A flexible framework for finetuning TimesFM models on custom datasets.\n",
+        "\"\"\"\n",
+        "\n",
+        "import logging\n",
+        "import os\n",
+        "from abc import ABC, abstractmethod\n",
+        "from dataclasses import dataclass, field\n",
+        "from typing import Any, Callable, Dict, List, Optional\n",
+        "\n",
+        "import torch\n",
+        "import torch.distributed as dist\n",
+        "import torch.nn as nn\n",
+        "from torch.nn.parallel import DistributedDataParallel as DDP\n",
+        "from torch.utils.data import DataLoader, Dataset\n",
+        "from timesfm.pytorch_patched_decoder import create_quantiles\n",
+        "\n",
+        "import wandb\n",
+        "\n",
+        "\n",
+        "class MetricsLogger(ABC):\n",
+        "  \"\"\"Abstract base class for logging metrics during training.\n",
+        "\n",
+        "    This class defines the interface for logging metrics during model training.\n",
+        "    Concrete implementations can log to different backends (e.g., WandB, TensorBoard).\n",
+        "    \"\"\"\n",
+        "\n",
+        "  @abstractmethod\n",
+        "  def log_metrics(self,\n",
+        "                  metrics: Dict[str, Any],\n",
+        "                  step: Optional[int] = None) -> None:\n",
+        "    \"\"\"Log metrics to the specified backend.\n",
+        "\n",
+        "        Args:\n",
+        "          metrics: Dictionary containing metric names and values.\n",
+        "          step: Optional step number or epoch for the metrics.\n",
+        "        \"\"\"\n",
+        "    pass\n",
+        "\n",
+        "  @abstractmethod\n",
+        "  def close(self) -> None:\n",
+        "    \"\"\"Clean up any resources used by the logger.\"\"\"\n",
+        "    pass\n",
+        "\n",
+        "\n",
+        "class WandBLogger(MetricsLogger):\n",
+        "  \"\"\"Weights & Biases implementation of metrics logging.\n",
+        "\n",
+        "    Args:\n",
+        "      project: Name of the W&B project.\n",
+        "      config: Configuration dictionary to log.\n",
+        "      rank: Process rank in distributed training.\n",
+        "    \"\"\"\n",
+        "\n",
+        "  def __init__(self, project: str, config: Dict[str, Any], rank: int = 0):\n",
+        "    self.rank = rank\n",
+        "    if rank == 0:\n",
+        "      wandb.init(project=project, config=config)\n",
+        "\n",
+        "  def log_metrics(self,\n",
+        "                  metrics: Dict[str, Any],\n",
+        "                  step: Optional[int] = None) -> None:\n",
+        "    \"\"\"Log metrics to W&B if on the main process.\n",
+        "\n",
+        "        Args:\n",
+        "          metrics: Dictionary of metrics to log.\n",
+        "          step: Current training step or epoch.\n",
+        "        \"\"\"\n",
+        "    if self.rank == 0:\n",
+        "      wandb.log(metrics, step=step)\n",
+        "\n",
+        "  def close(self) -> None:\n",
+        "    \"\"\"Finish the W&B run if on the main process.\"\"\"\n",
+        "    if self.rank == 0:\n",
+        "      wandb.finish()\n",
+        "\n",
+        "\n",
+        "class DistributedManager:\n",
+        "  \"\"\"Manages distributed training setup and cleanup.\n",
+        "\n",
+        "    Args:\n",
+        "      world_size: Total number of processes.\n",
+        "      rank: Process rank.\n",
+        "      master_addr: Address of the master process.\n",
+        "      master_port: Port for distributed communication.\n",
+        "      backend: PyTorch distributed backend to use.\n",
+        "    \"\"\"\n",
+        "\n",
+        "  def __init__(\n",
+        "      self,\n",
+        "      world_size: int,\n",
+        "      rank: int,\n",
+        "      master_addr: str = \"localhost\",\n",
+        "      master_port: str = \"12358\",\n",
+        "      backend: str = \"nccl\",\n",
+        "  ):\n",
+        "    self.world_size = world_size\n",
+        "    self.rank = rank\n",
+        "    self.master_addr = master_addr\n",
+        "    self.master_port = master_port\n",
+        "    self.backend = backend\n",
+        "\n",
+        "  def setup(self) -> None:\n",
+        "    \"\"\"Initialize the distributed environment.\"\"\"\n",
+        "    os.environ[\"MASTER_ADDR\"] = self.master_addr\n",
+        "    os.environ[\"MASTER_PORT\"] = self.master_port\n",
+        "\n",
+        "    if not dist.is_initialized():\n",
+        "      dist.init_process_group(backend=self.backend,\n",
+        "                              world_size=self.world_size,\n",
+        "                              rank=self.rank)\n",
+        "\n",
+        "  def cleanup(self) -> None:\n",
+        "    \"\"\"Clean up the distributed environment.\"\"\"\n",
+        "    if dist.is_initialized():\n",
+        "      dist.destroy_process_group()\n",
+        "\n",
+        "\n",
+        "@dataclass\n",
+        "class FinetuningConfig:\n",
+        "  \"\"\"Configuration for model training.\n",
+        "\n",
+        "    Args:\n",
+        "      batch_size: Number of samples per batch.\n",
+        "      num_epochs: Number of training epochs.\n",
+        "      learning_rate: Initial learning rate.\n",
+        "      weight_decay: L2 regularization factor.\n",
+        "      freq_type: Frequency, can be [0, 1, 2].\n",
+        "      use_quantile_loss: bool = False  # Flag to enable/disable quantile loss\n",
+        "      quantiles: Optional[List[float]] = None\n",
+        "      device: Device to train on ('cuda' or 'cpu').\n",
+        "      distributed: Whether to use distributed training.\n",
+        "      gpu_ids: List of GPU IDs to use.\n",
+        "      master_port: Port for distributed training.\n",
+        "      master_addr: Address for distributed training.\n",
+        "      use_wandb: Whether to use Weights & Biases logging.\n",
+        "      wandb_project: W&B project name.\n",
+        "      log_every_n_steps: Log metrics every N steps (batches), this is inspired from Pytorch Lightning\n",
+        "      val_check_interval: How often within one training epoch to check val metrics. (also from Pytorch Lightning)\n",
+        "        Can be: float (0.0-1.0): fraction of epoch (e.g., 0.5 = validate twice per epoch)\n",
+        "                int: validate every N batches\n",
+        "    \"\"\"\n",
+        "\n",
+        "  batch_size: int = 32\n",
+        "  num_epochs: int = 20\n",
+        "  learning_rate: float = 1e-4\n",
+        "  weight_decay: float = 0.01\n",
+        "  freq_type: int = 0\n",
+        "  use_quantile_loss: bool = False\n",
+        "  quantiles: Optional[List[float]] = None\n",
+        "  device: str = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+        "  distributed: bool = False\n",
+        "  gpu_ids: List[int] = field(default_factory=lambda: [0])\n",
+        "  master_port: str = \"12358\"\n",
+        "  master_addr: str = \"localhost\"\n",
+        "  use_wandb: bool = False\n",
+        "  wandb_project: str = \"timesfm-finetuning\"\n",
+        "  log_every_n_steps: int = 50\n",
+        "  val_check_interval: float = 0.5\n",
+        "\n",
+        "\n",
+        "class TimesFMFinetuner:\n",
+        "  \"\"\"Handles model training and validation.\n",
+        "\n",
+        "    Args:\n",
+        "      model: PyTorch model to train.\n",
+        "      config: Training configuration.\n",
+        "      rank: Process rank for distributed training.\n",
+        "      loss_fn: Loss function (defaults to MSE).\n",
+        "      logger: Optional logging.Logger instance.\n",
+        "    \"\"\"\n",
+        "\n",
+        "  def __init__(\n",
+        "      self,\n",
+        "      model: nn.Module,\n",
+        "      config: FinetuningConfig,\n",
+        "      rank: int = 0,\n",
+        "      loss_fn: Optional[Callable] = None,\n",
+        "      logger: Optional[logging.Logger] = None,\n",
+        "  ):\n",
+        "    self.model = model\n",
+        "    self.config = config\n",
+        "    self.rank = rank\n",
+        "    self.logger = logger or logging.getLogger(__name__)\n",
+        "    self.device = torch.device(\n",
+        "        f\"cuda:{rank}\" if torch.cuda.is_available() else \"cpu\")\n",
+        "    self.loss_fn = loss_fn or (lambda x, y: torch.mean((x - y.squeeze(-1))**2))\n",
+        "\n",
+        "    if config.use_wandb:\n",
+        "      self.metrics_logger = WandBLogger(config.wandb_project, config.__dict__,\n",
+        "                                        rank)\n",
+        "\n",
+        "    if config.distributed:\n",
+        "      self.dist_manager = DistributedManager(\n",
+        "          world_size=len(config.gpu_ids),\n",
+        "          rank=rank,\n",
+        "          master_addr=config.master_addr,\n",
+        "          master_port=config.master_port,\n",
+        "      )\n",
+        "      self.dist_manager.setup()\n",
+        "      self.model = self._setup_distributed_model()\n",
+        "\n",
+        "  def _setup_distributed_model(self) -> nn.Module:\n",
+        "    \"\"\"Configure model for distributed training.\"\"\"\n",
+        "    self.model = self.model.to(self.device)\n",
+        "    return DDP(self.model,\n",
+        "               device_ids=[self.config.gpu_ids[self.rank]],\n",
+        "               output_device=self.config.gpu_ids[self.rank])\n",
+        "\n",
+        "  def _create_dataloader(self, dataset: Dataset, is_train: bool) -> DataLoader:\n",
+        "    \"\"\"Create appropriate DataLoader based on training configuration.\n",
+        "\n",
+        "        Args:\n",
+        "          dataset: Dataset to create loader for.\n",
+        "          is_train: Whether this is for training (affects shuffling).\n",
+        "\n",
+        "        Returns:\n",
+        "          DataLoader instance.\n",
+        "        \"\"\"\n",
+        "    if self.config.distributed:\n",
+        "      sampler = torch.utils.data.distributed.DistributedSampler(\n",
+        "          dataset,\n",
+        "          num_replicas=len(self.config.gpu_ids),\n",
+        "          rank=dist.get_rank(),\n",
+        "          shuffle=is_train)\n",
+        "    else:\n",
+        "      sampler = None\n",
+        "\n",
+        "    return DataLoader(\n",
+        "        dataset,\n",
+        "        batch_size=self.config.batch_size,\n",
+        "        shuffle=(is_train and not self.config.distributed),\n",
+        "        sampler=sampler,\n",
+        "    )\n",
+        "\n",
+        "  def _quantile_loss(self, pred: torch.Tensor, actual: torch.Tensor,\n",
+        "                     quantile: float) -> torch.Tensor:\n",
+        "    \"\"\"Calculates quantile loss.\n",
+        "        Args:\n",
+        "            pred: Predicted values\n",
+        "            actual: Actual values\n",
+        "            quantile: Quantile at which loss is computed\n",
+        "        Returns:\n",
+        "            Quantile loss\n",
+        "        \"\"\"\n",
+        "    dev = actual - pred\n",
+        "    loss_first = dev * quantile\n",
+        "    loss_second = -dev * (1.0 - quantile)\n",
+        "    return 2 * torch.where(loss_first >= 0, loss_first, loss_second)\n",
+        "\n",
+        "  def _process_batch(self, batch: List[torch.Tensor]) -> tuple:\n",
+        "    \"\"\"Process a single batch of data.\n",
+        "\n",
+        "        Args:\n",
+        "          batch: List of input tensors.\n",
+        "\n",
+        "        Returns:\n",
+        "          Tuple of (loss, predictions).\n",
+        "        \"\"\"\n",
+        "    x_context, x_padding, freq, x_future = [\n",
+        "        t.to(self.device, non_blocking=True) for t in batch\n",
+        "    ]\n",
+        "\n",
+        "    predictions = self.model(x_context, x_padding.float(), freq)\n",
+        "    predictions_mean = predictions[..., 0]\n",
+        "    last_patch_pred = predictions_mean[:, -1, :]\n",
+        "\n",
+        "    loss = self.loss_fn(last_patch_pred, x_future.squeeze(-1))\n",
+        "    if self.config.use_quantile_loss:\n",
+        "      quantiles = self.config.quantiles or create_quantiles()\n",
+        "      for i, quantile in enumerate(quantiles):\n",
+        "        last_patch_quantile = predictions[:, -1, :, i + 1]\n",
+        "        loss += torch.mean(\n",
+        "            self._quantile_loss(last_patch_quantile, x_future.squeeze(-1),\n",
+        "                                quantile))\n",
+        "\n",
+        "    return loss, predictions\n",
+        "\n",
+        "  def _train_epoch(self, train_loader: DataLoader,\n",
+        "                   optimizer: torch.optim.Optimizer) -> float:\n",
+        "    \"\"\"Train for one epoch in a distributed setting.\n",
+        "\n",
+        "        Args:\n",
+        "            train_loader: DataLoader for training data.\n",
+        "            optimizer: Optimizer instance.\n",
+        "\n",
+        "        Returns:\n",
+        "            Average training loss for the epoch.\n",
+        "        \"\"\"\n",
+        "    self.model.train()\n",
+        "    total_loss = 0.0\n",
+        "    num_batches = len(train_loader)\n",
+        "\n",
+        "    for batch in train_loader:\n",
+        "      loss, _ = self._process_batch(batch)\n",
+        "\n",
+        "      optimizer.zero_grad()\n",
+        "      loss.backward()\n",
+        "      optimizer.step()\n",
+        "\n",
+        "      total_loss += loss.item()\n",
+        "\n",
+        "    avg_loss = total_loss / num_batches\n",
+        "\n",
+        "    if self.config.distributed:\n",
+        "      avg_loss_tensor = torch.tensor(avg_loss, device=self.device)\n",
+        "      dist.all_reduce(avg_loss_tensor, op=dist.ReduceOp.SUM)\n",
+        "      avg_loss = (avg_loss_tensor / dist.get_world_size()).item()\n",
+        "\n",
+        "    return avg_loss\n",
+        "\n",
+        "  def _validate(self, val_loader: DataLoader) -> float:\n",
+        "    \"\"\"Perform validation.\n",
+        "\n",
+        "        Args:\n",
+        "            val_loader: DataLoader for validation data.\n",
+        "\n",
+        "        Returns:\n",
+        "            Average validation loss.\n",
+        "        \"\"\"\n",
+        "    self.model.eval()\n",
+        "    total_loss = 0.0\n",
+        "    num_batches = len(val_loader)\n",
+        "\n",
+        "    with torch.no_grad():\n",
+        "      for batch in val_loader:\n",
+        "        loss, _ = self._process_batch(batch)\n",
+        "        total_loss += loss.item()\n",
+        "\n",
+        "    avg_loss = total_loss / num_batches\n",
+        "\n",
+        "    if self.config.distributed:\n",
+        "      avg_loss_tensor = torch.tensor(avg_loss, device=self.device)\n",
+        "      dist.all_reduce(avg_loss_tensor, op=dist.ReduceOp.SUM)\n",
+        "      avg_loss = (avg_loss_tensor / dist.get_world_size()).item()\n",
+        "\n",
+        "    return avg_loss\n",
+        "\n",
+        "  def finetune(self, train_dataset: Dataset,\n",
+        "               val_dataset: Dataset) -> Dict[str, Any]:\n",
+        "    \"\"\"Train the model.\n",
+        "\n",
+        "        Args:\n",
+        "          train_dataset: Training dataset.\n",
+        "          val_dataset: Validation dataset.\n",
+        "\n",
+        "        Returns:\n",
+        "          Dictionary containing training history.\n",
+        "        \"\"\"\n",
+        "    self.model = self.model.to(self.device)\n",
+        "    train_loader = self._create_dataloader(train_dataset, is_train=True)\n",
+        "    val_loader = self._create_dataloader(val_dataset, is_train=False)\n",
+        "\n",
+        "    optimizer = torch.optim.Adam(self.model.parameters(),\n",
+        "                                 lr=self.config.learning_rate,\n",
+        "                                 weight_decay=self.config.weight_decay)\n",
+        "\n",
+        "    history = {\"train_loss\": [], \"val_loss\": [], \"learning_rate\": []}\n",
+        "\n",
+        "    self.logger.info(\n",
+        "        f\"Starting training for {self.config.num_epochs} epochs...\")\n",
+        "    self.logger.info(f\"Training samples: {len(train_dataset)}\")\n",
+        "    self.logger.info(f\"Validation samples: {len(val_dataset)}\")\n",
+        "\n",
+        "    try:\n",
+        "      for epoch in range(self.config.num_epochs):\n",
+        "        train_loss = self._train_epoch(train_loader, optimizer)\n",
+        "        val_loss = self._validate(val_loader)\n",
+        "        current_lr = optimizer.param_groups[0][\"lr\"]\n",
+        "\n",
+        "        metrics = {\n",
+        "            \"train_loss\": train_loss,\n",
+        "            \"val_loss\": val_loss,\n",
+        "            \"learning_rate\": current_lr,\n",
+        "            \"epoch\": epoch + 1,\n",
+        "        }\n",
+        "\n",
+        "        if self.config.use_wandb:\n",
+        "          self.metrics_logger.log_metrics(metrics)\n",
+        "\n",
+        "        history[\"train_loss\"].append(train_loss)\n",
+        "        history[\"val_loss\"].append(val_loss)\n",
+        "        history[\"learning_rate\"].append(current_lr)\n",
+        "\n",
+        "        if self.rank == 0:\n",
+        "          self.logger.info(\n",
+        "              f\"[Epoch {epoch+1}] Train Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f}\"\n",
+        "          )\n",
+        "\n",
+        "    except KeyboardInterrupt:\n",
+        "      self.logger.info(\"Training interrupted by user\")\n",
+        "\n",
+        "    if self.config.distributed:\n",
+        "      self.dist_manager.cleanup()\n",
+        "\n",
+        "    if self.config.use_wandb:\n",
+        "      self.metrics_logger.close()\n",
+        "\n",
+        "    return {\"history\": history}\n",
+        "\n",
+        "import apache_beam as beam\n",
+        "import logging\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "import timesfm\n",
+        "from os import path\n",
+        "from timesfm import TimesFm, TimesFmCheckpoint, TimesFmHparams\n",
+        "from timesfm.pytorch_patched_decoder import PatchedTimeSeriesDecoder\n",
+        "from huggingface_hub import snapshot_download\n",
+        "from apache_beam.io.gcp.gcsio import GcsIO # Add this import\n",
+        "\n",
+        "from torch.utils.data import Dataset\n",
+        "from google.cloud import storage\n",
+        "from typing import Tuple\n",
+        "\n",
+        "\n",
+        "class TimeSeriesDataset(Dataset):\n",
+        "  \"\"\"Dataset for time series data compatible with TimesFM.\"\"\"\n",
+        "  def __init__(\n",
+        "      self,\n",
+        "      series: np.ndarray,\n",
+        "      context_length: int,\n",
+        "      horizon_length: int,\n",
+        "      freq_type: int = 0):\n",
+        "    \"\"\"\n",
+        "        Initialize dataset.\n",
+        "\n",
+        "        Args:\n",
+        "            series: Time series data\n",
+        "            context_length: Number of past timesteps to use as input\n",
+        "            horizon_length: Number of future timesteps to predict\n",
+        "            freq_type: Frequency type (0, 1, or 2)\n",
+        "        \"\"\"\n",
+        "    if freq_type not in [0, 1, 2]:\n",
+        "      raise ValueError(\"freq_type must be 0, 1, or 2\")\n",
+        "\n",
+        "    self.series = series\n",
+        "    self.context_length = context_length\n",
+        "    self.horizon_length = horizon_length\n",
+        "    self.freq_type = freq_type\n",
+        "    self._prepare_samples()\n",
+        "\n",
+        "  def _prepare_samples(self) -> None:\n",
+        "    \"\"\"Prepare sliding window samples from the time series.\"\"\"\n",
+        "    self.samples = []\n",
+        "    total_length = self.context_length + self.horizon_length\n",
+        "\n",
+        "    for start_idx in range(0, len(self.series) - total_length + 1):\n",
+        "      end_idx = start_idx + self.context_length\n",
+        "      x_context = self.series[start_idx:end_idx]\n",
+        "      x_future = self.series[end_idx:end_idx + self.horizon_length]\n",
+        "      self.samples.append((x_context, x_future))\n",
+        "\n",
+        "  def __len__(self) -> int:\n",
+        "    return len(self.samples)\n",
+        "\n",
+        "  def __getitem__(\n",
+        "      self, index: int\n",
+        "  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:\n",
+        "    x_context, x_future = self.samples[index]\n",
+        "\n",
+        "    x_context = torch.tensor(x_context, dtype=torch.float32)\n",
+        "    x_future = torch.tensor(x_future, dtype=torch.float32)\n",
+        "\n",
+        "    input_padding = torch.zeros_like(x_context)\n",
+        "    freq = torch.tensor([self.freq_type], dtype=torch.long)\n",
+        "\n",
+        "    return x_context, input_padding, freq, x_future\n",
+        "\n",
+        "\n",
+        "def prepare_datasets(\n",
+        "    series: np.ndarray,\n",
+        "    context_length: int,\n",
+        "    horizon_length: int,\n",
+        "    freq_type: int = 0,\n",
+        "    train_split: float = 0.8) -> Tuple[Dataset, Dataset]:\n",
+        "  \"\"\"\n",
+        "    Prepare training and validation datasets from time series data.\n",
+        "\n",
+        "    Args:\n",
+        "        series: Input time series data\n",
+        "        context_length: Number of past timesteps to use\n",
+        "        horizon_length: Number of future timesteps to predict\n",
+        "        freq_type: Frequency type (0, 1, or 2)\n",
+        "        train_split: Fraction of data to use for training\n",
+        "\n",
+        "    Returns:\n",
+        "        Tuple of (train_dataset, val_dataset)\n",
+        "    \"\"\"\n",
+        "  train_size = int(len(series) * train_split)\n",
+        "  train_data = series[:train_size]\n",
+        "  val_data = series[train_size:]\n",
+        "\n",
+        "  # Create datasets with specified frequency type\n",
+        "  train_dataset = TimeSeriesDataset(\n",
+        "      train_data,\n",
+        "      context_length=context_length,\n",
+        "      horizon_length=horizon_length,\n",
+        "      freq_type=freq_type)\n",
+        "\n",
+        "  val_dataset = TimeSeriesDataset(\n",
+        "      val_data,\n",
+        "      context_length=context_length,\n",
+        "      horizon_length=horizon_length,\n",
+        "      freq_type=freq_type)\n",
+        "\n",
+        "  return train_dataset, val_dataset\n",
+        "\n",
+        "\n",
+        "class BatchContinuousAndOrderedFn(beam.DoFn):\n",
+        "    \"\"\"\n",
+        "    A stateful DoFn that buffers elements, keeps them sorted, and emits\n",
+        "    a batch only when a full, continuous sequence of points is available.\n",
+        "    Includes detailed logging for debugging.\n",
+        "    \"\"\"\n",
+        "    BUFFER_STATE = ReadModifyWriteStateSpec('buffer', PickleCoder())\n",
+        "\n",
+        "    def __init__(self, batch_size, expected_interval_seconds=1):\n",
+        "        self.batch_size = batch_size\n",
+        "        self.interval = expected_interval_seconds\n",
+        "        # NEW LOGGING: Counter to avoid logging on every single element\n",
+        "        self.counter = 0\n",
+        "\n",
+        "    def process(self, element, buffer=beam.DoFn.StateParam(BUFFER_STATE)):\n",
+        "        key, data = element\n",
+        "        timestamp = data['timestamp']\n",
+        "        value = data['value']\n",
+        "\n",
+        "        # Increment the counter\n",
+        "        self.counter += 1\n",
+        "\n",
+        "        current_buffer = buffer.read() or []\n",
+        "        current_buffer.append((timestamp, value))\n",
+        "        current_buffer.sort(key=lambda x: x[0])\n",
+        "\n",
+        "        # NEW LOGGING: Periodically log the buffer status\n",
+        "        if self.counter % 100 == 0 and current_buffer:\n",
+        "            logging.info(\n",
+        "                f\"Batching buffer now contains {len(current_buffer)} points. \"\n",
+        "                f\"Timestamps range from {current_buffer[0][0]} to {current_buffer[-1][0]}.\"\n",
+        "            )\n",
+        "\n",
+        "        start_index = 0\n",
+        "        while start_index + self.batch_size <= len(current_buffer):\n",
+        "            is_continuous = True\n",
+        "            # Check for continuity in the slice of the buffer we are considering\n",
+        "            for i in range(start_index, start_index + self.batch_size - 1):\n",
+        "                ts1_seconds = current_buffer[i][0].seconds()\n",
+        "                ts2_seconds = current_buffer[i + 1][0].seconds()\n",
+        "\n",
+        "                if ts2_seconds - ts1_seconds != self.interval:\n",
+        "                    is_continuous = False\n",
+        "                    # If a gap is found, we should stop and wait for more data.\n",
+        "                    # We can't proceed past this point because the buffer is sorted.\n",
+        "                    logging.info(\n",
+        "                        f\"Gap detected at index {i}. \"\n",
+        "                        f\"Timestamp {current_buffer[i][0]} is followed by {current_buffer[i+1][0]}. \"\n",
+        "                        f\"Actual interval: {ts2_seconds - ts1_seconds}s, Expected: {self.interval}s. \"\n",
+        "                        f\"Waiting for missing data.\"\n",
+        "                    )\n",
+        "                    break\n",
+        "\n",
+        "            if not is_continuous:\n",
+        "                # Since the buffer is sorted, a gap at this point means we can't form any more continuous batches.\n",
+        "                break\n",
+        "\n",
+        "            # If we are here, the batch from start_index is continuous.\n",
+        "            logging.info(f\"Continuous sequence found! Emitting batch of size {self.batch_size} starting at index {start_index}.\")\n",
+        "\n",
+        "            batch_to_yield = current_buffer[start_index : start_index + self.batch_size]\n",
+        "\n",
+        "            formatted_batch = [{'timestamp': ts, 'value': val} for ts, val in batch_to_yield]\n",
+        "            yield formatted_batch\n",
+        "\n",
+        "            # Move the start_index to the next position after the yielded batch\n",
+        "            start_index += self.batch_size\n",
+        "\n",
+        "        # After the loop, remove all the yielded elements from the buffer.\n",
+        "        if start_index > 0:\n",
+        "            current_buffer = current_buffer[start_index:]\n",
+        "\n",
+        "        buffer.write(current_buffer)\n",
+        "\n",
+        "class RunFinetuningFn(beam.DoFn):\n",
+        "  \"\"\"\n",
+        "    Takes a batch of data, loads the LATEST model, runs fine-tuning,\n",
+        "    and uploads the new model to GCS.\n",
+        "  \"\"\"\n",
+        "  def __init__(\n",
+        "      self,\n",
+        "      initial_model_path, # Renamed from base_model_path\n",
+        "      finetuned_model_bucket,\n",
+        "      finetuned_model_prefix,\n",
+        "      hparams,\n",
+        "      config):\n",
+        "    # This is now a fallback for the very first run\n",
+        "    self.initial_model_path = initial_model_path\n",
+        "    self.finetuned_model_bucket = finetuned_model_bucket\n",
+        "    self.finetuned_model_prefix = finetuned_model_prefix\n",
+        "    self.hparams = hparams\n",
+        "    self.config = config\n",
+        "    self._storage_client = None\n",
+        "\n",
+        "  def setup(self):\n",
+        "    self._storage_client = storage.Client()\n",
+        "\n",
+        "  def _get_latest_model_from_gcs(self):\n",
+        "    \"\"\"Directly queries GCS for the most recently created model checkpoint.\"\"\"\n",
+        "    try:\n",
+        "        bucket = self._storage_client.get_bucket(self.finetuned_model_bucket)\n",
+        "        blobs = list(bucket.list_blobs(prefix=self.finetuned_model_prefix))\n",
+        "\n",
+        "        # Filter for actual model files and exclude the initial model if present\n",
+        "        model_blobs = [b for b in blobs if b.name.endswith(\".pth\") and \"initial\" not in b.name]\n",
+        "\n",
+        "        if not model_blobs:\n",
+        "            return None\n",
+        "\n",
+        "        # Find the blob with the latest creation time\n",
+        "        latest_blob = max(model_blobs, key=lambda b: b.time_created)\n",
+        "        latest_model_path = f\"gs://{self.finetuned_model_bucket}/{latest_blob.name}\"\n",
+        "        return latest_model_path\n",
+        "    except Exception as e:\n",
+        "        logging.error(f\"Error querying GCS for the latest model: {e}\")\n",
+        "        return None\n",
+        "\n",
+        "  # Add the side input parameter to the process method\n",
+        "  def process(self, batch_of_data):\n",
+        "    logging.info(\n",
+        "        f\"Received a batch of {len(batch_of_data)} points for finetuning.\")\n",
+        "\n",
+        "    # If a finetuned model exists, use it. Otherwise, use the initial base model.\n",
+        "    latest_model_path = self._get_latest_model_from_gcs()\n",
+        "\n",
+        "    if latest_model_path:\n",
+        "        model_to_load = latest_model_path\n",
+        "        logging.info(f\"Continuously finetuning from latest model: {model_to_load}\")\n",
+        "    else:\n",
+        "        model_to_load = self.initial_model_path\n",
+        "        logging.info(f\"No finetuned model found. Starting from initial model: {model_to_load}\")\n",
+        "\n",
+        "    # batch_of_data.sort(key=lambda x: x[1]['timestamp'])\n",
+        "    time_series_values = np.array([d['value'] for d in batch_of_data],\n",
+        "                                  dtype=np.float32)\n",
+        "    train_dataset, val_dataset = prepare_datasets(\n",
+        "        series=time_series_values,\n",
+        "        context_length=self.hparams.context_len,\n",
+        "        horizon_length=self.hparams.horizon_len,\n",
+        "        freq_type=self.config.freq_type,\n",
+        "        train_split=0.8\n",
+        "    )\n",
+        "\n",
+        "    logging.info(f\"Training dataset size: {train_dataset.series.tolist()}\")\n",
+        "    logging.info(f\"Validation dataset size: {val_dataset.series.tolist()}\")\n",
+        "\n",
+        "    # Load the model (base or latest finetuned)\n",
+        "    # The updated get_model function can handle both GCS and Hugging Face paths\n",
+        "    model = get_model(\n",
+        "        model_path=model_to_load, # Use the path we just determined\n",
+        "        hparams=self.hparams,\n",
+        "        load_weights=True\n",
+        "    )\n",
+        "\n",
+        "    # 4. Run fine-tuning (same as before)\n",
+        "    finetuner = TimesFMFinetuner(model, self.config)\n",
+        "    finetuner.finetune(train_dataset=train_dataset, val_dataset=val_dataset)\n",
+        "\n",
+        "    # 5. Save and upload the new model (same as before)\n",
+        "    from datetime import datetime\n",
+        "    timestamp_str = datetime.utcnow().strftime('%Y%m%d%H%M%S')\n",
+        "    model_filename = f\"timesfm_finetuned_{timestamp_str}.pth\"\n",
+        "    local_path = f\"/tmp/{model_filename}\"\n",
+        "    torch.save(model.state_dict(), local_path)\n",
+        "    bucket = self._storage_client.bucket(self.finetuned_model_bucket)\n",
+        "    blob_path = f\"{self.finetuned_model_prefix}/{model_filename}\"\n",
+        "    blob = bucket.blob(blob_path)\n",
+        "    blob.upload_from_filename(local_path)\n",
+        "    logging.info(\n",
+        "        f\"Successfully uploaded new model to gs://{self.finetuned_model_bucket}/{blob_path}\"\n",
+        "    )\n",
+        "    yield blob_path\n",
+        "\n",
+        "\n",
+        "def get_model(model_path: str, hparams: TimesFmHparams, load_weights: bool = False):\n",
+        "    \"\"\"\n",
+        "    Loads a TimesFM model from either a Hugging Face repo ID or a GCS path.\n",
+        "    The `load_weights` argument is kept for signature consistency but is\n",
+        "    effectively always True, as TimesFm handles loading.\n",
+        "    \"\"\"\n",
+        "    checkpoint_config = {}\n",
+        "\n",
+        "    # Case 1: The model path is a GCS URI.\n",
+        "    # We download it to a local file and tell TimesFmCheckpoint to load from that path.\n",
+        "    if model_path.startswith(\"gs://\"):\n",
+        "        logging.info(f\"Preparing to load model from GCS path: {model_path}\")\n",
+        "        local_temp_path = f\"/tmp/{path.basename(model_path)}\"\n",
+        "        with GcsIO().open(model_path, 'rb') as f_in, open(local_temp_path, 'wb') as f_out:\n",
+        "            f_out.write(f_in.read())\n",
+        "        # The key for a local file is 'path'\n",
+        "        checkpoint_config['path'] = local_temp_path\n",
+        "\n",
+        "    # Case 2: The model path is a Hugging Face repository ID.\n",
+        "    else:\n",
+        "        logging.info(f\"Preparing to load model from Hugging Face repo: {model_path}\")\n",
+        "        # The key for a Hugging Face repo is 'huggingface_repo_id'\n",
+        "        checkpoint_config['huggingface_repo_id'] = model_path\n",
+        "\n",
+        "    # Initialize the TimesFm object correctly with the dynamically created checkpoint config.\n",
+        "    # This single call handles model configuration and weight loading.\n",
+        "    tfm = TimesFm(\n",
+        "        hparams=hparams,\n",
+        "        checkpoint=TimesFmCheckpoint(**checkpoint_config)\n",
+        "    )\n",
+        "\n",
+        "    logging.info(\"Model loaded successfully inside get_model.\")\n",
+        "\n",
+        "    # The `TimesFm` object holds the configured model instance.\n",
+        "    # The model returned here will be a PatchedTimeSeriesDecoder instance with weights loaded.\n",
+        "    return tfm._model"
+      ],
+      "metadata": {
+        "id": "IzEE_R3SuwAR"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Load Time Series Data\n",
+        "\n",
+        "https://www.kaggle.com/datasets/julienjta/nyc-taxi-traffic/data"
+      ],
+      "metadata": {
+        "id": "Lz1OROouy9IV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "from google.colab import auth\n",
+        "auth.authenticate_user()\n",
+        "\n",
+        "auth.authenticate_user()\n",
+        "\n",
+        "# Define the path to your file in the GCS bucket\n",
+        "gcs_path = 'gs://apache-beam-samples/anomaly_detection/timesfm-dataset-example/nyc_taxi_timeseries.csv'\n",
+        "\n",
+        "# Read the CSV directly from GCS into a DataFrame\n",
+        "# All the gspread code is replaced by this single line\n",
+        "df = pd.read_csv(gcs_path)\n",
+        "\n",
+        "# --- The rest of your processing code remains the same ---\n",
+        "\n",
+        "# Convert 'value' column to a numpy array of integers\n",
+        "values_array = pd.to_numeric(df['value'], errors='coerce').astype(int).to_numpy()\n",
+        "\n",
+        "# Create the list of (timestamp, value) tuples\n",
+        "input_data = []\n",
+        "for i in range(len(values_array)):\n",
+        "  input_data.append((Timestamp(i + 1), values_array[i])) # Assuming Timestamp comes from pandas\n",
+        "\n",
+        "print(\"DataFrame loaded from GCS:\")\n",
+        "print(df.head())\n",
+        "print(\"\\nInput data created successfully (first 5 entries):\")\n",
+        "print(input_data[:5])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N7fAXDjXuDF4",
+        "outputId": "0ea32fa5-221d-44fb-9f83-4f524fd8f3c2"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "DataFrame loaded from GCS:\n",
+            "   Unnamed: 0           timestamp  value\n",
+            "0           0  2014-07-01 0:00:00  10844\n",
+            "1           1  2014-07-01 0:30:00   8127\n",
+            "2           2  2014-07-01 1:00:00   6210\n",
+            "3           3  2014-07-01 1:30:00   4656\n",
+            "4           4  2014-07-01 2:00:00   3820\n",
+            "\n",
+            "Input data created successfully (first 5 entries):\n",
+            "[(Timestamp(1), np.int64(10844)), (Timestamp(2), np.int64(8127)), (Timestamp(3), np.int64(6210)), (Timestamp(4), np.int64(4656)), (Timestamp(5), np.int64(3820))]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Beam Pipeline Setup"
+      ],
+      "metadata": {
+        "id": "LiQQF_IxquCK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import apache_beam as beam\n",
+        "from apache_beam.options.pipeline_options import PipelineOptions\n",
+        "from apache_beam.pvalue import AsDict, AsSingleton\n",
+        "from apache_beam.transforms.periodicsequence import PeriodicImpulse\n",
+        "import logging\n",
+        "import os\n",
+        "import json\n",
+        "import timesfm\n",
+        "from apache_beam.utils.timestamp import Timestamp\n",
+        "import csv\n",
+        "from apache_beam.ml.inference.base import RunInference\n",
+        "from apache_beam.ml.inference.utils import WatchFilePattern\n",
+        "import typing\n",
+        "from google.colab import userdata\n",
+        "import apache_beam.transforms.window as window\n",
+        "\n",
+        "logging.getLogger().setLevel(logging.INFO)\n",
+        "\n",
+        "# --- Pipeline Configuration ---\n",
+        "PROJECT_ID = os.environ.get(\"GCP_PROJECT\", \"apache-beam-testing\")\n",
+        "REGION = os.environ.get(\"GCP_REGION\", \"us-central1\")\n",
+        "TEMP_LOCATION = \"gs://apache-beam-testing-temp/timesfm_anomaly_detection/temp\"\n",
+        "STAGING_LOCATION = \"gs://apache-beam-testing-temp/timesfm_anomaly_detection/staging\"\n",
+        "FINETUNED_MODEL_BUCKET = \"apache-beam-testing-temp\"\n",
+        "FINETUNED_MODEL_PREFIX = \"timesfm_anomaly_detection/finetuned-models/timesfm/checkpoints\"\n",
+        "\n",
+        "# --- Model & Window Parameters ---\n",
+        "CONTEXT_LEN = 512\n",
+        "HORIZON_LEN = 128\n",
+        "WINDOW_SIZE = CONTEXT_LEN + HORIZON_LEN\n",
+        "SLIDE_INTERVAL = HORIZON_LEN\n",
+        "EXPECTED_INTERVAL = 1\n",
+        "INITIAL_MODEL = \"google/timesfm-1.0-200m-pytorch\"\n",
+        "\n",
+        "MODEL_CHECK_INTERVAL_SECONDS = 10  # Check for a new model every 5 seconds\n",
+        "FINETUNING_BATCH_SIZE = 7680 # 9600 # make larger later. minimum is WINDOW_SIZE for validation and training\n",
+        "FINETUNE_CONFIG = FinetuningConfig(\n",
+        "      batch_size=128,\n",
+        "      num_epochs=5,\n",
+        "      learning_rate=1e-4,\n",
+        "      use_wandb=False,\n",
+        "      freq_type=0, # should change based on your data\n",
+        "      log_every_n_steps=10,\n",
+        "      val_check_interval=0.5,\n",
+        "      use_quantile_loss=True\n",
+        "  )\n",
+        "\n",
+        "#Change to Dataflow if needed\n",
+        "options = PipelineOptions([\n",
+        "    \"--streaming\",\n",
+        "    \"--environment_type=LOOPBACK\",\n",
+        "    \"--runner=PrismRunner\",\n",
+        "    \"--logging_level=INFO\",\n",
+        "    \"--job_server_timeout=3600\"\n",
+        "])\n",
+        "\n",
+        "\n",
+        "\n",
+        "# HParams for the model\n",
+        "hparams = timesfm.TimesFmHparams(\n",
+        "    backend=\"gpu\",\n",
+        "    per_core_batch_size=32,\n",
+        "    horizon_len=HORIZON_LEN,\n",
+        "    context_len=CONTEXT_LEN,\n",
+        ")\n",
+        "model_handler = DynamicTimesFmModelHandler(model_uri=INITIAL_MODEL, hparams=hparams)\n",
+        "\n",
+        "def print_and_pass_through(label):\n",
+        "    def logger(element):\n",
+        "        print(f\"--- {label} --- \\nELEMENT: %s\", element)\n",
+        "        return element\n",
+        "    return logger\n",
+        "\n",
+        "\n",
+        "class CustomJsonEncoder(json.JSONEncoder):\n",
+        "    \"\"\"A custom JSON encoder that knows how to handle Beam's Timestamp objects.\"\"\"\n",
+        "    def default(self, obj):\n",
+        "        if isinstance(obj, Timestamp):\n",
+        "            # Convert Timestamp to a standard, readable ISO 8601 string format\n",
+        "            return obj.micros // 1e6\n",
+        "        # For all other types, fall back to the default behavior\n",
+        "        if isinstance(obj, np.integer):\n",
+        "            return int(obj)\n",
+        "\n",
+        "        # 3. Handle NumPy float types (this will fix your float32 error)\n",
+        "        if isinstance(obj, np.floating):\n",
+        "            return float(obj)\n",
+        "\n",
+        "        # 4. Handle NumPy arrays\n",
+        "        if isinstance(obj, np.ndarray):\n",
+        "            return obj.tolist()\n",
+        "\n",
+        "        # For all other types, fall back to the default behavior\n",
+        "        return super().default(obj)\n",
+        "        return json.JSONEncoder.default(self, obj)\n",
+        "\n",
+        "class WritePlotDataAndPassThrough(beam.DoFn):\n",
+        "    \"\"\"\n",
+        "    A DoFn that writes plotting data to a file as a side effect\n",
+        "    and then passes the original, unmodified element downstream.\n",
+        "    \"\"\"\n",
+        "    def __init__(self, output_path):\n",
+        "        self._output_path = output_path\n",
+        "        self._file_handle = None\n",
+        "\n",
+        "    def setup(self):\n",
+        "        self._file_handle = open(self._output_path, 'a')\n",
+        "\n",
+        "    def process(self, element):\n",
+        "        _original_window, payload_dict = element\n",
+        "\n",
+        "        # ✅ FIX: Use the custom encoder to handle Timestamp objects\n",
+        "        json_record = json.dumps(payload_dict, cls=CustomJsonEncoder)\n",
+        "        self._file_handle.write(json_record + '\\n')\n",
+        "\n",
+        "        # Pass the original element through, with the Timestamp object intact\n",
+        "        yield element\n",
+        "\n",
+        "    def teardown(self):\n",
+        "        if self._file_handle:\n",
+        "            self._file_handle.close()\n",
+        "\n",
+        "\n",
+        "# =================================================================\n",
+        "# 1. Get Latest Model Path (Side Input) - WatchFilePattern is not\n",
+        "#    currently supported on Prism. Uncomment the following to run\n",
+        "#    on Dataflow\n",
+        "# =================================================================\n",
+        "# model_pattern = os.path.join(\n",
+        "#     f\"gs://{FINETUNED_MODEL_BUCKET}\", FINETUNED_MODEL_PREFIX, \"*.pth\"\n",
+        "# )\n",
+        "\n",
+        "# model_metadata_pcoll = (\n",
+        "#     \"WatchForNewModels\" >> WatchFilePattern(\n",
+        "#         file_pattern=model_pattern,\n",
+        "#         interval=MODEL_CHECK_INTERVAL_SECONDS\n",
+        "#       )\n",
+        "#     | \"PrintModelLocation\" >> beam.Map(print_and_pass_through(\"Model Location\"))\n",
+        "\n",
+        "# )\n",
+        "\n",
+        "# =================================================================\n",
+        "# Ingest and Window Raw Data\n",
+        "# =================================================================\n",
+        "\n",
+        "\n",
+        "windowed_data = (\n",
+        "    PeriodicImpulse(data=input_data, fire_interval=0.01)\n",
+        "    | \"AddKey\" >> beam.WithKeys(lambda x: 0)\n",
+        "    | \"ApplySlidingWindow\" >> beam.ParDo(\n",
+        "        OrderedSlidingWindowFn(window_size=WINDOW_SIZE, slide_interval=SLIDE_INTERVAL))\n",
+        "    | \"FillGaps\" >> beam.ParDo(FillGapsFn(expected_interval=EXPECTED_INTERVAL)).with_output_types(\n",
+        "        typing.Tuple[int, typing.Tuple[Timestamp, Timestamp, typing.List[float]]])\n",
+        "    | \"Skip NaN Values for now\" >> beam.Filter(\n",
+        "      lambda batch: 'NaN' not in batch[1][2])\n",
+        "    | \"PrintWindowedData\" >> beam.Map(print_and_pass_through(\"Windowed Data\"))\n",
+        "\n",
+        ")\n",
+        "\n",
+        "# =================================================================\n",
+        "# Detect Anomalies using the Latest Model\n",
+        "# =================================================================\n",
+        "\n",
+        "inference_results = (\n",
+        "    \"DetectAnomalies\" >> RunInference(\n",
+        "        model_handler=model_handler,\n",
+        "        # model_metadata_pcoll=model_metadata_pcoll\n",
+        "      )\n",
+        "    | \"PrintInference\" >> beam.Map(print_and_pass_through(\"Inference Results\"))\n",
+        ")\n",
+        "\n",
+        "\n",
+        "# NEW BRANCH: For plotting. It takes the payload dictionary, converts\n",
+        "# it to JSON, and writes it to a file.\n",
+        "plotting_data_output = (\n",
+        "    \"WritePlotDataAsSideEffect\" >> beam.ParDo(\n",
+        "          WritePlotDataAndPassThrough('plot_data_original.jsonl'))\n",
+        ")\n",
+        "\n",
+        "def format_for_llm(result_tuple):\n",
+        "    \"\"\"\n",
+        "    Takes the output of RunInference (a PredictionResult) and formats it\n",
+        "    into the dictionary structure needed by the LLMClassifierFn.\n",
+        "    \"\"\"\n",
+        "    original_window_data, result_dict = result_tuple\n",
+        "\n",
+        "    list_of_anomalies = result_dict['anomalies']\n",
+        "\n",
+        "    key, (window_start_ts, _, values_array) = original_window_data\n",
+        "\n",
+        "    return (key, {\n",
+        "        'key': key,\n",
+        "        'window_start_ts': window_start_ts,\n",
+        "        'values_array': values_array,\n",
+        "        'anomalies': list_of_anomalies if list_of_anomalies else []\n",
+        "    })\n",
+        "\n",
+        "\n",
+        "data_for_llm = (\n",
+        "    \"FormatForLLM\" >> beam.Map(format_for_llm)\n",
+        "    | \"PrintDataForLLM\" >> beam.Map(print_and_pass_through(\"Data for LLM\"))\n",
+        ")\n",
+        "\n",
+        "\n",
+        "# =================================================================\n",
+        "# Classify with LLM and Create Clean Data for Finetuning\n",
+        "# =================================================================\n",
+        "api_key = \"userdata.get('GEMINI_API_KEY') # @param {type:'string'} \n",
+        "\n",
+        "llm_classifier = (\n",
+        "    \"LLMClassifierAndImputer\" >> beam.ParDo(\n",
+        "        LLMClassifierFn(\n",
+        "            secret=api_key,\n",
+        "            slide_interval=SLIDE_INTERVAL,\n",
+        "            expected_interval_secs=EXPECTED_INTERVAL\n",
+        "          )\n",
+        "      )\n",
+        "    # | \"PrintLLMResults\" >> beam.Map(print_and_pass_through(\"LLM Results\"))\n",
+        ")\n",
+        "\n",
+        "\n",
+        "# # =================================================================\n",
+        "# # Batch Clean Data and Trigger Finetuning\n",
+        "# # =================================================================\n",
+        "finetuning_job_input = (\n",
+        "    \"KeyForBatching\" >> beam.WithKeys(lambda _: \"finetune_batch\")\n",
+        "    # | \"BatchAndTrigger\" >> beam.ParDo(BatchAndTriggerFinetuningFn(FINETUNING_BATCH_SIZE))\n",
+        "    | \"BatchAndTrigger\" >> beam.ParDo(\n",
+        "        BatchContinuousAndOrderedFn(\n",
+        "            FINETUNING_BATCH_SIZE,\n",
+        "            expected_interval_seconds=1\n",
+        "            )\n",
+        "        )\n",
+        "    | \"PrintFinetuningJobInput\" >> beam.Map(print_and_pass_through(\"Finetuning Job Input\"))\n",
+        ")\n",
+        "\n",
+        "# # =================================================================\n",
+        "# # Run Finetuning and Save New Model to GCS\n",
+        "# # =================================================================\n",
+        "finetuning = (\n",
+        "    \"RunFinetuning\" >> beam.ParDo(\n",
+        "        RunFinetuningFn(\n",
+        "            initial_model_path=\"google/timesfm-1.0-200m-pytorch\",\n",
+        "            finetuned_model_bucket=FINETUNED_MODEL_BUCKET,\n",
+        "            finetuned_model_prefix=FINETUNED_MODEL_PREFIX,\n",
+        "            hparams=hparams,\n",
+        "            config=FINETUNE_CONFIG\n",
+        "        ),\n",
+        "    )\n",
+        ")\n"
+      ],
+      "metadata": {
+        "id": "Oud4wLTjqy2j",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1e0cdb8c-16e5-42ce-ef5a-b870677e954d"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:apache_beam.transforms.core:('No iterator is returned by the process method in %s.', <class '__main__.LLMClassifierFn'>)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Beam Pipeline"
+      ],
+      "metadata": {
+        "id": "ZMx8KhRyvj3Q"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "with beam.Pipeline(options=options) as p:\n",
+        "  (p\n",
+        "  | windowed_data\n",
+        "  | inference_results\n",
+        "  | plotting_data_output # comment this line if you dont want to save plot data\n",
+        "  | data_for_llm\n",
+        "  | llm_classifier\n",
+        "  | finetuning_job_input\n",
+        "  | finetuning\n",
+        "  )\n"
+      ],
+      "metadata": {
+        "id": "mKa6Qb_1vnNX"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Plot Data (Original)"
+      ],
+      "metadata": {
+        "id": "O7egp_5Alzz7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import json\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "CONTEXT_LEN = 512\n",
+        "HORIZON_LEN = 128\n",
+        "\n",
+        "def plot_anomalies_and_forecast(\n",
+        "    values_array,\n",
+        "    all_anomalies,\n",
+        "    all_predicted_values,\n",
+        "    all_q20_values,\n",
+        "    all_q30_values,\n",
+        "    all_q70_values,\n",
+        "    all_q80_values,\n",
+        "    title_suffix=\"\",\n",
+        "    x_lims=None,\n",
+        "    min_outlier_score_for_plot=0,\n",
+        "    context_len=512,\n",
+        "    output_filename=\"plot.png\"\n",
+        "):\n",
+        "    print(len(all_anomalies))\n",
+        "    # The key from your file is 'outlier_score'\n",
+        "    filtered_anomalies = [a for a in all_anomalies if a['outlier_score'] >= min_outlier_score_for_plot]\n",
+        "    # The key from your file is 'timestamp'\n",
+        "    anomaly_indices = [(a['timestamp'] - HORIZON_LEN) for a in filtered_anomalies]\n",
+        "    anomaly_values = [a['actual_value'] for a in filtered_anomalies]\n",
+        "\n",
+        "    Q1 = np.nanmean([all_q20_values, all_q30_values], axis=0)\n",
+        "    Q3 = np.nanmean([all_q70_values, all_q80_values], axis=0)\n",
+        "    IQR = Q3 - Q1\n",
+        "    upper_thresh = Q3 + 1.5 * IQR\n",
+        "    lower_thresh = Q1 - 1.5 * IQR\n",
+        "\n",
+        "    plt.figure(figsize=(18, 9))\n",
+        "    # This now plots the correct original data for the horizon\n",
+        "    plt.plot(values_array[context_len:], label='Original Time Series', color='blue', alpha=0.7, linewidth=1.5)\n",
+        "\n",
+        "    plt.plot(all_predicted_values, label='Predicted Mean', color='green', linestyle='--', linewidth=1.5)\n",
+        "    plt.plot(lower_thresh, label='Lower Threshold', color='orange', linestyle=':', linewidth=1.2)\n",
+        "    plt.plot(upper_thresh, label='Upper Threshold', color='purple', linestyle=':', linewidth=1.2)\n",
+        "\n",
+        "    plt.scatter([i - context_len for i in anomaly_indices], anomaly_values,\n",
+        "                color='red', s=70, zorder=5,\n",
+        "                label=f'Detected Anomalies (Score >= {min_outlier_score_for_plot:.1f})',\n",
+        "                marker='o', edgecolors='black', linewidths=0.8)\n",
+        "\n",
+        "    plt.title(f'Time Series Anomaly Detection {title_suffix}')\n",
+        "    plt.xlabel('Time Index')\n",
+        "    plt.ylabel('Value')\n",
+        "    if x_lims:\n",
+        "        plt.xlim(x_lims[0], x_lims[1])\n",
+        "    plt.legend()\n",
+        "    plt.grid(True, linestyle='--', alpha=0.6)\n",
+        "    plt.tight_layout()\n",
+        "    # plt.savefig(output_filename) # Save the plot to a file\n",
+        "    plt.show()\n",
+        "    plt.close() # Close the figure to free memory\n",
+        "\n",
+        "# --- Main Script Logic ---\n",
+        "\n",
+        "# 1. Read and parse the data from the Beam output file\n",
+        "all_window_data = []\n",
+        "# Make sure 'plot_data.jsonl' is in the same directory as this script\n",
+        "try:\n",
+        "    with open('plot_data_original.jsonl', 'r') as f:\n",
+        "        for line in f:\n",
+        "            # Check for empty lines that might have been added\n",
+        "            if line.strip():\n",
+        "                all_window_data.append(json.loads(line))\n",
+        "except FileNotFoundError:\n",
+        "    print(\"Error: 'plot_data.jsonl' not found. Please make sure the file is in the correct directory.\")\n",
+        "    exit()\n",
+        "\n",
+        "\n",
+        "# 2. Sort data by timestamp to ensure the correct order\n",
+        "all_window_data.sort(key=lambda x: x['start_ts_micros'])\n",
+        "\n",
+        "# 3. Reconstruct the full data arrays\n",
+        "all_anomalies = []\n",
+        "all_predicted_values = []\n",
+        "all_q20_values = []\n",
+        "all_q30_values = []\n",
+        "all_q70_values = []\n",
+        "all_q80_values = []\n",
+        "all_actual_horizon_values = [] # This will hold the real \"blue line\" data\n",
+        "\n",
+        "for window_data in all_window_data:\n",
+        "    all_predicted_values.extend(window_data['predicted_values'])\n",
+        "    all_q20_values.extend(window_data['q20_values'])\n",
+        "    all_q30_values.extend(window_data['q30_values'])\n",
+        "    all_q70_values.extend(window_data['q70_values'])\n",
+        "    all_q80_values.extend(window_data['q80_values'])\n",
+        "    # Populate the list with the actual values from the file\n",
+        "    all_actual_horizon_values.extend(window_data.get('actual_horizon_values', []))\n",
+        "    all_anomalies.extend(window_data.get('anomalies', []))\n",
+        "\n",
+        "# 4. Convert lists to NumPy arrays\n",
+        "all_predicted_values = np.array(all_predicted_values)\n",
+        "all_q20_values = np.array(all_q20_values)\n",
+        "all_q30_values = np.array(all_q30_values)\n",
+        "all_q70_values = np.array(all_q70_values)\n",
+        "all_q80_values = np.array(all_q80_values)\n",
+        "\n",
+        "# 5. Construct the `values_array` using the REAL data from your file\n",
+        "context_len = 512\n",
+        "# Create a dummy context so the array has the right shape for the plotting function.\n",
+        "# The first real value is used to make the context visually seamless.\n",
+        "if all_actual_horizon_values:\n",
+        "    dummy_context = [all_actual_horizon_values[0]] * context_len\n",
+        "    values_array = np.array(dummy_context + all_actual_horizon_values)\n",
+        "else:\n",
+        "    # Fallback in case the file is empty or missing the actual_horizon_values key\n",
+        "    print(\"Warning: 'actual_horizon_values' not found. The original time series plot will be empty.\")\n",
+        "    total_len = context_len + len(all_predicted_values)\n",
+        "    values_array = np.zeros(total_len)\n",
+        "\n",
+        "# 6. Call the plotting functions\n",
+        "if values_array.any():\n",
+        "    # Plotting function for full graph\n",
+        "    plot_anomalies_and_forecast(\n",
+        "        values_array, all_anomalies, all_predicted_values,\n",
+        "        all_q20_values, all_q30_values, all_q70_values, all_q80_values,\n",
+        "        title_suffix=\"(Full Graph with Correct Data)\",\n",
+        "        min_outlier_score_for_plot=1, # Set a score threshold\n",
+        "        context_len=context_len,\n",
+        "        output_filename=\"full_graph_correct.png\"\n",
+        "    )\n",
+        "\n",
+        "    # Plotting function for zoomed-in graphs - feel free to change\n",
+        "    zoom_ranges = [(2000, 2500), (8300, 9000), (9000, 9600)]\n",
+        "    for i, (start_idx, end_idx) in enumerate(zoom_ranges):\n",
+        "        # Adjust x_lims for the fact that the plotted array is sliced by context_len\n",
+        "        plot_x_start = max(0, start_idx)\n",
+        "        plot_x_end = end_idx\n",
+        "\n",
+        "        plot_anomalies_and_forecast(\n",
+        "            values_array, all_anomalies, all_predicted_values,\n",
+        "            all_q20_values, all_q30_values, all_q70_values, all_q80_values,\n",
+        "            title_suffix=f\"(Zoomed In: {start_idx} to {end_idx})\",\n",
+        "            x_lims=(plot_x_start, plot_x_end),\n",
+        "            min_outlier_score_for_plot=1,\n",
+        "            context_len=context_len,\n",
+        "            output_filename=f\"zoomed_graph_correct_{i}.png\"\n",
+        "        )\n",
+        "    print(\"Plots have been generated and saved with the corrected original data.\")\n",
+        "else:\n",
+        "    print(\"No data found to plot.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "_HzqIoZbl25b",
+        "outputId": "7f85032d-e2cf-4e7c-b9b1-cae9994dee37"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "948\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x900 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "948\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x900 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "948\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x900 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "948\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x900 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Plots have been generated and saved with the corrected original data.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Finetuned Model Predictions"
+      ],
+      "metadata": {
+        "id": "_KxBniHGqS3M"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import apache_beam as beam\n",
+        "from apache_beam.options.pipeline_options import PipelineOptions\n",
+        "from apache_beam.pvalue import AsDict, AsSingleton\n",
+        "from apache_beam.transforms.periodicsequence import PeriodicImpulse\n",
+        "import logging\n",
+        "import os\n",
+        "import json\n",
+        "import timesfm\n",
+        "from apache_beam.utils.timestamp import Timestamp\n",
+        "import csv\n",
+        "from apache_beam.ml.inference.base import RunInference\n",
+        "from apache_beam.ml.inference.utils import WatchFilePattern\n",
+        "import typing\n",
+        "from google.colab import userdata\n",
+        "import apache_beam.transforms.window as window\n",
+        "\n",
+        "logging.getLogger().setLevel(logging.INFO)\n",
+        "\n",
+        "# --- Pipeline Configuration ---\n",
+        "PROJECT_ID = os.environ.get(\"GCP_PROJECT\", \"apache-beam-testing\")\n",
+        "REGION = os.environ.get(\"GCP_REGION\", \"us-central1\")\n",
+        "TEMP_LOCATION = \"gs://apache-beam-testing-temp/timesfm_anomaly_detection/temp\"\n",
+        "STAGING_LOCATION = \"gs://apache-beam-testing-temp/timesfm_anomaly_detection/staging\"\n",
+        "FINETUNED_MODEL_BUCKET = \"apache-beam-testing-temp\"\n",
+        "FINETUNED_MODEL_PREFIX = \"timesfm_anomaly_detection/finetuned-models/timesfm/checkpoints\"\n",
+        "\n",
+        "# --- Model & Window Parameters ---\n",
+        "CONTEXT_LEN = 512\n",
+        "HORIZON_LEN = 128\n",
+        "WINDOW_SIZE = CONTEXT_LEN + HORIZON_LEN\n",
+        "SLIDE_INTERVAL = HORIZON_LEN\n",
+        "EXPECTED_INTERVAL = 1\n",
+        "# go to checkpoints bucket and select the correct model path\n",
+        "INITIAL_MODEL = \"gs://apache-beam-testing-temp/timesfm_anomaly_detection/finetuned-models/timesfm/checkpoints/timesfm_finetuned_20250814192006.pth\"\n",
+        "\n",
+        "MODEL_CHECK_INTERVAL_SECONDS = 10  # Check for a new model every 5 seconds\n",
+        "FINETUNING_BATCH_SIZE = 7680 # 9600 # make larger later. minimum is WINDOW_SIZE for validation and training\n",
+        "FINETUNE_CONFIG = FinetuningConfig(\n",
+        "      batch_size=128,\n",
+        "      num_epochs=5,\n",
+        "      learning_rate=1e-4,\n",
+        "      use_wandb=False,\n",
+        "      freq_type=0, # should change based on your data\n",
+        "      log_every_n_steps=10,\n",
+        "      val_check_interval=0.5,\n",
+        "      use_quantile_loss=True\n",
+        "  )\n",
+        "\n",
+        "\n",
+        "options = PipelineOptions([\n",
+        "    \"--streaming\",\n",
+        "    \"--environment_type=LOOPBACK\",\n",
+        "    \"--runner=PrismRunner\",\n",
+        "    \"--logging_level=INFO\",\n",
+        "    \"--job_server_timeout=3600\"\n",
+        "])\n",
+        "\n",
+        "\n",
+        "\n",
+        "# HParams for the model\n",
+        "hparams = timesfm.TimesFmHparams(\n",
+        "    backend=\"gpu\",\n",
+        "    per_core_batch_size=32,\n",
+        "    horizon_len=HORIZON_LEN,\n",
+        "    context_len=CONTEXT_LEN,\n",
+        ")\n",
+        "model_handler = DynamicTimesFmModelHandler(model_uri=INITIAL_MODEL, hparams=hparams)\n",
+        "\n",
+        "def print_and_pass_through(label):\n",
+        "    def logger(element):\n",
+        "        print(f\"--- {label} --- \\nELEMENT: %s\", element)\n",
+        "        return element\n",
+        "    return logger\n",
+        "\n",
+        "\n",
+        "class CustomJsonEncoder(json.JSONEncoder):\n",
+        "    \"\"\"A custom JSON encoder that knows how to handle Beam's Timestamp objects.\"\"\"\n",
+        "    def default(self, obj):\n",
+        "        if isinstance(obj, Timestamp):\n",
+        "            # Convert Timestamp to a standard, readable ISO 8601 string format\n",
+        "            return obj.micros // 1e6\n",
+        "        # For all other types, fall back to the default behavior\n",
+        "        if isinstance(obj, np.integer):\n",
+        "            return int(obj)\n",
+        "\n",
+        "        # 3. Handle NumPy float types (this will fix your float32 error)\n",
+        "        if isinstance(obj, np.floating):\n",
+        "            return float(obj)\n",
+        "\n",
+        "        # 4. Handle NumPy arrays\n",
+        "        if isinstance(obj, np.ndarray):\n",
+        "            return obj.tolist()\n",
+        "\n",
+        "        # For all other types, fall back to the default behavior\n",
+        "        return super().default(obj)\n",
+        "        return json.JSONEncoder.default(self, obj)\n",
+        "\n",
+        "class WritePlotDataAndPassThrough(beam.DoFn):\n",
+        "    \"\"\"\n",
+        "    A DoFn that writes plotting data to a file as a side effect\n",
+        "    and then passes the original, unmodified element downstream.\n",
+        "    \"\"\"\n",
+        "    def __init__(self, output_path):\n",
+        "        self._output_path = output_path\n",
+        "        self._file_handle = None\n",
+        "\n",
+        "    def setup(self):\n",
+        "        self._file_handle = open(self._output_path, 'a')\n",
+        "\n",
+        "    def process(self, element):\n",
+        "        _original_window, payload_dict = element\n",
+        "\n",
+        "        # ✅ FIX: Use the custom encoder to handle Timestamp objects\n",
+        "        json_record = json.dumps(payload_dict, cls=CustomJsonEncoder)\n",
+        "        self._file_handle.write(json_record + '\\n')\n",
+        "\n",
+        "        # Pass the original element through, with the Timestamp object intact\n",
+        "        yield element\n",
+        "\n",
+        "    def teardown(self):\n",
+        "        if self._file_handle:\n",
+        "            self._file_handle.close()\n",
+        "\n",
+        "\n",
+        "# =================================================================\n",
+        "# 1. Get Latest Model Path (Side Input) - WatchFilePattern is not\n",
+        "#    currently supported on Prism. Uncomment the following to run\n",
+        "#    on Dataflow\n",
+        "# =================================================================\n",
+        "# model_pattern = os.path.join(\n",
+        "#     f\"gs://{FINETUNED_MODEL_BUCKET}\", FINETUNED_MODEL_PREFIX, \"*.pth\"\n",
+        "# )\n",
+        "\n",
+        "# model_metadata_pcoll = (\n",
+        "#     \"WatchForNewModels\" >> WatchFilePattern(\n",
+        "#         file_pattern=model_pattern,\n",
+        "#         interval=MODEL_CHECK_INTERVAL_SECONDS\n",
+        "#       )\n",
+        "#     | \"PrintModelLocation\" >> beam.Map(print_and_pass_through(\"Model Location\"))\n",
+        "\n",
+        "# )\n",
+        "\n",
+        "# =================================================================\n",
+        "# Ingest and Window Raw Data\n",
+        "# =================================================================\n",
+        "\n",
+        "\n",
+        "windowed_data = (\n",
+        "    PeriodicImpulse(data=input_data, fire_interval=0.01)\n",
+        "    | \"AddKey\" >> beam.WithKeys(lambda x: 0)\n",
+        "    | \"ApplySlidingWindow\" >> beam.ParDo(\n",
+        "        OrderedSlidingWindowFn(window_size=WINDOW_SIZE, slide_interval=SLIDE_INTERVAL))\n",
+        "    | \"FillGaps\" >> beam.ParDo(FillGapsFn(expected_interval=EXPECTED_INTERVAL)).with_output_types(\n",
+        "        typing.Tuple[int, typing.Tuple[Timestamp, Timestamp, typing.List[float]]])\n",
+        "    | \"Skip NaN Values for now\" >> beam.Filter(\n",
+        "      lambda batch: 'NaN' not in batch[1][2])\n",
+        "    | \"PrintWindowedData\" >> beam.Map(print_and_pass_through(\"Windowed Data\"))\n",
+        "\n",
+        ")\n",
+        "\n",
+        "# =================================================================\n",
+        "# Detect Anomalies using the Latest Model\n",
+        "# =================================================================\n",
+        "\n",
+        "inference_results = (\n",
+        "    \"DetectAnomalies\" >> RunInference(\n",
+        "        model_handler=model_handler,\n",
+        "        # model_metadata_pcoll=model_metadata_pcoll\n",
+        "      )\n",
+        "    | \"PrintInference\" >> beam.Map(print_and_pass_through(\"Inference Results\"))\n",
+        ")\n",
+        "\n",
+        "\n",
+        "# NEW BRANCH: For plotting. It takes the payload dictionary, converts\n",
+        "# it to JSON, and writes it to a file.\n",
+        "plotting_data_output = (\n",
+        "    \"WritePlotDataAsSideEffect\" >> beam.ParDo(\n",
+        "          WritePlotDataAndPassThrough('plot_data_finetuned.jsonl'))\n",
+        ")\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "LRRg2QhPfEZr"
+      },
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "with beam.Pipeline(options=options) as p:\n",
+        "  (p\n",
+        "  | windowed_data\n",
+        "  | inference_results\n",
+        "  | plotting_data_output\n",
+        "  )\n"
+      ],
+      "metadata": {
+        "id": "qAJDAbdGqW_V"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Plot Data (After Finetuning)"
+      ],
+      "metadata": {
+        "id": "_fT-AjbrUOl5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import json\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def plot_anomalies_and_forecast(\n",
+        "    values_array,\n",
+        "    all_anomalies,\n",
+        "    all_predicted_values,\n",
+        "    all_q20_values,\n",
+        "    all_q30_values,\n",
+        "    all_q70_values,\n",
+        "    all_q80_values,\n",
+        "    title_suffix=\"\",\n",
+        "    x_lims=None,\n",
+        "    min_outlier_score_for_plot=0,\n",
+        "    context_len=512,\n",
+        "    output_filename=\"plot.png\"\n",
+        "):\n",
+        "    # The key from your file is 'outlier_score'\n",
+        "    filtered_anomalies = [a for a in all_anomalies if a['outlier_score'] >= min_outlier_score_for_plot]\n",
+        "    # The key from your file is 'timestamp'\n",
+        "    anomaly_indices = [(a['timestamp'] - 128) for a in filtered_anomalies]\n",
+        "    anomaly_values = [a['actual_value'] for a in filtered_anomalies]\n",
+        "\n",
+        "    Q1 = np.nanmean([all_q20_values, all_q30_values], axis=0)\n",
+        "    Q3 = np.nanmean([all_q70_values, all_q80_values], axis=0)\n",
+        "    IQR = Q3 - Q1\n",
+        "    upper_thresh = Q3 + 1.5 * IQR\n",
+        "    lower_thresh = Q1 - 1.5 * IQR\n",
+        "\n",
+        "    plt.figure(figsize=(18, 9))\n",
+        "    # This now plots the correct original data for the horizon\n",
+        "    plt.plot(values_array[context_len:], label='Original Time Series', color='blue', alpha=0.7, linewidth=1.5)\n",
+        "\n",
+        "    plt.plot(all_predicted_values, label='Predicted Mean', color='green', linestyle='--', linewidth=1.5)\n",
+        "    plt.plot(lower_thresh, label='Lower Threshold', color='orange', linestyle=':', linewidth=1.2)\n",
+        "    plt.plot(upper_thresh, label='Upper Threshold', color='purple', linestyle=':', linewidth=1.2)\n",
+        "\n",
+        "    plt.scatter([i - context_len for i in anomaly_indices], anomaly_values,\n",
+        "                color='red', s=70, zorder=5,\n",
+        "                label=f'Detected Anomalies (Score >= {min_outlier_score_for_plot:.1f})',\n",
+        "                marker='o', edgecolors='black', linewidths=0.8)\n",
+        "\n",
+        "    plt.title(f'Time Series Anomaly Detection {title_suffix}')\n",
+        "    plt.xlabel('Time Index')\n",
+        "    plt.ylabel('Value')\n",
+        "    if x_lims:\n",
+        "        plt.xlim(x_lims[0], x_lims[1])\n",
+        "    plt.legend()\n",
+        "    plt.grid(True, linestyle='--', alpha=0.6)\n",
+        "    plt.tight_layout()\n",
+        "    # plt.savefig(output_filename) # Save the plot to a file\n",
+        "    plt.show()\n",
+        "    plt.close() # Close the figure to free memory\n",
+        "\n",
+        "# --- Main Script Logic ---\n",
+        "\n",
+        "# 1. Read and parse the data from the Beam output file\n",
+        "all_window_data = []\n",
+        "# Make sure 'plot_data.jsonl' is in the same directory as this script\n",
+        "try:\n",
+        "    with open('plot_data_finetuned.jsonl', 'r') as f:\n",
+        "        for line in f:\n",
+        "            # Check for empty lines that might have been added\n",
+        "            if line.strip():\n",
+        "                all_window_data.append(json.loads(line))\n",
+        "except FileNotFoundError:\n",
+        "    print(\"Error: 'plot_data_finetuned.jsonl' not found. Please make sure the file is in the correct directory.\")\n",
+        "    exit()\n",
+        "\n",
+        "\n",
+        "# 2. Sort data by timestamp to ensure the correct order\n",
+        "all_window_data.sort(key=lambda x: x['start_ts_micros'])\n",
+        "\n",
+        "# 3. Reconstruct the full data arrays\n",
+        "all_anomalies = []\n",
+        "all_predicted_values = []\n",
+        "all_q20_values = []\n",
+        "all_q30_values = []\n",
+        "all_q70_values = []\n",
+        "all_q80_values = []\n",
+        "all_actual_horizon_values = [] # This will hold the real \"blue line\" data\n",
+        "\n",
+        "for window_data in all_window_data:\n",
+        "    all_predicted_values.extend(window_data['predicted_values'])\n",
+        "    all_q20_values.extend(window_data['q20_values'])\n",
+        "    all_q30_values.extend(window_data['q30_values'])\n",
+        "    all_q70_values.extend(window_data['q70_values'])\n",
+        "    all_q80_values.extend(window_data['q80_values'])\n",
+        "    # Populate the list with the actual values from the file\n",
+        "    all_actual_horizon_values.extend(window_data.get('actual_horizon_values', []))\n",
+        "    all_anomalies.extend(window_data.get('anomalies', []))\n",
+        "\n",
+        "# 4. Convert lists to NumPy arrays\n",
+        "all_predicted_values = np.array(all_predicted_values)\n",
+        "print(len(all_predicted_values))\n",
+        "all_q20_values = np.array(all_q20_values)\n",
+        "all_q30_values = np.array(all_q30_values)\n",
+        "all_q70_values = np.array(all_q70_values)\n",
+        "all_q80_values = np.array(all_q80_values)\n",
+        "\n",
+        "# 5. Construct the `values_array` using the REAL data from your file\n",
+        "context_len = 512\n",
+        "# Create a dummy context so the array has the right shape for the plotting function.\n",
+        "# The first real value is used to make the context visually seamless.\n",
+        "if all_actual_horizon_values:\n",
+        "    dummy_context = [all_actual_horizon_values[0]] * context_len\n",
+        "    values_array = np.array(dummy_context + all_actual_horizon_values)\n",
+        "else:\n",
+        "    # Fallback in case the file is empty or missing the actual_horizon_values key\n",
+        "    print(\"Warning: 'actual_horizon_values' not found. The original time series plot will be empty.\")\n",
+        "    total_len = context_len + len(all_predicted_values)\n",
+        "    values_array = np.zeros(total_len)\n",
+        "\n",
+        "# 6. Call the plotting functions\n",
+        "if values_array.any():\n",
+        "    # Plotting function for full graph\n",
+        "    plot_anomalies_and_forecast(\n",
+        "        values_array, all_anomalies, all_predicted_values,\n",
+        "        all_q20_values, all_q30_values, all_q70_values, all_q80_values,\n",
+        "        title_suffix=\"(Full Graph with Correct Data)\",\n",
+        "        min_outlier_score_for_plot=5, # Set a score threshold\n",
+        "        context_len=context_len,\n",
+        "        output_filename=\"full_graph_correct.png\"\n",
+        "    )\n",
+        "\n",
+        "    # Plotting function for zoomed-in graphs - feel free to change\n",
+        "    zoom_ranges = [(2000, 2500), (8300, 9000), (9000, 9600)]\n",
+        "    for i, (start_idx, end_idx) in enumerate(zoom_ranges):\n",
+        "        # Adjust x_lims for the fact that the plotted array is sliced by context_len\n",
+        "        plot_x_start = max(0, start_idx)\n",
+        "        plot_x_end = end_idx\n",
+        "\n",
+        "        plot_anomalies_and_forecast(\n",
+        "            values_array, all_anomalies, all_predicted_values,\n",
+        "            all_q20_values, all_q30_values, all_q70_values, all_q80_values,\n",
+        "            title_suffix=f\"(Zoomed In: {start_idx} to {end_idx})\",\n",
+        "            x_lims=(plot_x_start, plot_x_end),\n",
+        "            min_outlier_score_for_plot=5,\n",
+        "            context_len=context_len,\n",
+        "            output_filename=f\"zoomed_graph_correct_{i}.png\"\n",
+        "        )\n",
+        "    print(\"Plots have been generated and saved with the corrected original data.\")\n",
+        "else:\n",
+        "    print(\"No data found to plot.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "RZwpgtAr8nlD",
+        "outputId": "d27b08f1-1e77-4109-bfa6-c709edf4b5e8"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "9600\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x900 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x900 with 1 Axes>"
+            ],
+            "image/png": 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6yKxSpcot+40YMcKUZA4fPjzNe5m9/+Li4syAgADz0UcfNW02m6Pf+++/b0oyP/30U0dbrVq1TEnmnDlzHG27d+82JZkWi8X8/fffHe2p9+/1169Lly5mgQIFzH/++ccpptatW5thYWGO+3jChAmmJPPLL7909Ll48aJZsmRJt7f3TM05I0aMcOp73333pbnWqX3d2Qb3zJkzZr58+cyaNWs6tZcvX96sW7dumv47duwwJZkffvihaZqm+dVXX6X5vk311FNPmRERES6fMyMZ3fup13/WrFmOtpSUFDMmJsYMCQkxExISbnre9NSrV8+0Wq3muXPnnNofeOABc8KECeYPP/xgTp482bz33ntNSeakSZMcfU6fPp3uZ2eapvnBBx+Ykszdu3ebpnktb/j5+aUbQ968ec3WrVunaS9durT52GOPuTwnAAAAeAcr/QAAAHBXeOqppxQWFuZ4XaNGDUlS27Zt5e/v79SekpLiWKWyZMkSnT9/Xk8//bT++ecfx5efn59q1Kih5cuX33Tc8uXLa/PmzWrbtq0OHz6siRMnqlmzZsqfP78+/vhjRz93xnnhhRduOe+vvvpKYWFhql+/vtN5q1SpopCQEMd5c+bMKUn66aefbrkt342uXr2quXPnqlWrVo5tJOvWrat8+fJluNrv+eefd3pds2ZNnTlzRgkJCZKkBQsWSJL69evn1K9///6SlGabuXLlyikmJsbxOvXzrVu3rooUKZKm/fqtA4ODgx1/TkpK0j///KP7779fkrRp06YM522xWNSmTRvNmzfPaUXW7Nmz9cADD6hYsWIZHpsZISEhkuQ497+9F6XM3w9LlizRhQsX9MorrygoKMjpHNdvFZpZJ06c0ObNm9WxY0enFXUVKlRQ/fr1HZ/39W51j2TkzJkzypUr1037rFq1SsOHD1ft2rX16quvpnk/s/ff0qVLlZKSoj59+shi+b9/Pj/77LOyWq1p7tOQkBCnlYJlypRRzpw5FRUV5bg3pbT3qWma+uabb9SkSROZpun02cXGxio+Pt5xry5YsEAFChRQixYtHOfLnj270zaW7krvM7n+e0m6tvLMNM002wzfit1uV5s2bXT+/Hm99957Tu9dvnxZgYGBaY5JvTcvX77s9N+M+qa+78o5XbVgwQJFRETo6aefdrRly5ZNvXr1UmJiolauXOnS+UaNGqWlS5dqzJgxjjydavXq1erdu7cef/xxPf/889q4caPuvfde/e9//8v0Nbm+z+XLlxUQEJBuHDdev1S5cuXSP//849KcAAAA4D0U/QAAAHBXuL7wI8lRACxcuHC67anPN9u3b5+ka8WjvHnzOn0tXrxYcXFxtxy7dOnSmjlzpv755x9t3bpVo0aNkr+/v7p16+Z4bper4/j7+6tQoUK3HHvfvn2Kj49Xvnz50pw3MTHRcd5atWqpefPmGj58uPLkyaOmTZtq2rRpmXoO2OLFi3X69GlVr15d+/fv1/79+3Xo0CHVqVNHn3/+udNWmqlu/DxSizSp1/3IkSOyWCwqWbKkU7+IiAjlzJlTR44cuen5Mvv5StLZs2fVu3dv5c+fX8HBwcqbN6+jYBcfH3/Tubdv316XL1/Wd999J0nas2ePNm7cqHbt2t30uMxITEyUJIWGhkrKmnsxs/fDgQMHJEn33nvvv56HJMfnld5zDqOiovTPP//o4sWLTu23ukduxrzJtqxnzpzR008/rVy5cmn27NlOxbrr483M/ZfRvAICAlS8ePE092mhQoXSFE3DwsJueZ+ePn1a58+f10cffZTmc+vUqZMkOT67I0eOqGTJkmnG+TfPmJSuFX3y5s3r1JYrV65MfR6Z8eKLL+rnn3/W1KlTVbFiRaf3goOD081Fqc+xSy3cp/43o77XF/gze05XHTlyRKVKlUpzX0VFRTnez6y5c+fq1VdfVZcuXTL1Sx4BAQHq2bOnzp8/r40bN0q69TW5vk9wcLBSUlLSPfeN1y+VaZpu/SIAAAAAvMP/1l0AAACAO5+fn59L7alFg9SC1cyZMxUREZGm3/WrBDMTQ3R0tKKjoxUTE6M6depo9uzZqlevnsvjBAYGplusuJHdbr/pirvUH+IbhqGvv/5av//+u3788UctWrRInTt31rhx4/T77787Vp2lJ/XcLVu2TPf9lStXqk6dOk5tt7ruqTL7w2R3P1/pWtxr1qzRSy+9pEqVKikkJER2u10NGjRIt2B5vXLlyqlKlSqaNWuW2rdvr1mzZikgICDDa+GK7du3S5Kj8JQV92Jm74c7QWbvkRvlzp07w0KUaZrq0KGD/v77b/34448qWLDgTc+V1cWMf5uH2rZtqw4dOqTbt0KFClkQYcYyijErDB8+XJMmTdKYMWPSLZgXKFAg3WcEnjhxQpIcn2OBAgWc2m/se/3nndlzesuSJUvUvn17NWrUSB9++GGmj0stIJ89e1aSFB4ersDAwAyvieR8/Ww2m+Li4pQvXz5Hv5SUFJ05cybda3Lu3DmVKlUq8xMDAACAV1H0AwAAwH9aiRIlJEn58uVTvXr1suy8VatWlfR/P3S9XeOUKFFCS5cu1YMPPpiplSv333+/7r//fr3xxhuaM2eO2rRpoy+++EJdu3ZNt//Fixf1ww8/qFWrVk7bCabq1auXZs+enabodytFixaV3W7Xvn37HCtkJOnUqVM6f/68ihYt6tL5MnLu3DktW7ZMw4cP15AhQxztqavqMqN9+/bq16+fTpw4oTlz5qhRo0a33F4yM2bOnCnDMFS/fn1Jrt0jGRWrMns/pI61ffv2NKvdMjPOjVI/rz179qR5b/fu3cqTJ49y5MiRqXPdStmyZfXNN9+k+94777yj+fPnq2/fvmrUqNFN483M/Xf9vIoXL+7ol5KSokOHDmXZ93LevHkVGhoqm812y3MWLVpU27dvT7MCK71rfyf44IMPNGzYMPXp00cDBw5Mt0+lSpW0fPlyJSQkyGq1OtrXrVvneF+6tjLV399fGzZscCq8p6SkaPPmzU5tmT1nRjK694sWLaqtW7fKbrc7/WLG7t27He/fyrp16/TEE0+oatWq+vLLL1365ZLU7VZTC/gWi0XR0dHasGFDuuMUL17csZo4dc4bNmxQw4YNHf02bNggu92e5ppcvXpVx44d0+OPP57p+AAAAOBdbO8JAACA/7TY2FhZrVaNGjUq3WfdnT59+qbHr1q1Kt3jUp8Zlrrl3r8dJyMtW7aUzWbTyJEj07x39epVnT9/XtK14teNK6hSf8B7sy0+v/vuO128eFE9evRQixYt0nw1btxY33zzTaa2Cb1e6g+cJ0yY4NT+zjvvSNJNCzauSF29dOPcbxz3Zp5++mkZhqHevXvr4MGDatu27b+Oa8yYMVq8eLFatWrlWEXjyj2SWkBL/XxTZfZ+ePTRRxUaGqrRo0c7tgBMdf21ypEjxy23QJWurSCqVKmSZsyY4RTT9u3btXjxYqcCw78VExOjc+fOpXnW3Pr16zVo0CBVqVJFY8aMuek5Mnv/1atXTwEBAXr33Xedrssnn3yi+Pj4LL1Pmzdvrm+++caxAvR613/2DRs21N9//62vv/7a0Xbp0iV99NFHWRLLrZw4cUK7d+/O1LNB586dq169eqlNmzaOa5ueFi1ayGazOc0hOTlZ06ZNU40aNRyr28LCwlSvXj3NmjXL6TmbM2fOVGJiop566imXz5mRjO79hg0b6uTJk5o7d66j7erVq3rvvfcUEhKiWrVq3fS8u3btUqNGjRQZGamffvopw+J8en8nXLhwQRMmTFCePHlUpUoVp7muX7/eqfC3Z88e/fLLL07XpG7dugoPD9fkyZOdzjt58mRlz549zf28c+dOJSUl6YEHHrjpnAAAAHDnYKUfAAAA/tOsVqsmT56sdu3aqXLlymrdurXy5s2ro0ePav78+XrwwQf1/vvvZ3j8m2++qY0bN+rJJ590bL+3adMmffbZZwoPD1efPn2yZJyM1KpVS88995xGjx6tzZs369FHH1W2bNm0b98+ffXVV5o4caJatGihGTNmaNKkSXriiSdUokQJXbhwQR9//LGsVutNCzKzZ89W7ty5M/yh7+OPP66PP/5Y8+fP15NPPpnpuCtWrKgOHTroo48+0vnz51WrVi398ccfmjFjhpo1a+byysGMWK1WPfzww3rrrbd05coV3XPPPVq8eLEOHTqU6XPkzZtXDRo00FdffaWcOXO6VOi5evWqZs2aJenaM7OOHDmiefPmaevWrapTp45TQcKVeyT1B/69evVSbGys/Pz81Lp160zfD1arVePHj1fXrl1VrVo1PfPMM8qVK5e2bNmiS5cuacaMGY5x5s6dq379+qlatWoKCQlRkyZN0p3r22+/rccee0wxMTHq0qWLLl++rPfee09hYWEaNmxYpq/ZrTRq1Ej+/v5aunSpunXrJula0atVq1a6cuWKGjdurC+//DLdY/Pnz6/69etn+v7LmzevBg0apOHDh6tBgwZ6/PHHtWfPHk2aNEnVqlXLkgJwqjFjxmj58uWqUaOGnn32WZUrV05nz57Vpk2btHTpUsd2js8++6zef/99tW/fXhs3blSBAgU0c+ZMZc+ePctiuZlBgwZpxowZOnTokCIjIzPs98cff6h9+/bKnTu3HnnkkTRbzj7wwAOO1ZM1atTQU089pUGDBikuLk4lS5bUjBkzdPjwYX3yySdOx73xxht64IEHVKtWLXXr1k3Hjx/XuHHj9Oijj6pBgwaOfq6cMz0Z3fvdunXTlClT1LFjR23cuFGRkZH6+uuvtXr1ak2YMMGxqi49Fy5cUGxsrM6dO6eXXnpJ8+fPd3q/RIkSiomJkXRtheT333+vJk2aqEiRIjpx4oQ+/fRTHT16VDNnzlRAQIDjuO7du+vjjz9Wo0aNNGDAAGXLlk3vvPOO8ufPr/79+zv6BQcHa+TIkerRo4eeeuopxcbGatWqVZo1a5beeOMNhYeHO8WzZMkSZc+e3bEaGQA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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x900 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x900 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Plots have been generated and saved with the corrected original data.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Compare with RMSE (Original vs Finetuned on Test Data)"
+      ],
+      "metadata": {
+        "id": "WAyaVAEIt4Ey"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import json\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "def calculate_model_rmse(file_path, finetuning_cutoff_ts, outlier_threshold):\n",
+        "    \"\"\"\n",
+        "    Loads model output data, reconstructs the time series, and calculates RMSE\n",
+        "    on a test set after filtering outliers.\n",
+        "\n",
+        "    Args:\n",
+        "        file_path (str): Path to the model's prediction data in JSONL format.\n",
+        "        finetuning_cutoff_ts (int): Timestamp to split training and test data.\n",
+        "        outlier_threshold (float): Outlier score at or above which to exclude points.\n",
+        "\n",
+        "    Returns:\n",
+        "        float: The calculated Root Mean Squared Error.\n",
+        "    \"\"\"\n",
+        "    all_window_data = []\n",
+        "    with open(file_path, 'r') as f:\n",
+        "        for line in f:\n",
+        "            if line.strip():\n",
+        "                all_window_data.append(json.loads(line))\n",
+        "\n",
+        "    all_window_data.sort(key=lambda x: x['start_ts_micros'])\n",
+        "\n",
+        "    # --- Reconstruct the full time series from the windows ---\n",
+        "    timestamps = []\n",
+        "    all_predicted_values = []\n",
+        "    all_actual_values = []\n",
+        "    all_anomalies = []\n",
+        "\n",
+        "    current_ts = -1\n",
+        "    if all_window_data:\n",
+        "        # Initialize with the first window's start time\n",
+        "        current_ts = all_window_data[0]['start_ts_micros'] // 1000000\n",
+        "\n",
+        "    for window_data in all_window_data:\n",
+        "        # Extend the series lists\n",
+        "        all_predicted_values.extend(window_data['predicted_values'])\n",
+        "        all_actual_values.extend(window_data.get('actual_horizon_values', []))\n",
+        "        all_anomalies.extend(window_data.get('anomalies', []))\n",
+        "\n",
+        "        # Reconstruct the timestamps for each predicted point\n",
+        "        start_ts = window_data['start_ts_micros'] // 1000000\n",
+        "        for _ in window_data['predicted_values']:\n",
+        "            timestamps.append(start_ts)\n",
+        "            start_ts += 1\n",
+        "\n",
+        "    # Create a lookup for outlier scores\n",
+        "    outlier_scores_map = {item['timestamp']: item['outlier_score'] for item in all_anomalies}\n",
+        "\n",
+        "    # Ensure the actual values and predicted values align\n",
+        "    min_len = min(len(timestamps), len(all_predicted_values), len(all_actual_values))\n",
+        "\n",
+        "    # --- Create a DataFrame for easy filtering and calculation ---\n",
+        "    df = pd.DataFrame({\n",
+        "        'timestamp': timestamps[:min_len],\n",
+        "        'actual': all_actual_values[:min_len],\n",
+        "        'predicted': all_predicted_values[:min_len]\n",
+        "    })\n",
+        "    df['outlier_score'] = df['timestamp'].map(outlier_scores_map).fillna(0.0)\n",
+        "\n",
+        "    # 1. Isolate the test set\n",
+        "    df_test = df[df['timestamp'] > finetuning_cutoff_ts].copy()\n",
+        "    print(f\"\\n--- Analyzing: {file_path} ---\")\n",
+        "    print(f\"Test set size (before filtering): {len(df_test)} points\")\n",
+        "\n",
+        "    # 2. Filter out anomalies based on the threshold\n",
+        "    df_filtered = df_test[df_test['outlier_score'] < outlier_threshold]\n",
+        "    num_outliers = len(df_test) - len(df_filtered)\n",
+        "    print(f\"Test set size (after filtering): {len(df_filtered)} points\")\n",
+        "    print(f\"Removed {num_outliers} points with outlier_score >= {outlier_threshold}\")\n",
+        "\n",
+        "    # 3. Calculate RMSE\n",
+        "    y_true = df_filtered['actual']\n",
+        "    y_pred = df_filtered['predicted']\n",
+        "    rmse = np.sqrt(np.mean((y_true - y_pred)**2))\n",
+        "\n",
+        "    return rmse\n",
+        "\n",
+        "# --- Configuration ---\n",
+        "FINETUNING_CUTOFF_TS = 8320\n",
+        "ORIGINAL_OUTLIER_THRESHOLD = 1.0\n",
+        "FINETUNED_OUTLIER_THRESHOLD = 5.0\n",
+        "\n",
+        "# --- Execution & Comparison ---\n",
+        "original_rmse = calculate_model_rmse(\n",
+        "    file_path=\"plot_data_original.jsonl\",\n",
+        "    finetuning_cutoff_ts=FINETUNING_CUTOFF_TS,\n",
+        "    outlier_threshold=ORIGINAL_OUTLIER_THRESHOLD\n",
+        ")\n",
+        "\n",
+        "finetuned_rmse = calculate_model_rmse(\n",
+        "    file_path=\"plot_data_finetuned.jsonl\",\n",
+        "    finetuning_cutoff_ts=FINETUNING_CUTOFF_TS,\n",
+        "    outlier_threshold=FINETUNED_OUTLIER_THRESHOLD\n",
+        ")\n",
+        "\n",
+        "print(\"\\n--- Final Results ---\")\n",
+        "print(f\"Original Model RMSE: {original_rmse:.2f}\")\n",
+        "print(f\"Fine-tuned Model RMSE: {finetuned_rmse:.2f}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zrMOmc90lQhJ",
+        "outputId": "d0679b35-b375-4068-aa4b-bd22a8c6166e"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "--- Analyzing: plot_data_original.jsonl ---\n",
+            "Test set size (before filtering): 1407 points\n",
+            "Test set size (after filtering): 1369 points\n",
+            "Removed 38 points with outlier_score >= 1.0\n",
+            "\n",
+            "--- Analyzing: plot_data_finetuned.jsonl ---\n",
+            "Test set size (before filtering): 1407 points\n",
+            "Test set size (after filtering): 1384 points\n",
+            "Removed 23 points with outlier_score >= 5.0\n",
+            "\n",
+            "--- Final Results ---\n",
+            "Original Model RMSE: 7164.17\n",
+            "Fine-tuned Model RMSE: 3948.44\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
diff --git a/examples/notebooks/beam-ml/automatic_model_refresh.ipynb b/examples/notebooks/beam-ml/automatic_model_refresh.ipynb
index 2f80846f313b..c29881ea72fd 100644
--- a/examples/notebooks/beam-ml/automatic_model_refresh.ipynb
+++ b/examples/notebooks/beam-ml/automatic_model_refresh.ipynb
@@ -98,7 +98,7 @@
         }
       ],
       "source": [
-        "!pip install apache_beam[gcp]>=2.46.0  tensorflow==2.15.0 tensorflow_hub==0.16.1 keras==2.15.0 Pillow==11.0.0 --quiet"
+        "!pip install apache_beam[interactive,gcp]>=2.46.0  tensorflow==2.15.0 tensorflow_hub==0.16.1 keras==2.15.0 Pillow==11.0.0 --quiet"
       ]
     },
     {
diff --git a/examples/notebooks/beam-ml/bigquery_vector_ingestion_and_search.ipynb b/examples/notebooks/beam-ml/bigquery_vector_ingestion_and_search.ipynb
index 7608b83cb59c..b1becd294ff0 100644
--- a/examples/notebooks/beam-ml/bigquery_vector_ingestion_and_search.ipynb
+++ b/examples/notebooks/beam-ml/bigquery_vector_ingestion_and_search.ipynb
@@ -98,7 +98,7 @@
       "cell_type": "code",
       "source": [
         "# Apache Beam with GCP support\n",
-        "!pip install apache_beam[gcp]>=2.64.0 --quiet\n",
+        "!pip install apache_beam[interactive,gcp]>=2.64.0 --quiet\n",
         "# Huggingface sentence-transformers for embedding models\n",
         "!pip install sentence-transformers --quiet\n"
       ],
diff --git a/examples/notebooks/beam-ml/cloudsql_mysql_product_catalog_embeddings.ipynb b/examples/notebooks/beam-ml/cloudsql_mysql_product_catalog_embeddings.ipynb
new file mode 100644
index 000000000000..5abc119b1dab
--- /dev/null
+++ b/examples/notebooks/beam-ml/cloudsql_mysql_product_catalog_embeddings.ipynb
@@ -0,0 +1,2793 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "8ZekaWhZH2SX"
+      },
+      "outputs": [],
+      "source": [
+        "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+        "\n",
+        "# Licensed to the Apache Software Foundation (ASF) under one\n",
+        "# or more contributor license agreements. See the NOTICE file\n",
+        "# distributed with this work for additional information\n",
+        "# regarding copyright ownership. The ASF licenses this file\n",
+        "# to you under the Apache License, Version 2.0 (the\n",
+        "# \"License\"); you may not use this file except in compliance\n",
+        "# with the License. You may obtain a copy of the License at\n",
+        "#\n",
+        "#   http://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing,\n",
+        "# software distributed under the License is distributed on an\n",
+        "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+        "# KIND, either express or implied. See the License for the\n",
+        "# specific language governing permissions and limitations\n",
+        "# under the License"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K6-p-DVrIFTY"
+      },
+      "source": [
+        "# Vector Embedding Ingestion with Apache Beam and CloudSQL MySQL\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/cloudsql_mysql_product_catalog_embeddings.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/cloudsql_mysql_product_catalog_embeddings.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WWwFCLRHZPm4"
+      },
+      "source": [
+        "# Introduction\n",
+        "\n",
+        "This Colab demonstrates how to generate embeddings from data and ingest them into [CloudSQL MySQL](https://cloud.google.com/sql/docs/mysql). We'll use Apache Beam and Dataflow for scalable data processing.\n",
+        "\n",
+        "The goal of this notebook is to make it easy for users to get started with generating embeddings at scale using Apache Beam and storing them in CloudSQL MySQL. We focus on building efficient ingestion pipelines that can handle various data sources and embedding models.\n",
+        "\n",
+        "## Example: Furniture Product Catalog\n",
+        "\n",
+        "We'll work with a sample e-commerce dataset representing a furniture product catalog. Each product has:\n",
+        "\n",
+        "*   **Structured fields:** `id`, `name`, `category`, `price`\n",
+        "*   **Detailed text descriptions:** Longer text describing the product's features.\n",
+        "*   **Additional metadata:** `material`, `dimensions`\n",
+        "\n",
+        "## Pipeline Overview\n",
+        "We will build a pipeline to:\n",
+        "1. Read product data\n",
+        "2. Convert unstructured product data, to `Chunk`<sup>[1]</sup> type\n",
+        "2. Generate Embeddings: Use a pre-trained Hugging Face model (via MLTransform) to create vector embeddings\n",
+        "3. Write to CloudSQL MySQL: Store the embeddings in a CloudSQL MySQL vector database\n",
+        "\n",
+        "Here's a visualization of the data flow:\n",
+        "\n",
+        "| Stage                     | Data Representation                                      | Notes                                                                                                                   |\n",
+        "| :------------------------ | :------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------- |\n",
+        "| **1. Ingest Data**      | `{`<br> `  \"id\": \"desk-001\",`<br> `  \"name\": \"Modern Desk\",`<br> `  \"description\": \"Sleek...\",`<br> `  \"category\": \"Desks\",`<br> `  ...`<br> `}` | Supports:<br>- Reading from batch (e.g., files, databases)<br>- Streaming sources (e.g., Pub/Sub).                 |\n",
+        "| **2. Convert to Chunks** | `Chunk(` <br>  &nbsp;&nbsp;`id=\"desk-001\",` <br>  &nbsp;&nbsp;`content=Content(` <br>   &nbsp;&nbsp;&nbsp;&nbsp;`text=\"Modern Desk\"` <br> &nbsp;&nbsp; `),` <br>  &nbsp;&nbsp;`metadata={...}` <br> `)`       | - `Chunk` is the structured input for generating and ingesting embeddings.<br>- `chunk.content.text` is the field that is embedded.<br> - Converting to `Chunk` does not mean breaking data into smaller pieces,<br>&nbsp;&nbsp; it's simply organizing your data in a standard format for the embedding pipeline.<br> - `Chunk` allows data to flow seamlessly throughout embedding pipelines. |\n",
+        "| **3. Generate Embeddings**| `Chunk(` <br>  &nbsp;&nbsp;`id=\"desk-001\",`<br>  &nbsp;&nbsp;`embedding=[-0.1, 0.6, ...],`<br>  `...)`  | Supports:<br>- Local Hugging Face models<br>- Remote Vertex AI models<br>- Custom embedding implementations.          |\n",
+        "| **4. Write to CloudSQL MySQL** | **CloudSQL MySQL Table (Example Row):**<br>`id: desk-001`<br>`embedding: [-0.1, 0.6, ...]`<br> `name = \"Modern Desk\"`,<br>`Other fields ...` | Supports:<br>- Custom schemas<br>- Conflict resolution strategies for handling updates                               |\n",
+        "\n",
+        "\n",
+        "[1]: Chunk represents an embeddable unit of input. It specifies which fields should be embedded and which fields should be treated as metadata. Converting to Chunk does not necessarily mean breaking your text into smaller pieces - it's primarily about structuring your data for the embedding pipeline. For very long texts that exceed the embedding model's maximum input size, you can optionally [use Langchain TextSplitters](https://beam.apache.org/releases/pydoc/2.63.0/apache_beam.ml.rag.chunking.langchain.html) to break the text into smaller `Chunk`'s.\n",
+        "\n",
+        "## Execution Environments\n",
+        "\n",
+        "This notebook demonstrates two execution environments:\n",
+        "\n",
+        "1. **DirectRunner (Local Execution)**: All examples in this notebook run on DirectRunner by default, which executes the pipeline locally. This is ideal for development, testing, and processing small datasets.\n",
+        "\n",
+        "2. **DataflowRunner (Distributed Execution)**: The [Run on Dataflow](#scrollTo=Quick_Start_Run_on_Dataflow) section demonstrates how to execute the same pipeline on Google Cloud Dataflow for scalable, distributed processing. This is recommended for production workloads and large datasets.\n",
+        "\n",
+        "All examples in this notebook can be adapted to run on Dataflow by following the pattern shown in the \"Run on Dataflow\" section."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z2eAyRECIP3z"
+      },
+      "source": [
+        "# Connecting Apache Beam to CloudSQL MySQL\n",
+        "\n",
+        "Beam uses the [CloudSQL MySQL Java Connector](https://github.com/GoogleCloudPlatform/cloud-sql-jdbc-socket-factory/blob/main/docs/jdbc.md) to securely establish a connection to your database. Apache Beam supports any parameters that can be passed to the Java Connector e.g. IP types.\n",
+        "\n",
+        "# Setup and Prerequisites\n",
+        "\n",
+        "This example requires:\n",
+        "1. A CloudSQL MySQL instance with [cloudsql_vector](https://cloud.google.com/sql/docs/mysql/vector-search#requirements) flag enabled\n",
+        "2. Apache Beam 2.67.0 or later\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WhOOPUBa6PyW"
+      },
+      "source": [
+        "## Install Packages and Dependencies\n",
+        "\n",
+        "First, let's install the Python packages required for the embedding and ingestion pipeline:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gCWRw2YE11wN"
+      },
+      "outputs": [],
+      "source": [
+        "# Apache Beam with GCP support\n",
+        "!pip install apache_beam[interactive,gcp]>=2.67.0 --quiet\n",
+        "# Huggingface sentence-transformers for embedding models\n",
+        "!pip install sentence-transformers --quiet"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2FlMPmA0IUuv"
+      },
+      "outputs": [],
+      "source": [
+        "!pip show apache-beam"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4aqYZ_pG1oYb"
+      },
+      "source": [
+        "Next, let's install cloud-sql-python-connector to help set up our test database."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eOYjnVDR87IE"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install \"cloud-sql-python-connector[pymysql]>=1.0.0,<2.0.0\" sqlalchemy --quiet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VhgbpTKzI-zI"
+      },
+      "source": [
+        "## Database Setup\n",
+        "\n",
+        "To connect to CloudSQL MySQL, you'll need:\n",
+        "1. GCP project ID where the CloudSQL MySQL instance is located\n",
+        "2. The CloudSQL MySQL connection URI. This is the fully qualified connection name of the CloudSQL MySQL instance found in the google cloud console under CloudSQL > Instances > Instance > Connect to this Instance > Connection name.\n",
+        "3. Database name. This is the name of the mysql database within your CloudSQL MySQL instance. The default database name is mysql.\n",
+        "4. Database credentials\n",
+        "5. A CloudSQL MySQL instance with cloudsql_vector flag enabled\n",
+        "\n",
+        "Replace these placeholder values with your actual CloudSQL MySQL connection details:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oqKQT0c_JB5f"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"\" # @param {type:'string'}\n",
+        "\n",
+        "CONNECTION_NAME = \"\" # @param {type:'string'}\n",
+        "\n",
+        "DB_NAME = \"\" #  @param {type:'string'}\n",
+        "\n",
+        "DB_USER = \"\" # @param {type:'string'}\n",
+        "\n",
+        "DB_PASSWORD = \"\" # @param {type:'string'}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "doK840yZZNdl"
+      },
+      "source": [
+        "## Authenticate to Google Cloud\n",
+        "\n",
+        "To connect to the CloudSQL MySQL instance via the language conenctor, we authenticate with Google Cloud."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CLM12rbiZHTN"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "l_BBCKl7KKcb"
+      },
+      "outputs": [],
+      "source": [
+        " # @title SQLAlchemy + CloudSQL MySQL Connector helpers for creating tables and verifying data\n",
+        "\n",
+        "import sqlalchemy\n",
+        "from sqlalchemy import text\n",
+        "from sqlalchemy.exc import SQLAlchemyError\n",
+        "from google.cloud.sql.connector import Connector\n",
+        "\n",
+        "def get_db_engine(connection_name: str, user: str, password: str, db: str, **connect_kwargs) -> sqlalchemy.engine.Engine:\n",
+        "    \"\"\"\n",
+        "    Creates a SQLAlchemy engine configured for CloudSQL MySQL.\n",
+        "\n",
+        "    To use this function, you may need to install necessary libraries:\n",
+        "    'pip install google-cloud-sql-connector[pymysql] sqlalchemy'\n",
+        "\n",
+        "    Args:\n",
+        "        connection_name: CloudSQL MySQL instance connection name (e.g., \"project:region:instance\").\n",
+        "        user: The database user.\n",
+        "        password: The database password.\n",
+        "        db: The name of the database.\n",
+        "        connect_kwargs: Additional keyword arguments for the connector (e.g., ip_type=\"PUBLIC\").\n",
+        "\n",
+        "    Returns:\n",
+        "        A SQLAlchemy engine instance.\n",
+        "    \"\"\"\n",
+        "    connector = Connector()\n",
+        "\n",
+        "    def get_conn() -> sqlalchemy.engine.base.Connection:\n",
+        "        \"\"\"Helper function to create a database connection.\"\"\"\n",
+        "        conn = connector.connect(\n",
+        "            connection_name,\n",
+        "            \"pymysql\",  # Use the PyMySQL driver for MySQL\n",
+        "            user=user,\n",
+        "            password=password,\n",
+        "            db=db,\n",
+        "            **connect_kwargs\n",
+        "        )\n",
+        "        return conn\n",
+        "\n",
+        "    # Create the SQLAlchemy engine using the connection function\n",
+        "    engine = sqlalchemy.create_engine(\n",
+        "        \"mysql+pymysql://\",  # Use the MySQL+PyMySQL dialect\n",
+        "        creator=get_conn,\n",
+        "    )\n",
+        "\n",
+        "    # This hook ensures the connector is closed when the engine is disposed\n",
+        "    engine.pool.dispose = lambda: connector.close()\n",
+        "\n",
+        "    return engine\n",
+        "\n",
+        "def setup_db_table_sqlalchemy(connection_name: str,\n",
+        "                                 database: str,\n",
+        "                                 table_name: str,\n",
+        "                                 table_schema: str,\n",
+        "                                 user: str,\n",
+        "                                 password: str,\n",
+        "                                 **connect_kwargs):\n",
+        "    \"\"\"\n",
+        "    Sets up a CloudSQL MySQL table using SQLAlchemy.\n",
+        "\n",
+        "    This function will drop the table if it already exists and then create it\n",
+        "    based on the provided schema.\n",
+        "\n",
+        "    Args:\n",
+        "        connection_name: CloudSQL MySQL instance connection name.\n",
+        "        database: The name of the database.\n",
+        "        table_name: The name of the table to create.\n",
+        "        table_schema: SQL string defining the table columns. For MySQL, use types like\n",
+        "                      'INT AUTO_INCREMENT PRIMARY KEY'. For embeddings, consider using\n",
+        "                      'JSON' or 'BLOB' to store the vector data.\n",
+        "                      Example: \"id INT AUTO_INCREMENT PRIMARY KEY, embedding JSON\"\n",
+        "        user: The database user.\n",
+        "        password: The database password.\n",
+        "        connect_kwargs: Additional keyword arguments for the connector.\n",
+        "    \"\"\"\n",
+        "    engine = None\n",
+        "    try:\n",
+        "        engine = get_db_engine(connection_name, user, password, database, **connect_kwargs)\n",
+        "\n",
+        "        with engine.connect() as connection:\n",
+        "            # Use autocommit for DDL statements\n",
+        "            with connection.execution_options(isolation_level=\"AUTOCOMMIT\"):\n",
+        "                print(\"Connected to MySQL DB successfully via SQLAlchemy!\")\n",
+        "\n",
+        "                # Use backticks for table names for MySQL compatibility\n",
+        "                print(f\"Dropping table `{table_name}` if it exists...\")\n",
+        "                connection.execute(text(f\"DROP TABLE IF EXISTS `{table_name}`;\"))\n",
+        "\n",
+        "                print(f\"Creating table `{table_name}`...\")\n",
+        "                create_sql = f\"\"\"\n",
+        "                CREATE TABLE IF NOT EXISTS `{table_name}` (\n",
+        "                    {table_schema}\n",
+        "                );\n",
+        "                \"\"\"\n",
+        "                connection.execute(text(create_sql))\n",
+        "\n",
+        "        print(\"MySQL table setup completed successfully!\")\n",
+        "\n",
+        "    except SQLAlchemyError as e:\n",
+        "        print(f\"An SQLAlchemy error occurred during setup: {e}\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"An unexpected error occurred during setup: {e}\")\n",
+        "    finally:\n",
+        "        if engine:\n",
+        "            engine.dispose()\n",
+        "\n",
+        "def test_db_connection_sqlalchemy(connection_name: str,\n",
+        "                                     database: str,\n",
+        "                                     table_name: str,\n",
+        "                                     user: str,\n",
+        "                                     password: str,\n",
+        "                                     **connect_kwargs):\n",
+        "    \"\"\"\n",
+        "    Tests the CloudSQL MySQL connection and verifies table existence.\n",
+        "\n",
+        "    Args:\n",
+        "        connection_name: CloudSQL MySQL instance connection name.\n",
+        "        database: The name of the database.\n",
+        "        table_name: The name of the table to check for.\n",
+        "        user: The database user.\n",
+        "        password: The database password.\n",
+        "        connect_kwargs: Additional keyword arguments for the connector.\n",
+        "    \"\"\"\n",
+        "    engine = None\n",
+        "    try:\n",
+        "        engine = get_db_engine(connection_name, user, password, database, **connect_kwargs)\n",
+        "\n",
+        "        with engine.connect() as connection:\n",
+        "            print(\"Testing MySQL connection...\")\n",
+        "            connection.execute(text(\"SELECT 1\"))\n",
+        "            print(\"✓ Connection successful\")\n",
+        "\n",
+        "            # Check if table exists using information_schema.\n",
+        "            # In MySQL, schema is the database, which can be found with DATABASE().\n",
+        "            table_exists_query = text(\"\"\"\n",
+        "                SELECT EXISTS (\n",
+        "                    SELECT 1\n",
+        "                    FROM information_schema.tables\n",
+        "                    WHERE table_schema = DATABASE() AND table_name = :tname\n",
+        "                );\n",
+        "            \"\"\")\n",
+        "            table_exists = connection.execute(table_exists_query, {\"tname\": table_name}).scalar()\n",
+        "\n",
+        "            if table_exists:\n",
+        "                print(f\"✓ Table `{table_name}` exists in database `{database}`.\")\n",
+        "            else:\n",
+        "                print(f\"✗ Table `{table_name}` does NOT exist in database `{database}`.\")\n",
+        "\n",
+        "    except SQLAlchemyError as e:\n",
+        "        print(f\"Connection test failed (SQLAlchemy error): {e}\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"Connection test failed (Unexpected error): {e}\")\n",
+        "    finally:\n",
+        "        if engine:\n",
+        "            engine.dispose()\n",
+        "\n",
+        "def verify_embeddings_sqlalchemy(connection_name: str,\n",
+        "                                 database: str,\n",
+        "                                 table_name: str,\n",
+        "                                 user: str,\n",
+        "                                 password: str,\n",
+        "                                 embedding_column: str = \"embedding\",\n",
+        "                                 **connect_kwargs):\n",
+        "    \"\"\"\n",
+        "    Connects to a CloudSQL MySQL table and prints all of its rows.\n",
+        "\n",
+        "    Args:\n",
+        "        connection_name: CloudSQL MySQL instance connection name.\n",
+        "        database: The name of the database.\n",
+        "        table_name: The name of the table to query.\n",
+        "        user: The database user.\n",
+        "        password: The database password.\n",
+        "        connect_kwargs: Additional keyword arguments for the connector.\n",
+        "    \"\"\"\n",
+        "    engine = None\n",
+        "    try:\n",
+        "        engine = get_db_engine(connection_name, user, password, database, **connect_kwargs)\n",
+        "\n",
+        "        with engine.connect() as connection:\n",
+        "            # Use backticks for the table name for MySQL best practice\n",
+        "            column_query = text(f\"\"\"\n",
+        "                SELECT COLUMN_NAME\n",
+        "                FROM INFORMATION_SCHEMA.COLUMNS\n",
+        "                WHERE table_schema = :db_name\n",
+        "                  AND table_name = :t_name\n",
+        "                  AND COLUMN_NAME != '{embedding_column}'\n",
+        "            \"\"\")\n",
+        "\n",
+        "            column_result = connection.execute(\n",
+        "                column_query,\n",
+        "                {\"db_name\": database, \"t_name\": table_name}\n",
+        "            )\n",
+        "\n",
+        "            columns_to_select = [row[0] for row in column_result]\n",
+        "\n",
+        "            if not columns_to_select:\n",
+        "                print(f\"No columns to display in `{table_name}` (after excluding '{embedding_column}').\")\n",
+        "                return\n",
+        "\n",
+        "            # Construct the SELECT statement with the filtered columns, quoting them for safety\n",
+        "            select_columns_str = \", \".join([f\"`{col}`\" for col in columns_to_select])\n",
+        "            select_query = text(f\"SELECT {select_columns_str}, vector_to_string({embedding_column}) as {embedding_column} FROM `{table_name}`;\")\n",
+        "\n",
+        "            # Execute the query to get the data\n",
+        "            result = connection.execute(select_query)\n",
+        "            rows = result.mappings().all()\n",
+        "\n",
+        "            print(f\"\\nFound {len(rows)} rows in `{table_name}` (excluding '{embedding_column}' column):\")\n",
+        "            print(\"-\" * 80)\n",
+        "\n",
+        "            if not rows:\n",
+        "                print(\"Table is empty.\")\n",
+        "            else:\n",
+        "                # result.keys() will have the correct column names from the executed query\n",
+        "                columns = result.keys()\n",
+        "                for row in rows:\n",
+        "                    for col in columns:\n",
+        "                        print(f\"{col}: {row[col]}\")\n",
+        "                    print(\"-\" * 80)\n",
+        "    except SQLAlchemyError as e:\n",
+        "        # Check specifically for ProgrammingError if the table might not exist\n",
+        "        if isinstance(e, sqlalchemy.exc.ProgrammingError):\n",
+        "            print(f\"Failed to query table `{table_name}`. Does it exist? Error: {e}\")\n",
+        "        else:\n",
+        "            print(f\"Failed to verify data (SQLAlchemy error): {e}\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"Failed to verify data (Unexpected error): {e}\")\n",
+        "    finally:\n",
+        "        if engine:\n",
+        "            engine.dispose()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "70z2O4nbOuaM"
+      },
+      "source": [
+        "## Create Sample Product Catalog Data\n",
+        "\n",
+        "We'll create a typical e-commerce catalog where you might want to:\n",
+        "- Generate embeddings for product text\n",
+        "- Store vectors alongside product data\n",
+        "- Enable vector similarity features\n",
+        "\n",
+        "Example product:\n",
+        "```python\n",
+        "{\n",
+        "    \"id\": \"desk-001\",\n",
+        "    \"name\": \"Modern Minimalist Desk\",\n",
+        "    \"description\": \"Sleek minimalist desk with clean lines and a spacious work surface. \"\n",
+        "                  \"Features cable management system and sturdy steel frame. \"\n",
+        "                  \"Perfect for contemporary home offices and workspaces.\",\n",
+        "    \"category\": \"Desks\",\n",
+        "    \"price\": 399.99,\n",
+        "    \"material\": \"Engineered Wood, Steel\",\n",
+        "    \"dimensions\": \"60W x 30D x 29H inches\"\n",
+        "}\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7_J__S8JOwJ_"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Create sample data\n",
+        "PRODUCTS_DATA = [\n",
+        "    {\n",
+        "        \"id\": \"desk-001\",\n",
+        "        \"name\": \"Modern Minimalist Desk\",\n",
+        "        \"description\": \"Sleek minimalist desk with clean lines and a spacious work surface. \"\n",
+        "                      \"Features cable management system and sturdy steel frame. \"\n",
+        "                      \"Perfect for contemporary home offices and workspaces.\",\n",
+        "        \"category\": \"Desks\",\n",
+        "        \"price\": 399.99,\n",
+        "        \"material\": \"Engineered Wood, Steel\",\n",
+        "        \"dimensions\": \"60W x 30D x 29H inches\"\n",
+        "    },\n",
+        "    {\n",
+        "        \"id\": \"chair-001\",\n",
+        "        \"name\": \"Ergonomic Mesh Office Chair\",\n",
+        "        \"description\": \"Premium ergonomic office chair with breathable mesh back, \"\n",
+        "                      \"adjustable lumbar support, and 4D armrests. Features synchronized \"\n",
+        "                      \"tilt mechanism and memory foam seat cushion. Ideal for long work hours.\",\n",
+        "        \"category\": \"Office Chairs\",\n",
+        "        \"price\": 299.99,\n",
+        "        \"material\": \"Mesh, Metal, Premium Foam\",\n",
+        "        \"dimensions\": \"26W x 26D x 48H inches\"\n",
+        "    },\n",
+        "    {\n",
+        "        \"id\": \"sofa-001\",\n",
+        "        \"name\": \"Contemporary Sectional Sofa\",\n",
+        "        \"description\": \"Modern L-shaped sectional with chaise lounge. Upholstered in premium \"\n",
+        "                      \"performance fabric. Features deep seats, plush cushions, and solid \"\n",
+        "                      \"wood legs. Perfect for modern living rooms.\",\n",
+        "        \"category\": \"Sofas\",\n",
+        "        \"price\": 1299.99,\n",
+        "        \"material\": \"Performance Fabric, Solid Wood\",\n",
+        "        \"dimensions\": \"112W x 65D x 34H inches\"\n",
+        "    },\n",
+        "    {\n",
+        "        \"id\": \"table-001\",\n",
+        "        \"name\": \"Rustic Dining Table\",\n",
+        "        \"description\": \"Farmhouse-style dining table with solid wood construction. \"\n",
+        "                      \"Features distressed finish and trestle base. Seats 6-8 people \"\n",
+        "                      \"comfortably. Perfect for family gatherings.\",\n",
+        "        \"category\": \"Dining Tables\",\n",
+        "        \"price\": 899.99,\n",
+        "        \"material\": \"Solid Pine Wood\",\n",
+        "        \"dimensions\": \"72W x 42D x 30H inches\"\n",
+        "    },\n",
+        "    {\n",
+        "        \"id\": \"bed-001\",\n",
+        "        \"name\": \"Platform Storage Bed\",\n",
+        "        \"description\": \"Modern queen platform bed with integrated storage drawers. \"\n",
+        "                      \"Features upholstered headboard and durable wood slat support. \"\n",
+        "                      \"No box spring needed. Perfect for maximizing bedroom space.\",\n",
+        "        \"category\": \"Beds\",\n",
+        "        \"price\": 799.99,\n",
+        "        \"material\": \"Engineered Wood, Linen Fabric\",\n",
+        "        \"dimensions\": \"65W x 86D x 48H inches\"\n",
+        "    }\n",
+        "]\n",
+        "print(f\"\"\"✓ Created PRODUCTS_DATA with {len(PRODUCTS_DATA)} records\"\"\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KUHPsWzQFKpL"
+      },
+      "source": [
+        "## Importing Pipeline Components\n",
+        "\n",
+        "We import the following for configuring our embedding ingestion pipeline:\n",
+        "- `apache_beam.ml.rag.types.Chunk`, the structured input for generating and ingesting embeddings\n",
+        "- `apache_beam.ml.rag.ingestion.cloudsql.CloudSQLMySQLVectorWriterConfig` for configuring write behavior like schema mapping and conflict resolution\n",
+        "- `apache_beam.ml.rag.ingestion.cloudsql.LanguageConnectorConfig` to connect using the [CloudSQL MySQL language connector](https://github.com/GoogleCloudPlatform/cloud-sql-jdbc-socket-factory/blob/main/docs/jdbc.md)\n",
+        "- `apache_beam.ml.rag.ingestion.base import VectorDatabaseWriteTransform` to perform the write step using CloudSQL MySQL configs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fFMjPZaelTi2"
+      },
+      "outputs": [],
+      "source": [
+        "# CloudSQL imports\n",
+        "from apache_beam.ml.rag.ingestion.cloudsql import CloudSQLMySQLVectorWriterConfig\n",
+        "from apache_beam.ml.rag.ingestion.cloudsql import LanguageConnectorConfig\n",
+        "\n",
+        "\n",
+        "from apache_beam.ml.rag.ingestion.base import VectorDatabaseWriteTransform\n",
+        "from apache_beam.ml.rag.types import Chunk, Content\n",
+        "from apache_beam.ml.rag.embeddings.huggingface import HuggingfaceTextEmbeddings\n",
+        "\n",
+        "# Apache Beam core\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.options.pipeline_options import PipelineOptions\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "\n",
+        "# JDBC and MySQL utilities\n",
+        "from apache_beam.ml.rag.ingestion.jdbc_common import WriteConfig\n",
+        "from apache_beam.ml.rag.ingestion.mysql_common import ColumnSpecsBuilder, ConflictResolution"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FjUzsUtXzFof"
+      },
+      "source": [
+        "# What's next?\n",
+        "\n",
+        "This colab covers several use cases that you can explore based on your needs after completing the Setup and Prerequisites:\n",
+        "\n",
+        "🔰 **New to vector embeddings?**\n",
+        "- [Start with Quick Start](#scrollTo=Quick_Start_Basic_Vector_Ingestion)\n",
+        "- Uses simple out-of-box schema\n",
+        "- Perfect for initial testing\n",
+        "\n",
+        "🚀 **Need to scale to large datasets?**\n",
+        "- [Go to Run on Dataflow](#scrollTo=Quick_Start_Run_on_Dataflow)\n",
+        "- Learn how to execute the same pipeline at scale\n",
+        "- Fully managed\n",
+        "- Process large datasets efficiently\n",
+        "\n",
+        "🎯 **Have a specific schema?**\n",
+        "- [Go to Custom Schema](#scrollTo=Custom_Schema_with_Column_Mapping)\n",
+        "- Learn to use different column names\n",
+        "- Map metadata to individual columns\n",
+        "\n",
+        "🔄 **Need to update embeddings?**\n",
+        "- [Check out Updating Embeddings](#scrollTo=Update_Embeddings_and_Metadata_with_Conflict_Resolution)\n",
+        "- Handle conflicts\n",
+        "- Selective field updates\n",
+        "\n",
+        "🔗 **Need to generate and Store Embeddings for Existing CloudSQL MySQL Data??**\n",
+        "- [See Database Integration](#scrollTo=Adding_Embeddings_to_Existing_Database_Records)\n",
+        "- Read data from your CloudSQL MySQL table.\n",
+        "- Generate embeddings for the relevant fields.\n",
+        "- Update your table (or a related table) with the generated embeddings.\n",
+        "\n",
+        "🤖 **Want to use Google's AI models?**\n",
+        "- [Try Vertex AI Embeddings](#scrollTo=Generate_Embeddings_with_VertexAI_Text_Embeddings)\n",
+        "- Use Google's powerful embedding models\n",
+        "- Seamlessly integrate with other Google Cloud services\n",
+        "\n",
+        "🔄 Need real-time embedding  updates?\n",
+        "\n",
+        "- [Try Streaming Embeddings from PubSub](#scrollTo=Streaming_Embeddings_Updates_from_PubSub)\n",
+        "- Process continuous data streams\n",
+        "- Update embeddings in real-time as information changes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pLEi3Z4wKMOX"
+      },
+      "source": [
+        "<a name=\"quickstart\"></a>\n",
+        "# Quick Start: Basic Vector Ingestion\n",
+        "\n",
+        "This section shows the simplest way to generate embeddings and store them in CloudSQL MySQL."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LWqEgqjQOcbA"
+      },
+      "source": [
+        "## Create table with default schema\n",
+        "\n",
+        "Before running the pipeline, we need a table to store our embeddings:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "93YnjdJkFWOi"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"default_product_embeddings\"\n",
+        "table_schema = f\"\"\"\n",
+        "  id VARCHAR(255) PRIMARY KEY,\n",
+        "  embedding VECTOR(384) USING VARBINARY,\n",
+        "  content text,\n",
+        "  metadata JSON\n",
+        "\"\"\"\n",
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DikTnoGbOioG"
+      },
+      "source": [
+        "## Configure Pipeline Components\n",
+        "\n",
+        "Now define the components that control the pipeline behavior:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "M8rVyZ6o-Nep"
+      },
+      "source": [
+        "### Convert ingested product data to embeddable Chunks\n",
+        "- Our data is ingested as product dictionaries\n",
+        "- Embedding generation and ingestion processes `Chunks`\n",
+        "- We convert each product dictionary to a `Chunk` to configure what text to embed and what to treat as metadata"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Rm_IX5U6mP_r"
+      },
+      "outputs": [],
+      "source": [
+        "from typing import Dict, Any\n",
+        "\n",
+        "# The create_chunk function converts our product dictionaries to Chunks.\n",
+        "# This doesn't split the text - it simply structures it in the format\n",
+        "# expected by the embedding pipeline components.\n",
+        "def create_chunk(product: Dict[str, Any]) -> Chunk:\n",
+        "    \"\"\"Convert a product dictionary into a Chunk object.\n",
+        "\n",
+        "       The pipeline components (MLTransform, VectorDatabaseWriteTransform)\n",
+        "       work with Chunk objects. This function:\n",
+        "       1. Extracts text we want to embed\n",
+        "       2. Preserves product data as metadata\n",
+        "       3. Creates a Chunk in the expected format\n",
+        "\n",
+        "    Args:\n",
+        "        product: Dictionary containing product information\n",
+        "\n",
+        "    Returns:\n",
+        "        Chunk: A Chunk object ready for embedding\n",
+        "    \"\"\"\n",
+        "    return Chunk(\n",
+        "        content=Content(\n",
+        "            text=f\"{product['name']}: {product['description']}\"\n",
+        "        ), # The text that will be embedded\n",
+        "        id=product['id'],  # Use product ID as chunk ID\n",
+        "        metadata=product,  # Store all product info in metadata\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xJaI9m3D7Vw-"
+      },
+      "source": [
+        "### Generate embeddings with HuggingFace"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0dlm1fjQh2dX"
+      },
+      "source": [
+        "We use a local pre-trained Hugging Face model to create vector embeddings from the product descriptions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "E5LkHmjV7l2S"
+      },
+      "outputs": [],
+      "source": [
+        "huggingface_embedder = HuggingfaceTextEmbeddings(\n",
+        "    model_name=\"sentence-transformers/all-MiniLM-L6-v2\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vVv8hD5wQo3w"
+      },
+      "source": [
+        "### Write to CloudSQL MySQL\n",
+        "\n",
+        "The default CloudSQLMySQLVectorWriterConfig maps Chunk fields to database columns as:\n",
+        "\n",
+        "| Database Column | Chunk Field | Description |\n",
+        "|----------------|-------------|-------------|\n",
+        "| id             | chunk.id    | Unique identifier |\n",
+        "| embedding      | chunk.embedding.dense_embedding | Vector representation |\n",
+        "| content        | chunk.content.text | Text that was embedded |\n",
+        "| metadata       | chunk.metadata | Additional data as JSONB |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "moKsz_6xQt-E"
+      },
+      "outputs": [],
+      "source": [
+        "# Configure the language connector so we can connect securely\n",
+        "connector_config = LanguageConnectorConfig(\n",
+        "    username=DB_USER,\n",
+        "    password=DB_PASSWORD,\n",
+        "    database_name=DB_NAME,\n",
+        "    instance_name=CONNECTION_NAME\n",
+        ")\n",
+        "cloudsql_writer_config = CloudSQLMySQLVectorWriterConfig(\n",
+        "    connection_config=connector_config,\n",
+        "    table_name=table_name\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ww2BPxTNKmL2"
+      },
+      "source": [
+        "## Assemble and Run Pipeline\n",
+        "\n",
+        "Now we can create our pipeline that:\n",
+        "1. Takes our product data\n",
+        "2. Converts each product to a Chunk\n",
+        "3. Generates embeddings for each Chunk\n",
+        "4. Stores everything in CloudSQL MySQL"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lyS3IpNBDgYw"
+      },
+      "outputs": [],
+      "source": [
+        "import tempfile\n",
+        "\n",
+        "# Executing on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    _ = (\n",
+        "            p\n",
+        "            | 'Create Products' >> beam.Create(PRODUCTS_DATA)\n",
+        "            | 'Convert to Chunks' >> beam.Map(create_chunk)\n",
+        "            | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "              .with_transform(huggingface_embedder)\n",
+        "            | 'Write to CloudSQL' >> VectorDatabaseWriteTransform(\n",
+        "                cloudsql_writer_config\n",
+        "            )\n",
+        "        )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Qm97EAww6RvW"
+      },
+      "source": [
+        "## Verify Embeddings\n",
+        "Let's check what was written to our CloudSQL MySQL table:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-H3t2cIN6lO_"
+      },
+      "outputs": [],
+      "source": [
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lz5itufZ31KB"
+      },
+      "source": [
+        "## Quick Start Summary\n",
+        "\n",
+        "In this section, you learned how to:\n",
+        "- Convert product data to the Chunk format expected by embedding pipelines\n",
+        "- Generate embeddings using a HuggingFace model\n",
+        "- Configure and run a basic embedding ingestion pipeline\n",
+        "- Store embeddings and metadata in CloudSQL MySQL\n",
+        "\n",
+        "This basic pattern forms the foundation for all the advanced use cases covered in the following sections."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OqojLgpJKUGk"
+      },
+      "source": [
+        "# Quick Start: Run on Dataflow\n",
+        "\n",
+        "This section demonstrates how to launch the Quick Start embedding pipeline on Google Cloud Dataflow from the colab. While previous examples used DirectRunner for local execution, Dataflow provides a fully managed, distributed execution environment that is:\n",
+        "- Scalable: Automatically scales to handle large datasets\n",
+        "- Fault-tolerant: Handles worker failures and ensures exactly-once processing\n",
+        "- Fully managed: No need to provision or manage infrastructure\n",
+        "\n",
+        "For more in-depth documentation to package your pipeline into a python file and launch a DataFlow job from the command line see [Create Dataflow pipeline using Python](https://cloud.google.com/dataflow/docs/quickstarts/create-pipeline-python)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zrMJSm-JUVGY"
+      },
+      "source": [
+        "## Create the CloudSQL MySQL table with default schema\n",
+        "\n",
+        "Before running the pipeline, we need a table to store our embeddings:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tgAvMT-yUixY"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"default_dataflow_product_embeddings\"\n",
+        "table_schema = f\"\"\"\n",
+        "  id VARCHAR(255) PRIMARY KEY,\n",
+        "  embedding VECTOR(384) USING VARBINARY,\n",
+        "  content text,\n",
+        "  metadata JSON\n",
+        "\"\"\"\n",
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mcZATJbaOec0"
+      },
+      "source": [
+        "## Save our Pipeline to a python file\n",
+        "\n",
+        "To launch our pipeline job on DataFlow, we\n",
+        "1. Add command line arguments for passing pipeline options like CloudSQL MySQL credentioals\n",
+        "2. Save our pipeline code to a local file `basic_ingestion_pipeline.py`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CzhIiBdqOknd"
+      },
+      "outputs": [],
+      "source": [
+        "file_content = \"\"\"\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.options.pipeline_options import PipelineOptions\n",
+        "import argparse\n",
+        "import tempfile\n",
+        "\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "from apache_beam.ml.rag.types import Chunk, Content\n",
+        "from apache_beam.ml.rag.ingestion.base import VectorDatabaseWriteTransform\n",
+        "from apache_beam.ml.rag.ingestion.cloudsql import CloudSQLMySQLVectorWriterConfig, LanguageConnectorConfig\n",
+        "from apache_beam.ml.rag.embeddings.huggingface import HuggingfaceTextEmbeddings\n",
+        "from apache_beam.options.pipeline_options import SetupOptions\n",
+        "\n",
+        "PRODUCTS_DATA = [\n",
+        "    {\n",
+        "        \"id\": \"desk-001\",\n",
+        "        \"name\": \"Modern Minimalist Desk\",\n",
+        "        \"description\": \"Sleek minimalist desk with clean lines and a spacious work surface. \"\n",
+        "                      \"Features cable management system and sturdy steel frame. \"\n",
+        "                      \"Perfect for contemporary home offices and workspaces.\",\n",
+        "        \"category\": \"Desks\",\n",
+        "        \"price\": 399.99,\n",
+        "        \"material\": \"Engineered Wood, Steel\",\n",
+        "        \"dimensions\": \"60W x 30D x 29H inches\"\n",
+        "    },\n",
+        "    {\n",
+        "        \"id\": \"chair-001\",\n",
+        "        \"name\": \"Ergonomic Mesh Office Chair\",\n",
+        "        \"description\": \"Premium ergonomic office chair with breathable mesh back, \"\n",
+        "                      \"adjustable lumbar support, and 4D armrests. Features synchronized \"\n",
+        "                      \"tilt mechanism and memory foam seat cushion. Ideal for long work hours.\",\n",
+        "        \"category\": \"Office Chairs\",\n",
+        "        \"price\": 299.99,\n",
+        "        \"material\": \"Mesh, Metal, Premium Foam\",\n",
+        "        \"dimensions\": \"26W x 26D x 48H inches\"\n",
+        "    }\n",
+        "]\n",
+        "\n",
+        "def run(argv=None):\n",
+        "    parser = argparse.ArgumentParser()\n",
+        "    parser.add_argument(\n",
+        "        '--connection_name',\n",
+        "        required=True,\n",
+        "        help='CloudSQL MySQL instance uri'\n",
+        "    )\n",
+        "    parser.add_argument(\n",
+        "        '--cloudsql_database',\n",
+        "        default='mysql',\n",
+        "        help='CloudSQL MySQL database name'\n",
+        "    )\n",
+        "    parser.add_argument(\n",
+        "        '--cloudsql_table',\n",
+        "        required=True,\n",
+        "        help='CloudSQL MySQL table name'\n",
+        "    )\n",
+        "    parser.add_argument(\n",
+        "        '--cloudsql_username',\n",
+        "        required=True,\n",
+        "        help='CloudSQL MySQL user name'\n",
+        "    )\n",
+        "    parser.add_argument(\n",
+        "        '--cloudsql_password',\n",
+        "        required=True,\n",
+        "        help='CloudSQL MySQL password'\n",
+        "    )\n",
+        "    known_args, pipeline_args = parser.parse_known_args(argv)\n",
+        "\n",
+        "    pipeline_options = PipelineOptions(pipeline_args)\n",
+        "    pipeline_options.view_as(SetupOptions).save_main_session = True\n",
+        "\n",
+        "    with beam.Pipeline(options=pipeline_options) as p:\n",
+        "        _ = (\n",
+        "              p\n",
+        "              | 'Create Products' >> beam.Create(PRODUCTS_DATA)\n",
+        "              | 'Convert to Chunks' >> beam.Map(lambda product: Chunk(\n",
+        "                    content=Content(\n",
+        "                        text=f\"{product['name']}: {product['description']}\"\n",
+        "                    ), # The text that will be embedded\n",
+        "                    id=product['id'],  # Use product ID as chunk ID\n",
+        "                    metadata=product,  # Store all product info in metadata\n",
+        "                )\n",
+        "              )\n",
+        "              | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "                .with_transform(\n",
+        "                    HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+        "                )\n",
+        "              | 'Write to CloudSQL MySQL' >> VectorDatabaseWriteTransform(\n",
+        "                  CloudSQLMySQLVectorWriterConfig(\n",
+        "                      connection_config=LanguageConnectorConfig(\n",
+        "                        username=known_args.cloudsql_username,\n",
+        "                        password=known_args.cloudsql_password,\n",
+        "                        database_name=known_args.cloudsql_database,\n",
+        "                        instance_name=known_args.connection_name\n",
+        "                      ),\n",
+        "                      table_name=known_args.cloudsql_table\n",
+        "                  )\n",
+        "              )\n",
+        "          )\n",
+        "\n",
+        "if __name__ == '__main__':\n",
+        "    run()\n",
+        "\"\"\"\n",
+        "\n",
+        "with open(\"basic_ingestion_pipeline.py\", \"w\") as f:\n",
+        "    f.write(file_content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y_1IMXx7UuG4"
+      },
+      "source": [
+        "## Authenticate with Google Cloud\n",
+        "\n",
+        "To launch a pipeline on Google Cloud, authenticate this notebook. Replace `<PROJECT_ID>` with your Google Cloud project ID"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WxrW-zlgRDLk"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"<project_id_>\" # @param {type:'string'}\n",
+        "import os\n",
+        "os.environ['PROJECT_ID'] = PROJECT_ID"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GswFBa10Qxkx"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7sELV2KeRG2c"
+      },
+      "source": [
+        "## Configure the Pipeline options\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nVDW0Q9iS_Pk"
+      },
+      "source": [
+        "To run the pipeline on DataFlow we need\n",
+        "- A gcs bucket for staging DataFlow files. Replace `<BUCKET_NAME>`: the name of a valid Google Cloud Storage bucket.\n",
+        "- Optionally set the Google Cloud region that you want to run Dataflow in. Replace `<REGION>` with the desired location.\n",
+        "- Optionally provide `NETWORK` and `SUBNETWORK` for dataflow workers to run on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qxFJflLiTMua"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "BUCKET_NAME = '' # @param {type:'string'}\n",
+        "REGION = 'us-central1' # @param {type:'string'}\n",
+        "\n",
+        "NETWORK = '' # @param {type:'string'}\n",
+        "SUBNETWORK = '' # @param {type:'string'}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WWjjqwV-aJFi"
+      },
+      "source": [
+        "## Provide additional Python dependencies to be installed on Worker VM's\n",
+        "\n",
+        "We are making use of the HuggingFace `sentence-transformers` package to generate embeddings. Since this package is not installed on Worker VM's by default, we create a requirements.txt file with the additional dependencies to be installed on worker VM's.\n",
+        "\n",
+        "See [Managing Python Pipeline Dependencies](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/) for more details.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hkxmk6aTJSKW"
+      },
+      "outputs": [],
+      "source": [
+        "!echo \"sentence-transformers\" > ./requirements.txt\n",
+        "!cat ./requirements.txt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NXgGZeOsY2O7"
+      },
+      "source": [
+        "## Run Pipeline on Dataflow\n",
+        "\n",
+        "We launch the pipeline via the command line, passing\n",
+        "- CloudSQL MySQL pipeline arguments defined in `basic_ingestion_pipeline.py`\n",
+        "- GCP Project ID\n",
+        "- Job Region\n",
+        "- The runner (DataflowRunner)\n",
+        "- Temp and Staging GCS locations for Pipeline artifacts\n",
+        "- Requirement file location for additional dependencies\n",
+        "- (Optional) The VPC network and Subnetwork that has access to the CloudSQL MySQL instance\n",
+        "\n",
+        "Once the job is launched, you can monitor its progress in the Google Cloud Console:\n",
+        "1. Go to https://console.cloud.google.com/dataflow/jobs\n",
+        "2. Select your project\n",
+        "3. Click on the job named \"cloudsql-dataflow-basic-embedding-ingest\"\n",
+        "4. View detailed execution graphs, logs, and metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fUeG_hEb5Qbb"
+      },
+      "outputs": [],
+      "source": [
+        "command_parts = [\n",
+        "    \"python ./basic_ingestion_pipeline.py\",\n",
+        "    f\"--project={PROJECT_ID}\",\n",
+        "    f\"--cloudsql_username={DB_USER}\",\n",
+        "    f\"--connection_name={CONNECTION_NAME}\",\n",
+        "    f\"--cloudsql_password={DB_PASSWORD}\",\n",
+        "    f\"--cloudsql_table=default_dataflow_product_embeddings\",\n",
+        "    f\"--cloudsql_database={DB_NAME}\",\n",
+        "    f\"--job_name=cloudsql-dataflow-basic-embedding-ingest\",\n",
+        "    f\"--region={REGION}\",\n",
+        "    \"--runner=DataflowRunner\",\n",
+        "    f\"--temp_location=gs://{BUCKET_NAME}/temp\",\n",
+        "    f\"--staging_location=gs://{BUCKET_NAME}/staging\",\n",
+        "    \"--requirements_file=requirements.txt\",\n",
+        "]\n",
+        "\n",
+        "if NETWORK:\n",
+        "    command_parts.append(f\"--network={NETWORK}\")\n",
+        "\n",
+        "if SUBNETWORK:\n",
+        "    command_parts.append(f\"--subnetwork=regions/{REGION}/subnetworks/{SUBNETWORK}\")\n",
+        "\n",
+        "final_command = \" \".join(command_parts)\n",
+        "\n",
+        "print(\"Generated command:\\n\", final_command)\n",
+        "!{final_command}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Sp_M6tJbWXTw"
+      },
+      "source": [
+        "## Verify the Written Embeddings\n",
+        "\n",
+        "Let's check what was written to our CloudSQL MySQL table:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "A11PeldtWXvP"
+      },
+      "outputs": [],
+      "source": [
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name='default_dataflow_product_embeddings', user=DB_USER, password=DB_PASSWORD)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-2hTEi-jzYN6"
+      },
+      "source": [
+        "# Advanced Use Cases\n",
+        "\n",
+        "This section demonstrates more complex scenarios for using CloudSQL MySQL with Apache Beam for vector embeddings.\n",
+        "\n",
+        "🎯 **Have a specific schema?**\n",
+        "- [Go to Custom Schema](#scrollTo=Custom_Schema_with_Column_Mapping)\n",
+        "- Learn to use different column names and transform values\n",
+        "- Map metadata to individual columns\n",
+        "\n",
+        "🔄 **Need to update embeddings?**\n",
+        "- [Check out Updating Embeddings](#scrollTo=Update_Embeddings_and_Metadata_with_Conflict_Resolution)\n",
+        "- Handle conflicts\n",
+        "- Selective field updates\n",
+        "\n",
+        "🔗 **Need to generate and Store Embeddings for Existing CloudSQL MySQL Data??**\n",
+        "- [See Database Integration](#scrollTo=Adding_Embeddings_to_Existing_Database_Records)\n",
+        "- Read data from your CloudSQL MySQL table.\n",
+        "- Generate embeddings for the relevant fields.\n",
+        "- Update your table (or a related table) with the generated embeddings.\n",
+        "\n",
+        "🤖 **Want to use Google's AI models?**\n",
+        "- [Try Vertex AI Embeddings](#scrollTo=Generate_Embeddings_with_VertexAI_Text_Embeddings)\n",
+        "- Use Google's powerful embedding models\n",
+        "- Seamlessly integrate with other Google Cloud services\n",
+        "\n",
+        "🔄 Need real-time embedding  updates?\n",
+        "\n",
+        "- [Try Streaming Embeddings from PubSub](#scrollTo=Streaming_Embeddings_Updates_from_PubSub)\n",
+        "- Process continuous data streams\n",
+        "- Update embeddings in real-time as information changes\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qGaH_TqEzn8r"
+      },
+      "source": [
+        "## Custom Schema with Column Mapping <a name=\"custom-schema\"></a>\n",
+        "\n",
+        "In this example, we'll create a custom schema that:\n",
+        "- Uses different column names\n",
+        "- Maps metadata to individual columns\n",
+        "- Uses functions to transform values"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R4d9W6ry_CN8"
+      },
+      "source": [
+        "### ColumnSpec and ColumnSpecsBuilder\n",
+        "\n",
+        "\n",
+        "ColumnSpec specifies how to map data to a database column. For example:\n",
+        "```python\n",
+        "from apache_beam.ml.rag.ingestion.mysql_common import ColumnSpecsBuilder\n",
+        "\n",
+        "ColumnSpec(\n",
+        "    column_name=\"price\",          # Database column\n",
+        "    python_type=float,            # Python Type for the value\n",
+        "    value_fn=lambda c: c.metadata['price'],  # Extract price from Chunk metadata to get actual value\n",
+        "    placeholder=\"ROUND(?, 2)\"      # Optional SQL cast or function\n",
+        ")\n",
+        "```\n",
+        "creates an INSERT statement like:\n",
+        "```sql\n",
+        "INSERT INTO table (price) VALUES (?::decimal)\n",
+        "```\n",
+        "where the `?` placeholder is poulated with the value from our ingested data.\n",
+        "\n",
+        "`ColumnSpecsBuilder` provides a builder and convenience methods to create these `ColumnSpecs`:\n",
+        "\n",
+        "1. Core Field Mapping\n",
+        "   - `with_id_spec()` => Insert chunk.id as text in \"id\" column\n",
+        "   - `with_embedding_spec()` => Insert chunk.embedding casted to `VECTOR` via `string_to_vector(?)` in \"embedding\" column\n",
+        "   - `with_content_spec()` => Insert `chunk.content`.text as text in \"content\" column\n",
+        "\n",
+        "   Note: All `with_id_spec`, `with_embedding_spec`, etc. methods allow overriding `column_name`, `python_type`, and `value_fn`.\n",
+        "\n",
+        "2. Metadata Extraction\n",
+        "   - `add_metadata_field`: Creates a column from a `chunk.metadata` field\n",
+        "   - Handles type conversion based on specified SQL type\n",
+        "\n",
+        "3. Custom Fields\n",
+        "   - `add_custom_column_spec`: Grants complete control over mapping `Chunk` data to database rows using `ColumnSpec`\n",
+        "\n",
+        "Now, lets the table to store our embeddings:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6XpYLcCu80Dy"
+      },
+      "source": [
+        "### Create Custom Schema Table"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6bUd6vprzh7O"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"custom_product_embeddings\"\n",
+        "table_schema = \"\"\"\n",
+        "    product_id VARCHAR(255) PRIMARY KEY,\n",
+        "    vector_embedding VECTOR(384) USING VARBINARY,\n",
+        "    product_name VARCHAR(255),\n",
+        "    description TEXT,\n",
+        "    price DECIMAL,\n",
+        "    category VARCHAR(255),\n",
+        "    display_text VARCHAR(255),\n",
+        "    model_name VARCHAR(255),\n",
+        "    created_at TIMESTAMP\n",
+        "\"\"\"\n",
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ScCVCZFo-Fcv"
+      },
+      "source": [
+        "### Configure Pipeline Components"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g9-f7tcf-0qC"
+      },
+      "source": [
+        "#### Write to custom schema using ColumnSpecsBuilder\n",
+        "\n",
+        "\n",
+        "We configure ConlumnSpecsBuilder to map data as:\n",
+        "\n",
+        "| Database Column | Chunk Field                               |\n",
+        "|-----------------|-------------------------------------------|\n",
+        "| `product_id`    | `chunk.id`                                |\n",
+        "| `vector_embedding`| `chunk.embedding.dense_embedding`        |\n",
+        "| `description`   | `chunk.content.text`                      |\n",
+        "| `product_name`  | `chunk.metadata['name']`                  |\n",
+        "| `price`         | `chunk.metadata['price']`                 |\n",
+        "| `category`      | `chunk.metadata['category']`              |\n",
+        "| `display_text`  | *Function that combines product name and price* |\n",
+        "| `model_name`    | *Function that returns the model name: \"all-MiniLM-L6-v2\"* |\n",
+        "| `created_at`    | *Function that returns the current timestamp cast to a SQL timestamp* |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TAq6ydMn-5Uu"
+      },
+      "outputs": [],
+      "source": [
+        "from apache_beam.ml.rag.ingestion.mysql_common import ColumnSpecsBuilder\n",
+        "from apache_beam.ml.rag.ingestion.mysql_common import ColumnSpec\n",
+        "from datetime import datetime\n",
+        "\n",
+        "column_specs = (\n",
+        "    ColumnSpecsBuilder()\n",
+        "    # Write chunk.id to a column named \"product_id\"\n",
+        "    .with_id_spec(column_name='product_id')\n",
+        "    # Write chunk.embedding.dense_embedding to a column named \"vector_embedding\"\n",
+        "    .with_embedding_spec(column_name='vector_embedding')\n",
+        "    # Write chunk.content.text to a column named \"description\"\n",
+        "    .with_content_spec(column_name='description')\n",
+        "    # Write chunk.metadata.['product_name'] to a column named \"product_name\"\n",
+        "    .add_metadata_field(\n",
+        "        field='name',\n",
+        "        column_name='product_name',\n",
+        "        python_type=str\n",
+        "    )\n",
+        "    # Write chunk.metadata.['price'] to a column named \"price\"\n",
+        "    .add_metadata_field(\n",
+        "        field='price',\n",
+        "        column_name='price',\n",
+        "        python_type=float\n",
+        "    )\n",
+        "    # Write chunk.metadata.['category'] to a column named \"category\"\n",
+        "    .add_metadata_field(\n",
+        "        field='category',\n",
+        "        column_name='category',\n",
+        "        python_type=str\n",
+        "    )\n",
+        "    # Write custom field using value_fn to column named \"display_text\" using\n",
+        "    # ColumnSpec.text convenience method\n",
+        "    .add_custom_column_spec(\n",
+        "        ColumnSpec.text(\n",
+        "          column_name='display_text',\n",
+        "        value_fn=lambda chunk: \\\n",
+        "          f\"{chunk.metadata['name']} - ${chunk.metadata['price']:.2f}\"\n",
+        "        )\n",
+        "    )\n",
+        "    # Store model used to generate embedding using ColumnSpec constructor\n",
+        "    .add_custom_column_spec(\n",
+        "        ColumnSpec(\n",
+        "          column_name='model_name',\n",
+        "          python_type=str,\n",
+        "          value_fn=lambda _: \"all-MiniLM-L6-v2\"\n",
+        "        )\n",
+        "    )\n",
+        "    .add_custom_column_spec(\n",
+        "        ColumnSpec(\n",
+        "          column_name='created_at',\n",
+        "          python_type=str,\n",
+        "          value_fn=lambda _: datetime.now().isoformat()\n",
+        "        )\n",
+        "    )\n",
+        "    .build()\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MBfLVL6XX2mF"
+      },
+      "source": [
+        "### Assemble and Run Pipeline\n",
+        "\n",
+        "Now we can create our pipeline that will:\n",
+        "1. Take our product data\n",
+        "2. Convert each product to a Chunk\n",
+        "3. Generate embeddings for each Chunk\n",
+        "4. Store everything in CloudSQL MySQL with our custom schema configuration"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4V-ILUlWVVX8"
+      },
+      "outputs": [],
+      "source": [
+        "import tempfile # For storing MLTransform artifacts\n",
+        "\n",
+        "# Executing on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    _ = (\n",
+        "            p\n",
+        "            | 'Create Products' >> beam.Create(PRODUCTS_DATA)\n",
+        "            | 'Convert to Chunks' >> beam.Map(lambda product: Chunk(\n",
+        "                    content=Content(\n",
+        "                        text=f\"{product['name']}: {product['description']}\"\n",
+        "                    ), # The text that will be embedded\n",
+        "                    id=product['id'],  # Use product ID as chunk ID\n",
+        "                    metadata=product,  # Store all product info in metadata\n",
+        "                )\n",
+        "              )\n",
+        "            | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "              .with_transform(HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "            | 'Write to CloudSQL MySQL' >> VectorDatabaseWriteTransform(\n",
+        "                CloudSQLMySQLVectorWriterConfig(\n",
+        "                    connection_config=LanguageConnectorConfig(\n",
+        "                        username=DB_USER,\n",
+        "                        password=DB_PASSWORD,\n",
+        "                        database_name=DB_NAME,\n",
+        "                        instance_name=CONNECTION_NAME\n",
+        "                    ),\n",
+        "                    table_name=table_name,\n",
+        "                    column_specs=column_specs\n",
+        "                )\n",
+        "            )\n",
+        "        )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LCpoJkBpYEsH"
+      },
+      "source": [
+        "### Verify the Written Embeddings\n",
+        "\n",
+        "Let's check what was written to our CloudSQL MySQL table:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B2UDOZL0VZ-p"
+      },
+      "outputs": [],
+      "source": [
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD, embedding_column=\"vector_embedding\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DQyJoyZic9GT"
+      },
+      "source": [
+        "## Update Embeddings and Metadata with Conflict Resolution <a name=\"updating\"></a>\n",
+        "\n",
+        "This section demonstrates how to handle periodic updates to product descriptions and their embeddings using the default schema. We'll show how embeddings and metadata get updated when product descriptions change.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jwLHGKfNdbEG"
+      },
+      "source": [
+        "### Create table with desired schema\n",
+        "\n",
+        "Let's use the same default schema as in Quick Start:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0vK-b4xkXtgJ"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"mutable_product_embeddings\"\n",
+        "table_schema = f\"\"\"\n",
+        "  id VARCHAR(255) PRIMARY KEY,\n",
+        "  embedding VECTOR(384) USING VARBINARY,\n",
+        "  content text,\n",
+        "  metadata JSON,\n",
+        "  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n",
+        "\"\"\"\n",
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hhl2URWceSg_"
+      },
+      "source": [
+        "### Sample Data: Day 1 vs Day 2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "t4z8tM_leZV8"
+      },
+      "outputs": [],
+      "source": [
+        "PRODUCTS_DATA_DAY1 = [\n",
+        "    {\n",
+        "        \"id\": \"desk-001\",\n",
+        "        \"name\": \"Modern Minimalist Desk\",\n",
+        "        \"description\": \"Sleek minimalist desk with clean lines and a spacious work surface. \"\n",
+        "                      \"Features cable management system and sturdy steel frame.\",\n",
+        "        \"category\": \"Desks\",\n",
+        "        \"price\": 399.99,\n",
+        "        \"update_timestamp\": \"2024-02-18\"\n",
+        "    }\n",
+        "]\n",
+        "\n",
+        "PRODUCTS_DATA_DAY2 = [\n",
+        "    {\n",
+        "        \"id\": \"desk-001\",  # Same ID as Day 1\n",
+        "        \"name\": \"Modern Minimalist Desk\",\n",
+        "        \"description\": \"Updated: Sleek minimalist desk with built-in wireless charging. \"\n",
+        "                      \"Features cable management system, sturdy steel frame, and Qi charging pad. \"\n",
+        "                      \"Perfect for modern tech-enabled workspaces.\",\n",
+        "        \"category\": \"Smart Desks\",  # Category changed\n",
+        "        \"price\": 449.99,  # Price increased\n",
+        "        \"update_timestamp\": \"2024-02-19\"\n",
+        "    }\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W_UTcRz9eskE"
+      },
+      "source": [
+        "### Configure Pipeline Components"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PWvtwVmUedzw"
+      },
+      "source": [
+        "#### Writer with Conflict Resolution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Y2XEwxw6ee4b"
+      },
+      "outputs": [],
+      "source": [
+        "from apache_beam.ml.rag.ingestion.cloudsql import (\n",
+        "    CloudSQLMySQLVectorWriterConfig,\n",
+        "    LanguageConnectorConfig,\n",
+        ")\n",
+        "from apache_beam.ml.rag.ingestion.mysql_common import ConflictResolution\n",
+        "\n",
+        "# Define how to handle conflicts - update all fields when ID matches\n",
+        "conflict_resolution = ConflictResolution(\n",
+        "    action=\"UPDATE\",         # Update existing records\n",
+        "    update_fields=[\"embedding\", \"content\", \"metadata\"]\n",
+        ")\n",
+        "\n",
+        "# Create writer config with conflict resolution\n",
+        "cloudsql_writer_config = CloudSQLMySQLVectorWriterConfig(\n",
+        "    connection_config=LanguageConnectorConfig(\n",
+        "        username=DB_USER,\n",
+        "        password=DB_PASSWORD,\n",
+        "        database_name=DB_NAME,\n",
+        "        instance_name=CONNECTION_NAME\n",
+        "    ),\n",
+        "    table_name=table_name,\n",
+        "    conflict_resolution=conflict_resolution,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tzo43G9NfCr5"
+      },
+      "outputs": [],
+      "source": [
+        "huggingface_embedder = HuggingfaceTextEmbeddings(\n",
+        "    model_name=\"sentence-transformers/all-MiniLM-L6-v2\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "axMFW_DufKnO"
+      },
+      "source": [
+        "### Run Day 1 Pipeline\n",
+        "\n",
+        "First, let's ingest our initial product data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eA3TpkHMfLUq"
+      },
+      "outputs": [],
+      "source": [
+        "# Executing on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    _ = (\n",
+        "        p\n",
+        "        | 'Create Day 1 Products' >> beam.Create(PRODUCTS_DATA_DAY1)\n",
+        "        | 'Convert Day 1 to Chunks' >> beam.Map(lambda product: Chunk(\n",
+        "                content=Content(\n",
+        "                    text=f\"{product['name']}: {product['description']}\"\n",
+        "                ), # The text that will be embedded\n",
+        "                id=product['id'],  # Use product ID as chunk ID\n",
+        "                metadata=product,  # Store all product info in metadata\n",
+        "            )\n",
+        "          )\n",
+        "        | 'Generate Day1 Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "          .with_transform(HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "        | 'Write Day 1 to CloudSQL MySQL' >> VectorDatabaseWriteTransform(\n",
+        "            cloudsql_writer_config\n",
+        "        )\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hFjtKX9tZIrI"
+      },
+      "source": [
+        "#### Verify Initial Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lxSyaIhbZG52"
+      },
+      "outputs": [],
+      "source": [
+        "print(\"\\nAfter Day 1 ingestion:\")\n",
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yvOlen9qfSQ4"
+      },
+      "source": [
+        "### Run Day 2 Pipeline\n",
+        "\n",
+        "Now let's process our updated product data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "r19qQs6ifVq1"
+      },
+      "outputs": [],
+      "source": [
+        "# Executing on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    _ = (\n",
+        "        p\n",
+        "        | 'Create Day 2 Products' >> beam.Create(PRODUCTS_DATA_DAY2)\n",
+        "        | 'Convert Day 2 to Chunks' >> beam.Map(lambda product: Chunk(\n",
+        "                    content=Content(\n",
+        "                        text=f\"{product['name']}: {product['description']}\"\n",
+        "                    ), # The text that will be embedded\n",
+        "                    id=product['id'],  # Use product ID as chunk ID\n",
+        "                    metadata=product,  # Store all product info in metadata\n",
+        "                )\n",
+        "              )\n",
+        "        | 'Generate Day 2 Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "          .with_transform(HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "        | 'Write Day 2 to CloudSQL MySQL' >> VectorDatabaseWriteTransform(\n",
+        "            cloudsql_writer_config\n",
+        "        )\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QbcZOIdcZWA6"
+      },
+      "source": [
+        "#### Verify Updated Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_VpqhPAQZD4K"
+      },
+      "outputs": [],
+      "source": [
+        "print(\"\\nAfter Day 2 ingestion:\")\n",
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D5hImiN0fZo5"
+      },
+      "source": [
+        "### What Changed?\n",
+        "\n",
+        "Key points to notice:\n",
+        "\n",
+        "1. The embedding vector changed because the product description was updated\n",
+        "2. The metadata JSON field contains the updated category, price, and timestamp\n",
+        "3. The content field reflects the new description\n",
+        "4. The original ID remained the same\n",
+        "\n",
+        "This pattern allows you to:\n",
+        "- Update embeddings when source text changes\n",
+        "- Maintain referential integrity with consistent IDs\n",
+        "- Track changes through the metadata field\n",
+        "- Handle conflicts gracefully using CloudSQL MySQL's conflict resolution\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ndovzTB0mLdg"
+      },
+      "source": [
+        "## Adding Embeddings to Existing Database Records <a name=\"integration\" target=\"_blank\"></a>\n",
+        "\n",
+        "This section demonstrates how to:\n",
+        "1. Read existing product data from a database\n",
+        "2. Generate embeddings for that data\n",
+        "3. Write the embeddings back to the database"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "l3-wl9e1fjms"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"existing_products\"\n",
+        "table_schema = \"\"\"\n",
+        "    id VARCHAR(255) PRIMARY KEY,\n",
+        "    title VARCHAR(255) NOT NULL,\n",
+        "    description TEXT,\n",
+        "    price DECIMAL,\n",
+        "    embedding VECTOR(384) USING VARBINARY\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2cjjrbjUmaUN",
+        "cellView": "form"
+      },
+      "outputs": [],
+      "source": [
+        "#@title MySQL helper for inserting initial records\n",
+        "import sqlalchemy\n",
+        "from sqlalchemy import text\n",
+        "from sqlalchemy.exc import SQLAlchemyError\n",
+        "# The google.cloud.sql.connector and a driver like PyMySQL (`pip install pymysql`)\n",
+        "# are required for this to connect to Cloud SQL.\n",
+        "from google.cloud.sql.connector import Connector\n",
+        "\n",
+        "# Assume get_db_engine is defined elsewhere to connect using a MySQL dialect,\n",
+        "# e.g., 'mysql+pymysql'\n",
+        "# from your_utils import get_db_engine\n",
+        "\n",
+        "def setup_initial_data_sqlalchemy(connection_name: str,\n",
+        "                                  database: str,\n",
+        "                                  table_name: str,\n",
+        "                                  table_schema: str,\n",
+        "                                  user: str,\n",
+        "                                  password: str,\n",
+        "                                  **connect_kwargs):\n",
+        "    \"\"\"Sets up a table and inserts sample data into a MySQL database using SQLAlchemy.\n",
+        "\n",
+        "    This function will drop the specified table if it exists, recreate it based on the\n",
+        "    provided schema, and insert a predefined set of sample products.\n",
+        "\n",
+        "    Args:\n",
+        "        connection_name: Cloud SQL MySQL instance connection name string.\n",
+        "        database: Name of the database.\n",
+        "        table_name: Name of the table to create and populate.\n",
+        "        table_schema: A string containing MySQL-compatible column definitions\n",
+        "                      (e.g., \"id VARCHAR(255) PRIMARY KEY, title VARCHAR(255)\").\n",
+        "        user: Database username.\n",
+        "        password: Database password.\n",
+        "        connect_kwargs: Additional keyword arguments for the Cloud SQL connector.\n",
+        "    \"\"\"\n",
+        "    engine = None\n",
+        "    try:\n",
+        "        # Assumes get_db_engine returns a SQLAlchemy engine configured for MySQL\n",
+        "        engine = get_db_engine(connection_name, user, password, database, **connect_kwargs)\n",
+        "\n",
+        "        with engine.connect() as connection:\n",
+        "            print(\"✅ Connected to Cloud SQL MySQL successfully via SQLAlchemy!\")\n",
+        "\n",
+        "            # DDL operations (DROP/CREATE) in MySQL cause an implicit commit,\n",
+        "            # so they are run outside an explicit transaction block.\n",
+        "            print(f\"Dropping table `{table_name}` if it exists...\")\n",
+        "            connection.execute(text(f\"DROP TABLE IF EXISTS `{table_name}`;\"))\n",
+        "\n",
+        "            print(f\"Creating table `{table_name}`...\")\n",
+        "            # Note: Ensure the table_schema and sample data columns match.\n",
+        "            create_sql = f\"CREATE TABLE `{table_name}` ({table_schema});\"\n",
+        "            connection.execute(text(create_sql))\n",
+        "            print(f\"Table `{table_name}` created.\")\n",
+        "\n",
+        "            # Define the sample data to be inserted.\n",
+        "            sample_products_dicts = [\n",
+        "                 {\n",
+        "                    \"id\": \"lamp-001\", \"title\": \"Artisan Table Lamp\",\n",
+        "                    \"description\": \"Hand-crafted ceramic...\", \"price\": 129.99\n",
+        "                 },\n",
+        "                 {\n",
+        "                    \"id\": \"mirror-001\", \"title\": \"Floating Wall Mirror\",\n",
+        "                    \"description\": \"Modern circular mirror...\", \"price\": 199.99\n",
+        "                 },\n",
+        "                 {\n",
+        "                     \"id\": \"vase-001\", \"title\": \"Contemporary Ceramic Vase\",\n",
+        "                     \"description\": \"Minimalist vase...\", \"price\": 79.99\n",
+        "                 }\n",
+        "            ]\n",
+        "\n",
+        "            # The INSERT statement uses named placeholders matching the dictionary keys.\n",
+        "            insert_sql = text(f\"\"\"\n",
+        "                INSERT INTO `{table_name}` (id, title, description, price)\n",
+        "                VALUES (:id, :title, :description, :price)\n",
+        "            \"\"\")\n",
+        "\n",
+        "            print(f\"Inserting sample data into `{table_name}`...\")\n",
+        "            # SQLAlchemy executes the insert for each dictionary in the list.\n",
+        "            # This runs within a new transaction block started by the `connect()` context.\n",
+        "            connection.execute(insert_sql, sample_products_dicts)\n",
+        "\n",
+        "            # Explicitly commit the transaction that contains the INSERT statements.\n",
+        "            connection.commit()\n",
+        "            print(\"✓ Sample products inserted successfully.\")\n",
+        "\n",
+        "    except SQLAlchemyError as e:\n",
+        "        print(f\"❌ An SQLAlchemy error occurred during setup: {e}\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"❌ An unexpected error occurred during setup: {e}\")\n",
+        "    finally:\n",
+        "        if engine:\n",
+        "            # Dispose of the engine to close all connections in the pool.\n",
+        "            engine.dispose()\n",
+        "            print(\"Database engine pool disposed.\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HjHOJ0sRmrwu"
+      },
+      "outputs": [],
+      "source": [
+        "setup_initial_data_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, table_schema, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MCN0mI08m0Ba"
+      },
+      "source": [
+        "### Read from Database and Generate Embeddings\n",
+        "\n",
+        "Now let's create a pipeline to read the existing data, generate embeddings, and write back:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Q2gY_kh1m08Z"
+      },
+      "outputs": [],
+      "source": [
+        "from apache_beam.io.jdbc import ReadFromJdbc\n",
+        "from apache_beam.io.jdbc import WriteToJdbc\n",
+        "from apache_beam.ml.rag.ingestion.mysql_common import ColumnSpecsBuilder\n",
+        "\n",
+        "# Configure database writer\n",
+        "cloudsql_writer_config = CloudSQLMySQLVectorWriterConfig(\n",
+        "    connection_config=LanguageConnectorConfig(\n",
+        "        username=DB_USER,\n",
+        "        password=DB_PASSWORD,\n",
+        "        database_name=DB_NAME,\n",
+        "        instance_name=CONNECTION_NAME\n",
+        "    ),\n",
+        "    table_name=table_name,\n",
+        "    column_specs=(\n",
+        "        ColumnSpecsBuilder()\n",
+        "          .with_id_spec()\n",
+        "          .with_embedding_spec()\n",
+        "          # Add a placeholder value for the title column, because it has a\n",
+        "          # NOT NULL constraint. Insert with Conflict resolution statements in\n",
+        "          # MySQL requires all NOT NULL fields to have a value, even if the\n",
+        "          # value will not be updated (the original title is preserved).\n",
+        "          .add_custom_column_spec(\n",
+        "            ColumnSpec.text(\"title\", value_fn=lambda x: \"\")\n",
+        "           )\n",
+        "          .build()\n",
+        "    ),\n",
+        "    conflict_resolution=ConflictResolution(\n",
+        "        action=\"UPDATE\",\n",
+        "        update_fields=[\"embedding\"]  # Update the embedding field\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "# Create and run pipeline  on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    # Read existing products\n",
+        "    rows = (\n",
+        "        p\n",
+        "        | \"Read Products\" >> ReadFromJdbc(\n",
+        "            table_name=table_name,\n",
+        "            driver_class_name=\"com.mysql.cj.jdbc.Driver\",\n",
+        "            jdbc_url=cloudsql_writer_config.connector_config.to_connection_config(\n",
+        "              ).jdbc_url,\n",
+        "            username=DB_USER,\n",
+        "            password=DB_PASSWORD,\n",
+        "            query=f\"SELECT id, title, description FROM {table_name}\",\n",
+        "            classpath=cloudsql_writer_config.connector_config.additional_jdbc_args()['classpath']\n",
+        "        )\n",
+        "    )\n",
+        "\n",
+        "    # Generate and write embeddings\n",
+        "    _ = (\n",
+        "        rows\n",
+        "        | \"Convert to Chunks\" >> beam.Map(lambda row: Chunk(\n",
+        "              id=row.id,\n",
+        "              content=Content(text=f\"{row.title}: {row.description}\")\n",
+        "            )\n",
+        "          )\n",
+        "        | \"Generate Embeddings\" >> MLTransform(\n",
+        "            write_artifact_location=tempfile.mkdtemp()\n",
+        "        ).with_transform(HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "        | \"Write Back to CloudSQL MySQL\" >> VectorDatabaseWriteTransform(\n",
+        "            cloudsql_writer_config\n",
+        "        )\n",
+        "    )\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bBNl5DK3Zh58"
+      },
+      "source": [
+        "### Verify Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "elU53NLtZlTf"
+      },
+      "outputs": [],
+      "source": [
+        "print(\"\\nAfter embedding generation:\")\n",
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZFgpFarCp4Wo"
+      },
+      "source": [
+        "What Happened?\n",
+        "1. We started with a table containing product data but no embeddings\n",
+        "2. Read the existing records using ReadFromJdbc\n",
+        "3. Converted rows to Chunks, combining title and description for embedding\n",
+        "4. Generated embeddings using our model\n",
+        "5. Wrote back to the same table, updating only the embedding field\n",
+        "Preserved all other fields (price, etc.)\n",
+        "\n",
+        "This pattern is useful when:\n",
+        "\n",
+        "- You have an existing product database\n",
+        "- You want to add embeddings without disrupting current data\n",
+        "- You need to maintain existing schema and relationships\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-L8mGusPd83L"
+      },
+      "source": [
+        "## Generate Embeddings with VertexAI Text Embeddings"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dVB1qAARmOlc"
+      },
+      "source": [
+        "This section demonstrates how to use use the Vertex AI text-embeddings API to generate text embeddings that use Googles large generative artificial intelligence (AI) models.\n",
+        "\n",
+        "Vertex AI models are subject to [Rate Limits and Quotas](https://cloud.google.com/vertex-ai/generative-ai/docs/quotas#view-the-quotas-by-region-and-by-model) and Dataflow automatically retries throttled requests with exponential backoff.\n",
+        "\n",
+        "\n",
+        "For more information, see [Get text embeddings](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings) in the Vertex AI documentation."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-eLuZ78Tqm4w"
+      },
+      "source": [
+        "### Authenticate with Google Cloud\n",
+        "To use the Vertex AI API, we authenticate with Google Cloud."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "84p608l4ql8p"
+      },
+      "outputs": [],
+      "source": [
+        "# Replace <PROJECT_ID> with a valid Google Cloud project ID.\n",
+        "PROJECT_ID = '' # @param {type:'string'}\n",
+        "\n",
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9PZVv8S5oTHo"
+      },
+      "source": [
+        "### Create CloudSQL MySQL table with default schema\n",
+        "\n",
+        "First we create a table to store our embeddings:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cEuU4JkVkLBk"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"vertex_product_embeddings\"\n",
+        "table_schema = f\"\"\"\n",
+        "  id VARCHAR(255) PRIMARY KEY,\n",
+        "  embedding VECTOR(768) USING VARBINARY,\n",
+        "  content text,\n",
+        "  metadata JSON\n",
+        "\"\"\"\n",
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QZ7tSAfQpG_Z"
+      },
+      "source": [
+        "### Configure Embedding Handler\n",
+        "\n",
+        "Import the `VertexAITextEmbeddings` handler, and specify the desired `textembedding-gecko` model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ipv7R6G9pqnx"
+      },
+      "outputs": [],
+      "source": [
+        "from apache_beam.ml.rag.embeddings.vertex_ai import VertexAITextEmbeddings\n",
+        "\n",
+        "vertexai_embedder = VertexAITextEmbeddings(model_name=\"text-embedding-005\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D7VoYav9rQJU"
+      },
+      "source": [
+        "### Run the Pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fi5SMGpZrPEm"
+      },
+      "outputs": [],
+      "source": [
+        "import tempfile\n",
+        "\n",
+        "# Executing on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    _ = (\n",
+        "            p\n",
+        "            | 'Create Products' >> beam.Create(PRODUCTS_DATA)\n",
+        "            | 'Convert to Chunks' >> beam.Map(lambda product: Chunk(\n",
+        "                    content=Content(\n",
+        "                        text=f\"{product['name']}: {product['description']}\"\n",
+        "                    ), # The text that will be embedded\n",
+        "                    id=product['id'],  # Use product ID as chunk ID\n",
+        "                    metadata=product,  # Store all product info in metadata\n",
+        "                )\n",
+        "              )\n",
+        "              | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "                .with_transform(\n",
+        "                    vertexai_embedder\n",
+        "                )\n",
+        "              | 'Write to CloudSQL MySQL' >> VectorDatabaseWriteTransform(\n",
+        "                  CloudSQLMySQLVectorWriterConfig(\n",
+        "                    connection_config=LanguageConnectorConfig(\n",
+        "                        username=DB_USER,\n",
+        "                        password=DB_PASSWORD,\n",
+        "                        database_name=DB_NAME,\n",
+        "                        instance_name=CONNECTION_NAME\n",
+        "                    ),\n",
+        "                    table_name=table_name\n",
+        "                  )\n",
+        "              )\n",
+        "          )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9hVYw0rspp7Y"
+      },
+      "source": [
+        "### Verify Embeddings"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xSEY1IILsMvi"
+      },
+      "outputs": [],
+      "source": [
+        "print(\"\\nAfter embedding generation:\")\n",
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yv4Rd1ZvsB_M"
+      },
+      "source": [
+        "## Streaming Embeddings Updates from PubSub\n",
+        "\n",
+        "This section demonstrates how to build a real-time embedding pipeline that continuously processes product updates and maintains fresh embeddings in CloudSQL MySQL. This approach is ideal data that changes frequently.\n",
+        "\n",
+        "This example runs on Dataflow because streaming with DirectRunner and writing via JDBC is not supported.\n",
+        "\n",
+        "### Authenticate with Google Cloud\n",
+        "To use the PubSub, we authenticate with Google Cloud.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VCqJmaznt1nS"
+      },
+      "outputs": [],
+      "source": [
+        "# Replace <PROJECT_ID> with a valid Google Cloud project ID.\n",
+        "PROJECT_ID = '' # @param {type:'string'}\n",
+        "\n",
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2FsoFaugtsln"
+      },
+      "source": [
+        "### Setting Up PubSub Resources\n",
+        "\n",
+        "First, let's set up the necessary PubSub topics and subscriptions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nqMe0Brlt7Bk"
+      },
+      "outputs": [],
+      "source": [
+        "from google.cloud import pubsub_v1\n",
+        "from google.api_core.exceptions import AlreadyExists\n",
+        "import json\n",
+        "\n",
+        "# Define pubsub topic\n",
+        "TOPIC = \"product-updates\" # @param {type:'string'}\n",
+        "\n",
+        "# Create publisher client and topic\n",
+        "publisher = pubsub_v1.PublisherClient()\n",
+        "topic_path = publisher.topic_path(PROJECT_ID, TOPIC)\n",
+        "try:\n",
+        "    topic = publisher.create_topic(request={\"name\": topic_path})\n",
+        "    print(f\"Created topic: {topic.name}\")\n",
+        "except AlreadyExists:\n",
+        "    print(f\"Topic {topic_path} already exists.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "07ZFeGbMuFj_"
+      },
+      "source": [
+        "### Create CloudSQL MySQL Table for Streaming Updates\n",
+        "\n",
+        "Next, create a table to store the embedded data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3Xc70uV_uJy5"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"streaming_product_embeddings\"\n",
+        "table_schema = \"\"\"\n",
+        "  id VARCHAR(255) PRIMARY KEY,\n",
+        "  embedding VECTOR(384) USING VARBINARY,\n",
+        "  content text,\n",
+        "  metadata JSON,\n",
+        "  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8HPhUfAuorBP"
+      },
+      "outputs": [],
+      "source": [
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LSriDxtsn1wH"
+      },
+      "source": [
+        "### Configure the Pipeline options\n",
+        "To run the pipeline on DataFlow we need\n",
+        "- A gcs bucket for staging DataFlow files. Replace `<BUCKET_NAME>`: the name of a valid Google Cloud Storage bucket. Don't include a gs:// prefix or trailing slashes\n",
+        "- Optionally set the Google Cloud region that you want to run Dataflow in. Replace `<REGION>` with the desired location\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kR0x7vzTrUlZ"
+      },
+      "outputs": [],
+      "source": [
+        "from apache_beam.options.pipeline_options import PipelineOptions, StandardOptions, SetupOptions, GoogleCloudOptions, WorkerOptions\n",
+        "\n",
+        "options = PipelineOptions()\n",
+        "options.view_as(StandardOptions).streaming = True\n",
+        "\n",
+        "# Provide required pipeline options for the Dataflow Runner.\n",
+        "options.view_as(StandardOptions).runner = \"DataflowRunner\"\n",
+        "\n",
+        "# Set the Google Cloud region that you want to run Dataflow in.\n",
+        "REGION = 'us-central1' # @param {type:'string'}\n",
+        "options.view_as(GoogleCloudOptions).region = REGION\n",
+        "\n",
+        "NETWORK = '' # @param {type:'string'}\n",
+        "if NETWORK:\n",
+        "  options.view_as(WorkerOptions).network = NETWORK\n",
+        "\n",
+        "SUBNETWORK = '' # @param {type:'string'}\n",
+        "if SUBNETWORK:\n",
+        "  options.view_as(WorkerOptions).subnetwork = f\"regions/{REGION}/subnetworks/{SUBNETWORK}\"\n",
+        "\n",
+        "options.view_as(GoogleCloudOptions).project = PROJECT_ID\n",
+        "\n",
+        "BUCKET_NAME = '' # @param {type:'string'}\n",
+        "dataflow_gcs_location = \"gs://%s/dataflow\" % BUCKET_NAME\n",
+        "\n",
+        "# The Dataflow staging location. This location is used to stage the Dataflow pipeline and the SDK binary.\n",
+        "options.view_as(GoogleCloudOptions).staging_location = '%s/staging' % dataflow_gcs_location\n",
+        "\n",
+        "# The Dataflow temp location. This location is used to store temporary files or intermediate results before outputting to the sink.\n",
+        "options.view_as(GoogleCloudOptions).temp_location = '%s/temp' % dataflow_gcs_location\n",
+        "\n",
+        "import random\n",
+        "options.view_as(GoogleCloudOptions).job_name = f\"cloudsql-streaming-embedding-ingest{random.randint(0,1000)}\"\n",
+        "\n",
+        "# options.view_as(SetupOptions).save_main_session = True\n",
+        "options.view_as(SetupOptions).requirements_file = \"./requirements.txt\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gMKuccfHoDki"
+      },
+      "source": [
+        "### Provide additional Python dependencies to be installed on Worker VM's\n",
+        "\n",
+        "We are making use of the HuggingFace `sentence-transformers` package to generate embeddings. Since this package is not installed on Worker VM's by default, we create a requirements.txt file with the additional dependencies to be installed on worker VM's.\n",
+        "\n",
+        "See [Managing Python Pipeline Dependencies](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/) for more details.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RTGoA0SmoEvm"
+      },
+      "outputs": [],
+      "source": [
+        "!echo \"sentence-transformers\" > ./requirements.txt\n",
+        "!cat ./requirements.txt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eU0Sn19nqzLM"
+      },
+      "source": [
+        "### Configure and Run Pipeline\n",
+        "\n",
+        "Our pipeline contains these key components:\n",
+        "\n",
+        "1. **Source**: Continuously reads messages from PubSub\n",
+        "2. **Windowing**: Groups messages into 10-second windows for batch processing\n",
+        "3. **Transformation**: Converts JSON messages to Chunk objects for embedding\n",
+        "4. **ML Processing**: Generates embeddings using HuggingFace models\n",
+        "5. **Sink**: Writes results to CloudSQL MySQL with conflict resolution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "w2pmJn5fqXHx"
+      },
+      "outputs": [],
+      "source": [
+        "import apache_beam as beam\n",
+        "import tempfile\n",
+        "import json\n",
+        "\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "from apache_beam.ml.rag.types import Chunk, Content\n",
+        "from apache_beam.ml.rag.ingestion.base import VectorDatabaseWriteTransform\n",
+        "from apache_beam.ml.rag.ingestion.cloudsql import CloudSQLMySQLVectorWriterConfig\n",
+        "from apache_beam.ml.rag.ingestion.cloudsql import LanguageConnectorConfig\n",
+        "\n",
+        "from apache_beam.ml.rag.ingestion.mysql_common import ConflictResolution\n",
+        "\n",
+        "from apache_beam.ml.rag.embeddings.huggingface import HuggingfaceTextEmbeddings\n",
+        "from apache_beam.transforms.window import FixedWindows\n",
+        "\n",
+        "def parse_message(message):\n",
+        "  #Parse a message containing product data.\n",
+        "  product_json = json.loads(message.decode('utf-8'))\n",
+        "  return Chunk(\n",
+        "      content=Content(\n",
+        "          text=f\"{product_json.get('name', '')}: {product_json.get('description', '')}\"\n",
+        "      ),\n",
+        "      id=product_json.get('id', ''),\n",
+        "      metadata=product_json\n",
+        "  )\n",
+        "\n",
+        "pipeline = beam.Pipeline(options=options)\n",
+        "# Streaming pipeline\n",
+        "_ = (\n",
+        "    pipeline\n",
+        "    | \"Read from PubSub\" >> beam.io.ReadFromPubSub(\n",
+        "        topic=f\"projects/{PROJECT_ID}/topics/{TOPIC}\"\n",
+        "    )\n",
+        "    | \"Window\" >> beam.WindowInto(FixedWindows(10))\n",
+        "    | \"Parse Messages\" >> beam.Map(parse_message)\n",
+        "    | \"Generate Embeddings\" >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "        .with_transform(HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "    | \"Write to CloudSQL MySQL\" >> VectorDatabaseWriteTransform(\n",
+        "        CloudSQLMySQLVectorWriterConfig(\n",
+        "            connection_config=LanguageConnectorConfig(\n",
+        "                username=DB_USER,\n",
+        "                password=DB_PASSWORD,\n",
+        "                database_name=DB_NAME,\n",
+        "                instance_name=CONNECTION_NAME\n",
+        "            ),\n",
+        "            table_name=table_name,\n",
+        "            conflict_resolution=ConflictResolution(\n",
+        "                on_conflict_fields=\"id\",\n",
+        "                action=\"UPDATE\",\n",
+        "                update_fields=[\"embedding\", \"content\", \"metadata\"]\n",
+        "            )\n",
+        "        )\n",
+        "    )\n",
+        ")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r7nJdc09vs98"
+      },
+      "source": [
+        "### Create Publisher Subprocess\n",
+        "The publisher simulates real-time product updates by:\n",
+        "- Publishing sample product data to the PubSub topic every 5 seconds\n",
+        "- Modifying prices and descriptions to represent changes\n",
+        "- Adding timestamps to track update times\n",
+        "- Running for 25 minutes in the background while our pipeline processes the data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "C9Bf0Nb0vY7r"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Define PubSub publisher function\n",
+        "import threading\n",
+        "import time\n",
+        "import json\n",
+        "import logging\n",
+        "from google.cloud import pubsub_v1\n",
+        "import datetime\n",
+        "import os\n",
+        "import sys\n",
+        "log_file = os.path.join(os.getcwd(), \"publisher_log.txt\")\n",
+        "\n",
+        "print(f\"Log file will be created at: {log_file}\")\n",
+        "\n",
+        "def publisher_function(project_id, topic):\n",
+        "    \"\"\"Function that publishes sample product updates to a PubSub topic.\n",
+        "\n",
+        "    This function runs in a separate thread and continuously publishes\n",
+        "    messages to simulate real-time product updates.\n",
+        "    \"\"\"\n",
+        "    time.sleep(300)\n",
+        "    thread_id = threading.current_thread().ident\n",
+        "\n",
+        "    process_log_file = os.path.join(os.getcwd(), f\"publisher_{thread_id}.log\")\n",
+        "\n",
+        "    file_handler = logging.FileHandler(process_log_file)\n",
+        "    file_handler.setFormatter(logging.Formatter('%(asctime)s - ThreadID:%(thread)d - %(levelname)s - %(message)s'))\n",
+        "\n",
+        "    logger = logging.getLogger(f\"worker.{thread_id}\")\n",
+        "    logger.setLevel(logging.INFO)\n",
+        "    logger.addHandler(file_handler)\n",
+        "\n",
+        "    logger.info(f\"Publisher thread started with ID: {thread_id}\")\n",
+        "    file_handler.flush()\n",
+        "\n",
+        "    publisher = pubsub_v1.PublisherClient()\n",
+        "    topic_path = publisher.topic_path(project_id, topic)\n",
+        "\n",
+        "    logger.info(\"Starting to publish messages...\")\n",
+        "    file_handler.flush()\n",
+        "    for i in range(300):\n",
+        "        message_index = i % len(PRODUCTS_DATA)\n",
+        "        message = PRODUCTS_DATA[message_index].copy()\n",
+        "\n",
+        "\n",
+        "        dynamic_factor = 1.05 + (0.1 * ((i % 20) / 20))\n",
+        "        message[\"price\"] = round(message[\"price\"] * dynamic_factor, 2)\n",
+        "        message[\"description\"] = f\"PRICE UPDATE (factor: {dynamic_factor:.3f}): \" + message[\"description\"]\n",
+        "\n",
+        "        message[\"published_at\"] = datetime.datetime.now().isoformat()\n",
+        "\n",
+        "        data = json.dumps(message).encode('utf-8')\n",
+        "        publish_future = publisher.publish(topic_path, data)\n",
+        "\n",
+        "        try:\n",
+        "            logger.info(f\"Publishing message {message}\")\n",
+        "            file_handler.flush()\n",
+        "            message_id = publish_future.result()\n",
+        "            logger.info(f\"Published message {i+1}: {message['id']} (Message ID: {message_id})\")\n",
+        "            file_handler.flush()\n",
+        "        except Exception as e:\n",
+        "            logger.error(f\"Error publishing message: {e}\")\n",
+        "            file_handler.flush()\n",
+        "\n",
+        "        time.sleep(5)\n",
+        "\n",
+        "    logger.info(\"Finished publishing all messages.\")\n",
+        "    file_handler.flush()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jnUSynmjEmVr"
+      },
+      "source": [
+        "#### Start publishing to PuBSub in background"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZnBBTwZHw7Ex"
+      },
+      "outputs": [],
+      "source": [
+        "# Launch publisher in a separate thread\n",
+        "print(\"Starting publisher thread in 5 minutes...\")\n",
+        "publisher_thread = threading.Thread(\n",
+        "    target=publisher_function,\n",
+        "    args=(PROJECT_ID, TOPIC),\n",
+        "    daemon=True\n",
+        ")\n",
+        "publisher_thread.start()\n",
+        "print(f\"Publisher thread started with ID: {publisher_thread.ident}\")\n",
+        "print(f\"Publisher thread logging to file: publisher_{publisher_thread.ident}.log\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vGToqM9GoKOV"
+      },
+      "source": [
+        "### Run Pipeline on Dataflow\n",
+        "\n",
+        "We launch the pipeline to run remotely on Dataflow. Once the job is launched, you can monitor its progress in the Google Cloud Console:\n",
+        "1. Go to https://console.cloud.google.com/dataflow/jobs\n",
+        "2. Select your project\n",
+        "3. Click on the job named \"cloudsql-streaming-embedding-ingest\"\n",
+        "4. View detailed execution graphs, logs, and metrics\n",
+        "\n",
+        "**Note**: This streaming pipeline runs indefinitely until manually stopped. Be sure to monitor usage and terminate the job in the [dataflow job console](https://console.cloud.google.com/dataflow/jobs) when finished testing to avoid unnecessary costs.\n",
+        "\n",
+        "### What to Expect\n",
+        "After running this pipeline, you should see:\n",
+        "- Continuous updates to product embeddings in the CloudSQL MySQL table\n",
+        "- Price and description changes reflected in the metadata\n",
+        "- New embeddings generated for updated product descriptions\n",
+        "- Timestamps showing when each record was last modified"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NTibYI9rx46o"
+      },
+      "outputs": [],
+      "source": [
+        "# Run pipeline\n",
+        "pipeline.run().wait_until_finish()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vX9VxJ82CTum"
+      },
+      "source": [
+        "### Verify data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zSb1UoCSznkW"
+      },
+      "outputs": [],
+      "source": [
+        "# Verify the results\n",
+        "print(\"\\nAfter embedding generation:\")\n",
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [
+        "mcZATJbaOec0"
+      ],
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/examples/notebooks/beam-ml/cloudsql_postgres_product_catalog_embeddings.ipynb b/examples/notebooks/beam-ml/cloudsql_postgres_product_catalog_embeddings.ipynb
new file mode 100644
index 000000000000..6ac2d9b3a763
--- /dev/null
+++ b/examples/notebooks/beam-ml/cloudsql_postgres_product_catalog_embeddings.ipynb
@@ -0,0 +1,2770 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "8ZekaWhZH2SX"
+      },
+      "outputs": [],
+      "source": [
+        "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+        "\n",
+        "# Licensed to the Apache Software Foundation (ASF) under one\n",
+        "# or more contributor license agreements. See the NOTICE file\n",
+        "# distributed with this work for additional information\n",
+        "# regarding copyright ownership. The ASF licenses this file\n",
+        "# to you under the Apache License, Version 2.0 (the\n",
+        "# \"License\"); you may not use this file except in compliance\n",
+        "# with the License. You may obtain a copy of the License at\n",
+        "#\n",
+        "#   http://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing,\n",
+        "# software distributed under the License is distributed on an\n",
+        "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+        "# KIND, either express or implied. See the License for the\n",
+        "# specific language governing permissions and limitations\n",
+        "# under the License"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K6-p-DVrIFTY"
+      },
+      "source": [
+        "# Vector Embedding Ingestion with Apache Beam and CloudSQL Postgres\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/cloudsql_postgres_product_catalog_embeddings.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/cloudsql_postgres_product_catalog_embeddings.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WWwFCLRHZPm4"
+      },
+      "source": [
+        "# Introduction\n",
+        "\n",
+        "This Colab demonstrates how to generate embeddings from data and ingest them into [CloudSQL Postgres](https://cloud.google.com/sql/docs/postgres). We'll use Apache Beam and Dataflow for scalable data processing.\n",
+        "\n",
+        "The goal of this notebook is to make it easy for users to get started with generating embeddings at scale using Apache Beam and storing them in CloudSQL Postgres. We focus on building efficient ingestion pipelines that can handle various data sources and embedding models.\n",
+        "\n",
+        "## Example: Furniture Product Catalog\n",
+        "\n",
+        "We'll work with a sample e-commerce dataset representing a furniture product catalog. Each product has:\n",
+        "\n",
+        "*   **Structured fields:** `id`, `name`, `category`, `price`\n",
+        "*   **Detailed text descriptions:** Longer text describing the product's features.\n",
+        "*   **Additional metadata:** `material`, `dimensions`\n",
+        "\n",
+        "## Pipeline Overview\n",
+        "We will build a pipeline to:\n",
+        "1. Read product data\n",
+        "2. Convert unstructured product data, to `Chunk`<sup>[1]</sup> type\n",
+        "2. Generate Embeddings: Use a pre-trained Hugging Face model (via MLTransform) to create vector embeddings\n",
+        "3. Write to CloudSQL Postgres: Store the embeddings in an CloudSQL Postgres vector database\n",
+        "\n",
+        "Here's a visualization of the data flow:\n",
+        "\n",
+        "| Stage                     | Data Representation                                      | Notes                                                                                                                   |\n",
+        "| :------------------------ | :------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------- |\n",
+        "| **1. Ingest Data**      | `{`<br> `  \"id\": \"desk-001\",`<br> `  \"name\": \"Modern Desk\",`<br> `  \"description\": \"Sleek...\",`<br> `  \"category\": \"Desks\",`<br> `  ...`<br> `}` | Supports:<br>- Reading from batch (e.g., files, databases)<br>- Streaming sources (e.g., Pub/Sub).                 |\n",
+        "| **2. Convert to Chunks** | `Chunk(` <br>  &nbsp;&nbsp;`id=\"desk-001\",` <br>  &nbsp;&nbsp;`content=Content(` <br>   &nbsp;&nbsp;&nbsp;&nbsp;`text=\"Modern Desk\"` <br> &nbsp;&nbsp; `),` <br>  &nbsp;&nbsp;`metadata={...}` <br> `)`       | - `Chunk` is the structured input for generating and ingesting embeddings.<br>- `chunk.content.text` is the field that is embedded.<br> - Converting to `Chunk` does not mean breaking data into smaller pieces,<br>&nbsp;&nbsp; it's simply organizing your data in a standard format for the embedding pipeline.<br> - `Chunk` allows data to flow seamlessly throughout embedding pipelines. |\n",
+        "| **3. Generate Embeddings**| `Chunk(` <br>  &nbsp;&nbsp;`id=\"desk-001\",`<br>  &nbsp;&nbsp;`embedding=[-0.1, 0.6, ...],`<br>  `...)`  | Supports:<br>- Local Hugging Face models<br>- Remote Vertex AI models<br>- Custom embedding implementations.          |\n",
+        "| **4. Write to CloudSQL Postgres** | **CloudSQL Postgres Table (Example Row):**<br>`id: desk-001`<br>`embedding: [-0.1, 0.6, ...]`<br> `name = \"Modern Desk\"`,<br>`Other fields ...` | Supports:<br>- Custom schemas<br>- Conflict resolution strategies for handling updates                               |\n",
+        "\n",
+        "\n",
+        "[1]: Chunk represents an embeddable unit of input. It specifies which fields should be embedded and which fields should be treated as metadata. Converting to Chunk does not necessarily mean breaking your text into smaller pieces - it's primarily about structuring your data for the embedding pipeline. For very long texts that exceed the embedding model's maximum input size, you can optionally [use Langchain TextSplitters](https://beam.apache.org/releases/pydoc/2.63.0/apache_beam.ml.rag.chunking.langchain.html) to break the text into smaller `Chunk`'s.\n",
+        "\n",
+        "## Execution Environments\n",
+        "\n",
+        "This notebook demonstrates two execution environments:\n",
+        "\n",
+        "1. **DirectRunner (Local Execution)**: All examples in this notebook run on DirectRunner by default, which executes the pipeline locally. This is ideal for development, testing, and processing small datasets.\n",
+        "\n",
+        "2. **DataflowRunner (Distributed Execution)**: The [Run on Dataflow](#scrollTo=Quick_Start_Run_on_Dataflow) section demonstrates how to execute the same pipeline on Google Cloud Dataflow for scalable, distributed processing. This is recommended for production workloads and large datasets.\n",
+        "\n",
+        "All examples in this notebook can be adapted to run on Dataflow by following the pattern shown in the \"Run on Dataflow\" section."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z2eAyRECIP3z"
+      },
+      "source": [
+        "# Connecting Apache Beam to CloudSQL Postgres\n",
+        "\n",
+        "Beam uses the [CloudSQL Postgres Java Connector](https://github.com/GoogleCloudPlatform/cloud-sql-jdbc-socket-factory/blob/main/docs/jdbc.md) to securely establish a connection to your database. Apache Beam supports any parameters that can be passed to the Java Connector e.g. IP types.\n",
+        "\n",
+        "# Setup and Prerequisites\n",
+        "\n",
+        "This example requires:\n",
+        "1. An CloudSQL Postgres instance with pgvector extension\n",
+        "2. Apache Beam 2.66.0 or later\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WhOOPUBa6PyW"
+      },
+      "source": [
+        "## Install Packages and Dependencies\n",
+        "\n",
+        "First, let's install the Python packages required for the embedding and ingestion pipeline:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gCWRw2YE11wN"
+      },
+      "outputs": [],
+      "source": [
+        "# Apache Beam with GCP support\n",
+        "!pip install apache_beam[interactive,gcp]>=2.66.0 --quiet\n",
+        "# Huggingface sentence-transformers for embedding models\n",
+        "!pip install sentence-transformers --quiet"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2FlMPmA0IUuv"
+      },
+      "outputs": [],
+      "source": [
+        "!pip show apache-beam"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4aqYZ_pG1oYb"
+      },
+      "source": [
+        "Next, let's install cloud-sql-python-connector to help set up our test database."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eOYjnVDR87IE"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install \"cloud-sql-python-connector[pg8000]>=1.0.0,<2.0.0\" sqlalchemy --quiet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VhgbpTKzI-zI"
+      },
+      "source": [
+        "## Database Setup\n",
+        "\n",
+        "To connect to CloudSQL Postgres, you'll need:\n",
+        "1. GCP project ID where the CloudSQL Postgres instance is located\n",
+        "2. The CloudSQL Postgres connection URI. This is the fully qualified connection name of the CloudSQL Postgres instance found in the google cloud console under CloudSQL > Instances > Instance > Connect to this Instance > Connection name.\n",
+        "3. Database name. This is the name of the postgres database within your CloudSQL Postgres instance. The default database name is postgres.\n",
+        "4. Database credentials\n",
+        "5. The pgvector extension enabled in your database\n",
+        "\n",
+        "Replace these placeholder values with your actual CloudSQL Postgres connection details:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oqKQT0c_JB5f"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"\" # @param {type:'string'}\n",
+        "\n",
+        "CONNECTION_NAME = \"<project>:<region>:<instance>\" # @param {type:'string'}\n",
+        "\n",
+        "DB_NAME = \"postgres\" #  @param {type:'string'}\n",
+        "\n",
+        "DB_USER = \"postgres\" # @param {type:'string'}\n",
+        "\n",
+        "DB_PASSWORD = \"\" # @param {type:'string'}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "doK840yZZNdl"
+      },
+      "source": [
+        "## Authenticate to Google Cloud\n",
+        "\n",
+        "To connect to the CloudSQL Postgres instance via the language conenctor, we authenticate with Google Cloud."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CLM12rbiZHTN"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "l_BBCKl7KKcb"
+      },
+      "outputs": [],
+      "source": [
+        "# @title SQLAlchemy + CloudSQL Postgres Connector helpers for creating tables and verifying data\n",
+        "\n",
+        "import sqlalchemy\n",
+        "from sqlalchemy import text # Import text construct explicitly\n",
+        "from sqlalchemy.exc import SQLAlchemyError # Import specific exception type\n",
+        "from google.cloud.sql.connector import Connector\n",
+        "\n",
+        "def get_db_engine(connection_name: str, user: str, password: str, db: str, **connect_kwargs) -> sqlalchemy.engine.Engine:\n",
+        "    \"\"\"Creates a SQLAlchemy engine configured for CloudSQL Postgres.\"\"\"\n",
+        "    connector = Connector()\n",
+        "    connect_kwargs.setdefault('ip_type', 'PUBLIC')\n",
+        "    def get_conn() -> sqlalchemy.engine.base.Connection:\n",
+        "        conn = connector.connect(\n",
+        "            connection_name,\n",
+        "            \"pg8000\",\n",
+        "            user=user,\n",
+        "            password=password,\n",
+        "            db=db,\n",
+        "            **connect_kwargs # Pass additional options like ip_type='PUBLIC' if needed\n",
+        "        )\n",
+        "        return conn\n",
+        "\n",
+        "    # Create the SQLAlchemy engine using the connection function\n",
+        "    engine = sqlalchemy.create_engine(\n",
+        "        \"postgresql+pg8000://\",\n",
+        "        creator=get_conn,\n",
+        "    )\n",
+        "    engine.pool.dispose = lambda: connector.close()\n",
+        "\n",
+        "    return engine\n",
+        "\n",
+        "\n",
+        "def setup_db_table_sqlalchemy(connection_name: str,\n",
+        "                                   database: str,\n",
+        "                                   table_name: str,\n",
+        "                                   table_schema: str,\n",
+        "                                   user: str,\n",
+        "                                   password: str,\n",
+        "                                   **connect_kwargs):\n",
+        "    \"\"\"Set up CloudSQL Postgres table with vector extension and proper schema using SQLAlchemy.\n",
+        "\n",
+        "    Args:\n",
+        "        connection_name: CloudSQL Postgres instance URI (e.g., projects/.../locations/.../clusters/.../instances/...)\n",
+        "        database: Database name\n",
+        "        table_name: Name of the table to create.\n",
+        "        table_schema: SQL string defining the table columns (e.g., \"id SERIAL PRIMARY KEY, embedding VECTOR(768)\")\n",
+        "        user: Database user\n",
+        "        password: Database password\n",
+        "        connect_kwargs: Additional keyword arguments passed to connector.connect() (e.g., ip_type=\"PUBLIC\")\n",
+        "    \"\"\"\n",
+        "    engine = None\n",
+        "    try:\n",
+        "        engine = get_db_engine(connection_name, user, password, database, **connect_kwargs)\n",
+        "\n",
+        "        # Use a connection from the pool\n",
+        "        with engine.connect() as connection:\n",
+        "            # Use execution options for autocommit for DDL statements\n",
+        "            # Alternatively, execute outside an explicit transaction block (begin())\n",
+        "            with connection.execution_options(isolation_level=\"AUTOCOMMIT\"):\n",
+        "                print(\"Connected to DB successfully via SQLAlchemy!\")\n",
+        "\n",
+        "                # Create pgvector extension if it doesn't exist\n",
+        "                print(\"Creating pgvector extension...\")\n",
+        "                connection.execute(text(\"CREATE EXTENSION IF NOT EXISTS vector;\"))\n",
+        "\n",
+        "                # Drop the table if it exists\n",
+        "                print(f\"Dropping table {table_name} if exists...\")\n",
+        "                # Use f-string for table name (generally okay for DDL if source is trusted)\n",
+        "                connection.execute(text(f\"DROP TABLE IF EXISTS {table_name};\"))\n",
+        "\n",
+        "                # Create the table\n",
+        "                print(f\"Creating table {table_name}...\")\n",
+        "                # Use f-string for table name and schema (validate input if necessary)\n",
+        "                create_sql = f\"\"\"\n",
+        "                CREATE TABLE IF NOT EXISTS {table_name} (\n",
+        "                    {table_schema}\n",
+        "                );\n",
+        "                \"\"\"\n",
+        "                connection.execute(text(create_sql))\n",
+        "\n",
+        "            # Optional: Commit if not using autocommit (SQLAlchemy >= 2.0 often commits implicitly)\n",
+        "            # connection.commit() # Usually not needed with autocommit or implicit commit behavior\n",
+        "\n",
+        "        print(\"Setup completed successfully using SQLAlchemy!\")\n",
+        "\n",
+        "    except SQLAlchemyError as e:\n",
+        "        print(f\"An SQLAlchemy error occurred during setup: {e}\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"An unexpected error occurred during setup: {e}\")\n",
+        "    finally:\n",
+        "        if engine:\n",
+        "            engine.dispose() # Close connection pool and connector\n",
+        "\n",
+        "def test_db_connection_sqlalchemy(connection_name: str,\n",
+        "                                       database: str,\n",
+        "                                       table_name: str,\n",
+        "                                       user: str,\n",
+        "                                       password: str,\n",
+        "                                       **connect_kwargs):\n",
+        "    \"\"\"Test the CloudSQL Postgres connection and verify table/extension using SQLAlchemy.\n",
+        "\n",
+        "     Args:\n",
+        "        connection_name: CloudSQL Postgres instance URI\n",
+        "        database: Database name\n",
+        "        table_name: Name of the table to check.\n",
+        "        user: Database user\n",
+        "        password: Database password\n",
+        "        connect_kwargs: Additional keyword arguments passed to connector.connect()\n",
+        "    \"\"\"\n",
+        "    engine = None\n",
+        "    try:\n",
+        "        engine = get_db_engine(connection_name, user, password, database, **connect_kwargs)\n",
+        "\n",
+        "        with engine.connect() as connection:\n",
+        "            print(\"Testing connection...\")\n",
+        "            # Simple query to confirm connection\n",
+        "            connection.execute(text(\"SELECT 1\"))\n",
+        "            print(\"✓ Connection successful\")\n",
+        "\n",
+        "            # Check if table exists using information_schema\n",
+        "            # Use bind parameters (:tname) for safety, even though it's a table name here\n",
+        "            table_exists_query = text(\"\"\"\n",
+        "                SELECT EXISTS (\n",
+        "                    SELECT FROM information_schema.tables\n",
+        "                    WHERE table_schema = 'public' AND table_name = :tname\n",
+        "                );\n",
+        "            \"\"\")\n",
+        "            # .scalar() fetches the first column of the first row\n",
+        "            table_exists = connection.execute(table_exists_query, {\"tname\": table_name}).scalar()\n",
+        "\n",
+        "            if table_exists:\n",
+        "                print(f\"✓ '{table_name}' table exists\")\n",
+        "\n",
+        "                # Check if vector extension is installed\n",
+        "                ext_exists_query = text(\"\"\"\n",
+        "                    SELECT EXISTS (\n",
+        "                        SELECT FROM pg_extension WHERE extname = 'vector'\n",
+        "                    );\n",
+        "                \"\"\")\n",
+        "                vector_installed = connection.execute(ext_exists_query).scalar()\n",
+        "\n",
+        "                if vector_installed:\n",
+        "                    print(\"✓ pgvector extension is installed\")\n",
+        "                else:\n",
+        "                    print(\"✗ pgvector extension is NOT installed\")\n",
+        "            else:\n",
+        "                print(f\"✗ '{table_name}' table does NOT exist\")\n",
+        "\n",
+        "    except SQLAlchemyError as e:\n",
+        "        print(f\"Connection test failed (SQLAlchemy error): {e}\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"Connection test failed (Unexpected error): {e}\")\n",
+        "    finally:\n",
+        "        if engine:\n",
+        "            engine.dispose()\n",
+        "\n",
+        "def verify_embeddings_sqlalchemy(connection_name: str,\n",
+        "                                 database: str,\n",
+        "                                 table_name: str,\n",
+        "                                 user: str,\n",
+        "                                 password: str,\n",
+        "                                 **connect_kwargs):\n",
+        "    \"\"\"Connect to CloudSQL Postgres using SQLAlchemy and print all rows from the table.\"\"\"\n",
+        "    engine = None\n",
+        "    try:\n",
+        "        engine = get_db_engine(connection_name, user, password, database, **connect_kwargs)\n",
+        "\n",
+        "        with engine.connect() as connection:\n",
+        "            # Use f-string for table name in SELECT (ensure table_name is controlled)\n",
+        "            select_query = text(f\"SELECT * FROM {table_name};\")\n",
+        "            result = connection.execute(select_query)\n",
+        "\n",
+        "            # Get column names from the result keys\n",
+        "            columns = result.keys()\n",
+        "            # Fetch all rows as mapping objects (dict-like)\n",
+        "            rows = result.mappings().all()\n",
+        "\n",
+        "            print(f\"\\nFound {len(rows)} products in '{table_name}':\")\n",
+        "            print(\"-\" * 80)\n",
+        "\n",
+        "            if not rows:\n",
+        "                print(\"Table is empty.\")\n",
+        "                print(\"-\" * 80)\n",
+        "            else:\n",
+        "                # Print each row\n",
+        "                for row in rows:\n",
+        "                    for col in columns:\n",
+        "                        print(f\"{col}: {row[col]}\")\n",
+        "                    print(\"-\" * 80)\n",
+        "\n",
+        "    except SQLAlchemyError as e:\n",
+        "        print(f\"Failed to verify embeddings (SQLAlchemy error): {e}\")\n",
+        "        # You might want to check specifically for ProgrammingError if the table doesn't exist\n",
+        "        # from sqlalchemy.exc import ProgrammingError\n",
+        "        # except ProgrammingError as pe:\n",
+        "        #    print(f\"Failed to query table '{table_name}'. Does it exist? Error: {pe}\")\n",
+        "    except Exception as e:\n",
+        "        print(f\"Failed to verify embeddings (Unexpected error): {e}\")\n",
+        "    finally:\n",
+        "        if engine:\n",
+        "            engine.dispose()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "70z2O4nbOuaM"
+      },
+      "source": [
+        "## Create Sample Product Catalog Data\n",
+        "\n",
+        "We'll create a typical e-commerce catalog where you might want to:\n",
+        "- Generate embeddings for product text\n",
+        "- Store vectors alongside product data\n",
+        "- Enable vector similarity features\n",
+        "\n",
+        "Example product:\n",
+        "```python\n",
+        "{\n",
+        "    \"id\": \"desk-001\",\n",
+        "    \"name\": \"Modern Minimalist Desk\",\n",
+        "    \"description\": \"Sleek minimalist desk with clean lines and a spacious work surface. \"\n",
+        "                  \"Features cable management system and sturdy steel frame. \"\n",
+        "                  \"Perfect for contemporary home offices and workspaces.\",\n",
+        "    \"category\": \"Desks\",\n",
+        "    \"price\": 399.99,\n",
+        "    \"material\": \"Engineered Wood, Steel\",\n",
+        "    \"dimensions\": \"60W x 30D x 29H inches\"\n",
+        "}\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7_J__S8JOwJ_"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Create sample data\n",
+        "PRODUCTS_DATA = [\n",
+        "    {\n",
+        "        \"id\": \"desk-001\",\n",
+        "        \"name\": \"Modern Minimalist Desk\",\n",
+        "        \"description\": \"Sleek minimalist desk with clean lines and a spacious work surface. \"\n",
+        "                      \"Features cable management system and sturdy steel frame. \"\n",
+        "                      \"Perfect for contemporary home offices and workspaces.\",\n",
+        "        \"category\": \"Desks\",\n",
+        "        \"price\": 399.99,\n",
+        "        \"material\": \"Engineered Wood, Steel\",\n",
+        "        \"dimensions\": \"60W x 30D x 29H inches\"\n",
+        "    },\n",
+        "    {\n",
+        "        \"id\": \"chair-001\",\n",
+        "        \"name\": \"Ergonomic Mesh Office Chair\",\n",
+        "        \"description\": \"Premium ergonomic office chair with breathable mesh back, \"\n",
+        "                      \"adjustable lumbar support, and 4D armrests. Features synchronized \"\n",
+        "                      \"tilt mechanism and memory foam seat cushion. Ideal for long work hours.\",\n",
+        "        \"category\": \"Office Chairs\",\n",
+        "        \"price\": 299.99,\n",
+        "        \"material\": \"Mesh, Metal, Premium Foam\",\n",
+        "        \"dimensions\": \"26W x 26D x 48H inches\"\n",
+        "    },\n",
+        "    {\n",
+        "        \"id\": \"sofa-001\",\n",
+        "        \"name\": \"Contemporary Sectional Sofa\",\n",
+        "        \"description\": \"Modern L-shaped sectional with chaise lounge. Upholstered in premium \"\n",
+        "                      \"performance fabric. Features deep seats, plush cushions, and solid \"\n",
+        "                      \"wood legs. Perfect for modern living rooms.\",\n",
+        "        \"category\": \"Sofas\",\n",
+        "        \"price\": 1299.99,\n",
+        "        \"material\": \"Performance Fabric, Solid Wood\",\n",
+        "        \"dimensions\": \"112W x 65D x 34H inches\"\n",
+        "    },\n",
+        "    {\n",
+        "        \"id\": \"table-001\",\n",
+        "        \"name\": \"Rustic Dining Table\",\n",
+        "        \"description\": \"Farmhouse-style dining table with solid wood construction. \"\n",
+        "                      \"Features distressed finish and trestle base. Seats 6-8 people \"\n",
+        "                      \"comfortably. Perfect for family gatherings.\",\n",
+        "        \"category\": \"Dining Tables\",\n",
+        "        \"price\": 899.99,\n",
+        "        \"material\": \"Solid Pine Wood\",\n",
+        "        \"dimensions\": \"72W x 42D x 30H inches\"\n",
+        "    },\n",
+        "    {\n",
+        "        \"id\": \"bed-001\",\n",
+        "        \"name\": \"Platform Storage Bed\",\n",
+        "        \"description\": \"Modern queen platform bed with integrated storage drawers. \"\n",
+        "                      \"Features upholstered headboard and durable wood slat support. \"\n",
+        "                      \"No box spring needed. Perfect for maximizing bedroom space.\",\n",
+        "        \"category\": \"Beds\",\n",
+        "        \"price\": 799.99,\n",
+        "        \"material\": \"Engineered Wood, Linen Fabric\",\n",
+        "        \"dimensions\": \"65W x 86D x 48H inches\"\n",
+        "    }\n",
+        "]\n",
+        "print(f\"\"\"✓ Created PRODUCTS_DATA with {len(PRODUCTS_DATA)} records\"\"\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KUHPsWzQFKpL"
+      },
+      "source": [
+        "## Importing Pipeline Components\n",
+        "\n",
+        "We import the following for configuring our embedding ingestion pipeline:\n",
+        "- `apache_beam.ml.rag.types.Chunk`, the structured input for generating and ingesting embeddings\n",
+        "- `apache_beam.ml.rag.ingestion.cloudsql.CloudSQLPostgresVectorWriterConfig` for configuring write behavior like schema mapping and conflict resolution\n",
+        "- `apache_beam.ml.rag.ingestion.cloudsql.LanguageConnectorConfig` to connect using the [CloudSQL Postgres language connector](https://github.com/GoogleCloudPlatform/cloud-sql-jdbc-socket-factory/blob/main/docs/jdbc.md)\n",
+        "- `apache_beam.ml.rag.ingestion.base import VectorDatabaseWriteTransform` to perform the write step using CloudSQL Postgres configs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fFMjPZaelTi2"
+      },
+      "outputs": [],
+      "source": [
+        "# CloudSQL imports\n",
+        "from apache_beam.ml.rag.ingestion.cloudsql import CloudSQLPostgresVectorWriterConfig\n",
+        "from apache_beam.ml.rag.ingestion.cloudsql import LanguageConnectorConfig\n",
+        "\n",
+        "\n",
+        "from apache_beam.ml.rag.ingestion.base import VectorDatabaseWriteTransform\n",
+        "from apache_beam.ml.rag.types import Chunk, Content\n",
+        "from apache_beam.ml.rag.embeddings.huggingface import HuggingfaceTextEmbeddings\n",
+        "\n",
+        "# Apache Beam core\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.options.pipeline_options import PipelineOptions\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "\n",
+        "# JDBC and Postgres utilities\n",
+        "from apache_beam.ml.rag.ingestion.jdbc_common import WriteConfig\n",
+        "from apache_beam.ml.rag.ingestion.postgres_common import ColumnSpecsBuilder, ConflictResolution"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FjUzsUtXzFof"
+      },
+      "source": [
+        "# What's next?\n",
+        "\n",
+        "This colab covers several use cases that you can explore based on your needs after completing the Setup and Prerequisites:\n",
+        "\n",
+        "🔰 **New to vector embeddings?**\n",
+        "- [Start with Quick Start](#scrollTo=Quick_Start_Basic_Vector_Ingestion)\n",
+        "- Uses simple out-of-box schema\n",
+        "- Perfect for initial testing\n",
+        "\n",
+        "🚀 **Need to scale to large datasets?**\n",
+        "- [Go to Run on Dataflow](#scrollTo=Quick_Start_Run_on_Dataflow)\n",
+        "- Learn how to execute the same pipeline at scale\n",
+        "- Fully managed\n",
+        "- Process large datasets efficiently\n",
+        "\n",
+        "🎯 **Have a specific schema?**\n",
+        "- [Go to Custom Schema](#scrollTo=Custom_Schema_with_Column_Mapping)\n",
+        "- Learn to use different column names\n",
+        "- Map metadata to individual columns\n",
+        "\n",
+        "🔄 **Need to update embeddings?**\n",
+        "- [Check out Updating Embeddings](#scrollTo=Update_Embeddings_and_Metadata_with_Conflict_Resolution)\n",
+        "- Handle conflicts\n",
+        "- Selective field updates\n",
+        "\n",
+        "🔗 **Need to generate and Store Embeddings for Existing CloudSQL Postgres Data??**\n",
+        "- [See Database Integration](#scrollTo=Adding_Embeddings_to_Existing_Database_Records)\n",
+        "- Read data from your CloudSQL Postgres table.\n",
+        "- Generate embeddings for the relevant fields.\n",
+        "- Update your table (or a related table) with the generated embeddings.\n",
+        "\n",
+        "🤖 **Want to use Google's AI models?**\n",
+        "- [Try Vertex AI Embeddings](#scrollTo=Generate_Embeddings_with_VertexAI_Text_Embeddings)\n",
+        "- Use Google's powerful embedding models\n",
+        "- Seamlessly integrate with other Google Cloud services\n",
+        "\n",
+        "🔄 Need real-time embedding  updates?\n",
+        "\n",
+        "- [Try Streaming Embeddings from PubSub](#scrollTo=Streaming_Embeddings_Updates_from_PubSub)\n",
+        "- Process continuous data streams\n",
+        "- Update embeddings in real-time as information changes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pLEi3Z4wKMOX"
+      },
+      "source": [
+        "<a name=\"quickstart\"></a>\n",
+        "# Quick Start: Basic Vector Ingestion\n",
+        "\n",
+        "This section shows the simplest way to generate embeddings and store them in CloudSQL Postgres."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LWqEgqjQOcbA"
+      },
+      "source": [
+        "## Create table with default schema\n",
+        "\n",
+        "Before running the pipeline, we need a table to store our embeddings:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "93YnjdJkFWOi"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"default_product_embeddings\"\n",
+        "table_schema = f\"\"\"\n",
+        "  id VARCHAR PRIMARY KEY,\n",
+        "  embedding VECTOR(384) NOT NULL,\n",
+        "  content text,\n",
+        "  metadata JSONB\n",
+        "\"\"\"\n",
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DikTnoGbOioG"
+      },
+      "source": [
+        "## Configure Pipeline Components\n",
+        "\n",
+        "Now define the components that control the pipeline behavior:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "M8rVyZ6o-Nep"
+      },
+      "source": [
+        "### Convert ingested product data to embeddable Chunks\n",
+        "- Our data is ingested as product dictionaries\n",
+        "- Embedding generation and ingestion processes `Chunks`\n",
+        "- We convert each product dictionary to a `Chunk` to configure what text to embed and what to treat as metadata"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Rm_IX5U6mP_r"
+      },
+      "outputs": [],
+      "source": [
+        "from typing import Dict, Any\n",
+        "\n",
+        "# The create_chunk function converts our product dictionaries to Chunks.\n",
+        "# This doesn't split the text - it simply structures it in the format\n",
+        "# expected by the embedding pipeline components.\n",
+        "def create_chunk(product: Dict[str, Any]) -> Chunk:\n",
+        "    \"\"\"Convert a product dictionary into a Chunk object.\n",
+        "\n",
+        "       The pipeline components (MLTransform, VectorDatabaseWriteTransform)\n",
+        "       work with Chunk objects. This function:\n",
+        "       1. Extracts text we want to embed\n",
+        "       2. Preserves product data as metadata\n",
+        "       3. Creates a Chunk in the expected format\n",
+        "\n",
+        "    Args:\n",
+        "        product: Dictionary containing product information\n",
+        "\n",
+        "    Returns:\n",
+        "        Chunk: A Chunk object ready for embedding\n",
+        "    \"\"\"\n",
+        "    return Chunk(\n",
+        "        content=Content(\n",
+        "            text=f\"{product['name']}: {product['description']}\"\n",
+        "        ), # The text that will be embedded\n",
+        "        id=product['id'],  # Use product ID as chunk ID\n",
+        "        metadata=product,  # Store all product info in metadata\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xJaI9m3D7Vw-"
+      },
+      "source": [
+        "### Generate embeddings with HuggingFace"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0dlm1fjQh2dX"
+      },
+      "source": [
+        "We use a local pre-trained Hugging Face model to create vector embeddings from the product descriptions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "E5LkHmjV7l2S"
+      },
+      "outputs": [],
+      "source": [
+        "huggingface_embedder = HuggingfaceTextEmbeddings(\n",
+        "    model_name=\"sentence-transformers/all-MiniLM-L6-v2\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vVv8hD5wQo3w"
+      },
+      "source": [
+        "### Write to CloudSQL Postgres\n",
+        "\n",
+        "The default CloudSQLPostgresVectorWriterConfig maps Chunk fields to database columns as:\n",
+        "\n",
+        "| Database Column | Chunk Field | Description |\n",
+        "|----------------|-------------|-------------|\n",
+        "| id             | chunk.id    | Unique identifier |\n",
+        "| embedding      | chunk.embedding.dense_embedding | Vector representation |\n",
+        "| content        | chunk.content.text | Text that was embedded |\n",
+        "| metadata       | chunk.metadata | Additional data as JSONB |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "moKsz_6xQt-E"
+      },
+      "outputs": [],
+      "source": [
+        "# Configure the language connector so we can connect securely\n",
+        "connector_config = LanguageConnectorConfig(\n",
+        "    username=DB_USER,\n",
+        "    password=DB_PASSWORD,\n",
+        "    database_name=DB_NAME,\n",
+        "    instance_name=CONNECTION_NAME\n",
+        ")\n",
+        "cloudsql_writer_config = CloudSQLPostgresVectorWriterConfig(\n",
+        "    connection_config=connector_config,\n",
+        "    table_name=table_name\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ww2BPxTNKmL2"
+      },
+      "source": [
+        "## Assemble and Run Pipeline\n",
+        "\n",
+        "Now we can create our pipeline that:\n",
+        "1. Takes our product data\n",
+        "2. Converts each product to a Chunk\n",
+        "3. Generates embeddings for each Chunk\n",
+        "4. Stores everything in CloudSQL Postgres"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lyS3IpNBDgYw"
+      },
+      "outputs": [],
+      "source": [
+        "import tempfile\n",
+        "\n",
+        "# Executing on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    _ = (\n",
+        "            p\n",
+        "            | 'Create Products' >> beam.Create(PRODUCTS_DATA)\n",
+        "            | 'Convert to Chunks' >> beam.Map(create_chunk)\n",
+        "            | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "              .with_transform(huggingface_embedder)\n",
+        "            | 'Write to CloudSQL' >> VectorDatabaseWriteTransform(\n",
+        "                cloudsql_writer_config\n",
+        "            )\n",
+        "        )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Qm97EAww6RvW"
+      },
+      "source": [
+        "## Verify Embeddings\n",
+        "Let's check what was written to our CloudSQL Postgres table:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-H3t2cIN6lO_"
+      },
+      "outputs": [],
+      "source": [
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lz5itufZ31KB"
+      },
+      "source": [
+        "## Quick Start Summary\n",
+        "\n",
+        "In this section, you learned how to:\n",
+        "- Convert product data to the Chunk format expected by embedding pipelines\n",
+        "- Generate embeddings using a HuggingFace model\n",
+        "- Configure and run a basic embedding ingestion pipeline\n",
+        "- Store embeddings and metadata in CloudSQL Postgres\n",
+        "\n",
+        "This basic pattern forms the foundation for all the advanced use cases covered in the following sections."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OqojLgpJKUGk"
+      },
+      "source": [
+        "# Quick Start: Run on Dataflow\n",
+        "\n",
+        "This section demonstrates how to launch the Quick Start embedding pipeline on Google Cloud Dataflow from the colab. While previous examples used DirectRunner for local execution, Dataflow provides a fully managed, distributed execution environment that is:\n",
+        "- Scalable: Automatically scales to handle large datasets\n",
+        "- Fault-tolerant: Handles worker failures and ensures exactly-once processing\n",
+        "- Fully managed: No need to provision or manage infrastructure\n",
+        "\n",
+        "For more in-depth documentation to package your pipeline into a python file and launch a DataFlow job from the command line see [Create Dataflow pipeline using Python](https://cloud.google.com/dataflow/docs/quickstarts/create-pipeline-python)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zrMJSm-JUVGY"
+      },
+      "source": [
+        "## Create the CloudSQL Postgres table with default schema\n",
+        "\n",
+        "Before running the pipeline, we need a table to store our embeddings:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tgAvMT-yUixY"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"default_dataflow_product_embeddings\"\n",
+        "table_schema = f\"\"\"\n",
+        "  id VARCHAR PRIMARY KEY,\n",
+        "  embedding VECTOR(384) NOT NULL,\n",
+        "  content text,\n",
+        "  metadata JSONB\n",
+        "\"\"\"\n",
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mcZATJbaOec0"
+      },
+      "source": [
+        "## Save our Pipeline to a python file\n",
+        "\n",
+        "To launch our pipeline job on DataFlow, we\n",
+        "1. Add command line arguments for passing pipeline options like CloudSQL Postgres credentioals\n",
+        "2. Save our pipeline code to a local file `basic_ingestion_pipeline.py`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CzhIiBdqOknd"
+      },
+      "outputs": [],
+      "source": [
+        "file_content = \"\"\"\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.options.pipeline_options import PipelineOptions\n",
+        "import argparse\n",
+        "import tempfile\n",
+        "\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "from apache_beam.ml.rag.types import Chunk, Content\n",
+        "from apache_beam.ml.rag.ingestion.base import VectorDatabaseWriteTransform\n",
+        "from apache_beam.ml.rag.ingestion.cloudsql import CloudSQLPostgresVectorWriterConfig, LanguageConnectorConfig\n",
+        "from apache_beam.ml.rag.embeddings.huggingface import HuggingfaceTextEmbeddings\n",
+        "from apache_beam.options.pipeline_options import SetupOptions\n",
+        "\n",
+        "PRODUCTS_DATA = [\n",
+        "    {\n",
+        "        \"id\": \"desk-001\",\n",
+        "        \"name\": \"Modern Minimalist Desk\",\n",
+        "        \"description\": \"Sleek minimalist desk with clean lines and a spacious work surface. \"\n",
+        "                      \"Features cable management system and sturdy steel frame. \"\n",
+        "                      \"Perfect for contemporary home offices and workspaces.\",\n",
+        "        \"category\": \"Desks\",\n",
+        "        \"price\": 399.99,\n",
+        "        \"material\": \"Engineered Wood, Steel\",\n",
+        "        \"dimensions\": \"60W x 30D x 29H inches\"\n",
+        "    },\n",
+        "    {\n",
+        "        \"id\": \"chair-001\",\n",
+        "        \"name\": \"Ergonomic Mesh Office Chair\",\n",
+        "        \"description\": \"Premium ergonomic office chair with breathable mesh back, \"\n",
+        "                      \"adjustable lumbar support, and 4D armrests. Features synchronized \"\n",
+        "                      \"tilt mechanism and memory foam seat cushion. Ideal for long work hours.\",\n",
+        "        \"category\": \"Office Chairs\",\n",
+        "        \"price\": 299.99,\n",
+        "        \"material\": \"Mesh, Metal, Premium Foam\",\n",
+        "        \"dimensions\": \"26W x 26D x 48H inches\"\n",
+        "    }\n",
+        "]\n",
+        "\n",
+        "def run(argv=None):\n",
+        "    parser = argparse.ArgumentParser()\n",
+        "    parser.add_argument(\n",
+        "        '--connection_name',\n",
+        "        required=True,\n",
+        "        help='CloudSQL Postgres instance uri'\n",
+        "    )\n",
+        "    parser.add_argument(\n",
+        "        '--cloudsql_database',\n",
+        "        default='postgres',\n",
+        "        help='CloudSQL Postgres database name'\n",
+        "    )\n",
+        "    parser.add_argument(\n",
+        "        '--cloudsql_table',\n",
+        "        required=True,\n",
+        "        help='CloudSQL Postgres table name'\n",
+        "    )\n",
+        "    parser.add_argument(\n",
+        "        '--cloudsql_username',\n",
+        "        required=True,\n",
+        "        help='CloudSQL Postgres user name'\n",
+        "    )\n",
+        "    parser.add_argument(\n",
+        "        '--cloudsql_password',\n",
+        "        required=True,\n",
+        "        help='CloudSQL Postgres password'\n",
+        "    )\n",
+        "    known_args, pipeline_args = parser.parse_known_args(argv)\n",
+        "\n",
+        "    pipeline_options = PipelineOptions(pipeline_args)\n",
+        "    pipeline_options.view_as(SetupOptions).save_main_session = True\n",
+        "\n",
+        "    with beam.Pipeline(options=pipeline_options) as p:\n",
+        "        _ = (\n",
+        "              p\n",
+        "              | 'Create Products' >> beam.Create(PRODUCTS_DATA)\n",
+        "              | 'Convert to Chunks' >> beam.Map(lambda product: Chunk(\n",
+        "                    content=Content(\n",
+        "                        text=f\"{product['name']}: {product['description']}\"\n",
+        "                    ), # The text that will be embedded\n",
+        "                    id=product['id'],  # Use product ID as chunk ID\n",
+        "                    metadata=product,  # Store all product info in metadata\n",
+        "                )\n",
+        "              )\n",
+        "              | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "                .with_transform(\n",
+        "                    HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+        "                )\n",
+        "              | 'Write to CloudSQL Postgres' >> VectorDatabaseWriteTransform(\n",
+        "                  CloudSQLPostgresVectorWriterConfig(\n",
+        "                      connection_config=LanguageConnectorConfig(\n",
+        "                        username=known_args.cloudsql_username,\n",
+        "                        password=known_args.cloudsql_password,\n",
+        "                        database_name=known_args.cloudsql_database,\n",
+        "                        instance_name=known_args.connection_name\n",
+        "                      ),\n",
+        "                      table_name=known_args.cloudsql_table\n",
+        "                  )\n",
+        "              )\n",
+        "          )\n",
+        "\n",
+        "if __name__ == '__main__':\n",
+        "    run()\n",
+        "\"\"\"\n",
+        "\n",
+        "with open(\"basic_ingestion_pipeline.py\", \"w\") as f:\n",
+        "    f.write(file_content)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y_1IMXx7UuG4"
+      },
+      "source": [
+        "## Authenticate with Google Cloud\n",
+        "\n",
+        "To launch a pipeline on Google Cloud, authenticate this notebook. Replace `<PROJECT_ID` with your Google Cloud project ID"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WxrW-zlgRDLk"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"<project_id>\" # @param {type:'string'}\n",
+        "import os\n",
+        "os.environ['PROJECT_ID'] = PROJECT_ID"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GswFBa10Qxkx"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7sELV2KeRG2c"
+      },
+      "source": [
+        "## Configure the Pipeline options\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nVDW0Q9iS_Pk"
+      },
+      "source": [
+        "To run the pipeline on DataFlow we need\n",
+        "- A gcs bucket for staging DataFlow files. Replace `<BUCKET_NAME>`: the name of a valid Google Cloud Storage bucket.\n",
+        "- Optionally set the Google Cloud region that you want to run Dataflow in. Replace `<REGION>` with the desired location.\n",
+        "- Optionally provide `NETWORK` and `SUBNETWORK` for dataflow workers to run on."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qxFJflLiTMua"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "BUCKET_NAME = '<bucket_name>' # @param {type:'string'}\n",
+        "REGION = 'us-central1' # @param {type:'string'}\n",
+        "\n",
+        "NETWORK = '' # @param {type:'string'}\n",
+        "SUBNETWORK = '' # @param {type:'string'}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WWjjqwV-aJFi"
+      },
+      "source": [
+        "## Provide additional Python dependencies to be installed on Worker VM's\n",
+        "\n",
+        "We are making use of the HuggingFace `sentence-transformers` package to generate embeddings. Since this package is not installed on Worker VM's by default, we create a requirements.txt file with the additional dependencies to be installed on worker VM's.\n",
+        "\n",
+        "See [Managing Python Pipeline Dependencies](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/) for more details.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hkxmk6aTJSKW"
+      },
+      "outputs": [],
+      "source": [
+        "!echo \"sentence-transformers\" > ./requirements.txt\n",
+        "!cat ./requirements.txt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NXgGZeOsY2O7"
+      },
+      "source": [
+        "## Run Pipeline on Dataflow\n",
+        "\n",
+        "We launch the pipeline via the command line, passing\n",
+        "- CloudSQL Postgres pipeline arguments defined in `basic_ingestion_pipeline.py`\n",
+        "- GCP Project ID\n",
+        "- Job Region\n",
+        "- The runner (DataflowRunner)\n",
+        "- Temp and Staging GCS locations for Pipeline artifacts\n",
+        "- Requirement file location for additional dependencies\n",
+        "- (Optional) The VPC network and Subnetwork that has access to the CloudSQL Postgres instance\n",
+        "\n",
+        "Once the job is launched, you can monitor its progress in the Google Cloud Console:\n",
+        "1. Go to https://console.cloud.google.com/dataflow/jobs\n",
+        "2. Select your project\n",
+        "3. Click on the job named \"cloudsql-dataflow-basic-embedding-ingest\"\n",
+        "4. View detailed execution graphs, logs, and metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fUeG_hEb5Qbb"
+      },
+      "outputs": [],
+      "source": [
+        "command_parts = [\n",
+        "    \"python ./basic_ingestion_pipeline.py\",\n",
+        "    f\"--project={PROJECT_ID}\",\n",
+        "    f\"--cloudsql_username={DB_USER}\",\n",
+        "    f\"--connection_name={CONNECTION_NAME}\",\n",
+        "    f\"--cloudsql_password={DB_PASSWORD}\",\n",
+        "    f\"--cloudsql_table=default_dataflow_product_embeddings\",\n",
+        "    f\"--cloudsql_database={DB_NAME}\",\n",
+        "    f\"--job_name=cloudsql-dataflow-basic-embedding-ingest\",\n",
+        "    f\"--region={REGION}\",\n",
+        "    \"--runner=DataflowRunner\",\n",
+        "    f\"--temp_location=gs://{BUCKET_NAME}/temp\",\n",
+        "    f\"--staging_location=gs://{BUCKET_NAME}/staging\",\n",
+        "    \"--requirements_file=requirements.txt\",\n",
+        "]\n",
+        "\n",
+        "if NETWORK:\n",
+        "    command_parts.append(f\"--network={NETWORK}\")\n",
+        "\n",
+        "if SUBNETWORK:\n",
+        "    command_parts.append(f\"--subnetwork=regions/{REGION}/subnetworks/{SUBNETWORK}\")\n",
+        "\n",
+        "final_command = \" \".join(command_parts)\n",
+        "\n",
+        "print(\"Generated command:\\n\", final_command)\n",
+        "!{final_command}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Sp_M6tJbWXTw"
+      },
+      "source": [
+        "## Verify the Written Embeddings\n",
+        "\n",
+        "Let's check what was written to our CloudSQL Postgres table:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "A11PeldtWXvP"
+      },
+      "outputs": [],
+      "source": [
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name='default_dataflow_product_embeddings', user=DB_USER, password=DB_PASSWORD)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-2hTEi-jzYN6"
+      },
+      "source": [
+        "# Advanced Use Cases\n",
+        "\n",
+        "This section demonstrates more complex scenarios for using CloudSQL Postgres with Apache Beam for vector embeddings.\n",
+        "\n",
+        "🎯 **Have a specific schema?**\n",
+        "- [Go to Custom Schema](#scrollTo=Custom_Schema_with_Column_Mapping)\n",
+        "- Learn to use different column names and transform values\n",
+        "- Map metadata to individual columns\n",
+        "\n",
+        "🔄 **Need to update embeddings?**\n",
+        "- [Check out Updating Embeddings](#scrollTo=Update_Embeddings_and_Metadata_with_Conflict_Resolution)\n",
+        "- Handle conflicts\n",
+        "- Selective field updates\n",
+        "\n",
+        "🔗 **Need to generate and Store Embeddings for Existing CloudSQL Postgres Data??**\n",
+        "- [See Database Integration](#scrollTo=Adding_Embeddings_to_Existing_Database_Records)\n",
+        "- Read data from your CloudSQL Postgres table.\n",
+        "- Generate embeddings for the relevant fields.\n",
+        "- Update your table (or a related table) with the generated embeddings.\n",
+        "\n",
+        "🤖 **Want to use Google's AI models?**\n",
+        "- [Try Vertex AI Embeddings](#scrollTo=Generate_Embeddings_with_VertexAI_Text_Embeddings)\n",
+        "- Use Google's powerful embedding models\n",
+        "- Seamlessly integrate with other Google Cloud services\n",
+        "\n",
+        "🔄 Need real-time embedding  updates?\n",
+        "\n",
+        "- [Try Streaming Embeddings from PubSub](#scrollTo=Streaming_Embeddings_Updates_from_PubSub)\n",
+        "- Process continuous data streams\n",
+        "- Update embeddings in real-time as information changes\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qGaH_TqEzn8r"
+      },
+      "source": [
+        "## Custom Schema with Column Mapping <a name=\"custom-schema\"></a>\n",
+        "\n",
+        "In this example, we'll create a custom schema that:\n",
+        "- Uses different column names\n",
+        "- Maps metadata to individual columns\n",
+        "- Uses functions to transform values"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R4d9W6ry_CN8"
+      },
+      "source": [
+        "### ColumnSpec and ColumnSpecsBuilder\n",
+        "\n",
+        "\n",
+        "ColumnSpec specifies how to map data to a database column. For example:\n",
+        "```python\n",
+        "from apache_beam.ml.rag.ingestion.postgres_common import ColumnSpecsBuilder\n",
+        "\n",
+        "ColumnSpec(\n",
+        "    column_name=\"price\",          # Database column\n",
+        "    python_type=float,            # Python Type for the value\n",
+        "    value_fn=lambda c: c.metadata['price'],  # Extract price from Chunk metadata to get actual value\n",
+        "    sql_typecast=\"::decimal\"      # Optional SQL cast\n",
+        ")\n",
+        "```\n",
+        "creates an INSERT statement like:\n",
+        "```sql\n",
+        "INSERT INTO table (price) VALUES (?::decimal)\n",
+        "```\n",
+        "where the `?` placeholder is poulated with the value from our ingested data.\n",
+        "\n",
+        "`ColumnSpecsBuilder` provides a builder and convenience methods to create these `ColumnSpecs`:\n",
+        "\n",
+        "1. Core Field Mapping\n",
+        "   - `with_id_spec()` => Insert chunk.id as text in \"id\" column\n",
+        "   - `with_embedding_spec()` => Insert chunk.embedding as `float[]` in \"embedding\" column\n",
+        "   - `with_content_spec()` => Insert `chunk.content`.text as text in \"content\" column\n",
+        "\n",
+        "   Note: All `with_id_spec`, `with_embedding_spec`, etc. methods allow overriding `column_name`, `python_type`, and `value_fn`.\n",
+        "\n",
+        "2. Metadata Extraction\n",
+        "   - `add_metadata_field`: Creates a column from a `chunk.metadata` field\n",
+        "   - Handles type conversion based on specified SQL type\n",
+        "\n",
+        "3. Custom Fields\n",
+        "   - `add_custom_column_spec`: Grants complete control over mapping `Chunk` data to database rows using `ColumnSpec`\n",
+        "\n",
+        "Now, lets the table to store our embeddings:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6XpYLcCu80Dy"
+      },
+      "source": [
+        "### Create Custom Schema Table"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6bUd6vprzh7O"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"custom_product_embeddings\"\n",
+        "table_schema = \"\"\"\n",
+        "    product_id VARCHAR PRIMARY KEY,\n",
+        "    vector_embedding VECTOR(384) NOT NULL,\n",
+        "    product_name VARCHAR,\n",
+        "    description TEXT,\n",
+        "    price DECIMAL,\n",
+        "    category VARCHAR,\n",
+        "    display_text VARCHAR,\n",
+        "    model_name VARCHAR,\n",
+        "    created_at TIMESTAMP\n",
+        "\"\"\"\n",
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ScCVCZFo-Fcv"
+      },
+      "source": [
+        "### Configure Pipeline Components"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g9-f7tcf-0qC"
+      },
+      "source": [
+        "#### Write to custom schema using ColumnSpecsBuilder\n",
+        "\n",
+        "\n",
+        "We configure ConlumnSpecsBuilder to map data as:\n",
+        "\n",
+        "| Database Column | Chunk Field                               |\n",
+        "|-----------------|-------------------------------------------|\n",
+        "| `product_id`    | `chunk.id`                                |\n",
+        "| `vector_embedding`| `chunk.embedding.dense_embedding`        |\n",
+        "| `description`   | `chunk.content.text`                      |\n",
+        "| `product_name`  | `chunk.metadata['name']`                  |\n",
+        "| `price`         | `chunk.metadata['price']`                 |\n",
+        "| `category`      | `chunk.metadata['category']`              |\n",
+        "| `display_text`  | *Function that combines product name and price* |\n",
+        "| `model_name`    | *Function that returns the model name: \"all-MiniLM-L6-v2\"* |\n",
+        "| `created_at`    | *Function that returns the current timestamp cast to a SQL timestamp* |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TAq6ydMn-5Uu"
+      },
+      "outputs": [],
+      "source": [
+        "from apache_beam.ml.rag.ingestion.postgres_common import ColumnSpecsBuilder\n",
+        "from apache_beam.ml.rag.ingestion.postgres_common import ColumnSpec\n",
+        "from datetime import datetime\n",
+        "\n",
+        "column_specs = (\n",
+        "    ColumnSpecsBuilder()\n",
+        "    # Write chunk.id to a column named \"product_id\"\n",
+        "    .with_id_spec(column_name='product_id')\n",
+        "    # Write chunk.embedding.dense_embedding to a column named \"vector_embedding\"\n",
+        "    .with_embedding_spec(column_name='vector_embedding')\n",
+        "    # Write chunk.content.text to a column named \"description\"\n",
+        "    .with_content_spec(column_name='description')\n",
+        "    # Write chunk.metadata.['product_name'] to a column named \"product_name\"\n",
+        "    .add_metadata_field(\n",
+        "        field='name',\n",
+        "        column_name='product_name',\n",
+        "        python_type=str\n",
+        "    )\n",
+        "    # Write chunk.metadata.['price'] to a column named \"price\"\n",
+        "    .add_metadata_field(\n",
+        "        field='price',\n",
+        "        column_name='price',\n",
+        "        python_type=float\n",
+        "    )\n",
+        "    # Write chunk.metadata.['category'] to a column named \"category\"\n",
+        "    .add_metadata_field(\n",
+        "        field='category',\n",
+        "        column_name='category',\n",
+        "        python_type=str\n",
+        "    )\n",
+        "    # Write custom field using value_fn to column named \"display_text\" using\n",
+        "    # ColumnSpec.text convenience method\n",
+        "    .add_custom_column_spec(\n",
+        "        ColumnSpec.text(\n",
+        "          column_name='display_text',\n",
+        "        value_fn=lambda chunk: \\\n",
+        "          f\"{chunk.metadata['name']} - ${chunk.metadata['price']:.2f}\"\n",
+        "        )\n",
+        "    )\n",
+        "    # Store model used to generate embedding using ColumnSpec constructor\n",
+        "    .add_custom_column_spec(\n",
+        "        ColumnSpec(\n",
+        "          column_name='model_name',\n",
+        "          python_type=str,\n",
+        "          value_fn=lambda _: \"all-MiniLM-L6-v2\"\n",
+        "        )\n",
+        "    )\n",
+        "    .add_custom_column_spec(\n",
+        "        ColumnSpec(\n",
+        "          column_name='created_at',\n",
+        "          python_type=str,\n",
+        "          value_fn=lambda _: datetime.now().isoformat(),\n",
+        "          sql_typecast=\"::timestamp\"\n",
+        "        )\n",
+        "    )\n",
+        "    .build()\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MBfLVL6XX2mF"
+      },
+      "source": [
+        "### Assemble and Run Pipeline\n",
+        "\n",
+        "Now we can create our pipeline that will:\n",
+        "1. Take our product data\n",
+        "2. Convert each product to a Chunk\n",
+        "3. Generate embeddings for each Chunk\n",
+        "4. Store everything in CloudSQL Postgres with our custom schema configuration"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4V-ILUlWVVX8"
+      },
+      "outputs": [],
+      "source": [
+        "import tempfile # For storing MLTransform artifacts\n",
+        "\n",
+        "# Executing on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    _ = (\n",
+        "            p\n",
+        "            | 'Create Products' >> beam.Create(PRODUCTS_DATA)\n",
+        "            | 'Convert to Chunks' >> beam.Map(lambda product: Chunk(\n",
+        "                    content=Content(\n",
+        "                        text=f\"{product['name']}: {product['description']}\"\n",
+        "                    ), # The text that will be embedded\n",
+        "                    id=product['id'],  # Use product ID as chunk ID\n",
+        "                    metadata=product,  # Store all product info in metadata\n",
+        "                )\n",
+        "              )\n",
+        "            | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "              .with_transform(HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "            | 'Write to CloudSQL Postgres' >> VectorDatabaseWriteTransform(\n",
+        "                CloudSQLPostgresVectorWriterConfig(\n",
+        "                    connection_config=LanguageConnectorConfig(\n",
+        "                        username=DB_USER,\n",
+        "                        password=DB_PASSWORD,\n",
+        "                        database_name=DB_NAME,\n",
+        "                        instance_name=CONNECTION_NAME\n",
+        "                    ),\n",
+        "                    table_name=table_name,\n",
+        "                    column_specs=column_specs\n",
+        "                )\n",
+        "            )\n",
+        "        )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LCpoJkBpYEsH"
+      },
+      "source": [
+        "### Verify the Written Embeddings\n",
+        "\n",
+        "Let's check what was written to our CloudSQL Postgres table:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B2UDOZL0VZ-p"
+      },
+      "outputs": [],
+      "source": [
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DQyJoyZic9GT"
+      },
+      "source": [
+        "## Update Embeddings and Metadata with Conflict Resolution <a name=\"updating\"></a>\n",
+        "\n",
+        "This section demonstrates how to handle periodic updates to product descriptions and their embeddings using the default schema. We'll show how embeddings and metadata get updated when product descriptions change.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jwLHGKfNdbEG"
+      },
+      "source": [
+        "### Create table with desired schema\n",
+        "\n",
+        "Let's use the same default schema as in Quick Start:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0vK-b4xkXtgJ"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"mutable_product_embeddings\"\n",
+        "table_schema = f\"\"\"\n",
+        "  id VARCHAR PRIMARY KEY,\n",
+        "  embedding VECTOR(384) NOT NULL,\n",
+        "  content text,\n",
+        "  metadata JSONB,\n",
+        "  created_at TIMESTAMP NOT NULL DEFAULT NOW()\n",
+        "\"\"\"\n",
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hhl2URWceSg_"
+      },
+      "source": [
+        "### Sample Data: Day 1 vs Day 2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "t4z8tM_leZV8"
+      },
+      "outputs": [],
+      "source": [
+        "PRODUCTS_DATA_DAY1 = [\n",
+        "    {\n",
+        "        \"id\": \"desk-001\",\n",
+        "        \"name\": \"Modern Minimalist Desk\",\n",
+        "        \"description\": \"Sleek minimalist desk with clean lines and a spacious work surface. \"\n",
+        "                      \"Features cable management system and sturdy steel frame.\",\n",
+        "        \"category\": \"Desks\",\n",
+        "        \"price\": 399.99,\n",
+        "        \"update_timestamp\": \"2024-02-18\"\n",
+        "    }\n",
+        "]\n",
+        "\n",
+        "PRODUCTS_DATA_DAY2 = [\n",
+        "    {\n",
+        "        \"id\": \"desk-001\",  # Same ID as Day 1\n",
+        "        \"name\": \"Modern Minimalist Desk\",\n",
+        "        \"description\": \"Updated: Sleek minimalist desk with built-in wireless charging. \"\n",
+        "                      \"Features cable management system, sturdy steel frame, and Qi charging pad. \"\n",
+        "                      \"Perfect for modern tech-enabled workspaces.\",\n",
+        "        \"category\": \"Smart Desks\",  # Category changed\n",
+        "        \"price\": 449.99,  # Price increased\n",
+        "        \"update_timestamp\": \"2024-02-19\"\n",
+        "    }\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W_UTcRz9eskE"
+      },
+      "source": [
+        "### Configure Pipeline Components"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PWvtwVmUedzw"
+      },
+      "source": [
+        "#### Writer with Conflict Resolution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Y2XEwxw6ee4b"
+      },
+      "outputs": [],
+      "source": [
+        "from apache_beam.ml.rag.ingestion.cloudsql import (\n",
+        "    CloudSQLPostgresVectorWriterConfig,\n",
+        "    LanguageConnectorConfig,\n",
+        ")\n",
+        "from apache_beam.ml.rag.ingestion.postgres_common import ConflictResolution\n",
+        "\n",
+        "# Define how to handle conflicts - update all fields when ID matches\n",
+        "conflict_resolution = ConflictResolution(\n",
+        "    on_conflict_fields=\"id\",  # Identify records by ID\n",
+        "    action=\"UPDATE\",         # Update existing records\n",
+        "    update_fields=[\"embedding\", \"content\", \"metadata\"]\n",
+        ")\n",
+        "\n",
+        "# Create writer config with conflict resolution\n",
+        "cloudsql_writer_config = CloudSQLPostgresVectorWriterConfig(\n",
+        "    connection_config=LanguageConnectorConfig(\n",
+        "        username=DB_USER,\n",
+        "        password=DB_PASSWORD,\n",
+        "        database_name=DB_NAME,\n",
+        "        instance_name=CONNECTION_NAME\n",
+        "    ),\n",
+        "    table_name=table_name,\n",
+        "    conflict_resolution=conflict_resolution,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tzo43G9NfCr5"
+      },
+      "outputs": [],
+      "source": [
+        "huggingface_embedder = HuggingfaceTextEmbeddings(\n",
+        "    model_name=\"sentence-transformers/all-MiniLM-L6-v2\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "axMFW_DufKnO"
+      },
+      "source": [
+        "### Run Day 1 Pipeline\n",
+        "\n",
+        "First, let's ingest our initial product data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eA3TpkHMfLUq"
+      },
+      "outputs": [],
+      "source": [
+        "# Executing on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    _ = (\n",
+        "        p\n",
+        "        | 'Create Day 1 Products' >> beam.Create(PRODUCTS_DATA_DAY1)\n",
+        "        | 'Convert Day 1 to Chunks' >> beam.Map(lambda product: Chunk(\n",
+        "                content=Content(\n",
+        "                    text=f\"{product['name']}: {product['description']}\"\n",
+        "                ), # The text that will be embedded\n",
+        "                id=product['id'],  # Use product ID as chunk ID\n",
+        "                metadata=product,  # Store all product info in metadata\n",
+        "            )\n",
+        "          )\n",
+        "        | 'Generate Day1 Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "          .with_transform(HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "        | 'Write Day 1 to CloudSQL Postgres' >> VectorDatabaseWriteTransform(\n",
+        "            cloudsql_writer_config\n",
+        "        )\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hFjtKX9tZIrI"
+      },
+      "source": [
+        "#### Verify Initial Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lxSyaIhbZG52"
+      },
+      "outputs": [],
+      "source": [
+        "print(\"\\nAfter Day 1 ingestion:\")\n",
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yvOlen9qfSQ4"
+      },
+      "source": [
+        "### Run Day 2 Pipeline\n",
+        "\n",
+        "Now let's process our updated product data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "r19qQs6ifVq1"
+      },
+      "outputs": [],
+      "source": [
+        "# Executing on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    _ = (\n",
+        "        p\n",
+        "        | 'Create Day 2 Products' >> beam.Create(PRODUCTS_DATA_DAY2)\n",
+        "        | 'Convert Day 2 to Chunks' >> beam.Map(lambda product: Chunk(\n",
+        "                    content=Content(\n",
+        "                        text=f\"{product['name']}: {product['description']}\"\n",
+        "                    ), # The text that will be embedded\n",
+        "                    id=product['id'],  # Use product ID as chunk ID\n",
+        "                    metadata=product,  # Store all product info in metadata\n",
+        "                )\n",
+        "              )\n",
+        "        | 'Generate Day 2 Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "          .with_transform(HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "        | 'Write Day 2 to CloudSQL Postgres' >> VectorDatabaseWriteTransform(\n",
+        "            cloudsql_writer_config\n",
+        "        )\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QbcZOIdcZWA6"
+      },
+      "source": [
+        "#### Verify Updated Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_VpqhPAQZD4K"
+      },
+      "outputs": [],
+      "source": [
+        "print(\"\\nAfter Day 2 ingestion:\")\n",
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D5hImiN0fZo5"
+      },
+      "source": [
+        "### What Changed?\n",
+        "\n",
+        "Key points to notice:\n",
+        "\n",
+        "1. The embedding vector changed because the product description was updated\n",
+        "2. The metadata JSONB field contains the updated category, price, and timestamp\n",
+        "3. The content field reflects the new description\n",
+        "4. The original ID remained the same\n",
+        "\n",
+        "This pattern allows you to:\n",
+        "- Update embeddings when source text changes\n",
+        "- Maintain referential integrity with consistent IDs\n",
+        "- Track changes through the metadata field\n",
+        "- Handle conflicts gracefully using CloudSQL Postgres's conflict resolution\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ndovzTB0mLdg"
+      },
+      "source": [
+        "## Adding Embeddings to Existing Database Records <a name=\"integration\" target=\"_blank\"></a>\n",
+        "\n",
+        "This section demonstrates how to:\n",
+        "1. Read existing product data from a database\n",
+        "2. Generate embeddings for that data\n",
+        "3. Write the embeddings back to the database"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "l3-wl9e1fjms"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"existing_products\"\n",
+        "table_schema = \"\"\"\n",
+        "    id VARCHAR PRIMARY KEY,\n",
+        "    title VARCHAR NOT NULL,\n",
+        "    description TEXT,\n",
+        "    price DECIMAL,\n",
+        "    embedding VECTOR(384)\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "2cjjrbjUmaUN"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Postgres helpers for inserting initial records\n",
+        "import sqlalchemy\n",
+        "from sqlalchemy import text\n",
+        "from sqlalchemy.exc import SQLAlchemyError\n",
+        "from google.cloud.sql.connector import Connector\n",
+        "\n",
+        "def setup_initial_data_sqlalchemy(connection_name: str,\n",
+        "                                  database: str,\n",
+        "                                  table_name: str,\n",
+        "                                  table_schema: str,\n",
+        "                                  user: str,\n",
+        "                                  password: str,\n",
+        "                                  **connect_kwargs):\n",
+        "    \"\"\"Set up table and insert sample product data using SQLAlchemy.\n",
+        "\n",
+        "    (Revised to handle potential connection closing issue after DDL)\n",
+        "\n",
+        "    Args:\n",
+        "        connection_name: CloudSQL Postgres instance URI\n",
+        "        database: Database name\n",
+        "        table_name: Name of the table to create and populate.\n",
+        "        table_schema: SQL string defining the table columns.\n",
+        "        user: Database user\n",
+        "        password: Database password\n",
+        "        connect_kwargs: Additional keyword arguments for connector.connect().\n",
+        "    \"\"\"\n",
+        "    engine = None\n",
+        "    try:\n",
+        "        engine = get_db_engine(connection_name, user, password, database, **connect_kwargs)\n",
+        "\n",
+        "        # Use a single connection for both DDL and DML\n",
+        "        with engine.connect() as connection:\n",
+        "            print(\"Connected to CloudSQL Postgres successfully via SQLAlchemy!\")\n",
+        "\n",
+        "            # === DDL Operations (Relying on implicit autocommit for DDL) ===\n",
+        "            # Execute DDL directly on the connection outside an explicit transaction.\n",
+        "            # SQLAlchemy + Postgres drivers usually handle this correctly.\n",
+        "\n",
+        "            print(\"Ensuring pgvector extension exists...\")\n",
+        "            connection.execute(text(\"CREATE EXTENSION IF NOT EXISTS vector;\"))\n",
+        "\n",
+        "            print(f\"Dropping table {table_name} if exists...\")\n",
+        "            connection.execute(text(f\"DROP TABLE IF EXISTS {table_name};\"))\n",
+        "\n",
+        "            print(f\"Creating table {table_name}...\")\n",
+        "            create_sql = f\"CREATE TABLE {table_name} ({table_schema});\"\n",
+        "            connection.execute(text(create_sql))\n",
+        "            print(f\"Table {table_name} created.\")\n",
+        "\n",
+        "            # === DML Operations (Runs in default transaction started by connect()) ===\n",
+        "            sample_products_dicts = [\n",
+        "                # (Sample data dictionaries as defined in the previous version)\n",
+        "                 {\n",
+        "                    \"id\": \"lamp-001\", \"title\": \"Artisan Table Lamp\",\n",
+        "                    \"description\": \"Hand-crafted ceramic...\", \"price\": 129.99\n",
+        "                 },\n",
+        "                 {\n",
+        "                    \"id\": \"mirror-001\", \"title\": \"Floating Wall Mirror\",\n",
+        "                    \"description\": \"Modern circular mirror...\", \"price\": 199.99\n",
+        "                 },\n",
+        "                 {\n",
+        "                     \"id\": \"vase-001\", \"title\": \"Contemporary Ceramic Vase\",\n",
+        "                     \"description\": \"Minimalist vase...\", \"price\": 79.99\n",
+        "                 }\n",
+        "                 # Add embedding data if needed\n",
+        "            ]\n",
+        "\n",
+        "            insert_sql = text(f\"\"\"\n",
+        "                INSERT INTO {table_name} (id, title, description, price)\n",
+        "                VALUES (:id, :title, :description, :price)\n",
+        "            \"\"\") # Add other columns if needed\n",
+        "\n",
+        "            print(f\"Inserting sample data into {table_name}...\")\n",
+        "            # Execute DML within the connection's transaction\n",
+        "            connection.execute(insert_sql, sample_products_dicts)\n",
+        "\n",
+        "            # Commit the transaction containing the INSERTs\n",
+        "            print(\"Committing transaction...\")\n",
+        "            connection.commit()\n",
+        "            print(\"✓ Sample products inserted successfully\")\n",
+        "\n",
+        "        print(\"Initial data setup completed successfully using SQLAlchemy!\")\n",
+        "\n",
+        "    except SQLAlchemyError as e:\n",
+        "        print(f\"An SQLAlchemy error occurred during initial data setup: {e}\")\n",
+        "        # Note: If an error occurs *before* commit, the transaction is usually\n",
+        "        # rolled back automatically when the 'with engine.connect()' block exits.\n",
+        "    except Exception as e:\n",
+        "        print(f\"An unexpected error occurred during initial data setup: {e}\")\n",
+        "    finally:\n",
+        "        if engine:\n",
+        "            print(\"Disposing engine pool...\")\n",
+        "            engine.dispose()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HjHOJ0sRmrwu"
+      },
+      "outputs": [],
+      "source": [
+        "setup_initial_data_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, table_schema, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MCN0mI08m0Ba"
+      },
+      "source": [
+        "### Read from Database and Generate Embeddings\n",
+        "\n",
+        "Now let's create a pipeline to read the existing data, generate embeddings, and write back:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Q2gY_kh1m08Z"
+      },
+      "outputs": [],
+      "source": [
+        "from apache_beam.io.jdbc import ReadFromJdbc\n",
+        "from apache_beam.io.jdbc import WriteToJdbc\n",
+        "from apache_beam.ml.rag.ingestion.postgres_common import ColumnSpecsBuilder\n",
+        "\n",
+        "# Configure database writer\n",
+        "cloudsql_writer_config = CloudSQLPostgresVectorWriterConfig(\n",
+        "    connection_config=LanguageConnectorConfig(\n",
+        "        username=DB_USER,\n",
+        "        password=DB_PASSWORD,\n",
+        "        database_name=DB_NAME,\n",
+        "        instance_name=CONNECTION_NAME\n",
+        "    ),\n",
+        "    table_name=table_name,\n",
+        "    column_specs=(\n",
+        "        ColumnSpecsBuilder()\n",
+        "          .with_id_spec()\n",
+        "          .with_embedding_spec()\n",
+        "          # Add a placeholder value for the title column, because it has a\n",
+        "          # NOT NULL constraint. Insert with Conflict resolution statements in\n",
+        "          # Postgres requires all NOT NULL fields to have a value, even if the\n",
+        "          # value will not be updated (the original title is preserved).\n",
+        "          .add_custom_column_spec(\n",
+        "            ColumnSpec.text(\"title\", value_fn=lambda x: \"\")\n",
+        "           )\n",
+        "          .build()\n",
+        "    ),\n",
+        "    conflict_resolution=ConflictResolution(\n",
+        "        on_conflict_fields=\"id\",\n",
+        "        action=\"UPDATE\",\n",
+        "        update_fields=[\"embedding\"]  # Update the embedding field\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "# Create and run pipeline  on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    # Read existing products\n",
+        "    rows = (\n",
+        "        p\n",
+        "        | \"Read Products\" >> ReadFromJdbc(\n",
+        "            table_name=table_name,\n",
+        "            driver_class_name=\"org.postgresql.Driver\",\n",
+        "            jdbc_url=cloudsql_writer_config.connector_config.to_connection_config(\n",
+        "              ).jdbc_url,\n",
+        "            username=DB_USER,\n",
+        "            password=DB_PASSWORD,\n",
+        "            query=f\"SELECT id, title, description FROM {table_name}\",\n",
+        "            classpath=cloudsql_writer_config.connector_config.additional_jdbc_args()\n",
+        "              ['classpath']\n",
+        "        )\n",
+        "    )\n",
+        "\n",
+        "    # Generate and write embeddings\n",
+        "    _ = (\n",
+        "        rows\n",
+        "        | \"Convert to Chunks\" >> beam.Map(lambda row: Chunk(\n",
+        "              id=row.id,\n",
+        "              content=Content(text=f\"{row.title}: {row.description}\")\n",
+        "            )\n",
+        "          )\n",
+        "        | \"Generate Embeddings\" >> MLTransform(\n",
+        "            write_artifact_location=tempfile.mkdtemp()\n",
+        "        ).with_transform(HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "        | \"Write Back to CloudSQL Postgres\" >> VectorDatabaseWriteTransform(\n",
+        "            cloudsql_writer_config\n",
+        "        )\n",
+        "    )\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bBNl5DK3Zh58"
+      },
+      "source": [
+        "### Verify Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "elU53NLtZlTf"
+      },
+      "outputs": [],
+      "source": [
+        "print(\"\\nAfter embedding generation:\")\n",
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZFgpFarCp4Wo"
+      },
+      "source": [
+        "What Happened?\n",
+        "1. We started with a table containing product data but no embeddings\n",
+        "2. Read the existing records using ReadFromJdbc\n",
+        "3. Converted rows to Chunks, combining title and description for embedding\n",
+        "4. Generated embeddings using our model\n",
+        "5. Wrote back to the same table, updating only the embedding field\n",
+        "Preserved all other fields (price, etc.)\n",
+        "\n",
+        "This pattern is useful when:\n",
+        "\n",
+        "- You have an existing product database\n",
+        "- You want to add embeddings without disrupting current data\n",
+        "- You need to maintain existing schema and relationships\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-L8mGusPd83L"
+      },
+      "source": [
+        "## Generate Embeddings with VertexAI Text Embeddings"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dVB1qAARmOlc"
+      },
+      "source": [
+        "This section demonstrates how to use use the Vertex AI text-embeddings API to generate text embeddings that use Googles large generative artificial intelligence (AI) models.\n",
+        "\n",
+        "Vertex AI models are subject to [Rate Limits and Quotas](https://cloud.google.com/vertex-ai/generative-ai/docs/quotas#view-the-quotas-by-region-and-by-model) and Dataflow automatically retries throttled requests with exponential backoff.\n",
+        "\n",
+        "\n",
+        "For more information, see [Get text embeddings](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings) in the Vertex AI documentation."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-eLuZ78Tqm4w"
+      },
+      "source": [
+        "### Authenticate with Google Cloud\n",
+        "To use the Vertex AI API, we authenticate with Google Cloud."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "84p608l4ql8p"
+      },
+      "outputs": [],
+      "source": [
+        "# Replace <PROJECT_ID> with a valid Google Cloud project ID.\n",
+        "PROJECT_ID = '<project_id>' # @param {type:'string'}\n",
+        "\n",
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9PZVv8S5oTHo"
+      },
+      "source": [
+        "### Create CloudSQL Postgres table with default schema\n",
+        "\n",
+        "First we create a table to store our embeddings:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cEuU4JkVkLBk"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"vertex_product_embeddings\"\n",
+        "table_schema = f\"\"\"\n",
+        "  id VARCHAR PRIMARY KEY,\n",
+        "  embedding VECTOR(768) NOT NULL,\n",
+        "  content text,\n",
+        "  metadata JSONB\n",
+        "\"\"\"\n",
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QZ7tSAfQpG_Z"
+      },
+      "source": [
+        "### Configure Embedding Handler\n",
+        "\n",
+        "Import the `VertexAITextEmbeddings` handler, and specify the desired `textembedding-gecko` model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ipv7R6G9pqnx"
+      },
+      "outputs": [],
+      "source": [
+        "from apache_beam.ml.rag.embeddings.vertex_ai import VertexAITextEmbeddings\n",
+        "\n",
+        "vertexai_embedder = VertexAITextEmbeddings(model_name=\"text-embedding-005\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D7VoYav9rQJU"
+      },
+      "source": [
+        "### Run the Pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fi5SMGpZrPEm"
+      },
+      "outputs": [],
+      "source": [
+        "import tempfile\n",
+        "\n",
+        "# Executing on DirectRunner (local execution)\n",
+        "with beam.Pipeline() as p:\n",
+        "    _ = (\n",
+        "            p\n",
+        "            | 'Create Products' >> beam.Create(PRODUCTS_DATA)\n",
+        "            | 'Convert to Chunks' >> beam.Map(lambda product: Chunk(\n",
+        "                    content=Content(\n",
+        "                        text=f\"{product['name']}: {product['description']}\"\n",
+        "                    ), # The text that will be embedded\n",
+        "                    id=product['id'],  # Use product ID as chunk ID\n",
+        "                    metadata=product,  # Store all product info in metadata\n",
+        "                )\n",
+        "              )\n",
+        "              | 'Generate Embeddings' >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "                .with_transform(\n",
+        "                    vertexai_embedder\n",
+        "                )\n",
+        "              | 'Write to CloudSQL Postgres' >> VectorDatabaseWriteTransform(\n",
+        "                  CloudSQLPostgresVectorWriterConfig(\n",
+        "                    connection_config=LanguageConnectorConfig(\n",
+        "                        username=DB_USER,\n",
+        "                        password=DB_PASSWORD,\n",
+        "                        database_name=DB_NAME,\n",
+        "                        instance_name=CONNECTION_NAME\n",
+        "                    ),\n",
+        "                    table_name=table_name\n",
+        "                  )\n",
+        "              )\n",
+        "          )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9hVYw0rspp7Y"
+      },
+      "source": [
+        "### Verify Embeddings"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xSEY1IILsMvi"
+      },
+      "outputs": [],
+      "source": [
+        "print(\"\\nAfter embedding generation:\")\n",
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yv4Rd1ZvsB_M"
+      },
+      "source": [
+        "## Streaming Embeddings Updates from PubSub\n",
+        "\n",
+        "This section demonstrates how to build a real-time embedding pipeline that continuously processes product updates and maintains fresh embeddings in CloudSQL Postgres. This approach is ideal data that changes frequently.\n",
+        "\n",
+        "This example runs on Dataflow because streaming with DirectRunner and writing via JDBC is not supported.\n",
+        "\n",
+        "### Authenticate with Google Cloud\n",
+        "To use the PubSub, we authenticate with Google Cloud.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VCqJmaznt1nS"
+      },
+      "outputs": [],
+      "source": [
+        "# Replace <PROJECT_ID> with a valid Google Cloud project ID.\n",
+        "PROJECT_ID = '<project_id>' # @param {type:'string'}\n",
+        "\n",
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "  from google.colab import auth\n",
+        "  auth.authenticate_user(project_id=PROJECT_ID)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2FsoFaugtsln"
+      },
+      "source": [
+        "### Setting Up PubSub Resources\n",
+        "\n",
+        "First, let's set up the necessary PubSub topics and subscriptions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nqMe0Brlt7Bk"
+      },
+      "outputs": [],
+      "source": [
+        "from google.cloud import pubsub_v1\n",
+        "from google.api_core.exceptions import AlreadyExists\n",
+        "import json\n",
+        "\n",
+        "# Define pubsub topic\n",
+        "TOPIC = \"product-updates\" # @param {type:'string'}\n",
+        "\n",
+        "# Create publisher client and topic\n",
+        "publisher = pubsub_v1.PublisherClient()\n",
+        "topic_path = publisher.topic_path(PROJECT_ID, TOPIC)\n",
+        "try:\n",
+        "    topic = publisher.create_topic(request={\"name\": topic_path})\n",
+        "    print(f\"Created topic: {topic.name}\")\n",
+        "except AlreadyExists:\n",
+        "    print(f\"Topic {topic_path} already exists.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "07ZFeGbMuFj_"
+      },
+      "source": [
+        "### Create CloudSQL Postgres Table for Streaming Updates\n",
+        "\n",
+        "Next, create a table to store the embedded data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3Xc70uV_uJy5"
+      },
+      "outputs": [],
+      "source": [
+        "table_name = \"streaming_product_embeddings\"\n",
+        "table_schema = \"\"\"\n",
+        "  id VARCHAR PRIMARY KEY,\n",
+        "  embedding VECTOR(384) NOT NULL,\n",
+        "  content text,\n",
+        "  metadata JSONB,\n",
+        "  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8HPhUfAuorBP"
+      },
+      "outputs": [],
+      "source": [
+        "setup_db_table_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name,table_schema, DB_USER, DB_PASSWORD)\n",
+        "test_db_connection_sqlalchemy(CONNECTION_NAME, DB_NAME, table_name, DB_USER, DB_PASSWORD)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LSriDxtsn1wH"
+      },
+      "source": [
+        "### Configure the Pipeline options\n",
+        "To run the pipeline on DataFlow we need\n",
+        "- A gcs bucket for staging DataFlow files. Replace `<BUCKET_NAME>`: the name of a valid Google Cloud Storage bucket. Don't include a gs:// prefix or trailing slashes\n",
+        "- Optionally set the Google Cloud region that you want to run Dataflow in. Replace `<REGION>` with the desired location\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kR0x7vzTrUlZ"
+      },
+      "outputs": [],
+      "source": [
+        "from apache_beam.options.pipeline_options import PipelineOptions, StandardOptions, SetupOptions, GoogleCloudOptions, WorkerOptions\n",
+        "\n",
+        "options = PipelineOptions()\n",
+        "options.view_as(StandardOptions).streaming = True\n",
+        "\n",
+        "# Provide required pipeline options for the Dataflow Runner.\n",
+        "options.view_as(StandardOptions).runner = \"DataflowRunner\"\n",
+        "\n",
+        "# Set the Google Cloud region that you want to run Dataflow in.\n",
+        "REGION = 'us-central1' # @param {type:'string'}\n",
+        "options.view_as(GoogleCloudOptions).region = REGION\n",
+        "\n",
+        "NETWORK = '' # @param {type:'string'}\n",
+        "if NETWORK:\n",
+        "  options.view_as(WorkerOptions).network = NETWORK\n",
+        "\n",
+        "SUBNETWORK = '' # @param {type:'string'}\n",
+        "if SUBNETWORK:\n",
+        "  options.view_as(WorkerOptions).subnetwork = f\"regions/{REGION}/subnetworks/{SUBNETWORK}\"\n",
+        "\n",
+        "options.view_as(GoogleCloudOptions).project = PROJECT_ID\n",
+        "\n",
+        "BUCKET_NAME = '<bucket_name>' # @param {type:'string'}\n",
+        "dataflow_gcs_location = \"gs://%s/dataflow\" % BUCKET_NAME\n",
+        "\n",
+        "# The Dataflow staging location. This location is used to stage the Dataflow pipeline and the SDK binary.\n",
+        "options.view_as(GoogleCloudOptions).staging_location = '%s/staging' % dataflow_gcs_location\n",
+        "\n",
+        "# The Dataflow temp location. This location is used to store temporary files or intermediate results before outputting to the sink.\n",
+        "options.view_as(GoogleCloudOptions).temp_location = '%s/temp' % dataflow_gcs_location\n",
+        "\n",
+        "import random\n",
+        "options.view_as(GoogleCloudOptions).job_name = f\"cloudsql-streaming-embedding-ingest{random.randint(0,1000)}\"\n",
+        "\n",
+        "# options.view_as(SetupOptions).save_main_session = True\n",
+        "options.view_as(SetupOptions).requirements_file = \"./requirements.txt\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gMKuccfHoDki"
+      },
+      "source": [
+        "### Provide additional Python dependencies to be installed on Worker VM's\n",
+        "\n",
+        "We are making use of the HuggingFace `sentence-transformers` package to generate embeddings. Since this package is not installed on Worker VM's by default, we create a requirements.txt file with the additional dependencies to be installed on worker VM's.\n",
+        "\n",
+        "See [Managing Python Pipeline Dependencies](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/) for more details.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RTGoA0SmoEvm"
+      },
+      "outputs": [],
+      "source": [
+        "!echo \"sentence-transformers\" > ./requirements.txt\n",
+        "!cat ./requirements.txt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eU0Sn19nqzLM"
+      },
+      "source": [
+        "### Configure and Run Pipeline\n",
+        "\n",
+        "Our pipeline contains these key components:\n",
+        "\n",
+        "1. **Source**: Continuously reads messages from PubSub\n",
+        "2. **Windowing**: Groups messages into 10-second windows for batch processing\n",
+        "3. **Transformation**: Converts JSON messages to Chunk objects for embedding\n",
+        "4. **ML Processing**: Generates embeddings using HuggingFace models\n",
+        "5. **Sink**: Writes results to CloudSQL Postgres with conflict resolution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "w2pmJn5fqXHx"
+      },
+      "outputs": [],
+      "source": [
+        "import apache_beam as beam\n",
+        "import tempfile\n",
+        "import json\n",
+        "\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "from apache_beam.ml.rag.types import Chunk, Content\n",
+        "from apache_beam.ml.rag.ingestion.base import VectorDatabaseWriteTransform\n",
+        "from apache_beam.ml.rag.ingestion.cloudsql import CloudSQLPostgresVectorWriterConfig\n",
+        "from apache_beam.ml.rag.ingestion.cloudsql import LanguageConnectorConfig\n",
+        "\n",
+        "from apache_beam.ml.rag.ingestion.postgres_common import ConflictResolution\n",
+        "\n",
+        "from apache_beam.ml.rag.embeddings.huggingface import HuggingfaceTextEmbeddings\n",
+        "from apache_beam.transforms.window import FixedWindows\n",
+        "\n",
+        "def parse_message(message):\n",
+        "  #Parse a message containing product data.\n",
+        "  product_json = json.loads(message.decode('utf-8'))\n",
+        "  return Chunk(\n",
+        "      content=Content(\n",
+        "          text=f\"{product_json.get('name', '')}: {product_json.get('description', '')}\"\n",
+        "      ),\n",
+        "      id=product_json.get('id', ''),\n",
+        "      metadata=product_json\n",
+        "  )\n",
+        "\n",
+        "pipeline = beam.Pipeline(options=options)\n",
+        "# Streaming pipeline\n",
+        "_ = (\n",
+        "    pipeline\n",
+        "    | \"Read from PubSub\" >> beam.io.ReadFromPubSub(\n",
+        "        topic=f\"projects/{PROJECT_ID}/topics/{TOPIC}\"\n",
+        "    )\n",
+        "    | \"Window\" >> beam.WindowInto(FixedWindows(10))\n",
+        "    | \"Parse Messages\" >> beam.Map(parse_message)\n",
+        "    | \"Generate Embeddings\" >> MLTransform(write_artifact_location=tempfile.mkdtemp())\n",
+        "        .with_transform(HuggingfaceTextEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
+        "    | \"Write to CloudSQL Postgres\" >> VectorDatabaseWriteTransform(\n",
+        "        CloudSQLPostgresVectorWriterConfig(\n",
+        "            connection_config=LanguageConnectorConfig(\n",
+        "                username=DB_USER,\n",
+        "                password=DB_PASSWORD,\n",
+        "                database_name=DB_NAME,\n",
+        "                instance_name=CONNECTION_NAME\n",
+        "            ),\n",
+        "            table_name=table_name,\n",
+        "            conflict_resolution=ConflictResolution(\n",
+        "                on_conflict_fields=\"id\",\n",
+        "                action=\"UPDATE\",\n",
+        "                update_fields=[\"embedding\", \"content\", \"metadata\"]\n",
+        "            )\n",
+        "        )\n",
+        "    )\n",
+        ")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r7nJdc09vs98"
+      },
+      "source": [
+        "### Create Publisher Subprocess\n",
+        "The publisher simulates real-time product updates by:\n",
+        "- Publishing sample product data to the PubSub topic every 5 seconds\n",
+        "- Modifying prices and descriptions to represent changes\n",
+        "- Adding timestamps to track update times\n",
+        "- Running for 25 minutes in the background while our pipeline processes the data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "C9Bf0Nb0vY7r"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Define PubSub publisher function\n",
+        "import threading\n",
+        "import time\n",
+        "import json\n",
+        "import logging\n",
+        "from google.cloud import pubsub_v1\n",
+        "import datetime\n",
+        "import os\n",
+        "import sys\n",
+        "log_file = os.path.join(os.getcwd(), \"publisher_log.txt\")\n",
+        "\n",
+        "print(f\"Log file will be created at: {log_file}\")\n",
+        "\n",
+        "def publisher_function(project_id, topic):\n",
+        "    \"\"\"Function that publishes sample product updates to a PubSub topic.\n",
+        "\n",
+        "    This function runs in a separate thread and continuously publishes\n",
+        "    messages to simulate real-time product updates.\n",
+        "    \"\"\"\n",
+        "    time.sleep(300)\n",
+        "    thread_id = threading.current_thread().ident\n",
+        "\n",
+        "    process_log_file = os.path.join(os.getcwd(), f\"publisher_{thread_id}.log\")\n",
+        "\n",
+        "    file_handler = logging.FileHandler(process_log_file)\n",
+        "    file_handler.setFormatter(logging.Formatter('%(asctime)s - ThreadID:%(thread)d - %(levelname)s - %(message)s'))\n",
+        "\n",
+        "    logger = logging.getLogger(f\"worker.{thread_id}\")\n",
+        "    logger.setLevel(logging.INFO)\n",
+        "    logger.addHandler(file_handler)\n",
+        "\n",
+        "    logger.info(f\"Publisher thread started with ID: {thread_id}\")\n",
+        "    file_handler.flush()\n",
+        "\n",
+        "    publisher = pubsub_v1.PublisherClient()\n",
+        "    topic_path = publisher.topic_path(project_id, topic)\n",
+        "\n",
+        "    logger.info(\"Starting to publish messages...\")\n",
+        "    file_handler.flush()\n",
+        "    for i in range(300):\n",
+        "        message_index = i % len(PRODUCTS_DATA)\n",
+        "        message = PRODUCTS_DATA[message_index].copy()\n",
+        "\n",
+        "\n",
+        "        dynamic_factor = 1.05 + (0.1 * ((i % 20) / 20))\n",
+        "        message[\"price\"] = round(message[\"price\"] * dynamic_factor, 2)\n",
+        "        message[\"description\"] = f\"PRICE UPDATE (factor: {dynamic_factor:.3f}): \" + message[\"description\"]\n",
+        "\n",
+        "        message[\"published_at\"] = datetime.datetime.now().isoformat()\n",
+        "\n",
+        "        data = json.dumps(message).encode('utf-8')\n",
+        "        publish_future = publisher.publish(topic_path, data)\n",
+        "\n",
+        "        try:\n",
+        "            logger.info(f\"Publishing message {message}\")\n",
+        "            file_handler.flush()\n",
+        "            message_id = publish_future.result()\n",
+        "            logger.info(f\"Published message {i+1}: {message['id']} (Message ID: {message_id})\")\n",
+        "            file_handler.flush()\n",
+        "        except Exception as e:\n",
+        "            logger.error(f\"Error publishing message: {e}\")\n",
+        "            file_handler.flush()\n",
+        "\n",
+        "        time.sleep(5)\n",
+        "\n",
+        "    logger.info(\"Finished publishing all messages.\")\n",
+        "    file_handler.flush()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jnUSynmjEmVr"
+      },
+      "source": [
+        "#### Start publishing to PuBSub in background"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZnBBTwZHw7Ex"
+      },
+      "outputs": [],
+      "source": [
+        "# Launch publisher in a separate thread\n",
+        "print(\"Starting publisher thread in 5 minutes...\")\n",
+        "publisher_thread = threading.Thread(\n",
+        "    target=publisher_function,\n",
+        "    args=(PROJECT_ID, TOPIC),\n",
+        "    daemon=True\n",
+        ")\n",
+        "publisher_thread.start()\n",
+        "print(f\"Publisher thread started with ID: {publisher_thread.ident}\")\n",
+        "print(f\"Publisher thread logging to file: publisher_{publisher_thread.ident}.log\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vGToqM9GoKOV"
+      },
+      "source": [
+        "### Run Pipeline on Dataflow\n",
+        "\n",
+        "We launch the pipeline to run remotely on Dataflow. Once the job is launched, you can monitor its progress in the Google Cloud Console:\n",
+        "1. Go to https://console.cloud.google.com/dataflow/jobs\n",
+        "2. Select your project\n",
+        "3. Click on the job named \"cloudsql-streaming-embedding-ingest\"\n",
+        "4. View detailed execution graphs, logs, and metrics\n",
+        "\n",
+        "**Note**: This streaming pipeline runs indefinitely until manually stopped. Be sure to monitor usage and terminate the job in the [dataflow job console](https://console.cloud.google.com/dataflow/jobs) when finished testing to avoid unnecessary costs.\n",
+        "\n",
+        "### What to Expect\n",
+        "After running this pipeline, you should see:\n",
+        "- Continuous updates to product embeddings in the CloudSQL Postgres table\n",
+        "- Price and description changes reflected in the metadata\n",
+        "- New embeddings generated for updated product descriptions\n",
+        "- Timestamps showing when each record was last modified"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NTibYI9rx46o"
+      },
+      "outputs": [],
+      "source": [
+        "# Run pipeline\n",
+        "pipeline.run().wait_until_finish()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vX9VxJ82CTum"
+      },
+      "source": [
+        "### Verify data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zSb1UoCSznkW"
+      },
+      "outputs": [],
+      "source": [
+        "# Verify the results\n",
+        "print(\"\\nAfter embedding generation:\")\n",
+        "verify_embeddings_sqlalchemy(connection_name=CONNECTION_NAME, database=DB_NAME, table_name=table_name, user=DB_USER, password=DB_PASSWORD)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [
+        "mcZATJbaOec0"
+      ],
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/examples/notebooks/beam-ml/data_preprocessing/compute_and_apply_vocab.ipynb b/examples/notebooks/beam-ml/data_preprocessing/compute_and_apply_vocab.ipynb
index ecd4ded6e70c..779ec99903f5 100644
--- a/examples/notebooks/beam-ml/data_preprocessing/compute_and_apply_vocab.ipynb
+++ b/examples/notebooks/beam-ml/data_preprocessing/compute_and_apply_vocab.ipynb
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "! pip install apache_beam>=2.53.0 --quiet\n",
+        "! pip install apache_beam[interactive]>=2.53.0 --quiet\n",
         "! pip install tensorflow-transform --quiet"
       ]
     },
diff --git a/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb b/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb
index 8bbb9393157d..da445dd675b7 100644
--- a/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb
+++ b/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb
@@ -45,7 +45,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Generate text embeddings by using Hugging Face Hub models\n",
+        "# Generate text embeddings by using the EmbeddingGemma model from Hugging Face\n",
         "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
@@ -75,6 +75,8 @@
         "\n",
         "This notebook uses Apache Beam's `MLTransform` to generate embeddings from text data.\n",
         "\n",
+        "Using a small, highly efficient open model like EmbeddingGemma at the core of your pipeline makes the entire process self-contained, which can simplify management by eliminating the need for external network calls to other services for the embedding step. Because it's an open model, it can be hosted entirely within Dataflow. This provides the confidence to securely process large-scale, private datasets. For more information about the model, see the [model card](https://huggingface.co/google/embeddinggemma-300m)\n",
+        "\n",
         "Hugging Face's [`SentenceTransformers`](https://huggingface.co/sentence-transformers) framework uses Python to generate sentence, text, and image embeddings.\n",
         "\n",
         "To generate text embeddings that use Hugging Face models and `MLTransform`, use the `SentenceTransformerEmbeddings` module to specify the model configuration.\n"
@@ -97,7 +99,7 @@
     {
       "cell_type": "code",
       "source": [
-        "! pip install apache_beam>=2.53.0 --quiet\n",
+        "! pip install apache_beam[interactive]>=2.53.0 --quiet\n",
         "! pip install sentence-transformers --quiet"
       ],
       "metadata": {
@@ -120,6 +122,28 @@
       "execution_count": 29,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Authenticate with HuggingFace\n",
+        "\n",
+        "To ensure that you can pull the correct model, authenticate with HuggingFace by following the prompts in the cell."
+      ],
+      "metadata": {
+        "id": "kXDM8C7d3nPW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!hf auth login"
+      ],
+      "metadata": {
+        "id": "jVxSi2jS3M3c"
+      },
+      "execution_count": 29,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "source": [
@@ -170,7 +194,7 @@
         "    {'x': \"Should I sign up for Medicare Part B if I have Veterans' Benefits?\"}\n",
         "]\n",
         "\n",
-        "text_embedding_model_name = 'sentence-transformers/sentence-t5-large'\n",
+        "text_embedding_model_name = 'google/embeddinggemma-300m'\n",
         "\n",
         "\n",
         "# helper function that returns a dict containing only first\n",
@@ -191,7 +215,7 @@
       "source": [
         "\n",
         "### Generate text embeddings\n",
-        "This example uses the model `sentence-transformers/sentence-t5-large` to generate text embeddings. The model uses only the encoder from a `T5-large model`. The weights are stored in FP16. For more information about the model, see [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://arxiv.org/abs/2108.08877)."
+        "This example uses the model `google/embeddinggemma-300m` to generate text embeddings. For more information about the model, see [the model card](https://huggingface.co/google/embeddinggemma-300m)."
       ],
       "metadata": {
         "id": "SApMmlRLRv_e"
@@ -394,6 +418,21 @@
           ]
         }
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Next Steps\n",
+        "\n",
+        "Now that you've generated embeddings, you can use MLTransform and Sinks to ingest your data into a Vector Database. For this, along with more advanced concepts, check out the following notebooks:\n",
+        "\n",
+        "- [Vector Embedding Ingestion with Apache Beam and AlloyDB](https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/alloydb_product_catalog_embeddings.ipynb)\n",
+        "- [Embedding Ingestion and Vector Search with Apache Beam and BigQuery](https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/bigquery_vector_ingestion_and_search.ipynb)\n",
+        "- [Vector Embedding Ingestion with Apache Beam and CloudSQL Postgres](https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/cloudsql_postgres_product_catalog_embeddings.ipynb#scrollTo=K6-p-DVrIFTY)"
+      ],
+      "metadata": {
+        "id": "l31V3Q0Uo41z"
+      }
     }
   ]
 }
diff --git a/examples/notebooks/beam-ml/data_preprocessing/scale_data.ipynb b/examples/notebooks/beam-ml/data_preprocessing/scale_data.ipynb
index ba367fbc8177..ddf5a0c5c7e4 100644
--- a/examples/notebooks/beam-ml/data_preprocessing/scale_data.ipynb
+++ b/examples/notebooks/beam-ml/data_preprocessing/scale_data.ipynb
@@ -104,7 +104,7 @@
     {
       "cell_type": "code",
       "source": [
-        "! pip install apache_beam>=2.53.0 --quiet\n",
+        "! pip install apache_beam[interactive]>=2.53.0 --quiet\n",
         "! pip install tensorflow-transform --quiet"
       ],
       "metadata": {
diff --git a/examples/notebooks/beam-ml/data_preprocessing/vertex_ai_text_embeddings.ipynb b/examples/notebooks/beam-ml/data_preprocessing/vertex_ai_text_embeddings.ipynb
index 4d816ef97fb0..2d8cca4e44a0 100644
--- a/examples/notebooks/beam-ml/data_preprocessing/vertex_ai_text_embeddings.ipynb
+++ b/examples/notebooks/beam-ml/data_preprocessing/vertex_ai_text_embeddings.ipynb
@@ -117,7 +117,7 @@
       },
       "outputs": [],
       "source": [
-        "! pip install apache_beam[gcp]>=2.53.0 --quiet"
+        "! pip install apache_beam[interactive,gcp]>=2.53.0 --quiet"
       ]
     },
     {
diff --git a/examples/notebooks/beam-ml/dataflow_tpu_examples.ipynb b/examples/notebooks/beam-ml/dataflow_tpu_examples.ipynb
new file mode 100644
index 000000000000..e92ee73b02a2
--- /dev/null
+++ b/examples/notebooks/beam-ml/dataflow_tpu_examples.ipynb
@@ -0,0 +1,746 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "# Licensed to the Apache Software Foundation (ASF) under one\n",
+        "# or more contributor license agreements. See the NOTICE file\n",
+        "# distributed with this work for additional information\n",
+        "# regarding copyright ownership. The ASF licenses this file\n",
+        "# to you under the Apache License, Version 2.0 (the\n",
+        "# \"License\"); you may not use this file except in compliance\n",
+        "# with the License. You may obtain a copy of the License at\n",
+        "#\n",
+        "#   http://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing,\n",
+        "# software distributed under the License is distributed on an\n",
+        "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+        "# KIND, either express or implied. See the License for the\n",
+        "# specific language governing permissions and limitations\n",
+        "# under the License"
+      ],
+      "metadata": {
+        "id": "H-YbtpqChYYo"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a5343d14"
+      },
+      "source": [
+        "# Running Dataflow on TPUs: Quickstart examples\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/dataflow_tpu_examples.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/dataflow_tpu_examples.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n",
+        "<br/>\n",
+        "<br/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "This Colab notebook shows you how to set up two pipelines:\n",
+        "1. A pipeline that runs a trivial computation on a TPU.\n",
+        "2. A pipeline that runs inference using the [Gemma-3-27b-it model](https://huggingface.co/google/gemma-3-27b-it) on TPUs .\n",
+        "\n",
+        "Both pipelines use a custom Docker image. The Dataflow jobs will launch using a [Flex Template](https://cloud.google.com/dataflow/docs/guides/templates/using-flex-templates) to allow the same job to be reproduced in different Colab environments."
+      ],
+      "metadata": {
+        "id": "hAm4UpVHimSr"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8L0c_bikJt4d"
+      },
+      "source": [
+        "## Prerequisites"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i5IAopB4ewpu"
+      },
+      "source": [
+        "First, you need to authenticate to your Google Cloud Project. After running the cell below, you might need to **click on the text prompts in the cell** and enter inputs as prompted.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OdZ5bkvwesGg"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "    from google.colab import auth\n",
+        "    auth.authenticate_user()\n",
+        "!gcloud auth login"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dEFtSATYJp6p"
+      },
+      "source": [
+        "Now, set environment variables to access pipeline resources, such as a\n",
+        "Cloud Storage bucket or a repository to host container images in Artifact Registry."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": true,
+        "id": "cMJS0sYBfNkI"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import datetime\n",
+        "\n",
+        "project_id = \"some-project\" # @param {type:\"string\"}\n",
+        "gcs_bucket = \"some-bucket\" # @param {type:\"string\"}\n",
+        "ar_repository = \"some-ar-repo\" # @param {type:\"string\"}\n",
+        "\n",
+        "# Use a region where you have TPU accelerator quota.\n",
+        "region = \"some-region1\" # @param {type:\"string\"}\n",
+        "!gcloud config set project {project_id}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vIrXayHQL-d6"
+      },
+      "source": [
+        "Enable the necessary APIs if your project hasn't enabled them yet. If you have the appropriate permissions, you can enable the APIs by running the following cell."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_jKxVSK_MBFr"
+      },
+      "outputs": [],
+      "source": [
+        "!gcloud services enable \\\n",
+        "    dataflow.googleapis.com \\\n",
+        "    compute.googleapis.com \\\n",
+        "    logging.googleapis.com \\\n",
+        "    storage.googleapis.com \\\n",
+        "    cloudresourcemanager.googleapis.com \\\n",
+        "    artifactregistry.googleapis.com \\\n",
+        "    cloudbuild.googleapis.com"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lS3V0Sh5MbtT"
+      },
+      "source": [
+        "Now, you'll create a Cloud Storage bucket and Artifact Registry repository if you don't already have these resources."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8Wrs8yUhMas7"
+      },
+      "outputs": [],
+      "source": [
+        "!gcloud storage buckets describe gs://{gcs_bucket} >/dev/null 2>&1 || gcloud storage buckets create gs://{gcs_bucket} --location={region}\n",
+        "!gcloud artifacts repositories describe {ar_repository} --location={region} >/dev/null 2>&1 || gcloud artifacts repositories create {ar_repository} --repository-format=docker --location={region}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Uv12ZxPVcTEc"
+      },
+      "source": [
+        "# Example 1: Minimal computation pipeline using TPU V5E\n",
+        "\n",
+        "First, create a simple pipeline you can run to verify that TPUs are accessible, your custom Docker image has the necessary dependencies to interact with the TPUs and your Dataflow pipeline launch configuration is valid.\n",
+        "\n",
+        "With this sample you use the PyTorch library to interact with a TPU device."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "31f4cabb"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile minimal_tpu_pipeline.py\n",
+        "from __future__ import annotations\n",
+        "import torch\n",
+        "import torch_xla\n",
+        "import argparse\n",
+        "import logging\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.options.pipeline_options import PipelineOptions\n",
+        "\n",
+        "\n",
+        "class check_tpus(beam.DoFn):\n",
+        "    \"\"\"Validates that a TPU is accessible.\"\"\"\n",
+        "    def setup(self):\n",
+        "        tpu_devices = torch_xla.xm.get_xla_supported_devices()\n",
+        "        if not tpu_devices:\n",
+        "            raise RuntimeError(\"No TPUs found on the worker.\")\n",
+        "        logging.info(f\"Found TPU devices: {tpu_devices}\")\n",
+        "        tpu = torch_xla.device()\n",
+        "        t1 = torch.randn(3, 3, device=tpu)\n",
+        "        t2 = torch.randn(3, 3, device=tpu)\n",
+        "        result = t1 + t2\n",
+        "        logging.info(f\"Result of a sample TPU computation: {result}\")\n",
+        "\n",
+        "    def process(self, element):\n",
+        "        yield element\n",
+        "\n",
+        "\n",
+        "def run(input_text: str, beam_args: list[str] | None = None) -> None:\n",
+        "    beam_options = PipelineOptions(beam_args, save_main_session=True)\n",
+        "    pipeline = beam.Pipeline(options=beam_options)\n",
+        "    (\n",
+        "        pipeline\n",
+        "        | \"Create data\" >> beam.Create([input_text])\n",
+        "        | \"Check TPU availability\" >> beam.ParDo(check_tpus())\n",
+        "        | \"My transform\" >> beam.LogElements(level=logging.INFO)\n",
+        "    )\n",
+        "    pipeline.run()\n",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    logging.getLogger().setLevel(logging.INFO)\n",
+        "\n",
+        "    parser = argparse.ArgumentParser()\n",
+        "    parser.add_argument(\n",
+        "        \"--input-text\",\n",
+        "        default=\"Hello! This pipeline verified that TPUs are accessible.\",\n",
+        "        help=\"Input text to display.\",\n",
+        "    )\n",
+        "    args, beam_args = parser.parse_known_args()\n",
+        "\n",
+        "    run(args.input_text, beam_args)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4516f3e0"
+      },
+      "source": [
+        "## Create a Dockerfile for your TPU-compatible container image.\n",
+        "\n",
+        "In your Dockerfile you configure the environment variables to use with a `V5E` `1x1` TPU device.\n",
+        "\n",
+        "**You must use the region where you have V5E TPU quota to run this example.**\n",
+        "\n",
+        "To use a different TPU, adjust the configuration according to the [Dataflow documentation](https://cloud.google.com/dataflow/docs/tpu/use-tpus).\n",
+        "\n",
+        "This Dockerfile creates an image that serves both as a custom worker image for your Beam pipeline and also as a launcher image for your Flex template."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_EY1_rmXdAM5"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile Dockerfile\n",
+        "\n",
+        "FROM python:3.11-slim\n",
+        "\n",
+        "COPY minimal_tpu_pipeline.py minimal_tpu_pipeline.py\n",
+        "\n",
+        "# Copy the Apache Beam worker dependencies from the Beam Python 3.10 SDK image.\n",
+        "COPY --from=apache/beam_python3.10_sdk:2.67.0 /opt/apache/beam /opt/apache/beam\n",
+        "\n",
+        "# Copy Template Launcher dependencies\n",
+        "COPY --from=gcr.io/dataflow-templates-base/python310-template-launcher-base /opt/google/dataflow/python_template_launcher /opt/google/dataflow/python_template_launcher\n",
+        "\n",
+        "# Install TPU software and Apache Beam SDK\n",
+        "RUN pip install --no-cache-dir torch~=2.8.0 torch_xla[tpu]~=2.8.0 apache-beam[gcp]==2.67.0 -f https://storage.googleapis.com/libtpu-releases/index.html\n",
+        "\n",
+        "# Configuration for v5e 1x1 accelerator type.\n",
+        "ENV TPU_CHIPS_PER_HOST_BOUNDS=1,1,1\n",
+        "ENV TPU_ACCELERATOR_TYPE=v5litepod-1\n",
+        "ENV TPU_SKIP_MDS_QUERY=1\n",
+        "ENV TPU_HOST_BOUNDS=1,1,1\n",
+        "ENV TPU_WORKER_HOSTNAMES=localhost\n",
+        "ENV TPU_WORKER_ID=0\n",
+        "\n",
+        "ENV FLEX_TEMPLATE_PYTHON_PY_FILE=minimal_tpu_pipeline.py\n",
+        "\n",
+        "# Set the entrypoint to Apache Beam SDK worker launcher.\n",
+        "ENTRYPOINT [ \"/opt/apache/beam/boot\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XBFIEqNmenRj"
+      },
+      "source": [
+        "## Push your Docker image to Artifact Registry."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F9XQBZfrfbM2"
+      },
+      "source": [
+        "Finally, build your Docker image, and push it in Artifact Registry. This process should take about 15 minutes or so."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UaA-sBC1fabY"
+      },
+      "outputs": [],
+      "source": [
+        "container_tag = \"20250801\"\n",
+        "container_image = ''.join([\n",
+        "    region, \"-docker.pkg.dev/\",\n",
+        "    project_id, \"/\",\n",
+        "    ar_repository, \"/\",\n",
+        "    \"tpu-minimal-example\", \":\", container_tag\n",
+        "])\n",
+        "\n",
+        "!gcloud builds submit --tag {container_image}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1chqESwuSerP"
+      },
+      "source": [
+        "## Build the Dataflow Flex Template."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I3ukh2lwlmm3"
+      },
+      "source": [
+        "To create a reproducible environment for launching the pipeline, build a Flex Template.\n",
+        "\n",
+        "First, create a `metadata.json` file to change the default Dataflow worker disk size when launching the template.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GhlCMBnDl8-t"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile metadata.json\n",
+        "{\n",
+        "    \"name\": \"Minimal TPU Example on Dataflow\",\n",
+        "    \"description\": \"A Flex template launching a Dataflow Job doing a TPU computation \",\n",
+        "    \"parameters\": [\n",
+        "      {\n",
+        "        \"name\": \"disk_size_gb\",\n",
+        "        \"label\": \"disk_size_gb\",\n",
+        "        \"helpText\": \"disk_size_gb for worker\",\n",
+        "        \"isOptional\": true\n",
+        "      }\n",
+        "    ]\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eQAX8rzJVtDS"
+      },
+      "source": [
+        "Run the following cell to build the Flex Template and save it Cloud Storage."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CYLTC-jpSh6j"
+      },
+      "outputs": [],
+      "source": [
+        "!gcloud dataflow flex-template build gs://{gcs_bucket}/minimal_tpu_pipeline.json \\\n",
+        "  --image {container_image} \\\n",
+        "  --sdk-language \"PYTHON\" \\\n",
+        "  --metadata-file metadata.json \\\n",
+        "  --project {project_id}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cAhW0FdW5W7t"
+      },
+      "source": [
+        "## Submit your pipeline to Dataflow.\n",
+        "\n",
+        "Since you launch the pipeline as a Flex Template, make the following adjustments to the command line:\n",
+        "\n",
+        "* Use `--parameters` option to specify the container image and disk size.\n",
+        "* Use `--additional-experiments` option to specify the necessary Dataflow service options.\n",
+        "* To avoid using more than one process on a TPU simultaneously, limit process-level parallelism with the `no_use_multiple_sdk_containers` experiment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UVtBBPcWCzFu"
+      },
+      "outputs": [],
+      "source": [
+        "!gcloud dataflow flex-template run \"minimal-tpu-example-`date +%Y%m%d-%H%M%S`\" \\\n",
+        "  --template-file-gcs-location gs://{gcs_bucket}/minimal_tpu_pipeline.json \\\n",
+        "  --region {region} \\\n",
+        "  --project {project_id} \\\n",
+        "  --temp-location gs://{gcs_bucket}/tmp \\\n",
+        "  --parameters sdk_container_image={container_image} \\\n",
+        "  --worker-machine-type \"ct5lp-hightpu-1t\" \\\n",
+        "  --parameters disk_size_gb=50 \\\n",
+        "  --additional-experiments \"worker_accelerator=type:tpu-v5-lite-podslice;topology:1x1\" \\\n",
+        "  --additional-experiments \"no_use_multiple_sdk_containers\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Once the job is launched, use the following link to monitor its status: https://console.cloud.google.com/dataflow/jobs/\n",
+        "\n",
+        "Sample worker logs for the `Check TPU availability` step look like the following:\n",
+        "\n",
+        "```\n",
+        "Found TPU devices: ['xla:0']\n",
+        "Result of a sample TPU computation: tensor([[ 0.3355, -1.4628, -3.2610], [-1.4656, 0.3196, -2.8766], [ 0.8667, -1.5060, 0.7125]], device='xla:0')\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "xRW_d_i_tVel"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DpUAUjDlcMOR"
+      },
+      "source": [
+        "# Example 2: Inference Pipeline with Gemma 3 27B using TPU V6E\n",
+        "\n",
+        "This example shows you how to perform inference on a TPU using Gemma 3 27b model.\n",
+        "\n",
+        "To fit this model in TPU memory, you need four V6E TPU chips connected in 2x2 topology.\n",
+        "\n",
+        "**You must use the region where you have V6E TPU quota to run this example.**\n",
+        "\n",
+        "The example uses [Apache Beam RunInference APIs](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/) with the [VLLM Completions model handler](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.vllm_inference.html).\n",
+        "\n",
+        "The model is downloaded from HuggingFace at runtime, and running the example requires a [HuggingFace access token](https://huggingface.co/docs/hub/en/security-tokens).\n",
+        "\n",
+        "First, create a pipeline file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GGCqkzgXda97"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile gemma_tpu_pipeline.py\n",
+        "from __future__ import annotations\n",
+        "import argparse\n",
+        "import logging\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.ml.inference.base import RunInference\n",
+        "from apache_beam.options.pipeline_options import PipelineOptions\n",
+        "from apache_beam.ml.inference.vllm_inference import VLLMCompletionsModelHandler\n",
+        "\n",
+        "\n",
+        "def run(input_text: str, beam_args: list[str] | None = None) -> None:\n",
+        "    beam_options = PipelineOptions(beam_args, save_main_session=True)\n",
+        "    pipeline = beam.Pipeline(options=beam_options)\n",
+        "    (\n",
+        "        pipeline\n",
+        "        | \"Create data\" >> beam.Create([input_text])\n",
+        "        | \"Run Inference\" >> RunInference(\n",
+        "            model_handler=VLLMCompletionsModelHandler(\n",
+        "                'google/gemma-3-27b-it',\n",
+        "                {\n",
+        "                    'max-model-len': '4096',\n",
+        "                    'no-enable-prefix-caching': None,\n",
+        "                    'disable-log-requests': None,\n",
+        "                    'tensor-parallel-size': '4',\n",
+        "                    'limit-mm-per-prompt': '{\"image\": 0}'\n",
+        "                })\n",
+        "            )\n",
+        "        | \"Log Output\" >> beam.LogElements(level=logging.INFO)\n",
+        "    )\n",
+        "    pipeline.run()\n",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    logging.getLogger().setLevel(logging.INFO)\n",
+        "    parser = argparse.ArgumentParser()\n",
+        "    parser.add_argument(\n",
+        "        \"--input-text\",\n",
+        "        default=\"What are TPUs?\",\n",
+        "        help=\"Input text query.\",\n",
+        "    )\n",
+        "    args, beam_args = parser.parse_known_args()\n",
+        "    run(args.input_text, beam_args)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "emTd69gUonq-"
+      },
+      "source": [
+        "## Create a new Dockerfile for this pipeline with additional dependencies.\n",
+        "Note that this sample uses a different TPU device than the example 1, so the environment variables are different.\n",
+        "\n",
+        "**You must use your own HuggingFace Token in the Dockerfile.** For instructions on creating a token, see [User access tokens](https://huggingface.co/docs/hub/en/security-tokens)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6elKvBZ0_dc4"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile Dockerfile\n",
+        "# Use the official vLLM TPU base image, which has TPU dependencies.\n",
+        "# To use the latest version, use: vllm/vllm-tpu:nightly\n",
+        "FROM vllm/vllm-tpu:5964069367a7d54c3816ce3faba79e02110cde17\n",
+        "\n",
+        "# Copy your pipeline file.\n",
+        "COPY gemma_tpu_pipeline.py gemma_tpu_pipeline.py\n",
+        "\n",
+        "# You can use a more recent version of Apache Beam\n",
+        "COPY --from=apache/beam_python3.12_sdk:2.67.0 /opt/apache/beam /opt/apache/beam\n",
+        "RUN pip install --no-cache-dir apache-beam[gcp]==2.67.0\n",
+        "\n",
+        "# Copy Template Launcher dependencies\n",
+        "COPY --from=gcr.io/dataflow-templates-base/python310-template-launcher-base /opt/google/dataflow/python_template_launcher /opt/google/dataflow/python_template_launcher\n",
+        "\n",
+        "# Replace the Hugginface token here.\n",
+        "RUN python -c 'from huggingface_hub import HfFolder; HfFolder.save_token(\"YOUR HUGGINGFACE TOKEN\")'\n",
+        "\n",
+        "# TPU environment variables.\n",
+        "ENV TPU_SKIP_MDS_QUERY=1\n",
+        "\n",
+        "# Configuration for v6e 2x2 accelerator type.\n",
+        "ENV TPU_HOST_BOUNDS=1,1,1\n",
+        "ENV TPU_CHIPS_PER_HOST_BOUNDS=2,2,1\n",
+        "ENV TPU_ACCELERATOR_TYPE=v6e-4\n",
+        "ENV VLLM_USE_V1=1\n",
+        "\n",
+        "ENV FLEX_TEMPLATE_PYTHON_PY_FILE=gemma_tpu_pipeline.py\n",
+        "\n",
+        "# Set the entrypoint to Apache Beam SDK worker launcher.\n",
+        "ENTRYPOINT [ \"/opt/apache/beam/boot\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2V1PmAf1otG4"
+      },
+      "source": [
+        "Run the following cell to build the Docker image and push it to Artifact Registry. This process should take 15 min or so."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": true,
+        "id": "He5WkUAE_pYp"
+      },
+      "outputs": [],
+      "source": [
+        "container_tag = \"20250801\"\n",
+        "container_image = ''.join([\n",
+        "    region, \"-docker.pkg.dev/\",\n",
+        "    project_id, \"/\",\n",
+        "    ar_repository, \"/\",\n",
+        "    \"tpu-run-inference-example\", \":\", container_tag\n",
+        "])\n",
+        "!gcloud builds submit --tag {container_image}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EyYuSgDudcVK"
+      },
+      "source": [
+        "## Build the Flex Template for this pipeline."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "33V96JFAL_jk"
+      },
+      "source": [
+        "To create a reproducible environment for launching the pipeline, build a Flex Template.\n",
+        "\n",
+        "First, create a `metadata.json` file to change the default Dataflow worker disk size when launching the template."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "L8hylI64L_jl"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile metadata.json\n",
+        "{\n",
+        "    \"name\": \"Gemma 3 27b Run Inference pipeline with VLLM\",\n",
+        "    \"description\": \"A template for Dataflow RunInference pipeline with VLLM in a TPU-enabled environment with VLLM\",\n",
+        "    \"parameters\": [\n",
+        "      {\n",
+        "        \"name\": \"disk_size_gb\",\n",
+        "        \"label\": \"disk_size_gb\",\n",
+        "        \"helpText\": \"disk_size_gb for worker\",\n",
+        "        \"isOptional\": true\n",
+        "      }\n",
+        "    ]\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Run the following cell to build the Flex Template and save it in Cloud Storage."
+      ],
+      "metadata": {
+        "id": "yGRhrD1J2IIW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hvs2JWNydiBl"
+      },
+      "outputs": [],
+      "source": [
+        "!gcloud dataflow flex-template build gs://{gcs_bucket}/gemma_tpu_pipeline.json \\\n",
+        "  --image {container_image} \\\n",
+        "  --sdk-language \"PYTHON\" \\\n",
+        "  --metadata-file metadata.json \\\n",
+        "  --project {project_id}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VWWaf4cmdi7z"
+      },
+      "source": [
+        "## Finally, submit the job to Dataflow.\n",
+        "\n",
+        "Since you launch the pipeline as a Flex Template, you are making the following adjustments to the command line:\n",
+        "\n",
+        "* Use the `--parameters` option to specify the container image and disk size\n",
+        "* Use the `--additional-experiments` option to specify the necessary Dataflow service options.\n",
+        "* The VLLMCompletionsModelHandler from Beam RunInference APIs only loads the model onto TPUs from a single process. Still, limit the intra-worker parallelism by reducing the value of\n",
+        "`--number_of_worker_harness_threads`, which achieves better performance.\n",
+        "\n",
+        "Once the job is launched, use the following link to monitor its status: https://console.cloud.google.com/dataflow/jobs/"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OUX0E0XzdlLW"
+      },
+      "outputs": [],
+      "source": [
+        "!gcloud dataflow flex-template run \"gemma-tpu-example-`date +%Y%m%d-%H%M%S`\" \\\n",
+        "  --template-file-gcs-location gs://{gcs_bucket}/gemma_tpu_pipeline.json \\\n",
+        "  --region {region} \\\n",
+        "  --project {project_id} \\\n",
+        "  --temp-location gs://{gcs_bucket}/tmp \\\n",
+        "  --parameters number_of_worker_harness_threads=100 \\\n",
+        "  --parameters sdk_container_image={container_image} \\\n",
+        "  --parameters disk_size_gb=100 \\\n",
+        "  --worker-machine-type \"ct6e-standard-4t\" \\\n",
+        "  --additional-experiments \"worker_accelerator=type:tpu-v6e-slice;topology:2x2\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Due to model loading and initialization time, the pipeline takes 25 min or so to complete.\n",
+        "\n",
+        "Sample worker logs for the `Run Inference` step look like the following:\n",
+        "\n",
+        "```\n",
+        "PredictionResult(example='What are TPUs?', inference=Completion(id='cmpl-57ebbddeb1c04dc0a8a74f2b60d10f67', choices=[CompletionChoice(finish_reason='length', index=0, logprobs=None, text='\\n\\nTensor Processing Units (TPUs) are custom-developed AI accelerator ASICs', stop_reason=None, prompt_logprobs=None)], created=1755614936, model='google/gemma-3-27b-it', object='text_completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=16, prompt_tokens=6, total_tokens=22, completion_tokens_details=None, prompt_tokens_details=None), service_tier=None, kv_transfer_params=None), model_id=None)\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "1kpeVbdczt8u"
+      }
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/notebooks/beam-ml/gemma_2_sentiment_and_summarization.ipynb b/examples/notebooks/beam-ml/gemma_2_sentiment_and_summarization.ipynb
index 160c09f563b0..ad56b674ee5d 100644
--- a/examples/notebooks/beam-ml/gemma_2_sentiment_and_summarization.ipynb
+++ b/examples/notebooks/beam-ml/gemma_2_sentiment_and_summarization.ipynb
@@ -206,7 +206,7 @@
       },
       "source": [
         "```sh\n",
-        "apache_beam[gcp]==2.54.0\n",
+        "apache_beam[interactive,gcp]==2.54.0\n",
         "keras_nlp==0.14.3\n",
         "keras==3.4.1\n",
         "jax[cuda12]\n",
@@ -293,7 +293,7 @@
       },
       "outputs": [{"output_type": "stream", "name": "stdout", "text": ["\n"]}],
       "source": [
-        "%pip install apache_beam[gcp]==\"2.54.0\" keras_nlp==\"0.14.3\" keras==\"3.5.0\" jax[cuda12]"
+        "%pip install apache_beam[interactive,gcp]==\"2.54.0\" keras_nlp==\"0.14.3\" keras==\"3.5.0\" jax[cuda12]"
       ]
     },
     {
diff --git a/examples/notebooks/beam-ml/image_processing_tensorflow.ipynb b/examples/notebooks/beam-ml/image_processing_tensorflow.ipynb
index 45fca09addbb..c2e41c1f0cf5 100644
--- a/examples/notebooks/beam-ml/image_processing_tensorflow.ipynb
+++ b/examples/notebooks/beam-ml/image_processing_tensorflow.ipynb
@@ -87,7 +87,6 @@
       },
       "outputs": [],
       "source": [
-        "!pip install apache_beam --quiet\n",
         "!pip install apache-beam[interactive] --quiet"
       ]
     },
@@ -915,4 +914,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/examples/notebooks/beam-ml/mltransform_basic.ipynb b/examples/notebooks/beam-ml/mltransform_basic.ipynb
index b0af96d08593..470f100537e8 100644
--- a/examples/notebooks/beam-ml/mltransform_basic.ipynb
+++ b/examples/notebooks/beam-ml/mltransform_basic.ipynb
@@ -76,7 +76,7 @@
       "cell_type": "code",
       "source": [
         "!pip install tensorflow_transform --quiet\n",
-        "!pip install apache_beam>=2.50.0 --quiet"
+        "!pip install apache_beam[interactive]>=2.50.0 --quiet"
       ],
       "metadata": {
         "id": "MRWkC-n2DmjM"
diff --git a/examples/notebooks/beam-ml/per_key_models.ipynb b/examples/notebooks/beam-ml/per_key_models.ipynb
index 3e71c1d119a2..026a481dd2c4 100644
--- a/examples/notebooks/beam-ml/per_key_models.ipynb
+++ b/examples/notebooks/beam-ml/per_key_models.ipynb
@@ -107,7 +107,7 @@
         }
       ],
       "source": [
-        "!pip install apache_beam[gcp]>=2.51.0 --quiet\n",
+        "!pip install apache_beam[interactive,gcp]>=2.51.0 --quiet\n",
         "!pip install torch --quiet\n",
         "!pip install transformers --quiet\n",
         "\n",
diff --git a/examples/notebooks/beam-ml/rag_usecase/beam_rag_notebook.ipynb b/examples/notebooks/beam-ml/rag_usecase/beam_rag_notebook.ipynb
index e271074af555..4941f3f3ad63 100644
--- a/examples/notebooks/beam-ml/rag_usecase/beam_rag_notebook.ipynb
+++ b/examples/notebooks/beam-ml/rag_usecase/beam_rag_notebook.ipynb
@@ -108,7 +108,7 @@
     "#installing dependencies\n",
     "!pip install pandas==1.4.4\n",
     "!pip install numpy==1.24.4\n",
-    "!pip install apache_beam==2.56.0\n",
+    "!pip install apache_beam[interactive]==2.56.0\n",
     "!pip install redis==5.0.1\n",
     "!pip install langchain==0.1.14 #used for chunking"
    ]
diff --git a/examples/notebooks/beam-ml/rag_usecase/opensearch_rag_pipeline.ipynb b/examples/notebooks/beam-ml/rag_usecase/opensearch_rag_pipeline.ipynb
index aae86e31aa44..f11044426720 100644
--- a/examples/notebooks/beam-ml/rag_usecase/opensearch_rag_pipeline.ipynb
+++ b/examples/notebooks/beam-ml/rag_usecase/opensearch_rag_pipeline.ipynb
@@ -46,7 +46,7 @@
     "#installing dependencies\n",
     "!pip install pandas==1.4.4\n",
     "!pip install numpy==1.24.4\n",
-    "!pip install apache_beam==2.56.0\n",
+    "!pip install apache_beam[interactive]==2.56.0\n",
     "!pip install opensearch==2.1.0\n",
     "#used for chunking\n",
     "!pip install langchain==0.1.14 "
diff --git a/examples/notebooks/beam-ml/run_inference_gemma.ipynb b/examples/notebooks/beam-ml/run_inference_gemma.ipynb
index 489f01c4c9aa..12f5a03be109 100644
--- a/examples/notebooks/beam-ml/run_inference_gemma.ipynb
+++ b/examples/notebooks/beam-ml/run_inference_gemma.ipynb
@@ -130,7 +130,7 @@
       ],
       "source": [
         "!pip install -q -U protobuf\n",
-        "!pip install -q -U apache_beam[gcp]\n",
+        "!pip install -q -U apache_beam[interactive,gcp]\n",
         "!pip install -q -U keras_nlp>=0.8.0\n",
         "!pip install -q -U keras>3\n",
         "\n",
diff --git a/examples/notebooks/beam-ml/run_inference_generative_ai.ipynb b/examples/notebooks/beam-ml/run_inference_generative_ai.ipynb
index 40b283982b68..2ca2374abbf3 100644
--- a/examples/notebooks/beam-ml/run_inference_generative_ai.ipynb
+++ b/examples/notebooks/beam-ml/run_inference_generative_ai.ipynb
@@ -95,7 +95,7 @@
       },
       "outputs": [],
       "source": [
-        "!pip install apache_beam[gcp]==2.48.0\n",
+        "!pip install apache_beam[interactive,gcp]==2.48.0\n",
         "!pip install torch\n",
         "!pip install transformers"
       ]
diff --git a/examples/notebooks/beam-ml/run_inference_multi_model.ipynb b/examples/notebooks/beam-ml/run_inference_multi_model.ipynb
index 7cd144223cae..d6c616a62c56 100644
--- a/examples/notebooks/beam-ml/run_inference_multi_model.ipynb
+++ b/examples/notebooks/beam-ml/run_inference_multi_model.ipynb
@@ -195,7 +195,7 @@
         "!pip install ftfy==6.1.1 --quiet\n",
         "!pip install spacy==3.4.1 --quiet\n",
         "!pip install fairscale==0.4.4 --quiet\n",
-        "!pip install apache_beam[gcp]>=2.48.0\n",
+        "!pip install apache_beam[interactive,gcp]>=2.48.0\n",
         "\n",
         "# To use the newly installed versions, restart the runtime.\n",
         "exit()"
diff --git a/examples/notebooks/beam-ml/run_inference_pytorch.ipynb b/examples/notebooks/beam-ml/run_inference_pytorch.ipynb
index 93dd12dd20ab..a10d40e8f997 100644
--- a/examples/notebooks/beam-ml/run_inference_pytorch.ipynb
+++ b/examples/notebooks/beam-ml/run_inference_pytorch.ipynb
@@ -86,7 +86,7 @@
       },
       "outputs": [],
       "source": [
-        "!pip install apache_beam[gcp,dataframe] --quiet"
+        "!pip install apache_beam[interactive,gcp,dataframe] --quiet"
       ]
     },
     {
diff --git a/examples/notebooks/beam-ml/run_inference_pytorch_tensorflow_sklearn.ipynb b/examples/notebooks/beam-ml/run_inference_pytorch_tensorflow_sklearn.ipynb
index 115b70b11e94..4167cce47c4c 100644
--- a/examples/notebooks/beam-ml/run_inference_pytorch_tensorflow_sklearn.ipynb
+++ b/examples/notebooks/beam-ml/run_inference_pytorch_tensorflow_sklearn.ipynb
@@ -125,7 +125,7 @@
       "outputs": [],
       "source": [
         "!pip install --upgrade pip\n",
-        "!pip install apache_beam[gcp]>=2.40.0\n",
+        "!pip install apache_beam[interactive,gcp]>=2.40.0\n",
         "!pip install transformers\n",
         "!pip install google-api-core==1.32"
       ]
@@ -406,7 +406,7 @@
       "source": [
         "!pip install --upgrade pip\n",
         "!pip install google-api-core==1.32\n",
-        "!pip install apache_beam[gcp]==2.41.0\n",
+        "!pip install apache_beam[interactive,gcp]==2.41.0\n",
         "!pip install tensorflow==2.8\n",
         "!pip install tfx_bsl\n",
         "!pip install tensorflow-text==2.8.1"
@@ -649,7 +649,7 @@
       "source": [
         "!pip install --upgrade pip\n",
         "!pip install google-api-core==1.32\n",
-        "!pip install apache_beam[gcp]==2.41.0"
+        "!pip install apache_beam[interactive,gcp]==2.41.0"
       ]
     },
     {
diff --git a/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb b/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb
index c15e9b21ecf9..ebeff1f77dbc 100644
--- a/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb
+++ b/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb
@@ -105,7 +105,7 @@
       "outputs": [],
       "source": [
         "!pip install protobuf --quiet\n",
-        "!pip install apache_beam==2.46.0 --quiet\n",
+        "!pip install apache_beam[interactive]==2.46.0 --quiet\n",
         "\n",
         "# To use the newly installed versions, restart the runtime.\n",
         "exit()"
diff --git a/examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb b/examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb
index 2c2f6460651b..42b300d943e4 100644
--- a/examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb
+++ b/examples/notebooks/beam-ml/run_inference_tensorflow_with_tfx.ipynb
@@ -100,7 +100,7 @@
       "source": [
         "!pip install tfx_bsl==1.10.0 --quiet\n",
         "!pip install protobuf --quiet\n",
-        "!pip install apache_beam --quiet"
+        "!pip install apache_beam[interactive] --quiet"
       ]
     },
     {
diff --git a/examples/notebooks/beam-ml/run_inference_vertex_ai.ipynb b/examples/notebooks/beam-ml/run_inference_vertex_ai.ipynb
index 2ab45e0491a7..3c328348c7bf 100644
--- a/examples/notebooks/beam-ml/run_inference_vertex_ai.ipynb
+++ b/examples/notebooks/beam-ml/run_inference_vertex_ai.ipynb
@@ -109,7 +109,7 @@
       "outputs": [],
       "source": [
         "!pip install protobuf --quiet\n",
-        "!pip install apache_beam[gcp,interactive]==2.50.0 --quiet\n",
+        "!pip install apache_beam[interactive,gcp]==2.50.0 --quiet\n",
         "# Enforce shapely < 2.0.0 to avoid an issue with google.aiplatform\n",
         "!pip install shapely==1.7.1 --quiet\n",
         "\n",
diff --git a/examples/notebooks/beam-ml/run_inference_with_tensorflow_hub.ipynb b/examples/notebooks/beam-ml/run_inference_with_tensorflow_hub.ipynb
index b396851f9dcc..8ef185eaf0ff 100644
--- a/examples/notebooks/beam-ml/run_inference_with_tensorflow_hub.ipynb
+++ b/examples/notebooks/beam-ml/run_inference_with_tensorflow_hub.ipynb
@@ -95,7 +95,7 @@
       },
       "source": [
         "!pip install tensorflow\n",
-        "!pip install apache_beam==2.46.0"
+        "!pip install apache_beam[interactive]==2.46.0"
       ],
       "execution_count": null,
       "outputs": []
diff --git a/examples/notebooks/beam-ml/speech_emotion_tensorflow.ipynb b/examples/notebooks/beam-ml/speech_emotion_tensorflow.ipynb
index c2dfb06a6e67..3c47d7be0fb9 100644
--- a/examples/notebooks/beam-ml/speech_emotion_tensorflow.ipynb
+++ b/examples/notebooks/beam-ml/speech_emotion_tensorflow.ipynb
@@ -112,7 +112,7 @@
         }
       ],
       "source": [
-        "!pip install apache_beam --quiet"
+        "!pip install apache_beam[interactive] --quiet"
       ]
     },
     {
diff --git a/examples/notebooks/blog/unittests_in_beam.ipynb b/examples/notebooks/blog/unittests_in_beam.ipynb
index da3f39d02959..2eacc69914f7 100644
--- a/examples/notebooks/blog/unittests_in_beam.ipynb
+++ b/examples/notebooks/blog/unittests_in_beam.ipynb
@@ -58,7 +58,7 @@
       "cell_type": "code",
       "source": [
         "# Install the Apache Beam library\n",
-        "!pip install apache_beam[gcp] --quiet"
+        "!pip install apache_beam[interactive,gcp] --quiet"
       ],
       "metadata": {
         "id": "5W2nuV7uzlPg"
diff --git a/examples/yaml/README.md b/examples/yaml/README.md
new file mode 100644
index 000000000000..121b0b03bcb7
--- /dev/null
+++ b/examples/yaml/README.md
@@ -0,0 +1,54 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+## Example YAML Pipelines
+
+A suite of YAML pipeline examples is currently located under the directory
+[sdks/python/apache_beam/yaml/examples](../../sdks/python/apache_beam/yaml/examples).
+
+### [Aggregation](../../sdks/python/apache_beam/yaml/examples/transforms/aggregation)
+
+These examples leverage the built-in `Combine` transform for performing simple
+aggregations including sum, mean, count, etc.
+
+### [Blueprints](../../sdks/python/apache_beam/yaml/examples/transforms/blueprint)
+
+These examples leverage DF or other existing templates and convert them to yaml
+blueprints.
+
+### [Element-wise](../../sdks/python/apache_beam/yaml/examples/transforms/elementwise)
+
+These examples leverage the built-in mapping transforms including `MapToFields`,
+`Filter` and `Explode`.
+
+### [IO](../../sdks/python/apache_beam/yaml/examples/transforms/io)
+
+These examples leverage the built-in IO transforms to read from and write to
+various sources and sinks, including Iceberg, Kafka and Spanner.
+
+### [Jinja](../../sdks/python/apache_beam/yaml/examples/transforms/jinja)
+
+These examples use Jinja [templatization](https://beam.apache.org/documentation/sdks/yaml/#jinja-templatization)
+to build off of different contexts and/or with different
+configurations.
+
+### [ML](../../sdks/python/apache_beam/yaml/examples/transforms/ml)
+
+These examples include built-in ML-specific transforms such as `RunInference`,
+`MLTransform` and `Enrichment`.
diff --git a/gradle.properties b/gradle.properties
index be42e301d9d9..61e25944ccf3 100644
--- a/gradle.properties
+++ b/gradle.properties
@@ -30,8 +30,8 @@ signing.gnupg.useLegacyGpg=true
 # buildSrc/src/main/groovy/org/apache/beam/gradle/BeamModulePlugin.groovy.
 # To build a custom Beam version make sure you change it in both places, see
 # https://github.com/apache/beam/issues/21302.
-version=2.67.0-SNAPSHOT
-sdk_version=2.67.0.dev
+version=2.69.0-SNAPSHOT
+sdk_version=2.69.0.dev
 
 javaVersion=1.8
 
diff --git a/gradle/wrapper/gradle-wrapper.properties b/gradle/wrapper/gradle-wrapper.properties
index 3fa8f862f753..d4081da476bb 100644
--- a/gradle/wrapper/gradle-wrapper.properties
+++ b/gradle/wrapper/gradle-wrapper.properties
@@ -1,6 +1,6 @@
 distributionBase=GRADLE_USER_HOME
 distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-8.4-bin.zip
+distributionUrl=https\://services.gradle.org/distributions/gradle-8.14.3-bin.zip
 networkTimeout=10000
 validateDistributionUrl=true
 zipStoreBase=GRADLE_USER_HOME
diff --git a/infra/enforcement/README.md b/infra/enforcement/README.md
new file mode 100644
index 000000000000..8136081fed75
--- /dev/null
+++ b/infra/enforcement/README.md
@@ -0,0 +1,224 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+# Infrastructure rules enforcement
+
+This module is used to check that the infrastructure rules are being used and provides automated notifications for compliance violations.
+
+The enforcement tools support multiple notification methods:
+- **GitHub Issues**: Automatically create GitHub issues with detailed compliance reports
+- **Email Notifications**: Send email alerts via SMTP for compliance violations
+- **Console Output**: Print detailed reports to console for manual review
+
+## IAM Policies
+
+The enforcement is done by validating the IAM policies against the defined policies.
+The tool monitors and enforces compliance for user permissions, service account roles, and group memberships across your GCP project.
+
+### Usage
+
+You can specify the action either through the configuration file (`config.yml`) or via command-line arguments:
+
+```bash
+# Check compliance and report issues (default)
+python iam.py --action check
+
+# Create/update GitHub issue and send email if compliance violations are found
+python iam.py --action announce
+
+# Print announcement details for testing purposes (no actual issue created)
+python iam.py --action print
+
+# Generate new compliance file based on current IAM policy
+python iam.py --action generate
+```
+
+### Actions
+
+- **check**: Validates IAM policies against defined policies and reports any differences (default behavior)
+- **announce**: Creates or updates a GitHub issue and sends an email notification when IAM policies differ from the defined ones. If no open issue exists, creates a new one; if an open issue exists, updates the issue body with current violations
+- **print**: Prints announcement details for testing purposes without creating actual GitHub issues or sending emails
+- **generate**: Updates the compliance file to match the current GCP IAM policy, creating a new baseline from existing permissions
+
+### Features
+
+The IAM Policy enforcement tool provides the following capabilities:
+
+- **Comprehensive Policy Export**: Automatically exports all IAM bindings and roles from the GCP project
+- **Member Type Recognition**: Handles users, service accounts, and groups with proper parsing and identification
+- **Permission Comparison**: Detailed comparison between expected and actual permissions for each user
+- **Conditional Role Filtering**: Automatically excludes conditional roles (roles with conditions) from compliance checks
+- **Sorted Output**: Provides consistent, sorted output for easy comparison and review
+- **Detailed Reporting**: Comprehensive reporting of permission differences with clear before/after comparisons
+- **GitHub Integration**: Automatic issue creation with detailed compliance violation reports
+- **Email Notifications**: Optional email notifications for compliance issues via SMTP
+- **Issue Management**: Smart issue handling - creates new issues when none exist, updates existing open issues with current violations
+- **Testing Support**: Print action allows testing notification content without actually sending
+
+### Configuration
+
+The `config.yml` file supports the following parameters for IAM policies:
+
+- `project_id`: GCP project ID to check (default: `apache-beam-testing`)
+- `users_file`: Path to the YAML file containing expected IAM policies (default: `../iam/users.yml`)
+- `action`: Default action to perform (`check`, `announce`, `print`, or `generate`)
+- `logging`: Logging configuration (level and format)
+
+### Environment Variables (for announce action)
+
+When using the `announce` action, the following environment variables are required:
+
+- `GITHUB_TOKEN`: GitHub personal access token for creating issues
+- `GITHUB_REPOSITORY`: Repository in format `owner/repo` (default: `apache/beam`)
+- `SMTP_SERVER`: SMTP server for email notifications
+- `SMTP_PORT`: SMTP port (default: 587)
+- `EMAIL_ADDRESS`: Email address for sending notifications
+- `EMAIL_PASSWORD`: Email password for authentication
+- `EMAIL_RECIPIENT`: Email address to receive notifications
+
+### IAM Policy File Format
+
+The IAM policy file should follow this YAML structure:
+
+```yaml
+- username: john.doe
+  email: john.doe@example.com
+  permissions:
+    - role: roles/viewer
+    - role: roles/storage.objectViewer
+- username: service-account-name
+  email: service-account-name@project-id.iam.gserviceaccount.com
+  permissions:
+    - role: roles/compute.instanceAdmin
+    - role: roles/iam.serviceAccountUser
+```
+
+Each user entry includes:
+- `username`: The derived username (typically the part before @ in email addresses)
+- `email`: The full email address of the user or service account
+- `permissions`: List of IAM roles assigned to this member
+  - `role`: The full GCP IAM role name (e.g., `roles/viewer`, `roles/editor`)
+
+### Compliance Checking Process
+
+1. **Policy Extraction**: Retrieves current IAM policy from the GCP project
+2. **Member Parsing**: Parses all IAM members and extracts usernames, emails, and types
+3. **Role Processing**: Processes all roles while filtering out conditional bindings
+4. **Comparison**: Compares current permissions with expected permissions from the policy file
+5. **Reporting**: Generates detailed reports of any discrepancies found
+6. **Notification**: Sends notifications via GitHub issues and/or email when using announce action
+
+The `print` action can be used for testing notification content without actually creating GitHub issues or sending emails.
+
+Command-line arguments take precedence over configuration file settings.
+
+## GitHub Actions Integration
+
+The enforcement tools are integrated with GitHub Actions to provide automated compliance monitoring. The workflow is configured to run weekly and automatically create GitHub issues and send email notifications for any policy violations.
+
+### Workflow Configuration
+
+The GitHub Actions workflow (`.github/workflows/beam_Infrastructure_PolicyEnforcer.yml`) runs:
+- **Schedule**: Weekly on Mondays at 9:00 AM UTC
+- **Manual trigger**: Can be triggered manually via `workflow_dispatch`
+- **Actions**: Runs both IAM and Account Keys enforcement with the `announce` action
+
+**Note**:
+- The email service is configured to use gmail
+- The recipient email is set to `dev@beam.apache.org` for Apache Beam project notifications
+- The `GITHUB_TOKEN` is automatically provided by GitHub Actions and doesn't need to be configured manually
+
+## Account Keys
+
+The enforcement is also done by validating service account keys and their access permissions against the defined policies.
+The tool supports three different actions when discrepancies are found:
+
+### Usage
+
+You can specify the action either through the configuration file (`config.yml`) or via command-line arguments:
+
+```bash
+# Check compliance and report issues (default)
+python account_keys.py --action check
+
+# Create/update GitHub issue and send email if compliance violations are found
+python account_keys.py --action announce
+
+# Print announcement details for testing purposes (no actual issue created)
+python account_keys.py --action print
+
+# Generate new compliance file based on current service account keys policy
+python account_keys.py --action generate
+```
+
+### Actions
+
+- **check**: Validates service account keys and their permissions against defined policies and reports any differences (default behavior)
+- **announce**: Creates or updates a GitHub issue and sends an email notification when service account keys policies differ from the defined ones. If no open issue exists, creates a new one; if an open issue exists, updates the issue body with current violations
+- **print**: Prints announcement details for testing purposes without creating actual GitHub issues or sending emails
+- **generate**: Updates the compliance file to match the current GCP service account keys and Secret Manager permissions
+
+### Features
+
+The Account Keys enforcement tool provides the following capabilities:
+
+- **Service Account Discovery**: Automatically discovers all active (non-disabled) service accounts in the project
+- **Secret Manager Integration**: Monitors secrets created by the beam-infra-secret-manager service
+- **Permission Validation**: Ensures that Secret Manager permissions match the declared authorized users
+- **Compliance Reporting**: Identifies missing service accounts, undeclared managed secrets, and permission mismatches
+- **Automatic Remediation**: Can automatically update the compliance file to match current infrastructure state
+
+### Configuration
+
+The `config.yml` file supports the following parameters for account keys:
+
+- `project_id`: GCP project ID to check
+- `service_account_keys_file`: Path to the YAML file containing expected service account keys policies (default: `../keys/keys.yaml`)
+- `action`: Default action to perform (`check`, `announce`, `print`, or `generate`)
+- `logging`: Logging configuration (level and format)
+
+### Environment Variables (for announce action)
+
+When using the `announce` action, the following environment variables are required:
+
+- `GITHUB_TOKEN`: GitHub personal access token for creating issues
+- `GITHUB_REPOSITORY`: Repository in format `owner/repo` (default: `apache/beam`)
+- `SMTP_SERVER`: SMTP server for email notifications
+- `SMTP_PORT`: SMTP port (default: 587)
+- `EMAIL_ADDRESS`: Email address for sending notifications
+- `EMAIL_PASSWORD`: Email password for authentication
+- `EMAIL_RECIPIENT`: Email address to receive notifications
+
+### Service Account Keys File Format
+
+The service account keys file should follow this YAML structure:
+
+```yaml
+service_accounts:
+- account_id: example-service-account
+  display_name: example-service-account@project-id.iam.gserviceaccount.com
+  authorized_users:
+  - email: user1@example.com
+  - email: user2@example.com
+```
+
+Each service account entry includes:
+- `account_id`: The unique identifier for the service account (without the full email domain)
+- `display_name`: The full service account email address or any custom display name
+- `authorized_users`: List of users who should have access to the service account's secrets
diff --git a/infra/enforcement/account_keys.py b/infra/enforcement/account_keys.py
new file mode 100644
index 000000000000..4c3a8190d23f
--- /dev/null
+++ b/infra/enforcement/account_keys.py
@@ -0,0 +1,523 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import datetime
+import logging
+import sys
+import yaml
+import argparse
+import os
+from typing import List, Dict, TypedDict, Optional
+from google.cloud import secretmanager
+from google.cloud import iam_admin_v1
+from google.cloud.iam_admin_v1 import types
+from sending import SendingClient
+
+SECRET_MANAGER_LABEL = "beam-infra-secret-manager"
+
+class AuthorizedUser(TypedDict):
+    email: str
+
+class ServiceAccount(TypedDict):
+    account_id: str
+    display_name: str
+    authorized_users: List[AuthorizedUser]
+
+class ServiceAccountsConfig(TypedDict):
+    service_accounts: List[ServiceAccount]
+
+CONFIG_FILE = "config.yml"
+
+class AccountKeysPolicyComplianceCheck:
+    def __init__(self, project_id: str, service_account_keys_file: str, logger: logging.Logger, sending_client: Optional[SendingClient] = None):
+        self.project_id = project_id
+        self.service_account_keys_file = service_account_keys_file
+        self.logger = logger
+        self.sending_client = sending_client
+        self.secret_client = secretmanager.SecretManagerServiceClient()
+        self.service_account_client = iam_admin_v1.IAMClient()
+
+    def _normalize_account_email(self, account_id: str) -> str:
+        """
+        Normalizes the account identifier to a full email format.
+        
+        Args:
+            account_id (str): The unique identifier or email of the service account.
+            
+        Returns:
+            str: The full service account email address.
+        """
+        if "@" in account_id:
+            return account_id
+        else:
+            return f"{account_id}@{self.project_id}.iam.gserviceaccount.com"
+
+    def _denormalize_account_email(self, email: str) -> str:
+        """
+        Denormalizes the full service account email address to its unique identifier.
+
+        Args:
+            email (str): The full service account email address.
+
+        Returns:
+            str: The unique identifier for the service account.
+        """
+        if email.endswith(f"@{self.project_id}.iam.gserviceaccount.com"):
+            return email.split("@")[0]
+        return email
+
+    def _normalize_username(self, username: str) -> str:
+        """
+        Normalizes the username to a consistent format.
+
+        Args:
+            username (str): The username to normalize.
+
+        Returns:
+            str: The normalized username.
+        """
+        if not username.startswith("user:"):
+            return f"user:{username.strip().lower()}"
+        return username
+    
+    def _denormalize_username(self, username: str) -> str:
+        """
+        Denormalizes the username from the consistent format.
+
+        Args:
+            username (str): The normalized username.
+
+        Returns:
+            str: The denormalized username.
+        """
+        if username.startswith("user:"):
+            return username.split(":", 1)[1].strip().lower()
+        return username
+
+    def _get_all_live_service_accounts(self) -> List[str]:
+        """
+        Retrieves all service accounts that are currently active (not disabled) in the project.
+
+        Returns:
+            List[str]: A list of email addresses for all live service accounts.
+        """
+        request = types.ListServiceAccountsRequest()
+        request.name = f"projects/{self.project_id}"
+
+        try:
+            accounts = self.service_account_client.list_service_accounts(request=request)
+            self.logger.debug(f"Retrieved {len(accounts.accounts)} service accounts for project {self.project_id}")
+
+            if not accounts:
+                self.logger.warning(f"No service accounts found in project {self.project_id}.")
+                return []
+
+            return [self._normalize_account_email(account.email) for account in accounts.accounts if not account.disabled]
+        except Exception as e:
+            self.logger.error(f"Failed to retrieve service accounts for project {self.project_id}: {e}")
+            raise
+
+    def _get_all_live_managed_secrets(self) -> List[str]:
+        """
+        Retrieves the list of secrets from the Secret Manager that where created by the beam-secret-service
+
+        Returns:
+            List[str]: A list of secret ids 
+        """
+        try:
+            secrets = list(self.secret_client.list_secrets(request={"parent": f"projects/{self.project_id}"}))
+            self.logger.debug(f"Retrieved {len(secrets)} secrets for project {self.project_id}")
+
+            if not secrets:
+                self.logger.warning(f"No secrets found in project {self.project_id}.")
+                return []
+
+            return [secret.name.split("/")[-1] for secret in secrets if "created_by" in secret.labels and secret.labels["created_by"] == SECRET_MANAGER_LABEL]
+        except Exception as e:
+            self.logger.error(f"Failed to retrieve secrets for project {self.project_id}: {e}")
+            raise
+
+    def _get_all_secret_authorized_users(self, secret_id: str) -> List[str]:
+        """
+        Retrieves a list of all users who have access to the secrets in the Secret Manager.
+
+        Args:
+            secret_id (str): The ID of the secret to check access for.
+        Returns:
+            List[str]: A list of email addresses for all users authorized to access the secrets.
+        """
+        accessor_role = "roles/secretmanager.secretAccessor"
+        resource_name = self.secret_client.secret_path(self.project_id, secret_id)
+
+        try:
+            policy = self.secret_client.get_iam_policy(request={"resource": resource_name})
+            self.logger.debug(f"Retrieved IAM policy for secret '{secret_id}': {policy}")
+
+            if not policy.bindings:
+                self.logger.warning(f"No IAM bindings found for secret '{secret_id}'.")
+                return []
+            
+            authorized_users = []
+            for binding in policy.bindings:
+                if binding.role == accessor_role:
+                    for user in binding.members:
+                        authorized_users.append(self._normalize_username(user))
+            
+            return authorized_users
+        except Exception as e:
+            self.logger.error(f"Failed to get IAM policy for secret '{secret_id}': {e}")
+            raise
+
+    def _read_service_account_keys(self) -> ServiceAccountsConfig:
+        """
+        Reads the service account keys from a YAML file and returns a list of ServiceAccount objects.
+
+        Returns:
+            List[ServiceAccount]: A list of service account declarations.
+        """
+        try:
+            with open(self.service_account_keys_file, "r") as file:
+                keys = yaml.safe_load(file)
+
+                if not keys or keys.get("service_accounts") is None:
+                    return {"service_accounts": []}
+
+                return keys
+        except FileNotFoundError:
+            self.logger.info(f"Service account keys file {self.service_account_keys_file} not found, starting with empty configuration")
+            return {"service_accounts": []}
+        except IOError as e:
+            error_msg = f"Failed to read service account keys from {self.service_account_keys_file}: {e}"
+            self.logger.error(error_msg)
+            raise
+
+    def _to_yaml_file(self, data: List[ServiceAccount], output_file: str, header_info: str = "") -> None:
+        """
+        Writes a list of dictionaries to a YAML file.
+        Include the apache license header on the files
+
+        Args:
+            data: A list of dictionaries containing user permissions and details.
+            output_file: The file path where the YAML output will be written.
+            header_info: A string containing the header information to be included in the YAML file.
+        """
+
+        apache_license_header = """# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+    """
+
+        # Prepare the header with the Apache license
+        header = f"{apache_license_header}\n# {header_info}\n# Generated on {datetime.datetime.now(datetime.timezone.utc).strftime('%Y-%m-%d %H:%M:%S')} UTC\n\n"
+
+        try:
+            with open(output_file, "w") as file:
+                file.write(header)
+                yaml_data = {"service_accounts": data}
+                yaml.dump(yaml_data, file, sort_keys=False, default_flow_style=False, indent=2)
+            self.logger.info(f"Successfully wrote Service Account Keys policy data to {output_file}")
+        except IOError as e:
+            self.logger.error(f"Failed to write to {output_file}: {e}")
+            
+
+    def check_compliance(self) -> List[str]:
+        """
+        Checks the compliance of service account keys with the defined policies.
+
+        Returns:
+            List[str]: A list of compliance issue messages.
+        """
+
+        service_account_data = self._read_service_account_keys()
+        file_service_accounts = service_account_data.get("service_accounts")
+
+        if not file_service_accounts:
+            file_service_accounts = []
+            self.logger.info(f"No service account keys found in the {self.service_account_keys_file}.")
+        
+        compliance_issues = []
+
+        # Check that all service accounts that exist are declared
+        for service_account in self._get_all_live_service_accounts():
+            if self._denormalize_account_email(service_account) not in [account["account_id"] for account in file_service_accounts]:
+                msg = f"Service account '{service_account}' is not declared in the service account keys file."
+                compliance_issues.append(msg)
+                self.logger.warning(msg)
+
+        managed_secrets = self._get_all_live_managed_secrets()
+        extracted_secrets = [f"{self._denormalize_account_email(account['account_id'])}-key" for account in file_service_accounts]
+
+        # Check for managed secrets that are not declared
+        for secret in managed_secrets:
+            if secret not in extracted_secrets:
+                msg = f"Managed secret '{secret}' is not declared in the service account keys file."
+                compliance_issues.append(msg)
+                self.logger.warning(msg)
+
+        # Check for each managed secret if it has the correct permissions
+        for account in file_service_accounts:
+            secret_name = f"{self._denormalize_account_email(account['account_id'])}-key"
+            if secret_name not in managed_secrets:
+                # Skip accounts that don't have managed secrets
+                continue
+                
+            authorized_users = [user["email"] for user in account["authorized_users"]]
+            actual_users = [self._denormalize_username(user) for user in self._get_all_secret_authorized_users(secret_name)]
+            
+            # Sort both lists for proper comparison
+            authorized_users.sort()
+            actual_users.sort()
+            
+            if authorized_users != actual_users:
+                msg = f"Managed secret '{account['account_id']}' does not have the correct permissions. Expected: {authorized_users}, Actual: {actual_users}"
+                compliance_issues.append(msg)
+                self.logger.warning(msg)
+
+        return compliance_issues
+
+    def create_announcement(self, recipient: str) -> None:
+        """
+        Creates an announcement about compliance issues using the SendingClient.
+
+        Args:
+            recipient (str): The email address of the announcement recipient.
+        """
+        if not self.sending_client:
+            raise ValueError("SendingClient is required for creating announcements")
+            
+        diff = self.check_compliance()
+
+        if not diff:
+            self.logger.info("No compliance issues found, no announcement will be created.")
+            return  
+
+        title = f"Account Keys Compliance Issue Detected"
+        body = f"Account keys for project {self.project_id} are not compliant with the defined policies on {self.service_account_keys_file}\n\n"
+        for issue in diff:
+            body += f"- {issue}\n"
+
+        announcement = f"Dear team,\n\nThis is an automated notification about compliance issues detected in the Account Keys policy for project {self.project_id}.\n\n"
+        announcement += f"We found {len(diff)} compliance issue(s) that need your attention.\n"
+        announcement += f"\nPlease check the GitHub issue for detailed information and take appropriate action to resolve these compliance violations."
+
+        self.sending_client.create_announcement(title, body, recipient, announcement)
+
+    def print_announcement(self, recipient: str) -> None:
+        """
+        Prints announcement details instead of sending them (for testing purposes).
+        Args:
+            recipient (str): The email address of the announcement recipient.
+        """
+        if not self.sending_client:
+            raise ValueError("SendingClient is required for printing announcements")
+            
+        diff = self.check_compliance()
+
+        if not diff:
+            self.logger.info("No compliance issues found, no announcement will be printed.")
+            return
+
+        title = f"Account Keys Compliance Issue Detected"
+        body = f"Account keys for project {self.project_id} are not compliant with the defined policies on {self.service_account_keys_file}\n\n"
+        for issue in diff:
+            body += f"- {issue}\n"
+
+        announcement = f"Dear team,\n\nThis is an automated notification about compliance issues detected in the Account Keys policy for project {self.project_id}.\n\n"
+        announcement += f"We found {len(diff)} compliance issue(s) that need your attention.\n"
+        announcement += f"\nPlease check the GitHub issue for detailed information and take appropriate action to resolve these compliance violations."
+
+        self.sending_client.print_announcement(title, body, recipient, announcement)
+
+    def generate_compliance(self) -> None:
+        """
+        Modifies the service account keys file to match the current state of service accounts and secrets.
+        It will just add the non managed service accounts.
+        """
+
+        service_account_data = self._read_service_account_keys()
+        file_service_accounts = service_account_data.get("service_accounts", [])
+        
+        # Ensure file_service_accounts is a list
+        if file_service_accounts is None:
+            file_service_accounts = []
+
+        self.logger.info(f"Found {len(file_service_accounts)} existing service accounts in the keys file")
+        
+        # Check that all service accounts that exist are declared, if not, add them
+        for service_account in self._get_all_live_service_accounts():
+            if self._denormalize_account_email(service_account) not in [account["account_id"] for account in file_service_accounts]:
+                self.logger.info(f"Service account '{service_account}' is not declared in the service account keys file, adding it")
+                file_service_accounts.append({
+                    "account_id": self._denormalize_account_email(service_account),
+                    "display_name": service_account,
+                    "authorized_users": []
+                })
+
+        managed_secrets = self._get_all_live_managed_secrets()
+        extracted_secrets = [f"{self._denormalize_account_email(account['account_id'])}-key" for account in file_service_accounts]
+
+        # Check for managed secrets that are not declared, if not, add them
+        for secret in managed_secrets:
+            if secret not in extracted_secrets:
+                self.logger.info(f"Managed secret '{secret}' is not declared in the service account keys file, adding it")
+                file_service_accounts.append({
+                    "account_id": secret.strip("-key"),
+                    "display_name": self._normalize_account_email(secret.strip("-key")),
+                    "authorized_users": []
+                })
+
+        # Check for each managed secret if it has the correct permissions
+        for account in file_service_accounts:
+            secret_name = f"{self._denormalize_account_email(account['account_id'])}-key"
+            if secret_name not in managed_secrets:
+                continue
+
+            authorized_users = sorted([user["email"] for user in account["authorized_users"]])
+
+            if not authorized_users:
+                self.logger.info(f"Managed secret '{account}' is new, skipping permission check")
+                continue
+
+            actual_users_normalized = sorted(self._get_all_secret_authorized_users(secret_name))
+            actual_users = sorted([self._denormalize_username(user) for user in actual_users_normalized])
+
+            if authorized_users != actual_users:
+                self.logger.info(f"Managed secret '{account}' does not have the correct permissions, updating it")
+                account["authorized_users"] = [{"email": user} for user in actual_users]
+
+        # Remove duplicates based on account_id
+        seen_accounts = set()
+        deduplicated_accounts = []
+        for account in file_service_accounts:
+            if account["account_id"] not in seen_accounts:
+                seen_accounts.add(account["account_id"])
+                deduplicated_accounts.append(account)
+            else:
+                self.logger.info(f"Removing duplicate entry for account '{account['account_id']}'")
+
+        self._to_yaml_file(deduplicated_accounts, self.service_account_keys_file, header_info="Service Account Keys")
+
+def config_process() -> Dict[str, str]:
+    with open(CONFIG_FILE, "r") as file:
+        config = yaml.safe_load(file)
+
+    if not config:
+        raise ValueError("Configuration file is empty or invalid.")
+    
+    config_res = dict()
+
+    config_res["project_id"] = config.get("project_id", "apache-beam-testing")
+    config_res["logging_level"] = config.get("logging", {}).get("level", "INFO")
+    config_res["logging_format"] = config.get("logging", {}).get("format", "[%(asctime)s] %(levelname)s: %(message)s")
+    config_res["service_account_keys_file"] = config.get("service_account_keys_file", "../keys/keys.yaml")
+    config_res["action"] = config.get("action", "check")
+
+    # SendingClient configuration
+    config_res["github_token"] = os.getenv("GITHUB_TOKEN", "")
+    config_res["github_repo"] = os.getenv("GITHUB_REPOSITORY", "apache/beam")
+    config_res["smtp_server"] = os.getenv("SMTP_SERVER", "")
+    config_res["smtp_port"] = os.getenv("SMTP_PORT", 587)
+    config_res["email"] = os.getenv("EMAIL_ADDRESS", "")
+    config_res["password"] = os.getenv("EMAIL_PASSWORD", "")
+    config_res["recipient"] = os.getenv("EMAIL_RECIPIENT", "")
+
+    return config_res
+
+def main():
+    # Parse command line arguments
+    parser = argparse.ArgumentParser(description="Account Keys Compliance Checker")
+    parser.add_argument("--action", choices=["check", "announce", "print", "generate"], 
+                       help="Action to perform: check compliance, create announcement, print announcement, or generate new compliance")
+    args = parser.parse_args()
+
+    config = config_process()
+
+    # Command line argument takes precedence over config file
+    action = args.action if args.action else config.get("action", "check")
+
+    logging.basicConfig(level=getattr(logging, config["logging_level"].upper(), logging.INFO),
+                        format=config["logging_format"])
+    logger = logging.getLogger("AccountKeysPolicyComplianceCheck")
+
+    # Create SendingClient if needed for announcement actions
+    sending_client = None
+    if action in ["announce", "print"]:
+        try:
+            # Provide default values for testing, especially for print action
+            github_token = config["github_token"] or "dummy-token"
+            github_repo = config["github_repo"] or "dummy/repo"
+            smtp_server = config["smtp_server"] or "dummy-server"
+            smtp_port = int(config["smtp_port"]) if config["smtp_port"] else 587
+            email = config["email"] or "dummy@example.com"
+            password = config["password"] or "dummy-password"
+            
+            sending_client = SendingClient(
+                logger=logger,
+                github_token=github_token,
+                github_repo=github_repo,
+                smtp_server=smtp_server,
+                smtp_port=smtp_port,
+                email=email,
+                password=password
+            )
+        except Exception as e:
+            logger.error(f"Failed to initialize SendingClient: {e}")
+            return 1
+
+    logger.info(f"Starting Account Keys policy compliance check with action: {action}")
+    account_keys_checker = AccountKeysPolicyComplianceCheck(config["project_id"], config["service_account_keys_file"], logger, sending_client)
+
+    try:
+        if action == "check":
+            compliance_issues = account_keys_checker.check_compliance()
+            if compliance_issues:
+                logger.warning("Account Keys policy compliance issues found:")
+                for issue in compliance_issues:
+                    logger.warning(issue)
+            else:
+                logger.info("Account Keys policy is compliant.")
+        elif action == "announce":
+            logger.info("Creating announcement for compliance violations...")
+            recipient = config["recipient"] or "admin@example.com"
+            account_keys_checker.create_announcement(recipient)
+        elif action == "print":
+            logger.info("Printing announcement for compliance violations...")
+            recipient = config["recipient"] or "admin@example.com"
+            account_keys_checker.print_announcement(recipient)
+        elif action == "generate":
+            logger.info("Generating new compliance based on current Account Keys policy...")
+            account_keys_checker.generate_compliance()
+        else:
+            logger.error(f"Unknown action: {action}")
+            return 1
+    except Exception as e:
+        logger.error(f"Error executing action '{action}': {e}")
+        return 1
+
+    return 0
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/infra/enforcement/config.yml b/infra/enforcement/config.yml
new file mode 100644
index 000000000000..ae01931567af
--- /dev/null
+++ b/infra/enforcement/config.yml
@@ -0,0 +1,38 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Project ID
+project_id: apache-beam-testing
+
+# Logging
+logging:
+  level: DEBUG
+  format: "[%(asctime)s] %(levelname)s: %(message)s"
+
+# IAM
+
+# Working users file
+users_file: ../iam/users.yml
+
+# Service Account Keys
+service_account_keys_file: ../keys/keys.yaml
+
+# Action to perform when running the script
+# Options:
+#   - check: Check compliance and report issues (default)
+#   - announce: Create/update GitHub issue and send email if compliance violations are found
+#   - print: Print announcement details for testing purposes
+#   - generate: Generate new compliance file based on current IAM policy
+action: announce
diff --git a/infra/enforcement/iam.py b/infra/enforcement/iam.py
new file mode 100644
index 000000000000..5126c674e013
--- /dev/null
+++ b/infra/enforcement/iam.py
@@ -0,0 +1,414 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import datetime
+import logging
+import os
+import sys
+import yaml
+from google.api_core import exceptions
+from google.cloud import resourcemanager_v3
+from typing import Optional, List, Dict, Tuple
+from sending import SendingClient
+
+CONFIG_FILE = "config.yml"
+
+class IAMPolicyComplianceChecker:
+
+    def is_project_service_account_email(self, email: Optional[str]) -> bool:
+        """
+        Returns True if the email is not a service account, or if it is a service account and the email contains the project_id.
+        """
+        if email and email.endswith('.gserviceaccount.com'):
+            return self.project_id in email
+        return True
+
+    def __init__(self, project_id: str, users_file: str, logger: logging.Logger, sending_client: Optional[SendingClient] = None):
+        self.project_id = project_id
+        self.users_file = users_file
+        self.client = resourcemanager_v3.ProjectsClient()
+        self.logger = logger
+        self.sending_client = sending_client
+
+    def _parse_member(self, member: str) -> tuple[str, Optional[str], str]:
+        """Parses an IAM member string to extract type, email, and a derived username.
+
+        Args:
+            member: The IAM member string
+        Returns:
+            A tuple containing:
+                - username: The derived username from the member string.
+                - email: The email address if available, otherwise None.
+                - member_type: The type of the member (e.g., user, serviceAccount, group).
+        """
+        email = None
+        username = member
+
+        # Split the member string to determine type and identifier
+        parts = member.split(':', 1)
+        member_type = parts[0] if len(parts) > 1 else "unknown"
+        identifier = parts[1] if len(parts) > 1 else member
+
+        if member_type in ["user", "serviceAccount", "group"]:
+            email = identifier
+            if '@' in identifier:
+                username = identifier.split('@')[0]
+            else:
+                username = identifier
+        else:
+            username = identifier
+            member_type = "unknown"
+            email = None
+
+        return username, email, member_type
+
+    def _export_project_iam(self) -> List[Dict]:
+        """Exports the IAM policy for a given project to YAML format.
+
+        Returns:
+            A list of dictionaries containing the IAM policy details.
+        """
+
+        try:
+            policy = self.client.get_iam_policy(resource=f"projects/{self.project_id}")
+            self.logger.debug(f"Retrieved IAM policy for project {self.project_id}")
+        except exceptions.NotFound as e:
+            self.logger.error(f"Project {self.project_id} not found: {e}")
+            raise
+        except exceptions.PermissionDenied as e:
+            self.logger.error(f"Permission denied for project {self.project_id}: {e}")
+            raise
+        except Exception as e:
+            self.logger.error(f"An error occurred while retrieving IAM policy for project {self.project_id}: {e}")
+            raise
+
+        members_data = {}
+
+        for binding in policy.bindings:
+            role = binding.role
+
+            for member_str in binding.members:
+                if member_str not in members_data:
+                    username, email_address, member_type = self._parse_member(member_str)
+                    # Skip service accounts not matching the project_id
+                    if member_type == "serviceAccount" and not self.is_project_service_account_email(email_address):
+                        self.logger.debug(f"Skipping service account not matching project_id ({self.project_id}): {email_address}")
+                        continue
+                    if member_type == "unknown":
+                        self.logger.warning(f"Skipping member {member_str} with no email address")
+                        continue  # Skip if no email address is found, probably a malformed member
+                    members_data[member_str] = {
+                        "username": username,
+                        "email": email_address,
+                        "permissions": []
+                    }
+
+                # Skip permissions that have a condition
+                if "withcond" in role:
+                    continue
+
+                permission_entry = {}
+                permission_entry["role"] = role
+
+                members_data[member_str]["permissions"].append(permission_entry)
+
+        output_list = []
+        for data in members_data.values():
+            data["permissions"] = sorted(data["permissions"], key=lambda p: p["role"])
+            output_list.append({
+                "username": data["username"],
+                "email": data["email"],
+                "permissions": data["permissions"]
+            })
+
+        output_list.sort(key=lambda x: x["username"])
+        return output_list
+
+    def _read_project_iam_file(self) -> List[Dict]:
+        """Reads the IAM policy from a YAML file.
+
+        Returns:
+            A list of dictionaries containing the IAM policy details.
+        """
+        try:
+            with open(self.users_file, "r") as file:
+                iam_policy = yaml.safe_load(file)
+
+
+                self.logger.debug(f"Retrieved IAM policy from file for project {self.project_id}")
+                return iam_policy
+        except FileNotFoundError:
+            self.logger.error(f"IAM policy file not found for project {self.project_id}")
+            return []
+        except Exception as e:
+            self.logger.error(f"An error occurred while reading IAM policy file for project {self.project_id}: {e}")
+            return []
+
+    def _to_yaml_file(self, data: List[Dict], output_file: str, header_info: str = "") -> None:
+        """
+        Writes a list of dictionaries to a YAML file.
+        Include the apache license header on the files
+
+        Args:
+            data: A list of dictionaries containing user permissions and details.
+            output_file: The file path where the YAML output will be written.
+            header_info: A string containing the header information to be included in the YAML file.
+        """
+
+        apache_license_header = """# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+    """
+
+        # Prepare the header with the Apache license
+        header = f"{apache_license_header}\n# {header_info}\n# Generated on {datetime.datetime.now(datetime.timezone.utc).strftime('%Y-%m-%d %H:%M:%S')} UTC\n\n"
+
+        try:
+            with open(output_file, "w") as file:
+                file.write(header)
+                yaml.dump(data, file, sort_keys=False, default_flow_style=False, indent=2)
+            self.logger.info(f"Successfully wrote IAM policy data to {output_file}")
+        except IOError as e:
+            self.logger.error(f"Failed to write to {output_file}: {e}")
+            raise
+        
+    def check_compliance(self) -> List[str]:
+        """
+        Checks the compliance of the IAM policy against the defined policies.
+
+        Returns:
+            A list of strings describing any compliance issues found.
+        """
+
+        current_users = {user['email']: user for user in self._export_project_iam() if self.is_project_service_account_email(user.get('email'))}
+        existing_users = {user['email']: user for user in self._read_project_iam_file() if self.is_project_service_account_email(user.get('email'))}
+
+        if not existing_users:
+            error_msg = f"No IAM policy found in the {self.users_file}."
+            self.logger.info(error_msg)
+            raise RuntimeError(error_msg)
+
+        differences = []        
+
+        all_emails = set(current_users.keys()) | set(existing_users.keys())
+
+        for email in sorted(list(all_emails)):
+            current_user = current_users.get(email)
+            existing_user = existing_users.get(email)
+
+            if current_user and not existing_user:
+                differences.append(f"User {email} not found in existing policy.")
+            elif not current_user and existing_user:
+                differences.append(f"User {email} found in policy file but not in GCP.")
+            elif current_user and existing_user:
+                if current_user["permissions"] != existing_user["permissions"]:
+                    msg = f"\nPermissions for user {email} differ."
+                    msg += f"\nIn GCP: {current_user['permissions']}"
+                    msg += f"\nIn {self.users_file}: {existing_user['permissions']}"
+                    self.logger.info(msg)
+                    differences.append(msg)
+
+        return differences
+
+    def create_announcement(self, recipient: str) -> None:
+        """
+        Creates an announcement about compliance issues using the SendingClient.
+
+        Args:
+            recipient (str): The email address of the announcement recipient.
+        """
+        if not self.sending_client:
+            raise ValueError("SendingClient is required for creating announcements")
+            
+        diff = self.check_compliance()
+
+        if not diff:
+            self.logger.info("No compliance issues found, no announcement will be created.")
+            return
+
+        title = f"IAM Policy Non-Compliance Detected"
+        body = f"IAM policy for project {self.project_id} is not compliant with the defined policies on {self.users_file}\n\n"
+        for issue in diff:
+            body += f"- {issue}\n"
+
+        announcement = f"Dear team,\n\nThis is an automated notification about compliance issues detected in the IAM policy for project {self.project_id}.\n\n"
+        announcement += f"We found {len(diff)} compliance issue(s) that need your attention.\n"
+        announcement += f"\nPlease check the GitHub issue for detailed information and take appropriate action to resolve these compliance violations."
+
+        self.sending_client.create_announcement(title, body, recipient, announcement)
+
+    def print_announcement(self, recipient: str) -> None:
+        """
+        Prints announcement details instead of sending them (for testing purposes).
+        
+        Args:
+            recipient (str): The email address of the announcement recipient.
+        """
+        if not self.sending_client:
+            raise ValueError("SendingClient is required for printing announcements")
+            
+        diff = self.check_compliance()
+
+        if not diff:
+            self.logger.info("No compliance issues found, no announcement will be printed.")
+            return
+
+        title = f"IAM Policy Non-Compliance Detected"
+        body = f"IAM policy for project {self.project_id} is not compliant with the defined policies on {self.users_file}\n\n"
+        for issue in diff:
+            body += f"- {issue}\n"
+
+        announcement = f"Dear team,\n\nThis is an automated notification about compliance issues detected in the IAM policy for project {self.project_id}.\n\n"
+        announcement += f"We found {len(diff)} compliance issue(s) that need your attention.\n"
+        announcement += f"\nPlease check the GitHub issue for detailed information and take appropriate action to resolve these compliance violations."
+
+        self.sending_client.print_announcement(title, body, recipient, announcement)
+    
+    def generate_compliance(self) -> None:
+        """
+        Modifies the users file to match the current IAM policy.
+        If no changes are needed, no file will be written.
+        """
+        
+        try:
+            diff = self.check_compliance()
+        except RuntimeError:
+            self.logger.info("No existing IAM policy found.")
+            diff = ["No existing policy found"]
+
+        if not diff or (len(diff) == 1 and "No existing policy found" not in diff[0]):
+            self.logger.info("No compliance issues found, no changes will be made.")
+            return
+
+        current_policy = self._export_project_iam()
+        header_info = f"IAM policy for project {self.project_id}"
+        
+        self._to_yaml_file(current_policy, self.users_file, header_info)
+        self.logger.info(f"Generated new compliance file: {self.users_file}")
+
+def config_process() -> Dict[str, str]:
+    with open(CONFIG_FILE, "r") as file:
+        config = yaml.safe_load(file)
+
+    if not config:
+        raise ValueError("Configuration file is empty or invalid.")
+    
+    config_res = dict()
+
+    config_res["project_id"] = config.get("project_id", "apache-beam-testing")
+    config_res["logging_level"] = config.get("logging", {}).get("level", "INFO")
+    config_res["logging_format"] = config.get("logging", {}).get("format", "[%(asctime)s] %(levelname)s: %(message)s")
+    config_res["users_file"] = config.get("users_file", "../iam/users.yml")
+    config_res["action"] = config.get("action", "check")
+
+    # SendingClient configuration
+    config_res["github_token"] = os.getenv("GITHUB_TOKEN", "")
+    config_res["github_repo"] = os.getenv("GITHUB_REPOSITORY", "apache/beam")
+    config_res["smtp_server"] = os.getenv("SMTP_SERVER", "")
+    config_res["smtp_port"] = os.getenv("SMTP_PORT", 587)
+    config_res["email"] = os.getenv("EMAIL_ADDRESS", "")
+    config_res["password"] = os.getenv("EMAIL_PASSWORD", "")
+    config_res["recipient"] = os.getenv("EMAIL_RECIPIENT", "")
+
+    return config_res
+
+def main():
+    # Parse command line arguments
+    parser = argparse.ArgumentParser(description="IAM Policy Compliance Checker")
+    parser.add_argument("--action", choices=["check", "announce", "print", "generate"], 
+                       help="Action to perform: check compliance, create announcement, print announcement, or generate new compliance")
+    args = parser.parse_args()
+
+    config = config_process()
+
+    # Command line argument takes precedence over config file
+    action = args.action if args.action else config.get("action", "check")
+
+    logging.basicConfig(level=getattr(logging, config["logging_level"].upper(), logging.INFO),
+                        format=config["logging_format"])
+    logger = logging.getLogger("IAMPolicyComplianceChecker")
+
+    # Create SendingClient if needed for announcement actions
+    sending_client = None
+    if action in ["announce", "print"]:
+        try:
+            # Provide default values for testing, especially for print action
+            github_token = config["github_token"] or "dummy-token"
+            github_repo = config["github_repo"] or "dummy/repo"
+            smtp_server = config["smtp_server"] or "dummy-server"
+            smtp_port = int(config["smtp_port"]) if config["smtp_port"] else 587
+            email = config["email"] or "dummy@example.com"
+            password = config["password"] or "dummy-password"
+            
+            sending_client = SendingClient(
+                logger=logger,
+                github_token=github_token,
+                github_repo=github_repo,
+                smtp_server=smtp_server,
+                smtp_port=smtp_port,
+                email=email,
+                password=password
+            )
+        except Exception as e:
+            logger.error(f"Failed to initialize SendingClient: {e}")
+            return 1
+
+    logger.info(f"Starting IAM policy compliance check with action: {action}")
+    iam_checker = IAMPolicyComplianceChecker(config["project_id"], config["users_file"], logger, sending_client)
+
+    try:
+        if action == "check":
+            compliance_issues = iam_checker.check_compliance()
+            if compliance_issues:
+                logger.warning("IAM policy compliance issues found:")
+                for issue in compliance_issues:
+                    logger.warning(issue)
+            else:
+                logger.info("IAM policy is compliant.")
+        elif action == "announce":
+            logger.info("Creating announcement for compliance violations...")
+            recipient = config["recipient"] or "admin@example.com"
+            iam_checker.create_announcement(recipient)
+        elif action == "print":
+            logger.info("Printing announcement for compliance violations...")
+            recipient = config["recipient"] or "admin@example.com"
+            iam_checker.print_announcement(recipient)
+        elif action == "generate":
+            logger.info("Generating new compliance based on current IAM policy...")
+            iam_checker.generate_compliance()
+        else:
+            logger.error(f"Unknown action: {action}")
+            return 1
+    except Exception as e:
+        logger.error(f"Error executing action '{action}': {e}")
+        return 1
+
+    return 0
+
+if __name__ == "__main__":
+    
+    sys.exit(main())
diff --git a/infra/enforcement/requirements.txt b/infra/enforcement/requirements.txt
new file mode 100644
index 000000000000..1015266195cf
--- /dev/null
+++ b/infra/enforcement/requirements.txt
@@ -0,0 +1,24 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This file is used to install the dependencies for the infrastructure
+
+PyYAML==6.0.2
+google-cloud-iam==2.19.0
+google-cloud-resource-manager==1.14.1
+google-cloud-secret-manager==2.24.0
+google-crc32c==1.7.1
+requests==2.32.4
diff --git a/infra/enforcement/sending.py b/infra/enforcement/sending.py
new file mode 100644
index 000000000000..961674ca2f17
--- /dev/null
+++ b/infra/enforcement/sending.py
@@ -0,0 +1,179 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import requests
+import logging
+import smtplib, ssl
+from typing import List, Optional
+from dataclasses import dataclass
+
+@dataclass
+class GitHubIssue:
+    """
+    Represents a GitHub issue.
+    """
+    number: int
+    title: str
+    body: str
+    state: str
+    html_url: str
+    created_at: str
+    updated_at: str
+
+class SendingClient:
+    """
+    Sends notifications about GitHub issues.
+    """
+    def __init__(self, logger: logging.Logger, github_token: str, github_repo: str,
+                 smtp_server: str, smtp_port: int, email: str, password: str):
+
+        required_keys = [github_token, github_repo, smtp_server, smtp_port, email, password]
+
+        if not all(required_keys):
+            raise ValueError("All parameters must be provided.")
+
+        self.github_repo = github_repo
+        self.headers = {
+            "Authorization": f"Bearer {github_token}",
+            "X-GitHub-Api-Version": "2022-11-28",
+            "Accept": "application/vnd.github+json"
+        }
+
+        self.smtp_server = smtp_server
+        self.smtp_port = smtp_port
+        self.email = email
+        self.password = password
+
+        self.logger = logger
+        self.github_api_url = "https://api.github.com"
+
+    def _make_github_request(self, method: str, endpoint: str, json: Optional[dict] = None) -> requests.Response:
+        """
+        Makes a request to the GitHub API.
+
+        Args:
+            method (str): The HTTP method to use (e.g., "GET", "POST", "PATCH").
+            endpoint (str): The API endpoint to call.
+            json (Optional[dict]): The JSON payload to send with the request.
+
+        Returns:
+            requests.Response: The response from the API.
+        """
+        url = f"{self.github_api_url}/{endpoint}"
+        response = requests.request(method, url, headers=self.headers, json=json)
+        
+        if not response.ok:
+            self.logger.error(f"Failed GitHub API request to {endpoint}: {response.status_code} - {response.text}")
+            response.raise_for_status()
+            
+        return response
+
+    def _send_email(self, title: str, body: str, recipient: str) -> None:
+        """
+        Sends an email notification.
+
+        Args:
+            title (str): The title of the email.
+            body (str): The body content of the email.
+            recipient (str): The email address of the recipient.
+        """
+        message = f"Subject: {title}\n\n{body}"
+        context = ssl.create_default_context()
+        with smtplib.SMTP_SSL(self.smtp_server, self.smtp_port, context=context) as server:
+            server.login(self.email, self.password)
+            server.sendmail(self.email, recipient, message)
+
+    def _get_open_issues(self, title: str) -> List[GitHubIssue]:
+        """
+        Retrieves the number of open GitHub issues with a given title.
+
+        Args:
+            title (str): The title of the GitHub issue.
+        """
+        endpoint = f"search/issues/?q=is:issue+repo:{self.github_repo}+in:title+{title}+is:open"
+        response = self._make_github_request("GET", endpoint)
+        issues = response.json().get('items', [])
+        return [GitHubIssue(**issue) for issue in issues]
+
+    def create_issue(self, title: str, body: str) -> GitHubIssue:
+        """
+        Creates a GitHub issue in the specified repository.
+
+        Args:
+            title (str): The title of the GitHub issue.
+            body (str): The body content of the GitHub issue.
+        """
+        endpoint = f"repos/{self.github_repo}/issues"
+        payload = {"title": title, "body": body}
+        response = self._make_github_request("POST", endpoint, json=payload)
+        self.logger.info(f"Successfully created GitHub issue: {title}")
+        return GitHubIssue(**response.json())
+
+    def update_issue_body(self, issue_number: int, new_body: str) -> None:
+        """
+        Updates the body of a GitHub issue in the specified repository.
+
+        Args:
+            issue_number (int): The number of the GitHub issue to update.
+            new_body (str): The new body content for the GitHub issue.
+        """
+        endpoint = f"repos/{self.github_repo}/issues/{issue_number}"
+        payload = {"body": new_body}
+        self._make_github_request("PATCH", endpoint, json=payload)
+        self.logger.info(f"Successfully updated body on GitHub issue: #{issue_number}")
+
+    def create_announcement(self, title: str, body: str, recipient: str, announcement: str) -> None:
+        """
+        This method sends an email with an announcement. The email will point to a GitHub issue.
+
+        Creates a GitHub issue in the specified repository if it doesn't already exist.
+        If multiple open versions exist, the most recent one will be updated.
+
+        Args:
+            title (str): The title of the GitHub issue.
+            body (str): The body content of the GitHub issue.
+            recipient (str): The email address of the recipient.
+            announcement (str): The announcement message to include in the email.
+        """
+        open_issues = self._get_open_issues(title)
+        open_issues.sort(key=lambda x: x.updated_at, reverse=True)
+        if open_issues:
+            self.logger.info(f"Issue with title '{title}' already exists: #{open_issues[0].number}")
+            announcement += f"\n\nRelated GitHub Issue: {open_issues[0].html_url}"
+
+            if open_issues[0].body != body:
+                self.logger.info(f"Updating body of issue #{open_issues[0].number}")
+                self.update_issue_body(open_issues[0].number, body)
+            else:
+                self.logger.info(f"No changes detected for issue #{open_issues[0].number}")
+            self._send_email(title, announcement, recipient)
+        else:
+            new_issue = self.create_issue(title, body)
+            announcement += f"\n\nRelated GitHub Issue: {new_issue.html_url}"
+            self._send_email(title, announcement, recipient)
+
+    def print_announcement(self, title: str, body: str, recipient: str, announcement: str) -> None:
+        """
+        This method prints the data instead of sending the email or creating an issue.
+        This is used for testing.
+        """
+        self.logger.info("Printing announcement...")
+        print(f"Simulating email sending...")
+        print(f"Recipient: {recipient}")
+        print(f"Announcement: {announcement}")
+
+        print("\nSimulating GitHub issue creation...")
+        print(f"Title: {title}")
+        print(f"Body: {body}")
diff --git a/infra/iam/.terraform.lock.hcl b/infra/iam/.terraform.lock.hcl
new file mode 100644
index 000000000000..7347ee97418f
--- /dev/null
+++ b/infra/iam/.terraform.lock.hcl
@@ -0,0 +1,21 @@
+# This file is maintained automatically by "terraform init".
+# Manual edits may be lost in future updates.
+
+provider "registry.terraform.io/hashicorp/google" {
+  version = "6.37.0"
+  hashes = [
+    "h1:uOHr5EZKLKbwco32NyMRrGEwMpD21N/ACPTeYTDQfP0=",
+    "zh:0527880f838690bc32bf3d4bba42b3adefdf81e6614a169b09def759f341e11e",
+    "zh:39b5bf4ddebb7289db800faa14acd92e3591bcc711082058a3ecfbf868c43fdf",
+    "zh:3b0fb69d504d01801fa54dc1b5e8fad59f56a6a1866a7a7475a450e95a690fbf",
+    "zh:6b354bc2d89ee2a0f55fb11a2360ce94d185e7957b21a6b1a5f2cb01aff35e0b",
+    "zh:8c8783c892f3b20b425885f78dcd7fbb68fb10c4b8825b7f807eb4de950d963c",
+    "zh:9291034807a9d4799ecd2cbac33bf3d78aa59c6b734147b9579cd7a3d9ea207c",
+    "zh:9396293aed1fabc476452a2c6d14775f8e03b0d27ad558a18875fee1dc7fa8f8",
+    "zh:9e95308ce490dcf8efb45cd945ecf46c7a8b74ad9c65e25800b65ffd2125e4e1",
+    "zh:9fa9bdd07efd4eaeae1fea44e7926b1abb3d065c938c6cd5fcb0f88b12e09b68",
+    "zh:b684074bc12e46e671aa627849d8f515045983b53fcc56b7d6ded28abcaf4f10",
+    "zh:e35d5e5d89469324b8baf68b1d9599ccc1cfacb43f2cfa73107d1de7ce7f3aa9",
+    "zh:f569b65999264a9416862bca5cd2a6177d94ccb0424f3a4ef424428912b9cb3c",
+  ]
+}
diff --git a/infra/iam/README.md b/infra/iam/README.md
new file mode 100644
index 000000000000..0322881aa856
--- /dev/null
+++ b/infra/iam/README.md
@@ -0,0 +1,186 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+# Infrastructure Permissions Management
+
+This document outlines the structure of the Beam project control of infrastructure permissions and
+ provides instructions on how to manage a user or role's permissions.
+
+## Overview
+
+### Managing User Roles
+
+To manage user roles, edit the `users.yml` file. Add or modify entries under the `users` section to
+ reflect the desired roles for each user. Remember to follow the YAML format:
+
+```yaml
+users:
+  - username: <username>
+    email: <email>
+    permissions:
+      - role: <role>
+        title: <title> (optional)
+        description: <description> (optional)
+        expiry_date: <expiry_date> (optional, format: YYYY-MM-DD)
+      - role: <role> (optional, for multiple roles)
+```
+
+> **Note**: `role/owner` roles are handled separately, adding them to the `users.yml` file will be ignored.
+
+### Applying Changes
+
+After modifying the `users.yml` file, open a Pull Request (PR) to the `infra/iam` directory.
+The changes will be reviewed and when approved, they will be merged into the main branch.
+
+Once the PR is merged, [the GitHub Actions workflow](../../.github/workflows/beam_UserRoles.yml)
+ will automatically trigger and apply the changes to the IAM policies in the GCP project using Terraform.
+
+This will update the IAM policies in the GCP project based on the changes made in the `users.yml` file.
+
+## Directory Structure
+
+### Terraform Configuration
+
+- **main.tf**: The main Terraform configuration file that defines the infrastructure resources and their permissions.
+- **config.auto.tfvars**: Contains the configuration variables for the Terraform project.
+- **users.tf**: Processes the `users.yml` file to associate users with their respective roles.
+- **users.yml**: A YAML file that contains the IAM policies and permissions for users and roles in the Beam project.
+
+### Migration and Automation
+
+- **migrate_roles.py**: Python script for migrating existing IAM policies to the new custom roles structure
+
+## Custom Roles
+
+The Beam project uses custom IAM roles to provide granular permissions for different levels of access to GCP resources. These roles follow a hierarchical structure where higher-level roles inherit permissions from lower-level roles.
+
+### Role Hierarchy
+
+The custom roles are structured in the following hierarchy:
+
+```
+beam_viewer < beam_writer < beam_infra_manager < beam_admin
+```
+
+### Available Roles
+
+#### beam_viewer
+- **Description**: Read-only access to the Beam project resources
+- **Permissions**: View-only access to all services used by Beam
+- **Exclusions**: Secret management permissions, destructive actions
+- **Use case**: For team members who need to monitor and observe project resources
+
+#### beam_writer
+- **Description**: User access to resources in the Beam project
+- **Permissions**: Inherits all `beam_viewer` permissions plus additional permissions for:
+  - BigQuery data access and querying
+  - Cloud SQL instance usage
+  - Container cluster viewing and development
+  - Datastore usage
+  - Network viewing
+- **Exclusions**: Destructive actions, administrative operations
+- **Use case**: For active contributors who need to work with project resources
+
+#### beam_infra_manager
+- **Description**: Editor access to the Beam project infrastructure
+- **Permissions**: Inherits all `beam_writer` permissions plus:
+  - Cloud Build editor access
+  - Service account token creation and usage
+  - Storage object creation and viewing
+  - General editor role (with exclusions)
+- **Exclusions**: Destructive permissions, full administrative access
+- **Use case**: For infrastructure maintainers who manage deployments and resources
+
+#### beam_admin
+- **Description**: Full administrative access to the Beam project
+- **Permissions**: Complete access including:
+  - All previous role permissions
+  - Administrative access to all services
+  - Secret management capabilities
+  - Destructive operations
+- **Exclusions**: None
+- **Use case**: For project administrators and senior maintainers
+
+### Managing Custom Roles
+
+Custom roles are defined and managed through configuration files in the `roles/` directory:
+
+- **roles_config.yaml**: Defines the roles, their hierarchy, services, and base permissions
+- **generate_roles.py**: Python script that generates YAML role definitions from the configuration
+- **roles.tf**: Terraform configuration that applies the custom roles to the GCP project
+
+To modify custom roles:
+
+1. Edit the `roles_config.yaml` file to update role definitions
+2. Run `generate_roles.py` to regenerate the role YAML files
+3. Apply changes through Terraform or via pull request
+
+For detailed information about custom roles management, see the [roles directory README](roles/README.md).
+
+### Migrating from Legacy Roles
+
+The `migrate_roles.py` script helps migrate existing GCP project IAM policies to the new custom roles structure. This is useful when transitioning from standard GCP roles to the custom Beam roles.
+
+#### Migration Rules
+
+The script applies the following hierarchical migration rules:
+
+- **Owner roles**: Left unchanged (highest privilege)
+- **Admin/Secret roles**: Migrated to `beam_admin` (includes all lower roles)
+- **Editor roles**: Migrated to `beam_infra_manager` (includes writer and viewer)
+- **User roles**: Migrated to `beam_writer` (includes viewer)
+- **Viewer roles**: Migrated to `beam_viewer`
+
+#### Using the Migration Script
+
+**Prerequisites:**
+- Google Cloud SDK installed and authenticated
+- Required Python dependencies (install with `pip install -r requirements.txt`)
+- Appropriate GCP permissions to read IAM policies
+
+**Export and migrate IAM policies:**
+```bash
+python migrate_roles.py <PROJECT_ID>
+```
+
+This generates two files:
+- `<PROJECT_ID>.original-roles.yaml`: Current IAM policy export
+- `<PROJECT_ID>.migrated-roles.yaml`: Proposed migration to custom roles
+
+**Analyze permission differences for a specific user:**
+```bash
+python migrate_roles.py <PROJECT_ID> --difference <USER_EMAIL>
+```
+
+This generates:
+- `<PROJECT_ID>.permission-differences.yaml`: Detailed comparison of permissions before and after migration
+
+**Example workflow:**
+```bash
+# Export current IAM policies and generate migration
+python migrate_roles.py apache-beam-testing
+
+# Check permission differences for a specific user
+python migrate_roles.py apache-beam-testing --difference user@example.com
+
+# Review the generated files before applying changes
+# Then apply via Terraform or manual IAM policy updates
+```
+
+The migration script helps ensure a smooth transition to the custom roles while maintaining appropriate access levels for all users.
diff --git a/infra/iam/config.auto.tfvars b/infra/iam/config.auto.tfvars
new file mode 100644
index 000000000000..befa8fcd6000
--- /dev/null
+++ b/infra/iam/config.auto.tfvars
@@ -0,0 +1,21 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This file is used for general configuration of the Terraform project,
+
+# GCP Project ID
+project_id = "apache-beam-testing"
diff --git a/infra/iam/main.tf b/infra/iam/main.tf
new file mode 100644
index 000000000000..42d1ceb62fc8
--- /dev/null
+++ b/infra/iam/main.tf
@@ -0,0 +1,40 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This file defines the general configuration for the Terraform project.
+terraform {
+  required_providers {
+    google = {
+      source  = "hashicorp/google"
+      version = "6.37.0"
+    }
+  }
+
+  backend "gcs" {
+    bucket  = "beam-terraform-infra-state"
+    prefix  = "terraform/state"
+  }
+}
+
+variable "project_id" {
+  description = "The GCP project ID."
+  type        = string
+}
+
+module "beam_roles" {
+  source     = "./roles"
+  project_id = var.project_id
+}
\ No newline at end of file
diff --git a/infra/iam/migrate_roles.py b/infra/iam/migrate_roles.py
new file mode 100644
index 000000000000..3abb9b7bcb0b
--- /dev/null
+++ b/infra/iam/migrate_roles.py
@@ -0,0 +1,340 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This script is used to export the IAM policy of a Google Cloud project to a YAML format.
+# It retrieves the IAM policy bindings, parses the members, and formats the output in a structured
+# YAML format, excluding service accounts and groups. The output includes usernames, emails, and
+# their associated permissions, with optional conditions for roles that have conditions attached.
+# You need to have the Google Cloud SDK installed and authenticated to run this script.
+
+import argparse
+import os
+import sys
+import yaml
+import roles.generate_roles as generate_roles
+from generate import export_project_iam, to_yaml_file
+from google.cloud.iam_admin_v1 import GetRoleRequest, IAMClient
+
+def migrate_permissions(data: list) -> list:
+    """
+    Migrates permissions from the permissions to the new roles defined on beam_roles/ directory.
+
+    The rules are:
+    - If the user has owner role, leave it as is, remove any other role as it is redundant.
+    - If the user has any admin or secret related role, it will be migrated to the beam_admin role.
+    - If the user has an editor role or any user role but not an admin or secret related role, it will be migrated to the beam_infra_manager role.
+    - If the user has a role that is not only viewer, it will be migrated to the beam_committer role.
+    - The users with just viewer roles will be migrated to the beam_viewer role.
+
+    The rules are in a hierarchical order, meaning that if a user has a high role, it will also have the lower roles.
+
+    Args:
+        data: A list of dictionaries containing user permissions and details.
+    Returns:
+        A list of dictionaries with migrated permissions.
+    """
+
+    migrated_data = []
+
+    for item in data:
+        username = item["username"]
+        email = item["email"]
+        permissions = item["permissions"]
+
+        # Initialize the new roles
+        new_roles = {
+            "beam_owner": False,
+            "beam_admin": False,
+            "beam_infra_manager": False,
+            "beam_committer": False,
+            "beam_viewer": False
+        }
+
+        for permission in permissions:
+            role = permission["role"]
+
+            # If the role is 'roles/owner', it is considered an owner role.
+            if role == "roles/owner":
+                new_roles["beam_owner"] = True
+            # If it ends with 'admin' or containes 'secretmanager' in the role, it is considered an admin role. Case insensitive.
+            elif 'admin' in role.lower() or 'secretmanager' in role.lower():
+                new_roles["beam_admin"] = True
+                new_roles["beam_infra_manager"] = True
+                new_roles["beam_committer"] = True
+                new_roles["beam_viewer"] = True
+            # If it is an editor role, it will be migrated to the beam_infra_manager.
+            elif role == "roles/editor":
+                new_roles["beam_infra_manager"] = True
+                new_roles["beam_committer"] = True
+                new_roles["beam_viewer"] = True
+            elif role != "roles/viewer":
+                # If it is a role that is not only viewer, it will be migrated to the beam_committer role.
+                new_roles["beam_committer"] = True
+                new_roles["beam_viewer"] = True
+            # If it is a viewer role, it will be migrated to the beam_viewer role.
+            else:
+                new_roles["beam_viewer"] = True
+
+        # Create the migrated entry
+        migrated_entry = {
+            "username": username,
+            "email": email,
+            "permissions": []
+        }
+
+        if new_roles["beam_owner"]:
+            migrated_entry["permissions"].append({"role": "roles/owner"})
+        else:
+            if new_roles["beam_admin"]:
+                migrated_entry["permissions"].append({"role": "projects/PROJECT-ID/roles/beam_admin"})
+            if new_roles["beam_infra_manager"]:
+                migrated_entry["permissions"].append({"role": "projects/PROJECT-ID/roles/beam_infra_manager"})
+            if new_roles["beam_committer"]:
+                migrated_entry["permissions"].append({"role": "projects/PROJECT-ID/roles/beam_committer"})
+            if new_roles["beam_viewer"]:
+                migrated_entry["permissions"].append({"role": "projects/PROJECT-ID/roles/beam_viewer"})
+
+        migrated_data.append(migrated_entry)
+
+    return migrated_data
+
+def get_gcp_role_permissions(role_id: str) -> list:
+    """
+    Retrieves the permissions associated to a google cloud role.
+    Args:
+        project_id: The ID of the Google Cloud project.
+        role_id: The name of the role to retrieve permissions for.
+    Returns:
+        A list of permissions associated with the specified role.
+    """
+    client = IAMClient()
+
+    request = GetRoleRequest(name=role_id)
+    role = client.get_role(request=request)
+
+    return list(role.included_permissions)
+
+def get_roles_from_file(file_path: str) -> list:
+    """
+    Reads a YAML file containing roles and returns a list of dictionaries with user data.
+
+    Args:
+        file_path: The path to the YAML file containing roles.
+    Returns:
+        A list of dictionaries with user data.
+    """
+    with open(file_path, 'r') as file:
+        data = yaml.safe_load(file)
+
+    roles = []
+    for role in data:
+        email = role.get("email")
+        username = role.get("username")
+        permissions = role.get("permissions", [])
+
+        roles.append({
+            "email": email,
+            "username": username,
+            "permissions": permissions
+        })
+
+    return roles
+
+def permission_differences(project_id: str, user_email: str) -> list:
+    """
+    Generates a list of differences between the original and migrated permissions for a user.
+    It gets the permission from the generated files, so it is expected that the files are already generated and up to date.
+
+    Args:
+        project_id: The ID of the Google Cloud project.
+        user_email: The email of the user to compare permissions for.
+    Returns:
+        A list of dictionaries containing the differences in permissions for the specified user.
+    """
+
+    cache = {}
+    user_differences = {}
+
+    original = get_roles_from_file(f"{project_id}.original-roles.yaml")
+    migrated = get_roles_from_file(f"{project_id}.migrated-roles.yaml")
+
+    # Get the permissions on the beam_roles
+    beam_roles = generate_roles.get_roles()
+    for role_name, role_data in beam_roles.items():
+        permissions = role_data["permissions"]
+        cache[role_name] = permissions
+
+    # Get the permissions for the original roles
+    for user in original:
+        username = user["username"]
+        email = user["email"]
+
+        # Skip if the user email does not match the specified user_email
+        if user_email and email != user_email:
+            continue
+
+        original_roles = user["permissions"]
+
+        original_permissions = []
+
+        for role in original_roles:
+            if '_withcond_' in role['role']:
+                # Skip roles with conditions, as they are not supported in the new roles
+                continue
+            if 'organizations/' in role['role']:
+                # Skip organization roles, as they are not supported in the new roles
+                continue
+
+            if role['role'] not in cache:
+                permissions = get_gcp_role_permissions(role["role"])
+                cache[role['role']] = sorted(permissions)
+            original_permissions.extend(cache[role['role']])
+
+        # Initialize the user differences entry
+        user_differences[username] = {
+            "email": email,
+            "original_roles": original_roles,
+            "original_permissions": sorted(original_permissions),
+            "migrated_roles": [],
+            "migrated_permissions": [],
+            "differences": []
+        }
+
+    # Get the permissions for the migrated roles
+    for user in migrated:
+        username = user["username"]
+        email = user["email"]
+
+        # Skip if the user email does not match the specified user_email
+        if user_email and email != user_email:
+            continue
+
+        migrated_roles = user["permissions"]
+
+        migrated_permissions = []
+
+        for role in migrated_roles:
+            full_role_name = role["role"]
+            # Owner is a special case, it should not be migrated to any other role.
+            if "roles/owner" in full_role_name:
+                migrated_permissions.extend(get_gcp_role_permissions(full_role_name))
+            else:
+                role_name = full_role_name.split('roles/')[1]
+                migrated_permissions.extend(cache[role_name])
+
+        user_differences[username]["migrated_roles"] = migrated_roles
+        user_differences[username]["migrated_permissions"] = sorted(migrated_permissions)
+
+    # Compare original and migrated permissions
+    differences_list = []
+
+    for username, user_data in user_differences.items():
+        original_permissions = user_data["original_permissions"]
+        migrated_permissions = user_data["migrated_permissions"]
+
+        # Find differences in permissions
+        original_set = set(original_permissions)
+        migrated_set = set(migrated_permissions)
+
+        added_permissions = migrated_set.difference(original_set)
+        removed_permissions = original_set.difference(migrated_set)
+
+        if added_permissions or removed_permissions:
+            differences = {
+                "username": username,
+                "email": user_data["email"],
+                "added_permissions": sorted(list(added_permissions)),
+                "removed_permissions": sorted(list(removed_permissions))
+            }
+            differences_list.append(differences)
+
+    return differences_list
+
+def main():
+    """
+    Main function to run the script.
+
+    This function parses command-line arguments to either export IAM policies
+    or generate permission differences for a specified GCP project.
+    """
+    parser = argparse.ArgumentParser(
+        description="Export IAM policies or generate permission differences for a GCP project."
+    )
+    parser.add_argument(
+        "project_id",
+        help="The Google Cloud project ID."
+    )
+    parser.add_argument(
+        "--difference",
+        dest="user_email",
+        metavar="USER_EMAIL",
+        help="Generate permission differences for the specified user email."
+    )
+
+    args = parser.parse_args()
+
+    project_id = args.project_id
+    user_email = args.user_email
+
+    if user_email:
+        # If the iam policy has not been generated yet, it will generate the original IAM policy first.
+        if not os.path.exists(f"{project_id}.original-roles.yaml") or not os.path.exists(f"{project_id}.migrated-roles.yaml"):
+            print(f"Original IAM policy for project {project_id} not found. Generating original and migrated roles first.")
+
+            print(f"Exporting IAM policy for project {project_id}...")
+            iam_data = export_project_iam(project_id)
+
+            original_filename = f"{project_id}.original-roles.yaml"
+            original_header = f"Exported original IAM policy for project {project_id}"
+            to_yaml_file(iam_data, original_filename, header_info=original_header)
+
+            print("Migrating permissions to new roles...")
+            migrated_data = migrate_permissions(iam_data)
+            migrated_filename = f"{project_id}.migrated-roles.yaml"
+            migrated_header = f"Migrated IAM policy for project {project_id} to new beam_roles"
+            to_yaml_file(migrated_data, migrated_filename, header_info=migrated_header)
+
+            print(f"Generated {original_filename} and {migrated_filename}")
+
+        print(f"Generating permission differences for {user_email} in project {project_id}...")
+        differences = permission_differences(project_id, user_email)
+        if differences:
+            output_filename = f"{project_id}.permission-differences.yaml"
+            header = f"Permission differences for user {user_email} in project {project_id}"
+            to_yaml_file(differences, output_filename, header_info=header)
+            print(f"Generated {output_filename}")
+        else:
+            print(f"No permission differences found for user {user_email} in project {project_id}.")
+    else:
+        print(f"Exporting IAM policy for project {project_id}...")
+        iam_data = export_project_iam(project_id)
+
+        original_filename = f"{project_id}.original-roles.yaml"
+        original_header = f"Exported original IAM policy for project {project_id}"
+        to_yaml_file(iam_data, original_filename, header_info=original_header)
+
+        print("Migrating permissions to new roles...")
+        migrated_data = migrate_permissions(iam_data)
+        migrated_filename = f"{project_id}.migrated-roles.yaml"
+        migrated_header = f"Migrated IAM policy for project {project_id} to new beam_roles"
+        to_yaml_file(migrated_data, migrated_filename, header_info=migrated_header)
+
+        print(f"Generated {original_filename} and {migrated_filename}")
+        print(f"To generate permission differences, run: python {sys.argv[0]} {project_id} --difference <user_email>")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/infra/iam/requirements.txt b/infra/iam/requirements.txt
new file mode 100644
index 000000000000..4e4ee15bbe10
--- /dev/null
+++ b/infra/iam/requirements.txt
@@ -0,0 +1,22 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This file is used to install the dependencies for the infrastructure
+
+PyYAML==6.0.2
+google-cloud==0.34.0
+google-cloud-iam==2.19.0
+google-cloud-resource-manager==1.14.1
\ No newline at end of file
diff --git a/infra/iam/roles/README.md b/infra/iam/roles/README.md
new file mode 100644
index 000000000000..94b04b8f27b5
--- /dev/null
+++ b/infra/iam/roles/README.md
@@ -0,0 +1,75 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+# Beam custom roles
+
+This document describes the custom roles defined for the Beam project and their associated permissions.
+
+
+## Roles
+
+The following files are used to define and manage roles:
+
+- `roles_config.yaml`: A YAML file that defines the roles and their associated services.
+- `generate_roles.py`: A Python script that generates yaml files for the roles.
+- `roles.tf`: A Terraform file that applies that generate the roles described over the custom roles created.
+
+### Defined roles
+
+The roles are defined in the `roles_config.yaml` file. Each role includes a name, description, and a list of services associated with it.
+
+The defined roles are:
+
+- `beam_viewer`: Read-only access to the Beam project. Excludes secret management permissions.
+- `beam_writer`: User access to the the resources in the Beam project.
+- `beam_infra_manager`: Editor access to the Beam project, excluding destructive permissions.
+- `beam_admin`: Full access to the Beam project, including destructive capabilities and secret management.
+
+Roles are structured in a hierarchy, allowing for inheritance of permissions. Each role builds upon the previous one. The hierarchy is as follows:
+
+```plaintext
+beam_viewer < beam_writer < beam_infra_manager < beam_admin
+```
+
+### Modifying Roles services
+
+Each role can have its associated base roles and services. The `roles_config.yaml` file defines the services associated with each role. For example, the `beam_viewer` role has read-only access to the project, while the `beam_infra_manager` role has editor access but excludes destructive permissions.
+
+To modify the services associated with a role, edit the `roles_config.yaml` file and update the relevant service and roles lists under each role. After making changes, re-run the `generate_roles.py` script to apply the updates.
+
+The `generate_roles.py` script, install the dependencies using:
+
+```bash
+pip install -r requirements.txt
+```
+
+After modifying the `roles_config.yaml` file, run the script to generate the yaml files for the roles:
+
+```bash
+python3 generate_roles.py
+```
+
+This will update the `beam_roles` directory with the new role definitions. You do not need any GCP permissions to run this script, as it only generates local files.
+
+To apply the changes to the GCP project, ensure you have a owner role in the GCP project, go to the main `infra/iam` directory and run the following Terraform commands:
+
+```bash
+terraform plan
+terraform apply
+```
diff --git a/infra/iam/roles/beam_admin.role.yaml b/infra/iam/roles/beam_admin.role.yaml
new file mode 100644
index 000000000000..4296196c495e
--- /dev/null
+++ b/infra/iam/roles/beam_admin.role.yaml
@@ -0,0 +1,674 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This file is auto-generated by generate_roles.py.
+# Do not edit manually.
+
+# This file was generated on 2025-08-11 14:34:54 UTC
+
+description: This is the beam_admin role
+permissions:
+- artifactregistry.attachments.delete
+- artifactregistry.files.delete
+- artifactregistry.packages.delete
+- artifactregistry.repositories.createTagBinding
+- artifactregistry.repositories.delete
+- artifactregistry.repositories.deleteTagBinding
+- artifactregistry.repositories.setIamPolicy
+- artifactregistry.rules.delete
+- artifactregistry.tags.delete
+- artifactregistry.versions.delete
+- biglake.catalogs.delete
+- biglake.catalogs.setIamPolicy
+- biglake.databases.delete
+- biglake.locks.delete
+- biglake.namespaces.delete
+- biglake.namespaces.setIamPolicy
+- biglake.tables.delete
+- biglake.tables.setIamPolicy
+- bigquery.capacityCommitments.create
+- bigquery.capacityCommitments.delete
+- bigquery.connections.delegate
+- bigquery.connections.delete
+- bigquery.connections.setIamPolicy
+- bigquery.dataPolicies.delete
+- bigquery.dataPolicies.setIamPolicy
+- bigquery.datasets.createTagBinding
+- bigquery.datasets.delete
+- bigquery.datasets.deleteTagBinding
+- bigquery.datasets.link
+- bigquery.datasets.listSharedDatasetUsage
+- bigquery.datasets.setIamPolicy
+- bigquery.datasets.update
+- bigquery.jobs.delete
+- bigquery.jobs.listAll
+- bigquery.jobs.update
+- bigquery.models.delete
+- bigquery.reservationAssignments.delete
+- bigquery.reservationGroups.delete
+- bigquery.reservations.delete
+- bigquery.routines.delete
+- bigquery.rowAccessPolicies.delete
+- bigquery.rowAccessPolicies.setIamPolicy
+- bigquery.savedqueries.delete
+- bigquery.tables.create
+- bigquery.tables.createTagBinding
+- bigquery.tables.delete
+- bigquery.tables.deleteSnapshot
+- bigquery.tables.deleteTagBinding
+- bigquery.tables.setCategory
+- bigquery.tables.setIamPolicy
+- bigquery.tables.update
+- bigquery.tables.updateData
+- bigquery.tables.updateTag
+- bigquerymigration.workflows.delete
+- cloudasset.feeds.list
+- cloudasset.othercloudconnections.delete
+- cloudasset.savedqueries.delete
+- cloudbuild.connections.delete
+- cloudbuild.connections.setIamPolicy
+- cloudbuild.integrations.delete
+- cloudbuild.repositories.delete
+- cloudbuild.workerpools.delete
+- cloudfunctions.functions.delete
+- cloudfunctions.functions.setIamPolicy
+- cloudkms.cryptoKeys.setIamPolicy
+- cloudkms.ekmConfigs.setIamPolicy
+- cloudkms.ekmConnections.setIamPolicy
+- cloudkms.importJobs.setIamPolicy
+- cloudkms.keyRings.setIamPolicy
+- cloudsql.backupRuns.delete
+- cloudsql.databases.delete
+- cloudsql.instances.delete
+- cloudsql.sslCerts.delete
+- cloudsql.users.delete
+- compute.addresses.createTagBinding
+- compute.addresses.delete
+- compute.addresses.deleteTagBinding
+- compute.advice.calendarMode
+- compute.autoscalers.delete
+- compute.backendBuckets.createTagBinding
+- compute.backendBuckets.delete
+- compute.backendBuckets.deleteTagBinding
+- compute.backendBuckets.setIamPolicy
+- compute.backendServices.createTagBinding
+- compute.backendServices.delete
+- compute.backendServices.deleteTagBinding
+- compute.backendServices.setIamPolicy
+- compute.crossSiteNetworks.delete
+- compute.disks.createTagBinding
+- compute.disks.delete
+- compute.disks.deleteTagBinding
+- compute.disks.setIamPolicy
+- compute.externalVpnGateways.createTagBinding
+- compute.externalVpnGateways.delete
+- compute.externalVpnGateways.deleteTagBinding
+- compute.firewallPolicies.createTagBinding
+- compute.firewallPolicies.delete
+- compute.firewallPolicies.deleteTagBinding
+- compute.firewallPolicies.setIamPolicy
+- compute.firewalls.createTagBinding
+- compute.firewalls.delete
+- compute.firewalls.deleteTagBinding
+- compute.forwardingRules.createTagBinding
+- compute.forwardingRules.delete
+- compute.forwardingRules.deleteTagBinding
+- compute.futureReservations.cancel
+- compute.futureReservations.delete
+- compute.futureReservations.setIamPolicy
+- compute.globalAddresses.createTagBinding
+- compute.globalAddresses.delete
+- compute.globalAddresses.deleteTagBinding
+- compute.globalForwardingRules.createTagBinding
+- compute.globalForwardingRules.delete
+- compute.globalForwardingRules.deleteTagBinding
+- compute.globalNetworkEndpointGroups.createTagBinding
+- compute.globalNetworkEndpointGroups.delete
+- compute.globalNetworkEndpointGroups.deleteTagBinding
+- compute.globalOperations.delete
+- compute.globalPublicDelegatedPrefixes.delete
+- compute.healthChecks.createTagBinding
+- compute.healthChecks.delete
+- compute.healthChecks.deleteTagBinding
+- compute.httpHealthChecks.createTagBinding
+- compute.httpHealthChecks.delete
+- compute.httpHealthChecks.deleteTagBinding
+- compute.httpsHealthChecks.createTagBinding
+- compute.httpsHealthChecks.delete
+- compute.httpsHealthChecks.deleteTagBinding
+- compute.images.createTagBinding
+- compute.images.delete
+- compute.images.deleteTagBinding
+- compute.instanceGroupManagers.createTagBinding
+- compute.instanceGroupManagers.delete
+- compute.instanceGroupManagers.deleteTagBinding
+- compute.instanceGroups.createTagBinding
+- compute.instanceGroups.delete
+- compute.instanceGroups.deleteTagBinding
+- compute.instanceTemplates.delete
+- compute.instanceTemplates.setIamPolicy
+- compute.instances.createTagBinding
+- compute.instances.delete
+- compute.instances.deleteTagBinding
+- compute.instances.setIamPolicy
+- compute.instances.stop
+- compute.instantSnapshots.delete
+- compute.instantSnapshots.setIamPolicy
+- compute.interconnectAttachmentGroups.delete
+- compute.interconnectAttachments.createTagBinding
+- compute.interconnectAttachments.deleteTagBinding
+- compute.interconnectGroups.delete
+- compute.interconnects.createTagBinding
+- compute.interconnects.deleteTagBinding
+- compute.interconnects.getMacsecConfig
+- compute.licenseCodes.setIamPolicy
+- compute.licenses.setIamPolicy
+- compute.machineImages.delete
+- compute.machineImages.setIamPolicy
+- compute.multiMig.delete
+- compute.networkAttachments.createTagBinding
+- compute.networkAttachments.delete
+- compute.networkAttachments.deleteTagBinding
+- compute.networkAttachments.setIamPolicy
+- compute.networkEdgeSecurityServices.createTagBinding
+- compute.networkEdgeSecurityServices.delete
+- compute.networkEdgeSecurityServices.deleteTagBinding
+- compute.networkEndpointGroups.createTagBinding
+- compute.networkEndpointGroups.delete
+- compute.networkEndpointGroups.deleteTagBinding
+- compute.networks.createTagBinding
+- compute.networks.delete
+- compute.networks.deleteTagBinding
+- compute.nodeGroups.delete
+- compute.nodeGroups.setIamPolicy
+- compute.nodeTemplates.delete
+- compute.nodeTemplates.setIamPolicy
+- compute.organizations.disableXpnHost
+- compute.organizations.disableXpnResource
+- compute.organizations.enableXpnHost
+- compute.organizations.enableXpnResource
+- compute.packetMirrorings.createTagBinding
+- compute.packetMirrorings.delete
+- compute.packetMirrorings.deleteTagBinding
+- compute.publicAdvertisedPrefixes.delete
+- compute.publicDelegatedPrefixes.createTagBinding
+- compute.publicDelegatedPrefixes.delete
+- compute.publicDelegatedPrefixes.deleteTagBinding
+- compute.regionBackendServices.createTagBinding
+- compute.regionBackendServices.delete
+- compute.regionBackendServices.deleteTagBinding
+- compute.regionBackendServices.setIamPolicy
+- compute.regionFirewallPolicies.createTagBinding
+- compute.regionFirewallPolicies.delete
+- compute.regionFirewallPolicies.deleteTagBinding
+- compute.regionFirewallPolicies.setIamPolicy
+- compute.regionHealthCheckServices.delete
+- compute.regionHealthChecks.createTagBinding
+- compute.regionHealthChecks.delete
+- compute.regionHealthChecks.deleteTagBinding
+- compute.regionNetworkEndpointGroups.createTagBinding
+- compute.regionNetworkEndpointGroups.delete
+- compute.regionNetworkEndpointGroups.deleteTagBinding
+- compute.regionNotificationEndpoints.delete
+- compute.regionOperations.delete
+- compute.regionSecurityPolicies.createTagBinding
+- compute.regionSecurityPolicies.delete
+- compute.regionSecurityPolicies.deleteTagBinding
+- compute.regionSslCertificates.createTagBinding
+- compute.regionSslCertificates.delete
+- compute.regionSslCertificates.deleteTagBinding
+- compute.regionSslPolicies.createTagBinding
+- compute.regionSslPolicies.delete
+- compute.regionSslPolicies.deleteTagBinding
+- compute.regionTargetHttpProxies.createTagBinding
+- compute.regionTargetHttpProxies.delete
+- compute.regionTargetHttpProxies.deleteTagBinding
+- compute.regionTargetHttpsProxies.createTagBinding
+- compute.regionTargetHttpsProxies.delete
+- compute.regionTargetHttpsProxies.deleteTagBinding
+- compute.regionTargetTcpProxies.createTagBinding
+- compute.regionTargetTcpProxies.delete
+- compute.regionTargetTcpProxies.deleteTagBinding
+- compute.regionUrlMaps.createTagBinding
+- compute.regionUrlMaps.delete
+- compute.regionUrlMaps.deleteTagBinding
+- compute.reservations.delete
+- compute.resourcePolicies.delete
+- compute.resourcePolicies.setIamPolicy
+- compute.routers.createTagBinding
+- compute.routers.delete
+- compute.routers.deleteTagBinding
+- compute.routes.createTagBinding
+- compute.routes.delete
+- compute.routes.deleteTagBinding
+- compute.securityPolicies.createTagBinding
+- compute.securityPolicies.deleteTagBinding
+- compute.serviceAttachments.createTagBinding
+- compute.serviceAttachments.delete
+- compute.serviceAttachments.deleteTagBinding
+- compute.serviceAttachments.setIamPolicy
+- compute.snapshots.createTagBinding
+- compute.snapshots.delete
+- compute.snapshots.deleteTagBinding
+- compute.snapshots.setIamPolicy
+- compute.sslCertificates.createTagBinding
+- compute.sslCertificates.delete
+- compute.sslCertificates.deleteTagBinding
+- compute.sslPolicies.createTagBinding
+- compute.sslPolicies.deleteTagBinding
+- compute.storagePools.delete
+- compute.storagePools.setIamPolicy
+- compute.subnetworks.createTagBinding
+- compute.subnetworks.delete
+- compute.subnetworks.deleteTagBinding
+- compute.subnetworks.setIamPolicy
+- compute.targetGrpcProxies.createTagBinding
+- compute.targetGrpcProxies.delete
+- compute.targetGrpcProxies.deleteTagBinding
+- compute.targetHttpProxies.createTagBinding
+- compute.targetHttpProxies.delete
+- compute.targetHttpProxies.deleteTagBinding
+- compute.targetHttpsProxies.createTagBinding
+- compute.targetHttpsProxies.delete
+- compute.targetHttpsProxies.deleteTagBinding
+- compute.targetInstances.createTagBinding
+- compute.targetInstances.delete
+- compute.targetInstances.deleteTagBinding
+- compute.targetPools.createTagBinding
+- compute.targetPools.delete
+- compute.targetPools.deleteTagBinding
+- compute.targetSslProxies.createTagBinding
+- compute.targetSslProxies.delete
+- compute.targetSslProxies.deleteTagBinding
+- compute.targetTcpProxies.createTagBinding
+- compute.targetTcpProxies.delete
+- compute.targetTcpProxies.deleteTagBinding
+- compute.targetVpnGateways.createTagBinding
+- compute.targetVpnGateways.delete
+- compute.targetVpnGateways.deleteTagBinding
+- compute.urlMaps.createTagBinding
+- compute.urlMaps.deleteTagBinding
+- compute.vpnGateways.createTagBinding
+- compute.vpnGateways.delete
+- compute.vpnGateways.deleteTagBinding
+- compute.vpnTunnels.createTagBinding
+- compute.vpnTunnels.delete
+- compute.vpnTunnels.deleteTagBinding
+- compute.wireGroups.delete
+- compute.zoneOperations.delete
+- container.apiServices.delete
+- container.auditSinks.delete
+- container.backendConfigs.delete
+- container.certificateSigningRequests.approve
+- container.certificateSigningRequests.delete
+- container.clusterRoleBindings.create
+- container.clusterRoleBindings.delete
+- container.clusterRoleBindings.update
+- container.clusterRoles.bind
+- container.clusterRoles.create
+- container.clusterRoles.delete
+- container.clusterRoles.update
+- container.clusters.createTagBinding
+- container.clusters.delete
+- container.clusters.deleteTagBinding
+- container.configMaps.delete
+- container.controllerRevisions.delete
+- container.cronJobs.delete
+- container.csiDrivers.delete
+- container.csiNodeInfos.delete
+- container.csiNodes.delete
+- container.customResourceDefinitions.delete
+- container.daemonSets.delete
+- container.deployments.delete
+- container.endpointSlices.delete
+- container.endpoints.delete
+- container.events.delete
+- container.frontendConfigs.delete
+- container.horizontalPodAutoscalers.delete
+- container.hostServiceAgent.use
+- container.ingresses.delete
+- container.jobs.delete
+- container.leases.delete
+- container.limitRanges.delete
+- container.managedCertificates.delete
+- container.mutatingWebhookConfigurations.delete
+- container.namespaces.delete
+- container.networkPolicies.delete
+- container.nodes.delete
+- container.persistentVolumeClaims.delete
+- container.persistentVolumes.delete
+- container.podDisruptionBudgets.delete
+- container.podSecurityPolicies.delete
+- container.podTemplates.delete
+- container.pods.delete
+- container.priorityClasses.delete
+- container.replicaSets.delete
+- container.replicationControllers.delete
+- container.resourceQuotas.delete
+- container.roleBindings.create
+- container.roleBindings.delete
+- container.roleBindings.update
+- container.roles.bind
+- container.roles.create
+- container.roles.delete
+- container.roles.update
+- container.runtimeClasses.delete
+- container.secrets.delete
+- container.serviceAccounts.delete
+- container.services.delete
+- container.statefulSets.delete
+- container.storageClasses.delete
+- container.storageStates.delete
+- container.storageVersionMigrations.delete
+- container.thirdPartyObjects.delete
+- container.updateInfos.delete
+- container.validatingWebhookConfigurations.delete
+- container.volumeAttachments.delete
+- container.volumeSnapshotClasses.delete
+- container.volumeSnapshotContents.delete
+- container.volumeSnapshots.delete
+- containeranalysis.notes.delete
+- containeranalysis.notes.setIamPolicy
+- containeranalysis.occurrences.delete
+- containeranalysis.occurrences.setIamPolicy
+- dataflow.jobs.cancel
+- dataflow.snapshots.delete
+- dataform.commentThreads.delete
+- dataform.comments.delete
+- dataform.releaseConfigs.delete
+- dataform.repositories.delete
+- dataform.repositories.setIamPolicy
+- dataform.workflowConfigs.delete
+- dataform.workflowInvocations.cancel
+- dataform.workflowInvocations.delete
+- dataform.workspaces.delete
+- dataform.workspaces.setIamPolicy
+- dataplex.aspectTypes.delete
+- dataplex.aspectTypes.setIamPolicy
+- dataplex.assets.delete
+- dataplex.assets.setIamPolicy
+- dataplex.content.delete
+- dataplex.content.setIamPolicy
+- dataplex.dataAttributeBindings.delete
+- dataplex.dataAttributeBindings.setIamPolicy
+- dataplex.dataAttributes.delete
+- dataplex.dataAttributes.setIamPolicy
+- dataplex.dataTaxonomies.delete
+- dataplex.dataTaxonomies.setIamPolicy
+- dataplex.datascans.delete
+- dataplex.datascans.setIamPolicy
+- dataplex.entities.delete
+- dataplex.entries.delete
+- dataplex.entryGroups.delete
+- dataplex.entryGroups.setIamPolicy
+- dataplex.entryLinks.delete
+- dataplex.entryTypes.delete
+- dataplex.entryTypes.setIamPolicy
+- dataplex.environments.delete
+- dataplex.environments.setIamPolicy
+- dataplex.glossaries.delete
+- dataplex.glossaries.setIamPolicy
+- dataplex.glossaryCategories.delete
+- dataplex.glossaryTerms.delete
+- dataplex.lakes.delete
+- dataplex.lakes.setIamPolicy
+- dataplex.metadataJobs.cancel
+- dataplex.operations.cancel
+- dataplex.operations.delete
+- dataplex.partitions.delete
+- dataplex.tasks.cancel
+- dataplex.tasks.delete
+- dataplex.tasks.setIamPolicy
+- dataplex.zones.delete
+- dataplex.zones.setIamPolicy
+- dataproc.agents.delete
+- dataproc.autoscalingPolicies.delete
+- dataproc.autoscalingPolicies.setIamPolicy
+- dataproc.batches.cancel
+- dataproc.batches.delete
+- dataproc.clusters.delete
+- dataproc.clusters.setIamPolicy
+- dataproc.clusters.stop
+- dataproc.jobs.cancel
+- dataproc.jobs.delete
+- dataproc.jobs.setIamPolicy
+- dataproc.operations.cancel
+- dataproc.operations.delete
+- dataproc.operations.setIamPolicy
+- dataproc.sessionTemplates.delete
+- dataproc.sessions.delete
+- dataproc.sessions.terminate
+- dataproc.workflowTemplates.delete
+- dataproc.workflowTemplates.setIamPolicy
+- dataprocrm.nodePools.delete
+- dataprocrm.operations.cancel
+- dataprocrm.operations.delete
+- dataprocrm.workloads.cancel
+- dataprocrm.workloads.delete
+- datastore.backupSchedules.delete
+- datastore.backups.delete
+- datastore.backups.restoreDatabase
+- datastore.databases.bulkDelete
+- datastore.databases.clone
+- datastore.databases.create
+- datastore.databases.createTagBinding
+- datastore.databases.delete
+- datastore.databases.deleteTagBinding
+- datastore.databases.export
+- datastore.databases.import
+- datastore.entities.delete
+- datastore.indexes.delete
+- datastore.locations.get
+- datastore.locations.list
+- datastore.operations.cancel
+- datastore.operations.delete
+- datastore.userCreds.delete
+- dns.managedZones.delete
+- dns.managedZones.setIamPolicy
+- dns.policies.delete
+- dns.resourceRecordSets.delete
+- dns.responsePolicies.delete
+- dns.responsePolicyRules.delete
+- firebase.billingPlans.update
+- firebase.clients.delete
+- firebase.links.create
+- firebase.links.delete
+- firebase.links.update
+- firebase.playLinks.update
+- firebase.projects.delete
+- firebaseabt.experiments.delete
+- firebaseappcheck.appCheckTokens.verify
+- firebaseappcheck.automations.delete
+- firebaseauth.users.delete
+- firebasedatabase.instances.delete
+- firebasedataconnect.connectorRevisions.delete
+- firebasedataconnect.connectors.delete
+- firebasedataconnect.operations.cancel
+- firebasedataconnect.operations.delete
+- firebasedataconnect.schemaRevisions.delete
+- firebasedataconnect.schemas.delete
+- firebasedataconnect.services.delete
+- firebasedynamiclinks.destinations.update
+- firebasedynamiclinks.domains.delete
+- firebaseextensions.configs.create
+- firebaseextensions.configs.delete
+- firebaseextensions.configs.update
+- firebaseextensionspublisher.extensions.delete
+- firebasehosting.sites.delete
+- firebaseinappmessaging.campaigns.delete
+- firebasemessagingcampaigns.campaigns.delete
+- firebasemessagingcampaigns.campaigns.stop
+- firebaseml.models.delete
+- firebasenotifications.messages.delete
+- firebaserules.releases.delete
+- firebaserules.rulesets.delete
+- firebasestorage.defaultBucket.delete
+- iam.googleapis.com/workloadIdentityPoolProviderKeys.create
+- iam.googleapis.com/workloadIdentityPoolProviderKeys.delete
+- iam.googleapis.com/workloadIdentityPoolProviderKeys.undelete
+- iam.googleapis.com/workloadIdentityPoolProviders.create
+- iam.googleapis.com/workloadIdentityPoolProviders.delete
+- iam.googleapis.com/workloadIdentityPoolProviders.undelete
+- iam.googleapis.com/workloadIdentityPoolProviders.update
+- iam.googleapis.com/workloadIdentityPools.create
+- iam.googleapis.com/workloadIdentityPools.delete
+- iam.googleapis.com/workloadIdentityPools.undelete
+- iam.googleapis.com/workloadIdentityPools.update
+- iam.roles.create
+- iam.roles.delete
+- iam.roles.undelete
+- iam.roles.update
+- iam.serviceAccountApiKeyBindings.delete
+- iam.serviceAccountKeys.delete
+- iam.serviceAccounts.createTagBinding
+- iam.serviceAccounts.delete
+- iam.serviceAccounts.deleteTagBinding
+- iam.serviceAccounts.setIamPolicy
+- iam.serviceAccounts.undelete
+- iap.tunnel.getIamPolicy
+- iap.tunnel.setIamPolicy
+- iap.tunnelDestGroups.delete
+- iap.tunnelDestGroups.getIamPolicy
+- iap.tunnelDestGroups.setIamPolicy
+- iap.tunnelInstances.getIamPolicy
+- iap.tunnelInstances.setIamPolicy
+- iap.tunnelLocations.getIamPolicy
+- iap.tunnelLocations.setIamPolicy
+- iap.tunnelZones.getIamPolicy
+- iap.tunnelZones.setIamPolicy
+- iap.web.getIamPolicy
+- iap.web.setIamPolicy
+- iap.webServiceVersions.getIamPolicy
+- iap.webServiceVersions.setIamPolicy
+- iap.webServices.getIamPolicy
+- iap.webServices.setIamPolicy
+- iap.webTypes.getIamPolicy
+- iap.webTypes.setIamPolicy
+- monitoring.alertPolicies.createTagBinding
+- monitoring.alertPolicies.delete
+- monitoring.alertPolicies.deleteTagBinding
+- monitoring.dashboards.createTagBinding
+- monitoring.dashboards.delete
+- monitoring.dashboards.deleteTagBinding
+- monitoring.groups.delete
+- monitoring.metricDescriptors.delete
+- monitoring.metricsScopes.link
+- monitoring.services.delete
+- monitoring.slos.delete
+- monitoring.uptimeCheckConfigs.delete
+- pubsub.schemas.delete
+- pubsub.schemas.setIamPolicy
+- pubsub.snapshots.delete
+- pubsub.subscriptions.delete
+- pubsub.subscriptions.getIamPolicy
+- pubsub.subscriptions.setIamPolicy
+- pubsub.topics.delete
+- pubsub.topics.getIamPolicy
+- pubsub.topics.setIamPolicy
+- pubsublite.reservations.delete
+- pubsublite.subscriptions.delete
+- pubsublite.topics.delete
+- redis.backupCollections.delete
+- redis.backups.delete
+- redis.clusters.delete
+- redis.instances.createTagBinding
+- redis.instances.delete
+- redis.instances.deleteTagBinding
+- redis.operations.cancel
+- redis.operations.delete
+- resourcemanager.projects.setIamPolicy
+- resourcemanager.tagHolds.delete
+- resourcemanager.tagKeys.delete
+- resourcemanager.tagKeys.setIamPolicy
+- resourcemanager.tagValueBindings.delete
+- resourcemanager.tagValues.delete
+- resourcemanager.tagValues.setIamPolicy
+- secretmanager.secrets.createTagBinding
+- secretmanager.secrets.delete
+- secretmanager.secrets.deleteTagBinding
+- secretmanager.secrets.setIamPolicy
+- secretmanager.versions.access
+- secretmanager.versions.destroy
+- servicemanagement.services.delete
+- servicemanagement.services.getIamPolicy
+- servicemanagement.services.setIamPolicy
+- spanner.backupOperations.cancel
+- spanner.backupSchedules.delete
+- spanner.backupSchedules.setIamPolicy
+- spanner.backups.delete
+- spanner.backups.setIamPolicy
+- spanner.databaseOperations.cancel
+- spanner.databases.setIamPolicy
+- spanner.instanceConfigOperations.cancel
+- spanner.instanceConfigOperations.delete
+- spanner.instanceConfigs.delete
+- spanner.instanceOperations.cancel
+- spanner.instanceOperations.delete
+- spanner.instancePartitionOperations.cancel
+- spanner.instancePartitionOperations.delete
+- spanner.instancePartitions.delete
+- spanner.instances.createTagBinding
+- spanner.instances.delete
+- spanner.instances.deleteTagBinding
+- spanner.instances.setIamPolicy
+- spanner.sessions.delete
+- storage.anywhereCaches.create
+- storage.anywhereCaches.disable
+- storage.anywhereCaches.get
+- storage.anywhereCaches.list
+- storage.anywhereCaches.pause
+- storage.anywhereCaches.resume
+- storage.anywhereCaches.update
+- storage.bucketOperations.cancel
+- storage.bucketOperations.get
+- storage.bucketOperations.list
+- storage.buckets.createTagBinding
+- storage.buckets.delete
+- storage.buckets.deleteTagBinding
+- storage.buckets.enableObjectRetention
+- storage.buckets.get
+- storage.buckets.getIamPolicy
+- storage.buckets.getIpFilter
+- storage.buckets.getObjectInsights
+- storage.buckets.relocate
+- storage.buckets.restore
+- storage.buckets.setIamPolicy
+- storage.buckets.setIpFilter
+- storage.buckets.update
+- storage.folders.delete
+- storage.hmacKeys.delete
+- storage.intelligenceConfigs.update
+- storage.managedFolders.delete
+- storage.managedFolders.getIamPolicy
+- storage.managedFolders.setIamPolicy
+- storage.multipartUploads.list
+- storage.objects.delete
+- storage.objects.getIamPolicy
+- storage.objects.move
+- storage.objects.overrideUnlockedRetention
+- storage.objects.restore
+- storage.objects.setIamPolicy
+- storage.objects.setRetention
+- storage.objects.update
+- storageinsights.datasetConfigs.delete
+- storageinsights.operations.cancel
+- storageinsights.operations.delete
+- storageinsights.reportConfigs.delete
+- storagetransfer.agentpools.delete
+- storagetransfer.jobs.delete
+- storagetransfer.operations.cancel
+role_id: beam_admin
+stage: GA
+title: beam_admin
diff --git a/infra/iam/roles/beam_infra_manager.role.yaml b/infra/iam/roles/beam_infra_manager.role.yaml
new file mode 100644
index 000000000000..169bebd7fbc3
--- /dev/null
+++ b/infra/iam/roles/beam_infra_manager.role.yaml
@@ -0,0 +1,848 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This file is auto-generated by generate_roles.py.
+# Do not edit manually.
+
+# This file was generated on 2025-08-11 14:34:54 UTC
+
+description: This is the beam_infra_manager role
+permissions:
+- artifactregistry.aptartifacts.create
+- artifactregistry.attachments.create
+- artifactregistry.files.update
+- artifactregistry.files.upload
+- artifactregistry.kfpartifacts.create
+- artifactregistry.packages.update
+- artifactregistry.projectsettings.update
+- artifactregistry.repositories.create
+- artifactregistry.repositories.createOnPush
+- artifactregistry.repositories.deleteArtifacts
+- artifactregistry.repositories.update
+- artifactregistry.repositories.uploadArtifacts
+- artifactregistry.rules.create
+- artifactregistry.rules.update
+- artifactregistry.tags.create
+- artifactregistry.tags.update
+- artifactregistry.versions.update
+- artifactregistry.yumartifacts.create
+- biglake.catalogs.create
+- biglake.databases.create
+- biglake.databases.update
+- biglake.locks.check
+- biglake.locks.create
+- biglake.namespaces.create
+- biglake.namespaces.update
+- biglake.tables.create
+- biglake.tables.lock
+- biglake.tables.update
+- biglake.tables.updateData
+- bigquery.bireservations.update
+- bigquery.capacityCommitments.update
+- bigquery.config.update
+- bigquery.connections.create
+- bigquery.connections.update
+- bigquery.connections.updateTag
+- bigquery.dataPolicies.create
+- bigquery.dataPolicies.update
+- bigquery.datasets.updateTag
+- bigquery.models.create
+- bigquery.models.updateData
+- bigquery.models.updateMetadata
+- bigquery.models.updateTag
+- bigquery.objectRefs.write
+- bigquery.reservationAssignments.create
+- bigquery.reservationGroups.create
+- bigquery.reservations.create
+- bigquery.reservations.update
+- bigquery.routines.create
+- bigquery.routines.update
+- bigquery.routines.updateTag
+- bigquery.rowAccessPolicies.create
+- bigquery.rowAccessPolicies.update
+- bigquery.savedqueries.create
+- bigquery.savedqueries.update
+- bigquery.tables.createIndex
+- bigquery.tables.deleteIndex
+- bigquery.tables.restoreSnapshot
+- bigquery.tables.updateIndex
+- bigquery.transfers.update
+- bigquerymigration.workflows.create
+- bigquerymigration.workflows.enableAiOutputTypes
+- bigquerymigration.workflows.enableLineageOutputTypes
+- bigquerymigration.workflows.enableOutputTypePermissions
+- bigquerymigration.workflows.update
+- cloudasset.othercloudconnections.create
+- cloudasset.othercloudconnections.update
+- cloudasset.othercloudconnections.verify
+- cloudasset.savedqueries.create
+- cloudasset.savedqueries.update
+- cloudbuild.builds.approve
+- cloudbuild.builds.create
+- cloudbuild.builds.update
+- cloudbuild.connections.create
+- cloudbuild.connections.update
+- cloudbuild.integrations.create
+- cloudbuild.integrations.update
+- cloudbuild.repositories.create
+- cloudbuild.workerpools.create
+- cloudbuild.workerpools.update
+- cloudbuild.workerpools.use
+- cloudfunctions.functions.call
+- cloudfunctions.functions.create
+- cloudfunctions.functions.generationUpgrade
+- cloudfunctions.functions.invoke
+- cloudfunctions.functions.sourceCodeSet
+- cloudfunctions.functions.update
+- cloudkms.cryptoKeyVersions.create
+- cloudkms.cryptoKeyVersions.update
+- cloudkms.cryptoKeys.create
+- cloudkms.cryptoKeys.update
+- cloudkms.ekmConfigs.update
+- cloudkms.ekmConnections.create
+- cloudkms.ekmConnections.update
+- cloudkms.ekmConnections.use
+- cloudkms.importJobs.create
+- cloudkms.importJobs.useToImport
+- cloudkms.kajPolicyConfigs.update
+- cloudkms.keyRings.create
+- cloudsql.backupRuns.create
+- cloudsql.backupRuns.update
+- cloudsql.databases.create
+- cloudsql.databases.update
+- cloudsql.instances.addServerCa
+- cloudsql.instances.addServerCertificate
+- cloudsql.instances.clone
+- cloudsql.instances.connect
+- cloudsql.instances.create
+- cloudsql.instances.demoteMaster
+- cloudsql.instances.executeSql
+- cloudsql.instances.failover
+- cloudsql.instances.import
+- cloudsql.instances.migrate
+- cloudsql.instances.performDiskShrink
+- cloudsql.instances.promoteReplica
+- cloudsql.instances.reencrypt
+- cloudsql.instances.resetReplicaSize
+- cloudsql.instances.resetSslConfig
+- cloudsql.instances.restart
+- cloudsql.instances.restoreBackup
+- cloudsql.instances.rotateServerCa
+- cloudsql.instances.rotateServerCertificate
+- cloudsql.instances.startReplica
+- cloudsql.instances.stopReplica
+- cloudsql.instances.truncateLog
+- cloudsql.instances.update
+- cloudsql.instances.updateBackupDrConfig
+- cloudsql.sslCerts.create
+- cloudsql.users.create
+- cloudsql.users.update
+- compute.addresses.create
+- compute.addresses.use
+- compute.autoscalers.create
+- compute.autoscalers.update
+- compute.backendBuckets.addSignedUrlKey
+- compute.backendBuckets.create
+- compute.backendBuckets.deleteSignedUrlKey
+- compute.backendBuckets.setSecurityPolicy
+- compute.backendBuckets.update
+- compute.backendBuckets.use
+- compute.backendServices.addSignedUrlKey
+- compute.backendServices.create
+- compute.backendServices.deleteSignedUrlKey
+- compute.backendServices.setSecurityPolicy
+- compute.backendServices.update
+- compute.backendServices.use
+- compute.commitments.create
+- compute.commitments.update
+- compute.commitments.updateReservations
+- compute.crossSiteNetworks.create
+- compute.crossSiteNetworks.update
+- compute.diskSettings.update
+- compute.disks.addResourcePolicies
+- compute.disks.create
+- compute.disks.removeResourcePolicies
+- compute.disks.resize
+- compute.disks.setLabels
+- compute.disks.startAsyncReplication
+- compute.disks.stopAsyncReplication
+- compute.disks.stopGroupAsyncReplication
+- compute.disks.update
+- compute.disks.use
+- compute.externalVpnGateways.create
+- compute.externalVpnGateways.setLabels
+- compute.externalVpnGateways.use
+- compute.firewallPolicies.cloneRules
+- compute.firewallPolicies.create
+- compute.firewallPolicies.update
+- compute.firewallPolicies.use
+- compute.firewalls.create
+- compute.firewalls.update
+- compute.forwardingRules.create
+- compute.forwardingRules.pscCreate
+- compute.forwardingRules.pscDelete
+- compute.forwardingRules.pscSetLabels
+- compute.forwardingRules.pscUpdate
+- compute.forwardingRules.setTarget
+- compute.forwardingRules.update
+- compute.forwardingRules.use
+- compute.futureReservations.create
+- compute.futureReservations.update
+- compute.globalAddresses.create
+- compute.globalAddresses.createInternal
+- compute.globalAddresses.deleteInternal
+- compute.globalAddresses.use
+- compute.globalForwardingRules.create
+- compute.globalForwardingRules.pscCreate
+- compute.globalForwardingRules.pscDelete
+- compute.globalForwardingRules.pscSetLabels
+- compute.globalForwardingRules.pscUpdate
+- compute.globalForwardingRules.update
+- compute.globalNetworkEndpointGroups.attachNetworkEndpoints
+- compute.globalNetworkEndpointGroups.create
+- compute.globalNetworkEndpointGroups.detachNetworkEndpoints
+- compute.globalNetworkEndpointGroups.use
+- compute.globalPublicDelegatedPrefixes.create
+- compute.globalPublicDelegatedPrefixes.updatePolicy
+- compute.healthChecks.create
+- compute.healthChecks.update
+- compute.healthChecks.use
+- compute.httpHealthChecks.create
+- compute.httpHealthChecks.update
+- compute.httpsHealthChecks.create
+- compute.httpsHealthChecks.update
+- compute.images.create
+- compute.images.deprecate
+- compute.images.setLabels
+- compute.images.update
+- compute.instanceGroupManagers.create
+- compute.instanceGroupManagers.update
+- compute.instanceGroupManagers.use
+- compute.instanceGroups.create
+- compute.instanceGroups.update
+- compute.instanceGroups.use
+- compute.instanceSettings.update
+- compute.instanceTemplates.create
+- compute.instances.addAccessConfig
+- compute.instances.addNetworkInterface
+- compute.instances.addResourcePolicies
+- compute.instances.attachDisk
+- compute.instances.create
+- compute.instances.deleteAccessConfig
+- compute.instances.deleteNetworkInterface
+- compute.instances.detachDisk
+- compute.instances.osAdminLogin
+- compute.instances.osLogin
+- compute.instances.pscInterfaceCreate
+- compute.instances.removeResourcePolicies
+- compute.instances.reset
+- compute.instances.resume
+- compute.instances.sendDiagnosticInterrupt
+- compute.instances.setDiskAutoDelete
+- compute.instances.setLabels
+- compute.instances.setMachineResources
+- compute.instances.setMachineType
+- compute.instances.setMetadata
+- compute.instances.setMinCpuPlatform
+- compute.instances.setName
+- compute.instances.setScheduling
+- compute.instances.setSecurityPolicy
+- compute.instances.setServiceAccount
+- compute.instances.setShieldedInstanceIntegrityPolicy
+- compute.instances.setShieldedVmIntegrityPolicy
+- compute.instances.setTags
+- compute.instances.simulateMaintenanceEvent
+- compute.instances.start
+- compute.instances.startWithEncryptionKey
+- compute.instances.suspend
+- compute.instances.update
+- compute.instances.updateAccessConfig
+- compute.instances.updateDisplayDevice
+- compute.instances.updateNetworkInterface
+- compute.instances.updateSecurity
+- compute.instances.updateShieldedInstanceConfig
+- compute.instances.updateShieldedVmConfig
+- compute.instances.use
+- compute.instantSnapshots.create
+- compute.instantSnapshots.export
+- compute.instantSnapshots.setLabels
+- compute.interconnectAttachmentGroups.create
+- compute.interconnectAttachmentGroups.patch
+- compute.interconnectGroups.create
+- compute.interconnectGroups.patch
+- compute.licenses.update
+- compute.machineImages.create
+- compute.machineImages.setLabels
+- compute.multiMig.create
+- compute.networkAttachments.create
+- compute.networkAttachments.update
+- compute.networkAttachments.use
+- compute.networkEdgeSecurityServices.create
+- compute.networkEdgeSecurityServices.update
+- compute.networkEndpointGroups.attachNetworkEndpoints
+- compute.networkEndpointGroups.create
+- compute.networkEndpointGroups.detachNetworkEndpoints
+- compute.networkEndpointGroups.use
+- compute.networks.access
+- compute.networks.create
+- compute.networks.mirror
+- compute.networks.setFirewallPolicy
+- compute.networks.updatePeering
+- compute.networks.updatePolicy
+- compute.networks.use
+- compute.networks.useExternalIp
+- compute.nodeGroups.addNodes
+- compute.nodeGroups.create
+- compute.nodeGroups.deleteNodes
+- compute.nodeGroups.performMaintenance
+- compute.nodeGroups.setNodeTemplate
+- compute.nodeGroups.simulateMaintenanceEvent
+- compute.nodeGroups.update
+- compute.nodeTemplates.create
+- compute.organizations.setFirewallPolicy
+- compute.organizations.setSecurityPolicy
+- compute.packetMirrorings.create
+- compute.packetMirrorings.update
+- compute.previewFeatures.update
+- compute.projects.setCloudArmorTier
+- compute.projects.setCommonInstanceMetadata
+- compute.projects.setManagedProtectionTier
+- compute.projects.setUsageExportBucket
+- compute.publicAdvertisedPrefixes.create
+- compute.publicAdvertisedPrefixes.update
+- compute.publicAdvertisedPrefixes.updatePolicy
+- compute.publicDelegatedPrefixes.create
+- compute.publicDelegatedPrefixes.update
+- compute.publicDelegatedPrefixes.updatePolicy
+- compute.publicDelegatedPrefixes.use
+- compute.regionBackendServices.create
+- compute.regionBackendServices.setSecurityPolicy
+- compute.regionBackendServices.update
+- compute.regionBackendServices.use
+- compute.regionFirewallPolicies.cloneRules
+- compute.regionFirewallPolicies.create
+- compute.regionFirewallPolicies.update
+- compute.regionFirewallPolicies.use
+- compute.regionHealthCheckServices.create
+- compute.regionHealthCheckServices.update
+- compute.regionHealthCheckServices.use
+- compute.regionHealthChecks.create
+- compute.regionHealthChecks.update
+- compute.regionHealthChecks.use
+- compute.regionNetworkEndpointGroups.attachNetworkEndpoints
+- compute.regionNetworkEndpointGroups.create
+- compute.regionNetworkEndpointGroups.detachNetworkEndpoints
+- compute.regionNetworkEndpointGroups.use
+- compute.regionNotificationEndpoints.create
+- compute.regionNotificationEndpoints.update
+- compute.regionNotificationEndpoints.use
+- compute.regionSecurityPolicies.create
+- compute.regionSecurityPolicies.update
+- compute.regionSecurityPolicies.use
+- compute.regionSslCertificates.create
+- compute.regionSslPolicies.create
+- compute.regionSslPolicies.update
+- compute.regionSslPolicies.use
+- compute.regionTargetHttpProxies.create
+- compute.regionTargetHttpProxies.setUrlMap
+- compute.regionTargetHttpProxies.use
+- compute.regionTargetHttpsProxies.create
+- compute.regionTargetHttpsProxies.setSslCertificates
+- compute.regionTargetHttpsProxies.setUrlMap
+- compute.regionTargetHttpsProxies.update
+- compute.regionTargetHttpsProxies.use
+- compute.regionTargetTcpProxies.create
+- compute.regionTargetTcpProxies.use
+- compute.regionUrlMaps.create
+- compute.regionUrlMaps.invalidateCache
+- compute.regionUrlMaps.update
+- compute.regionUrlMaps.use
+- compute.reservationBlocks.performMaintenance
+- compute.reservationSubBlocks.performMaintenance
+- compute.reservations.create
+- compute.reservations.performMaintenance
+- compute.reservations.resize
+- compute.reservations.update
+- compute.resourcePolicies.create
+- compute.resourcePolicies.update
+- compute.resourcePolicies.use
+- compute.routers.create
+- compute.routers.deleteRoutePolicy
+- compute.routers.update
+- compute.routers.updateRoutePolicy
+- compute.routers.use
+- compute.routes.create
+- compute.securityPolicies.setLabels
+- compute.serviceAttachments.create
+- compute.serviceAttachments.update
+- compute.serviceAttachments.use
+- compute.snapshotSettings.update
+- compute.snapshots.create
+- compute.snapshots.setLabels
+- compute.sslCertificates.create
+- compute.storagePools.create
+- compute.storagePools.update
+- compute.storagePools.use
+- compute.subnetworks.create
+- compute.subnetworks.expandIpCidrRange
+- compute.subnetworks.mirror
+- compute.subnetworks.setPrivateIpGoogleAccess
+- compute.subnetworks.update
+- compute.subnetworks.use
+- compute.subnetworks.useExternalIp
+- compute.subnetworks.usePeerMigration
+- compute.targetGrpcProxies.create
+- compute.targetGrpcProxies.update
+- compute.targetGrpcProxies.use
+- compute.targetHttpProxies.create
+- compute.targetHttpProxies.setUrlMap
+- compute.targetHttpProxies.update
+- compute.targetHttpProxies.use
+- compute.targetHttpsProxies.create
+- compute.targetHttpsProxies.setCertificateMap
+- compute.targetHttpsProxies.setQuicOverride
+- compute.targetHttpsProxies.setSslCertificates
+- compute.targetHttpsProxies.setUrlMap
+- compute.targetHttpsProxies.update
+- compute.targetHttpsProxies.use
+- compute.targetInstances.create
+- compute.targetInstances.setSecurityPolicy
+- compute.targetInstances.use
+- compute.targetPools.addHealthCheck
+- compute.targetPools.addInstance
+- compute.targetPools.create
+- compute.targetPools.removeHealthCheck
+- compute.targetPools.removeInstance
+- compute.targetPools.setSecurityPolicy
+- compute.targetPools.update
+- compute.targetPools.use
+- compute.targetSslProxies.create
+- compute.targetSslProxies.setBackendService
+- compute.targetSslProxies.setCertificateMap
+- compute.targetSslProxies.setProxyHeader
+- compute.targetSslProxies.setSslCertificates
+- compute.targetSslProxies.setSslPolicy
+- compute.targetSslProxies.update
+- compute.targetSslProxies.use
+- compute.targetTcpProxies.create
+- compute.targetTcpProxies.update
+- compute.targetTcpProxies.use
+- compute.targetVpnGateways.create
+- compute.targetVpnGateways.use
+- compute.vpnGateways.create
+- compute.vpnGateways.setLabels
+- compute.vpnGateways.use
+- compute.vpnTunnels.create
+- compute.wireGroups.create
+- compute.wireGroups.update
+- container.clusters.create
+- container.clusters.getCredentials
+- container.clusters.update
+- container.controllerRevisions.create
+- container.controllerRevisions.update
+- container.mutatingWebhookConfigurations.create
+- container.mutatingWebhookConfigurations.update
+- container.podSecurityPolicies.create
+- container.podSecurityPolicies.update
+- container.validatingWebhookConfigurations.create
+- container.validatingWebhookConfigurations.update
+- containeranalysis.notes.attachOccurrence
+- containeranalysis.notes.create
+- containeranalysis.notes.listOccurrences
+- containeranalysis.notes.update
+- containeranalysis.occurrences.create
+- containeranalysis.occurrences.update
+- containersecurity.clusterSummaries.list
+- containersecurity.findings.list
+- dataflow.jobs.create
+- dataflow.jobs.snapshot
+- dataflow.jobs.updateContents
+- dataflow.shuffle.read
+- dataflow.shuffle.write
+- dataflow.streamingWorkItems.ImportState
+- dataflow.streamingWorkItems.commitWork
+- dataflow.streamingWorkItems.getData
+- dataflow.streamingWorkItems.getWork
+- dataflow.streamingWorkItems.getWorkerMetadata
+- dataflow.workItems.lease
+- dataflow.workItems.sendMessage
+- dataflow.workItems.update
+- dataform.commentThreads.create
+- dataform.commentThreads.update
+- dataform.comments.create
+- dataform.comments.update
+- dataform.compilationResults.create
+- dataform.config.update
+- dataform.releaseConfigs.create
+- dataform.releaseConfigs.update
+- dataform.repositories.commit
+- dataform.repositories.update
+- dataform.workflowConfigs.create
+- dataform.workflowConfigs.update
+- dataform.workflowInvocations.create
+- dataform.workspaces.commit
+- dataform.workspaces.create
+- dataform.workspaces.installNpmPackages
+- dataform.workspaces.makeDirectory
+- dataform.workspaces.moveDirectory
+- dataform.workspaces.moveFile
+- dataform.workspaces.pull
+- dataform.workspaces.push
+- dataform.workspaces.removeDirectory
+- dataform.workspaces.removeFile
+- dataform.workspaces.reset
+- dataform.workspaces.writeFile
+- dataplex.aspectTypes.create
+- dataplex.aspectTypes.update
+- dataplex.aspectTypes.use
+- dataplex.assets.create
+- dataplex.assets.update
+- dataplex.content.create
+- dataplex.content.update
+- dataplex.dataAttributeBindings.create
+- dataplex.dataAttributeBindings.update
+- dataplex.dataAttributes.bind
+- dataplex.dataAttributes.create
+- dataplex.dataAttributes.update
+- dataplex.dataTaxonomies.configureDataAccess
+- dataplex.dataTaxonomies.configureResourceAccess
+- dataplex.dataTaxonomies.create
+- dataplex.dataTaxonomies.update
+- dataplex.datascans.create
+- dataplex.datascans.run
+- dataplex.datascans.update
+- dataplex.entities.create
+- dataplex.entities.update
+- dataplex.entries.create
+- dataplex.entries.link
+- dataplex.entries.update
+- dataplex.entryGroups.create
+- dataplex.entryGroups.import
+- dataplex.entryGroups.update
+- dataplex.entryGroups.useContactsAspect
+- dataplex.entryGroups.useDataQualityScorecardAspect
+- dataplex.entryGroups.useDefinitionEntryLink
+- dataplex.entryGroups.useGenericAspect
+- dataplex.entryGroups.useGenericEntry
+- dataplex.entryGroups.useOverviewAspect
+- dataplex.entryGroups.useRelatedEntryLink
+- dataplex.entryGroups.useSchemaAspect
+- dataplex.entryGroups.useSynonymEntryLink
+- dataplex.entryLinks.create
+- dataplex.entryLinks.reference
+- dataplex.entryTypes.create
+- dataplex.entryTypes.update
+- dataplex.entryTypes.use
+- dataplex.environments.create
+- dataplex.environments.execute
+- dataplex.environments.update
+- dataplex.glossaries.create
+- dataplex.glossaries.import
+- dataplex.glossaries.update
+- dataplex.glossaryCategories.create
+- dataplex.glossaryCategories.update
+- dataplex.glossaryTerms.create
+- dataplex.glossaryTerms.update
+- dataplex.glossaryTerms.use
+- dataplex.lakes.create
+- dataplex.lakes.update
+- dataplex.metadataJobs.create
+- dataplex.partitions.create
+- dataplex.partitions.update
+- dataplex.tasks.create
+- dataplex.tasks.run
+- dataplex.tasks.update
+- dataplex.zones.create
+- dataplex.zones.update
+- dataproc.agents.create
+- dataproc.agents.update
+- dataproc.autoscalingPolicies.create
+- dataproc.autoscalingPolicies.update
+- dataproc.batches.analyze
+- dataproc.batches.create
+- dataproc.batches.sparkApplicationWrite
+- dataproc.clusters.create
+- dataproc.clusters.start
+- dataproc.clusters.update
+- dataproc.clusters.use
+- dataproc.jobs.create
+- dataproc.jobs.update
+- dataproc.nodeGroups.create
+- dataproc.nodeGroups.update
+- dataproc.sessionTemplates.create
+- dataproc.sessionTemplates.update
+- dataproc.sessions.create
+- dataproc.sessions.sparkApplicationWrite
+- dataproc.tasks.lease
+- dataproc.tasks.reportStatus
+- dataproc.workflowTemplates.create
+- dataproc.workflowTemplates.instantiate
+- dataproc.workflowTemplates.instantiateInline
+- dataproc.workflowTemplates.update
+- dataprocessing.datasources.update
+- dataprocrm.nodePools.create
+- dataprocrm.nodePools.deleteNodes
+- dataprocrm.nodePools.resize
+- dataprocrm.nodes.heartbeat
+- dataprocrm.nodes.update
+- dataprocrm.workloads.create
+- datastore.backupSchedules.create
+- datastore.backupSchedules.update
+- datastore.databases.update
+- datastore.indexes.create
+- datastore.indexes.update
+- datastore.userCreds.create
+- datastore.userCreds.update
+- dns.changes.create
+- dns.gkeClusters.bindDNSResponsePolicy
+- dns.gkeClusters.bindPrivateDNSZone
+- dns.managedZones.create
+- dns.managedZones.update
+- dns.networks.bindDNSResponsePolicy
+- dns.networks.bindPrivateDNSPolicy
+- dns.networks.bindPrivateDNSZone
+- dns.networks.targetWithPeeringZone
+- dns.networks.useHealthSignals
+- dns.policies.create
+- dns.policies.update
+- dns.resourceRecordSets.create
+- dns.resourceRecordSets.update
+- dns.responsePolicies.create
+- dns.responsePolicies.update
+- dns.responsePolicyRules.create
+- dns.responsePolicyRules.update
+- firebase.clients.create
+- firebase.clients.undelete
+- firebase.clients.update
+- firebase.projects.update
+- firebaseabt.experiments.create
+- firebaseabt.experiments.update
+- firebaseanalytics.resources.googleAnalyticsEdit
+- firebaseappcheck.appAttestConfig.update
+- firebaseappcheck.automations.create
+- firebaseappcheck.automations.resume
+- firebaseappcheck.automations.suspend
+- firebaseappcheck.automations.update
+- firebaseappcheck.debugTokens.update
+- firebaseappcheck.deviceCheckConfig.update
+- firebaseappcheck.playIntegrityConfig.update
+- firebaseappcheck.recaptchaEnterpriseConfig.update
+- firebaseappcheck.recaptchaV3Config.update
+- firebaseappcheck.resourcePolicies.update
+- firebaseappcheck.safetyNetConfig.update
+- firebaseappcheck.services.update
+- firebaseappdistro.groups.update
+- firebaseappdistro.releases.update
+- firebaseappdistro.testers.update
+- firebaseauth.configs.create
+- firebaseauth.configs.getHashConfig
+- firebaseauth.configs.getSecret
+- firebaseauth.configs.update
+- firebaseauth.users.create
+- firebaseauth.users.createSession
+- firebaseauth.users.sendEmail
+- firebaseauth.users.update
+- firebasecrash.issues.update
+- firebasecrashlytics.config.update
+- firebasecrashlytics.issues.update
+- firebasedatabase.instances.create
+- firebasedatabase.instances.disable
+- firebasedatabase.instances.reenable
+- firebasedatabase.instances.undelete
+- firebasedatabase.instances.update
+- firebasedataconnect.connectors.create
+- firebasedataconnect.connectors.update
+- firebasedataconnect.schemas.create
+- firebasedataconnect.schemas.update
+- firebasedataconnect.services.create
+- firebasedataconnect.services.executeGraphql
+- firebasedataconnect.services.executeGraphqlRead
+- firebasedataconnect.services.update
+- firebasedynamiclinks.domains.create
+- firebasedynamiclinks.domains.update
+- firebasedynamiclinks.links.create
+- firebasedynamiclinks.links.update
+- firebaseextensionspublisher.extensions.create
+- firebasehosting.sites.create
+- firebasehosting.sites.update
+- firebaseinappmessaging.campaigns.create
+- firebaseinappmessaging.campaigns.update
+- firebasemessagingcampaigns.campaigns.create
+- firebasemessagingcampaigns.campaigns.start
+- firebasemessagingcampaigns.campaigns.update
+- firebaseml.models.create
+- firebaseml.models.update
+- firebaseml.modelversions.create
+- firebaseml.modelversions.update
+- firebasenotifications.messages.create
+- firebasenotifications.messages.update
+- firebaseperformance.config.update
+- firebaserules.releases.create
+- firebaserules.releases.update
+- firebaserules.rulesets.create
+- firebaserules.rulesets.get
+- firebasestorage.buckets.addFirebase
+- firebasestorage.buckets.removeFirebase
+- firebasestorage.defaultBucket.create
+- firebasevertexai.configs.update
+- iam.serviceAccountApiKeyBindings.create
+- iam.serviceAccountApiKeyBindings.undelete
+- iam.serviceAccountKeys.create
+- iam.serviceAccountKeys.disable
+- iam.serviceAccountKeys.enable
+- iam.serviceAccounts.actAs
+- iam.serviceAccounts.create
+- iam.serviceAccounts.disable
+- iam.serviceAccounts.enable
+- iam.serviceAccounts.getAccessToken
+- iam.serviceAccounts.getOpenIdToken
+- iam.serviceAccounts.implicitDelegation
+- iam.serviceAccounts.signBlob
+- iam.serviceAccounts.signJwt
+- iam.serviceAccounts.update
+- iap.tunnelDestGroups.create
+- iap.tunnelDestGroups.update
+- monitoring.alertPolicies.create
+- monitoring.alertPolicies.update
+- monitoring.dashboards.create
+- monitoring.dashboards.update
+- monitoring.groups.create
+- monitoring.groups.update
+- monitoring.metricDescriptors.create
+- monitoring.services.create
+- monitoring.services.update
+- monitoring.slos.create
+- monitoring.slos.update
+- monitoring.snoozes.create
+- monitoring.snoozes.update
+- monitoring.timeSeries.create
+- monitoring.uptimeCheckConfigs.create
+- monitoring.uptimeCheckConfigs.update
+- pubsub.schemas.commit
+- pubsub.schemas.create
+- pubsub.schemas.rollback
+- pubsub.snapshots.create
+- pubsub.subscriptions.consume
+- pubsub.subscriptions.create
+- pubsub.subscriptions.update
+- pubsub.topics.attachSubscription
+- pubsub.topics.create
+- pubsub.topics.detachSubscription
+- pubsub.topics.publish
+- pubsub.topics.update
+- pubsub.topics.updateTag
+- pubsublite.reservations.attachTopic
+- pubsublite.reservations.create
+- pubsublite.reservations.update
+- pubsublite.subscriptions.create
+- pubsublite.subscriptions.seek
+- pubsublite.subscriptions.setCursor
+- pubsublite.subscriptions.update
+- pubsublite.topics.create
+- pubsublite.topics.publish
+- pubsublite.topics.update
+- redis.backupCollections.create
+- redis.backups.create
+- redis.clusters.backup
+- redis.clusters.connect
+- redis.clusters.create
+- redis.clusters.update
+- redis.instances.create
+- redis.instances.export
+- redis.instances.failover
+- redis.instances.getAuthString
+- redis.instances.import
+- redis.instances.rescheduleMaintenance
+- redis.instances.update
+- redis.instances.updateAuth
+- redis.instances.upgrade
+- resourcemanager.hierarchyNodes.createTagBinding
+- resourcemanager.hierarchyNodes.deleteTagBinding
+- resourcemanager.projects.move
+- resourcemanager.projects.update
+- resourcemanager.tagHolds.create
+- resourcemanager.tagKeys.create
+- resourcemanager.tagKeys.update
+- resourcemanager.tagValueBindings.create
+- resourcemanager.tagValues.create
+- resourcemanager.tagValues.update
+- secretmanager.secrets.create
+- secretmanager.secrets.update
+- secretmanager.versions.add
+- secretmanager.versions.disable
+- secretmanager.versions.enable
+- servicemanagement.services.bind
+- servicemanagement.services.check
+- servicemanagement.services.create
+- servicemanagement.services.quota
+- servicemanagement.services.report
+- servicemanagement.services.update
+- serviceusage.services.disable
+- serviceusage.services.enable
+- serviceusage.services.use
+- spanner.backupSchedules.create
+- spanner.backupSchedules.update
+- spanner.backups.copy
+- spanner.backups.create
+- spanner.backups.restoreDatabase
+- spanner.backups.update
+- spanner.databases.adapt
+- spanner.databases.addSplitPoints
+- spanner.databases.beginOrRollbackReadWriteTransaction
+- spanner.databases.beginPartitionedDmlTransaction
+- spanner.databases.changequorum
+- spanner.databases.create
+- spanner.databases.createBackup
+- spanner.databases.drop
+- spanner.databases.update
+- spanner.databases.updateDdl
+- spanner.databases.useRoleBasedAccess
+- spanner.databases.write
+- spanner.instanceConfigs.create
+- spanner.instanceConfigs.update
+- spanner.instancePartitions.create
+- spanner.instancePartitions.update
+- spanner.instances.create
+- spanner.instances.update
+- storage.buckets.create
+- storage.folders.create
+- storage.folders.rename
+- storage.hmacKeys.create
+- storage.hmacKeys.update
+- storage.managedFolders.create
+- storage.managedFolders.get
+- storage.managedFolders.list
+- storage.multipartUploads.abort
+- storage.multipartUploads.create
+- storage.multipartUploads.listParts
+- storage.objects.create
+- storage.objects.get
+- storage.objects.list
+- storageinsights.datasetConfigs.create
+- storageinsights.datasetConfigs.linkDataset
+- storageinsights.datasetConfigs.unlinkDataset
+- storageinsights.datasetConfigs.update
+- storageinsights.reportConfigs.create
+- storageinsights.reportConfigs.update
+- storagetransfer.agentpools.create
+- storagetransfer.agentpools.update
+- storagetransfer.jobs.create
+- storagetransfer.jobs.run
+- storagetransfer.jobs.update
+- storagetransfer.operations.pause
+- storagetransfer.operations.resume
+role_id: beam_infra_manager
+stage: GA
+title: beam_infra_manager
diff --git a/infra/iam/roles/beam_viewer.role.yaml b/infra/iam/roles/beam_viewer.role.yaml
new file mode 100644
index 000000000000..0525fda09560
--- /dev/null
+++ b/infra/iam/roles/beam_viewer.role.yaml
@@ -0,0 +1,1113 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This file is auto-generated by generate_roles.py.
+# Do not edit manually.
+
+# This file was generated on 2025-08-11 14:34:54 UTC
+
+description: This is the beam_viewer role
+permissions:
+- artifactregistry.attachments.get
+- artifactregistry.attachments.list
+- artifactregistry.dockerimages.get
+- artifactregistry.dockerimages.list
+- artifactregistry.files.download
+- artifactregistry.files.get
+- artifactregistry.files.list
+- artifactregistry.locations.get
+- artifactregistry.locations.list
+- artifactregistry.mavenartifacts.get
+- artifactregistry.mavenartifacts.list
+- artifactregistry.npmpackages.get
+- artifactregistry.npmpackages.list
+- artifactregistry.packages.get
+- artifactregistry.packages.list
+- artifactregistry.projectsettings.get
+- artifactregistry.pythonpackages.get
+- artifactregistry.pythonpackages.list
+- artifactregistry.repositories.downloadArtifacts
+- artifactregistry.repositories.get
+- artifactregistry.repositories.getIamPolicy
+- artifactregistry.repositories.list
+- artifactregistry.repositories.listEffectiveTags
+- artifactregistry.repositories.listTagBindings
+- artifactregistry.repositories.readViaVirtualRepository
+- artifactregistry.rules.get
+- artifactregistry.rules.list
+- artifactregistry.tags.get
+- artifactregistry.tags.list
+- artifactregistry.versions.get
+- artifactregistry.versions.list
+- biglake.catalogs.get
+- biglake.catalogs.getIamPolicy
+- biglake.catalogs.list
+- biglake.databases.get
+- biglake.databases.list
+- biglake.locks.list
+- biglake.namespaces.get
+- biglake.namespaces.getIamPolicy
+- biglake.namespaces.list
+- biglake.tables.get
+- biglake.tables.getData
+- biglake.tables.getIamPolicy
+- biglake.tables.list
+- bigquery.bireservations.get
+- bigquery.capacityCommitments.get
+- bigquery.capacityCommitments.list
+- bigquery.config.get
+- bigquery.connections.get
+- bigquery.connections.getIamPolicy
+- bigquery.connections.list
+- bigquery.connections.use
+- bigquery.dataPolicies.get
+- bigquery.dataPolicies.getIamPolicy
+- bigquery.dataPolicies.list
+- bigquery.datasets.get
+- bigquery.datasets.getIamPolicy
+- bigquery.datasets.listEffectiveTags
+- bigquery.datasets.listTagBindings
+- bigquery.jobs.create
+- bigquery.jobs.get
+- bigquery.jobs.list
+- bigquery.jobs.listExecutionMetadata
+- bigquery.models.export
+- bigquery.models.getData
+- bigquery.models.getMetadata
+- bigquery.models.list
+- bigquery.objectRefs.read
+- bigquery.readsessions.create
+- bigquery.readsessions.getData
+- bigquery.readsessions.update
+- bigquery.reservationAssignments.list
+- bigquery.reservationAssignments.search
+- bigquery.reservationGroups.get
+- bigquery.reservationGroups.list
+- bigquery.reservations.get
+- bigquery.reservations.list
+- bigquery.reservations.listFailoverDatasets
+- bigquery.reservations.use
+- bigquery.routines.get
+- bigquery.routines.list
+- bigquery.rowAccessPolicies.get
+- bigquery.rowAccessPolicies.getIamPolicy
+- bigquery.rowAccessPolicies.list
+- bigquery.savedqueries.get
+- bigquery.savedqueries.list
+- bigquery.tables.createSnapshot
+- bigquery.tables.getIamPolicy
+- bigquery.tables.listEffectiveTags
+- bigquery.tables.listTagBindings
+- bigquery.tables.replicateData
+- bigquery.transfers.get
+- bigquerymigration.subtasks.get
+- bigquerymigration.subtasks.list
+- bigquerymigration.workflows.get
+- bigquerymigration.workflows.list
+- cloudasset.assets.analyzeIamPolicy
+- cloudasset.assets.analyzeMove
+- cloudasset.assets.analyzeOrgPolicy
+- cloudasset.assets.exportAppengineApplications
+- cloudasset.assets.exportAppengineServices
+- cloudasset.assets.exportAppengineVersions
+- cloudasset.assets.exportBigqueryDatasets
+- cloudasset.assets.exportBigqueryModels
+- cloudasset.assets.exportBigqueryTables
+- cloudasset.assets.exportCloudDocumentAIEvaluation
+- cloudasset.assets.exportCloudDocumentAIHumanReviewConfig
+- cloudasset.assets.exportCloudDocumentAILabelerPool
+- cloudasset.assets.exportCloudDocumentAIProcessor
+- cloudasset.assets.exportCloudDocumentAIProcessorVersion
+- cloudasset.assets.exportCloudbillingBillingAccounts
+- cloudasset.assets.exportCloudkmsCryptoKeyVersions
+- cloudasset.assets.exportCloudkmsCryptoKeys
+- cloudasset.assets.exportCloudkmsKeyRings
+- cloudasset.assets.exportCloudmemcacheInstances
+- cloudasset.assets.exportCloudresourcemanagerFolders
+- cloudasset.assets.exportCloudresourcemanagerOrganizations
+- cloudasset.assets.exportCloudresourcemanagerProjects
+- cloudasset.assets.exportCloudresourcemanagerTagBindings
+- cloudasset.assets.exportCloudresourcemanagerTagKeys
+- cloudasset.assets.exportCloudresourcemanagerTagValues
+- cloudasset.assets.exportComputeAddress
+- cloudasset.assets.exportComputeAutoscalers
+- cloudasset.assets.exportComputeBackendBuckets
+- cloudasset.assets.exportComputeBackendServices
+- cloudasset.assets.exportComputeDisks
+- cloudasset.assets.exportComputeFirewalls
+- cloudasset.assets.exportComputeForwardingRules
+- cloudasset.assets.exportComputeGlobalForwardingRules
+- cloudasset.assets.exportComputeHealthChecks
+- cloudasset.assets.exportComputeHttpHealthChecks
+- cloudasset.assets.exportComputeHttpsHealthChecks
+- cloudasset.assets.exportComputeImages
+- cloudasset.assets.exportComputeInstanceGroupManagers
+- cloudasset.assets.exportComputeInstanceGroups
+- cloudasset.assets.exportComputeInstanceTemplates
+- cloudasset.assets.exportComputeInstances
+- cloudasset.assets.exportComputeInterconnect
+- cloudasset.assets.exportComputeInterconnectAttachment
+- cloudasset.assets.exportComputeLicenses
+- cloudasset.assets.exportComputeNetworkEndpointGroups
+- cloudasset.assets.exportComputeNetworks
+- cloudasset.assets.exportComputeProjects
+- cloudasset.assets.exportComputeRegionBackendServices
+- cloudasset.assets.exportComputeRouters
+- cloudasset.assets.exportComputeRoutes
+- cloudasset.assets.exportComputeSecurityPolicy
+- cloudasset.assets.exportComputeSnapshots
+- cloudasset.assets.exportComputeSslCertificates
+- cloudasset.assets.exportComputeSslPolicies
+- cloudasset.assets.exportComputeSubnetworks
+- cloudasset.assets.exportComputeTargetHttpProxies
+- cloudasset.assets.exportComputeTargetHttpsProxies
+- cloudasset.assets.exportComputeTargetInstances
+- cloudasset.assets.exportComputeTargetPools
+- cloudasset.assets.exportComputeTargetSslProxies
+- cloudasset.assets.exportComputeTargetTcpProxies
+- cloudasset.assets.exportComputeTargetVpnGateways
+- cloudasset.assets.exportComputeUrlMaps
+- cloudasset.assets.exportComputeVpnTunnels
+- cloudasset.assets.exportContainerClusters
+- cloudasset.assets.exportDataprocClusters
+- cloudasset.assets.exportDataprocJobs
+- cloudasset.assets.exportDnsManagedZones
+- cloudasset.assets.exportDnsPolicies
+- cloudasset.assets.exportIamRoles
+- cloudasset.assets.exportIamServiceAccountKeys
+- cloudasset.assets.exportIamServiceAccounts
+- cloudasset.assets.exportOSConfigOSPolicyAssignmentReports
+- cloudasset.assets.exportOSConfigOSPolicyAssignments
+- cloudasset.assets.exportPubsubSnapshots
+- cloudasset.assets.exportPubsubSubscriptions
+- cloudasset.assets.exportPubsubTopics
+- cloudasset.assets.exportServicemanagementServices
+- cloudasset.assets.exportSpannerBackups
+- cloudasset.assets.exportSpannerDatabases
+- cloudasset.assets.exportSpannerInstances
+- cloudasset.assets.exportSqladminBackupRuns
+- cloudasset.assets.exportSqladminInstances
+- cloudasset.assets.exportStorageBuckets
+- cloudasset.assets.listCloudDocumentAIEvaluation
+- cloudasset.assets.listCloudDocumentAIHumanReviewConfig
+- cloudasset.assets.listCloudDocumentAILabelerPool
+- cloudasset.assets.listCloudDocumentAIProcessor
+- cloudasset.assets.listCloudDocumentAIProcessorVersion
+- cloudasset.assets.listSqladminBackupRuns
+- cloudasset.assets.searchAllIamPolicies
+- cloudasset.assets.searchAllResources
+- cloudasset.othercloudconnections.get
+- cloudasset.othercloudconnections.list
+- cloudasset.savedqueries.get
+- cloudasset.savedqueries.list
+- cloudbuild.builds.get
+- cloudbuild.builds.list
+- cloudbuild.connections.fetchLinkableRepositories
+- cloudbuild.connections.get
+- cloudbuild.connections.getIamPolicy
+- cloudbuild.connections.list
+- cloudbuild.integrations.get
+- cloudbuild.integrations.list
+- cloudbuild.locations.get
+- cloudbuild.locations.list
+- cloudbuild.operations.get
+- cloudbuild.operations.list
+- cloudbuild.repositories.fetchGitRefs
+- cloudbuild.repositories.get
+- cloudbuild.repositories.list
+- cloudbuild.workerpools.get
+- cloudbuild.workerpools.list
+- cloudfunctions.functions.get
+- cloudfunctions.functions.getIamPolicy
+- cloudfunctions.functions.list
+- cloudfunctions.functions.sourceCodeGet
+- cloudfunctions.locations.list
+- cloudfunctions.operations.get
+- cloudfunctions.operations.list
+- cloudsql.backupRuns.export
+- cloudsql.backupRuns.get
+- cloudsql.backupRuns.list
+- cloudsql.databases.get
+- cloudsql.databases.list
+- cloudsql.instances.createBackupDrBackup
+- cloudsql.instances.export
+- cloudsql.instances.get
+- cloudsql.instances.getDiskShrinkConfig
+- cloudsql.instances.list
+- cloudsql.instances.listEffectiveTags
+- cloudsql.instances.listServerCas
+- cloudsql.instances.listServerCertificates
+- cloudsql.instances.listTagBindings
+- cloudsql.schemas.view
+- cloudsql.sslCerts.get
+- cloudsql.sslCerts.list
+- cloudsql.users.get
+- cloudsql.users.list
+- compute.acceleratorTypes.get
+- compute.acceleratorTypes.list
+- compute.addresses.get
+- compute.addresses.list
+- compute.addresses.listEffectiveTags
+- compute.addresses.listTagBindings
+- compute.autoscalers.get
+- compute.autoscalers.list
+- compute.backendBuckets.get
+- compute.backendBuckets.getIamPolicy
+- compute.backendBuckets.list
+- compute.backendBuckets.listEffectiveTags
+- compute.backendBuckets.listTagBindings
+- compute.backendServices.get
+- compute.backendServices.getIamPolicy
+- compute.backendServices.list
+- compute.backendServices.listEffectiveTags
+- compute.backendServices.listTagBindings
+- compute.commitments.get
+- compute.commitments.list
+- compute.crossSiteNetworks.get
+- compute.crossSiteNetworks.list
+- compute.diskSettings.get
+- compute.diskTypes.get
+- compute.diskTypes.list
+- compute.disks.createSnapshot
+- compute.disks.get
+- compute.disks.getIamPolicy
+- compute.disks.list
+- compute.disks.listEffectiveTags
+- compute.disks.listTagBindings
+- compute.disks.useReadOnly
+- compute.externalVpnGateways.get
+- compute.externalVpnGateways.list
+- compute.externalVpnGateways.listEffectiveTags
+- compute.externalVpnGateways.listTagBindings
+- compute.firewallPolicies.get
+- compute.firewallPolicies.getIamPolicy
+- compute.firewallPolicies.list
+- compute.firewallPolicies.listEffectiveTags
+- compute.firewallPolicies.listTagBindings
+- compute.firewalls.get
+- compute.firewalls.list
+- compute.firewalls.listEffectiveTags
+- compute.firewalls.listTagBindings
+- compute.forwardingRules.get
+- compute.forwardingRules.list
+- compute.forwardingRules.listEffectiveTags
+- compute.forwardingRules.listTagBindings
+- compute.futureReservations.get
+- compute.futureReservations.getIamPolicy
+- compute.futureReservations.list
+- compute.globalAddresses.get
+- compute.globalAddresses.list
+- compute.globalAddresses.listEffectiveTags
+- compute.globalAddresses.listTagBindings
+- compute.globalForwardingRules.get
+- compute.globalForwardingRules.list
+- compute.globalForwardingRules.listEffectiveTags
+- compute.globalForwardingRules.listTagBindings
+- compute.globalNetworkEndpointGroups.get
+- compute.globalNetworkEndpointGroups.list
+- compute.globalNetworkEndpointGroups.listEffectiveTags
+- compute.globalNetworkEndpointGroups.listTagBindings
+- compute.globalOperations.get
+- compute.globalOperations.list
+- compute.globalPublicDelegatedPrefixes.get
+- compute.globalPublicDelegatedPrefixes.list
+- compute.healthChecks.get
+- compute.healthChecks.list
+- compute.healthChecks.listEffectiveTags
+- compute.healthChecks.listTagBindings
+- compute.healthChecks.useReadOnly
+- compute.httpHealthChecks.get
+- compute.httpHealthChecks.list
+- compute.httpHealthChecks.listEffectiveTags
+- compute.httpHealthChecks.listTagBindings
+- compute.httpHealthChecks.useReadOnly
+- compute.httpsHealthChecks.get
+- compute.httpsHealthChecks.list
+- compute.httpsHealthChecks.listEffectiveTags
+- compute.httpsHealthChecks.listTagBindings
+- compute.httpsHealthChecks.useReadOnly
+- compute.images.get
+- compute.images.getFromFamily
+- compute.images.getIamPolicy
+- compute.images.list
+- compute.images.listEffectiveTags
+- compute.images.listTagBindings
+- compute.images.useReadOnly
+- compute.instanceGroupManagers.get
+- compute.instanceGroupManagers.list
+- compute.instanceGroupManagers.listEffectiveTags
+- compute.instanceGroupManagers.listTagBindings
+- compute.instanceGroups.get
+- compute.instanceGroups.list
+- compute.instanceGroups.listEffectiveTags
+- compute.instanceGroups.listTagBindings
+- compute.instanceSettings.get
+- compute.instanceTemplates.get
+- compute.instanceTemplates.getIamPolicy
+- compute.instanceTemplates.list
+- compute.instanceTemplates.useReadOnly
+- compute.instances.get
+- compute.instances.getEffectiveFirewalls
+- compute.instances.getIamPolicy
+- compute.instances.getScreenshot
+- compute.instances.getSerialPortOutput
+- compute.instances.getShieldedInstanceIdentity
+- compute.instances.getShieldedVmIdentity
+- compute.instances.list
+- compute.instances.listEffectiveTags
+- compute.instances.listReferrers
+- compute.instances.listTagBindings
+- compute.instances.useReadOnly
+- compute.instantSnapshots.get
+- compute.instantSnapshots.getIamPolicy
+- compute.instantSnapshots.list
+- compute.instantSnapshots.useReadOnly
+- compute.interconnectAttachmentGroups.get
+- compute.interconnectAttachmentGroups.list
+- compute.interconnectAttachments.listEffectiveTags
+- compute.interconnectAttachments.listTagBindings
+- compute.interconnectGroups.get
+- compute.interconnectGroups.list
+- compute.interconnectRemoteLocations.get
+- compute.interconnectRemoteLocations.list
+- compute.interconnects.listEffectiveTags
+- compute.interconnects.listTagBindings
+- compute.licenseCodes.getIamPolicy
+- compute.licenses.get
+- compute.licenses.getIamPolicy
+- compute.machineImages.get
+- compute.machineImages.getIamPolicy
+- compute.machineImages.list
+- compute.machineImages.useReadOnly
+- compute.machineTypes.get
+- compute.machineTypes.list
+- compute.multiMig.get
+- compute.multiMig.list
+- compute.multiMigMembers.get
+- compute.multiMigMembers.list
+- compute.networkAttachments.get
+- compute.networkAttachments.getIamPolicy
+- compute.networkAttachments.list
+- compute.networkAttachments.listEffectiveTags
+- compute.networkAttachments.listTagBindings
+- compute.networkEdgeSecurityServices.get
+- compute.networkEdgeSecurityServices.list
+- compute.networkEdgeSecurityServices.listEffectiveTags
+- compute.networkEdgeSecurityServices.listTagBindings
+- compute.networkEndpointGroups.get
+- compute.networkEndpointGroups.list
+- compute.networkEndpointGroups.listEffectiveTags
+- compute.networkEndpointGroups.listTagBindings
+- compute.networkProfiles.get
+- compute.networkProfiles.list
+- compute.networks.get
+- compute.networks.getEffectiveFirewalls
+- compute.networks.getRegionEffectiveFirewalls
+- compute.networks.list
+- compute.networks.listEffectiveTags
+- compute.networks.listPeeringRoutes
+- compute.networks.listTagBindings
+- compute.nodeGroups.get
+- compute.nodeGroups.getIamPolicy
+- compute.nodeGroups.list
+- compute.nodeTemplates.get
+- compute.nodeTemplates.getIamPolicy
+- compute.nodeTemplates.list
+- compute.nodeTypes.get
+- compute.nodeTypes.list
+- compute.organizations.listAssociations
+- compute.packetMirrorings.get
+- compute.packetMirrorings.list
+- compute.packetMirrorings.listEffectiveTags
+- compute.packetMirrorings.listTagBindings
+- compute.previewFeatures.get
+- compute.previewFeatures.list
+- compute.projects.get
+- compute.publicAdvertisedPrefixes.get
+- compute.publicAdvertisedPrefixes.list
+- compute.publicDelegatedPrefixes.get
+- compute.publicDelegatedPrefixes.list
+- compute.publicDelegatedPrefixes.listEffectiveTags
+- compute.publicDelegatedPrefixes.listTagBindings
+- compute.regionBackendServices.get
+- compute.regionBackendServices.getIamPolicy
+- compute.regionBackendServices.list
+- compute.regionBackendServices.listEffectiveTags
+- compute.regionBackendServices.listTagBindings
+- compute.regionFirewallPolicies.get
+- compute.regionFirewallPolicies.getIamPolicy
+- compute.regionFirewallPolicies.list
+- compute.regionFirewallPolicies.listEffectiveTags
+- compute.regionFirewallPolicies.listTagBindings
+- compute.regionHealthCheckServices.get
+- compute.regionHealthCheckServices.list
+- compute.regionHealthChecks.get
+- compute.regionHealthChecks.list
+- compute.regionHealthChecks.listEffectiveTags
+- compute.regionHealthChecks.listTagBindings
+- compute.regionHealthChecks.useReadOnly
+- compute.regionNetworkEndpointGroups.get
+- compute.regionNetworkEndpointGroups.list
+- compute.regionNetworkEndpointGroups.listEffectiveTags
+- compute.regionNetworkEndpointGroups.listTagBindings
+- compute.regionNotificationEndpoints.get
+- compute.regionNotificationEndpoints.list
+- compute.regionOperations.get
+- compute.regionOperations.list
+- compute.regionSecurityPolicies.get
+- compute.regionSecurityPolicies.list
+- compute.regionSecurityPolicies.listEffectiveTags
+- compute.regionSecurityPolicies.listTagBindings
+- compute.regionSslCertificates.get
+- compute.regionSslCertificates.list
+- compute.regionSslCertificates.listEffectiveTags
+- compute.regionSslCertificates.listTagBindings
+- compute.regionSslPolicies.get
+- compute.regionSslPolicies.list
+- compute.regionSslPolicies.listAvailableFeatures
+- compute.regionSslPolicies.listEffectiveTags
+- compute.regionSslPolicies.listTagBindings
+- compute.regionTargetHttpProxies.get
+- compute.regionTargetHttpProxies.list
+- compute.regionTargetHttpProxies.listEffectiveTags
+- compute.regionTargetHttpProxies.listTagBindings
+- compute.regionTargetHttpsProxies.get
+- compute.regionTargetHttpsProxies.list
+- compute.regionTargetHttpsProxies.listEffectiveTags
+- compute.regionTargetHttpsProxies.listTagBindings
+- compute.regionTargetTcpProxies.get
+- compute.regionTargetTcpProxies.list
+- compute.regionTargetTcpProxies.listEffectiveTags
+- compute.regionTargetTcpProxies.listTagBindings
+- compute.regionUrlMaps.get
+- compute.regionUrlMaps.list
+- compute.regionUrlMaps.listEffectiveTags
+- compute.regionUrlMaps.listTagBindings
+- compute.regionUrlMaps.validate
+- compute.regions.get
+- compute.regions.list
+- compute.reservationBlocks.get
+- compute.reservationBlocks.list
+- compute.reservationSubBlocks.get
+- compute.reservationSubBlocks.list
+- compute.reservations.get
+- compute.reservations.list
+- compute.resourcePolicies.get
+- compute.resourcePolicies.getIamPolicy
+- compute.resourcePolicies.list
+- compute.resourcePolicies.useReadOnly
+- compute.routers.get
+- compute.routers.getRoutePolicy
+- compute.routers.list
+- compute.routers.listBgpRoutes
+- compute.routers.listEffectiveTags
+- compute.routers.listRoutePolicies
+- compute.routers.listTagBindings
+- compute.routes.get
+- compute.routes.list
+- compute.routes.listEffectiveTags
+- compute.routes.listTagBindings
+- compute.securityPolicies.listEffectiveTags
+- compute.securityPolicies.listTagBindings
+- compute.serviceAttachments.get
+- compute.serviceAttachments.getIamPolicy
+- compute.serviceAttachments.list
+- compute.serviceAttachments.listEffectiveTags
+- compute.serviceAttachments.listTagBindings
+- compute.snapshotSettings.get
+- compute.snapshots.get
+- compute.snapshots.getIamPolicy
+- compute.snapshots.list
+- compute.snapshots.listEffectiveTags
+- compute.snapshots.listTagBindings
+- compute.snapshots.useReadOnly
+- compute.spotAssistants.get
+- compute.sslCertificates.get
+- compute.sslCertificates.list
+- compute.sslCertificates.listEffectiveTags
+- compute.sslCertificates.listTagBindings
+- compute.sslPolicies.listEffectiveTags
+- compute.sslPolicies.listTagBindings
+- compute.storagePools.get
+- compute.storagePools.getIamPolicy
+- compute.storagePools.list
+- compute.subnetworks.get
+- compute.subnetworks.getIamPolicy
+- compute.subnetworks.list
+- compute.subnetworks.listEffectiveTags
+- compute.subnetworks.listTagBindings
+- compute.targetGrpcProxies.get
+- compute.targetGrpcProxies.list
+- compute.targetGrpcProxies.listEffectiveTags
+- compute.targetGrpcProxies.listTagBindings
+- compute.targetHttpProxies.get
+- compute.targetHttpProxies.list
+- compute.targetHttpProxies.listEffectiveTags
+- compute.targetHttpProxies.listTagBindings
+- compute.targetHttpsProxies.get
+- compute.targetHttpsProxies.list
+- compute.targetHttpsProxies.listEffectiveTags
+- compute.targetHttpsProxies.listTagBindings
+- compute.targetInstances.get
+- compute.targetInstances.list
+- compute.targetInstances.listEffectiveTags
+- compute.targetInstances.listTagBindings
+- compute.targetPools.get
+- compute.targetPools.list
+- compute.targetPools.listEffectiveTags
+- compute.targetPools.listTagBindings
+- compute.targetSslProxies.get
+- compute.targetSslProxies.list
+- compute.targetSslProxies.listEffectiveTags
+- compute.targetSslProxies.listTagBindings
+- compute.targetTcpProxies.get
+- compute.targetTcpProxies.list
+- compute.targetTcpProxies.listEffectiveTags
+- compute.targetTcpProxies.listTagBindings
+- compute.targetVpnGateways.get
+- compute.targetVpnGateways.list
+- compute.targetVpnGateways.listEffectiveTags
+- compute.targetVpnGateways.listTagBindings
+- compute.urlMaps.listEffectiveTags
+- compute.urlMaps.listTagBindings
+- compute.vpnGateways.get
+- compute.vpnGateways.list
+- compute.vpnGateways.listEffectiveTags
+- compute.vpnGateways.listTagBindings
+- compute.vpnTunnels.get
+- compute.vpnTunnels.list
+- compute.vpnTunnels.listEffectiveTags
+- compute.vpnTunnels.listTagBindings
+- compute.wireGroups.get
+- compute.wireGroups.list
+- compute.zoneOperations.get
+- compute.zoneOperations.list
+- compute.zones.get
+- compute.zones.list
+- container.apiServices.get
+- container.apiServices.getStatus
+- container.apiServices.list
+- container.auditSinks.get
+- container.auditSinks.list
+- container.backendConfigs.get
+- container.backendConfigs.list
+- container.certificateSigningRequests.get
+- container.certificateSigningRequests.getStatus
+- container.certificateSigningRequests.list
+- container.clusterRoleBindings.get
+- container.clusterRoleBindings.list
+- container.clusterRoles.get
+- container.clusterRoles.list
+- container.clusters.connect
+- container.clusters.get
+- container.clusters.list
+- container.clusters.listEffectiveTags
+- container.clusters.listTagBindings
+- container.componentStatuses.get
+- container.componentStatuses.list
+- container.configMaps.get
+- container.configMaps.list
+- container.controllerRevisions.get
+- container.controllerRevisions.list
+- container.cronJobs.get
+- container.cronJobs.getStatus
+- container.cronJobs.list
+- container.csiDrivers.get
+- container.csiDrivers.list
+- container.csiNodeInfos.get
+- container.csiNodeInfos.list
+- container.csiNodes.get
+- container.csiNodes.list
+- container.customResourceDefinitions.get
+- container.customResourceDefinitions.getStatus
+- container.customResourceDefinitions.list
+- container.daemonSets.get
+- container.daemonSets.getStatus
+- container.daemonSets.list
+- container.deployments.get
+- container.deployments.getStatus
+- container.deployments.list
+- container.endpointSlices.get
+- container.endpointSlices.list
+- container.endpoints.get
+- container.endpoints.list
+- container.events.get
+- container.events.list
+- container.frontendConfigs.get
+- container.frontendConfigs.list
+- container.horizontalPodAutoscalers.get
+- container.horizontalPodAutoscalers.getStatus
+- container.horizontalPodAutoscalers.list
+- container.ingresses.get
+- container.ingresses.getStatus
+- container.ingresses.list
+- container.jobs.get
+- container.jobs.getStatus
+- container.jobs.list
+- container.leases.get
+- container.leases.list
+- container.limitRanges.get
+- container.limitRanges.list
+- container.managedCertificates.get
+- container.managedCertificates.list
+- container.mutatingWebhookConfigurations.get
+- container.mutatingWebhookConfigurations.list
+- container.namespaces.get
+- container.namespaces.getStatus
+- container.namespaces.list
+- container.networkPolicies.get
+- container.networkPolicies.list
+- container.nodes.get
+- container.nodes.getStatus
+- container.nodes.list
+- container.operations.get
+- container.operations.list
+- container.persistentVolumeClaims.get
+- container.persistentVolumeClaims.getStatus
+- container.persistentVolumeClaims.list
+- container.persistentVolumes.get
+- container.persistentVolumes.getStatus
+- container.persistentVolumes.list
+- container.podDisruptionBudgets.get
+- container.podDisruptionBudgets.getStatus
+- container.podDisruptionBudgets.list
+- container.podSecurityPolicies.get
+- container.podSecurityPolicies.list
+- container.podTemplates.get
+- container.podTemplates.list
+- container.pods.get
+- container.pods.getLogs
+- container.pods.getStatus
+- container.pods.list
+- container.priorityClasses.get
+- container.priorityClasses.list
+- container.replicaSets.get
+- container.replicaSets.getScale
+- container.replicaSets.getStatus
+- container.replicaSets.list
+- container.replicationControllers.get
+- container.replicationControllers.getScale
+- container.replicationControllers.getStatus
+- container.replicationControllers.list
+- container.resourceQuotas.get
+- container.resourceQuotas.getStatus
+- container.resourceQuotas.list
+- container.roleBindings.get
+- container.roleBindings.list
+- container.roles.get
+- container.roles.list
+- container.runtimeClasses.get
+- container.runtimeClasses.list
+- container.selfSubjectAccessReviews.create
+- container.selfSubjectRulesReviews.create
+- container.serviceAccounts.get
+- container.serviceAccounts.list
+- container.services.get
+- container.services.getStatus
+- container.services.list
+- container.statefulSets.get
+- container.statefulSets.getScale
+- container.statefulSets.getStatus
+- container.statefulSets.list
+- container.storageClasses.get
+- container.storageClasses.list
+- container.storageStates.get
+- container.storageStates.getStatus
+- container.storageStates.list
+- container.storageVersionMigrations.get
+- container.storageVersionMigrations.getStatus
+- container.storageVersionMigrations.list
+- container.thirdPartyObjects.get
+- container.thirdPartyObjects.list
+- container.tokenReviews.create
+- container.updateInfos.get
+- container.updateInfos.list
+- container.validatingWebhookConfigurations.get
+- container.validatingWebhookConfigurations.list
+- container.volumeAttachments.get
+- container.volumeAttachments.getStatus
+- container.volumeAttachments.list
+- container.volumeSnapshotClasses.get
+- container.volumeSnapshotClasses.list
+- container.volumeSnapshotContents.get
+- container.volumeSnapshotContents.getStatus
+- container.volumeSnapshotContents.list
+- container.volumeSnapshots.get
+- container.volumeSnapshots.getStatus
+- container.volumeSnapshots.list
+- containeranalysis.notes.get
+- containeranalysis.notes.getIamPolicy
+- containeranalysis.notes.list
+- containeranalysis.occurrences.get
+- containeranalysis.occurrences.getIamPolicy
+- containeranalysis.occurrences.list
+- containersecurity.locations.get
+- containersecurity.locations.list
+- dataflow.jobs.get
+- dataflow.jobs.list
+- dataflow.messages.list
+- dataflow.metrics.get
+- dataflow.snapshots.get
+- dataflow.snapshots.list
+- dataproc.agents.get
+- dataproc.agents.list
+- dataproc.autoscalingPolicies.get
+- dataproc.autoscalingPolicies.getIamPolicy
+- dataproc.autoscalingPolicies.list
+- dataproc.autoscalingPolicies.use
+- dataproc.batches.get
+- dataproc.batches.list
+- dataproc.batches.sparkApplicationRead
+- dataproc.clusters.get
+- dataproc.clusters.getIamPolicy
+- dataproc.clusters.list
+- dataproc.jobs.get
+- dataproc.jobs.getIamPolicy
+- dataproc.jobs.list
+- dataproc.nodeGroups.get
+- dataproc.operations.get
+- dataproc.operations.getIamPolicy
+- dataproc.operations.list
+- dataproc.sessionTemplates.get
+- dataproc.sessionTemplates.list
+- dataproc.sessions.get
+- dataproc.sessions.list
+- dataproc.sessions.sparkApplicationRead
+- dataproc.tasks.listInvalidatedLeases
+- dataproc.workflowTemplates.get
+- dataproc.workflowTemplates.getIamPolicy
+- dataproc.workflowTemplates.list
+- dataprocessing.datasources.get
+- dataprocessing.datasources.list
+- dataprocessing.featurecontrols.list
+- dataprocessing.groupcontrols.get
+- dataprocessing.groupcontrols.list
+- dataprocrm.locations.get
+- dataprocrm.locations.list
+- dataprocrm.nodePools.get
+- dataprocrm.nodePools.list
+- dataprocrm.nodes.get
+- dataprocrm.nodes.list
+- dataprocrm.nodes.mintOAuthToken
+- dataprocrm.operations.get
+- dataprocrm.operations.list
+- dataprocrm.workloads.get
+- dataprocrm.workloads.list
+- datastore.backupSchedules.get
+- datastore.backupSchedules.list
+- datastore.backups.get
+- datastore.backups.list
+- datastore.databases.get
+- datastore.databases.getMetadata
+- datastore.databases.list
+- datastore.databases.listEffectiveTags
+- datastore.databases.listTagBindings
+- datastore.entities.get
+- datastore.entities.list
+- datastore.indexes.get
+- datastore.indexes.list
+- datastore.insights.get
+- datastore.keyVisualizerScans.get
+- datastore.keyVisualizerScans.list
+- datastore.namespaces.get
+- datastore.namespaces.list
+- datastore.operations.get
+- datastore.operations.list
+- datastore.statistics.get
+- datastore.statistics.list
+- datastore.userCreds.get
+- datastore.userCreds.list
+- dns.changes.get
+- dns.changes.list
+- dns.dnsKeys.get
+- dns.dnsKeys.list
+- dns.managedZoneOperations.get
+- dns.managedZoneOperations.list
+- dns.managedZones.get
+- dns.managedZones.getIamPolicy
+- dns.managedZones.list
+- dns.policies.get
+- dns.policies.list
+- dns.projects.get
+- dns.resourceRecordSets.get
+- dns.resourceRecordSets.list
+- dns.responsePolicies.get
+- dns.responsePolicies.list
+- dns.responsePolicyRules.get
+- dns.responsePolicyRules.list
+- firebase.billingPlans.get
+- firebase.clients.get
+- firebase.clients.list
+- firebase.links.list
+- firebase.playLinks.get
+- firebase.playLinks.list
+- firebase.projects.get
+- firebaseabt.experimentresults.get
+- firebaseabt.experiments.get
+- firebaseabt.experiments.list
+- firebaseabt.projectmetadata.get
+- firebaseanalytics.resources.googleAnalyticsReadAndAnalyze
+- firebaseappcheck.appAttestConfig.get
+- firebaseappcheck.automations.get
+- firebaseappcheck.automations.list
+- firebaseappcheck.debugTokens.get
+- firebaseappcheck.deviceCheckConfig.get
+- firebaseappcheck.playIntegrityConfig.get
+- firebaseappcheck.recaptchaEnterpriseConfig.get
+- firebaseappcheck.recaptchaV3Config.get
+- firebaseappcheck.resourcePolicies.get
+- firebaseappcheck.safetyNetConfig.get
+- firebaseappcheck.services.get
+- firebaseappdistro.groups.list
+- firebaseappdistro.releases.list
+- firebaseappdistro.testers.list
+- firebaseauth.configs.get
+- firebaseauth.users.get
+- firebasecrash.reports.get
+- firebasecrashlytics.config.get
+- firebasecrashlytics.data.get
+- firebasecrashlytics.issues.get
+- firebasecrashlytics.issues.list
+- firebasecrashlytics.sessions.get
+- firebasedatabase.instances.get
+- firebasedatabase.instances.list
+- firebasedataconnect.connectorRevisions.get
+- firebasedataconnect.connectorRevisions.list
+- firebasedataconnect.connectors.get
+- firebasedataconnect.connectors.list
+- firebasedataconnect.locations.get
+- firebasedataconnect.locations.list
+- firebasedataconnect.operations.get
+- firebasedataconnect.operations.list
+- firebasedataconnect.schemaRevisions.get
+- firebasedataconnect.schemaRevisions.list
+- firebasedataconnect.schemas.get
+- firebasedataconnect.schemas.list
+- firebasedataconnect.services.get
+- firebasedataconnect.services.list
+- firebasedynamiclinks.destinations.list
+- firebasedynamiclinks.domains.get
+- firebasedynamiclinks.domains.list
+- firebasedynamiclinks.links.get
+- firebasedynamiclinks.links.list
+- firebasedynamiclinks.stats.get
+- firebaseextensions.configs.list
+- firebaseextensionspublisher.extensions.get
+- firebaseextensionspublisher.extensions.list
+- firebasehosting.sites.get
+- firebasehosting.sites.list
+- firebaseinappmessaging.campaigns.get
+- firebaseinappmessaging.campaigns.list
+- firebasemessagingcampaigns.campaigns.get
+- firebasemessagingcampaigns.campaigns.list
+- firebaseml.models.get
+- firebaseml.models.list
+- firebaseml.modelversions.get
+- firebaseml.modelversions.list
+- firebasenotifications.messages.get
+- firebasenotifications.messages.list
+- firebaseperformance.data.get
+- firebaserules.releases.get
+- firebaserules.releases.getExecutable
+- firebaserules.releases.list
+- firebaserules.rulesets.list
+- firebaserules.rulesets.test
+- firebasestorage.buckets.get
+- firebasestorage.buckets.list
+- firebasestorage.defaultBucket.get
+- firebasevertexai.configs.get
+- iam.denypolicies.get
+- iam.denypolicies.list
+- iam.googleapis.com/oauthClientCredentials.get
+- iam.googleapis.com/oauthClientCredentials.list
+- iam.googleapis.com/oauthClients.get
+- iam.googleapis.com/oauthClients.list
+- iam.googleapis.com/workloadIdentityPoolProviderKeys.get
+- iam.googleapis.com/workloadIdentityPoolProviderKeys.list
+- iam.googleapis.com/workloadIdentityPoolProviders.get
+- iam.googleapis.com/workloadIdentityPoolProviders.list
+- iam.googleapis.com/workloadIdentityPools.get
+- iam.googleapis.com/workloadIdentityPools.list
+- iam.roles.get
+- iam.roles.list
+- iam.serviceAccountKeys.get
+- iam.serviceAccountKeys.list
+- iam.serviceAccounts.get
+- iam.serviceAccounts.getIamPolicy
+- iam.serviceAccounts.list
+- iam.serviceAccounts.listEffectiveTags
+- iam.serviceAccounts.listTagBindings
+- iap.tunnelDestGroups.get
+- iap.tunnelDestGroups.list
+- monitoring.alertPolicies.get
+- monitoring.alertPolicies.list
+- monitoring.alertPolicies.listEffectiveTags
+- monitoring.alertPolicies.listTagBindings
+- monitoring.dashboards.get
+- monitoring.dashboards.list
+- monitoring.dashboards.listEffectiveTags
+- monitoring.dashboards.listTagBindings
+- monitoring.groups.get
+- monitoring.groups.list
+- monitoring.metricDescriptors.get
+- monitoring.metricDescriptors.list
+- monitoring.monitoredResourceDescriptors.get
+- monitoring.monitoredResourceDescriptors.list
+- monitoring.services.get
+- monitoring.services.list
+- monitoring.slos.get
+- monitoring.slos.list
+- monitoring.snoozes.get
+- monitoring.snoozes.list
+- monitoring.timeSeries.list
+- monitoring.uptimeCheckConfigs.get
+- monitoring.uptimeCheckConfigs.list
+- pubsub.messageTransforms.validate
+- pubsub.schemas.attach
+- pubsub.schemas.get
+- pubsub.schemas.getIamPolicy
+- pubsub.schemas.list
+- pubsub.schemas.listRevisions
+- pubsub.schemas.validate
+- pubsub.snapshots.list
+- pubsub.subscriptions.get
+- pubsub.subscriptions.list
+- pubsub.topics.get
+- pubsub.topics.list
+- pubsublite.locations.openKafkaStream
+- pubsublite.operations.get
+- pubsublite.operations.list
+- pubsublite.reservations.get
+- pubsublite.reservations.list
+- pubsublite.reservations.listTopics
+- pubsublite.subscriptions.get
+- pubsublite.subscriptions.getCursor
+- pubsublite.subscriptions.list
+- pubsublite.subscriptions.subscribe
+- pubsublite.topics.computeHeadCursor
+- pubsublite.topics.computeMessageStats
+- pubsublite.topics.computeTimeCursor
+- pubsublite.topics.get
+- pubsublite.topics.getPartitions
+- pubsublite.topics.list
+- pubsublite.topics.listSubscriptions
+- pubsublite.topics.subscribe
+- redis.backupCollections.get
+- redis.backupCollections.list
+- redis.backups.export
+- redis.backups.get
+- redis.backups.list
+- redis.clusters.get
+- redis.clusters.list
+- redis.instances.get
+- redis.instances.list
+- redis.instances.listEffectiveTags
+- redis.instances.listTagBindings
+- redis.locations.get
+- redis.locations.list
+- redis.operations.get
+- redis.operations.list
+- resourcemanager.hierarchyNodes.listEffectiveTags
+- resourcemanager.hierarchyNodes.listTagBindings
+- resourcemanager.projects.get
+- resourcemanager.projects.getIamPolicy
+- resourcemanager.tagHolds.list
+- resourcemanager.tagKeys.get
+- resourcemanager.tagKeys.getIamPolicy
+- resourcemanager.tagKeys.list
+- resourcemanager.tagValues.get
+- resourcemanager.tagValues.getIamPolicy
+- resourcemanager.tagValues.list
+- secretmanager.locations.get
+- secretmanager.locations.list
+- secretmanager.secrets.get
+- secretmanager.secrets.getIamPolicy
+- secretmanager.secrets.list
+- secretmanager.secrets.listEffectiveTags
+- secretmanager.secrets.listTagBindings
+- secretmanager.versions.get
+- secretmanager.versions.list
+- servicemanagement.services.get
+- servicemanagement.services.list
+- serviceusage.services.get
+- serviceusage.services.list
+- spanner.backupOperations.get
+- spanner.backupOperations.list
+- spanner.backupSchedules.get
+- spanner.backupSchedules.getIamPolicy
+- spanner.backupSchedules.list
+- spanner.backups.get
+- spanner.backups.getIamPolicy
+- spanner.backups.list
+- spanner.databaseOperations.get
+- spanner.databaseOperations.list
+- spanner.databaseRoles.list
+- spanner.databases.beginReadOnlyTransaction
+- spanner.databases.get
+- spanner.databases.getDdl
+- spanner.databases.getIamPolicy
+- spanner.databases.list
+- spanner.databases.partitionQuery
+- spanner.databases.partitionRead
+- spanner.databases.read
+- spanner.databases.select
+- spanner.databases.useDataBoost
+- spanner.instanceConfigOperations.get
+- spanner.instanceConfigOperations.list
+- spanner.instanceConfigs.get
+- spanner.instanceConfigs.list
+- spanner.instanceOperations.get
+- spanner.instanceOperations.list
+- spanner.instancePartitionOperations.get
+- spanner.instancePartitionOperations.list
+- spanner.instancePartitions.get
+- spanner.instancePartitions.list
+- spanner.instances.get
+- spanner.instances.getIamPolicy
+- spanner.instances.list
+- spanner.instances.listEffectiveTags
+- spanner.instances.listTagBindings
+- spanner.sessions.create
+- spanner.sessions.get
+- spanner.sessions.list
+- storage.buckets.list
+- storage.buckets.listEffectiveTags
+- storage.buckets.listTagBindings
+- storage.folders.get
+- storage.folders.list
+- storage.hmacKeys.get
+- storage.hmacKeys.list
+- storage.intelligenceConfigs.get
+- storageinsights.datasetConfigs.get
+- storageinsights.datasetConfigs.list
+- storageinsights.locations.get
+- storageinsights.locations.list
+- storageinsights.operations.get
+- storageinsights.operations.list
+- storageinsights.reportConfigs.get
+- storageinsights.reportConfigs.list
+- storageinsights.reportDetails.get
+- storageinsights.reportDetails.list
+- storagetransfer.agentpools.get
+- storagetransfer.agentpools.list
+- storagetransfer.jobs.get
+- storagetransfer.jobs.list
+- storagetransfer.operations.get
+- storagetransfer.operations.list
+- storagetransfer.projects.getServiceAccount
+- trafficdirector.networks.getConfigs
+role_id: beam_viewer
+stage: GA
+title: beam_viewer
diff --git a/infra/iam/roles/beam_writer.role.yaml b/infra/iam/roles/beam_writer.role.yaml
new file mode 100644
index 000000000000..947757b0d6d9
--- /dev/null
+++ b/infra/iam/roles/beam_writer.role.yaml
@@ -0,0 +1,306 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This file is auto-generated by generate_roles.py.
+# Do not edit manually.
+
+# This file was generated on 2025-08-11 15:53:17 UTC
+
+description: This is the beam_writer role
+permissions:
+- bigquery.datasets.create
+- bigquery.tables.export
+- bigquery.tables.get
+- bigquery.tables.getData
+- bigquery.tables.list
+- bigquerymigration.translation.translate
+- cloudkms.cryptoKeyVersions.get
+- cloudkms.cryptoKeyVersions.list
+- cloudkms.cryptoKeys.get
+- cloudkms.cryptoKeys.getIamPolicy
+- cloudkms.cryptoKeys.list
+- cloudkms.ekmConfigs.get
+- cloudkms.ekmConfigs.getIamPolicy
+- cloudkms.ekmConnections.get
+- cloudkms.ekmConnections.getIamPolicy
+- cloudkms.ekmConnections.list
+- cloudkms.ekmConnections.verifyConnectivity
+- cloudkms.importJobs.get
+- cloudkms.importJobs.getIamPolicy
+- cloudkms.importJobs.list
+- cloudkms.kajPolicyConfigs.get
+- cloudkms.keyHandles.create
+- cloudkms.keyHandles.get
+- cloudkms.keyHandles.list
+- cloudkms.keyRings.get
+- cloudkms.keyRings.getIamPolicy
+- cloudkms.keyRings.list
+- cloudkms.keyRings.listEffectiveTags
+- cloudkms.keyRings.listTagBindings
+- cloudkms.locations.generateRandomBytes
+- cloudkms.locations.get
+- cloudkms.locations.list
+- cloudkms.operations.get
+- cloudkms.projects.showEffectiveAutokeyConfig
+- cloudkms.projects.showEffectiveKajEnrollmentConfig
+- cloudkms.projects.showEffectiveKajPolicyConfig
+- cloudsql.instances.login
+- container.apiServices.create
+- container.apiServices.update
+- container.apiServices.updateStatus
+- container.auditSinks.create
+- container.auditSinks.update
+- container.backendConfigs.create
+- container.backendConfigs.update
+- container.bindings.create
+- container.certificateSigningRequests.create
+- container.certificateSigningRequests.update
+- container.certificateSigningRequests.updateStatus
+- container.configMaps.create
+- container.configMaps.update
+- container.cronJobs.create
+- container.cronJobs.update
+- container.cronJobs.updateStatus
+- container.csiDrivers.create
+- container.csiDrivers.update
+- container.csiNodeInfos.create
+- container.csiNodeInfos.update
+- container.csiNodes.create
+- container.csiNodes.update
+- container.customResourceDefinitions.create
+- container.customResourceDefinitions.update
+- container.customResourceDefinitions.updateStatus
+- container.daemonSets.create
+- container.daemonSets.update
+- container.daemonSets.updateStatus
+- container.deployments.create
+- container.deployments.getScale
+- container.deployments.rollback
+- container.deployments.update
+- container.deployments.updateScale
+- container.deployments.updateStatus
+- container.endpointSlices.create
+- container.endpointSlices.update
+- container.endpoints.create
+- container.endpoints.update
+- container.events.create
+- container.events.update
+- container.frontendConfigs.create
+- container.frontendConfigs.update
+- container.horizontalPodAutoscalers.create
+- container.horizontalPodAutoscalers.update
+- container.horizontalPodAutoscalers.updateStatus
+- container.ingresses.create
+- container.ingresses.update
+- container.ingresses.updateStatus
+- container.jobs.create
+- container.jobs.update
+- container.jobs.updateStatus
+- container.leases.create
+- container.leases.update
+- container.limitRanges.create
+- container.limitRanges.update
+- container.localSubjectAccessReviews.create
+- container.managedCertificates.create
+- container.managedCertificates.update
+- container.namespaces.create
+- container.namespaces.update
+- container.namespaces.updateStatus
+- container.networkPolicies.create
+- container.networkPolicies.update
+- container.nodes.create
+- container.nodes.proxy
+- container.nodes.update
+- container.nodes.updateStatus
+- container.persistentVolumeClaims.create
+- container.persistentVolumeClaims.update
+- container.persistentVolumeClaims.updateStatus
+- container.persistentVolumes.create
+- container.persistentVolumes.update
+- container.persistentVolumes.updateStatus
+- container.podDisruptionBudgets.create
+- container.podDisruptionBudgets.update
+- container.podDisruptionBudgets.updateStatus
+- container.podTemplates.create
+- container.podTemplates.update
+- container.pods.attach
+- container.pods.create
+- container.pods.evict
+- container.pods.exec
+- container.pods.portForward
+- container.pods.proxy
+- container.pods.update
+- container.pods.updateStatus
+- container.priorityClasses.create
+- container.priorityClasses.update
+- container.replicaSets.create
+- container.replicaSets.update
+- container.replicaSets.updateScale
+- container.replicaSets.updateStatus
+- container.replicationControllers.create
+- container.replicationControllers.update
+- container.replicationControllers.updateScale
+- container.replicationControllers.updateStatus
+- container.resourceQuotas.create
+- container.resourceQuotas.update
+- container.resourceQuotas.updateStatus
+- container.runtimeClasses.create
+- container.runtimeClasses.update
+- container.secrets.create
+- container.secrets.get
+- container.secrets.list
+- container.secrets.update
+- container.serviceAccounts.create
+- container.serviceAccounts.createToken
+- container.serviceAccounts.update
+- container.services.create
+- container.services.proxy
+- container.services.update
+- container.services.updateStatus
+- container.statefulSets.create
+- container.statefulSets.update
+- container.statefulSets.updateScale
+- container.statefulSets.updateStatus
+- container.storageClasses.create
+- container.storageClasses.update
+- container.storageStates.create
+- container.storageStates.update
+- container.storageStates.updateStatus
+- container.storageVersionMigrations.create
+- container.storageVersionMigrations.update
+- container.storageVersionMigrations.updateStatus
+- container.subjectAccessReviews.create
+- container.thirdPartyObjects.create
+- container.thirdPartyObjects.update
+- container.updateInfos.create
+- container.updateInfos.update
+- container.volumeAttachments.create
+- container.volumeAttachments.update
+- container.volumeAttachments.updateStatus
+- container.volumeSnapshotClasses.create
+- container.volumeSnapshotClasses.update
+- container.volumeSnapshotContents.create
+- container.volumeSnapshotContents.update
+- container.volumeSnapshotContents.updateStatus
+- container.volumeSnapshots.create
+- container.volumeSnapshots.update
+- container.volumeSnapshots.updateStatus
+- dataform.commentThreads.get
+- dataform.commentThreads.list
+- dataform.comments.get
+- dataform.comments.list
+- dataform.compilationResults.get
+- dataform.compilationResults.list
+- dataform.compilationResults.query
+- dataform.config.get
+- dataform.locations.get
+- dataform.locations.list
+- dataform.releaseConfigs.get
+- dataform.releaseConfigs.list
+- dataform.repositories.computeAccessTokenStatus
+- dataform.repositories.create
+- dataform.repositories.fetchHistory
+- dataform.repositories.fetchRemoteBranches
+- dataform.repositories.get
+- dataform.repositories.getIamPolicy
+- dataform.repositories.list
+- dataform.repositories.queryDirectoryContents
+- dataform.repositories.readFile
+- dataform.workflowConfigs.get
+- dataform.workflowConfigs.list
+- dataform.workflowInvocations.get
+- dataform.workflowInvocations.list
+- dataform.workflowInvocations.query
+- dataform.workspaces.fetchFileDiff
+- dataform.workspaces.fetchFileGitStatuses
+- dataform.workspaces.fetchGitAheadBehind
+- dataform.workspaces.get
+- dataform.workspaces.getIamPolicy
+- dataform.workspaces.list
+- dataform.workspaces.queryDirectoryContents
+- dataform.workspaces.readFile
+- dataform.workspaces.searchFiles
+- dataplex.aspectTypes.get
+- dataplex.aspectTypes.getIamPolicy
+- dataplex.aspectTypes.list
+- dataplex.assetActions.list
+- dataplex.assets.get
+- dataplex.assets.getIamPolicy
+- dataplex.assets.list
+- dataplex.content.get
+- dataplex.content.getIamPolicy
+- dataplex.content.list
+- dataplex.dataAttributeBindings.get
+- dataplex.dataAttributeBindings.getIamPolicy
+- dataplex.dataAttributeBindings.list
+- dataplex.dataAttributes.get
+- dataplex.dataAttributes.getIamPolicy
+- dataplex.dataAttributes.list
+- dataplex.dataTaxonomies.get
+- dataplex.dataTaxonomies.getIamPolicy
+- dataplex.dataTaxonomies.list
+- dataplex.datascans.get
+- dataplex.datascans.getData
+- dataplex.datascans.getIamPolicy
+- dataplex.datascans.list
+- dataplex.entities.get
+- dataplex.entities.list
+- dataplex.entries.get
+- dataplex.entries.list
+- dataplex.entryGroups.export
+- dataplex.entryGroups.get
+- dataplex.entryGroups.getIamPolicy
+- dataplex.entryGroups.list
+- dataplex.entryLinks.get
+- dataplex.entryTypes.get
+- dataplex.entryTypes.getIamPolicy
+- dataplex.entryTypes.list
+- dataplex.environments.get
+- dataplex.environments.getIamPolicy
+- dataplex.environments.list
+- dataplex.glossaries.get
+- dataplex.glossaries.getIamPolicy
+- dataplex.glossaries.list
+- dataplex.glossaryCategories.get
+- dataplex.glossaryCategories.list
+- dataplex.glossaryTerms.get
+- dataplex.glossaryTerms.list
+- dataplex.lakeActions.list
+- dataplex.lakes.get
+- dataplex.lakes.getIamPolicy
+- dataplex.lakes.list
+- dataplex.locations.get
+- dataplex.locations.list
+- dataplex.metadataJobs.get
+- dataplex.metadataJobs.list
+- dataplex.operations.get
+- dataplex.operations.list
+- dataplex.partitions.get
+- dataplex.partitions.list
+- dataplex.projects.search
+- dataplex.tasks.get
+- dataplex.tasks.getIamPolicy
+- dataplex.tasks.list
+- dataplex.zoneActions.list
+- dataplex.zones.get
+- dataplex.zones.getIamPolicy
+- dataplex.zones.list
+- datastore.entities.allocateIds
+- datastore.entities.create
+- datastore.entities.update
+- trafficdirector.networks.reportMetrics
+role_id: beam_writer
+stage: GA
+title: beam_writer
diff --git a/infra/iam/roles/generate_roles.py b/infra/iam/roles/generate_roles.py
new file mode 100644
index 000000000000..2d2b4d294ef6
--- /dev/null
+++ b/infra/iam/roles/generate_roles.py
@@ -0,0 +1,277 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script generates roles based on what Apache Beam uses in GCP.
+# The roles are defined in a YAML file.
+
+import yaml
+import datetime
+import os
+from google.cloud import iam_admin_v1
+from google.api_core import exceptions
+
+# Permissions cache to avoid repeated API calls.
+permissions_cache = {}
+
+ASF_LICENSE_HEADER = """# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the \"License\"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an \"AS IS\" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This file is auto-generated by generate_roles.py.
+# Do not edit manually.
+\n"""
+
+def get_permission_stage(permission_name: str, project_id: str) -> str:
+    """
+    Finds the support level of a specific IAM permission for a given project. This function caches the results to avoid repeated API calls.
+
+    Args:
+        permission_name: The name of the permission to check, e.g., 'storage.buckets.create'.
+        project_id: The ID of the GCP project to check against.
+    Returns:
+        The support level of the permission as a string, or "" if the permission is not found.
+    """
+    global permissions_cache
+
+    try:
+        if f"{project_id}-stage" in permissions_cache:
+            return permissions_cache[f"{project_id}-stage"].get(permission_name, "")
+        else:
+            permissions_cache[f"{project_id}-stage"] = {}
+
+        client = iam_admin_v1.IAMClient()
+        resource = f"//cloudresourcemanager.googleapis.com/projects/{project_id}"
+
+        request = iam_admin_v1.QueryTestablePermissionsRequest(
+            full_resource_name=resource,
+            page_size=1000
+        )
+
+        for permission in client.query_testable_permissions(request=request):
+            permissions_cache[f"{project_id}-stage"][permission.name] = permission.custom_roles_support_level
+
+        return permissions_cache[f"{project_id}-stage"].get(permission_name, "")
+
+    except exceptions.PermissionDenied as e:
+        print(f"Error: Permission denied. Ensure you have 'resourcemanager.projects.get' on project '{project_id}'.")
+        print(f"Details: {e}")
+        return ""
+    except exceptions.NotFound as e:
+        print(f"Error: Project '{project_id}' not found.")
+        print(f"Details: {e}")
+        return ""
+    except Exception as e:
+        print(f"An unexpected error occurred while fetching permissions: {e}")
+        return ""
+
+def get_role_permissions(role_name: str, project_id: str = "") -> list[str]:
+    """
+    Gets the permissions included in a predefined or custom IAM role, filtered to only GA permissions.
+
+    Args:
+        role_name: The full name of the role.
+                   For predefined roles, e.g., 'roles/secretmanager.viewer'.
+                   For custom roles, e.g., 'projects/your-project-id/roles/your-custom-role'.
+
+        project_id: Optional, used for permission metadata lookup.
+    Returns:
+        A list of GA permissions associated with the role.
+    """
+
+    global permissions_cache
+    print(f"Fetching permissions for role: {role_name} in project: {project_id}")
+
+    try:
+        if f"{project_id}-role" in permissions_cache and role_name in permissions_cache[f"{project_id}-role"]:
+            return permissions_cache[f"{project_id}-role"].get(role_name, [])
+        else:
+            if f"{project_id}-role" not in permissions_cache:
+                permissions_cache[f"{project_id}-role"] = {}
+
+        client = iam_admin_v1.IAMClient()
+        request = iam_admin_v1.GetRoleRequest(
+            name=role_name,
+        )
+        role = client.get_role(request=request)
+        all_perms = list(role.included_permissions)
+        ga_perms = []
+        for perm in all_perms:
+            stage = get_permission_stage(perm, project_id)
+            if stage == iam_admin_v1.Permission.CustomRolesSupportLevel.SUPPORTED:
+                ga_perms.append(perm)
+        
+        permissions_cache[f"{project_id}-role"][role_name] = ga_perms
+        return ga_perms
+    except exceptions.NotFound:
+        print(f"Error: The role '{role_name}' was not found.")
+        return []
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+        return []
+
+def filter_permissions(permissions: list[str], allowed_prefixes: list[str] = [], denied_suffixes: list[str] = []) -> set[str]:
+    """
+    Filters permissions based on the provided services.
+
+    Args:
+        permissions: A list of permissions to filter.
+        allowed_prefixes: A list of strings that permissions must contain to be included.
+        denied_suffixes: A list of strings that permissions must not contain to be included.
+    Returns:
+        A list of permissions that match the specified services.
+    """
+
+    filtered_permissions = set()
+
+    for perm in permissions:
+        if any(perm.startswith(prefix) for prefix in allowed_prefixes):
+            if not any(perm.endswith(suffix) for suffix in denied_suffixes):
+                filtered_permissions.add(perm)
+
+    return filtered_permissions
+
+def generate_role(role_name: str , perms: set[str]) -> dict:
+    return {
+        "role_id": f"{role_name}",
+        "title": f"{role_name}",
+        "stage": "GA",
+        "description": f"This is the {role_name} role",
+        "permissions": sorted(list(perms)),
+    }
+
+def write_role_yaml(filename, role_data):
+    if not role_data.get("permissions"):
+        print(f"No permissions to write for {filename}. Skipping.")
+        return
+    with open(filename, "w") as f:
+        f.write(ASF_LICENSE_HEADER)
+        f.write(f"# This file was generated on {datetime.datetime.now(datetime.timezone.utc).strftime('%Y-%m-%d %H:%M:%S')} UTC\n\n")
+        yaml.dump(role_data, f, default_flow_style=False)
+
+def get_config():
+    """
+    Reads the roles configuration from the YAML file and returns it as a dictionary.
+    The configuration includes services, roles, and suffixes for filtering permissions.
+    """
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    config_path = os.path.join(script_dir, "roles_config.yaml")
+    with open(config_path, "r") as f:
+        config = yaml.safe_load(f)
+
+    # Each role inherits permissions from the previous role.
+    # This means that the viewer role has all the permissions of the committer role, and so on.
+    # The roles are defined in the order of viewer, committer, infra_manager, and admin.
+    # The viewer role is the base role, so its file contains all its
+
+    response = {
+        "project_id": config.get("project_id", "apache-beam-testing"),
+        "roles_prefix": config.get("roles_prefix", "beam"),
+        "role": {}
+    }
+
+    # Add suffixes to the response
+    suffixes = {}
+    for suffix in config.get("suffixes", []):
+        suffixes[suffix["name"]] = suffix["values"]
+
+    services = set()
+    roles = set()
+
+    # Sort roles by hierarchy to ensure they are processed in the correct order.
+    config["roles"].sort(key=lambda x: int(x.get("hierarchy", 0)))
+
+    for role in config["roles"]:
+        services.update(role.get("services", []))
+        roles.update(role.get("roles", []))
+
+        response["role"][role["name"]] = {
+            "name": role["name"],
+            "description": role.get("description", f"This is the {role['name']} role"),
+            "services": services.copy(),
+            "roles": roles.copy(),
+            "except_suffixes": [],
+        }
+
+        # If the role has except_suffixes, add them to the response
+        suffix_set = set()
+        for except_suffix in role.get("except_suffixes", []):
+            if except_suffix in suffixes:
+                suffix_set.update(suffixes[except_suffix])
+            else:
+                raise ValueError(f"Unknown suffix '{except_suffix}' in role '{role['name']}'")
+        if suffix_set:
+            response["role"][role["name"]]["except_suffixes"] = list(suffix_set)
+
+    return response
+
+def get_roles():
+    """
+    Generates the roles based on the predefined services and permissions.
+    This function creates roles for Beam Viewer, Committer, Infra Manager, and Admin.
+    It filters permissions based on the allowed and denied strings defined in the configuration.
+    """
+
+    config = get_config()
+    response = {}
+
+    project_id = config["project_id"]
+
+    permissions_added = set()
+
+    for role in config["role"].values():
+        print(f"Generating role: {config['roles_prefix']}_{role['name']} with services: {role['services']} and roles: {role['roles']}")
+        # Get the permissions for each base role.
+        role_permissions = set()
+        for role_name in role["roles"]:
+            role_permissions.update(get_role_permissions(role_name, project_id))
+        role["permissions"] = filter_permissions(
+            permissions=list(role_permissions),
+            allowed_prefixes=list(role["services"]),
+            denied_suffixes=role.get("except_suffixes", [])
+        )
+        # Remove already added permissions to avoid duplicates.
+        role["permissions"] = role["permissions"].difference(permissions_added)
+        permissions_added.update(role["permissions"])
+        response[f"{config['roles_prefix']}_{role['name']}"] = generate_role(f"{config['roles_prefix']}_{role['name']}", role["permissions"])
+
+    return response
+
+def main():
+    """
+    Main function to generate the roles and write them to YAML files.
+    It creates a directory for the roles if it doesn't exist and writes each role to its respective file.
+    """
+
+    roles = get_roles()
+
+    for role_name, role_data in roles.items():
+        filename = f"{role_name}.role.yaml"
+        write_role_yaml(filename, role_data)
+        print(f"Generated {filename} with {len(role_data['permissions'])} permissions.")
+
+if __name__ == "__main__":
+    main()
diff --git a/infra/iam/roles/roles.tf b/infra/iam/roles/roles.tf
new file mode 100644
index 000000000000..d3348fa31b1e
--- /dev/null
+++ b/infra/iam/roles/roles.tf
@@ -0,0 +1,45 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This Terraform configuration file is used to manage custom IAM roles
+# in a Google Cloud Platform (GCP) project. It reads role definitions
+# from YAML files located in the same directory and creates custom roles
+# in the specified GCP project.
+
+locals {
+  role_files = fileset(path.module, "*.role.yaml")
+  roles_data = {
+    for f in local.role_files :
+    trimsuffix(f, ".role.yaml") => yamldecode(file("${path.module}/${f}"))
+  }
+}
+
+variable "project_id" {
+  description = "The GCP project ID."
+  type        = string
+}
+
+resource "google_project_iam_custom_role" "custom_roles" {
+  for_each = local.roles_data
+
+  project     = var.project_id
+  role_id     = each.value.role_id
+  title       = each.value.title
+  description = lookup(each.value, "description", null)
+  permissions = each.value.permissions
+  stage       = lookup(each.value, "stage", "GA")
+}
diff --git a/infra/iam/roles/roles_config.yaml b/infra/iam/roles/roles_config.yaml
new file mode 100644
index 000000000000..1e94cdc2ccbd
--- /dev/null
+++ b/infra/iam/roles/roles_config.yaml
@@ -0,0 +1,150 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Configuration for Apache Beam roles in GCP.
+# This file defines the roles, their hierarchy, the services they can access and the roles they inherit from.
+
+project-id: "apache-beam-testing" # Default project ID for Apache Beam in GCP.
+roles-prefix: "beam" # Prefix for the roles generated by this configuration.
+
+# Each custom role is defined here.
+# name: The name of the role.
+# hierarchy: The hierarchy level of the role, lower numbers indicate fewer permissions,
+# the higher hierarchy level also gets the permissions of lower hierarchy levels.
+# description: A brief description of the role.
+# services: The list of services that the role can access.
+# roles: The list of base roles that this role inherits permissions from.
+# except_suffixes: A list of suffixes that indicate permissions that should not be included in the role.
+# The suffixes are defined in the `suffixes` section below.
+roles:
+  - name: viewer
+    hierarchy: 0
+    description: "Viewer role for Apache Beam in GCP, it has read-only access to all services used by Beam."
+    services:
+      - artifactregistry
+      - biglake
+      - bigquery
+      - cloudasset
+      - cloudbuild
+      - cloudfunctions
+      - cloudsql
+      - compute
+      - container
+      - dataflow
+      - dataproc
+      - datastore
+      - dns
+      - firebase
+      - iam
+      - iap
+      - meshconfig
+      - monitoring
+      - pubsub
+      - redis
+      - resourcemanager
+      - secretmanager
+      - servicemanagement
+      - serviceusage
+      - spanner
+      - storage
+      - trafficdirector
+    roles:
+      - roles/viewer
+    except_suffixes:
+      - destructive
+  - name: writer
+    description: "Writer role for Apache Beam in GCP, it has additional permissions for managing resources."
+    hierarchy: 1
+    services:
+      - cloudkms
+      - dataform
+      - dataplex
+    roles:
+      - roles/viewer
+      - roles/bigquery.user
+      - roles/bigquery.dataViewer
+      - roles/cloudsql.instanceUser
+      - roles/container.clusterViewer
+      - roles/container.developer
+      - roles/compute.networkViewer
+      - roles/datastore.user
+      - roles/trafficdirector.client
+    except_suffixes:
+      - destructive
+  - name: infra_manager
+    description: "Infrastructure Manager role for Apache Beam in GCP, it has permissions for managing infrastructure resources but not for destructive actions."
+    hierarchy: 2
+    services: []
+    roles:
+      - roles/cloudbuild.builds.editor
+      - roles/iam.serviceAccountTokenCreator
+      - roles/iam.serviceAccountUser
+      - roles/storage.objectCreator
+      - roles/storage.objectViewer
+      - roles/editor
+    except_suffixes:
+      - destructive
+  - name: admin
+    description: "Admin role for Apache Beam in GCP, it has permissions for managing all services used by Beam, it can perform destructive actions and access secrets."
+    hierarchy: 3
+    services:
+      - secretmanager
+    roles:
+      - roles/editor
+      - roles/artifactregistry.admin
+      - roles/biglake.admin
+      - roles/bigquery.admin
+      - roles/cloudfunctions.admin
+      - roles/compute.admin
+      - roles/compute.instanceAdmin.v1
+      - roles/compute.networkAdmin
+      - roles/container.admin
+      - roles/dataflow.admin
+      - roles/dataproc.admin
+      - roles/datastore.indexAdmin
+      - roles/dns.admin
+      - roles/firebase.admin
+      - roles/iam.roleAdmin
+      - roles/iam.securityAdmin
+      - roles/iam.serviceAccountAdmin
+      - roles/iam.workloadIdentityPoolAdmin
+      - roles/meshconfig.admin
+      - roles/monitoring.admin
+      - roles/pubsub.admin
+      - roles/redis.admin
+      - roles/resourcemanager.projectIamAdmin
+      - roles/secretmanager.admin
+      - roles/secretmanager.secretAccessor
+      - roles/secretmanager.viewer
+      - roles/servicemanagement.quotaAdmin
+      - roles/serviceusage.serviceUsageAdmin
+      - roles/spanner.admin
+      - roles/spanner.databaseAdmin
+      - roles/storage.admin
+      - roles/storage.objectAdmin
+    except_suffixes: []
+
+suffixes:
+  - name: destructive
+    description: "Suffixes that indicate destructive actions in GCP."
+    values:
+      - ".delete"
+      - ".remove"
+      - ".destroy"
+      - ".purge"
+      - ".cancel"
+      - ".stop"
+      - ".terminate"
diff --git a/infra/iam/roles/test_generate_roles.py b/infra/iam/roles/test_generate_roles.py
new file mode 100644
index 000000000000..f5ebc5948e7c
--- /dev/null
+++ b/infra/iam/roles/test_generate_roles.py
@@ -0,0 +1,82 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+ # Tests for generate_roles.py
+
+import unittest
+from unittest.mock import MagicMock
+import sys
+import types
+import generate_roles
+
+# Patch yaml and google.cloud imports before importing the script
+sys.modules['yaml'] = MagicMock()
+sys.modules['google.cloud'] = types.SimpleNamespace(iam_admin_v1=MagicMock())
+sys.modules['google.api_core'] = types.SimpleNamespace(exceptions=MagicMock())
+
+class TestGenerateRoles(unittest.TestCase):
+    def test_filter_permissions(self):
+        perms = [
+            'compute.instances.create',
+            'compute.instances.delete',
+            'storage.buckets.create',
+            'storage.buckets.delete',
+            'storage.objects.get',
+            'bigquery.tables.get',
+            'bigquery.tables.delete',
+        ]
+        allowed = ['storage', 'bigquery']
+        denied = ['delete']
+        filtered = generate_roles.filter_permissions(perms, allowed, denied)
+        self.assertIn('storage.buckets.create', filtered)
+        self.assertIn('storage.objects.get', filtered)
+        self.assertIn('bigquery.tables.get', filtered)
+        self.assertNotIn('storage.buckets.delete', filtered)
+        self.assertNotIn('bigquery.tables.delete', filtered)
+        self.assertNotIn('compute.instances.create', filtered)
+        self.assertNotIn('compute.instances.delete', filtered)
+
+    def test_generate_role(self):
+        perms = {'a.b.c', 'd.e.f'}
+        role = generate_roles.generate_role('test_role', perms)
+        self.assertEqual(role['role_id'], 'test_role')
+        self.assertEqual(role['title'], 'test_role')
+        self.assertEqual(role['stage'], 'GA')
+        self.assertIn('a.b.c', role['permissions'])
+        self.assertIn('d.e.f', role['permissions'])
+
+    def test_write_role_yaml(self):
+        import tempfile
+        import os
+        role_data = {
+            'role_id': 'test_role',
+            'title': 'test_role',
+            'stage': 'GA',
+            'description': 'desc',
+            'permissions': ['a.b.c', 'd.e.f'],
+        }
+        with tempfile.TemporaryDirectory() as tmpdir:
+            filename = os.path.join(tmpdir, 'role.yaml')
+            generate_roles.ASF_LICENSE_HEADER = ''  # Avoid header for test
+            generate_roles.write_role_yaml(filename, role_data)
+            with open(filename) as f:
+                content = f.read()
+            self.assertIn('role_id', content)
+            self.assertIn('a.b.c', content)
+            self.assertIn('d.e.f', content)
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/infra/iam/users.tf b/infra/iam/users.tf
new file mode 100644
index 000000000000..30d5bfddf8f8
--- /dev/null
+++ b/infra/iam/users.tf
@@ -0,0 +1,60 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This Terraform configuration file is used to manage users in a Google Cloud Platform (GCP) project.
+# It reads user definitions from a YAML file (`users.yml`) and configures the corresponding IAM
+# roles and permissions for each user in the specified GCP project.
+
+locals {
+  users = yamldecode(file("${path.module}/users.yml"))
+
+  user_permissions = flatten([
+    for user in (local.users == null ? [] : local.users) : [
+      for perm in (user.permissions == null ? [] : user.permissions) :
+        {
+          username              = user.username
+          email                 = user.email
+          role                  = replace(perm.role, "PROJECT-ID", var.project_id)
+          title                 = lookup(perm, "title", null)
+          description           = lookup(perm, "description", null)
+          request_description   = lookup(perm, "request_description", null)
+          expiry_date           = lookup(perm, "expiry_date", null)
+        # Owner roles need to be handled separately, they require the user
+        # to accept their assignment.
+        } if perm != null && lookup(perm, "role", null) != null && perm.role != "roles/owner"
+    ]
+  ])
+}
+
+resource "google_project_iam_member" "project_members" {
+  for_each = {
+    for up in local.user_permissions : "${up.email}-${up.role}" => up
+  }
+  project = var.project_id
+  role    = each.value.role
+  member = can(regex(".*\\.gserviceaccount\\.com$", each.value.email)) ? "serviceAccount:${each.value.email}" : "user:${each.value.email}"
+
+  dynamic "condition" {
+    # Condition is only created if expiry_date is set
+    for_each = each.value.expiry_date != null && each.value.expiry_date != "" ? [true] : []
+    content {
+      title       = "${each.value.title}"
+      description = "${each.value.description}"
+      expression  = "request.time < timestamp('${each.value.expiry_date}T00:00:00Z')"
+    }
+  }
+}
diff --git a/infra/iam/users.yml b/infra/iam/users.yml
new file mode 100644
index 000000000000..9bb5349e329a
--- /dev/null
+++ b/infra/iam/users.yml
@@ -0,0 +1,1059 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+    
+# IAM policy for project apache-beam-testing
+# Generated on 2025-09-19 18:17:58 UTC
+
+- username: WhatWouldAustinDo
+  email: WhatWouldAustinDo@gmail.com
+  permissions:
+  - role: roles/editor
+- username: a.khorbaladze
+  email: a.khorbaladze@akvelon.us
+  permissions:
+  - role: roles/bigquery.admin
+  - role: roles/container.admin
+  - role: roles/editor
+  - role: roles/iam.serviceAccountUser
+  - role: roles/secretmanager.admin
+- username: aaronleeiv
+  email: aaronleeiv@google.com
+  permissions:
+  - role: roles/editor
+- username: abbymotley
+  email: abbymotley@google.com
+  permissions:
+  - role: roles/viewer
+- username: adudko-runner-gke-sa
+  email: adudko-runner-gke-sa@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/container.admin
+  - role: roles/container.clusterAdmin
+  - role: roles/dataflow.admin
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+  - role: roles/artifactregistry.admin
+  - role: roles/compute.admin
+  - role: roles/container.admin
+  - role: roles/datastore.indexAdmin
+  - role: roles/dns.admin
+  - role: roles/editor
+  - role: roles/iam.roleAdmin
+  - role: roles/iam.securityAdmin
+  - role: roles/iam.serviceAccountAdmin
+  - role: roles/iam.serviceAccountCreator
+  - role: roles/iam.serviceAccountUser
+  - role: roles/redis.admin
+  - role: roles/servicemanagement.quotaAdmin
+  - role: roles/storage.admin
+- username: ryanmadden
+  email: ryanmadden@google.com
+  permissions:
+  - role: roles/editor
+- username: saadatssu
+  email: saadatssu@gmail.com
+  permissions:
+  - role: roles/editor
+- username: samuelw
+  email: samuelw@google.com
+  permissions:
+  - role: roles/editor
+- username: secrets-manager-40
+  email: secrets-manager-40@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/compute.instanceAdmin.v1
+  - role: roles/secretmanager.secretAccessor
+- username: sergey.makarkin
+  email: sergey.makarkin@akvelon.com
+  permissions:
+  - role: roles/editor
+  - role: roles/iam.workloadIdentityPoolAdmin
+  - role: roles/secretmanager.admin
+- username: shunping
+  email: shunping@google.com
+  permissions:
+  - role: roles/editor
+  - role: roles/iam.serviceAccountTokenCreator
+  - role: roles/iam.serviceAccountUser
+  - role: roles/owner
+- username: siyuez
+  email: siyuez@google.com
+  permissions:
+  - role: roles/editor
+  - role: roles/viewer
+- username: skp
+  email: skp@google.com
+  permissions:
+  - role: roles/editor
+- username: sniemitz
+  email: sniemitz@google.com
+  permissions:
+  - role: roles/editor
+- username: stg-playground-sa
+  email: stg-playground-sa@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/artifactregistry.reader
+  - role: roles/bigquery.dataViewer
+  - role: roles/bigquery.jobUser
+  - role: roles/bigquery.readSessionUser
+  - role: roles/container.nodeServiceAccount
+  - role: roles/datastore.viewer
+  - role: roles/logging.logWriter
+  - role: roles/monitoring.metricWriter
+  - role: roles/stackdriver.resourceMetadata.writer
+- username: stg-playground-sa-cf
+  email: stg-playground-sa-cf@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/cloudfunctions.invoker
+  - role: roles/datastore.user
+  - role: roles/storage.objectViewer
+- username: stg-tourofbeam-cb-cd
+  email: stg-tourofbeam-cb-cd@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: organizations/433637338589/roles/GcsBucketLister
+  - role: roles/datastore.user
+  - role: roles/secretmanager.secretAccessor
+  - role: roles/storage.admin
+  - role: roles/storage.insightsCollectorService
+  - role: roles/storage.objectAdmin
+- username: stg-tourofbeam-cb-ci
+  email: stg-tourofbeam-cb-ci@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/secretmanager.secretAccessor
+  - role: roles/storage.insightsCollectorService
+  - role: roles/storage.objectAdmin
+- username: stg-tourofbeam-cb-deploy
+  email: stg-tourofbeam-cb-deploy@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/cloudfunctions.admin
+  - role: roles/container.clusterViewer
+  - role: roles/datastore.indexAdmin
+  - role: roles/datastore.user
+  - role: roles/firebase.admin
+  - role: roles/iam.serviceAccountCreator
+  - role: roles/iam.serviceAccountUser
+  - role: roles/logging.logWriter
+  - role: roles/serviceusage.serviceUsageAdmin
+  - role: roles/storage.admin
+- username: svetaksundhar
+  email: svetaksundhar@google.com
+  permissions:
+  - role: roles/editor
+- username: svetaksundhar-233
+  email: svetaksundhar-233@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/bigquery.admin
+  - role: roles/bigquery.dataEditor
+  - role: roles/bigquery.dataOwner
+  - role: roles/bigquery.jobUser
+- username: talatu
+  email: talatu@google.com
+  permissions:
+  - role: roles/owner
+- username: tannapareddy
+  email: tannapareddy@google.com
+  permissions:
+  - role: organizations/433637338589/roles/GcsBucketOwner
+  - role: roles/alloydb.admin
+  - role: roles/artifactregistry.admin
+  - role: roles/biglake.admin
+  - role: roles/bigquery.admin
+  - role: roles/dataproc.admin
+  - role: roles/editor
+  - role: roles/owner
+  - role: roles/pubsub.admin
+  - role: roles/storage.admin
+- username: tanusharmaa
+  email: tanusharmaa@google.com
+  permissions:
+  - role: roles/editor
+- username: tarun-926
+  email: tarun-926@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/alloydb.admin
+  - role: roles/artifactregistry.admin
+  - role: roles/biglake.admin
+  - role: roles/bigquery.admin
+  - role: roles/dataflow.worker
+  - role: roles/iam.serviceAccountAdmin
+  - role: roles/logging.logWriter
+  - role: roles/monitoring.metricWriter
+  - role: roles/pubsub.admin
+  - role: roles/pubsub.subscriber
+  - role: roles/resourcemanager.projectIamAdmin
+  - role: roles/storage.admin
+  - role: roles/tpu.admin
+- username: tarunannapareddy1997
+  email: tarunannapareddy1997@gmail.com
+  permissions:
+  - role: roles/bigquery.admin
+  - role: roles/iam.serviceAccountAdmin
+  - role: roles/resourcemanager.projectIamAdmin
+  - role: roles/tpu.admin
+- username: tf-test-dataflow-egyosq0h66-0
+  email: tf-test-dataflow-egyosq0h66-0@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/dataflow.worker
+  - role: roles/storage.admin
+- username: tf-test-dataflow-egyosq0h66-1
+  email: tf-test-dataflow-egyosq0h66-1@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/dataflow.worker
+  - role: roles/storage.admin
+- username: tf-test-dataflow-ntgfw3y4q6-0
+  email: tf-test-dataflow-ntgfw3y4q6-0@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/dataflow.worker
+  - role: roles/storage.admin
+- username: tf-test-dataflow-ntgfw3y4q6-1
+  email: tf-test-dataflow-ntgfw3y4q6-1@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/dataflow.worker
+  - role: roles/storage.admin
+- username: tf-test-dataflow-odmv2iiu6v-0
+  email: tf-test-dataflow-odmv2iiu6v-0@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/dataflow.worker
+  - role: roles/storage.admin
+- username: tf-test-dataflow-odmv2iiu6v-1
+  email: tf-test-dataflow-odmv2iiu6v-1@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/dataflow.worker
+  - role: roles/storage.admin
+- username: tf-test-dataflow-uzgihx18zf-0
+  email: tf-test-dataflow-uzgihx18zf-0@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/dataflow.worker
+  - role: roles/storage.admin
+- username: tf-test-dataflow-uzgihx18zf-1
+  email: tf-test-dataflow-uzgihx18zf-1@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/dataflow.worker
+  - role: roles/storage.admin
+- username: timur.sultanov.akvelon
+  email: timur.sultanov.akvelon@gmail.com
+  permissions:
+  - role: roles/editor
+- username: tourofbeam-cb-cd-prod
+  email: tourofbeam-cb-cd-prod@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/datastore.user
+  - role: roles/secretmanager.secretAccessor
+  - role: roles/storage.insightsCollectorService
+  - role: roles/storage.objectAdmin
+- username: tourofbeam-cb-ci-prod
+  email: tourofbeam-cb-ci-prod@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/secretmanager.secretAccessor
+  - role: roles/storage.insightsCollectorService
+  - role: roles/storage.objectAdmin
+- username: tourofbeam-cb-deploy-prod
+  email: tourofbeam-cb-deploy-prod@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/cloudfunctions.admin
+  - role: roles/container.clusterViewer
+  - role: roles/datastore.indexAdmin
+  - role: roles/datastore.user
+  - role: roles/firebase.admin
+  - role: roles/iam.serviceAccountCreator
+  - role: roles/iam.serviceAccountUser
+  - role: roles/logging.logWriter
+  - role: roles/serviceusage.serviceUsageAdmin
+  - role: roles/storage.admin
+- username: tourofbeam-cf-sa-prod
+  email: tourofbeam-cf-sa-prod@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/cloudfunctions.admin
+  - role: roles/datastore.user
+  - role: roles/firebaseauth.viewer
+  - role: roles/iam.serviceAccountUser
+  - role: roles/storage.objectViewer
+- username: tourofbeam-cf-sa-stg
+  email: tourofbeam-cf-sa-stg@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/cloudfunctions.admin
+  - role: roles/datastore.user
+  - role: roles/firebaseauth.viewer
+  - role: roles/iam.serviceAccountUser
+  - role: roles/storage.objectViewer
+- username: tourofbeam-stg3-cloudfunc-sa
+  email: tourofbeam-stg3-cloudfunc-sa@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/cloudfunctions.admin
+  - role: roles/datastore.user
+  - role: roles/firebaseauth.viewer
+  - role: roles/iam.serviceAccountUser
+  - role: roles/storage.objectViewer
+- username: valentyn
+  email: valentyn@google.com
+  permissions:
+  - role: roles/owner
+- username: valentyn-dataflow-deployer
+  email: valentyn-dataflow-deployer@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/dataflow.admin
+  - role: roles/iam.serviceAccountUser
+- username: valentyn-test
+  email: valentyn-test@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/compute.admin
+  - role: roles/dataflow.admin
+  - role: roles/editor
+  - role: roles/storage.admin
+- username: vdjerek-test
+  email: vdjerek-test@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: organizations/433637338589/roles/GceStorageAdmin
+  - role: roles/automlrecommendations.editor
+  - role: roles/bigquery.dataEditor
+  - role: roles/bigquery.jobUser
+  - role: roles/bigtable.admin
+  - role: roles/cloudsql.admin
+  - role: roles/cloudsql.client
+  - role: roles/cloudsql.editor
+  - role: roles/container.admin
+  - role: roles/dataflow.admin
+  - role: roles/dataproc.admin
+  - role: roles/healthcare.dicomEditor
+  - role: roles/healthcare.dicomStoreAdmin
+  - role: roles/healthcare.fhirResourceEditor
+  - role: roles/healthcare.fhirStoreAdmin
+  - role: roles/iam.serviceAccountTokenCreator
+  - role: roles/iam.serviceAccountUser
+  - role: roles/pubsub.editor
+- username: vitaly-terentyev
+  email: vitaly-terentyev@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/container.clusterViewer
+  - role: roles/container.viewer
+  - role: roles/iam.serviceAccountTokenCreator
+  - role: roles/iam.serviceAccountUser
+  - role: roles/storage.objectAdmin
+  - role: roles/storage.objectCreator
+- username: vitaly.terentyev.akv
+  email: vitaly.terentyev.akv@gmail.com
+  permissions:
+  - role: roles/container.admin
+  - role: roles/editor
+  - role: roles/iam.serviceAccountAdmin
+  - role: roles/iam.workloadIdentityPoolAdmin
+  - role: roles/secretmanager.secretAccessor
+- username: vladislav.chunikhin
+  email: vladislav.chunikhin@akvelon.com
+  permissions:
+  - role: roles/editor
+- username: vlado.djerek
+  email: vlado.djerek@akvelon.com
+  permissions:
+  - role: organizations/433637338589/roles/GceStorageAdmin
+  - role: roles/cloudfunctions.admin
+  - role: roles/container.admin
+  - role: roles/dataproc.admin
+  - role: roles/owner
+  - role: roles/secretmanager.secretAccessor
+- username: wasmx-jbdthx
+  email: wasmx-jbdthx@apache-beam-testing.iam.gserviceaccount.com
+  permissions:
+  - role: roles/autoscaling.metricsWriter
+  - role: roles/logging.logWriter
+  - role: roles/monitoring.metricWriter
+  - role: roles/monitoring.viewer
+  - role: roles/stackdriver.resourceMetadata.writer
+- username: wdg-team
+  email: wdg-team@google.com
+  permissions:
+  - role: roles/looker.instanceUser
+- username: xqhu
+  email: xqhu@google.com
+  permissions:
+  - role: roles/editor
+  - role: roles/iam.serviceAccountTokenCreator
+  - role: roles/owner
+  - role: roles/storage.admin
+- username: yathu
+  email: yathu@google.com
+  permissions:
+  - role: roles/editor
+  - role: roles/iam.serviceAccountTokenCreator
+  - role: roles/owner
+- username: ylabur
+  email: ylabur@google.com
+  permissions:
+  - role: roles/editor
+- username: yyingwang
+  email: yyingwang@google.com
+  permissions:
+  - role: roles/editor
+- username: zhoufek
+  email: zhoufek@google.com
+  permissions:
+  - role: roles/editor
diff --git a/infra/keys/README.md b/infra/keys/README.md
new file mode 100644
index 000000000000..f7bf1d927c16
--- /dev/null
+++ b/infra/keys/README.md
@@ -0,0 +1,103 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+# Service Account Management
+
+This module is used to manage Google Cloud service accounts, including creating, retrieving, enabling, and deleting service accounts and their keys. It uses the Google Cloud IAM API to perform these operations.
+
+## User usage
+
+We use the `keys.py` script to manage service account keys. In order to use this script you will need to:
+
+1. Generate a change over `keys.yaml` file, where your email address is listed as an authorized user for the service accounts you want to manage.
+2. Open a pull request with the changes to the `keys.yaml` file. The change will be reviewed and merged by the Infra team.
+3. Once your changes are merged, install the required python packages: `pip install -r requirements.txt` and authenticate with Google Cloud using the `gcloud auth application-default login` command.
+4. Run `keys.py --get-key <service_account_id>` to get the latest key for a service account. The key will be printed to the console, and you can use it to authenticate with Google Cloud services.
+
+> Remember this keys are rotated regularly, so you will need to run the command again to get the latest key after a rotation, the rotation days are defined in the `config.yaml` file.
+
+## Administrative usage
+
+This section is intended for developers who need to manage service accounts and their keys at a higher level. A regular user should not need to access this section.
+
+### Prerequisites
+
+- Google Cloud SDK installed and configured.
+- Appropriate permissions to manage service accounts and secrets in your Google Cloud project.
+- Required Python packages installed (see requirements.txt).
+
+### How it works
+
+This module provide a script `keys.py` that allows you to manage the service accounts and their keys. This script is run automatically by a GitHub Action to ensure that service account keys are rotated regularly and that the latest keys are available for authorized users. It is also run every time a PR is merged over the `keys.yaml` file to ensure that the service accounts, their keys and authorized users are up to date.
+
+### Automation with GitHub Actions
+
+A GitHub Actions workflow is set up to automate the execution of the `keys.py` script. This workflow is defined in `.github/workflows/beam_Infrastructure_ServiceAccountKeys.yml`.
+
+The workflow is triggered automatically on the following events:
+- A push to the `main` branch that includes changes to the `infra/keys/keys.yaml` file.
+- A manual trigger (`workflow_dispatch`) by a developer.
+
+When triggered, the workflow runs the `python keys.py --cron` command, which handles the creation and rotation of service account keys based on the configuration in `keys.yaml` and `config.yaml`.
+
+### Files
+
+#### config.yaml
+
+This file contains configuration settings for the service account management, including project ID, key rotation settings, and logging configuration.
+
+#### keys.yaml
+
+All the service accounts are managed through a configuration file in YAML format, `keys.yaml`. This file contains the necessary information about each service account, including its ID, display name, and authorized users.
+
+```yaml
+service_accounts:
+  - account_id: my-service-account
+    display_name: My Service Account
+    authorized_users:
+      - email: user1@example.com
+      - email: user2@example.com
+```
+
+Where:
+
+- `account_id`: The unique identifier for the service account. The email address of the service account will be `<account_id>@<project_id>.iam.gserviceaccount.com`.
+- `display_name`: A human-readable name for the service account.
+- `authorized_users`: A list of users who will be granted access to the service account's keys. Each user is specified by their email address. This users will be able to retrieve the keys and act on behalf of the service account.
+
+Service accounts are created the first time the cron is run, or when the `keys.yaml` file is updated with a new service account. The script checks if the service account already exists in Google Cloud, and if not, it creates it. If the service account already exists but it is not managed by the Secret Manager, it creates a new key, storing it in the Secret Manager and ignore the rest. This ensures that the service account is always up to date with the latest keys and authorized users.
+
+### Rotation
+
+Service account keys should be rotated regularly to maintain security. To automate key rotation, you can set up a cron job that runs the command:
+
+```bash
+python keys.py --cron
+```
+
+This will rotate keys for all service accounts defined in the `keys.yaml` file that have achieved the age threshold (e.g., 30 days). The age threshold can be adjusted in the `config.yaml` file.
+
+### Retrieval
+
+To retrieve the latest service account key, use the `--get-key` flag:
+
+```bash
+python keys.py --get-key my-service-account
+```
+
diff --git a/infra/keys/config.yaml b/infra/keys/config.yaml
new file mode 100644
index 000000000000..e28c3a589537
--- /dev/null
+++ b/infra/keys/config.yaml
@@ -0,0 +1,34 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This is the configuration file for the secrets rotation service.
+# It defines the parameters for the secrets rotation process.
+
+# GENERAL CONFIGURATION
+
+# The project ID where the secrets rotation service will run
+project_id: "apache-beam-testing"
+
+# Secret service configuration
+
+# Default secret rotation interval in days
+rotation_interval: 7
+# Time the disabled secret versions will be kept before deletion
+grace_period: 2
+
+# LOGGING
+
+# Logging level for the secrets rotation service
+logging_level: "DEBUG" # Options: DEBUG, INFO, WARNING, ERROR, CRITICAL
diff --git a/infra/keys/keys.py b/infra/keys/keys.py
new file mode 100644
index 000000000000..c06307ecb24f
--- /dev/null
+++ b/infra/keys/keys.py
@@ -0,0 +1,383 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import traceback
+import yaml
+import logging
+import argparse
+import sys
+from typing import List, TypedDict
+from google.api_core.exceptions import PermissionDenied
+# Importing custom modules
+from secret_manager import SecretManager
+from service_account import ServiceAccountManager
+
+
+# --- Configuration ---
+CONFIG_FILE = 'config.yaml'
+KEYS_FILE = 'keys.yaml'
+
+class ConfigDict(TypedDict):
+    project_id: str
+    rotation_interval: int
+    grace_period: int
+    logging_level: str
+
+class AuthorizedUser(TypedDict):
+    email: str
+
+class ServiceAccount(TypedDict):
+    account_id: str
+    display_name: str
+    authorized_users: List[AuthorizedUser]
+
+class ServiceAccountsConfig(TypedDict):
+    service_accounts: List[ServiceAccount]
+
+def load_config() -> ConfigDict:
+    """Loads the configuration from the YAML file."""
+    with open(CONFIG_FILE, 'r') as f:
+        config = yaml.safe_load(f)
+
+    if not config:
+        raise ValueError("Configuration file is empty or invalid.")
+
+    required_keys = set(['project_id', 'rotation_interval', 'grace_period'])
+    missing_keys = required_keys - config.keys()
+    if missing_keys:
+        raise ValueError(f"Missing required configuration keys: {', '.join(missing_keys)}")
+    
+    if not isinstance(config['rotation_interval'], int) or config['rotation_interval'] <= 0:
+        raise ValueError("Configuration 'rotation_interval' must be a positive integer.")
+    if not isinstance(config['grace_period'], int) or config['grace_period'] < 0:
+        raise ValueError("Configuration 'grace_period' must be a non-negative integer.")
+    if 'logging_level' in config:
+        if not isinstance(config['logging_level'], str) or config['logging_level'].strip() not in logging._nameToLevel:
+            raise ValueError("Configuration 'logging_level' must be one of: " + ", ".join(logging._nameToLevel.keys()))
+    else:
+        config['logging_level'] = 'INFO'
+
+    return config
+
+def load_service_accounts_config() -> ServiceAccountsConfig:
+    """Loads the service accounts configuration from the YAML file."""
+    with open(KEYS_FILE, 'r') as f:
+        service_accounts_config = yaml.safe_load(f)
+
+    if not service_accounts_config or 'service_accounts' not in service_accounts_config:
+        raise ValueError("Service accounts configuration file is empty or invalid.")
+    
+    if not isinstance(service_accounts_config['service_accounts'], list):
+        raise ValueError("Service accounts configuration must be a list of service accounts.")
+    
+    for account in service_accounts_config['service_accounts']:
+        if 'account_id' not in account or 'display_name' not in account:
+            raise ValueError("Each service account must have 'account_id' and 'display_name'.")
+        if 'authorized_users' not in account or not isinstance(account['authorized_users'], list):
+            raise ValueError("Each service account must have a list of 'authorized_users'.")
+        
+    return service_accounts_config
+
+def parse_arguments():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(
+        description="KeyService - GCP Service Account Key Management",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  python keys.py --cron                 # Run key rotation for accounts that need it, ran only by cron job
+  python keys.py --cron-dry-run         # Run a dry run of the key rotation cron job
+  python keys.py --get-key my-sa        # Get the latest key for service account 'my-sa', ran by users
+        """
+    )
+    
+    group = parser.add_mutually_exclusive_group()
+    group.add_argument(
+        '--cron',
+        action='store_true',
+        help='Run the cron job to rotate keys that require rotation'
+    )
+    group.add_argument(
+        '--cron-dry-run',
+        action='store_true',
+        help='Run a dry run of the cron job to see what would be rotated'
+    )
+    group.add_argument(
+        '--get-key',
+        metavar='ACCOUNT_ID',
+        type=str,
+        help='Get the latest key for the specified service account ID'
+    )
+    
+    return parser.parse_args()
+
+class KeyService:
+    """Service to manage GCP API keys rotation."""
+
+    # Configuration
+    project_id: str
+    service_accounts: List[ServiceAccount]
+    enable_logging: bool
+
+    # Clients
+    secret_manager_client: SecretManager
+    service_account_manager: ServiceAccountManager
+    logger: logging.Logger
+
+    def __init__(self, config: ConfigDict, service_accounts_config: ServiceAccountsConfig, enable_logging: bool = True) -> None:
+        """
+        Initializes the KeyService with the provided configuration.
+
+        Args:
+            config (ConfigDict): Configuration dictionary containing:
+                - project_id: GCP project ID
+                - rotation_interval: Interval in days for secret rotation 
+                - max_versions_to_keep: Maximum number of secret versions to keep
+                - bucket_name: GCS bucket name for logging
+                - log_file_prefix: Prefix for log file names
+                - logging_level: Logging level (e.g., 'INFO', 'DEBUG')
+            service_accounts_config (ServiceAccountsConfig): Configuration for service accounts.
+                - service_accounts: List of service accounts to manage and their configuration
+            enable_logging (bool): Whether to enable logging. Defaults to True.
+        Raises:
+            ValueError: If any required configuration parameter is missing.
+        """
+
+        self.project_id = config['project_id']
+        rotation_interval = config['rotation_interval']
+        grace_period = config['grace_period']
+        logging_level = config['logging_level']
+
+        self.service_accounts = service_accounts_config['service_accounts']
+        self.enable_logging = enable_logging
+
+        self.logger = logging.getLogger("KeyService")
+        if self.enable_logging:
+            self.logger.setLevel(logging_level)
+            handler = logging.StreamHandler(sys.stdout)
+            formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+            handler.setFormatter(formatter)
+            self.logger.addHandler(handler)
+        else:
+            # Create a null logger that doesn't actually log anything
+            self.logger.setLevel(logging.CRITICAL + 1)  # Set to a level higher than CRITICAL to disable all logging
+
+        self.secret_manager_client = SecretManager(self.project_id, self.logger, rotation_interval, grace_period)
+        self.service_account_manager = ServiceAccountManager(self.project_id, self.logger)
+
+        if self.enable_logging:
+            self.logger.info(f"Initialized KeyService for project: {self.project_id}")
+
+    def _start_all_service_accounts(self) -> None:
+        """
+        Reads the service accounts configuration and checks for service accounts.
+
+        1. If a service account exists and is managed, it checks if the secret exists and updates access if needed.
+        2. If the service account exists but the secret does not, it creates the secret and clears the service account
+              keys as now keys will be managed by the Secret Manager.
+        3. If neither the service account nor the secret exists, it creates and initializes both.
+        4. If any other case is encountered, it logs an error and skips the account.
+        """
+
+        self.logger.debug("Creating service accounts if they do not exist")
+        for account in self.service_accounts:
+            account_id = account['account_id']
+            authorized_users = [user['email'] for user in account.get('authorized_users', [])]
+
+            try:
+                secret_name = f"{account_id}-key"
+                # If service account and secret exists and is managed, just check permissions
+                if self.service_account_manager._service_account_exists(account_id) and self.secret_manager_client._secret_is_managed(secret_name):
+                    self.logger.debug(f"Service account {account_id} and secret {secret_name} already exist and are managed")
+                    if self.secret_manager_client.is_different_user_access(secret_name, authorized_users):
+                        self.logger.debug(f"Updating access policy for secret {secret_name}")
+                        self.secret_manager_client.update_secret_access(secret_name, authorized_users)
+
+                # If the service account exists but the secret does not, create the secret and a key and ignore the existing keys
+                elif self.service_account_manager._service_account_exists(account_id) and not self.secret_manager_client._secret_exists(secret_name):
+                    self.logger.debug(f"Service account {account_id} exists but secret {secret_name} does not, creating secret and a new key")
+                    self.secret_manager_client.create_secret(secret_name)
+                    self.secret_manager_client.update_secret_access(secret_name, authorized_users)
+
+                    new_key = self.service_account_manager.create_service_account_key(account_id)
+                    new_key_id = new_key.name.split('/')[-1]
+                    self.secret_manager_client.add_secret_version(secret_name, new_key_id, new_key.private_key_data)
+
+                # If neither secret nor service account exists, create and initialize both
+                elif not self.service_account_manager._service_account_exists(account_id) and not self.secret_manager_client._secret_exists(secret_name):
+                    self.logger.debug(f"Service account {account_id} and secret {secret_name} do not exist, creating both")
+                    display_name = account['display_name']
+                    
+                    self.service_account_manager.create_service_account(account_id, display_name)
+
+                    secret_name = self.secret_manager_client.create_secret(secret_name)
+                    self.secret_manager_client.update_secret_access(secret_name, authorized_users)
+
+                    new_key = self.service_account_manager.create_service_account_key(account_id)
+                    new_key_id = new_key.name.split('/')[-1]
+                    self.secret_manager_client.add_secret_version(secret_name, new_key_id, new_key.private_key_data)
+
+                else:
+                    # Any other case is not supported
+                    self.logger.error(f"Unexpected state for service account {account_id}")
+
+            except Exception as e:
+                self.logger.error(f"Error creating service account or secret for {account_id}: {e}")
+
+    def cron(self, dry_run: bool = False) -> None:
+        """
+        Cron job to rotate service account keys and secrets.
+
+        This method should be called periodically based on the rotation interval.
+        It will:
+
+        1. Check each service account to see if its key is due for rotation.
+            1.1. If the key is due for rotation, it will rotate the key and update the secret in Secret Manager.
+            1.2. If the key is not due for rotation, it will log that no action is needed.
+        2. Check for keys that have expired the grace period and delete them from both the service account and Secret Manager.
+        
+        Args:
+            dry_run (bool): If True, the method will only log the actions that would be taken.
+        """
+
+        if dry_run:
+            self.logger.info("Starting cron job DRY RUN for service account key rotation")
+        else:
+            self.logger.info("Starting cron job for service account key rotation")
+        
+        if not dry_run:
+            self._start_all_service_accounts()
+
+        for account in self.service_accounts:
+            account_id = account['account_id']
+            secret_name = f"{account_id}-key"
+            try:
+                if self.secret_manager_client._is_key_rotation_due(secret_name):
+                    if dry_run:
+                        self.logger.info(f"[DRY RUN] Service account key for {account_id} is due for rotation, would rotate key.")
+                    else:
+                        self.logger.info(f"Service account key for {account_id} is due for rotation, rotating key")
+                        new_key = self.service_account_manager.create_service_account_key(account_id)
+                        new_key_id = new_key.name.split('/')[-1]
+                        self.secret_manager_client.add_secret_version(secret_name, new_key_id, new_key.private_key_data)
+                else:
+                    self.logger.debug(f"Service account key for {account_id} is not due for rotation")
+            except Exception as e:
+                self.logger.error(f"Error during cron job for service account {account_id}: {e}")
+
+        # Check for keys that have expired the grace period and delete them
+        
+        self.logger.info("Checking for keys that have expired the grace period")
+        keys_to_delete = self.secret_manager_client.cron()
+        for secret_id, key_ids in keys_to_delete:
+            try:
+                for key_id in key_ids:
+                    if dry_run:
+                        self.logger.info(f"[DRY RUN] Would delete expired key {key_id} for secret {secret_id}")
+                    else:
+                        self.logger.info(f"Deleting expired key {key_id} for secret {secret_id}")
+                        self.service_account_manager.delete_service_account_key(secret_id, key_id)
+            except Exception as e:
+                self.logger.error(f"Error deleting expired keys for secret {secret_id}: {e}")
+                continue
+
+        if dry_run:
+            self.logger.info("Cron job DRY RUN for service account key rotation completed")
+        else:
+            self.logger.info("Cron job for service account key rotation completed")
+
+    def get_latest_service_account_key(self, account_id: str) -> str:
+        """
+        Retrieves the latest service account key for a given service account.
+         
+        Args:
+            account_id (str): The ID of the service account to retrieve the key for.
+        
+        Returns:
+            str: The latest service account key.
+        """
+        self.logger.info(f"Retrieving latest service account key for {account_id}")
+        try:
+            secret_name = f"{account_id}-key"
+            key_bytes = self.secret_manager_client.get_latest_secret_version(secret_name)
+            if not key_bytes:
+                self.logger.warning(f"No key found for service account {account_id}.")
+                raise ValueError(f"No key found for service account {account_id}.")
+
+            self.logger.debug(f"Latest service account key for {account_id} retrieved successfully.")
+            return key_bytes[1].decode('utf-8')
+        except Exception as e:
+            self.logger.error(f"Error retrieving latest service account key for {account_id}: {e}")
+            return ""
+
+def main():
+    """
+    Main function to run the KeyService.
+    
+    Loads configuration, initializes the KeyService, and handles CLI arguments.
+    """
+    args = parse_arguments()
+    
+    key_service = None
+    try:
+        config = load_config()
+        service_accounts_config = load_service_accounts_config()
+        
+        if args.cron or args.cron_dry_run:
+            is_dry_run = args.cron_dry_run
+            run_type = "dry run" if is_dry_run else "job"
+            print(f"Running cron {run_type} for key rotation...")
+            key_service = KeyService(config, service_accounts_config)
+            key_service.cron(dry_run=is_dry_run)
+            print(f"Cron {run_type} completed successfully.")
+            
+        elif args.get_key:
+            account_id = args.get_key
+            # If just a user getting the key, disable logging
+            key_service = KeyService(config, service_accounts_config, enable_logging=False)
+            print(f"Retrieving latest key for service account: {account_id}")
+            
+            # Validate that the account exists in configuration
+            account_ids = [account['account_id'] for account in service_accounts_config['service_accounts']]
+            if account_id not in account_ids:
+                print(f"Error: Service account '{account_id}' not found in configuration.")
+                print(f"Available accounts: {', '.join(account_ids)}")
+                sys.exit(1)
+            
+            try:
+                key = key_service.get_latest_service_account_key(account_id)
+                if key:
+                    print(f"Latest key for {account_id}:")
+                    print(key)
+                else:
+                    print(f"No key found for service account: {account_id}")
+                    sys.exit(1)
+            except PermissionDenied as e:
+                print(f"Permission denied when accessing the key for {account_id}: {e}")
+                sys.exit(1)
+            except Exception as e:
+                print(f"Error retrieving key for {account_id}: {e}")
+                sys.exit(1)
+        else:
+            print("You must specify either --cron to run the cron job or --get-key <ACCOUNT_ID> to retrieve a key.")
+            
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        logging.error(f"An error occurred: {e}")
+        logging.error(f"Full traceback: {traceback.format_exc()}")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/infra/keys/keys.yaml b/infra/keys/keys.yaml
new file mode 100644
index 000000000000..269a2841d91a
--- /dev/null
+++ b/infra/keys/keys.yaml
@@ -0,0 +1,26 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+    
+# Service Account Keys
+# This file contains the service account for the project, the account id
+# and the users authorized to use it
+# service_accounts:
+# - account_id: account_id
+#   display_name: account_@project_id.iam.gserviceaccount.com
+#   authorized_users:
+#     - email: "user1@google.com"
+#     - email: "user2@google.com"
+
+service_accounts: []
diff --git a/infra/keys/requirements.txt b/infra/keys/requirements.txt
new file mode 100644
index 000000000000..98814332cf01
--- /dev/null
+++ b/infra/keys/requirements.txt
@@ -0,0 +1,23 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This file is used to install the dependencies for the infrastructure
+
+PyYAML==6.0.2
+google-cloud-iam==2.19.1
+google-cloud-secret-manager==2.24.0
+google-cloud-storage==3.2.0
+google-crc32c==1.7.1
diff --git a/infra/keys/secret_manager.py b/infra/keys/secret_manager.py
new file mode 100644
index 000000000000..102b629bbfee
--- /dev/null
+++ b/infra/keys/secret_manager.py
@@ -0,0 +1,787 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import google_crc32c
+import logging
+import time
+from datetime import datetime, timezone, timedelta
+from google.cloud import secretmanager
+from typing import List, Union, Tuple, Dict
+
+# What the "created_by" label is set to for secrets created by this service.
+SECRET_MANAGER_LABEL = "beam-infra-secret-manager"
+
+class SecretManagerLoggerAdapter(logging.LoggerAdapter):
+    """Logger adapter that adds a prefix to all log messages."""
+    
+    def process(self, msg, kwargs):
+        return f"[SecretManager] {msg}", kwargs
+
+class SecretManager:
+    """Service to manage GCP API keys rotation."""
+
+    project_id: str # The GCP project ID where secrets are managed
+    rotation_interval: int # The interval (in days) at which to rotate secrets
+    grace_period: int # The grace period (in days) before a secret is considered for rotation
+    max_retries: int # The maximum number of retries for API calls
+    client: secretmanager.SecretManagerServiceClient # GCP Secret Manager client
+    logger: Union[logging.Logger, logging.LoggerAdapter] # Logger for logging messages
+
+    def __init__(self, project_id: str, logger: logging.Logger, rotation_interval: int = 30, grace_period: int = 7, max_retries: int = 3) -> None:
+        self.project_id = project_id
+        self.rotation_interval = rotation_interval
+        self.grace_period = grace_period
+        self.max_retries = max_retries
+        self.client = secretmanager.SecretManagerServiceClient()
+        self.logger = SecretManagerLoggerAdapter(logger, {})
+        self.logger.info(f"Initialized SecretManager for project '{self.project_id}'")
+
+    def _get_secret_ids(self) -> List[str]:
+        """
+        Retrieves the list of secrets from the Secret Manager and populates the `secrets_ids` list.
+        This method filters secrets based on a specific label indicating they were created by this service.
+
+        Returns:
+            List[str]: A list of secret IDs that were created by this service.
+        """
+        self.logger.debug(f"Retrieving secrets with the label from project '{self.project_id}'")
+        secret_ids = []
+
+        try:
+            for secret in self.client.list_secrets(request={"parent": f"projects/{self.project_id}"}):
+                secret_id = secret.name.split("/")[-1]
+                if "created_by" in secret.labels and secret.labels["created_by"] == SECRET_MANAGER_LABEL:
+                    secret_ids.append(secret_id)
+        except Exception as e:
+            self.logger.error(f"Error retrieving secrets: {e}")
+
+        self.logger.debug(f"Found {len(secret_ids)} secrets created by {SECRET_MANAGER_LABEL} in project '{self.project_id}'")
+        return secret_ids
+
+    def _secret_exists(self, secret_id: str) -> bool:
+        """
+        Checks if a secret with the given ID exists.
+
+        Args:
+            secret_id (str): The ID of the secret to check.
+        Returns:
+            bool: True if the secret exists, False otherwise.
+        """
+        self.logger.debug(f"Checking if secret '{secret_id}' exists")
+        try:
+            name = self.client.secret_path(self.project_id, secret_id)
+            self.client.get_secret(request={"name": name})
+            self.logger.debug(f"Secret '{secret_id}' exists")
+            return True
+        except Exception as e:
+            self.logger.debug(f"Secret '{secret_id}' does not exist: {e}")
+            return False
+
+    def _secret_is_managed(self, secret_id: str) -> bool:
+        """
+        Checks if a secret with the given ID exists and is managed by this service.
+
+        Args:
+            secret_id (str): The ID of the secret to check.
+        Returns:
+            bool: True if the secret is managed by this service, False otherwise.
+        """
+        self.logger.debug(f"Checking if secret '{secret_id}' exists and is managed by {SECRET_MANAGER_LABEL}")
+        if not self._secret_exists(secret_id):
+            self.logger.debug(f"Secret '{secret_id}' does not exist, cannot be managed")
+            return False
+
+        name = self.client.secret_path(self.project_id, secret_id)
+        secret = self.client.get_secret(request={"name": name})
+
+        is_managed = "created_by" in secret.labels and secret.labels["created_by"] == SECRET_MANAGER_LABEL
+        self.logger.debug(f"Secret '{secret_id}' is managed by {SECRET_MANAGER_LABEL}: {is_managed}")
+        return is_managed
+
+    def create_secret(self, secret_id: str) -> str:
+        """
+        Create a new secret with the given name. A secret is a logical wrapper
+        around a collection of secret versions. Secret versions hold the actual
+        secret material. This method creates a new secret with automatic replication
+        and labels for tracking.
+
+        Args:
+            secret_id (str): The ID to assign to the new secret. This ID must be unique within the project.
+        Returns:
+            str: The secret path of the newly created secret.
+        """
+        if self._secret_is_managed(secret_id):
+            self.logger.debug(f"Secret '{secret_id}' already exists, returning existing secret path")
+            name = self.client.secret_path(self.project_id, secret_id)
+            return name
+
+        self.logger.info(f"Creating new secret '{secret_id}' with rotation interval of {self.rotation_interval} days")
+        response = self.client.create_secret(
+            request={
+                "parent": f"projects/{self.project_id}",
+                "secret_id": f"{secret_id}",
+                "secret": {
+                    "replication": {
+                        "automatic": {}
+                    },
+                    "labels": {
+                        "created_by": SECRET_MANAGER_LABEL,
+                        "created_at": datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S"),
+                        "rotation_interval_days": str(self.rotation_interval),
+                        "grace_period_days": str(self.grace_period),
+                        "last_version_created_at": datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S"),
+                    }
+                }
+            }
+        )
+
+        # created_by : This label is used to identify secrets created by this service.
+        # created_at : This label stores the timestamp when the secret was created.
+        # rotation_interval_days : This label specifies the rotation interval for the secret.
+        # grace_period_days : This label specifies the grace period for the secret.
+        # last_version_created_at : This label stores the timestamp when the last version of the secret was created, this
+        #   helps with the rotation and grace period calculations.
+
+        # Wait for the secret to be created
+        self.logger.debug(f"Waiting for secret '{secret_id}' to be created")
+        delay = 1
+        for _ in range(self.max_retries):
+            if self._secret_is_managed(secret_id):
+                self.logger.debug(f"Secret '{secret_id}' is now available")
+                break
+            self.logger.debug(f"Secret '{secret_id}' not found, retrying in {delay} seconds")
+            time.sleep(delay)
+            delay *= 2
+        else:
+            error_msg = f"Could not verify creation of secret '{secret_id}' after {self.max_retries} retries."
+            self.logger.error(error_msg)
+            raise RuntimeError(error_msg)
+
+        self.logger.info(f"Successfully created secret '{secret_id}'")
+        return response.name
+
+    def get_secret(self, secret_id: str) -> secretmanager.Secret:
+        """
+        Retrieves the specified secret by its ID.
+
+        Args:
+            secret_id (str): The ID of the secret to retrieve.
+        Returns:
+            secretmanager.Secret: The requested secret.
+        """
+        self.logger.info(f"Retrieving secret '{secret_id}'")
+
+        if not self._secret_exists(secret_id):
+            error_msg = f"Secret {secret_id} does not exist. Please create it first."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+        if not self._secret_is_managed(secret_id):
+            error_msg = f"Secret {secret_id} is not managed by this service."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        name = self.client.secret_path(self.project_id, secret_id)
+        return self.client.get_secret(request={"name": name})
+
+    def delete_secret(self, secret_id: str) -> None:
+        """
+        Deletes the specified secret and all its versions.
+
+        Args:
+            secret_id (str): The ID of the secret to delete.
+        """
+        if not self._secret_is_managed(secret_id):
+            self.logger.debug(f"Secret '{secret_id}' is not managed by this service, cannot delete")
+            return
+
+        self.logger.info(f"Deleting secret '{secret_id}' and all its versions")
+        name = self.client.secret_path(self.project_id, secret_id)
+        self.client.delete_secret(request={"name": name})
+        
+        # Wait for the secret to be deleted
+        self.logger.debug(f"Waiting for secret '{secret_id}' to be deleted")
+        delay = 1 
+        for _ in range(self.max_retries):
+            if not self._secret_exists(secret_id):
+                self.logger.debug(f"Secret '{secret_id}' is now deleted")
+                break
+            self.logger.debug(f"Secret '{secret_id}' still exists, retrying in {delay} seconds")
+            time.sleep(delay)
+            delay *= 2
+        else:
+            error_msg = f"Could not verify deletion of secret '{secret_id}' after {self.max_retries} retries."
+            self.logger.error(error_msg)
+            raise RuntimeError(error_msg)
+
+        self.logger.info(f"Successfully deleted secret '{secret_id}'")
+
+    def is_different_user_access(self, secret_id: str, allowed_users: List[str]) -> bool:
+        """
+        Checks if the current access policy of a secret allows only the specified users to read it.
+        This is used to determine if an update is needed.
+
+        Args:
+            secret_id (str): The ID of the secret to check access for.
+            allowed_users (List[str]): A list of user emails to check against the current access policy.
+        Returns:
+            bool: True if the current access policy is different from the specified users, False otherwise.
+        """
+        self.logger.debug(f"Checking if access for secret '{secret_id}' differs from allowed users: {allowed_users}")
+
+        if not self._secret_is_managed(secret_id):
+            self.logger.debug(f"Secret '{secret_id}' is not managed by this service, cannot check access")
+            return True
+        
+        accessor_role = "roles/secretmanager.secretAccessor"
+        resource_name = self.client.secret_path(self.project_id, secret_id)
+        
+        try:
+            policy = self.client.get_iam_policy(request={"resource": resource_name})
+        except Exception as e:
+            self.logger.error(f"Failed to get IAM policy for secret '{secret_id}': {e}")
+            return True
+
+        current_members = set()
+        for binding in policy.bindings:
+            if binding.role == accessor_role:
+                current_members.update(binding.members)
+
+        allowed_members = {f"user:{user_email}" for user_email in allowed_users}
+        
+        is_different = current_members != allowed_members
+        self.logger.debug(f"Current members: {current_members}")
+        self.logger.debug(f"Allowed members: {allowed_members}")
+        self.logger.debug(f"Access for secret '{secret_id}' differs: {is_different}")
+        return is_different
+
+    def update_secret_access(self, secret_id: str, allowed_users: List[str]) -> None:
+        """
+        Updates the access policy of a secret to allow only the specified users to read it.
+        Any existing users will be removed and replaced with the new list.
+
+        Args:
+            secret_id (str): The ID of the secret to update access for.
+            allowed_users (List[str]): A list of user emails to grant read access to.
+        """
+        self.logger.debug(f"Updating access for secret '{secret_id}' to allow users: {allowed_users}")
+
+        if not self._secret_is_managed(secret_id):
+            error_msg = f"Secret {secret_id} is not managed by this service, cannot update access."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+        
+        accessor_role = "roles/secretmanager.secretAccessor"
+        resource_name = self.client.secret_path(self.project_id, secret_id)
+        policy = self.client.get_iam_policy(request={"resource": resource_name})
+
+        members = [f"user:{user_email}" for user_email in allowed_users]
+
+        binding_found = False
+        for binding in policy.bindings:
+            if binding.role == accessor_role:
+                binding.members[:] = members
+                self.logger.debug(f"Replaced members for role '{accessor_role}' in secret '{secret_id}' with: {allowed_users}")
+                binding_found = True
+                break
+        
+        if not binding_found:
+            policy.bindings.add(
+                role=accessor_role,
+                members=members
+            )
+            self.logger.debug(f"Created new binding for role '{accessor_role}' in secret '{secret_id}'")
+
+        self.client.set_iam_policy(
+            request={
+                "resource": resource_name,
+                "policy": policy
+            }
+        )
+
+        self.logger.info(f"Successfully updated access for secret '{secret_id}' to allow users: {allowed_users}")
+
+    def _get_secret_versions(self, secret_id: str) -> List[secretmanager.SecretVersion]:
+        """
+       Retrieves all versions of a secret.
+
+        Args:
+            secret_id (str): The ID of the secret to list versions for.
+        Returns:
+            List[secretmanager.SecretVersion]: A list of secret versions.
+        """
+        self.logger.debug(f"Retrieving versions for secret '{secret_id}'")
+
+        if not self._secret_is_managed(secret_id):
+            self.logger.debug(f"Secret '{secret_id}' is not managed by this service, cannot retrieve versions")
+            return []
+
+        parent = self.client.secret_path(self.project_id, secret_id)
+        versions = list(self.client.list_secret_versions(request={"parent": parent}))
+        self.logger.debug(f"Found {len(versions)} versions for secret '{secret_id}'")
+        return versions
+
+    def _secret_version_exists(self, secret_id: str, version_id: str) -> bool:
+        """
+        Checks if a specific version of a secret exists.
+
+        Args:
+            secret_id (str): The ID of the secret to check.
+            version_id (str): The ID of the version to check.
+        Returns:
+            bool: True if the version exists, False otherwise.
+        """
+        self.logger.debug(f"Checking if version '{version_id}' exists for secret '{secret_id}'")
+        if not self._secret_is_managed(secret_id):
+            self.logger.debug(f"Secret '{secret_id}' is not managed by this service, cannot check version existence")
+            return False
+        
+        versions = self._get_secret_versions(secret_id)
+        exists = any(version.name.split("/")[-1] == version_id for version in versions)
+        self.logger.debug(f"Version '{version_id}' exists: {exists}")
+        return exists
+    
+    def _secret_version_is_enabled(self, secret_id: str, version_id: str) -> bool:
+        """
+        Checks if a specific version of a secret is enabled.
+
+        Args:
+            secret_id (str): The ID of the secret to check.
+            version_id (str): The ID of the version to check.
+        Returns:
+            bool: True if the version is enabled, False otherwise.
+        """
+        self.logger.debug(f"Checking if version '{version_id}' of secret '{secret_id}' is enabled")
+        if not self._secret_is_managed(secret_id):
+            self.logger.debug(f"Secret '{secret_id}' is not managed by this service, version cannot be enabled")
+            return False
+        
+        versions = self._get_secret_versions(secret_id)
+        for version in versions:
+            if version.name.split("/")[-1] == version_id:
+                is_enabled = version.state == secretmanager.SecretVersion.State.ENABLED
+                self.logger.debug(f"Version '{version_id}' is enabled: {is_enabled}")
+                return is_enabled
+        self.logger.debug(f"Version '{version_id}' does not exist for secret '{secret_id}'")
+        return False
+    
+    def _secret_version_is_destroyed(self, secret_id: str, version_id: str) -> bool:
+        """
+        Checks if a specific version of a secret is destroyed.
+        
+        Args:
+            secret_id (str): The ID of the secret to check.
+            version_id (str): The ID of the version to check.
+        Returns:
+            bool: True if the version is destroyed, False otherwise.
+        """
+        self.logger.debug(f"Checking if version '{version_id}' of secret '{secret_id}' is destroyed")
+        if not self._secret_is_managed(secret_id):
+            self.logger.debug(f"Secret '{secret_id}' is not managed by this service, version cannot be destroyed")
+            return False
+        
+        versions = self._get_secret_versions(secret_id)
+        for version in versions:
+            if version.name.split("/")[-1] == version_id:
+                is_destroyed = version.state == secretmanager.SecretVersion.State.DESTROYED
+                self.logger.debug(f"Version '{version_id}' is destroyed: {is_destroyed}")
+                return is_destroyed
+        self.logger.debug(f"Version '{version_id}' does not exist for secret '{secret_id}'")
+        return False
+
+    def _get_latest_secret_version_id(self, secret_id: str) -> str:
+        """
+        Retrieves the latest enabled version of a secret.
+
+        Args:
+            secret_id (str): The ID of the secret to retrieve the latest version for.
+        Returns:
+            str: The name of the latest secret version.
+        """
+        self.logger.debug(f"Retrieving latest enabled version of secret '{secret_id}'")
+        if not self._secret_is_managed(secret_id):
+            error_msg = f"Secret {secret_id} does not exist or is not managed by this service, cannot retrieve latest version."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        for version in self._get_secret_versions(secret_id):
+            if version.state == secretmanager.SecretVersion.State.ENABLED:
+                version_id = version.name.split("/")[-1]
+                self.logger.debug(f"Found latest enabled version '{version_id}' for secret '{secret_id}'")
+                return version_id
+        error_msg = f"No enabled versions found for secret {secret_id}."
+        self.logger.error(error_msg)
+        raise ValueError(error_msg)
+    
+    def _is_key_rotation_due(self, secret_id: str) -> bool:
+        """
+        Checks if the key rotation is due based on the last version created timestamp.
+
+        Args:
+            secret_id (str): The ID of the secret to check.
+        Returns:
+            bool: True if the key rotation is due, False otherwise.
+        """
+        self.logger.debug(f"Checking if key rotation is due for secret '{secret_id}'")
+        if not self._secret_is_managed(secret_id):
+            error_msg = f"Secret {secret_id} does not exist or is not managed by this service, cannot check rotation."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        secret = self.get_secret(secret_id)
+        last_version_created_at = secret.labels["last_version_created_at"]
+        last_version_date = datetime.strptime(last_version_created_at, "%Y%m%d_%H%M%S").replace(tzinfo=timezone.utc)
+        due_date = last_version_date + timedelta(days=self.rotation_interval)
+        
+        is_due = datetime.now(timezone.utc) >= due_date
+        self.logger.debug(f"Key rotation due for secret '{secret_id}': {is_due}")
+        return is_due
+    
+    def add_secret_version(self, secret_id: str, data_id: str, payload: Union[bytes, str]) -> str:
+        """
+        Adds a new version to the specified secret with the given data ID and payload.
+        If the secret does not exist, it will be created first. All previous versions will be disabled.
+
+        Args:
+            secret_id (str): The ID of the secret to which the version will be added.
+            data_id (str): The ID of the data to be stored in the new version.
+            payload (bytes): The secret data to be stored in the new version.
+        Returns:
+            str: The name of the newly created secret version.
+        """
+        self.logger.info(f"Adding new version to secret '{secret_id}'")
+
+        secret_path = self.create_secret(secret_id)
+
+        if not isinstance(payload, (bytes, str)):
+            error_msg = "Payload must be a bytes object or a string that can be encoded to bytes."
+            self.logger.error(error_msg)
+            raise TypeError(error_msg)
+        
+        # Join data_id and payload to form the payload
+        if isinstance(payload, str):
+            payload = f"{data_id}:{payload}"
+        else:
+            payload = f"{data_id}:{payload.decode('utf-8')}" if isinstance(payload, bytes) else payload
+
+        # Ensure payload is bytes
+        payload_bytes = payload.encode('utf-8') if isinstance(payload, str) else payload
+
+        crc32c = google_crc32c.Checksum()
+        crc32c.update(payload_bytes)
+
+        self.logger.debug(f"Creating secret version with CRC32C checksum")
+        response = self.client.add_secret_version(
+            request={
+                "parent": secret_path,
+                "payload": {
+                    "data": payload_bytes,
+                    "data_crc32c": int(crc32c.hexdigest(), 16),
+                }
+            }
+        )
+
+        version_id = response.name.split("/")[-1]
+
+        # Update the last version created timestamp
+        self.logger.debug(f"Updating last version created timestamp for secret '{secret_id}'")
+        secret_obj = self.get_secret(secret_id)
+        labels = dict(secret_obj.labels)
+        labels["last_version_created_at"] = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
+        secret = {"name": secret_obj.name, "labels": labels}
+        update_mask = {"paths": ["labels"]}
+        self.client.update_secret(request={"secret": secret, "update_mask": update_mask})
+
+        # Wait for the new version to be available
+        self.logger.debug(f"Waiting for new version '{version_id}' of secret '{secret_id}' to be available")
+        delay = 1
+        for _ in range(self.max_retries):
+            if self._secret_version_exists(secret_id, version_id):
+                self.logger.debug(f"Version '{version_id}' of secret '{secret_id}' is now available")
+                break
+            self.logger.debug(f"Version '{version_id}' of secret '{secret_id}' not found, retrying in {delay} seconds")
+            time.sleep(delay)
+            delay *= 2
+        else:
+            error_msg = f"Could not verify creation of secret version '{version_id}' after {self.max_retries} retries."
+            self.logger.error(error_msg)
+            raise RuntimeError(error_msg)
+
+        # Disable all the previous versions except the newly created one
+        for ver in self._get_secret_versions(secret_id):
+            if ver.name != response.name and ver.state == secretmanager.SecretVersion.State.ENABLED:
+                self.logger.debug(f"Disabling previous version '{ver.name}' of secret '{secret_id}'")
+                self.disable_secret_version(secret_id, ver.name.split("/")[-1])
+
+        self.logger.info(f"Successfully added version '{version_id}' to secret '{secret_id}'")
+        return response.name
+
+    def get_latest_secret_version(self, secret_id: str) -> Tuple[str, bytes]:
+        """
+        Retrieves the latest enabled version of a secret.
+
+        Args:
+            secret_id (str): The ID of the secret from which to retrieve the version.
+
+        Returns:
+            Tuple[str, bytes]: A tuple containing the data ID and the payload of the latest secret version.
+        """
+        self.logger.info(f"Retrieving latest version of secret '{secret_id}'")
+
+        if not self._secret_is_managed(secret_id):
+            error_msg = f"Secret {secret_id} does not exist or is not managed by this service, cannot retrieve latest version."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        version_id = self._get_latest_secret_version_id(secret_id)
+        name = f"projects/{self.project_id}/secrets/{secret_id}/versions/{version_id}"
+
+        self.logger.debug(f"Accessing secret version '{version_id}' of secret '{secret_id}'")
+        response = self.client.access_secret_version(request={"name": name})
+
+        crc32c = google_crc32c.Checksum()
+        crc32c.update(response.payload.data)
+
+        if int(crc32c.hexdigest(), 16) != response.payload.data_crc32c:
+            error_msg = "CRC32C checksum mismatch. The data may be corrupted."
+            self.logger.error(f"{error_msg} for secret '{secret_id}' version '{version_id}'")
+            raise ValueError(error_msg)
+
+        self.logger.info(f"Successfully retrieved version '{version_id}' of secret '{secret_id}'")
+
+        data_str = response.payload.data.decode('utf-8')
+        data_id, payload = data_str.split(":", 1)
+        return data_id, payload.encode('utf-8')
+
+    def enable_secret_version(self, secret_id: str, version_id: str) -> None:
+        """
+        Enables a specific version of a secret.
+
+        Args:
+            secret_id (str): The ID of the secret from which to enable the version.
+            version_id (str): The version ID to enable.
+        """
+        self.logger.info(f"Enabling version '{version_id}' of secret '{secret_id}'")
+
+        if not self._secret_is_managed(secret_id):
+            error_msg = f"Secret {secret_id} does not exist or is not managed by this service, cannot enable version."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+        
+        self.logger.debug(f"Verifying version '{version_id}' exists for secret '{secret_id}'")
+        version_exists = any(
+            version.name.split("/")[-1] == version_id and version.state == secretmanager.SecretVersion.State.DISABLED
+            for version in self._get_secret_versions(secret_id)
+        )
+        if not version_exists:
+            error_msg = f"Version {version_id} does not exist or is not disabled for secret {secret_id}."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+        
+        name = f"projects/{self.project_id}/secrets/{secret_id}/versions/{version_id}"
+        self.logger.debug(f"Enabling version '{version_id}' of secret '{secret_id}'")
+        response = self.client.enable_secret_version(request={"name": name})
+
+        if response.name.split("/")[-1] != version_id or response.state != secretmanager.SecretVersion.State.ENABLED:
+            error_msg = f"Failed to enable secret version {version_id} for secret {secret_id}."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+        
+        # Wait for the version to be enabled
+        self.logger.debug(f"Waiting for version '{version_id}' of secret '{secret_id}' to be enabled")
+        delay = 1
+        for _ in range(self.max_retries):
+            if self._secret_version_is_enabled(secret_id, version_id):
+                self.logger.debug(f"Version '{version_id}' of secret '{secret_id}' is now enabled")
+                break
+            self.logger.debug(f"Version '{version_id}' of secret '{secret_id}' still disabled, retrying in {delay} seconds")
+            time.sleep(delay)
+            delay *= 2
+        else:
+            error_msg = f"Could not verify enabling of version '{version_id}' of secret '{secret_id}' after {self.max_retries} retries."
+            self.logger.error(error_msg)
+            raise RuntimeError(error_msg)
+        
+        self.logger.info(f"Successfully enabled version '{version_id}' of secret '{secret_id}'")
+
+    def disable_secret_version(self, secret_id: str, version_id: str) -> None:
+        """
+        Disables a specific version of a secret.
+
+        Args:
+            secret_id (str): The ID of the secret from which to delete the version.
+            version_id (str): The version ID to delete.
+        """
+        self.logger.info(f"Disabling version '{version_id}' of secret '{secret_id}'")
+
+        if not self._secret_is_managed(secret_id):
+            error_msg = f"Secret {secret_id} does not exist or is not managed by this service, cannot disable version."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        self.logger.debug(f"Verifying version '{version_id}' exists for secret '{secret_id}'")
+
+        version_exists = any(
+            version.name.split("/")[-1] == version_id
+            for version in self._get_secret_versions(secret_id)
+        )
+        if not version_exists:
+            error_msg = f"Version {version_id} does not exist for secret {secret_id}."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        name = f"projects/{self.project_id}/secrets/{secret_id}/versions/{version_id}"
+        self.logger.debug(f"Disabling version '{version_id}' of secret '{secret_id}'")
+        response = self.client.disable_secret_version(request={"name": name})
+
+        if response.name.split("/")[-1] != version_id or response.state != secretmanager.SecretVersion.State.DISABLED:
+            error_msg = f"Failed to disable secret version {version_id} for secret {secret_id}."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+        
+        # Wait for the version to be disabled
+        self.logger.debug(f"Waiting for version '{version_id}' of secret '{secret_id}' to be disabled")
+        delay = 1
+        for _ in range(self.max_retries):
+            if not self._secret_version_is_enabled(secret_id, version_id):
+                self.logger.debug(f"Version '{version_id}' of secret '{secret_id}' is now disabled")
+                break
+            self.logger.debug(f"Version '{version_id}' of secret '{secret_id}' still enabled, retrying in {delay} seconds")
+            time.sleep(delay)
+            delay *= 2
+        else:
+            error_msg = f"Could not verify disabling of version '{version_id}' of secret '{secret_id}' after {self.max_retries} retries."
+            self.logger.error(error_msg)
+            raise RuntimeError(error_msg)
+
+        self.logger.info(f"Successfully disabled version '{version_id}' of secret '{secret_id}'")
+
+    def destroy_secret_version(self, secret_id: str, version_id: str) -> str:
+        """
+        Destroys a specific version of a secret.
+
+        Args:
+            secret_id (str): The ID of the secret from which to delete the version.
+            version_id (str): The version ID to delete.
+        Returns:
+            str: The data ID of the destroyed version.
+        """
+        self.logger.info(f"Destroying version '{version_id}' of secret '{secret_id}'")
+
+        if not self._secret_is_managed(secret_id):
+            error_msg = f"Secret {secret_id} does not exist or is not managed by this service, cannot destroy version."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        self.logger.debug(f"Verifying version '{version_id}' exists for secret '{secret_id}'")
+
+        version_exists = any(
+            version.name.split("/")[-1] == version_id
+            for version in self._get_secret_versions(secret_id)
+        )
+        if not version_exists:
+            error_msg = f"Version {version_id} does not exist for secret {secret_id}."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+        
+        # Enable the version before destroying it to get the data ID
+        if not self._secret_version_is_enabled(secret_id, version_id):
+            self.logger.debug(f"Version '{version_id}' of secret '{secret_id}' is not enabled, enabling it before destruction")
+            self.enable_secret_version(secret_id, version_id)
+        
+        # Get the data ID from the specific version we're about to destroy
+        name = f"projects/{self.project_id}/secrets/{secret_id}/versions/{version_id}"
+        response = self.client.access_secret_version(request={"name": name})
+        data_str = response.payload.data.decode('utf-8')
+        data_id, _ = data_str.split(":", 1)
+        self.logger.debug(f"Data ID for version '{version_id}' of secret '{secret_id}': {data_id}")
+    
+        # Now destroy the version
+        self.logger.debug(f"Destroying version '{version_id}' of secret '{secret_id}'")
+        response = self.client.destroy_secret_version(request={"name": name})
+
+        if response.name.split("/")[-1] != version_id or response.state != secretmanager.SecretVersion.State.DESTROYED:
+            error_msg = f"Failed to destroy secret version {version_id} for secret {secret_id}."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+        
+        # Wait for the version to be destroyed
+        self.logger.debug(f"Waiting for version '{version_id}' of secret '{secret_id}' to be destroyed")
+        delay = 1
+        for _ in range(self.max_retries):
+            if self._secret_version_is_destroyed(secret_id, version_id):
+                self.logger.debug(f"Version '{version_id}' of secret '{secret_id}' is now destroyed")
+                break
+            self.logger.debug(f"Version '{version_id}' of secret '{secret_id}' still not destroyed, retrying in {delay} seconds")
+            time.sleep(delay)
+            delay *= 2
+        else:
+            error_msg = f"Could not verify destruction of version '{version_id}' of secret '{secret_id}' after {self.max_retries} retries."
+            self.logger.error(error_msg)
+            raise RuntimeError(error_msg)
+
+        self.logger.info(f"Successfully destroyed version '{version_id}' of secret '{secret_id}'")
+        return data_id
+
+    def purge_disabled_secret_versions(self, secret_id: str) -> List[str]:
+        """
+        Purges (destroys) all disabled versions of a secret that are older than the grace period.
+        To determine if a version is older than the grace period, it checks the creation time of each version,
+        if the latest version was created more than the grace period ago, it will purge the disabled versions.
+
+        Args:
+            secret_id (str): The ID of the secret for which to purge disabled versions.
+        Returns:
+            List[str]: A list of data IDs of the destroyed versions.
+        """
+        self.logger.info(f"Purging disabled versions of secret '{secret_id}'")
+
+        if not self._secret_is_managed(secret_id):
+            error_msg = f"Secret {secret_id} does not exist or is not managed by this service, cannot purge versions."
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+        
+        data_ids = []
+
+        for version in self._get_secret_versions(secret_id):
+            if version.state == secretmanager.SecretVersion.State.DISABLED:
+                version_id = version.name.split("/")[-1]
+                create_time = datetime.fromtimestamp(version.create_time.timestamp(), tz=timezone.utc)  # type: ignore
+                if create_time < datetime.now(timezone.utc) - timedelta(days=self.grace_period):
+                    self.logger.debug(f"Destroying disabled version '{version_id}' of secret '{secret_id}'")
+                    data_ids.append(self.destroy_secret_version(secret_id, version_id))
+                else:
+                    self.logger.debug(f"Skipping version '{version_id}' of secret '{secret_id}' as it is within the grace period")
+
+        return data_ids
+
+    def cron(self) -> Dict[str, List[str]]:
+        """
+        Performs periodic maintenance tasks:
+        - Purges disabled secret versions that are older than the grace period.
+
+        Returns:
+            Dict[str, List[str]]: A dictionary with secret IDs as keys and lists of destroyed data IDs as values.
+        """
+        self.logger.info("Starting periodic maintenance tasks (cron)")
+        destroyed_secret_ids = {}
+
+        for secret_id in self._get_secret_ids():
+            self.logger.debug(f"Processing secret '{secret_id}' for maintenance")
+            purged_data_ids = self.purge_disabled_secret_versions(secret_id)
+            if purged_data_ids:
+                self.logger.info(f"Purged disabled versions of secret '{secret_id}': {purged_data_ids}")
+                destroyed_secret_ids[secret_id] = purged_data_ids
+
+        return destroyed_secret_ids
\ No newline at end of file
diff --git a/infra/keys/service_account.py b/infra/keys/service_account.py
new file mode 100644
index 000000000000..a1036bf88a47
--- /dev/null
+++ b/infra/keys/service_account.py
@@ -0,0 +1,425 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import json
+import time
+from typing import List,Optional
+from google.cloud import iam_admin_v1
+from google.cloud.iam_admin_v1 import types
+from google.oauth2 import service_account
+from google.auth.transport.requests import Request
+from google.api_core import exceptions
+
+class ServiceAccountManagerLoggerAdapter(logging.LoggerAdapter):
+    """Logger adapter that adds a prefix to all log messages."""
+    
+    def process(self, msg, kwargs):
+        return f"[ServiceAccountManager] {msg}", kwargs
+
+class ServiceAccountManager:
+    def __init__(self, project_id: str, logger: logging.Logger, max_retries: int = 3) -> None:
+        self.project_id = project_id
+        self.client = iam_admin_v1.IAMClient()
+        self.logger = ServiceAccountManagerLoggerAdapter(logger, {})
+        self.max_retries = max_retries
+        self.logger.info(f"Initialized ServiceAccountManager for project: {self.project_id}")
+
+    def _normalize_account_email(self, account_id: str) -> str:
+        """
+        Normalizes the account identifier to a full email format.
+        
+        Args:
+            account_id (str): The unique identifier or email of the service account.
+            
+        Returns:
+            str: The full service account email address.
+        """
+        # Handle both account ID and full email formats
+        if "@" in account_id and account_id.endswith(".iam.gserviceaccount.com"):
+            # account_id is already a full email
+            return account_id
+        else:
+            # account_id is just the account name
+            return f"{account_id}@{self.project_id}.iam.gserviceaccount.com"
+
+    def _get_service_accounts(self) -> List[iam_admin_v1.ServiceAccount]:
+        """
+        Retrieves all service accounts in the specified project.
+
+        Returns:
+            List[iam_admin_v1.ServiceAccount]: A list of service account objects.
+        """
+        request = types.ListServiceAccountsRequest()
+        request.name = f"projects/{self.project_id}"
+
+        accounts = self.client.list_service_accounts(request=request)
+        self.logger.debug(f"Listed service accounts: {[account.email for account in accounts.accounts]}")
+        return list(accounts.accounts)
+    
+    def _service_account_exists(self, account_id: str) -> bool:
+        """
+        Checks if a service account with the given account_id exists in the project.
+
+        Args:
+            account_id (str): The unique identifier or email of the service account.
+
+        Returns:
+            bool: True if the service account exists, False otherwise.
+        """
+        try:
+            self.get_service_account(account_id)
+            return True
+        except exceptions.NotFound:
+            return False
+        
+    def _service_account_is_enabled(self, account_id: str) -> bool:
+        """
+        Checks if a service account is enabled.
+
+        Args:
+            account_id (str): The unique identifier or email of the service account.
+
+        Returns:
+            bool: True if the service account is enabled, False otherwise.
+        """
+        try:
+            service_account = self.get_service_account(account_id)
+            return not service_account.disabled
+        except exceptions.NotFound:
+            self.logger.error(f"Service account {account_id} not found")
+            return False
+
+    def create_service_account(self, account_id: str, display_name: Optional[str] = None) -> types.ServiceAccount:
+        """
+        Creates a service account in the specified project.
+        If the service account already exists, returns the existing account (idempotent operation).
+
+        Args:
+            account_id (str): The unique identifier for the service account.
+            display_name (Optional[str]): A human-readable name for the service account.
+        Returns:
+            types.ServiceAccount: The created or existing service account object.
+        """
+        request = types.CreateServiceAccountRequest()
+        request.account_id = account_id
+        request.name = f"projects/{self.project_id}"
+
+        service_account = types.ServiceAccount()
+        service_account.display_name = display_name or account_id
+        request.service_account = service_account
+
+        try:
+            account = self.client.create_service_account(request=request)
+
+            # Wait for the service account to be created
+            delay = 1
+            for _ in range(self.max_retries):
+                if self._service_account_exists(account_id):
+                    break
+                time.sleep(delay)
+                delay *= 2
+            else:
+                self.logger.error(f"Service account {account_id} creation timed out after {self.max_retries} retries.")
+                raise exceptions.DeadlineExceeded(f"Service account {account_id} creation timed out.")
+
+            self.logger.info(f"Created service account: {account.email}")
+            return account
+        except exceptions.Conflict:
+            existing_account = self.get_service_account(account_id)
+            self.logger.info(f"Service account already exists: {existing_account.email}")
+            return existing_account
+    
+    def get_service_account(self, account_id: str) -> types.ServiceAccount:
+        """
+        Retrieves a service account by its unique identifier or email.
+
+        Args:
+            account_id (str): The unique identifier or email of the service account.
+        
+        Returns:
+            types.ServiceAccount: The service account object.
+        """
+        service_account_email = self._normalize_account_email(account_id)
+
+        request = types.GetServiceAccountRequest()
+        request.name = f"projects/{self.project_id}/serviceAccounts/{service_account_email}"
+
+        try:
+            service_account = self.client.get_service_account(request=request)
+            self.logger.info(f"Retrieved service account: {service_account.email}")
+            return service_account
+        except exceptions.NotFound:
+            self.logger.error(f"Service account {account_id} not found")
+            raise
+    
+    def enable_service_account(self, account_id: str) -> None:
+        """
+        Enables a service account in the specified project.
+
+        Args:
+            account_id (str): The unique identifier or email of the service account to enable.
+        """
+        service_account_email = self._normalize_account_email(account_id)
+        request = types.EnableServiceAccountRequest()
+        request.name = f"projects/{self.project_id}/serviceAccounts/{service_account_email}"
+
+        self.client.enable_service_account(request=request)
+
+        # Wait for the service account to be enabled
+        delay = 1
+        for _ in range(self.max_retries):
+            if self._service_account_is_enabled(account_id):
+                break
+            time.sleep(delay)
+            delay *= 2
+        else:
+            self.logger.error(f"Service account {account_id} enabling timed out after {self.max_retries} retries.")
+            raise exceptions.DeadlineExceeded(f"Service account {account_id} enabling timed out.")
+        
+        self.logger.info(f"Enabled service account: {account_id}")
+
+    def disable_service_account(self, account_id: str) -> None:
+        """
+        Disables a service account in the specified project.
+
+        Args:
+            account_id (str): The unique identifier or email of the service account to disable.
+        """
+        service_account_email = self._normalize_account_email(account_id)
+        request = types.DisableServiceAccountRequest()
+        request.name = f"projects/{self.project_id}/serviceAccounts/{service_account_email}"
+
+        self.client.disable_service_account(request=request)
+
+        # Wait for the service account to be disabled
+        delay = 1
+        for _ in range(self.max_retries):
+            if not self._service_account_is_enabled(account_id):
+                break
+            time.sleep(delay)
+            delay *= 2
+        else:
+            self.logger.error(f"Service account {account_id} disabling timed out after {self.max_retries} retries.")
+            raise exceptions.DeadlineExceeded(f"Service account {account_id} disabling timed out.")
+        
+        self.logger.info(f"Disabled service account: {account_id}")
+
+    def delete_service_account(self, account_id: str) -> None:
+        """
+        Deletes a service account in the specified project.
+
+        Args:
+            account_id (str): The unique identifier or email of the service account to delete.
+        """
+        service_account_email = self._normalize_account_email(account_id)
+        request = types.DeleteServiceAccountRequest()
+        request.name = f"projects/{self.project_id}/serviceAccounts/{service_account_email}"
+
+        self.client.delete_service_account(request=request)
+
+        # Wait for the service account to be deleted
+        delay = 1
+        for _ in range(self.max_retries):
+            if not self._service_account_exists(account_id):
+                break
+            time.sleep(delay)
+            delay *= 2
+        else:
+            self.logger.error(f"Service account {account_id} deletion timed out after {self.max_retries} retries.")
+            raise exceptions.DeadlineExceeded(f"Service account {account_id} deletion timed out.")
+
+        self.logger.info(f"Deleted service account: {account_id}")
+
+    def _get_service_account_keys(self, account_id: str) -> List[iam_admin_v1.ServiceAccountKey]:
+        """
+        Retrieves all keys for the specified service account.
+
+        Args:
+            account_id (str): The unique identifier or email of the service account.
+        
+        Returns:
+            List[iam_admin_v1.ServiceAccountKey]: A list of service account key objects.
+        """
+        service_account_email = self._normalize_account_email(account_id)
+        request = types.ListServiceAccountKeysRequest()
+        request.name = f"projects/{self.project_id}/serviceAccounts/{service_account_email}"
+
+        response = self.client.list_service_account_keys(request=request)
+        self.logger.debug(f"Listed keys for service account: {account_id}")
+        return list(response.keys)
+
+    def _service_account_key_exists(self, account_id: str, key_id: str) -> bool:
+        """
+        Checks if a service account key exists for the specified service account.
+
+        Args:
+            account_id (str): The unique identifier or email of the service account.
+            key_id (str): The ID of the service account key to check.
+        
+        Returns:
+            bool: True if the key exists, False otherwise.
+        """
+        keys = self._get_service_account_keys(account_id)
+        return any(key.name.split('/')[-1] == key_id for key in keys)
+    
+    def create_service_account_key(self, account_id: str) -> types.ServiceAccountKey:
+        """
+        Creates a key for the specified service account.
+        Remember the private key ID is only returned once.
+        If the service account is disabled, it will be enabled first.
+        Includes retry logic to handle service account propagation delays.
+
+        Args:
+            account_id (str): The unique identifier or email of the service account.
+        
+        Returns:
+            types.ServiceAccountKey: The created service account key object.
+            str: The private key ID of the created key.
+        """
+        service_account_email = self._normalize_account_email(account_id)
+        
+        # Retry logic for service account access and key creation
+        delay = 1
+        for attempt in range(self.max_retries):
+            try:
+                # Check if service account exists and get its state
+                get_request = types.GetServiceAccountRequest()
+                get_request.name = f"projects/{self.project_id}/serviceAccounts/{service_account_email}"
+                
+                service_account = self.client.get_service_account(request=get_request)
+                if service_account.disabled:
+                    self.logger.info(f"Service account {account_id} is disabled. Enabling it first.")
+                    self.enable_service_account(account_id)
+                
+                # Create the key
+                request = types.CreateServiceAccountKeyRequest()
+                request.name = f"projects/{self.project_id}/serviceAccounts/{service_account_email}"
+                
+                key = self.client.create_service_account_key(request=request)
+                
+                # Wait for the key to be created and available
+                key_delay = 1
+                for _ in range(self.max_retries):
+                    if self._service_account_key_exists(account_id, key.name.split('/')[-1]):
+                        break
+                    time.sleep(key_delay)
+                    key_delay *= 2
+                else:
+                    self.logger.error(f"Service account key creation for {account_id} timed out after {self.max_retries} retries.")
+                    raise exceptions.DeadlineExceeded(f"Service account key creation for {account_id} timed out.")
+
+                self.logger.info(f"Created service account key for {account_id}")
+                return key
+                
+            except exceptions.NotFound as e:
+                if attempt < self.max_retries - 1:
+                    self.logger.warning(f"Service account {account_id} not found (attempt {attempt + 1}/{self.max_retries}), retrying in {delay}s. This may be due to propagation delay.")
+                    time.sleep(delay)
+                    delay *= 2
+                else:
+                    self.logger.error(f"Service account {account_id} not found after {self.max_retries} attempts")
+                    raise
+            except Exception as e:
+                # For other exceptions, don't retry
+                self.logger.error(f"Error creating service account key for {account_id}: {e}")
+                raise
+        
+        # This should not be reached due to the raise in the except block
+        raise exceptions.NotFound(f"Service account {account_id} not found after {self.max_retries} attempts")
+    
+    def delete_service_account_key(self, account_id: str, key_id: str) -> None:
+        """
+        Deletes a key for the specified service account.
+
+        Args:
+            account_id (str): The unique identifier or email of the service account.
+            key_id (str): The ID of the key to delete.
+        
+        Raises:
+            exceptions.NotFound: If the key does not exist.
+            exceptions.FailedPrecondition: If the key cannot be deleted due to constraints.
+        """
+        service_account_email = self._normalize_account_email(account_id)
+        request = types.DeleteServiceAccountKeyRequest()
+        request.name = f"projects/{self.project_id}/serviceAccounts/{service_account_email}/keys/{key_id}"
+
+        try:
+            self.client.delete_service_account_key(request=request)
+        except exceptions.NotFound:
+            self.logger.warning(f"Service account key {key_id} not found for account: {account_id} (may have been already deleted)")
+            raise
+        except exceptions.FailedPrecondition as e:
+            self.logger.warning(f"Failed to delete service account key {key_id} for account: {account_id}. Error: {e}")
+            raise
+        except Exception as e:
+            self.logger.error(f"Unexpected error deleting service account key {key_id} for account: {account_id}. Error: {e}")
+            raise
+
+        # Wait for the key to be deleted
+        delay = 1
+        for _ in range(self.max_retries):
+            if not self._service_account_key_exists(account_id, key_id):
+                break
+            time.sleep(delay)
+            delay *= 2
+        else:
+            self.logger.error(f"Service account key deletion for {account_id} timed out after {self.max_retries} retries.")
+            raise exceptions.DeadlineExceeded(f"Service account key deletion for {account_id} timed out.")
+
+        self.logger.info(f"Deleted service account key: {key_id} for account: {account_id}")
+
+    def test_service_account_key(self, key_data: bytes) -> bool:
+        """
+        Tests if a service account key is valid by attempting to authenticate and make an API call.
+        Includes retry logic to handle key propagation delays.
+
+        Args:
+            key_data (bytes): The private key data from the service account key.
+        
+        Returns:
+            bool: True if the key is valid and can authenticate, False otherwise.
+        """
+        try:
+            key_info = json.loads(key_data.decode('utf-8'))
+        except json.JSONDecodeError as json_error:
+            self.logger.error(f"Invalid JSON in service account key: {json_error}")
+            return False
+
+        delay = 1
+        for attempt in range(self.max_retries):
+            try:
+                credentials = service_account.Credentials.from_service_account_info(
+                    key_info,
+                    scopes=['https://www.googleapis.com/auth/cloud-platform']
+                )
+                
+                request = Request()
+                credentials.refresh(request)
+
+                self.logger.info(f"Service account key is valid and can authenticate")
+                return True
+                    
+            except Exception as auth_error:
+                if attempt < self.max_retries - 1:  # Don't log on the last attempt
+                    delay *= 2
+                    self.logger.warning(f"Authentication attempt {attempt + 1} failed (will retry in {delay}s): {auth_error}")
+                    time.sleep(delay)
+                else:
+                    self.logger.error(f"Authentication failed with service account key after {self.max_retries} attempts: {auth_error}")
+                    return False
+        
+        return False
+    
\ No newline at end of file
diff --git a/infra/keys/test_secret_manager.py b/infra/keys/test_secret_manager.py
new file mode 100644
index 000000000000..4b301e10c58a
--- /dev/null
+++ b/infra/keys/test_secret_manager.py
@@ -0,0 +1,839 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import logging
+import unittest
+import time
+from unittest import mock
+from datetime import datetime, timezone, timedelta
+from secret_manager import SecretManager, SECRET_MANAGER_LABEL, SecretManagerLoggerAdapter
+from google.cloud import secretmanager
+from google.api_core import exceptions
+
+class TestSecretManagerLoggerAdapter(unittest.TestCase):
+    """Unit tests for SecretManagerLoggerAdapter class."""
+
+    def test_process_adds_prefix(self):
+        """Test that the logger adapter adds the correct prefix."""
+        logger = logging.getLogger("test")
+        adapter = SecretManagerLoggerAdapter(logger, {})
+        
+        msg, kwargs = adapter.process("test message", {"key": "value"})
+        
+        self.assertEqual(msg, "[SecretManager] test message")
+        self.assertEqual(kwargs, {"key": "value"})
+
+class TestSecretManager(unittest.TestCase):
+    """Unit tests for SecretManager class."""
+
+    def setUp(self):
+        """Set up test fixtures."""
+        self.project_id = "test-project"
+        self.logger = logging.getLogger("test")
+        self.logger.setLevel(logging.CRITICAL)  # Suppress logging during tests
+        
+        # Mock the SecretManagerServiceClient
+        with mock.patch('secret_manager.secretmanager.SecretManagerServiceClient'):
+            self.manager = SecretManager(
+                self.project_id, 
+                self.logger, 
+                rotation_interval=30, 
+                grace_period=7, 
+                max_retries=3
+            )
+        
+        self.test_secret_id = "test-secret"
+        self.test_data_id = "test-data"
+        self.test_payload = b"test-payload"
+
+    def test_init(self):
+        """Test SecretManager initialization."""
+        with mock.patch('secret_manager.secretmanager.SecretManagerServiceClient'):
+            manager = SecretManager("test-project", self.logger, 15, 3, 5)
+        
+        self.assertEqual(manager.project_id, "test-project")
+        self.assertEqual(manager.rotation_interval, 15)
+        self.assertEqual(manager.grace_period, 3)
+        self.assertEqual(manager.max_retries, 5)
+        self.assertIsInstance(manager.logger, SecretManagerLoggerAdapter)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_get_secret_ids(self, mock_client):
+        """Test _get_secret_ids method."""
+        # Mock response with secrets having the correct label
+        mock_secret1 = mock.Mock()
+        mock_secret1.name = "projects/test-project/secrets/secret1"
+        mock_secret1.labels = {"created_by": SECRET_MANAGER_LABEL}
+        
+        mock_secret2 = mock.Mock()
+        mock_secret2.name = "projects/test-project/secrets/secret2"
+        mock_secret2.labels = {"created_by": "other"}
+        
+        mock_secret3 = mock.Mock()
+        mock_secret3.name = "projects/test-project/secrets/secret3"
+        mock_secret3.labels = {"created_by": SECRET_MANAGER_LABEL}
+        
+        mock_client.return_value.list_secrets.return_value = [mock_secret1, mock_secret2, mock_secret3]
+        
+        manager = SecretManager(self.project_id, self.logger)
+        secret_ids = manager._get_secret_ids()
+        
+        self.assertEqual(secret_ids, ["secret1", "secret3"])
+        mock_client.return_value.list_secrets.assert_called_once()
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_get_secret_ids_exception(self, mock_client):
+        """Test _get_secret_ids method with exception."""
+        mock_client.return_value.list_secrets.side_effect = Exception("API Error")
+        
+        manager = SecretManager(self.project_id, self.logger)
+        secret_ids = manager._get_secret_ids()
+        
+        self.assertEqual(secret_ids, [])
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_secret_exists_true(self, mock_client):
+        """Test _secret_exists method when secret exists."""
+        mock_client.return_value.get_secret.return_value = mock.Mock()
+        
+        manager = SecretManager(self.project_id, self.logger)
+        exists = manager._secret_exists(self.test_secret_id)
+        
+        self.assertTrue(exists)
+        mock_client.return_value.get_secret.assert_called_once()
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_secret_exists_false(self, mock_client):
+        """Test _secret_exists method when secret doesn't exist."""
+        mock_client.return_value.get_secret.side_effect = exceptions.NotFound("Secret not found")
+        
+        manager = SecretManager(self.project_id, self.logger)
+        exists = manager._secret_exists(self.test_secret_id)
+        
+        self.assertFalse(exists)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_secret_is_managed_true(self, mock_client):
+        """Test _secret_is_managed method when secret is managed."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        manager = SecretManager(self.project_id, self.logger)
+        is_managed = manager._secret_is_managed(self.test_secret_id)
+        
+        self.assertTrue(is_managed)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_secret_is_managed_false(self, mock_client):
+        """Test _secret_is_managed method when secret is not managed."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": "other"}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        manager = SecretManager(self.project_id, self.logger)
+        is_managed = manager._secret_is_managed(self.test_secret_id)
+        
+        self.assertFalse(is_managed)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_secret_is_managed_not_exists(self, mock_client):
+        """Test _secret_is_managed method when secret doesn't exist."""
+        mock_client.return_value.get_secret.side_effect = exceptions.NotFound("Secret not found")
+        
+        manager = SecretManager(self.project_id, self.logger)
+        is_managed = manager._secret_is_managed(self.test_secret_id)
+        
+        self.assertFalse(is_managed)
+
+    @mock.patch('time.sleep')
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_create_secret_success(self, mock_client, mock_sleep):
+        """Test create_secret method success."""
+        mock_response = mock.Mock()
+        mock_response.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}"
+        mock_client.return_value.create_secret.return_value = mock_response
+        
+        # Mock the sequence of get_secret calls: first raises NotFound, then succeeds
+        call_count = [0]  # Use list to make it mutable in nested function
+        
+        def get_secret_side_effect(*args, **kwargs):
+            call_count[0] += 1
+            if call_count[0] == 1:
+                raise exceptions.NotFound("Not found")  # _secret_is_managed returns False
+            else:
+                # For waiting loop - return a mock secret with proper labels
+                mock_secret = mock.Mock()
+                mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+                return mock_secret
+        
+        mock_client.return_value.get_secret.side_effect = get_secret_side_effect
+        
+        manager = SecretManager(self.project_id, self.logger)
+        result = manager.create_secret(self.test_secret_id)
+        
+        self.assertEqual(result, mock_response.name)
+        mock_client.return_value.create_secret.assert_called_once()
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_create_secret_already_managed(self, mock_client):
+        """Test create_secret method when secret already managed."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_secret.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}"
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        # Mock the secret_path method to return the expected path
+        expected_path = f"projects/{self.project_id}/secrets/{self.test_secret_id}"
+        mock_client.return_value.secret_path.return_value = expected_path
+        
+        manager = SecretManager(self.project_id, self.logger)
+        result = manager.create_secret(self.test_secret_id)
+        
+        self.assertEqual(result, expected_path)
+        mock_client.return_value.create_secret.assert_not_called()
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_get_secret_success(self, mock_client):
+        """Test get_secret method success."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        manager = SecretManager(self.project_id, self.logger)
+        result = manager.get_secret(self.test_secret_id)
+        
+        self.assertEqual(result, mock_secret)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_get_secret_not_exists(self, mock_client):
+        """Test get_secret method when secret doesn't exist."""
+        mock_client.return_value.get_secret.side_effect = exceptions.NotFound("Not found")
+        
+        manager = SecretManager(self.project_id, self.logger)
+        
+        with self.assertRaises(ValueError):
+            manager.get_secret(self.test_secret_id)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_get_secret_not_managed(self, mock_client):
+        """Test get_secret method when secret is not managed."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": "other"}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        manager = SecretManager(self.project_id, self.logger)
+        
+        with self.assertRaises(ValueError):
+            manager.get_secret(self.test_secret_id)
+
+    @mock.patch.object(SecretManager, '_secret_exists')
+    @mock.patch.object(SecretManager, '_secret_is_managed')
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_delete_secret_success(self, mock_client, mock_is_managed, mock_exists):
+        """Test delete_secret method success."""
+        # Mock that secret is managed
+        mock_is_managed.return_value = True
+        
+        # Mock that secret doesn't exist after deletion
+        mock_exists.return_value = False
+        
+        manager = SecretManager(self.project_id, self.logger)
+        manager.delete_secret(self.test_secret_id)
+        
+        mock_client.return_value.delete_secret.assert_called_once()
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_delete_secret_not_managed(self, mock_client):
+        """Test delete_secret method when secret is not managed."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": "other"}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        manager = SecretManager(self.project_id, self.logger)
+        
+        # The method should return early without raising exception when secret is not managed
+        manager.delete_secret(self.test_secret_id)
+        
+        # Verify that delete_secret was not called since the secret is not managed
+        mock_client.return_value.delete_secret.assert_not_called()
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_is_different_user_access_same(self, mock_client):
+        """Test is_different_user_access method when access is the same."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        mock_policy = mock.Mock()
+        mock_binding = mock.Mock()
+        mock_binding.role = "roles/secretmanager.secretAccessor"
+        mock_binding.members = ["user:test@example.com", "user:test2@example.com"]
+        mock_policy.bindings = [mock_binding]
+        mock_client.return_value.get_iam_policy.return_value = mock_policy
+        
+        manager = SecretManager(self.project_id, self.logger)
+        is_different = manager.is_different_user_access(
+            self.test_secret_id, 
+            ["test@example.com", "test2@example.com"]
+        )
+        
+        self.assertFalse(is_different)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_is_different_user_access_different(self, mock_client):
+        """Test is_different_user_access method when access is different."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        mock_policy = mock.Mock()
+        mock_binding = mock.Mock()
+        mock_binding.role = "roles/secretmanager.secretAccessor"
+        mock_binding.members = ["user:different@example.com"]
+        mock_policy.bindings = [mock_binding]
+        mock_client.return_value.get_iam_policy.return_value = mock_policy
+        
+        manager = SecretManager(self.project_id, self.logger)
+        is_different = manager.is_different_user_access(
+            self.test_secret_id, 
+            ["test@example.com"]
+        )
+        
+        self.assertTrue(is_different)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_update_secret_access_success(self, mock_client):
+        """Test update_secret_access method success."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        mock_policy = mock.Mock()
+        mock_binding = mock.Mock()
+        mock_binding.role = "roles/secretmanager.secretAccessor"
+        mock_binding.members = ["user:old@example.com"]
+        mock_policy.bindings = [mock_binding]
+        mock_client.return_value.get_iam_policy.return_value = mock_policy
+        
+        manager = SecretManager(self.project_id, self.logger)
+        manager.update_secret_access(self.test_secret_id, ["new@example.com"])
+        
+        mock_client.return_value.set_iam_policy.assert_called_once()
+        self.assertEqual(mock_binding.members, ["user:new@example.com"])
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_get_secret_versions_success(self, mock_client):
+        """Test _get_secret_versions method success."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        mock_versions = [mock.Mock(), mock.Mock()]
+        mock_client.return_value.list_secret_versions.return_value = mock_versions
+        
+        manager = SecretManager(self.project_id, self.logger)
+        versions = manager._get_secret_versions(self.test_secret_id)
+        
+        self.assertEqual(versions, mock_versions)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_secret_version_exists_true(self, mock_client):
+        """Test _secret_version_exists method when version exists."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        mock_version = mock.Mock()
+        mock_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_client.return_value.list_secret_versions.return_value = [mock_version]
+        
+        manager = SecretManager(self.project_id, self.logger)
+        exists = manager._secret_version_exists(self.test_secret_id, "1")
+        
+        self.assertTrue(exists)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_secret_version_exists_false(self, mock_client):
+        """Test _secret_version_exists method when version doesn't exist."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        mock_version = mock.Mock()
+        mock_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/2"
+        mock_client.return_value.list_secret_versions.return_value = [mock_version]
+        
+        manager = SecretManager(self.project_id, self.logger)
+        exists = manager._secret_version_exists(self.test_secret_id, "1")
+        
+        self.assertFalse(exists)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_secret_version_is_enabled_true(self, mock_client):
+        """Test _secret_version_is_enabled method when version is enabled."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        mock_version = mock.Mock()
+        mock_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_version.state = secretmanager.SecretVersion.State.ENABLED
+        mock_client.return_value.list_secret_versions.return_value = [mock_version]
+        
+        manager = SecretManager(self.project_id, self.logger)
+        is_enabled = manager._secret_version_is_enabled(self.test_secret_id, "1")
+        
+        self.assertTrue(is_enabled)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_secret_version_is_enabled_false(self, mock_client):
+        """Test _secret_version_is_enabled method when version is not enabled."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        mock_version = mock.Mock()
+        mock_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_version.state = secretmanager.SecretVersion.State.DISABLED
+        mock_client.return_value.list_secret_versions.return_value = [mock_version]
+        
+        manager = SecretManager(self.project_id, self.logger)
+        is_enabled = manager._secret_version_is_enabled(self.test_secret_id, "1")
+        
+        self.assertFalse(is_enabled)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_get_latest_secret_version_id_success(self, mock_client):
+        """Test _get_latest_secret_version_id method success."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        mock_version1 = mock.Mock()
+        mock_version1.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_version1.state = secretmanager.SecretVersion.State.ENABLED
+        mock_version1.create_time.timestamp.return_value = 1000
+        
+        mock_version2 = mock.Mock()
+        mock_version2.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/2"
+        mock_version2.state = secretmanager.SecretVersion.State.ENABLED
+        mock_version2.create_time.timestamp.return_value = 2000
+        
+        # Return versions in reverse order (latest first) as Google API does
+        mock_client.return_value.list_secret_versions.return_value = [mock_version2, mock_version1]
+        
+        manager = SecretManager(self.project_id, self.logger)
+        latest_id = manager._get_latest_secret_version_id(self.test_secret_id)
+        
+        self.assertEqual(latest_id, "2")
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_get_latest_secret_version_id_no_enabled(self, mock_client):
+        """Test _get_latest_secret_version_id method when no enabled versions."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        mock_version = mock.Mock()
+        mock_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_version.state = secretmanager.SecretVersion.State.DISABLED
+        mock_client.return_value.list_secret_versions.return_value = [mock_version]
+        
+        manager = SecretManager(self.project_id, self.logger)
+        
+        with self.assertRaises(ValueError):
+            manager._get_latest_secret_version_id(self.test_secret_id)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_is_key_rotation_due_true(self, mock_client):
+        """Test _is_key_rotation_due method when rotation is due."""
+        past_date = datetime.now(timezone.utc) - timedelta(days=40)
+        mock_secret = mock.Mock()
+        mock_secret.labels = {
+            "created_by": SECRET_MANAGER_LABEL,
+            "last_version_created_at": past_date.strftime("%Y%m%d_%H%M%S")
+        }
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        manager = SecretManager(self.project_id, self.logger, rotation_interval=30)
+        is_due = manager._is_key_rotation_due(self.test_secret_id)
+        
+        self.assertTrue(is_due)
+
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_is_key_rotation_due_false(self, mock_client):
+        """Test _is_key_rotation_due method when rotation is not due."""
+        recent_date = datetime.now(timezone.utc) - timedelta(days=10)
+        mock_secret = mock.Mock()
+        mock_secret.labels = {
+            "created_by": SECRET_MANAGER_LABEL,
+            "last_version_created_at": recent_date.strftime("%Y%m%d_%H%M%S")
+        }
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        manager = SecretManager(self.project_id, self.logger, rotation_interval=30)
+        is_due = manager._is_key_rotation_due(self.test_secret_id)
+        
+        self.assertFalse(is_due)
+
+    @mock.patch('time.sleep')
+    @mock.patch('google_crc32c.Checksum')
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_add_secret_version_success(self, mock_client, mock_checksum, mock_sleep):
+        """Test add_secret_version method success."""
+        # Mock checksum
+        mock_checksum_instance = mock.Mock()
+        mock_checksum_instance.hexdigest.return_value = "abcd1234"
+        mock_checksum.return_value = mock_checksum_instance
+        
+        # Mock create_secret behavior - secret already exists
+        mock_secret = mock.Mock()
+        mock_secret.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}"
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        # Mock add_secret_version
+        mock_response = mock.Mock()
+        mock_response.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_client.return_value.add_secret_version.return_value = mock_response
+        
+        # Mock list_secret_versions for waiting and disabling
+        mock_version = mock.Mock()
+        mock_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_version.state = secretmanager.SecretVersion.State.ENABLED
+        mock_client.return_value.list_secret_versions.return_value = [mock_version]
+        
+        manager = SecretManager(self.project_id, self.logger)
+        result = manager.add_secret_version(self.test_secret_id, self.test_data_id, self.test_payload)
+        
+        self.assertEqual(result, mock_response.name)
+        mock_client.return_value.add_secret_version.assert_called_once()
+        mock_client.return_value.update_secret.assert_called_once()
+
+    @mock.patch('google_crc32c.Checksum')
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_get_latest_secret_version_success(self, mock_client, mock_checksum):
+        """Test get_latest_secret_version method success."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        # Mock latest version
+        mock_version = mock.Mock()
+        mock_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_version.state = secretmanager.SecretVersion.State.ENABLED
+        mock_version.create_time.timestamp.return_value = 1000
+        mock_client.return_value.list_secret_versions.return_value = [mock_version]
+        
+        # Mock access_secret_version
+        mock_response = mock.Mock()
+        mock_response.payload.data = b"test-data:test-payload"
+        mock_response.payload.data_crc32c = int("abcd1234", 16)
+        mock_client.return_value.access_secret_version.return_value = mock_response
+        
+        # Mock checksum
+        mock_checksum_instance = mock.Mock()
+        mock_checksum_instance.hexdigest.return_value = "abcd1234"
+        mock_checksum.return_value = mock_checksum_instance
+        
+        manager = SecretManager(self.project_id, self.logger)
+        data_id, payload = manager.get_latest_secret_version(self.test_secret_id)
+        
+        self.assertEqual(data_id, "test-data")
+        self.assertEqual(payload, b"test-payload")
+
+    @mock.patch('time.sleep')
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_enable_secret_version_success(self, mock_client, mock_sleep):
+        """Test enable_secret_version method success."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        # Mock version exists and is not enabled initially
+        mock_disabled_version = mock.Mock()
+        mock_disabled_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_disabled_version.state = secretmanager.SecretVersion.State.DISABLED
+        
+        # Mock version becomes enabled after the operation
+        mock_enabled_version = mock.Mock()
+        mock_enabled_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_enabled_version.state = secretmanager.SecretVersion.State.ENABLED
+        
+        # First call returns disabled version, second call returns enabled version
+        mock_client.return_value.list_secret_versions.side_effect = [
+            [mock_disabled_version],  # Initial check
+            [mock_enabled_version]    # After enabling
+        ]
+        
+        # Mock enable response
+        mock_response = mock.Mock()
+        mock_response.name = mock_disabled_version.name
+        mock_response.state = secretmanager.SecretVersion.State.ENABLED
+        mock_client.return_value.enable_secret_version.return_value = mock_response
+        
+        manager = SecretManager(self.project_id, self.logger)
+        manager.enable_secret_version(self.test_secret_id, "1")
+        
+        mock_client.return_value.enable_secret_version.assert_called_once()
+
+    @mock.patch('time.sleep')
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_disable_secret_version_success(self, mock_client, mock_sleep):
+        """Test disable_secret_version method success."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        # Mock version exists and is enabled initially
+        mock_enabled_version = mock.Mock()
+        mock_enabled_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_enabled_version.state = secretmanager.SecretVersion.State.ENABLED
+        
+        # Mock version becomes disabled after the operation
+        mock_disabled_version = mock.Mock()
+        mock_disabled_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_disabled_version.state = secretmanager.SecretVersion.State.DISABLED
+        
+        # First call returns enabled version, second call returns disabled version
+        mock_client.return_value.list_secret_versions.side_effect = [
+            [mock_enabled_version],   # Initial check
+            [mock_disabled_version]   # After disabling
+        ]
+        
+        # Mock disable response
+        mock_response = mock.Mock()
+        mock_response.name = mock_enabled_version.name
+        mock_response.state = secretmanager.SecretVersion.State.DISABLED
+        mock_client.return_value.disable_secret_version.return_value = mock_response
+        
+        manager = SecretManager(self.project_id, self.logger)
+        manager.disable_secret_version(self.test_secret_id, "1")
+        
+        mock_client.return_value.disable_secret_version.assert_called_once()
+
+    @mock.patch('time.sleep')
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_destroy_secret_version_success(self, mock_client, mock_sleep):
+        """Test destroy_secret_version method success."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        # Mock version exists and is enabled initially
+        mock_enabled_version = mock.Mock()
+        mock_enabled_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_enabled_version.state = secretmanager.SecretVersion.State.ENABLED
+        
+        # Mock version becomes destroyed after the operation
+        mock_destroyed_version = mock.Mock()
+        mock_destroyed_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_destroyed_version.state = secretmanager.SecretVersion.State.DESTROYED
+        
+        # Multiple calls to list_secret_versions for different operations
+        mock_client.return_value.list_secret_versions.side_effect = [
+            [mock_enabled_version],   # Initial check in _secret_version_is_enabled
+            [mock_enabled_version],   # Check in enable_secret_version before enabling
+            [mock_enabled_version],   # After enabling check
+            [mock_destroyed_version]  # After destroying check
+        ]
+        
+        # Mock access_secret_version for getting data_id
+        mock_access_response = mock.Mock()
+        mock_access_response.payload.data = b"test-data:test-payload"
+        mock_client.return_value.access_secret_version.return_value = mock_access_response
+        
+        # Mock destroy response
+        mock_destroy_response = mock.Mock()
+        mock_destroy_response.name = mock_enabled_version.name
+        mock_destroy_response.state = secretmanager.SecretVersion.State.DESTROYED
+        mock_client.return_value.destroy_secret_version.return_value = mock_destroy_response
+        
+        # Mock enable response (needed since version is already enabled)
+        mock_enable_response = mock.Mock()
+        mock_enable_response.name = mock_enabled_version.name
+        mock_enable_response.state = secretmanager.SecretVersion.State.ENABLED
+        mock_client.return_value.enable_secret_version.return_value = mock_enable_response
+        
+        manager = SecretManager(self.project_id, self.logger)
+        data_id = manager.destroy_secret_version(self.test_secret_id, "1")
+        
+        self.assertEqual(data_id, "test-data")
+        mock_client.return_value.destroy_secret_version.assert_called_once()
+
+    @mock.patch.object(SecretManager, 'destroy_secret_version')
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_purge_disabled_secret_versions_success(self, mock_client, mock_destroy):
+        """Test purge_disabled_secret_versions method success."""
+        mock_secret = mock.Mock()
+        mock_secret.labels = {"created_by": SECRET_MANAGER_LABEL}
+        mock_client.return_value.get_secret.return_value = mock_secret
+        
+        # Mock old disabled version
+        old_time = datetime.now(timezone.utc) - timedelta(days=10)
+        mock_old_version = mock.Mock()
+        mock_old_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/1"
+        mock_old_version.state = secretmanager.SecretVersion.State.DISABLED
+        mock_old_version.create_time.timestamp.return_value = old_time.timestamp()
+        
+        # Mock recent disabled version (within grace period)
+        recent_time = datetime.now(timezone.utc) - timedelta(days=2)
+        mock_recent_version = mock.Mock()
+        mock_recent_version.name = f"projects/{self.project_id}/secrets/{self.test_secret_id}/versions/2"
+        mock_recent_version.state = secretmanager.SecretVersion.State.DISABLED
+        mock_recent_version.create_time.timestamp.return_value = recent_time.timestamp()
+        
+        mock_client.return_value.list_secret_versions.return_value = [mock_old_version, mock_recent_version]
+        
+        # Mock destroy method to return data_id
+        mock_destroy.return_value = "old-data"
+        
+        manager = SecretManager(self.project_id, self.logger, grace_period=7)
+        data_ids = manager.purge_disabled_secret_versions(self.test_secret_id)
+        
+        self.assertEqual(data_ids, ["old-data"])
+        mock_destroy.assert_called_once_with(self.test_secret_id, "1")
+
+    @mock.patch.object(SecretManager, 'purge_disabled_secret_versions')
+    @mock.patch('secret_manager.secretmanager.SecretManagerServiceClient')
+    def test_cron_success(self, mock_client, mock_purge):
+        """Test cron method success."""
+        # Mock _get_secret_ids
+        mock_secret1 = mock.Mock()
+        mock_secret1.name = f"projects/{self.project_id}/secrets/secret1"
+        mock_secret1.labels = {"created_by": SECRET_MANAGER_LABEL}
+        
+        mock_secret2 = mock.Mock()
+        mock_secret2.name = f"projects/{self.project_id}/secrets/secret2"
+        mock_secret2.labels = {"created_by": SECRET_MANAGER_LABEL}
+        
+        mock_client.return_value.list_secrets.return_value = [mock_secret1, mock_secret2]
+        
+        # Mock purge_disabled_secret_versions behavior
+        def mock_purge_side_effect(secret_id):
+            if secret_id == "secret1":
+                return ["purged-data"]
+            else:
+                return []  # secret2 has no versions to purge
+        
+        mock_purge.side_effect = mock_purge_side_effect
+        
+        manager = SecretManager(self.project_id, self.logger, grace_period=7)
+        result = manager.cron()
+        
+        self.assertIn("secret1", result)
+        self.assertEqual(result["secret1"], ["purged-data"])
+        # secret2 should not be in result since it had no purged versions
+        self.assertNotIn("secret2", result)
+
+
+
+
+# Integration tests (skipped unless environment variables are set)
+@unittest.skipUnless(
+    'GOOGLE_CLOUD_PROJECT' in os.environ,
+    "Skipping tests because environment variables are not set for Google Cloud project."
+)
+class TestSecretManagerIntegration(unittest.TestCase):
+    """Integration tests for SecretManager with real Google Cloud Secret Manager client."""
+
+    def setUp(self):
+        """Set up test fixtures."""
+        self.project_id = os.environ['GOOGLE_CLOUD_PROJECT']
+        # Create a logger for integration tests
+        self.logger = logging.getLogger(__name__)
+        self.manager = SecretManager(self.project_id, self.logger, rotation_interval=0, grace_period=0, max_retries=3)
+        self.test_secret_id = f"integration-test-secret-{int(time.time())}"
+        self.test_data_id = f"integration-test-data-{int(time.time())}"
+        self.test_payload = b"integration-test-payload"
+        self.test_allowed_users = ["pabloem@google.com"]
+
+    def tearDown(self):
+        """Tear down test fixtures."""
+        # Clean up any secrets created during tests
+        try:
+            if self.test_secret_id in self.manager._get_secret_ids():
+                self.manager.delete_secret(self.test_secret_id)
+        except Exception as e:
+            self.logger.warning(f"Failed to clean up test secret: {e}")
+
+    def test_full_secret_lifecycle(self):
+        """Test creating, adding versions, rotating, and deleting a secret."""
+        # Test creating a secret
+        self.manager.create_secret(self.test_secret_id)
+        self.assertTrue(self.manager._secret_exists(self.test_secret_id))
+
+        # Test allowing users to access the secret
+        self.manager.update_secret_access(self.test_secret_id, self.test_allowed_users)
+        self.assertFalse(self.manager.is_different_user_access(self.test_secret_id, self.test_allowed_users))
+
+        # Add first version (creates the secret)
+        version1 = self.manager.add_secret_version(self.test_secret_id, self.test_data_id, self.test_payload)
+        self.assertIsNotNone(version1)
+        
+        # Verify secret exists
+        secret = self.manager.get_secret(self.test_secret_id)
+        self.assertEqual(secret.labels["created_by"], SECRET_MANAGER_LABEL)
+        
+        # Add second version
+        version2 = self.manager.add_secret_version(self.test_secret_id, f"{self.test_data_id}-v2", b"second-payload")
+        self.assertIsNotNone(version2)
+        
+        # List versions
+        versions = self.manager._get_secret_versions(self.test_secret_id)
+        self.assertGreaterEqual(len(versions), 2)
+        
+        # Get latest version
+        retrieved_payload = self.manager.get_latest_secret_version(self.test_secret_id)
+        self.assertEqual(retrieved_payload, (f"{self.test_data_id}-v2", b"second-payload"))
+        
+        # Rotate secret
+        latest_version = self.manager.add_secret_version(self.test_secret_id, f"{self.test_data_id}-rotated", b"rotated-payload")
+
+        # Verify latest version has rotated payload
+        latest_payload = self.manager.get_latest_secret_version(self.test_secret_id)
+        self.assertEqual(latest_payload, (f"{self.test_data_id}-rotated", b"rotated-payload"))
+
+        # Verify all the other versions are disabled
+        versions = self.manager._get_secret_versions(self.test_secret_id)
+        for version in versions:
+            if version.name != latest_version:
+                self.assertEqual(version.state, secretmanager.SecretVersion.State.DISABLED)
+
+        # Try cron method (should be no-op since grace period is 0)
+        cron_result = self.manager.cron()
+        self.assertIn(self.test_secret_id, cron_result)
+        self.assertEqual(len(cron_result[self.test_secret_id]), len(versions) - 1)  # All but the latest should be purged
+        self.assertNotIn(f"{self.test_data_id}-rotated", cron_result[self.test_secret_id]) # Latest id should not be purged
+
+        # Try to get the latest version after cron
+        latest_payload_after_cron = self.manager.get_latest_secret_version(self.test_secret_id)
+        self.assertEqual(latest_payload_after_cron, (f"{self.test_data_id}-rotated", b"rotated-payload"))
+
+        # Delete secret
+        self.manager.delete_secret(self.test_secret_id)
+        
+        # Verify secret is removed from secret_ids
+        self.assertNotIn(self.test_secret_id, self.manager._get_secret_ids())
+
+if __name__ == '__main__':
+    # Configure logging to reduce noise during testing
+    logging.getLogger('google.cloud').setLevel(logging.WARNING)
+    logging.getLogger('google.auth').setLevel(logging.WARNING)
+    
+    # Run the tests
+    unittest.main()
diff --git a/infra/keys/test_service_account.py b/infra/keys/test_service_account.py
new file mode 100644
index 000000000000..370216653267
--- /dev/null
+++ b/infra/keys/test_service_account.py
@@ -0,0 +1,601 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import logging
+import unittest
+import time
+from unittest import mock
+from service_account import ServiceAccountManager
+from google.cloud.iam_admin_v1 import types
+from google.api_core import exceptions
+
+class TestServiceAccountManagerUnit(unittest.TestCase):
+    """Unit tests for ServiceAccountManager with mocked Google Cloud IAM client."""
+
+    def setUp(self):
+        """Set up test fixtures."""
+        self.project_id = "test-project-123"
+        self.test_account_id = "test-service-account"
+        self.test_display_name = "Test Service Account"
+        
+        # Patch the IAM client
+        self.iam_client_patcher = mock.patch('service_account.iam_admin_v1.IAMClient')
+        self.mock_iam_client_class = self.iam_client_patcher.start()
+        self.mock_iam_client = self.mock_iam_client_class.return_value
+        
+        # Create a mock logger
+        self.mock_logger = mock.MagicMock()
+        
+        # Create the service account manager
+        self.manager = ServiceAccountManager(self.project_id, self.mock_logger)
+
+    def tearDown(self):
+        """Tear down test fixtures."""
+        self.iam_client_patcher.stop()
+
+    def _create_mock_service_account(self, account_id: str, disabled: bool = False) -> types.ServiceAccount:
+        """Helper method to create a mock service account."""
+        mock_account = types.ServiceAccount()
+        mock_account.name = f"projects/{self.project_id}/serviceAccounts/{account_id}@{self.project_id}.iam.gserviceaccount.com"
+        mock_account.email = f"{account_id}@{self.project_id}.iam.gserviceaccount.com"
+        mock_account.display_name = account_id
+        mock_account.disabled = disabled
+        mock_account.project_id = self.project_id
+        mock_account.unique_id = f"123456789{account_id}"
+        return mock_account
+
+    def _create_mock_service_account_key(self, account_id: str, key_id: str = "test-key-id") -> types.ServiceAccountKey:
+        """Helper method to create a mock service account key."""
+        mock_key = types.ServiceAccountKey()
+        mock_key.name = f"projects/{self.project_id}/serviceAccounts/{account_id}@{self.project_id}.iam.gserviceaccount.com/keys/{key_id}"
+        mock_key.private_key_data = b'{"type": "service_account", "project_id": "test-project"}'
+        return mock_key
+
+    def test_init(self):
+        """Test ServiceAccountManager initialization."""
+        self.assertEqual(self.manager.project_id, self.project_id)
+        self.mock_iam_client_class.assert_called_once()
+
+    def test_create_service_account_success(self):
+        """Test successful service account creation."""
+        expected_account = self._create_mock_service_account(self.test_account_id)
+        self.mock_iam_client.create_service_account.return_value = expected_account
+
+        with mock.patch.object(self.manager, '_service_account_exists', return_value=True):
+            result = self.manager.create_service_account(self.test_account_id, self.test_display_name)
+
+        self.assertEqual(result, expected_account)
+        self.mock_iam_client.create_service_account.assert_called_once()
+
+        # Verify the request structure
+        call_args = self.mock_iam_client.create_service_account.call_args
+        request = call_args[1]['request']
+        self.assertEqual(request.account_id, self.test_account_id)
+        self.assertEqual(request.name, f"projects/{self.project_id}")
+        self.assertEqual(request.service_account.display_name, self.test_display_name)
+
+    def test_create_service_account_already_exists(self):
+        """Test service account creation when account already exists."""
+        existing_account = self._create_mock_service_account(self.test_account_id)
+        
+        # Mock the conflict exception and then successful get
+        self.mock_iam_client.create_service_account.side_effect = exceptions.Conflict("Account already exists")
+        self.mock_iam_client.get_service_account.return_value = existing_account
+
+        result = self.manager.create_service_account(self.test_account_id, self.test_display_name)
+
+        self.assertEqual(result, existing_account)
+        self.mock_iam_client.create_service_account.assert_called_once()
+        self.mock_iam_client.get_service_account.assert_called_once()
+
+    def test_enable_service_account(self):
+        """Test enabling a service account."""
+        enabled_account = self._create_mock_service_account(self.test_account_id, disabled=False)
+        
+        with mock.patch.object(self.manager, '_service_account_is_enabled', return_value=True):
+            self.manager.enable_service_account(self.test_account_id)
+
+        self.mock_iam_client.enable_service_account.assert_called_once()
+        
+        # Verify the request structure
+        call_args = self.mock_iam_client.enable_service_account.call_args
+        request = call_args[1]['request']
+        expected_name = f"projects/{self.project_id}/serviceAccounts/{self.test_account_id}@{self.project_id}.iam.gserviceaccount.com"
+        self.assertEqual(request.name, expected_name)
+
+    def test_disable_service_account(self):
+        """Test disabling a service account."""
+        disabled_account = self._create_mock_service_account(self.test_account_id, disabled=True)
+        
+        with mock.patch.object(self.manager, '_service_account_is_enabled', return_value=False):
+            self.manager.disable_service_account(self.test_account_id)
+
+        self.mock_iam_client.disable_service_account.assert_called_once()
+        
+        # Verify the request structure
+        call_args = self.mock_iam_client.disable_service_account.call_args
+        request = call_args[1]['request']
+        expected_name = f"projects/{self.project_id}/serviceAccounts/{self.test_account_id}@{self.project_id}.iam.gserviceaccount.com"
+        self.assertEqual(request.name, expected_name)
+
+    def test_delete_service_account(self):
+        """Test deleting a service account."""
+        with mock.patch.object(self.manager, '_service_account_exists', return_value=False):
+            self.manager.delete_service_account(self.test_account_id)
+
+        self.mock_iam_client.delete_service_account.assert_called_once()
+
+        # Verify the request structure
+        call_args = self.mock_iam_client.delete_service_account.call_args
+        request = call_args[1]['request']
+        expected_name = f"projects/{self.project_id}/serviceAccounts/{self.test_account_id}@{self.project_id}.iam.gserviceaccount.com"
+        self.assertEqual(request.name, expected_name)
+
+    def test_list_service_accounts(self):
+        """Test listing all service accounts in the project."""
+        mock_accounts = [
+            self._create_mock_service_account("account1"),
+            self._create_mock_service_account("account2", disabled=True),
+            self._create_mock_service_account("account3"),
+        ]
+        
+        mock_response = mock.MagicMock()
+        mock_response.accounts = mock_accounts
+        # Make the mock response iterable so list(accounts) works
+        mock_response.__iter__ = lambda self: iter(mock_accounts)
+        self.mock_iam_client.list_service_accounts.return_value = mock_response
+
+        result = self.manager._get_service_accounts()
+
+        self.assertEqual(result, mock_accounts)
+        self.mock_iam_client.list_service_accounts.assert_called_once()
+
+        # Verify the request structure
+        call_args = self.mock_iam_client.list_service_accounts.call_args
+        request = call_args[1]['request']
+        self.assertEqual(request.name, f"projects/{self.project_id}")
+
+    def test_create_service_account_key_enabled_account(self):
+        """Test creating a key for an enabled service account."""
+        enabled_account = self._create_mock_service_account(self.test_account_id, disabled=False)
+        mock_key = self._create_mock_service_account_key(self.test_account_id)
+        
+        self.mock_iam_client.get_service_account.return_value = enabled_account
+        self.mock_iam_client.create_service_account_key.return_value = mock_key
+        
+        with mock.patch.object(self.manager, '_service_account_key_exists', return_value=True):
+            result = self.manager.create_service_account_key(self.test_account_id)
+
+        self.assertEqual(result, mock_key)
+        self.mock_iam_client.get_service_account.assert_called_once()
+        self.mock_iam_client.create_service_account_key.assert_called_once()
+
+    def test_create_service_account_key_disabled_account(self):
+        """Test creating a key for a disabled service account."""
+        disabled_account = self._create_mock_service_account(self.test_account_id, disabled=True)
+        enabled_account = self._create_mock_service_account(self.test_account_id, disabled=False)
+        mock_key = self._create_mock_service_account_key(self.test_account_id)
+        
+        # First call returns disabled account, then we mock the enable flow
+        self.mock_iam_client.get_service_account.return_value = disabled_account
+        self.mock_iam_client.create_service_account_key.return_value = mock_key
+
+        with mock.patch.object(self.manager, '_service_account_is_enabled', return_value=True), \
+             mock.patch.object(self.manager, '_service_account_key_exists', return_value=True):
+            result = self.manager.create_service_account_key(self.test_account_id)
+
+        self.assertEqual(result, mock_key)
+        # Should call get_service_account once to check if it's disabled
+        self.mock_iam_client.get_service_account.assert_called_once()
+        self.mock_iam_client.enable_service_account.assert_called_once()
+        self.mock_iam_client.create_service_account_key.assert_called_once()
+
+    def test_create_service_account_key_not_found(self):
+        """Test creating a key for a non-existent service account."""
+        self.mock_iam_client.get_service_account.side_effect = exceptions.NotFound("Account not found")
+
+        with self.assertRaises(exceptions.NotFound):
+            self.manager.create_service_account_key(self.test_account_id)
+
+    def test_delete_service_account_key(self):
+        """Test deleting a service account key."""
+        key_id = "test-key-id"
+        
+        with mock.patch.object(self.manager, '_service_account_key_exists', return_value=False):
+            self.manager.delete_service_account_key(self.test_account_id, key_id)
+
+        self.mock_iam_client.delete_service_account_key.assert_called_once()
+
+    def test_list_service_account_keys(self):
+        """Test listing service account keys."""
+        mock_keys = [
+            self._create_mock_service_account_key(self.test_account_id, "key1"),
+            self._create_mock_service_account_key(self.test_account_id, "key2"),
+        ]
+        
+        mock_response = mock.MagicMock()
+        mock_response.keys = mock_keys
+        self.mock_iam_client.list_service_account_keys.return_value = mock_response
+
+        result = self.manager._get_service_account_keys(self.test_account_id)
+
+        self.assertEqual(result, mock_keys)
+        self.mock_iam_client.list_service_account_keys.assert_called_once()
+
+    @mock.patch('service_account.service_account.Credentials.from_service_account_info')
+    @mock.patch('service_account.Request')
+    def test_test_service_account_key_valid(self, mock_request_class, mock_credentials_class):
+        """Test testing a valid service account key."""
+        mock_credentials = mock.MagicMock()
+        mock_credentials_class.return_value = mock_credentials
+        
+        key_data = b'{"type": "service_account", "project_id": "test-project"}'
+        
+        result = self.manager.test_service_account_key(key_data)
+
+        self.assertTrue(result)
+        mock_credentials_class.assert_called_once()
+        mock_credentials.refresh.assert_called_once()
+
+    @mock.patch('service_account.service_account.Credentials.from_service_account_info')
+    def test_test_service_account_key_invalid_json(self, mock_credentials_class):
+        """Test testing an invalid JSON service account key."""
+        key_data = b'invalid json'
+        
+        result = self.manager.test_service_account_key(key_data)
+
+        self.assertFalse(result)
+        mock_credentials_class.assert_not_called()
+
+    @mock.patch('service_account.service_account.Credentials.from_service_account_info')
+    def test_test_service_account_key_auth_error(self, mock_credentials_class):
+        """Test testing a service account key with authentication error."""
+        mock_credentials = mock.MagicMock()
+        mock_credentials.refresh.side_effect = Exception("Authentication failed")
+        mock_credentials_class.return_value = mock_credentials
+        
+        key_data = b'{"type": "service_account", "project_id": "test-project"}'
+        
+        result = self.manager.test_service_account_key(key_data)
+
+        self.assertFalse(result)
+
+    def test_normalize_account_email_with_email(self):
+        """Test normalizing account email when input is already a full email."""
+        full_email = f"{self.test_account_id}@{self.project_id}.iam.gserviceaccount.com"
+        result = self.manager._normalize_account_email(full_email)
+        self.assertEqual(result, full_email)
+
+    def test_normalize_account_email_with_id(self):
+        """Test normalizing account email when input is just the account ID."""
+        result = self.manager._normalize_account_email(self.test_account_id)
+        expected_email = f"{self.test_account_id}@{self.project_id}.iam.gserviceaccount.com"
+        self.assertEqual(result, expected_email)
+
+    def test_service_account_exists_true(self):
+        """Test _service_account_exists when service account exists."""
+        mock_account = self._create_mock_service_account(self.test_account_id)
+        self.mock_iam_client.get_service_account.return_value = mock_account
+        
+        result = self.manager._service_account_exists(self.test_account_id)
+        
+        self.assertTrue(result)
+        self.mock_iam_client.get_service_account.assert_called_once()
+
+    def test_service_account_exists_false(self):
+        """Test _service_account_exists when service account does not exist."""
+        self.mock_iam_client.get_service_account.side_effect = exceptions.NotFound("Not found")
+        
+        result = self.manager._service_account_exists(self.test_account_id)
+        
+        self.assertFalse(result)
+        self.mock_iam_client.get_service_account.assert_called_once()
+
+    def test_service_account_is_enabled_true(self):
+        """Test _service_account_is_enabled when service account is enabled."""
+        mock_account = self._create_mock_service_account(self.test_account_id, disabled=False)
+        self.mock_iam_client.get_service_account.return_value = mock_account
+        
+        result = self.manager._service_account_is_enabled(self.test_account_id)
+        
+        self.assertTrue(result)
+        self.mock_iam_client.get_service_account.assert_called_once()
+
+    def test_service_account_is_enabled_false(self):
+        """Test _service_account_is_enabled when service account is disabled."""
+        mock_account = self._create_mock_service_account(self.test_account_id, disabled=True)
+        self.mock_iam_client.get_service_account.return_value = mock_account
+        
+        result = self.manager._service_account_is_enabled(self.test_account_id)
+        
+        self.assertFalse(result)
+        self.mock_iam_client.get_service_account.assert_called_once()
+
+    def test_service_account_is_enabled_not_found(self):
+        """Test _service_account_is_enabled when service account does not exist."""
+        self.mock_iam_client.get_service_account.side_effect = exceptions.NotFound("Not found")
+        
+        result = self.manager._service_account_is_enabled(self.test_account_id)
+        
+        self.assertFalse(result)
+        self.mock_iam_client.get_service_account.assert_called_once()
+
+    def test_get_service_account_success(self):
+        """Test successful retrieval of a service account."""
+        mock_account = self._create_mock_service_account(self.test_account_id)
+        self.mock_iam_client.get_service_account.return_value = mock_account
+        
+        result = self.manager.get_service_account(self.test_account_id)
+        
+        self.assertEqual(result, mock_account)
+        self.mock_iam_client.get_service_account.assert_called_once()
+
+    def test_get_service_account_not_found(self):
+        """Test retrieval of a non-existent service account."""
+        self.mock_iam_client.get_service_account.side_effect = exceptions.NotFound("Not found")
+        
+        with self.assertRaises(exceptions.NotFound):
+            self.manager.get_service_account(self.test_account_id)
+        
+        self.mock_iam_client.get_service_account.assert_called_once()
+
+    def test_service_account_key_exists_true(self):
+        """Test _service_account_key_exists when key exists."""
+        key_id = "test-key-id"
+        mock_key = self._create_mock_service_account_key(self.test_account_id, key_id)
+        mock_response = mock.MagicMock()
+        mock_response.keys = [mock_key]
+        self.mock_iam_client.list_service_account_keys.return_value = mock_response
+        
+        result = self.manager._service_account_key_exists(self.test_account_id, key_id)
+        
+        self.assertTrue(result)
+        self.mock_iam_client.list_service_account_keys.assert_called_once()
+
+    def test_service_account_key_exists_false(self):
+        """Test _service_account_key_exists when key does not exist."""
+        key_id = "test-key-id"
+        other_key = self._create_mock_service_account_key(self.test_account_id, "other-key-id")
+        mock_response = mock.MagicMock()
+        mock_response.keys = [other_key]
+        self.mock_iam_client.list_service_account_keys.return_value = mock_response
+        
+        result = self.manager._service_account_key_exists(self.test_account_id, key_id)
+        
+        self.assertFalse(result)
+        self.mock_iam_client.list_service_account_keys.assert_called_once()
+
+    def test_delete_service_account_key_not_found(self):
+        """Test deleting a non-existent service account key."""
+        key_id = "non-existent-key"
+        self.mock_iam_client.delete_service_account_key.side_effect = exceptions.NotFound("Key not found")
+        
+        with self.assertRaises(exceptions.NotFound):
+            self.manager.delete_service_account_key(self.test_account_id, key_id)
+        
+        self.mock_iam_client.delete_service_account_key.assert_called_once()
+
+    def test_delete_service_account_key_failed_precondition(self):
+        """Test deleting a service account key with failed precondition."""
+        key_id = "test-key-id"
+        self.mock_iam_client.delete_service_account_key.side_effect = exceptions.FailedPrecondition("Cannot delete")
+        
+        with self.assertRaises(exceptions.FailedPrecondition):
+            self.manager.delete_service_account_key(self.test_account_id, key_id)
+        
+        self.mock_iam_client.delete_service_account_key.assert_called_once()
+
+    def test_delete_service_account_key_unexpected_error(self):
+        """Test deleting a service account key with unexpected error."""
+        key_id = "test-key-id"
+        self.mock_iam_client.delete_service_account_key.side_effect = Exception("Unexpected error")
+        
+        with self.assertRaises(Exception):
+            self.manager.delete_service_account_key(self.test_account_id, key_id)
+        
+        self.mock_iam_client.delete_service_account_key.assert_called_once()
+
+    @mock.patch('service_account.time.sleep')
+    def test_test_service_account_key_retry_success(self, mock_sleep):
+        """Test service account key testing with retry logic success."""
+        mock_credentials = mock.MagicMock()
+        
+        # First attempt fails, second succeeds
+        mock_credentials.refresh.side_effect = [Exception("Auth failed"), None]
+        
+        with mock.patch('service_account.service_account.Credentials.from_service_account_info', return_value=mock_credentials):
+            key_data = b'{"type": "service_account", "project_id": "test-project"}'
+            result = self.manager.test_service_account_key(key_data)
+        
+        self.assertTrue(result)
+        self.assertEqual(mock_credentials.refresh.call_count, 2)
+        mock_sleep.assert_called_once_with(2)  # delay is doubled before sleep (1 * 2 = 2)
+
+    @mock.patch('service_account.time.sleep')
+    def test_test_service_account_key_retry_exhausted(self, mock_sleep):
+        """Test service account key testing when all retries are exhausted."""
+        mock_credentials = mock.MagicMock()
+        mock_credentials.refresh.side_effect = Exception("Auth failed")
+        
+        with mock.patch('service_account.service_account.Credentials.from_service_account_info', return_value=mock_credentials):
+            key_data = b'{"type": "service_account", "project_id": "test-project"}'
+            result = self.manager.test_service_account_key(key_data)
+        
+        self.assertFalse(result)
+        self.assertEqual(mock_credentials.refresh.call_count, 3)  # max_retries
+        # Sleep is called with 2, then 4 (delay is doubled each time)
+        self.assertEqual(mock_sleep.call_count, 2)  # 2 retry delays
+        mock_sleep.assert_any_call(2)  # First retry delay (1 * 2)
+        mock_sleep.assert_any_call(4)  # Second retry delay (2 * 2)
+
+    def test_create_service_account_timeout(self):
+        """Test service account creation timeout scenario."""
+        expected_account = self._create_mock_service_account(self.test_account_id)
+        self.mock_iam_client.create_service_account.return_value = expected_account
+
+        # Mock the helper method to always return False (service account never exists)
+        with mock.patch.object(self.manager, '_service_account_exists', return_value=False):
+            with self.assertRaises(exceptions.DeadlineExceeded):
+                self.manager.create_service_account(self.test_account_id, self.test_display_name)
+
+    def test_enable_service_account_timeout(self):
+        """Test service account enabling timeout scenario."""
+        # Mock the helper method to always return False (service account never gets enabled)
+        with mock.patch.object(self.manager, '_service_account_is_enabled', return_value=False):
+            with self.assertRaises(exceptions.DeadlineExceeded):
+                self.manager.enable_service_account(self.test_account_id)
+
+    def test_disable_service_account_timeout(self):
+        """Test service account disabling timeout scenario."""
+        # Mock the helper method to always return True (service account never gets disabled)
+        with mock.patch.object(self.manager, '_service_account_is_enabled', return_value=True):
+            with self.assertRaises(exceptions.DeadlineExceeded):
+                self.manager.disable_service_account(self.test_account_id)
+
+    def test_delete_service_account_timeout(self):
+        """Test service account deletion timeout scenario."""
+        # Mock the helper method to always return True (service account never gets deleted)
+        with mock.patch.object(self.manager, '_service_account_exists', return_value=True):
+            with self.assertRaises(exceptions.DeadlineExceeded):
+                self.manager.delete_service_account(self.test_account_id)
+
+    def test_create_service_account_key_timeout(self):
+        """Test service account key creation timeout scenario."""
+        enabled_account = self._create_mock_service_account(self.test_account_id, disabled=False)
+        mock_key = self._create_mock_service_account_key(self.test_account_id)
+        
+        self.mock_iam_client.get_service_account.return_value = enabled_account
+        self.mock_iam_client.create_service_account_key.return_value = mock_key
+        
+        # Mock the helper method to always return False (key never gets created)
+        with mock.patch.object(self.manager, '_service_account_key_exists', return_value=False):
+            with self.assertRaises(exceptions.DeadlineExceeded):
+                self.manager.create_service_account_key(self.test_account_id)
+
+    def test_delete_service_account_key_timeout(self):
+        """Test service account key deletion timeout scenario."""
+        key_id = "test-key-id"
+        
+        # Mock the helper method to always return True (key never gets deleted)
+        with mock.patch.object(self.manager, '_service_account_key_exists', return_value=True):
+            with self.assertRaises(exceptions.DeadlineExceeded):
+                self.manager.delete_service_account_key(self.test_account_id, key_id)
+
+# Run these real tests just if the environment variables are set correctly
+# export GOOGLE_CLOUD_PROJECT = "your-project-id"
+
+# Verify that the variables are set before running the tests
+@unittest.skipUnless(
+    'GOOGLE_CLOUD_PROJECT' in os.environ,
+    "Skipping tests because environment variables are not set for Google Cloud project."
+)
+class TestServiceAccountManagerIntegration(unittest.TestCase):
+    """Integration tests for ServiceAccountManager with real Google Cloud IAM client."""
+
+    def setUp(self):
+        """Set up test fixtures."""
+        self.project_id = os.environ['GOOGLE_CLOUD_PROJECT']
+        self.logger = logging.getLogger(__name__)
+        self.manager = ServiceAccountManager(self.project_id, self.logger, 5)
+
+    def tearDown(self):
+        """Tear down test fixtures."""
+        # Clean up any service accounts created during tests
+        try:
+            accounts = self.manager._get_service_accounts()
+            for account in accounts:
+                if account.email.startswith("test-account-"):
+                    try:
+                        self.manager.delete_service_account(account.email)
+                    except Exception as e:
+                        self.logger.warning(f"Failed to delete service account {account.email}: {e}")
+        except Exception as e:
+            self.logger.warning(f"Failed to list service accounts during tearDown: {e}")
+
+    def test_full_service_account_lifecycle(self):
+        """Test creating and deleting a service account."""
+        account_id = "test-account-" + str(os.getpid())
+        display_name = "Test Account"
+
+        # Create service account
+        account = self.manager.create_service_account(account_id, display_name)
+        service_account_email = account.email
+        self.assertEqual(account.display_name, display_name)
+
+        # Wait until service account is created (with retries)
+        for i in range(5):
+            if service_account_email in [a.email for a in self.manager._get_service_accounts()]:
+                break
+            time.sleep(i ** 2)  # Exponential backoff
+        # Verify service account exists
+        self.assertIn(service_account_email, [a.email for a in self.manager._get_service_accounts()])
+
+        # Create a key for the service account
+        key = self.manager.create_service_account_key(service_account_email)
+        self.assertIsNotNone(key.private_key_data)
+
+        # Test the key (now includes retry logic for propagation delays)
+        key_valid = self.manager.test_service_account_key(key.private_key_data)
+        self.assertTrue(key_valid)
+
+        # List keys for the service account - with delayed check
+        self.assertIn(key.name, [k.name for k in self.manager._get_service_account_keys(service_account_email)])
+
+        # Delete the service account key
+        self.manager.delete_service_account_key(service_account_email, key.name.split('/')[-1])
+
+        # Create a new key to ensure we have multiple keys
+        new_key = self.manager.create_service_account_key(service_account_email)
+        new_key_valid = self.manager.test_service_account_key(new_key.private_key_data)
+        self.assertTrue(new_key_valid)
+
+        # Verify that we have 2 keys now
+        all_keys = self.manager._get_service_account_keys(service_account_email)
+        self.assertEqual(len(all_keys), 2)  # 1 old key + 1 new key
+
+        # Disable the service account
+        self.manager.disable_service_account(service_account_email)
+
+        # Verify service account is disabled
+        account = self.manager.get_service_account(service_account_email)
+        self.assertTrue(account.disabled)
+
+        # Enable the service account
+        self.manager.enable_service_account(service_account_email)
+
+        # Verify service account is enabled
+        account = self.manager.get_service_account(service_account_email)
+        self.assertFalse(account.disabled)
+
+        # Test again the key after enabling the service account
+        key_valid = self.manager.test_service_account_key(new_key.private_key_data)
+        self.assertTrue(key_valid)
+
+        # Delete the service account
+        self.manager.delete_service_account(service_account_email)
+
+        # Verify service account is deleted - using get_service_account with exception handling
+        with self.assertRaises(exceptions.NotFound):
+            self.manager.get_service_account(service_account_email)
+
+if __name__ == '__main__':
+    # Configure logging to reduce noise during testing
+    import logging
+    logging.getLogger('google.cloud').setLevel(logging.WARNING)
+    logging.getLogger('google.auth').setLevel(logging.WARNING)
+    
+    # Run the tests
+    unittest.main()
diff --git a/infra/security/README.md b/infra/security/README.md
new file mode 100644
index 000000000000..0e60c4b33043
--- /dev/null
+++ b/infra/security/README.md
@@ -0,0 +1,84 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+# GCP Security Analyzer
+
+This document describes the implementation of a security analyzer for Google Cloud Platform (GCP) resources. The analyzer is designed to enhance security monitoring within our GCP environment by capturing critical events and generating alerts for specific security-sensitive actions.
+
+## How It Works
+
+1.  **Log Sinks**: The system uses [GCP Log Sinks](https://cloud.google.com/logging/docs/export/configure_export_v2) to capture specific security-related log entries. These sinks are configured to filter for events like IAM policy changes or service account key creation.
+2.  **Log Storage**: The filtered logs are routed to a dedicated Google Cloud Storage (GCS) bucket for persistence and analysis.
+3.  **Report Generation**: A scheduled job runs weekly, executing the `log_analyzer.py` script.
+4.  **Email Alerts**: The script analyzes the logs from the past week, compiles a summary of security events, and sends a report to a configured email address.
+
+## Configuration
+
+The behavior of the log analyzer is controlled by a `config.yml` file. Here’s an overview of the configuration options:
+
+-   `project_id`: The GCP project ID where the resources are located.
+-   `bucket_name`: The name of the GCS bucket where logs will be stored.
+-   `logging`: Configures the logging level and format for the script.
+-   `sinks`: A list of log sinks to be created. Each sink has the following properties:
+    -   `name`: A unique name for the sink.
+    -   `description`: A brief description of what the sink monitors.
+    -   `filter_methods`: A list of GCP API methods to include in the filter (e.g., `SetIamPolicy`).
+    -   `excluded_principals`: A list of service accounts or user emails to exclude from monitoring, such as CI/CD service accounts.
+
+### Example Configuration (`config.yml`)
+
+```yaml
+project_id: your-gcp-project-id
+bucket_name: your-log-storage-bucket
+
+sinks:
+  - name: iam-policy-changes
+    description: Monitors changes to IAM policies.
+    filter_methods:
+      - "SetIamPolicy"
+    excluded_principals:
+      - "ci-cd-account@your-project.iam.gserviceaccount.com"
+```
+
+## Usage
+
+The `log_analyzer.py` script provides two main commands for managing the security analyzer.
+
+### Initializing Sinks
+
+To create or update the log sinks in GCP based on your `config.yml` file, run the following command:
+
+```bash
+python log_analyzer.py --config config.yml initialize
+```
+
+This command ensures that the log sinks are correctly configured to capture the desired security events.
+
+### Generating Weekly Reports
+
+To generate and send the weekly security report, run this command:
+
+```bash
+python log_analyzer.py --config config.yml generate-report
+```
+
+This is typically run as a scheduled job (GitHub Action) to automate the delivery of weekly security reports.
+
+
+
diff --git a/infra/security/config.yml b/infra/security/config.yml
new file mode 100644
index 000000000000..e2c3659040cc
--- /dev/null
+++ b/infra/security/config.yml
@@ -0,0 +1,43 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+project_id: apache-beam-testing
+
+# Logging
+logging:
+  level: DEBUG
+  format: "[%(asctime)s] %(levelname)s: %(message)s"
+
+# gcloud storage bucket
+bucket_name: "beam-sec-analytics-and-logging"
+
+# GCP Log sinks
+sinks:
+  - name: iam-policy-changes
+    description: Monitors changes to IAM policies, excluding approved CI/CD service accounts.
+    filter_methods:
+      - "SetIamPolicy"
+    excluded_principals:
+      - beam-github-actions@apache-beam-testing.iam.gserviceaccount.com
+      - github-self-hosted-runners@apache-beam-testing.iam.gserviceaccount.com
+
+  - name: sa-key-management
+    description: Monitors creation and deletion of service account keys.
+    filter_methods:
+      - "google.iam.admin.v1.IAM.CreateServiceAccountKey"
+      - "google.iam.admin.v1.IAM.DeleteServiceAccountKey"
+    excluded_principals:
+      - beam-github-actions@apache-beam-testing.iam.gserviceaccount.com
+      - github-self-hosted-runners@apache-beam-testing.iam.gserviceaccount.com
diff --git a/infra/security/log_analyzer.py b/infra/security/log_analyzer.py
new file mode 100644
index 000000000000..55ab4495e24f
--- /dev/null
+++ b/infra/security/log_analyzer.py
@@ -0,0 +1,333 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import ssl
+import yaml
+import logging
+import smtplib
+import os
+from dataclasses import dataclass
+from datetime import datetime, timedelta, timezone
+from google.cloud import logging_v2
+from google.cloud import storage
+from typing import List, Dict, Any
+import argparse
+
+REPORT_SUBJECT = "Weekly IAM Security Events Report"
+REPORT_BODY_TEMPLATE = """
+Hello Team,
+
+Please find below the summary of IAM security events for the past week:
+
+{event_summary}
+
+Best Regards,
+Automated GitHub Action
+"""
+
+@dataclass
+class SinkCls:
+    name: str
+    description: str
+    filter_methods: List[str]
+    excluded_principals: List[str]
+
+class LogAnalyzer():
+    def __init__(self, project_id: str, gcp_bucket: str, logger: logging.Logger, sinks: List[SinkCls]):
+        self.project_id = project_id
+        self.bucket = gcp_bucket
+        self.logger = logger
+        self.sinks = sinks
+
+    def _construct_filter(self, sink: SinkCls) -> str:
+        """
+        Constructs a filter string for a given sink.
+
+        Args:
+            sink (Sink): The sink object containing filter information.
+
+        Returns:
+            str: The constructed filter string.
+        """
+
+        method_filters = []
+        for method in sink.filter_methods:
+            method_filters.append(f'protoPayload.methodName="{method}"')
+
+        exclusion_filters = []
+        for principal in sink.excluded_principals:
+            exclusion_filters.append(f'protoPayload.authenticationInfo.principalEmail != "{principal}"')
+
+        if method_filters and exclusion_filters:
+            filter_ = f"({' OR '.join(method_filters)}) AND ({' AND '.join(exclusion_filters)})"
+        elif method_filters:
+            filter_ = f"({' OR '.join(method_filters)})"
+        elif exclusion_filters:
+            filter_ = f"({' AND '.join(exclusion_filters)})"
+        else:
+            filter_ = ""
+
+        return filter_
+
+    def _create_log_sink(self, sink: SinkCls) -> None:
+        """
+        Creates a log sink in GCP if it doesn't already exist.
+        If it already exists, it updates the sink with the new filter in case the filter has changed.
+
+        Args:
+            sink (Sink): The sink object to create.
+        """
+        logging_client = logging_v2.Client(project=self.project_id)
+        filter_ = self._construct_filter(sink)
+        destination = "storage.googleapis.com/{bucket}".format(bucket=self.bucket)
+
+        sink_client = logging_client.sink(sink.name, filter_=filter_, destination=destination)
+
+        if sink_client.exists():
+            self.logger.debug(f"Sink {sink.name} already exists.")
+            sink_client.reload()
+            if sink_client.filter_ != filter_:
+                sink_client.filter_ = filter_
+                sink_client.update()
+                self.logger.info(f"Updated sink {sink.name}'s filter.")
+        else:
+            sink_client.create()
+            self.logger.info(f"Created sink {sink.name}.")
+            # Reload the sink to get the writer_identity, this may take a few moments
+            sink_client.reload()
+
+        self._grant_bucket_permissions(sink_client)
+
+        logging_client.close()
+
+    def _grant_bucket_permissions(self, sink: logging_v2.Sink) -> None:
+        """
+        Grants a log sink's writer identity permissions to write to the bucket.
+        """
+        logging_client = logging_v2.Client(project=self.project_id)
+        storage_client = storage.Client(project=self.project_id)
+
+        sink.reload()    
+        writer_identity = sink.writer_identity
+        if not writer_identity:
+            self.logger.warning(f"Could not retrieve writer identity for sink {sink.name}. "
+                                f"Manual permission granting might be required.")
+            return
+
+        bucket = storage_client.get_bucket(self.bucket)
+        policy = bucket.get_iam_policy(requested_policy_version=3)
+        iam_role = "roles/storage.objectCreator"
+
+        # Workaround for projects where the writer_identity is not a valid service account.
+        if writer_identity == "serviceAccount:cloud-logs@system.gserviceaccount.com":
+            member = "group:cloud-logs@google.com"
+        else:
+            member = f"serviceAccount:{writer_identity}"
+
+        # Check if the policy is already configured
+        if any(member in b.get("members", []) and b.get("role") == iam_role for b in policy.bindings):
+            self.logger.debug(f"Sink {sink.name} already has the necessary permissions.")
+            return
+
+        policy.bindings.append({
+            "role": iam_role,
+            "members": {member}
+        })
+
+        bucket.set_iam_policy(policy)
+        self.logger.info(f"Granted {iam_role} to {member} on bucket {self.bucket} for sink {sink.name}.")
+
+    def initialize_sinks(self) -> None:
+        for sink in self.sinks:
+            self._create_log_sink(sink)
+            self.logger.info(f"Initialized sink: {sink.name}")
+
+    def get_event_logs(self, days: int = 7) -> List[Dict[str, Any]]:
+        """
+        Reads and retrieves log events from the specified time range from the GCP Cloud Storage bucket.
+
+        Args:
+            days (int): The number of days to look back for log analysis.
+
+        Returns:
+            List[Dict[str, Any]]: A list of log entries that match the specified time range.
+        """
+        found_events = []
+        storage_client = storage.Client(project=self.project_id)
+
+        now = datetime.now(timezone.utc)
+        end_time = now.replace(minute=0, second=0, microsecond=0) - timedelta(minutes=30)
+        start_time = end_time - timedelta(days=days)
+
+        blobs = storage_client.list_blobs(self.bucket)
+        for blob in blobs:
+            if not (start_time <= blob.time_created < end_time):
+                continue
+
+            self.logger.debug(f"Processing blob: {blob.name}")
+            content = blob.download_as_string().decode("utf-8")
+
+            for num, line in enumerate(content.splitlines(), 1):
+                try:
+                    log_entry = json.loads(line)
+                    payload = log_entry.get("protoPayload")
+                    if not payload:
+                        self.logger.warning(f"Skipping log in blob {blob.name}, line {num}: no protoPayload found.")
+                        continue
+
+                    event_details = {
+                        "timestamp": log_entry.get("timestamp", "N/A"),
+                        "principal": payload.get("authenticationInfo", {}).get("principalEmail", "N/A"),
+                        "method": payload.get("methodName", "N/A"),
+                        "resource": payload.get("resourceName", "N/A"),
+                        "project_id": log_entry.get("resource", {}).get("labels", {}).get("project_id", "N/A"),
+                        "file_name": blob.name
+                    }
+                    found_events.append(event_details)
+                except json.JSONDecodeError:
+                    self.logger.warning(f"Skipping invalid JSON log in blob {blob.name}, line {num}.")
+                    continue
+
+        storage_client.close()
+        return found_events
+
+    def create_weekly_email_report(self, dry_run: bool = False) -> None:
+        """
+        Creates an email report based on the events found this week.
+        If `dry_run` is True, it will print the report to the console instead of sending it.
+        """
+        events = self.get_event_logs(days=7)
+        if not events:
+            self.logger.info("No events found for the weekly report.")
+            return
+
+        events.sort(key=lambda x: x['timestamp'], reverse=True)
+        event_summary = "\n".join(
+            f"Timestamp: {event['timestamp']}, Principal: {event['principal']}, Method: {event['method']}, Resource: {event['resource']}, Project ID: {event['project_id']}, File: {event['file_name']}"
+            for event in events
+        )
+
+        report_subject = REPORT_SUBJECT
+        report_body = REPORT_BODY_TEMPLATE.format(event_summary=event_summary)
+
+        if dry_run:
+            self.logger.info("Dry run: printing email report to console.")
+            print(f"Subject: {report_subject}\n")
+            print(f"Body:\n{report_body}")
+            return
+
+        self.send_email(report_subject, report_body)
+
+    def send_email(self, subject: str, body: str) -> None:
+        """
+        Sends an email with the specified subject and body.
+        If email configuration is not fully set, it prints the email instead.
+
+        Args:
+            subject (str): The subject of the email.
+            body (str): The body of the email.
+        """
+        smtp_server = os.getenv("SMTP_SERVER")
+        smtp_port_str = os.getenv("SMTP_PORT")
+        recipient = os.getenv("EMAIL_RECIPIENT")
+        email = os.getenv("EMAIL_ADDRESS")
+        password = os.getenv("EMAIL_PASSWORD")
+
+        if not all([smtp_server, smtp_port_str, recipient, email, password]):
+            self.logger.warning("Email configuration is not fully set. Printing email instead.")
+            print(f"Subject: {subject}\n")
+            print(f"Body:\n{body}")
+            return
+
+        assert smtp_server is not None
+        assert smtp_port_str is not None
+        assert recipient is not None
+        assert email is not None
+        assert password is not None
+
+        message = f"Subject: {subject}\n\n{body}"
+        context = ssl.create_default_context()
+
+        try:
+            smtp_port = int(smtp_port_str)
+            with smtplib.SMTP_SSL(smtp_server, smtp_port, context=context) as server:
+                server.login(email, password)
+                server.sendmail(email, recipient, message)
+            self.logger.info(f"Successfully sent email report to {recipient}")
+        except Exception as e:
+            self.logger.error(f"Failed to send email report: {e}")
+
+def load_config_from_yaml(config_path: str) -> Dict[str, Any]:
+    with open(config_path, 'r') as file:
+        config = yaml.safe_load(file)
+
+    c = {
+        "project_id": config.get("project_id"),
+        "gcp_bucket": config.get("bucket_name"),
+        "sinks": [],
+        "logger": logging.getLogger(__name__)
+    }
+
+    for sink_config in config.get("sinks", []):
+        sink = SinkCls(
+            name=sink_config["name"],
+            description=sink_config["description"],
+            filter_methods=sink_config.get("filter_methods", []),
+            excluded_principals=sink_config.get("excluded_principals", [])
+        )
+        c["sinks"].append(sink)
+
+    logging_config = config.get("logging", {})
+    log_level = logging_config.get("level", "INFO")
+    log_format = logging_config.get("format", "[%(asctime)s] %(levelname)s: %(message)s")
+
+    c["logger"].setLevel(log_level)
+    logging.basicConfig(level=log_level, format=log_format)
+
+    return c
+
+def main():
+    """
+    Main entry point for the script.
+    """
+    parser = argparse.ArgumentParser(description="GCP IAM Log Analyzer")
+    parser.add_argument("--config", required=True, help="Path to the configuration YAML file.")
+
+    subparsers = parser.add_subparsers(dest="command", required=True)
+
+    subparsers.add_parser("initialize", help="Initialize/update log sinks in GCP.")
+    report_parser = subparsers.add_parser("generate-report", help="Generate and send the weekly IAM security report.")
+    report_parser.add_argument("--dry-run", action="store_true", help="Do not send email, print report to console.")
+
+    args = parser.parse_args()
+
+    config = load_config_from_yaml(args.config)
+    log_analyzer = LogAnalyzer(
+        project_id=config["project_id"],
+        gcp_bucket=config["gcp_bucket"],
+        logger=config["logger"],
+        sinks=config["sinks"]
+    )
+
+    if args.command == "initialize":
+        log_analyzer.initialize_sinks()
+        log_analyzer.logger.info("Sinks initialized successfully.")
+    elif args.command == "generate-report":
+        log_analyzer.create_weekly_email_report(dry_run=args.dry_run)
+        log_analyzer.logger.info("Weekly report generation process completed.")
+
+if __name__ == "__main__":
+    main()
diff --git a/infra/security/requirements.txt b/infra/security/requirements.txt
new file mode 100644
index 000000000000..a4abb8bc5acf
--- /dev/null
+++ b/infra/security/requirements.txt
@@ -0,0 +1,19 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+PyYAML==6.0.2
+google-cloud-storage==3.3.0
+google-cloud-logging==3.12.1
diff --git a/it/common/src/main/java/org/apache/beam/it/common/utils/ResourceManagerUtils.java b/it/common/src/main/java/org/apache/beam/it/common/utils/ResourceManagerUtils.java
index 0492206643f0..e7911f9ba4c7 100644
--- a/it/common/src/main/java/org/apache/beam/it/common/utils/ResourceManagerUtils.java
+++ b/it/common/src/main/java/org/apache/beam/it/common/utils/ResourceManagerUtils.java
@@ -175,8 +175,7 @@ public static void cleanResources(boolean failOnCleanup, ResourceManager... mana
       throw new RuntimeException("Error cleaning up resources", bubbleException);
     } else if (bubbleException != null) {
       LOG.warn(
-          "Error cleaning up resources. This is not configured to fail the test: {}",
-          bubbleException.getMessage());
+          "Error cleaning up resources. This is not configured to fail the test", bubbleException);
     }
   }
 
diff --git a/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/IOLoadTestBase.java b/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/IOLoadTestBase.java
index bbf9dd0519ec..14770a429731 100644
--- a/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/IOLoadTestBase.java
+++ b/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/IOLoadTestBase.java
@@ -121,7 +121,7 @@ protected void exportMetrics(
     try {
       metrics = getMetrics(launchInfo, metricsConfig);
     } catch (Exception e) {
-      LOG.warn("Unable to get metrics due to error: {}", e.getMessage());
+      LOG.warn("Unable to get metrics due to error", e);
       return;
     }
     String testId = UUID.randomUUID().toString();
diff --git a/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/artifacts/matchers/ArtifactsSubject.java b/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/artifacts/matchers/ArtifactsSubject.java
index 06ea14d0390b..ab5b49699484 100644
--- a/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/artifacts/matchers/ArtifactsSubject.java
+++ b/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/artifacts/matchers/ArtifactsSubject.java
@@ -72,6 +72,7 @@ public void hasFiles() {
    *
    * @param expectedFiles Expected files
    */
+  @SuppressWarnings("LenientFormatStringValidation")
   public void hasFiles(int expectedFiles) {
     check("there are %d files", expectedFiles).that(actual.size()).isEqualTo(expectedFiles);
   }
diff --git a/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/dataflow/DefaultPipelineLauncher.java b/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/dataflow/DefaultPipelineLauncher.java
index e0f9f6c2f63d..7e496aa18938 100644
--- a/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/dataflow/DefaultPipelineLauncher.java
+++ b/it/google-cloud-platform/src/main/java/org/apache/beam/it/gcp/dataflow/DefaultPipelineLauncher.java
@@ -49,9 +49,11 @@
 import org.apache.beam.sdk.metrics.MetricQueryResults;
 import org.apache.beam.sdk.metrics.MetricResult;
 import org.apache.beam.sdk.metrics.MetricsFilter;
+import org.apache.beam.sdk.options.ExperimentalOptions;
 import org.apache.beam.sdk.options.PipelineOptions;
 import org.apache.beam.sdk.options.PipelineOptionsFactory;
 import org.apache.beam.sdk.options.StreamingOptions;
+import org.apache.beam.sdk.util.ReleaseInfo;
 import org.apache.beam.sdk.util.common.ReflectHelpers;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Strings;
@@ -312,6 +314,13 @@ public LaunchInfo launch(String project, String region, LaunchConfig options) th
             optionFromConfig.add(
                 String.format("--tempLocation=%s", pipelineOptions.getTempLocation()));
           }
+          // for snapshot Beam running on runner v2, need to use staged SDK harness
+          if (ReleaseInfo.getReleaseInfo().isDevSdkVersion()
+              && (ExperimentalOptions.hasExperiment(pipelineOptions, "use_runner_v2")
+                  || (!Strings.isNullOrEmpty(options.parameters().get("experiments"))
+                      && options.parameters().get("experiments").contains("use_runner_v2")))) {
+            optionFromConfig.add("--experiments=use_staged_dataflow_worker_jar");
+          }
 
           // dataflow runner specific options
           PipelineOptions updatedOptions =
diff --git a/it/google-cloud-platform/src/test/java/org/apache/beam/it/gcp/bigquery/BigQueryStreamingLT.java b/it/google-cloud-platform/src/test/java/org/apache/beam/it/gcp/bigquery/BigQueryStreamingLT.java
index e89fe1dc8524..d68c0f07865e 100644
--- a/it/google-cloud-platform/src/test/java/org/apache/beam/it/gcp/bigquery/BigQueryStreamingLT.java
+++ b/it/google-cloud-platform/src/test/java/org/apache/beam/it/gcp/bigquery/BigQueryStreamingLT.java
@@ -132,7 +132,7 @@ public void setUpTest() {
     String expectedTable =
         TestProperties.getProperty("expectedTable", "", TestProperties.Type.PROPERTY);
     if (!Strings.isNullOrEmpty(expectedTable)) {
-      config.toBuilder().setExpectedTable(expectedTable).build();
+      config = config.toBuilder().setExpectedTable(expectedTable).build();
     }
 
     crashIntervalSeconds =
@@ -396,7 +396,7 @@ public void runTest(BigQueryIO.Write.Method writeMethod)
       } catch (Exception e) {
         // Just log the error. Don't re-throw because we have accuracy checks that are more
         // important below
-        LOG.error("Encountered an error while exporting metrics to BigQuery:\n{}", e);
+        LOG.error("Encountered an error while exporting metrics to BigQuery:", e);
       }
     }
     // If we're not publishing metrics, just run the pipeline normally
diff --git a/it/mongodb/build.gradle b/it/mongodb/build.gradle
index 6be9b91f5b34..960e15af8394 100644
--- a/it/mongodb/build.gradle
+++ b/it/mongodb/build.gradle
@@ -35,6 +35,7 @@ dependencies {
     implementation library.java.testcontainers_mongodb
     implementation library.java.google_code_gson
     implementation library.java.mongo_java_driver
+    implementation library.java.mongo_bson
     implementation library.java.vendored_guava_32_1_2_jre
 
     testImplementation library.java.mockito_core
diff --git a/learning/tour-of-beam/learning-content/common-transforms/filter/description.md b/learning/tour-of-beam/learning-content/common-transforms/filter/description.md
index 96f4b549625b..b4ea26be3758 100644
--- a/learning/tour-of-beam/learning-content/common-transforms/filter/description.md
+++ b/learning/tour-of-beam/learning-content/common-transforms/filter/description.md
@@ -17,7 +17,7 @@ limitations under the License.
 {{if (eq .Sdk "go")}}
 ```
 import (
-  "github.com/apache/fbeam/sdks/go/pkg/beam"
+  "github.com/apache/beam/sdks/go/pkg/beam"
   "github.com/apache/beam/sdks/go/pkg/beam/transforms/filter"
 )
 
diff --git a/learning/tour-of-beam/learning-content/introduction/introduction-concepts/creating-collections/reading-from-text/description.md b/learning/tour-of-beam/learning-content/introduction/introduction-concepts/creating-collections/reading-from-text/description.md
index 0924d2fceb17..1ad7d3eaad90 100644
--- a/learning/tour-of-beam/learning-content/introduction/introduction-concepts/creating-collections/reading-from-text/description.md
+++ b/learning/tour-of-beam/learning-content/introduction/introduction-concepts/creating-collections/reading-from-text/description.md
@@ -23,7 +23,7 @@ Each data source adapter has a Read transform; to read, you must apply that tran
 func main() {
     ctx := context.Background()
 
-    // First create pipline
+    // First create pipeline
     p, s := beam.NewPipelineWithRoot()
 
     // Now create the PCollection by reading text files. Separate elements will be added for each line in the input file
@@ -49,7 +49,7 @@ public static void main(String[] args) {
 {{end}}
 {{if (eq .Sdk "python")}}
 ```
-# First create pipline
+# First create pipeline
 with beam.Pipeline() as p:
 
     # Now create the PCollection by reading text files. Separate elements will be added for each line in the input file
diff --git a/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/external_transforms.proto b/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/external_transforms.proto
index add8a1999caf..043a72dd34f2 100644
--- a/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/external_transforms.proto
+++ b/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/external_transforms.proto
@@ -76,6 +76,18 @@ message ManagedTransforms {
       "beam:schematransform:org.apache.beam:bigquery_write:v1"];
     ICEBERG_CDC_READ = 6 [(org.apache.beam.model.pipeline.v1.beam_urn) =
       "beam:schematransform:org.apache.beam:iceberg_cdc_read:v1"];
+    POSTGRES_READ = 7 [(org.apache.beam.model.pipeline.v1.beam_urn) =
+      "beam:schematransform:org.apache.beam:postgres_read:v1"];
+    POSTGRES_WRITE = 8 [(org.apache.beam.model.pipeline.v1.beam_urn) =
+      "beam:schematransform:org.apache.beam:postgres_write:v1"];
+    MYSQL_READ = 9 [(org.apache.beam.model.pipeline.v1.beam_urn) =
+      "beam:schematransform:org.apache.beam:mysql_read:v1"];
+    MYSQL_WRITE = 10 [(org.apache.beam.model.pipeline.v1.beam_urn) =
+      "beam:schematransform:org.apache.beam:mysql_write:v1"];
+    SQL_SERVER_READ = 11 [(org.apache.beam.model.pipeline.v1.beam_urn) =
+      "beam:schematransform:org.apache.beam:sql_server_read:v1"];
+    SQL_SERVER_WRITE = 12 [(org.apache.beam.model.pipeline.v1.beam_urn) =
+      "beam:schematransform:org.apache.beam:sql_server_write:v1"];
   }
 }
 
diff --git a/playground/backend/containers/router/Dockerfile b/playground/backend/containers/router/Dockerfile
index 863461013a45..1fcb98062201 100644
--- a/playground/backend/containers/router/Dockerfile
+++ b/playground/backend/containers/router/Dockerfile
@@ -44,16 +44,19 @@ RUN cd cmd/migration_tool &&\
     go build -o /go/bin/migration_tool
 
 # Null image
-FROM debian:stable-20221114-slim
+FROM debian:bullseye-slim
 
+ENV DEBIAN_FRONTEND=noninteractive
 # Install deps being used by sh files
-RUN apt-get update \
-    && apt-get install -y --no-install-recommends \
-        ca-certificates \
-        curl \
-    && apt-get autoremove -yqq --purge \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
+RUN set -eux; \
+  # 1) use existing HTTP sources to bootstrap CAs
+  apt-get update; \
+  apt-get install -y --no-install-recommends ca-certificates; \
+  # 2) now it’s safe to use HTTPS
+  sed -ri 's|http://deb\.debian\.org|https://deb.debian.org|g' /etc/apt/sources.list; \
+  apt-get update; \
+  apt-get install -y --no-install-recommends curl; \
+  rm -rf /var/lib/apt/lists/*
 
 # Set Environment
 ENV SERVER_IP=0.0.0.0
diff --git a/release/build.gradle.kts b/release/build.gradle.kts
index 7ec49b86aac2..a13ad34b00fc 100644
--- a/release/build.gradle.kts
+++ b/release/build.gradle.kts
@@ -41,7 +41,9 @@ task("runJavaExamplesValidationTask") {
   dependsOn(":runners:spark:3:runQuickstartJavaSpark")
   dependsOn(":runners:flink:1.19:runQuickstartJavaFlinkLocal")
   dependsOn(":runners:direct-java:runMobileGamingJavaDirect")
-  dependsOn(":runners:google-cloud-dataflow-java:runMobileGamingJavaDataflow")
-  dependsOn(":runners:twister2:runQuickstartJavaTwister2")
+  if (project.hasProperty("ver") || !project.version.toString().endsWith("SNAPSHOT")) {
+    // only run one variant of MobileGaming on Dataflow for nightly
+    dependsOn(":runners:google-cloud-dataflow-java:runMobileGamingJavaDataflow")
+  }
   dependsOn(":runners:google-cloud-dataflow-java:runMobileGamingJavaDataflowBom")
 }
diff --git a/runners/core-java/build.gradle b/runners/core-java/build.gradle
index ea7989873712..9f24ce39b974 100644
--- a/runners/core-java/build.gradle
+++ b/runners/core-java/build.gradle
@@ -48,6 +48,7 @@ dependencies {
   implementation library.java.slf4j_api
   implementation library.java.jackson_core
   implementation library.java.jackson_databind
+  implementation library.java.hamcrest
   testImplementation project(path: ":sdks:java:core", configuration: "shadowTest")
   testImplementation library.java.junit
   testImplementation library.java.mockito_core
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/LateDataUtils.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/LateDataUtils.java
index fbb7b315c3b1..65084120f922 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/LateDataUtils.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/LateDataUtils.java
@@ -81,7 +81,9 @@ public static <K, V> Iterable<WindowedValue<V>> dropExpiredWindows(
               if (input == null) {
                 return null;
               }
-              return input.explodeWindows();
+              // The generics in this chain of calls line up best if we drop the covariance
+              // in the return value of explodeWindows()
+              return (Iterable<WindowedValue<V>>) input.explodeWindows();
             })
         .filter(
             input -> {
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/OutputAndTimeBoundedSplittableProcessElementInvoker.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/OutputAndTimeBoundedSplittableProcessElementInvoker.java
index f8dbfd61e836..9bda4dd2cbca 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/OutputAndTimeBoundedSplittableProcessElementInvoker.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/OutputAndTimeBoundedSplittableProcessElementInvoker.java
@@ -45,6 +45,7 @@
 import org.apache.beam.sdk.transforms.splittabledofn.WatermarkEstimator;
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.apache.beam.sdk.util.OutputBuilderSuppliers;
 import org.apache.beam.sdk.util.WindowedValueMultiReceiver;
 import org.apache.beam.sdk.values.KV;
 import org.apache.beam.sdk.values.PCollectionView;
@@ -180,7 +181,8 @@ public TimeDomain timeDomain(DoFn<InputT, OutputT> doFn) {
 
               @Override
               public OutputReceiver<OutputT> outputReceiver(DoFn<InputT, OutputT> doFn) {
-                return DoFnOutputReceivers.windowedReceiver(processContext, null);
+                return DoFnOutputReceivers.windowedReceiver(
+                    processContext, OutputBuilderSuppliers.supplierForElement(element), null);
               }
 
               @Override
@@ -190,7 +192,8 @@ public OutputReceiver<Row> outputRowReceiver(DoFn<InputT, OutputT> doFn) {
 
               @Override
               public MultiOutputReceiver taggedOutputReceiver(DoFn<InputT, OutputT> doFn) {
-                return DoFnOutputReceivers.windowedMultiReceiver(processContext, null);
+                return DoFnOutputReceivers.windowedMultiReceiver(
+                    processContext, OutputBuilderSuppliers.supplierForElement(element));
               }
 
               @Override
@@ -383,6 +386,16 @@ public PaneInfo pane() {
       return element.getPaneInfo();
     }
 
+    @Override
+    public String currentRecordId() {
+      return element.getRecordId();
+    }
+
+    @Override
+    public Long currentRecordOffset() {
+      return element.getRecordOffset();
+    }
+
     @Override
     public PipelineOptions getPipelineOptions() {
       return pipelineOptions;
@@ -411,6 +424,24 @@ public void outputWindowedValue(
       outputReceiver.output(mainOutputTag, WindowedValues.of(value, timestamp, windows, paneInfo));
     }
 
+    @Override
+    public void outputWindowedValue(
+        OutputT value,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      noteOutput();
+      if (watermarkEstimator instanceof TimestampObservingWatermarkEstimator) {
+        ((TimestampObservingWatermarkEstimator) watermarkEstimator).observeTimestamp(timestamp);
+      }
+      outputReceiver.output(
+          mainOutputTag,
+          WindowedValues.of(
+              value, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
+    }
+
     @Override
     public <T> void output(TupleTag<T> tag, T value) {
       outputWithTimestamp(tag, value, element.getTimestamp());
@@ -429,11 +460,26 @@ public <T> void outputWindowedValue(
         Instant timestamp,
         Collection<? extends BoundedWindow> windows,
         PaneInfo paneInfo) {
+      outputWindowedValue(tag, value, timestamp, windows, paneInfo, null, null);
+    }
+
+    @Override
+    public <T> void outputWindowedValue(
+        TupleTag<T> tag,
+        T value,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
       noteOutput();
       if (watermarkEstimator instanceof TimestampObservingWatermarkEstimator) {
         ((TimestampObservingWatermarkEstimator) watermarkEstimator).observeTimestamp(timestamp);
       }
-      outputReceiver.output(tag, WindowedValues.of(value, timestamp, windows, paneInfo));
+      outputReceiver.output(
+          tag,
+          WindowedValues.of(
+              value, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
     }
 
     private void noteOutput() {
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/PaneInfoTracker.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/PaneInfoTracker.java
index 543b2cb5a741..69e6225a33ea 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/PaneInfoTracker.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/PaneInfoTracker.java
@@ -93,7 +93,7 @@ public void storeCurrentPaneInfo(ReduceFn<?, ?, ?, ?>.Context context, PaneInfo
     context.state().access(PANE_INFO_TAG).write(currentPane);
   }
 
-  private <W> PaneInfo describePane(
+  private PaneInfo describePane(
       Object key, Instant windowMaxTimestamp, PaneInfo previousPane, boolean isFinal) {
     boolean isFirst = previousPane == null;
     Timing previousTiming = isFirst ? null : previousPane.getTiming();
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/ReduceFnRunner.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/ReduceFnRunner.java
index 4e10dd471b40..b08bd42b0b22 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/ReduceFnRunner.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/ReduceFnRunner.java
@@ -1057,8 +1057,13 @@ private void prefetchOnTrigger(
                 }
 
                 // Output the actual value.
-                outputter.output(
-                    WindowedValues.of(KV.of(key, toOutput), outputTimestamp, windows, paneInfo));
+                WindowedValues.<KV<K, OutputT>>builder()
+                    .setValue(KV.of(key, toOutput))
+                    .setTimestamp(outputTimestamp)
+                    .setWindows(windows)
+                    .setPaneInfo(paneInfo)
+                    .setReceiver(outputter)
+                    .output();
               });
 
       reduceFn.onTrigger(renamedTriggerContext);
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/SimpleDoFnRunner.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/SimpleDoFnRunner.java
index 840245edf7ad..3af90ea9a0a1 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/SimpleDoFnRunner.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/SimpleDoFnRunner.java
@@ -51,6 +51,8 @@
 import org.apache.beam.sdk.transforms.splittabledofn.WatermarkEstimator;
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.apache.beam.sdk.util.OutputBuilderSupplier;
+import org.apache.beam.sdk.util.OutputBuilderSuppliers;
 import org.apache.beam.sdk.util.SystemDoFnInternal;
 import org.apache.beam.sdk.util.UserCodeException;
 import org.apache.beam.sdk.util.WindowedValueMultiReceiver;
@@ -113,7 +115,7 @@ public class SimpleDoFnRunner<InputT, OutputT> implements DoFnRunner<InputT, Out
 
   final @Nullable SchemaCoder<OutputT> mainOutputSchemaCoder;
 
-  private @Nullable Map<TupleTag<?>, Coder<?>> outputCoders;
+  private final @Nullable Map<TupleTag<?>, Coder<?>> outputCoders;
 
   private final @Nullable DoFnSchemaInformation doFnSchemaInformation;
 
@@ -334,6 +336,35 @@ public void output(OutputT output, Instant timestamp, BoundedWindow window) {
       public <T> void output(TupleTag<T> tag, T output, Instant timestamp, BoundedWindow window) {
         outputWindowedValue(tag, WindowedValues.of(output, timestamp, window, PaneInfo.NO_FIRING));
       }
+
+      @Override
+      public void output(
+          OutputT output,
+          Instant timestamp,
+          BoundedWindow window,
+          @Nullable String currentRecordId,
+          @Nullable Long currentRecordOffset) {
+        output(mainOutputTag, output, timestamp, window, currentRecordId, currentRecordOffset);
+      }
+
+      @Override
+      public <T> void output(
+          TupleTag<T> tag,
+          T output,
+          Instant timestamp,
+          BoundedWindow window,
+          @Nullable String currentRecordId,
+          @Nullable Long currentRecordOffset) {
+        outputWindowedValue(
+            tag,
+            WindowedValues.of(
+                output,
+                timestamp,
+                Collections.singletonList(window),
+                PaneInfo.NO_FIRING,
+                currentRecordId,
+                currentRecordOffset));
+      }
     }
 
     private final DoFnFinishBundleArgumentProvider.Context context =
@@ -366,6 +397,8 @@ private class DoFnProcessContext extends DoFn<InputT, OutputT>.ProcessContext
     /** Lazily initialized; should only be accessed via {@link #getNamespace()}. */
     private @Nullable StateNamespace namespace;
 
+    private final OutputBuilderSupplier builderSupplier;
+
     /**
      * The state namespace for this context.
      *
@@ -383,6 +416,7 @@ private StateNamespace getNamespace() {
     private DoFnProcessContext(WindowedValue<InputT> elem) {
       fn.super();
       this.elem = elem;
+      this.builderSupplier = OutputBuilderSuppliers.supplierForElement(elem);
     }
 
     @Override
@@ -427,6 +461,24 @@ public void outputWindowedValue(
       outputWindowedValue(mainOutputTag, output, timestamp, windows, paneInfo);
     }
 
+    @Override
+    public void outputWindowedValue(
+        OutputT output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      outputWindowedValue(
+          mainOutputTag,
+          output,
+          timestamp,
+          windows,
+          paneInfo,
+          currentRecordId,
+          currentRecordOffset);
+    }
+
     @Override
     public <T> void output(TupleTag<T> tag, T output) {
       checkNotNull(tag, "Tag passed to output cannot be null");
@@ -447,8 +499,32 @@ public <T> void outputWindowedValue(
         Instant timestamp,
         Collection<? extends BoundedWindow> windows,
         PaneInfo paneInfo) {
+      builderSupplier
+          .builder(output)
+          .setTimestamp(timestamp)
+          .setWindows(windows)
+          .setPaneInfo(paneInfo)
+          .setReceiver(
+              wv -> {
+                checkTimestamp(elem.getTimestamp(), wv.getTimestamp());
+                SimpleDoFnRunner.this.outputWindowedValue(tag, wv);
+              })
+          .output();
+    }
+
+    @Override
+    public <T> void outputWindowedValue(
+        TupleTag<T> tag,
+        T output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
       SimpleDoFnRunner.this.outputWindowedValue(
-          tag, WindowedValues.of(output, timestamp, windows, paneInfo));
+          tag,
+          WindowedValues.of(
+              output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
     }
 
     @Override
@@ -456,6 +532,16 @@ public Instant timestamp() {
       return elem.getTimestamp();
     }
 
+    @Override
+    public String currentRecordId() {
+      return elem.getRecordId();
+    }
+
+    @Override
+    public Long currentRecordOffset() {
+      return elem.getRecordOffset();
+    }
+
     public Collection<? extends BoundedWindow> windows() {
       return elem.getWindows();
     }
@@ -532,17 +618,18 @@ public TimeDomain timeDomain(DoFn<InputT, OutputT> doFn) {
 
     @Override
     public OutputReceiver<OutputT> outputReceiver(DoFn<InputT, OutputT> doFn) {
-      return DoFnOutputReceivers.windowedReceiver(this, mainOutputTag);
+      return DoFnOutputReceivers.windowedReceiver(this, builderSupplier, mainOutputTag);
     }
 
     @Override
     public OutputReceiver<Row> outputRowReceiver(DoFn<InputT, OutputT> doFn) {
-      return DoFnOutputReceivers.rowReceiver(this, mainOutputTag, mainOutputSchemaCoder);
+      return DoFnOutputReceivers.rowReceiver(
+          this, builderSupplier, mainOutputTag, mainOutputSchemaCoder);
     }
 
     @Override
     public MultiOutputReceiver taggedOutputReceiver(DoFn<InputT, OutputT> doFn) {
-      return DoFnOutputReceivers.windowedMultiReceiver(this, outputCoders);
+      return DoFnOutputReceivers.windowedMultiReceiver(this, builderSupplier, outputCoders);
     }
 
     @Override
@@ -638,6 +725,7 @@ private class OnTimerArgumentProvider<KeyT> extends DoFn<InputT, OutputT>.OnTime
     private final TimeDomain timeDomain;
     private final String timerId;
     private final KeyT key;
+    private final OutputBuilderSupplier builderSupplier;
 
     /** Lazily initialized; should only be accessed via {@link #getNamespace()}. */
     private @Nullable StateNamespace namespace;
@@ -670,6 +758,13 @@ private OnTimerArgumentProvider(
       this.timestamp = timestamp;
       this.timeDomain = timeDomain;
       this.key = key;
+      this.builderSupplier =
+          OutputBuilderSuppliers.supplierForElement(
+              WindowedValues.builder()
+                  .setValue(null)
+                  .setTimestamp(timestamp)
+                  .setWindow(window)
+                  .setPaneInfo(PaneInfo.NO_FIRING));
     }
 
     @Override
@@ -756,17 +851,19 @@ public TimeDomain timeDomain(DoFn<InputT, OutputT> doFn) {
 
     @Override
     public OutputReceiver<OutputT> outputReceiver(DoFn<InputT, OutputT> doFn) {
-      return DoFnOutputReceivers.windowedReceiver(this, mainOutputTag);
+      return DoFnOutputReceivers.windowedReceiver(this, builderSupplier, mainOutputTag);
     }
 
     @Override
     public OutputReceiver<Row> outputRowReceiver(DoFn<InputT, OutputT> doFn) {
-      return DoFnOutputReceivers.rowReceiver(this, mainOutputTag, mainOutputSchemaCoder);
+      return DoFnOutputReceivers.rowReceiver(
+          this, builderSupplier, mainOutputTag, mainOutputSchemaCoder);
     }
 
     @Override
     public MultiOutputReceiver taggedOutputReceiver(DoFn<InputT, OutputT> doFn) {
-      return DoFnOutputReceivers.windowedMultiReceiver(this, outputCoders);
+      // ... what to doooo 0...
+      return DoFnOutputReceivers.windowedMultiReceiver(this, builderSupplier, outputCoders);
     }
 
     @Override
@@ -867,6 +964,24 @@ public void outputWindowedValue(
       outputWindowedValue(mainOutputTag, output, timestamp, windows, paneInfo);
     }
 
+    @Override
+    public void outputWindowedValue(
+        OutputT output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      outputWindowedValue(
+          mainOutputTag,
+          output,
+          timestamp,
+          windows,
+          paneInfo,
+          currentRecordId,
+          currentRecordOffset);
+    }
+
     @Override
     public <T> void output(TupleTag<T> tag, T output) {
       checkTimestamp(timestamp(), timestamp);
@@ -888,8 +1003,30 @@ public <T> void outputWindowedValue(
         Collection<? extends BoundedWindow> windows,
         PaneInfo paneInfo) {
       checkTimestamp(timestamp(), timestamp);
+
+      builderSupplier
+          .builder(output)
+          .setTimestamp(timestamp)
+          .setWindows(windows)
+          .setPaneInfo(paneInfo)
+          .setReceiver(wv -> SimpleDoFnRunner.this.outputWindowedValue(tag, wv))
+          .output();
+    }
+
+    @Override
+    public <T> void outputWindowedValue(
+        TupleTag<T> tag,
+        T output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      checkTimestamp(timestamp(), timestamp);
       SimpleDoFnRunner.this.outputWindowedValue(
-          tag, WindowedValues.of(output, timestamp, windows, paneInfo));
+          tag,
+          WindowedValues.of(
+              output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
     }
 
     @Override
@@ -909,6 +1046,8 @@ private class OnWindowExpirationArgumentProvider<KeyT>
     private final BoundedWindow window;
     private final Instant timestamp;
     private final KeyT key;
+    private final OutputBuilderSupplier builderSupplier;
+
     /** Lazily initialized; should only be accessed via {@link #getNamespace()}. */
     private @Nullable StateNamespace namespace;
 
@@ -931,6 +1070,13 @@ private OnWindowExpirationArgumentProvider(BoundedWindow window, Instant timesta
       this.window = window;
       this.timestamp = timestamp;
       this.key = key;
+      this.builderSupplier =
+          OutputBuilderSuppliers.supplierForElement(
+              WindowedValues.<Void>builder()
+                  .setValue(null)
+                  .setWindow(window)
+                  .setTimestamp(timestamp)
+                  .setPaneInfo(PaneInfo.NO_FIRING));
     }
 
     @Override
@@ -1003,17 +1149,18 @@ public KeyT key() {
 
     @Override
     public OutputReceiver<OutputT> outputReceiver(DoFn<InputT, OutputT> doFn) {
-      return DoFnOutputReceivers.windowedReceiver(this, mainOutputTag);
+      return DoFnOutputReceivers.windowedReceiver(this, builderSupplier, mainOutputTag);
     }
 
     @Override
     public OutputReceiver<Row> outputRowReceiver(DoFn<InputT, OutputT> doFn) {
-      return DoFnOutputReceivers.rowReceiver(this, mainOutputTag, mainOutputSchemaCoder);
+      return DoFnOutputReceivers.rowReceiver(
+          this, builderSupplier, mainOutputTag, mainOutputSchemaCoder);
     }
 
     @Override
     public MultiOutputReceiver taggedOutputReceiver(DoFn<InputT, OutputT> doFn) {
-      return DoFnOutputReceivers.windowedMultiReceiver(this, outputCoders);
+      return DoFnOutputReceivers.windowedMultiReceiver(this, builderSupplier, outputCoders);
     }
 
     @Override
@@ -1096,6 +1243,24 @@ public void outputWindowedValue(
       outputWindowedValue(mainOutputTag, output, timestamp, windows, paneInfo);
     }
 
+    @Override
+    public void outputWindowedValue(
+        OutputT output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      outputWindowedValue(
+          mainOutputTag,
+          output,
+          timestamp,
+          windows,
+          paneInfo,
+          currentRecordId,
+          currentRecordOffset);
+    }
+
     @Override
     public <T> void output(TupleTag<T> tag, T output) {
       checkTimestamp(this.timestamp, timestamp);
@@ -1117,8 +1282,29 @@ public <T> void outputWindowedValue(
         Collection<? extends BoundedWindow> windows,
         PaneInfo paneInfo) {
       checkTimestamp(this.timestamp, timestamp);
+      builderSupplier
+          .builder(output)
+          .setTimestamp(timestamp)
+          .setWindows(windows)
+          .setPaneInfo(paneInfo)
+          .setReceiver(wv -> SimpleDoFnRunner.this.outputWindowedValue(tag, wv))
+          .output();
+    }
+
+    @Override
+    public <T> void outputWindowedValue(
+        TupleTag<T> tag,
+        T output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      checkTimestamp(this.timestamp, timestamp);
       SimpleDoFnRunner.this.outputWindowedValue(
-          tag, WindowedValues.of(output, timestamp, windows, paneInfo));
+          tag,
+          WindowedValues.of(
+              output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
     }
 
     @Override
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/SplittableParDoViaKeyedWorkItems.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/SplittableParDoViaKeyedWorkItems.java
index fafb02f9dd0b..6af54da0a08b 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/SplittableParDoViaKeyedWorkItems.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/SplittableParDoViaKeyedWorkItems.java
@@ -42,7 +42,6 @@
 import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker;
 import org.apache.beam.sdk.transforms.splittabledofn.WatermarkEstimator;
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
-import org.apache.beam.sdk.transforms.windowing.GlobalWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
 import org.apache.beam.sdk.transforms.windowing.TimestampCombiner;
 import org.apache.beam.sdk.util.construction.PTransformReplacements;
@@ -250,7 +249,7 @@ public static class ProcessFn<InputT, OutputT, RestrictionT, PositionT, Watermar
      */
     private static final StateTag<WatermarkHoldState> watermarkHoldTag =
         StateTags.makeSystemTagInternal(
-            StateTags.<GlobalWindow>watermarkStateInternal("hold", TimestampCombiner.LATEST));
+            StateTags.watermarkStateInternal("hold", TimestampCombiner.LATEST));
 
     /**
      * The state cell containing a copy of the element. Written during the first {@link
@@ -663,6 +662,27 @@ public <T> void output(
               throwUnsupportedOutput();
             }
 
+            @Override
+            public void output(
+                OutputT output,
+                Instant timestamp,
+                BoundedWindow window,
+                @Nullable String currentRecordId,
+                @Nullable Long currentRecordOffset) {
+              throwUnsupportedOutput();
+            }
+
+            @Override
+            public <T> void output(
+                TupleTag<T> tag,
+                T output,
+                Instant timestamp,
+                BoundedWindow window,
+                @Nullable String currentRecordId,
+                @Nullable Long currentRecordOffset) {
+              throwUnsupportedOutput();
+            }
+
             @Override
             public PipelineOptions getPipelineOptions() {
               return baseContext.getPipelineOptions();
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/StateMerging.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/StateMerging.java
index e490e3188d81..f5a2ff679ed8 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/StateMerging.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/StateMerging.java
@@ -76,8 +76,7 @@ public static <K, T, W extends BoundedWindow> void mergeBags(
   /**
    * Merge all bag state in {@code sources} (which may include {@code result}) into {@code result}.
    */
-  public static <T, W extends BoundedWindow> void mergeBags(
-      Collection<BagState<T>> sources, BagState<T> result) {
+  public static <T> void mergeBags(Collection<BagState<T>> sources, BagState<T> result) {
     if (sources.isEmpty()) {
       // Nothing to merge.
       return;
@@ -117,8 +116,7 @@ public static <K, T, W extends BoundedWindow> void mergeSets(
   /**
    * Merge all set state in {@code sources} (which may include {@code result}) into {@code result}.
    */
-  public static <T, W extends BoundedWindow> void mergeSets(
-      Collection<SetState<T>> sources, SetState<T> result) {
+  public static <T> void mergeSets(Collection<SetState<T>> sources, SetState<T> result) {
     if (sources.isEmpty()) {
       // Nothing to merge.
       return;
@@ -172,7 +170,7 @@ public static <K, InputT, AccumT, OutputT, W extends BoundedWindow> void mergeCo
    * Merge all value state from {@code sources} (which may include {@code result}) into {@code
    * result}.
    */
-  public static <InputT, AccumT, OutputT, W extends BoundedWindow> void mergeCombiningValues(
+  public static <InputT, AccumT, OutputT> void mergeCombiningValues(
       Collection<CombiningState<InputT, AccumT, OutputT>> sources,
       CombiningState<InputT, AccumT, OutputT> result) {
     if (sources.isEmpty()) {
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/StateTags.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/StateTags.java
index 6ed7f8525fdc..ba5478be6c77 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/StateTags.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/StateTags.java
@@ -35,7 +35,6 @@
 import org.apache.beam.sdk.state.WatermarkHoldState;
 import org.apache.beam.sdk.transforms.Combine.CombineFn;
 import org.apache.beam.sdk.transforms.CombineWithContext.CombineFnWithContext;
-import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.TimestampCombiner;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Equivalence;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.MoreObjects;
@@ -223,7 +222,7 @@ public static <T> StateTag<OrderedListState<T>> orderedList(String id, Coder<T>
   }
 
   /** Create a state tag for holding the watermark. */
-  public static <W extends BoundedWindow> StateTag<WatermarkHoldState> watermarkStateInternal(
+  public static StateTag<WatermarkHoldState> watermarkStateInternal(
       String id, TimestampCombiner timestampCombiner) {
     return new SimpleStateTag<>(
         new StructuredId(id), StateSpecs.watermarkStateInternal(timestampCombiner));
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/TimerInternals.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/TimerInternals.java
index 6c92d234b86f..254e6f5fcf5b 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/TimerInternals.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/TimerInternals.java
@@ -222,12 +222,7 @@ public static TimerData of(
      */
     public static TimerData of(
         StateNamespace namespace, Instant timestamp, Instant outputTimestamp, TimeDomain domain) {
-      String timerId =
-          new StringBuilder()
-              .append(domain.ordinal())
-              .append(':')
-              .append(timestamp.getMillis())
-              .toString();
+      String timerId = String.valueOf(domain.ordinal()) + ':' + timestamp.getMillis();
       return of(timerId, namespace, timestamp, outputTimestamp, domain);
     }
 
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/WatermarkHold.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/WatermarkHold.java
index e5a5f90587c1..15ae8dfe5f1a 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/WatermarkHold.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/WatermarkHold.java
@@ -55,11 +55,10 @@
 })
 class WatermarkHold<W extends BoundedWindow> implements Serializable {
   /** Return tag for state containing the output watermark hold used for elements. */
-  public static <W extends BoundedWindow>
-      StateTag<WatermarkHoldState> watermarkHoldTagForTimestampCombiner(
-          TimestampCombiner timestampCombiner) {
+  public static StateTag<WatermarkHoldState> watermarkHoldTagForTimestampCombiner(
+      TimestampCombiner timestampCombiner) {
     return StateTags.makeSystemTagInternal(
-        StateTags.<W>watermarkStateInternal("hold", timestampCombiner));
+        StateTags.watermarkStateInternal("hold", timestampCombiner));
   }
 
   /**
diff --git a/runners/core-java/src/test/java/org/apache/beam/runners/core/WindowMatchers.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/WindowMatchers.java
similarity index 91%
rename from runners/core-java/src/test/java/org/apache/beam/runners/core/WindowMatchers.java
rename to runners/core-java/src/main/java/org/apache/beam/runners/core/WindowMatchers.java
index 33ae2f68b48f..463cb9320237 100644
--- a/runners/core-java/src/test/java/org/apache/beam/runners/core/WindowMatchers.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/WindowMatchers.java
@@ -20,6 +20,7 @@
 import java.util.Collection;
 import java.util.Objects;
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.transforms.windowing.GlobalWindow;
 import org.apache.beam.sdk.transforms.windowing.IntervalWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
 import org.apache.beam.sdk.values.WindowedValue;
@@ -31,6 +32,9 @@
 import org.joda.time.Instant;
 
 /** Matchers that are useful for working with Windowing, Timestamps, etc. */
+@SuppressWarnings({
+  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
+})
 public class WindowMatchers {
 
   public static <T> Matcher<WindowedValue<? extends T>> isWindowedValue(
@@ -99,6 +103,15 @@ public static <T> Matcher<WindowedValue<? extends T>> isSingleWindowedValue(
         Matchers.equalTo(value), Matchers.equalTo(timestamp), Matchers.equalTo(window));
   }
 
+  public static <T> Matcher<WindowedValue<? extends T>> isSingleWindowedValue(
+      T value, BoundedWindow window) {
+    return WindowMatchers.isSingleWindowedValue(
+        Matchers.equalTo(value),
+        Matchers.anything(),
+        Matchers.equalTo(window),
+        Matchers.anything());
+  }
+
   public static <T> Matcher<WindowedValue<? extends T>> isSingleWindowedValue(
       Matcher<T> valueMatcher, long timestamp, long windowStart, long windowEnd) {
     IntervalWindow intervalWindow =
@@ -166,6 +179,15 @@ protected void describeMismatchSafely(
     };
   }
 
+  public static <T> Matcher<WindowedValue<? extends T>> isValueInGlobalWindow(T value) {
+    return isSingleWindowedValue(value, GlobalWindow.INSTANCE);
+  }
+
+  public static <T> Matcher<WindowedValue<? extends T>> isValueInGlobalWindow(
+      T value, Instant timestamp) {
+    return isSingleWindowedValue(value, timestamp, GlobalWindow.INSTANCE);
+  }
+
   @SuppressWarnings({"unchecked", "rawtypes"})
   @SafeVarargs
   public static final <W extends BoundedWindow> Matcher<Iterable<W>> ofWindows(
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/metrics/MetricsContainerImpl.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/metrics/MetricsContainerImpl.java
index 3fc078e83d7e..3532cd3be111 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/metrics/MetricsContainerImpl.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/metrics/MetricsContainerImpl.java
@@ -51,7 +51,6 @@
 import org.apache.beam.runners.core.metrics.MetricUpdates.MetricUpdate;
 import org.apache.beam.sdk.metrics.Distribution;
 import org.apache.beam.sdk.metrics.Histogram;
-import org.apache.beam.sdk.metrics.Metric;
 import org.apache.beam.sdk.metrics.MetricKey;
 import org.apache.beam.sdk.metrics.MetricName;
 import org.apache.beam.sdk.metrics.MetricsContainer;
@@ -608,7 +607,7 @@ public void commitUpdates() {
         });
   }
 
-  private <UserT extends Metric, UpdateT, CellT extends MetricCell<UpdateT>>
+  private <UpdateT, CellT extends MetricCell<UpdateT>>
       ImmutableList<MetricUpdate<UpdateT>> extractCumulatives(MetricsMap<MetricName, CellT> cells) {
     ImmutableList.Builder<MetricUpdate<UpdateT>> updates = ImmutableList.builder();
     cells.forEach(
@@ -619,7 +618,7 @@ ImmutableList<MetricUpdate<UpdateT>> extractCumulatives(MetricsMap<MetricName, C
     return updates.build();
   }
 
-  private <UserT extends Metric, UpdateT, CellT extends MetricCell<UpdateT>>
+  private <UpdateT, CellT extends MetricCell<UpdateT>>
       ImmutableList<MetricUpdate<UpdateT>> extractHistogramCumulatives(
           MetricsMap<KV<MetricName, HistogramData.BucketType>, CellT> cells) {
     ImmutableList.Builder<MetricUpdate<UpdateT>> updates = ImmutableList.builder();
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/metrics/SimpleExecutionState.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/metrics/SimpleExecutionState.java
index f1b7bd07c71c..821a7e06c526 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/metrics/SimpleExecutionState.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/metrics/SimpleExecutionState.java
@@ -65,7 +65,7 @@ public class SimpleExecutionState extends ExecutionState {
 
   /**
    * @param stateName A state name to be used in lull logging when stuck in a state.
-   * @param urn A optional string urn for an execution time metric.
+   * @param urn An optional string urn for an execution time metric.
    * @param labelsMetadata arbitrary metadata to use for reporting purposes.
    */
   public SimpleExecutionState(
diff --git a/runners/core-java/src/main/java/org/apache/beam/runners/core/triggers/ExecutableTriggerStateMachine.java b/runners/core-java/src/main/java/org/apache/beam/runners/core/triggers/ExecutableTriggerStateMachine.java
index 006c34fe153c..2d64986f51a0 100644
--- a/runners/core-java/src/main/java/org/apache/beam/runners/core/triggers/ExecutableTriggerStateMachine.java
+++ b/runners/core-java/src/main/java/org/apache/beam/runners/core/triggers/ExecutableTriggerStateMachine.java
@@ -23,7 +23,6 @@
 import java.io.Serializable;
 import java.util.ArrayList;
 import java.util.List;
-import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 
 /**
  * A wrapper around a trigger used during execution. While an actual trigger may appear multiple
@@ -42,18 +41,17 @@ public class ExecutableTriggerStateMachine implements Serializable {
   private final List<ExecutableTriggerStateMachine> subTriggers = new ArrayList<>();
   private final TriggerStateMachine trigger;
 
-  public static <W extends BoundedWindow> ExecutableTriggerStateMachine create(
-      TriggerStateMachine trigger) {
+  public static ExecutableTriggerStateMachine create(TriggerStateMachine trigger) {
     return create(trigger, 0);
   }
 
-  private static <W extends BoundedWindow> ExecutableTriggerStateMachine create(
+  private static ExecutableTriggerStateMachine create(
       TriggerStateMachine trigger, int nextUnusedIndex) {
 
     return new ExecutableTriggerStateMachine(trigger, nextUnusedIndex);
   }
 
-  public static <W extends BoundedWindow> ExecutableTriggerStateMachine createForOnceTrigger(
+  public static ExecutableTriggerStateMachine createForOnceTrigger(
       TriggerStateMachine trigger, int nextUnusedIndex) {
     return new ExecutableTriggerStateMachine(trigger, nextUnusedIndex);
   }
diff --git a/runners/core-java/src/test/java/org/apache/beam/runners/core/WindowMatchersTest.java b/runners/core-java/src/test/java/org/apache/beam/runners/core/WindowMatchersTest.java
index 9dd8ac502fde..06995a515fcf 100644
--- a/runners/core-java/src/test/java/org/apache/beam/runners/core/WindowMatchersTest.java
+++ b/runners/core-java/src/test/java/org/apache/beam/runners/core/WindowMatchersTest.java
@@ -19,6 +19,7 @@
 
 import static org.hamcrest.MatcherAssert.assertThat;
 
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.IntervalWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
 import org.apache.beam.sdk.values.WindowedValues;
@@ -75,4 +76,29 @@ public void testIsWindowedValueReorderedWindows() {
                 new IntervalWindow(new Instant(windowStart2), new Instant(windowEnd2))),
             PaneInfo.NO_FIRING));
   }
+
+  @Test
+  public void test_IsValueInGlobalWindow_TimestampedValueInGlobalWindow() {
+    assertThat(
+        WindowedValues.timestampedValueInGlobalWindow("foo", new Instant(7)),
+        WindowMatchers.isValueInGlobalWindow("foo", new Instant(7)));
+
+    assertThat(
+        WindowedValues.timestampedValueInGlobalWindow("foo", BoundedWindow.TIMESTAMP_MIN_VALUE),
+        WindowMatchers.isValueInGlobalWindow("foo", BoundedWindow.TIMESTAMP_MIN_VALUE));
+
+    assertThat(
+        WindowedValues.timestampedValueInGlobalWindow("foo", BoundedWindow.TIMESTAMP_MIN_VALUE),
+        WindowMatchers.isValueInGlobalWindow("foo"));
+  }
+
+  @Test
+  public void test_IsValueInGlobalWindow_ValueInGlobalWindow() {
+    assertThat(
+        WindowedValues.valueInGlobalWindow("foo"), WindowMatchers.isValueInGlobalWindow("foo"));
+
+    assertThat(
+        WindowedValues.valueInGlobalWindow("foo"),
+        WindowMatchers.isValueInGlobalWindow("foo", BoundedWindow.TIMESTAMP_MIN_VALUE));
+  }
 }
diff --git a/runners/core-java/src/test/java/org/apache/beam/runners/core/metrics/MetricsContainerStepMapTest.java b/runners/core-java/src/test/java/org/apache/beam/runners/core/metrics/MetricsContainerStepMapTest.java
index dcc3651e66fe..432f7c588191 100644
--- a/runners/core-java/src/test/java/org/apache/beam/runners/core/metrics/MetricsContainerStepMapTest.java
+++ b/runners/core-java/src/test/java/org/apache/beam/runners/core/metrics/MetricsContainerStepMapTest.java
@@ -96,7 +96,7 @@ public class MetricsContainerStepMapTest {
       stringSet.add(FIRST_STRING, SECOND_STRING);
       boundedTrie.add(FIRST_STRING, SECOND_STRING);
     } catch (IOException e) {
-      LOG.error(e.getMessage(), e);
+      LOG.error("Suppressed Exception.", e);
     }
   }
 
diff --git a/runners/core-java/src/test/java/org/apache/beam/runners/core/metrics/MetricsPusherTest.java b/runners/core-java/src/test/java/org/apache/beam/runners/core/metrics/MetricsPusherTest.java
index 5c8902fd7ca5..1509b46dd4e6 100644
--- a/runners/core-java/src/test/java/org/apache/beam/runners/core/metrics/MetricsPusherTest.java
+++ b/runners/core-java/src/test/java/org/apache/beam/runners/core/metrics/MetricsPusherTest.java
@@ -104,7 +104,7 @@ public void processElement(ProcessContext context) {
         counter.inc();
         context.output(context.element());
       } catch (Exception e) {
-        LOG.error(e.getMessage(), e);
+        LOG.error("Suppressed Exception.", e);
       }
     }
   }
diff --git a/runners/core-java/src/test/java/org/apache/beam/runners/core/triggers/ReshuffleTriggerStateMachineTest.java b/runners/core-java/src/test/java/org/apache/beam/runners/core/triggers/ReshuffleTriggerStateMachineTest.java
index 89e100eccda2..191efc33c572 100644
--- a/runners/core-java/src/test/java/org/apache/beam/runners/core/triggers/ReshuffleTriggerStateMachineTest.java
+++ b/runners/core-java/src/test/java/org/apache/beam/runners/core/triggers/ReshuffleTriggerStateMachineTest.java
@@ -21,7 +21,6 @@
 import static org.junit.Assert.assertFalse;
 import static org.junit.Assert.assertTrue;
 
-import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.FixedWindows;
 import org.apache.beam.sdk.transforms.windowing.IntervalWindow;
 import org.joda.time.Duration;
@@ -35,7 +34,7 @@
 public class ReshuffleTriggerStateMachineTest {
 
   /** Public so that other tests can instantiate {@link ReshuffleTriggerStateMachine}. */
-  public static <W extends BoundedWindow> ReshuffleTriggerStateMachine forTest() {
+  public static ReshuffleTriggerStateMachine forTest() {
     return ReshuffleTriggerStateMachine.create();
   }
 
diff --git a/runners/direct-java/build.gradle b/runners/direct-java/build.gradle
index 1d9ba7600966..1ab702da3213 100644
--- a/runners/direct-java/build.gradle
+++ b/runners/direct-java/build.gradle
@@ -118,7 +118,7 @@ def sickbayTests = [
 
 task needsRunnerTests(type: Test) {
   group = "Verification"
-  description = "Runs tests that require a runner to validate that piplines/transforms work correctly"
+  description = "Runs tests that require a runner to validate that pipelines/transforms work correctly"
 
   testLogging.showStandardStreams = true
 
diff --git a/runners/direct-java/src/main/java/org/apache/beam/runners/direct/GroupAlsoByWindowEvaluatorFactory.java b/runners/direct-java/src/main/java/org/apache/beam/runners/direct/GroupAlsoByWindowEvaluatorFactory.java
index 0e011aa5cd9b..c6726fb3463f 100644
--- a/runners/direct-java/src/main/java/org/apache/beam/runners/direct/GroupAlsoByWindowEvaluatorFactory.java
+++ b/runners/direct-java/src/main/java/org/apache/beam/runners/direct/GroupAlsoByWindowEvaluatorFactory.java
@@ -246,8 +246,8 @@ private BundleWindowedValueReceiver(UncommittedBundle<KV<K, Iterable<V>>> bundle
     }
 
     @Override
-    public void output(WindowedValue<KV<K, Iterable<V>>> valueWithMetadata) {
-      bundle.add(valueWithMetadata);
+    public void output(WindowedValue<KV<K, Iterable<V>>> windowedValue) {
+      bundle.add(windowedValue);
     }
   }
 }
diff --git a/runners/direct-java/src/main/java/org/apache/beam/runners/direct/SplittableProcessElementsEvaluatorFactory.java b/runners/direct-java/src/main/java/org/apache/beam/runners/direct/SplittableProcessElementsEvaluatorFactory.java
index 57ac8a4e73d2..b134e872b65d 100644
--- a/runners/direct-java/src/main/java/org/apache/beam/runners/direct/SplittableProcessElementsEvaluatorFactory.java
+++ b/runners/direct-java/src/main/java/org/apache/beam/runners/direct/SplittableProcessElementsEvaluatorFactory.java
@@ -65,7 +65,8 @@ class SplittableProcessElementsEvaluatorFactory<
               public DoFnLifecycleManager load(final AppliedPTransform<?, ?, ?> application) {
                 checkArgument(
                     ProcessElements.class.isInstance(application.getTransform()),
-                    "No know extraction of the fn from " + application);
+                    "No know extraction of the fn from %s",
+                    application);
                 final ProcessElements<
                         InputT, OutputT, RestrictionT, PositionT, WatermarkEstimatorStateT>
                     transform =
diff --git a/runners/direct-java/src/main/java/org/apache/beam/runners/direct/WindowEvaluatorFactory.java b/runners/direct-java/src/main/java/org/apache/beam/runners/direct/WindowEvaluatorFactory.java
index 27de46bf102b..2724312c99a7 100644
--- a/runners/direct-java/src/main/java/org/apache/beam/runners/direct/WindowEvaluatorFactory.java
+++ b/runners/direct-java/src/main/java/org/apache/beam/runners/direct/WindowEvaluatorFactory.java
@@ -90,9 +90,7 @@ public WindowIntoEvaluator(
     public void processElement(WindowedValue<InputT> compressedElement) throws Exception {
       for (WindowedValue<InputT> element : compressedElement.explodeWindows()) {
         Collection<? extends BoundedWindow> windows = assignWindows(windowFn, element);
-        outputBundle.add(
-            WindowedValues.of(
-                element.getValue(), element.getTimestamp(), windows, element.getPaneInfo()));
+        WindowedValues.builder(element).setWindows(windows).setReceiver(outputBundle::add).output();
       }
     }
 
diff --git a/runners/direct-java/src/test/java/org/apache/beam/runners/direct/DirectRunnerTest.java b/runners/direct-java/src/test/java/org/apache/beam/runners/direct/DirectRunnerTest.java
index 155f5566cc83..dc45de20002f 100644
--- a/runners/direct-java/src/test/java/org/apache/beam/runners/direct/DirectRunnerTest.java
+++ b/runners/direct-java/src/test/java/org/apache/beam/runners/direct/DirectRunnerTest.java
@@ -797,6 +797,8 @@ public Coder<T> getOutputCoder() {
     }
   }
 
+  @SuppressWarnings(
+      "NullableOptional") // null value used to indicates no elements put to the queue yet
   private static class StaticQueue<T> implements Serializable {
 
     static class StaticQueueSource<T> extends UnboundedSource<T, StaticQueueSource.Checkpoint<T>> {
diff --git a/runners/flink/src/main/java/org/apache/beam/runners/flink/FlinkDetachedRunnerResult.java b/runners/flink/src/main/java/org/apache/beam/runners/flink/FlinkDetachedRunnerResult.java
index 77d0e7d3434c..f7d82065b658 100644
--- a/runners/flink/src/main/java/org/apache/beam/runners/flink/FlinkDetachedRunnerResult.java
+++ b/runners/flink/src/main/java/org/apache/beam/runners/flink/FlinkDetachedRunnerResult.java
@@ -98,7 +98,7 @@ public State waitUntilFinish(Duration duration) {
         return state;
       }
       try {
-        Thread.sleep(jobCheckIntervalInSecs * 1000);
+        Thread.sleep(jobCheckIntervalInSecs * 1000L);
       } catch (InterruptedException e) {
         throw new RuntimeException(e);
       }
diff --git a/runners/flink/src/main/java/org/apache/beam/runners/flink/FlinkStreamingTransformTranslators.java b/runners/flink/src/main/java/org/apache/beam/runners/flink/FlinkStreamingTransformTranslators.java
index 19ccdb76af58..79a90c554027 100644
--- a/runners/flink/src/main/java/org/apache/beam/runners/flink/FlinkStreamingTransformTranslators.java
+++ b/runners/flink/src/main/java/org/apache/beam/runners/flink/FlinkStreamingTransformTranslators.java
@@ -1403,20 +1403,16 @@ private SourceContextWrapper(SourceContext<WindowedValue<OutputT>> ctx) {
       @Override
       public void collect(WindowedValue<ValueWithRecordId<OutputT>> element) {
         OutputT originalValue = element.getValue().getValue();
-        WindowedValue<OutputT> output =
-            WindowedValues.of(
-                originalValue, element.getTimestamp(), element.getWindows(), element.getPaneInfo());
-        ctx.collect(output);
+        WindowedValues.builder(element).withValue(originalValue).setReceiver(ctx::collect).output();
       }
 
       @Override
       public void collectWithTimestamp(
           WindowedValue<ValueWithRecordId<OutputT>> element, long timestamp) {
         OutputT originalValue = element.getValue().getValue();
-        WindowedValue<OutputT> output =
-            WindowedValues.of(
-                originalValue, element.getTimestamp(), element.getWindows(), element.getPaneInfo());
-        ctx.collectWithTimestamp(output, timestamp);
+        WindowedValues.builder(element)
+            .withValue(originalValue)
+            .setReceiver(wv -> ctx.collectWithTimestamp(wv, timestamp));
       }
 
       @Override
diff --git a/runners/flink/src/main/java/org/apache/beam/runners/flink/translation/functions/FlinkDoFnFunction.java b/runners/flink/src/main/java/org/apache/beam/runners/flink/translation/functions/FlinkDoFnFunction.java
index a707e366c8a5..882e7dfe46b1 100644
--- a/runners/flink/src/main/java/org/apache/beam/runners/flink/translation/functions/FlinkDoFnFunction.java
+++ b/runners/flink/src/main/java/org/apache/beam/runners/flink/translation/functions/FlinkDoFnFunction.java
@@ -223,12 +223,10 @@ public void setCollector(Collector<WindowedValue<RawUnionValue>> collector) {
     @Override
     public <T> void output(TupleTag<T> tag, WindowedValue<T> output) {
       checkStateNotNull(collector);
-      collector.collect(
-          WindowedValues.of(
-              new RawUnionValue(0 /* single output */, output.getValue()),
-              output.getTimestamp(),
-              output.getWindows(),
-              output.getPaneInfo()));
+      WindowedValues.builder(output)
+          .withValue(new RawUnionValue(0 /* single output */, output.getValue()))
+          .setReceiver(collector::collect)
+          .output();
     }
   }
 
@@ -257,13 +255,10 @@ public void setCollector(Collector<WindowedValue<RawUnionValue>> collector) {
     @Override
     public <T> void output(TupleTag<T> tag, WindowedValue<T> output) {
       checkStateNotNull(collector);
-
-      collector.collect(
-          WindowedValues.of(
-              new RawUnionValue(outputMap.get(tag), output.getValue()),
-              output.getTimestamp(),
-              output.getWindows(),
-              output.getPaneInfo()));
+      WindowedValues.builder(output)
+          .withValue(new RawUnionValue(outputMap.get(tag), output.getValue()))
+          .setReceiver(collector::collect)
+          .output();
     }
   }
 }
diff --git a/runners/flink/src/main/java/org/apache/beam/runners/flink/translation/functions/FlinkNonMergingReduceFunction.java b/runners/flink/src/main/java/org/apache/beam/runners/flink/translation/functions/FlinkNonMergingReduceFunction.java
index bcc5a244d3b1..38c6ad27cf12 100644
--- a/runners/flink/src/main/java/org/apache/beam/runners/flink/translation/functions/FlinkNonMergingReduceFunction.java
+++ b/runners/flink/src/main/java/org/apache/beam/runners/flink/translation/functions/FlinkNonMergingReduceFunction.java
@@ -101,11 +101,11 @@ public void reduce(
                   (WindowedValue<KV<K, InputT>> wv) ->
                       Objects.requireNonNull(wv).getValue().getValue()));
     }
-    coll.collect(
-        WindowedValues.of(
-            KV.of(first.getValue().getKey(), values),
-            combinedTimestamp,
-            first.getWindows(),
-            PaneInfo.ON_TIME_AND_ONLY_FIRING));
+    WindowedValues.builder(first)
+        .withValue(KV.of(first.getValue().getKey(), values))
+        .setReceiver(coll::collect)
+        .setPaneInfo(PaneInfo.ON_TIME_AND_ONLY_FIRING)
+        .setTimestamp(combinedTimestamp)
+        .output();
   }
 }
diff --git a/runners/google-cloud-dataflow-java/build.gradle b/runners/google-cloud-dataflow-java/build.gradle
index 90d388b8bb68..d6f860382605 100644
--- a/runners/google-cloud-dataflow-java/build.gradle
+++ b/runners/google-cloud-dataflow-java/build.gradle
@@ -52,8 +52,8 @@ evaluationDependsOn(":sdks:java:container:java11")
 
 ext.dataflowLegacyEnvironmentMajorVersion = '8'
 ext.dataflowFnapiEnvironmentMajorVersion = '8'
-ext.dataflowLegacyContainerVersion = 'beam-master-20250605'
-ext.dataflowFnapiContainerVersion = 'beam-master-20250605'
+ext.dataflowLegacyContainerVersion = 'beam-master-20250811'
+ext.dataflowFnapiContainerVersion = 'beam-master-20250811'
 ext.dataflowContainerBaseRepository = 'gcr.io/cloud-dataflow/v1beta3'
 
 processResources {
@@ -153,9 +153,11 @@ def firestoreDb = project.findProperty('firestoreDb') ?: 'firestoredb'
 
 def dockerImageRoot = project.findProperty('dockerImageRoot') ?: "us.gcr.io/${gcpProject.replaceAll(':', '/')}/java-postcommit-it"
 def dockerJavaImageContainer = "${dockerImageRoot}/java"
+def dockerJavaDistrolessImageContainer = "${dockerImageRoot}/java_distroless"
 def dockerPythonImageContainer = "${dockerImageRoot}/python"
 def dockerTag = new Date().format('yyyyMMddHHmmss')
 ext.dockerJavaImageName = "${dockerJavaImageContainer}:${dockerTag}"
+ext.dockerJavaDistrolessImageName = "${dockerJavaDistrolessImageContainer}:${dockerTag}"
 ext.dockerPythonImageName = "${dockerPythonImageContainer}:${dockerTag}"
 
 def legacyPipelineOptions = [
@@ -164,21 +166,35 @@ def legacyPipelineOptions = [
   "--region=${gcpRegion}",
   "--tempRoot=${dataflowValidatesTempRoot}",
   "--dataflowWorkerJar=${dataflowLegacyWorkerJar}",
-  "--workerHarnessContainerImage=",
   "--experiments=enable_lineage"
 ]
 
-def runnerV2PipelineOptions = [
+// For the following test tasks using legacy worker, set workerHarnessContainerImage to empty to
+// make Dataflow pick up the non-versioned container image, which handles a staged worker jar,
+// unless testJavaVersion is specified, then the client picks up the current beam-master container.
+if (!project.hasProperty('testJavaVersion')) {
+  legacyPipelineOptions += ["--workerHarnessContainerImage="]
+}
+
+def runnerV2CommonPipelineOptions = [
   "--runner=TestDataflowRunner",
   "--project=${gcpProject}",
   "--region=${gcpRegion}",
   "--tempRoot=${dataflowValidatesTempRoot}",
-  "--sdkContainerImage=${dockerJavaImageContainer}:${dockerTag}",
   "--experiments=use_unified_worker,use_runner_v2",
   "--firestoreDb=${firestoreDb}",
   "--experiments=enable_lineage"
 ]
 
+def runnerV2PipelineOptions = runnerV2CommonPipelineOptions + [
+  "--sdkContainerImage=${dockerJavaImageContainer}:${dockerTag}"
+]
+
+def runnerV2DistrolessPipelineOptions = runnerV2CommonPipelineOptions + [
+  "--sdkContainerImage=${dockerJavaDistrolessImageContainer}:${dockerTag}"
+]
+
+
 def commonLegacyExcludeCategories = [
   // Should be run only in a properly configured SDK harness environment
   'org.apache.beam.sdk.testing.UsesSdkHarnessEnvironment',
@@ -209,8 +225,6 @@ def commonRunnerV2ExcludeCategories = [
         'org.apache.beam.sdk.testing.UsesBoundedTrieMetrics', // Dataflow QM as of now does not support returning back BoundedTrie in metric result.
 ]
 
-// For the following test tasks using legacy worker, set workerHarnessContainerImage to empty to
-// make Dataflow pick up the non-versioned container image, which handles a staged worker jar.
 def createLegacyWorkerValidatesRunnerTest = { Map args ->
   def name = args.name
   def pipelineOptions = args.pipelineOptions ?: legacyPipelineOptions
@@ -278,91 +292,52 @@ def createRunnerV2ValidatesRunnerTest = { Map args ->
   }
 }
 
-tasks.register('examplesJavaRunnerV2IntegrationTestDistroless', Test.class) {
-  group = "verification"
-  dependsOn 'buildAndPushDistrolessContainerImage'
-  def javaVer = getSupportedJavaVersion(project.findProperty('testJavaVersion') as String)
-  def repository = "us.gcr.io/apache-beam-testing/${System.getenv('USER')}"
-  def tag = project.findProperty('dockerTag')
-  def imageURL = "${repository}/beam_${javaVer}_sdk_distroless:${tag}"
-  def pipelineOptions = [
-          "--runner=TestDataflowRunner",
-          "--project=${gcpProject}",
-          "--region=${gcpRegion}",
-          "--tempRoot=${dataflowValidatesTempRoot}",
-          "--sdkContainerImage=${imageURL}",
-          "--experiments=use_unified_worker,use_runner_v2",
-          "--firestoreDb=${firestoreDb}",
-  ]
-  systemProperty "beamTestPipelineOptions", JsonOutput.toJson(pipelineOptions)
-
-  include '**/*IT.class'
+// ************************************************************************************************
+// Tasks for pushing containers for testing. These ensure that Dataflow integration tests run with
+// containers built from HEAD, for testing in-progress code changes.
+//
+// Tasks which consume docker images from the registry should depend on these
+// tasks directly ('dependsOn buildAndPushDockerJavaContainer'). This ensures the correct
+// task ordering such that the registry doesn't get cleaned up prior to task completion.
+// ************************************************************************************************
 
-  maxParallelForks 4
-  classpath = configurations.examplesJavaIntegrationTest
-  testClassesDirs = files(project(":examples:java").sourceSets.test.output.classesDirs)
-  useJUnit { }
-}
+def buildAndPushDockerJavaContainer = tasks.register("buildAndPushDockerJavaContainer") {
+  def javaVer = getSupportedJavaVersion(project.findProperty('testJavaVersion') as String)
 
-tasks.register('buildAndPushDistrolessContainerImage', Task.class) {
-  // Only Java 17 and 21 are supported.
-  // See https://github.com/GoogleContainerTools/distroless/tree/main/java#image-contents.
-  def allowed = ["java17", "java21"]
+  dependsOn ":sdks:java:container:${javaVer}:docker"
+  def defaultDockerImageName = containerImageName(
+          name: "${project.docker_image_default_repo_prefix}${javaVer}_sdk",
+          root: "apache",
+          tag: project.sdk_version)
   doLast {
-    def javaVer = getSupportedJavaVersion(project.findProperty('testJavaVersion') as String)
-    if (!allowed.contains(javaVer)) {
-      throw new GradleException("testJavaVersion must be one of ${allowed}, got: ${javaVer}")
-    }
-    if (!project.hasProperty('dockerTag')) {
-      throw new GradleException("dockerTag is missing but required")
-    }
-    def repository = "us.gcr.io/apache-beam-testing/${System.getenv('USER')}"
-    def tag = project.findProperty('dockerTag')
-    def imageURL = "${repository}/beam_${javaVer}_sdk_distroless:${tag}"
     exec {
-      executable 'docker'
-      workingDir rootDir
-      args = [
-              'buildx',
-              'build',
-              '-t',
-              imageURL,
-              '-f',
-              'sdks/java/container/distroless/Dockerfile',
-              "--build-arg=BEAM_BASE=gcr.io/apache-beam-testing/beam-sdk/beam_${javaVer}_sdk",
-              "--build-arg=DISTROLESS_BASE=gcr.io/distroless/${javaVer}-debian12",
-              '.'
-      ]
+      commandLine "docker", "tag", "${defaultDockerImageName}", "${dockerJavaImageName}"
     }
     exec {
-      executable 'docker'
-      args = ['push', imageURL]
+      commandLine "gcloud", "docker", "--", "push", "${dockerJavaImageName}"
     }
   }
 }
 
-// Push docker images to a container registry for use within tests.
-// NB: Tasks which consume docker images from the registry should depend on this
-// task directly ('dependsOn buildAndPushDockerJavaContainer'). This ensures the correct
-// task ordering such that the registry doesn't get cleaned up prior to task completion.
-def buildAndPushDockerJavaContainer = tasks.register("buildAndPushDockerJavaContainer") {
+def buildAndPushDistrolessDockerJavaContainer = tasks.register("buildAndPushDistrolessDockerJavaContainer") {
   def javaVer = getSupportedJavaVersion(project.findProperty('testJavaVersion') as String)
 
-  dependsOn ":sdks:java:container:${javaVer}:docker"
+  dependsOn ":sdks:java:container:distroless:${javaVer}:docker"
   def defaultDockerImageName = containerImageName(
-          name: "${project.docker_image_default_repo_prefix}${javaVer}_sdk",
-          root: "apache",
-          tag: project.sdk_version)
+    name: "${project.docker_image_default_repo_prefix}${javaVer}_sdk_distroless",
+    root: "apache",
+    tag: project.sdk_version)
   doLast {
     exec {
-      commandLine "docker", "tag", "${defaultDockerImageName}", "${dockerJavaImageName}"
+      commandLine "docker", "tag", "${defaultDockerImageName}", "${dockerJavaDistrolessImageName}"
     }
     exec {
-      commandLine "gcloud", "docker", "--", "push", "${dockerJavaImageName}"
+      commandLine "gcloud", "docker", "--", "push", "${dockerJavaDistrolessImageName}"
     }
   }
 }
 
+
 // Clean up built Java images
 def cleanUpDockerJavaImages = tasks.register("cleanUpDockerJavaImages") {
   doLast {
@@ -709,20 +684,7 @@ task googleCloudPlatformRunnerV2IntegrationTest(type: Test) {
   }
 }
 
-task examplesJavaRunnerV2PreCommit(type: Test) {
-  group = "Verification"
-  dependsOn buildAndPushDockerJavaContainer
-  systemProperty "beamTestPipelineOptions", JsonOutput.toJson(runnerV2PipelineOptions)
-  include '**/WordCountIT.class'
-  include '**/WindowedWordCountIT.class'
-
-  maxParallelForks 4
-  classpath = configurations.examplesJavaIntegrationTest
-  testClassesDirs = files(project(":examples:java").sourceSets.test.output.classesDirs)
-  useJUnit { }
-}
-
-task examplesJavaRunnerV2IntegrationTest(type: Test) {
+tasks.register('examplesJavaRunnerV2IntegrationTest', Test.class) {
   group = "Verification"
   dependsOn buildAndPushDockerJavaContainer
   if (project.hasProperty("testJavaVersion")) {
@@ -747,6 +709,23 @@ task examplesJavaRunnerV2IntegrationTest(type: Test) {
   useJUnit { }
 }
 
+tasks.register('examplesJavaRunnerV2IntegrationTestDistroless', Test.class) {
+  group = "Verification"
+  dependsOn buildAndPushDistrolessDockerJavaContainer
+  if (project.hasProperty("testJavaVersion")) {
+    dependsOn ":sdks:java:testing:test-utils:verifyJavaVersion${project.property("testJavaVersion")}"
+  }
+
+  systemProperty "beamTestPipelineOptions", JsonOutput.toJson(runnerV2DistrolessPipelineOptions)
+
+  include '**/*IT.class'
+
+  maxParallelForks 4
+  classpath = configurations.examplesJavaIntegrationTest
+  testClassesDirs = files(project(":examples:java").sourceSets.test.output.classesDirs)
+  useJUnit { }
+}
+
 task coreSDKJavaLegacyWorkerIntegrationTest(type: Test) {
   group = "Verification"
   dependsOn ":runners:google-cloud-dataflow-java:worker:shadowJar"
@@ -784,6 +763,38 @@ task coreSDKJavaRunnerV2IntegrationTest(type: Test) {
   useJUnit { }
 }
 
+// ****************************************************************************************
+// Tasks for easy invocation from GitHub Actions and command line.
+// These tasks reference whether they are expected to be run "precommit" or "postcommit"
+// in CI/CD settings.
+// ****************************************************************************************
+
+tasks.register("examplesJavaRunnerV2PreCommit", Test.class) {
+  group = "Verification"
+  dependsOn buildAndPushDockerJavaContainer
+  systemProperty "beamTestPipelineOptions", JsonOutput.toJson(runnerV2PipelineOptions)
+  include '**/WordCountIT.class'
+  include '**/WindowedWordCountIT.class'
+
+  maxParallelForks 4
+  classpath = configurations.examplesJavaIntegrationTest
+  testClassesDirs = files(project(":examples:java").sourceSets.test.output.classesDirs)
+  useJUnit { }
+}
+
+tasks.register("examplesJavaDistrolessRunnerV2PreCommit", Test.class) {
+  group = "Verification"
+  dependsOn buildAndPushDistrolessDockerJavaContainer
+  systemProperty "beamTestPipelineOptions", JsonOutput.toJson(runnerV2DistrolessPipelineOptions)
+  include '**/WordCountIT.class'
+  include '**/WindowedWordCountIT.class'
+
+  maxParallelForks 4
+  classpath = configurations.examplesJavaIntegrationTest
+  testClassesDirs = files(project(":examples:java").sourceSets.test.output.classesDirs)
+  useJUnit { }
+}
+
 task postCommit {
   group = "Verification"
   description = "Various integration tests using the Dataflow runner."
@@ -799,6 +810,10 @@ task postCommitRunnerV2 {
   dependsOn coreSDKJavaRunnerV2IntegrationTest
 }
 
+//
+// Archetype validations
+//
+
 def gcsBucket = project.findProperty('gcsBucket') ?: 'temp-storage-for-release-validation-tests/nightly-snapshot-validation'
 def bqDataset = project.findProperty('bqDataset') ?: 'beam_postrelease_mobile_gaming'
 def pubsubTopic = project.findProperty('pubsubTopic') ?: 'java_mobile_gaming_topic'
@@ -835,33 +850,3 @@ task GCSUpload(type: JavaExec) {
   args "--stagingLocation=${dataflowUploadTemp}/staging",
        "--filesToStage=${testFilesToStage}"
 }
-
-def buildAndPushDistrolessDockerJavaContainer = tasks.register("buildAndPushDistrolessDockerJavaContainer") {
-  def javaVer = getSupportedJavaVersion(project.findProperty('testJavaVersion') as String)
-  dependsOn ":sdks:java:container:distroless:${javaVer}:docker"
-  def defaultDockerImageName = containerImageName(
-          name: "${project.docker_image_default_repo_prefix}${javaVer}_sdk_distroless",
-          root: "apache",
-          tag: project.sdk_version)
-  doLast {
-    exec {
-      commandLine "docker", "tag", "${defaultDockerImageName}", "${dockerJavaImageName}"
-    }
-    exec {
-      commandLine "gcloud", "docker", "--", "push", "${dockerJavaImageName}"
-    }
-  }
-}
-
-task examplesJavaDistrolessRunnerV2PreCommit(type: Test) {
-  group = "Verification"
-  dependsOn buildAndPushDistrolessDockerJavaContainer
-  systemProperty "beamTestPipelineOptions", JsonOutput.toJson(runnerV2PipelineOptions)
-  include '**/WordCountIT.class'
-  include '**/WindowedWordCountIT.class'
-
-  maxParallelForks 4
-  classpath = configurations.examplesJavaIntegrationTest
-  testClassesDirs = files(project(":examples:java").sourceSets.test.output.classesDirs)
-  useJUnit { }
-}
diff --git a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/BatchViewOverrides.java b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/BatchViewOverrides.java
index 537a2d855921..10b41bb5b5ba 100644
--- a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/BatchViewOverrides.java
+++ b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/BatchViewOverrides.java
@@ -215,8 +215,7 @@ public PCollectionView<Map<K, V>> expand(PCollection<KV<K, V>> input) {
       return this.applyInternal(input);
     }
 
-    private <W extends BoundedWindow> PCollectionView<Map<K, V>> applyInternal(
-        PCollection<KV<K, V>> input) {
+    private PCollectionView<Map<K, V>> applyInternal(PCollection<KV<K, V>> input) {
       try {
         return BatchViewAsMultimap.applyForMapLike(runner, input, view, true /* unique keys */);
       } catch (NonDeterministicException e) {
@@ -704,8 +703,7 @@ public PCollectionView<Map<K, Iterable<V>>> expand(PCollection<KV<K, V>> input)
       return this.applyInternal(input);
     }
 
-    private <W extends BoundedWindow> PCollectionView<Map<K, Iterable<V>>> applyInternal(
-        PCollection<KV<K, V>> input) {
+    private PCollectionView<Map<K, Iterable<V>>> applyInternal(PCollection<KV<K, V>> input) {
       try {
         return applyForMapLike(runner, input, view, false /* unique keys not expected */);
       } catch (NonDeterministicException e) {
@@ -1395,6 +1393,16 @@ public PaneInfo getPaneInfo() {
       return PaneInfo.NO_FIRING;
     }
 
+    @Override
+    public @Nullable String getRecordId() {
+      return null;
+    }
+
+    @Override
+    public @Nullable Long getRecordOffset() {
+      return null;
+    }
+
     @Override
     public Iterable<WindowedValue<T>> explodeWindows() {
       return Collections.emptyList();
diff --git a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/DataflowPipelineJob.java b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/DataflowPipelineJob.java
index 3838534c6aee..2be4b569ca9b 100644
--- a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/DataflowPipelineJob.java
+++ b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/DataflowPipelineJob.java
@@ -54,7 +54,8 @@
 
 /** A DataflowPipelineJob represents a job submitted to Dataflow using {@link DataflowRunner}. */
 @SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
+  "nullness", // TODO(https://github.com/apache/beam/issues/20497)
+  "Slf4jDoNotLogMessageOfExceptionExplicitly", // intended, sent full stacktrace to LOG.debug
 })
 public class DataflowPipelineJob implements PipelineResult {
 
diff --git a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/DataflowRunner.java b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/DataflowRunner.java
index d25a37e92dc3..de6a039b7077 100644
--- a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/DataflowRunner.java
+++ b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/DataflowRunner.java
@@ -1040,9 +1040,12 @@ protected RunnerApi.Pipeline applySdkEnvironmentOverrides(
             && !updated
             // don't update if the container image is already configured by DataflowRunner
             && !containerImage.equals(getContainerImageForJob(options))) {
+          String imageAndTag =
+              normalizeDataflowImageAndTag(
+                  containerImage.substring(containerImage.lastIndexOf("/")));
           containerImage =
               DataflowRunnerInfo.getDataflowRunnerInfo().getContainerImageBaseRepository()
-                  + containerImage.substring(containerImage.lastIndexOf("/"));
+                  + imageAndTag;
         }
         environmentBuilder.setPayload(
             RunnerApi.DockerPayload.newBuilder()
@@ -1055,6 +1058,23 @@ protected RunnerApi.Pipeline applySdkEnvironmentOverrides(
     return pipelineBuilder.build();
   }
 
+  static String normalizeDataflowImageAndTag(String imageAndTag) {
+    if (imageAndTag.startsWith("/beam_java")
+        || imageAndTag.startsWith("/beam_python")
+        || imageAndTag.startsWith("/beam_go_")) {
+      int tagIdx = imageAndTag.lastIndexOf(":");
+      if (tagIdx > 0) {
+        // For release candidates, apache/beam_ images has rc tag while Dataflow does not
+        String tag = imageAndTag.substring(tagIdx); // e,g, ":2.xx.0rc1"
+        int mayRc = tag.toLowerCase().lastIndexOf("rc");
+        if (mayRc > 0) {
+          imageAndTag = imageAndTag.substring(0, tagIdx) + tag.substring(0, mayRc);
+        }
+      }
+    }
+    return imageAndTag;
+  }
+
   @VisibleForTesting
   protected RunnerApi.Pipeline resolveArtifacts(RunnerApi.Pipeline pipeline) {
     RunnerApi.Pipeline.Builder pipelineBuilder = pipeline.toBuilder();
diff --git a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/RedistributeByKeyOverrideFactory.java b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/RedistributeByKeyOverrideFactory.java
index cea195ed2013..4375cc5adcfe 100644
--- a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/RedistributeByKeyOverrideFactory.java
+++ b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/RedistributeByKeyOverrideFactory.java
@@ -17,7 +17,6 @@
  */
 package org.apache.beam.runners.dataflow;
 
-import java.util.Collections;
 import org.apache.beam.runners.dataflow.internal.DataflowGroupByKey;
 import org.apache.beam.sdk.runners.AppliedPTransform;
 import org.apache.beam.sdk.runners.PTransformOverrideFactory;
@@ -134,12 +133,14 @@ public Duration getAllowedTimestampSkew() {
 
                 @ProcessElement
                 public void processElement(
-                    @Element KV<K, ValueInSingleWindow<V>> kv, OutputReceiver<KV<K, V>> r) {
-                  r.outputWindowedValue(
-                      KV.of(kv.getKey(), kv.getValue().getValue()),
-                      kv.getValue().getTimestamp(),
-                      Collections.singleton(kv.getValue().getWindow()),
-                      kv.getValue().getPaneInfo());
+                    @Element KV<K, ValueInSingleWindow<V>> kv,
+                    OutputReceiver<KV<K, V>> outputReceiver) {
+                  outputReceiver
+                      .builder(KV.of(kv.getKey(), kv.getValue().getValue()))
+                      .setTimestamp(kv.getValue().getTimestamp())
+                      .setWindow(kv.getValue().getWindow())
+                      .setPaneInfo(kv.getValue().getPaneInfo())
+                      .output();
                 }
               }));
     }
diff --git a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/CloudObjectTranslators.java b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/CloudObjectTranslators.java
index c0a83c5a8226..a85a9c1addf3 100644
--- a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/CloudObjectTranslators.java
+++ b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/CloudObjectTranslators.java
@@ -290,7 +290,7 @@ public CloudObject toCloudObject(FullWindowedValueCoder target, SdkComponents sd
       @Override
       public FullWindowedValueCoder fromCloudObject(CloudObject object) {
         List<Coder<?>> components = getComponents(object);
-        checkArgument(components.size() == 2, "Expecting 2 components, got " + components.size());
+        checkArgument(components.size() == 2, "Expecting 2 components, got %s", components.size());
         @SuppressWarnings("unchecked")
         Coder<? extends BoundedWindow> window = (Coder<? extends BoundedWindow>) components.get(1);
         return FullWindowedValueCoder.of(components.get(0), window);
diff --git a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/RowCoderCloudObjectTranslator.java b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/RowCoderCloudObjectTranslator.java
index 9e11ffe2a794..df062aad645a 100644
--- a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/RowCoderCloudObjectTranslator.java
+++ b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/RowCoderCloudObjectTranslator.java
@@ -56,7 +56,6 @@ public RowCoder fromCloudObject(CloudObject cloudObject) {
       SchemaApi.Schema.Builder schemaBuilder = SchemaApi.Schema.newBuilder();
       JsonFormat.parser().merge(Structs.getString(cloudObject, SCHEMA), schemaBuilder);
       Schema schema = SchemaTranslation.schemaFromProto(schemaBuilder.build());
-      SchemaCoderCloudObjectTranslator.overrideEncodingPositions(schema);
       return RowCoder.of(schema);
     } catch (IOException e) {
       throw new RuntimeException(e);
diff --git a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/SchemaCoderCloudObjectTranslator.java b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/SchemaCoderCloudObjectTranslator.java
index fa58590ba798..029f6c3c61d7 100644
--- a/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/SchemaCoderCloudObjectTranslator.java
+++ b/runners/google-cloud-dataflow-java/src/main/java/org/apache/beam/runners/dataflow/util/SchemaCoderCloudObjectTranslator.java
@@ -18,15 +18,11 @@
 package org.apache.beam.runners.dataflow.util;
 
 import java.io.IOException;
-import java.util.UUID;
-import javax.annotation.Nullable;
 import org.apache.beam.model.pipeline.v1.SchemaApi;
-import org.apache.beam.sdk.coders.RowCoder;
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.schemas.SchemaCoder;
 import org.apache.beam.sdk.schemas.SchemaTranslation;
 import org.apache.beam.sdk.transforms.SerializableFunction;
-import org.apache.beam.sdk.util.Preconditions;
 import org.apache.beam.sdk.util.SerializableUtils;
 import org.apache.beam.sdk.util.StringUtils;
 import org.apache.beam.sdk.util.construction.SdkComponents;
@@ -102,50 +98,12 @@ public SchemaCoder fromCloudObject(CloudObject cloudObject) {
       SchemaApi.Schema.Builder schemaBuilder = SchemaApi.Schema.newBuilder();
       JsonFormat.parser().merge(Structs.getString(cloudObject, SCHEMA), schemaBuilder);
       Schema schema = SchemaTranslation.schemaFromProto(schemaBuilder.build());
-      overrideEncodingPositions(schema);
       return SchemaCoder.of(schema, typeDescriptor, toRowFunction, fromRowFunction);
     } catch (IOException e) {
       throw new RuntimeException(e);
     }
   }
 
-  static void overrideEncodingPositions(Schema schema) {
-    @Nullable UUID uuid = schema.getUUID();
-    if (schema.isEncodingPositionsOverridden() && uuid != null) {
-      RowCoder.overrideEncodingPositions(uuid, schema.getEncodingPositions());
-    }
-    schema.getFields().stream()
-        .map(Schema.Field::getType)
-        .forEach(SchemaCoderCloudObjectTranslator::overrideEncodingPositions);
-  }
-
-  private static void overrideEncodingPositions(Schema.FieldType fieldType) {
-    switch (fieldType.getTypeName()) {
-      case ROW:
-        overrideEncodingPositions(Preconditions.checkArgumentNotNull(fieldType.getRowSchema()));
-        break;
-      case ARRAY:
-      case ITERABLE:
-        overrideEncodingPositions(
-            Preconditions.checkArgumentNotNull(fieldType.getCollectionElementType()));
-        break;
-      case MAP:
-        overrideEncodingPositions(Preconditions.checkArgumentNotNull(fieldType.getMapKeyType()));
-        overrideEncodingPositions(Preconditions.checkArgumentNotNull(fieldType.getMapValueType()));
-        break;
-      case LOGICAL_TYPE:
-        Schema.LogicalType logicalType =
-            Preconditions.checkArgumentNotNull(fieldType.getLogicalType());
-        @Nullable Schema.FieldType argumentType = logicalType.getArgumentType();
-        if (argumentType != null) {
-          overrideEncodingPositions(argumentType);
-        }
-        overrideEncodingPositions(logicalType.getBaseType());
-        break;
-      default:
-    }
-  }
-
   @Override
   public Class<? extends SchemaCoder> getSupportedClass() {
     return SchemaCoder.class;
diff --git a/runners/google-cloud-dataflow-java/src/test/java/org/apache/beam/runners/dataflow/DataflowRunnerTest.java b/runners/google-cloud-dataflow-java/src/test/java/org/apache/beam/runners/dataflow/DataflowRunnerTest.java
index c9bd50da0a56..db8fbd525ac1 100644
--- a/runners/google-cloud-dataflow-java/src/test/java/org/apache/beam/runners/dataflow/DataflowRunnerTest.java
+++ b/runners/google-cloud-dataflow-java/src/test/java/org/apache/beam/runners/dataflow/DataflowRunnerTest.java
@@ -1224,6 +1224,23 @@ public void testNoStagingLocationAndNoTempLocationFails() {
     DataflowRunner.fromOptions(options);
   }
 
+  private static RunnerApi.Pipeline containerUrlToPipeline(String url) {
+    return RunnerApi.Pipeline.newBuilder()
+        .setComponents(
+            RunnerApi.Components.newBuilder()
+                .putEnvironments(
+                    "env",
+                    RunnerApi.Environment.newBuilder()
+                        .setUrn(BeamUrns.getUrn(RunnerApi.StandardEnvironments.Environments.DOCKER))
+                        .setPayload(
+                            RunnerApi.DockerPayload.newBuilder()
+                                .setContainerImage(url)
+                                .build()
+                                .toByteString())
+                        .build()))
+        .build();
+  }
+
   @Test
   public void testApplySdkEnvironmentOverrides() throws IOException {
     DataflowPipelineOptions options = buildPipelineOptions();
@@ -1231,38 +1248,8 @@ public void testApplySdkEnvironmentOverrides() throws IOException {
     String gcrPythonContainerUrl = "gcr.io/apache-beam-testing/beam-sdk/beam_python3.9_sdk:latest";
     options.setSdkHarnessContainerImageOverrides(".*python.*," + gcrPythonContainerUrl);
     DataflowRunner runner = DataflowRunner.fromOptions(options);
-    RunnerApi.Pipeline pipeline =
-        RunnerApi.Pipeline.newBuilder()
-            .setComponents(
-                RunnerApi.Components.newBuilder()
-                    .putEnvironments(
-                        "env",
-                        RunnerApi.Environment.newBuilder()
-                            .setUrn(
-                                BeamUrns.getUrn(RunnerApi.StandardEnvironments.Environments.DOCKER))
-                            .setPayload(
-                                RunnerApi.DockerPayload.newBuilder()
-                                    .setContainerImage(dockerHubPythonContainerUrl)
-                                    .build()
-                                    .toByteString())
-                            .build()))
-            .build();
-    RunnerApi.Pipeline expectedPipeline =
-        RunnerApi.Pipeline.newBuilder()
-            .setComponents(
-                RunnerApi.Components.newBuilder()
-                    .putEnvironments(
-                        "env",
-                        RunnerApi.Environment.newBuilder()
-                            .setUrn(
-                                BeamUrns.getUrn(RunnerApi.StandardEnvironments.Environments.DOCKER))
-                            .setPayload(
-                                RunnerApi.DockerPayload.newBuilder()
-                                    .setContainerImage(gcrPythonContainerUrl)
-                                    .build()
-                                    .toByteString())
-                            .build()))
-            .build();
+    RunnerApi.Pipeline pipeline = containerUrlToPipeline(dockerHubPythonContainerUrl);
+    RunnerApi.Pipeline expectedPipeline = containerUrlToPipeline(gcrPythonContainerUrl);
     assertThat(runner.applySdkEnvironmentOverrides(pipeline, options), equalTo(expectedPipeline));
   }
 
@@ -1272,38 +1259,19 @@ public void testApplySdkEnvironmentOverridesByDefault() throws IOException {
     String dockerHubPythonContainerUrl = "apache/beam_python3.9_sdk:latest";
     String gcrPythonContainerUrl = "gcr.io/cloud-dataflow/v1beta3/beam_python3.9_sdk:latest";
     DataflowRunner runner = DataflowRunner.fromOptions(options);
-    RunnerApi.Pipeline pipeline =
-        RunnerApi.Pipeline.newBuilder()
-            .setComponents(
-                RunnerApi.Components.newBuilder()
-                    .putEnvironments(
-                        "env",
-                        RunnerApi.Environment.newBuilder()
-                            .setUrn(
-                                BeamUrns.getUrn(RunnerApi.StandardEnvironments.Environments.DOCKER))
-                            .setPayload(
-                                RunnerApi.DockerPayload.newBuilder()
-                                    .setContainerImage(dockerHubPythonContainerUrl)
-                                    .build()
-                                    .toByteString())
-                            .build()))
-            .build();
-    RunnerApi.Pipeline expectedPipeline =
-        RunnerApi.Pipeline.newBuilder()
-            .setComponents(
-                RunnerApi.Components.newBuilder()
-                    .putEnvironments(
-                        "env",
-                        RunnerApi.Environment.newBuilder()
-                            .setUrn(
-                                BeamUrns.getUrn(RunnerApi.StandardEnvironments.Environments.DOCKER))
-                            .setPayload(
-                                RunnerApi.DockerPayload.newBuilder()
-                                    .setContainerImage(gcrPythonContainerUrl)
-                                    .build()
-                                    .toByteString())
-                            .build()))
-            .build();
+    RunnerApi.Pipeline pipeline = containerUrlToPipeline(dockerHubPythonContainerUrl);
+    RunnerApi.Pipeline expectedPipeline = containerUrlToPipeline(gcrPythonContainerUrl);
+    assertThat(runner.applySdkEnvironmentOverrides(pipeline, options), equalTo(expectedPipeline));
+  }
+
+  @Test
+  public void testApplySdkEnvironmentOverridesRcByDefault() throws IOException {
+    DataflowPipelineOptions options = buildPipelineOptions();
+    String dockerHubPythonContainerUrl = "apache/beam_python3.9_sdk:2.68.0rc2";
+    String gcrPythonContainerUrl = "gcr.io/cloud-dataflow/v1beta3/beam_python3.9_sdk:2.68.0";
+    DataflowRunner runner = DataflowRunner.fromOptions(options);
+    RunnerApi.Pipeline pipeline = containerUrlToPipeline(dockerHubPythonContainerUrl);
+    RunnerApi.Pipeline expectedPipeline = containerUrlToPipeline(gcrPythonContainerUrl);
     assertThat(runner.applySdkEnvironmentOverrides(pipeline, options), equalTo(expectedPipeline));
   }
 
diff --git a/runners/google-cloud-dataflow-java/worker/build.gradle b/runners/google-cloud-dataflow-java/worker/build.gradle
index fe7e3b93dd0e..4068c5f88e4f 100644
--- a/runners/google-cloud-dataflow-java/worker/build.gradle
+++ b/runners/google-cloud-dataflow-java/worker/build.gradle
@@ -131,7 +131,7 @@ applyJavaNature(
             dependencies {
                 // We have to include jetty-server/jetty-servlet and all of its transitive dependencies
                 // which includes several org.eclipse.jetty artifacts + servlet-api
-                include(dependency("org.eclipse.jetty:.*:9.4.54.v20240208"))
+                include(dependency("org.eclipse.jetty:.*:9.4.57.v20241219"))
                 include(dependency("javax.servlet:javax.servlet-api:3.1.0"))
             }
             relocate("org.eclipse.jetty", getWorkerRelocatedPath("org.eclipse.jetty"))
@@ -200,8 +200,8 @@ dependencies {
     compileOnly "org.conscrypt:conscrypt-openjdk-uber:2.5.1"
 
     implementation "javax.servlet:javax.servlet-api:3.1.0"
-    implementation "org.eclipse.jetty:jetty-server:9.4.54.v20240208"
-    implementation "org.eclipse.jetty:jetty-servlet:9.4.54.v20240208"
+    implementation "org.eclipse.jetty:jetty-server:9.4.57.v20241219"
+    implementation "org.eclipse.jetty:jetty-servlet:9.4.57.v20241219"
     implementation library.java.avro
     implementation library.java.jackson_annotations
     implementation library.java.jackson_core
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/AssignWindowsParDoFnFactory.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/AssignWindowsParDoFnFactory.java
index d45e1f3a4e46..83cbc3aa62c7 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/AssignWindowsParDoFnFactory.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/AssignWindowsParDoFnFactory.java
@@ -111,9 +111,7 @@ public BoundedWindow window() {
                 }
               });
 
-      WindowedValue<T> res =
-          WindowedValues.of(elem.getValue(), elem.getTimestamp(), windows, elem.getPaneInfo());
-      receiver.process(res);
+      WindowedValues.builder(elem).setWindows(windows).setReceiver(receiver::process).output();
     }
 
     @Override
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/GroupAlsoByWindowParDoFnFactory.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/GroupAlsoByWindowParDoFnFactory.java
index 8f84020f1329..b69a45373ede 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/GroupAlsoByWindowParDoFnFactory.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/GroupAlsoByWindowParDoFnFactory.java
@@ -101,7 +101,8 @@ public ParDoFn create(
           SerializableUtils.deserializeFromByteArray(serializedCombineFn, "serialized combine fn");
       checkArgument(
           combineFnObj instanceof AppliedCombineFn,
-          "unexpected kind of AppliedCombineFn: " + combineFnObj.getClass().getName());
+          "unexpected kind of AppliedCombineFn: %s",
+          combineFnObj.getClass().getName());
       combineFn = (AppliedCombineFn<?, ?, ?, ?>) combineFnObj;
     }
 
@@ -110,14 +111,16 @@ public ParDoFn create(
     Coder<?> inputCoder = CloudObjects.coderFromCloudObject(CloudObject.fromSpec(inputCoderObject));
     checkArgument(
         inputCoder instanceof WindowedValueCoder,
-        "Expected WindowedValueCoder for inputCoder, got: " + inputCoder.getClass().getName());
+        "Expected WindowedValueCoder for inputCoder, got: %s",
+        inputCoder.getClass().getName());
     @SuppressWarnings("unchecked")
     WindowedValueCoder<?> windowedValueCoder = (WindowedValueCoder<?>) inputCoder;
 
     Coder<?> elemCoder = windowedValueCoder.getValueCoder();
     checkArgument(
         elemCoder instanceof KvCoder,
-        "Expected KvCoder for inputCoder, got: " + elemCoder.getClass().getName());
+        "Expected KvCoder for inputCoder, got: %s",
+        elemCoder.getClass().getName());
     @SuppressWarnings("unchecked")
     KvCoder<?, ?> kvCoder = (KvCoder<?, ?>) elemCoder;
 
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/InMemoryReader.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/InMemoryReader.java
index 9ce5fad93d99..d986418056ca 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/InMemoryReader.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/InMemoryReader.java
@@ -64,10 +64,12 @@ public InMemoryReader(
     int maxIndex = encodedElements.size();
     this.startIndex = Math.min(maxIndex, firstNonNull(startIndex, 0));
     this.endIndex = Math.min(maxIndex, firstNonNull(endIndex, maxIndex));
-    checkArgument(this.startIndex >= 0, "negative start index: " + startIndex);
+    checkArgument(this.startIndex >= 0, "negative start index: %s", startIndex);
     checkArgument(
         this.endIndex >= this.startIndex,
-        "end index before start: [" + this.startIndex + ", " + this.endIndex + ")");
+        "end index before start: [%s, %s)",
+        this.startIndex,
+        this.endIndex);
     this.coder = coder;
   }
 
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/PartialGroupByKeyParDoFns.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/PartialGroupByKeyParDoFns.java
index 05f537948072..399258d7dbb9 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/PartialGroupByKeyParDoFns.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/PartialGroupByKeyParDoFns.java
@@ -243,8 +243,12 @@ public WindowingCoderGroupingKeyCreator(Coder<K> coder) {
     public Object createGroupingKey(WindowedValue<K> key) throws Exception {
       // Ignore timestamp for grouping purposes.
       // The PGBK output will inherit the timestamp of one of its inputs.
-      return WindowedValues.of(
-          coder.structuralValue(key.getValue()), ignored, key.getWindows(), key.getPaneInfo());
+      return WindowedValues.builder(key)
+          .withValue(coder.structuralValue(key.getValue()))
+          .setTimestamp(ignored)
+          .setWindows(key.getWindows())
+          .setPaneInfo(key.getPaneInfo())
+          .build();
     }
   }
 
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/ReifyTimestampAndWindowsParDoFnFactory.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/ReifyTimestampAndWindowsParDoFnFactory.java
index 31d846d1102d..248ed34e8c40 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/ReifyTimestampAndWindowsParDoFnFactory.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/ReifyTimestampAndWindowsParDoFnFactory.java
@@ -70,18 +70,17 @@ public void startBundle(Receiver... receivers) throws Exception {
     public void processElement(Object untypedElem) throws Exception {
       WindowedValue<KV<?, ?>> typedElem = (WindowedValue<KV<?, ?>>) untypedElem;
 
-      receiver.process(
-          WindowedValues.of(
+      WindowedValues.builder(typedElem)
+          .withValue(
               KV.of(
                   typedElem.getValue().getKey(),
                   WindowedValues.of(
                       typedElem.getValue().getValue(),
                       typedElem.getTimestamp(),
                       typedElem.getWindows(),
-                      typedElem.getPaneInfo())),
-              typedElem.getTimestamp(),
-              typedElem.getWindows(),
-              typedElem.getPaneInfo()));
+                      typedElem.getPaneInfo())))
+          .setReceiver(receiver::process)
+          .output();
     }
 
     @Override
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WindmillSink.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WindmillSink.java
index b156ff45caf6..7cb6f2223472 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WindmillSink.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WindmillSink.java
@@ -18,11 +18,11 @@
 package org.apache.beam.runners.dataflow.worker;
 
 import static org.apache.beam.runners.dataflow.util.Structs.getString;
-import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkState;
 
 import com.google.auto.service.AutoService;
 import java.io.IOException;
 import java.io.InputStream;
+import java.nio.charset.StandardCharsets;
 import java.util.Collection;
 import java.util.HashMap;
 import java.util.Map;
@@ -43,6 +43,7 @@
 import org.apache.beam.sdk.values.WindowedValues.FullWindowedValueCoder;
 import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.ByteString;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.primitives.Longs;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -52,6 +53,7 @@
   "nullness" // TODO(https://github.com/apache/beam/issues/20497)
 })
 class WindmillSink<T> extends Sink<WindowedValue<T>> {
+
   private WindmillStreamWriter writer;
   private final Coder<T> valueCoder;
   private final Coder<Collection<? extends BoundedWindow>> windowsCoder;
@@ -69,15 +71,29 @@ class WindmillSink<T> extends Sink<WindowedValue<T>> {
     this.context = context;
   }
 
+  private static ByteString encodeMetadata(
+      ByteStringOutputStream stream,
+      Coder<Collection<? extends BoundedWindow>> windowsCoder,
+      Collection<? extends BoundedWindow> windows,
+      PaneInfo paneInfo)
+      throws IOException {
+    try {
+      PaneInfoCoder.INSTANCE.encode(paneInfo, stream);
+      windowsCoder.encode(windows, stream, Coder.Context.OUTER);
+      return stream.toByteStringAndReset();
+    } catch (Exception e) {
+      stream.reset();
+      throw e;
+    }
+  }
+
   public static ByteString encodeMetadata(
       Coder<Collection<? extends BoundedWindow>> windowsCoder,
       Collection<? extends BoundedWindow> windows,
       PaneInfo paneInfo)
       throws IOException {
     ByteStringOutputStream stream = new ByteStringOutputStream();
-    PaneInfoCoder.INSTANCE.encode(paneInfo, stream);
-    windowsCoder.encode(windows, stream, Coder.Context.OUTER);
-    return stream.toByteString();
+    return encodeMetadata(stream, windowsCoder, windows, paneInfo);
   }
 
   public static PaneInfo decodeMetadataPane(ByteString metadata) throws IOException {
@@ -107,6 +123,7 @@ public Map<String, SinkFactory> factories() {
   }
 
   public static class Factory implements SinkFactory {
+
     @Override
     public WindmillSink<?> create(
         CloudObject spec,
@@ -131,23 +148,35 @@ public SinkWriter<WindowedValue<T>> writer() {
   }
 
   class WindmillStreamWriter implements SinkWriter<WindowedValue<T>> {
+
     private Map<ByteString, Windmill.KeyedMessageBundle.Builder> productionMap;
     private final String destinationName;
     private final ByteStringOutputStream stream; // Kept across encodes for buffer reuse.
 
+    // Builders are reused to reduce GC overhead.
+    private final Windmill.Message.Builder messageBuilder;
+    private final Windmill.OutputMessageBundle.Builder outputBuilder;
+
     private WindmillStreamWriter(String destinationName) {
       this.destinationName = destinationName;
       productionMap = new HashMap<>();
       stream = new ByteStringOutputStream();
+      messageBuilder = Windmill.Message.newBuilder();
+      outputBuilder = Windmill.OutputMessageBundle.newBuilder();
     }
 
     private <EncodeT> ByteString encode(Coder<EncodeT> coder, EncodeT object) throws IOException {
-      checkState(
-          stream.size() == 0,
-          "Expected output stream to be empty but had %s",
-          stream.toByteString());
-      coder.encode(object, stream, Coder.Context.OUTER);
-      return stream.toByteStringAndReset();
+      if (stream.size() != 0) {
+        throw new IllegalStateException(
+            "Expected output stream to be empty but had " + stream.toByteString());
+      }
+      try {
+        coder.encode(object, stream, Coder.Context.OUTER);
+        return stream.toByteStringAndReset();
+      } catch (Exception e) {
+        stream.reset();
+        throw e;
+      }
     }
 
     @Override
@@ -155,7 +184,8 @@ private <EncodeT> ByteString encode(Coder<EncodeT> coder, EncodeT object) throws
     public long add(WindowedValue<T> data) throws IOException {
       ByteString key, value;
       ByteString id = ByteString.EMPTY;
-      ByteString metadata = encodeMetadata(windowsCoder, data.getWindows(), data.getPaneInfo());
+      ByteString metadata =
+          encodeMetadata(stream, windowsCoder, data.getWindows(), data.getPaneInfo());
       if (valueCoder instanceof KvCoder) {
         KvCoder kvCoder = (KvCoder) valueCoder;
         KV kv = (KV) data.getValue();
@@ -208,28 +238,41 @@ public long add(WindowedValue<T> data) throws IOException {
         productionMap.put(key, keyedOutput);
       }
 
-      Windmill.Message.Builder builder =
-          Windmill.Message.newBuilder()
-              .setTimestamp(WindmillTimeUtils.harnessToWindmillTimestamp(data.getTimestamp()))
-              .setData(value)
-              .setMetadata(metadata);
-      keyedOutput.addMessages(builder.build());
-
+      try {
+        messageBuilder
+            .setTimestamp(WindmillTimeUtils.harnessToWindmillTimestamp(data.getTimestamp()))
+            .setData(value)
+            .setMetadata(metadata);
+        keyedOutput.addMessages(messageBuilder.build());
+      } finally {
+        messageBuilder.clear();
+      }
       long offsetSize = 0;
       if (context.offsetBasedDeduplicationSupported()) {
         if (id.size() > 0) {
           throw new RuntimeException(
               "Unexpected record ID via ValueWithRecordIdCoder while offset-based deduplication enabled.");
         }
-        byte[] rawId = context.getCurrentRecordId();
-        if (rawId.length == 0) {
+        byte[] rawId = null;
+
+        if (data.getRecordId() != null) {
+          rawId = data.getRecordId().getBytes(StandardCharsets.UTF_8);
+        } else {
+          rawId = context.getCurrentRecordId();
+        }
+        if (rawId == null || rawId.length == 0) {
           throw new RuntimeException(
               "Unexpected empty record ID while offset-based deduplication enabled.");
         }
         id = ByteString.copyFrom(rawId);
 
-        byte[] rawOffset = context.getCurrentRecordOffset();
-        if (rawOffset.length == 0) {
+        byte[] rawOffset = null;
+        if (data.getRecordOffset() != null) {
+          rawOffset = Longs.toByteArray(data.getRecordOffset());
+        } else {
+          rawOffset = context.getCurrentRecordOffset();
+        }
+        if (rawOffset == null || rawOffset.length == 0) {
           throw new RuntimeException(
               "Unexpected empty record offset while offset-based deduplication enabled.");
         }
@@ -245,14 +288,17 @@ public long add(WindowedValue<T> data) throws IOException {
 
     @Override
     public void close() throws IOException {
-      Windmill.OutputMessageBundle.Builder outputBuilder =
-          Windmill.OutputMessageBundle.newBuilder().setDestinationStreamId(destinationName);
+      try {
+        outputBuilder.setDestinationStreamId(destinationName);
 
-      for (Windmill.KeyedMessageBundle.Builder keyedOutput : productionMap.values()) {
-        outputBuilder.addBundles(keyedOutput.build());
-      }
-      if (outputBuilder.getBundlesCount() > 0) {
-        context.getOutputBuilder().addOutputMessages(outputBuilder.build());
+        for (Windmill.KeyedMessageBundle.Builder keyedOutput : productionMap.values()) {
+          outputBuilder.addBundles(keyedOutput.build());
+        }
+        if (outputBuilder.getBundlesCount() > 0) {
+          context.getOutputBuilder().addOutputMessages(outputBuilder.build());
+        }
+      } finally {
+        outputBuilder.clear();
       }
       productionMap.clear();
     }
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WindmillTimerInternals.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WindmillTimerInternals.java
index c8143cae864d..1dbc7b005345 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WindmillTimerInternals.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WindmillTimerInternals.java
@@ -408,23 +408,25 @@ public static ByteString timerTag(WindmillNamespacePrefix prefix, TimerData time
     String tagString;
     if (useNewTimerTagEncoding(timerData)) {
       tagString =
-          new StringBuilder()
-              .append(prefix.byteString().toStringUtf8()) // this never ends with a slash
-              .append(timerData.getNamespace().stringKey()) // this must begin and end with a slash
-              .append('+')
-              .append(timerData.getTimerId()) // this is arbitrary; currently unescaped
-              .append('+')
-              .append(timerData.getTimerFamilyId())
-              .toString();
+          prefix.byteString().toStringUtf8()
+              + // this never ends with a slash
+              timerData.getNamespace().stringKey()
+              + // this must begin and end with a slash
+              '+'
+              + timerData.getTimerId()
+              + // this is arbitrary; currently unescaped
+              '+'
+              + timerData.getTimerFamilyId();
     } else {
       // Timers without timerFamily would have timerFamily would be an empty string
       tagString =
-          new StringBuilder()
-              .append(prefix.byteString().toStringUtf8()) // this never ends with a slash
-              .append(timerData.getNamespace().stringKey()) // this must begin and end with a slash
-              .append('+')
-              .append(timerData.getTimerId()) // this is arbitrary; currently unescaped
-              .toString();
+          prefix.byteString().toStringUtf8()
+              + // this never ends with a slash
+              timerData.getNamespace().stringKey()
+              + // this must begin and end with a slash
+              '+'
+              + timerData.getTimerId() // this is arbitrary; currently unescaped
+      ;
     }
     return ByteString.copyFromUtf8(tagString);
   }
@@ -437,26 +439,30 @@ public static ByteString timerHoldTag(WindmillNamespacePrefix prefix, TimerData
     String tagString;
     if ("".equals(timerData.getTimerFamilyId())) {
       tagString =
-          new StringBuilder()
-              .append(prefix.byteString().toStringUtf8()) // this never ends with a slash
-              .append(TIMER_HOLD_PREFIX) // this never ends with a slash
-              .append(timerData.getNamespace().stringKey()) // this must begin and end with a slash
-              .append('+')
-              .append(timerData.getTimerId()) // this is arbitrary; currently unescaped
-              .toString();
+          prefix.byteString().toStringUtf8()
+              + // this never ends with a slash
+              TIMER_HOLD_PREFIX
+              + // this never ends with a slash
+              timerData.getNamespace().stringKey()
+              + // this must begin and end with a slash
+              '+'
+              + timerData.getTimerId() // this is arbitrary; currently unescaped
+      ;
     } else {
       tagString =
-          new StringBuilder()
-              .append(prefix.byteString().toStringUtf8()) // this never ends with a slash
-              .append(TIMER_HOLD_PREFIX) // this never ends with a slash
-              .append(timerData.getNamespace().stringKey()) // this must begin and end with a slash
-              .append('+')
-              .append(timerData.getTimerId()) // this is arbitrary; currently unescaped
-              .append('+')
-              .append(
-                  timerData.getTimerFamilyId()) // use to differentiate same timerId in different
-              // timerMap
-              .toString();
+          prefix.byteString().toStringUtf8()
+              + // this never ends with a slash
+              TIMER_HOLD_PREFIX
+              + // this never ends with a slash
+              timerData.getNamespace().stringKey()
+              + // this must begin and end with a slash
+              '+'
+              + timerData.getTimerId()
+              + // this is arbitrary; currently unescaped
+              '+'
+              + timerData.getTimerFamilyId() // use to differentiate same timerId in different
+      // timerMap
+      ;
     }
     return ByteString.copyFromUtf8(tagString);
   }
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WorkerCustomSources.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WorkerCustomSources.java
index 6323e757561e..0181e647ac7b 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WorkerCustomSources.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WorkerCustomSources.java
@@ -254,7 +254,8 @@ private static <T> SourceOperationResponse performSplitTyped(
     // the sources into numBundlesLimit compressed serialized bundles.
     while (serializedSize > apiByteLimit || bundles.size() > numBundlesLimit) {
       // bundle size constrained by API limit, adds 5% allowance
-      int targetBundleSizeApiLimit = (int) (bundles.size() * apiByteLimit / serializedSize * 0.95);
+      int targetBundleSizeApiLimit =
+          (int) ((double) (bundles.size() * apiByteLimit) / serializedSize * 0.95);
       // bundle size constrained by numBundlesLimit
       int targetBundleSizeBundleLimit = Math.min(numBundlesLimit, bundles.size() - 1);
       int targetBundleSize = Math.min(targetBundleSizeApiLimit, targetBundleSizeBundleLimit);
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingHandler.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingHandler.java
index 572f9354ca93..864887f9bd36 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingHandler.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingHandler.java
@@ -35,10 +35,13 @@
 import java.text.SimpleDateFormat;
 import java.util.Date;
 import java.util.EnumMap;
+import java.util.Map;
 import java.util.logging.ErrorManager;
 import java.util.logging.Handler;
 import java.util.logging.LogRecord;
 import java.util.logging.SimpleFormatter;
+import javax.annotation.Nullable;
+import javax.annotation.concurrent.GuardedBy;
 import org.apache.beam.model.fnexecution.v1.BeamFnApi;
 import org.apache.beam.runners.core.metrics.ExecutionStateTracker;
 import org.apache.beam.runners.core.metrics.ExecutionStateTracker.ExecutionState;
@@ -47,6 +50,7 @@
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.MoreObjects;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Supplier;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.io.CountingOutputStream;
+import org.slf4j.MDC;
 
 /**
  * Formats {@link LogRecord} into JSON format for Cloud Logging. Any exception is represented using
@@ -83,6 +87,10 @@ public class DataflowWorkerLoggingHandler extends Handler {
    */
   private static final int LOGGING_WRITER_BUFFER_SIZE = 262144; // 256kb
 
+  /** If true, add SLF4J MDC to custom_data of the log message. */
+  @GuardedBy("this")
+  private boolean logCustomMdc = false;
+
   /**
    * Formats the throwable as per {@link Throwable#printStackTrace()}.
    *
@@ -123,6 +131,10 @@ public DataflowWorkerLoggingHandler(String filename, long sizeLimit) throws IOEx
     createOutputStream();
   }
 
+  public synchronized void setLogMdc(boolean enabled) {
+    this.logCustomMdc = enabled;
+  }
+
   @Override
   public synchronized void publish(LogRecord record) {
     DataflowExecutionState currrentDataflowState = null;
@@ -171,6 +183,24 @@ public synchronized void publish(DataflowExecutionState currentExecutionState, L
       writeIfNotEmpty("work", DataflowWorkerLoggingMDC.getWorkId());
       writeIfNotEmpty("logger", record.getLoggerName());
       writeIfNotEmpty("exception", formatException(record.getThrown()));
+      if (logCustomMdc) {
+        @Nullable Map<String, String> mdcMap = MDC.getCopyOfContextMap();
+        if (mdcMap != null && !mdcMap.isEmpty()) {
+          generator.writeFieldName("custom_data");
+          generator.writeStartObject();
+          mdcMap.entrySet().stream()
+              .sorted(Map.Entry.comparingByKey())
+              .forEach(
+                  (entry) -> {
+                    try {
+                      generator.writeStringField(entry.getKey(), entry.getValue());
+                    } catch (IOException e) {
+                      throw new RuntimeException(e);
+                    }
+                  });
+          generator.writeEndObject();
+        }
+      }
       generator.writeEndObject();
       generator.writeRaw(System.lineSeparator());
     } catch (IOException | RuntimeException e) {
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingInitializer.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingInitializer.java
index 0673ae790eaf..a56c62e92315 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingInitializer.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingInitializer.java
@@ -247,6 +247,10 @@ public static synchronized void configure(DataflowWorkerLoggingOptions options)
               Charset.defaultCharset()));
     }
 
+    if (harnessOptions.getLogMdc()) {
+      loggingHandler.setLogMdc(true);
+    }
+
     if (usedDeprecated) {
       LOG.warn(
           "Deprecated DataflowWorkerLoggingOptions are used for log level settings."
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/util/BatchGroupAlsoByWindowAndCombineFn.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/util/BatchGroupAlsoByWindowAndCombineFn.java
index 1a66f4484292..c028ed4c58d7 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/util/BatchGroupAlsoByWindowAndCombineFn.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/util/BatchGroupAlsoByWindowAndCombineFn.java
@@ -190,7 +190,8 @@ private void closeWindow(
       W window,
       Map<W, AccumT> accumulators,
       Map<W, Instant> accumulatorOutputTimes,
-      WindowedValueReceiver<KV<K, OutputT>> output) {
+      WindowedValueReceiver<KV<K, OutputT>> output)
+      throws Exception {
     AccumT accum = accumulators.remove(window);
     Instant timestamp = accumulatorOutputTimes.remove(window);
     checkState(accum != null && timestamp != null);
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/util/ValueInEmptyWindows.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/util/ValueInEmptyWindows.java
index 42174629b3b8..a51c9ed419e1 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/util/ValueInEmptyWindows.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/util/ValueInEmptyWindows.java
@@ -49,6 +49,16 @@ public PaneInfo getPaneInfo() {
     return PaneInfo.NO_FIRING;
   }
 
+  @Override
+  public @Nullable String getRecordId() {
+    return null;
+  }
+
+  @Override
+  public @Nullable Long getRecordOffset() {
+    return null;
+  }
+
   @Override
   public Iterable<WindowedValue<T>> explodeWindows() {
     return Collections.emptyList();
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/AbstractWindmillStream.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/AbstractWindmillStream.java
index cf46e1f984dc..ed99ae1bbd6f 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/AbstractWindmillStream.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/AbstractWindmillStream.java
@@ -17,13 +17,22 @@
  */
 package org.apache.beam.runners.dataflow.worker.windmill.client;
 
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkState;
+
 import com.google.errorprone.annotations.CanIgnoreReturnValue;
 import java.io.PrintWriter;
+import java.time.Duration;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.IdentityHashMap;
+import java.util.List;
 import java.util.Set;
+import java.util.concurrent.ConcurrentHashMap;
 import java.util.concurrent.CountDownLatch;
-import java.util.concurrent.ExecutorService;
-import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
 import java.util.concurrent.RejectedExecutionException;
+import java.util.concurrent.ScheduledExecutorService;
 import java.util.concurrent.TimeUnit;
 import java.util.concurrent.atomic.AtomicBoolean;
 import java.util.concurrent.atomic.AtomicReference;
@@ -37,9 +46,7 @@
 import org.apache.beam.vendor.grpc.v1p69p0.com.google.api.client.util.Sleeper;
 import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.Status;
 import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.stub.StreamObserver;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.util.concurrent.ThreadFactoryBuilder;
 import org.checkerframework.checker.nullness.qual.NonNull;
-import org.joda.time.DateTime;
 import org.joda.time.Instant;
 import org.slf4j.Logger;
 
@@ -75,11 +82,10 @@ public abstract class AbstractWindmillStream<RequestT, ResponseT> implements Win
   // shutdown.
   private static final Status OK_STATUS = Status.fromCode(Status.Code.OK);
   private static final String NEVER_RECEIVED_RESPONSE_LOG_STRING = "never received response";
-  private static final String NOT_SHUTDOWN = "not shutdown";
   protected final Sleeper sleeper;
 
   private final Logger logger;
-  private final ExecutorService executor;
+  private final ScheduledExecutorService executor;
   private final BackOff backoff;
   private final CountDownLatch finishLatch;
   private final Set<AbstractWindmillStream<?, ?>> streamRegistry;
@@ -89,6 +95,7 @@ public abstract class AbstractWindmillStream<RequestT, ResponseT> implements Win
   private final Function<StreamObserver<ResponseT>, TerminatingStreamObserver<RequestT>>
       physicalStreamFactory;
   protected final long physicalStreamDeadlineSeconds;
+  private final Duration halfClosePhysicalStreamAfter;
   private final ResettableThrowingStreamObserver<RequestT> requestObserver;
 
   private final StreamDebugMetrics debugMetrics;
@@ -106,6 +113,17 @@ public abstract class AbstractWindmillStream<RequestT, ResponseT> implements Win
   @GuardedBy("this")
   protected @Nullable PhysicalStreamHandler currentPhysicalStream;
 
+  @GuardedBy("this")
+  @Nullable
+  Future<?> halfCloseFuture = null;
+
+  // Physical streams that have been half-closed and are waiting for responses or stream failure.
+  @GuardedBy("this")
+  protected final Set<PhysicalStreamHandler> closingPhysicalStreams;
+
+  private final Set<PhysicalStreamHandler> closingPhysicalStreamsForDebug =
+      Collections.newSetFromMap(new ConcurrentHashMap<PhysicalStreamHandler, Boolean>());
+
   // Generally the same as currentPhysicalStream, set under synchronization of this but can be read
   // without.
   private final AtomicReference<PhysicalStreamHandler> currentPhysicalStreamForDebug =
@@ -114,25 +132,33 @@ public abstract class AbstractWindmillStream<RequestT, ResponseT> implements Win
   @GuardedBy("this")
   private boolean started;
 
+  // If halfClosePhysicalStream is non-zero, substreams created for the logical
+  // AbstractWindmillStream
+  // will be half-closed and a new physical stream will be created after this duraction.
   protected AbstractWindmillStream(
       Logger logger,
-      String debugStreamType,
       Function<StreamObserver<ResponseT>, StreamObserver<RequestT>> clientFactory,
       BackOff backoff,
       StreamObserverFactory streamObserverFactory,
       Set<AbstractWindmillStream<?, ?>> streamRegistry,
       int logEveryNStreamFailures,
-      String backendWorkerToken) {
+      String backendWorkerToken,
+      Duration halfClosePhysicalStreamAfter,
+      ScheduledExecutorService executor) {
+    checkArgument(!halfClosePhysicalStreamAfter.isNegative());
     this.backendWorkerToken = backendWorkerToken;
     this.physicalStreamFactory =
         (StreamObserver<ResponseT> observer) -> streamObserverFactory.from(clientFactory, observer);
     this.physicalStreamDeadlineSeconds = streamObserverFactory.getDeadlineSeconds();
-    this.executor =
-        Executors.newSingleThreadExecutor(
-            new ThreadFactoryBuilder()
-                .setDaemon(true)
-                .setNameFormat(createThreadName(debugStreamType, backendWorkerToken))
-                .build());
+    if (!halfClosePhysicalStreamAfter.isZero()
+        && halfClosePhysicalStreamAfter.compareTo(Duration.ofSeconds(physicalStreamDeadlineSeconds))
+            >= 0) {
+      logger.debug("Not attempting to half-close cleanly as stream deadline is shorter.");
+      halfClosePhysicalStreamAfter = Duration.ZERO;
+    }
+    this.halfClosePhysicalStreamAfter = halfClosePhysicalStreamAfter;
+    this.closingPhysicalStreams = Collections.newSetFromMap(new IdentityHashMap<>());
+    this.executor = executor;
     this.backoff = backoff;
     this.streamRegistry = streamRegistry;
     this.logEveryNStreamFailures = logEveryNStreamFailures;
@@ -147,12 +173,6 @@ protected AbstractWindmillStream(
     this.debugMetrics = StreamDebugMetrics.create();
   }
 
-  private static String createThreadName(String streamType, String backendWorkerToken) {
-    return !backendWorkerToken.isEmpty()
-        ? String.format("%s-%s-WindmillStream-thread", streamType, backendWorkerToken)
-        : String.format("%s-WindmillStream-thread", streamType);
-  }
-
   /** Represents a physical grpc stream that is part of the logical windmill stream. */
   protected abstract class PhysicalStreamHandler {
 
@@ -178,11 +198,23 @@ protected abstract class PhysicalStreamHandler {
     public abstract void appendHtml(PrintWriter writer);
 
     private final StreamDebugMetrics streamDebugMetrics = StreamDebugMetrics.create();
+
+    @Override
+    public final boolean equals(@Nullable Object obj) {
+      return this == obj;
+    }
+
+    @Override
+    public final int hashCode() {
+      return System.identityHashCode(this);
+    }
   }
 
+  /* Constructs and returns a new handler to be associated with a physical stream. */
   protected abstract PhysicalStreamHandler newResponseHandler();
 
-  protected abstract void onNewStream() throws WindmillStreamShutdownException;
+  protected abstract void onFlushPending(boolean isNewStream)
+      throws WindmillStreamShutdownException;
 
   /** Try to send a request to the server. Returns true if the request was successfully sent. */
   @CanIgnoreReturnValue
@@ -214,54 +246,68 @@ public final void start() {
     }
 
     if (shouldStartStream) {
+      // Add the stream to the registry after it has been fully constructed.
+      streamRegistry.add(this);
       startStream();
     }
   }
 
   /** Starts the underlying stream. */
   private void startStream() {
-    // Add the stream to the registry after it has been fully constructed.
-    streamRegistry.add(this);
     while (true) {
       @NonNull PhysicalStreamHandler streamHandler = newResponseHandler();
-      try {
-        synchronized (this) {
+      synchronized (this) {
+        try {
+          checkState(currentPhysicalStream == null, "Overwriting existing physical stream");
+          checkState(halfCloseFuture == null, "Unexpected half-close future");
+          if (isShutdown) {
+            // No need to start the stream. shutdown() or onPhysicalStreamCompletion will be
+            // responsible for completing shutdown.
+            return;
+          }
           debugMetrics.recordStart();
           streamHandler.streamDebugMetrics.recordStart();
           currentPhysicalStream = streamHandler;
           currentPhysicalStreamForDebug.set(currentPhysicalStream);
           requestObserver.reset(physicalStreamFactory.apply(new ResponseObserver(streamHandler)));
-          onNewStream();
+          onFlushPending(true);
           if (clientClosed) {
-            halfClose();
+            // The logical stream is half-closed so after flushing the remaining requests close the
+            // physical stream.
+            streamHandler.streamDebugMetrics.recordHalfClose();
+            requestObserver.onCompleted();
+          } else if (!halfClosePhysicalStreamAfter.isZero()) {
+            halfCloseFuture =
+                executor.schedule(
+                    () -> onHalfClosePhysicalStreamTimeout(streamHandler),
+                    halfClosePhysicalStreamAfter.getSeconds(),
+                    TimeUnit.SECONDS);
           }
           return;
-        }
-      } catch (WindmillStreamShutdownException e) {
-        // shutdown() is responsible for cleaning up pending requests.
-        logger.debug("Stream was shutdown while creating new stream.", e);
-        break;
-      } catch (Exception e) {
-        logger.error("Failed to create new stream, retrying: ", e);
-        try {
-          long sleep = backoff.nextBackOffMillis();
-          debugMetrics.recordSleep(sleep);
-          sleeper.sleep(sleep);
-        } catch (InterruptedException ie) {
-          Thread.currentThread().interrupt();
-          logger.info(
-              "Interrupted during {} creation backoff. The stream will not be created.",
-              getClass());
-          // Shutdown the stream to clean up any dangling resources and pending requests.
-          shutdown();
+        } catch (WindmillStreamShutdownException e) {
+          logger.debug("Stream was shutdown while creating new stream.", e);
+          clearCurrentPhysicalStream(true);
           break;
+        } catch (Exception e) {
+          logger.error("Failed to create new stream, retrying: ", e);
+          clearCurrentPhysicalStream(true);
+          debugMetrics.recordRestartReason("Failed to create new stream, retrying: " + e);
         }
       }
+      // Backoff outside the synchronized block.
+      try {
+        long sleep = backoff.nextBackOffMillis();
+        debugMetrics.recordSleep(sleep);
+        sleeper.sleep(sleep);
+      } catch (InterruptedException ie) {
+        Thread.currentThread().interrupt();
+        logger.info(
+            "Interrupted during {} creation backoff. The stream will not be created.", getClass());
+        // Shutdown the stream to clean up any dangling resources and pending requests.
+        shutdown();
+        break;
+      }
     }
-
-    // We were never able to start the stream, remove it from the stream registry. Otherwise, it is
-    // removed when closed.
-    streamRegistry.remove(this);
   }
 
   /**
@@ -317,23 +363,6 @@ public final void maybeScheduleHealthCheck(Instant lastSendThreshold) {
    */
   public final void appendSummaryHtml(PrintWriter writer) {
     appendSpecificHtml(writer);
-
-    @Nullable PhysicalStreamHandler currentHandler = currentPhysicalStreamForDebug.get();
-    if (currentHandler != null) {
-      writer.format("Physical stream: ");
-      currentHandler.appendHtml(writer);
-      StreamDebugMetrics.Snapshot summaryMetrics =
-          currentHandler.streamDebugMetrics.getSummaryMetrics();
-      if (summaryMetrics.isClientClosed()) {
-        writer.write(" client closed");
-      }
-      writer.format(
-          " current stream is %dms old, last send %dms, last response %dms\n",
-          summaryMetrics.streamAge(),
-          summaryMetrics.timeSinceLastSend(),
-          summaryMetrics.timeSinceLastResponse());
-    }
-
     StreamDebugMetrics.Snapshot summaryMetrics = debugMetrics.getSummaryMetrics();
     summaryMetrics
         .restartMetrics()
@@ -356,13 +385,44 @@ public final void appendSummaryHtml(PrintWriter writer) {
     }
 
     writer.format(
-        ", current stream is %dms old, last send %dms, last response %dms, closed: %s, "
-            + "shutdown time: %s",
+        ", stream is %dms old, last send %dms, last response %dms",
         summaryMetrics.streamAge(),
         summaryMetrics.timeSinceLastSend(),
-        summaryMetrics.timeSinceLastResponse(),
-        requestObserver.isClosed(),
-        summaryMetrics.shutdownTime().map(DateTime::toString).orElse(NOT_SHUTDOWN));
+        summaryMetrics.timeSinceLastResponse());
+    if (requestObserver.isClosed()) {
+      writer.append(", observer closed");
+    }
+    summaryMetrics
+        .shutdownTime()
+        .ifPresent(dateTime -> writer.format(", shutdown at %s", dateTime));
+
+    @Nullable PhysicalStreamHandler currentHandler = currentPhysicalStreamForDebug.get();
+    if (currentHandler != null) {
+      writer.format("<br>current physical stream: ");
+      appendPhysicalStream(writer, currentHandler);
+    }
+
+    List<PhysicalStreamHandler> closingStreamsSnapshot =
+        new ArrayList<>(closingPhysicalStreamsForDebug);
+    for (int i = 0; i < closingStreamsSnapshot.size(); ++i) {
+      writer.format("<br>closing physical stream #%d: ", i);
+      appendPhysicalStream(writer, closingStreamsSnapshot.get(i));
+    }
+  }
+
+  private void appendPhysicalStream(
+      PrintWriter writer, PhysicalStreamHandler physicalStreamHandler) {
+    physicalStreamHandler.appendHtml(writer);
+    StreamDebugMetrics.Snapshot summaryMetrics =
+        physicalStreamHandler.streamDebugMetrics.getSummaryMetrics();
+    if (summaryMetrics.isClientClosed()) {
+      writer.write(" client closed");
+    }
+    writer.format(
+        " started %dms ago, last send %dms, last response %dms\n",
+        summaryMetrics.streamAge(),
+        summaryMetrics.timeSinceLastSend(),
+        summaryMetrics.timeSinceLastResponse());
   }
 
   /**
@@ -375,7 +435,12 @@ public final void appendSummaryHtml(PrintWriter writer) {
 
   @Override
   public final synchronized void halfClose() {
-    // Synchronization of close and onCompleted necessary for correct retry logic in onNewStream.
+    if (clientClosed) {
+      logger.warn("Stream was previously closed.");
+      return;
+    }
+    // Synchronization of close and onCompleted necessary for correct retry logic in
+    // onPhysicalStreamCompleted.
     debugMetrics.recordHalfClose();
     clientClosed = true;
     try {
@@ -399,7 +464,7 @@ public final boolean awaitTermination(int time, TimeUnit unit) throws Interrupte
 
   @Override
   public final Instant startTime() {
-    return new Instant(debugMetrics.getStartTimeMs());
+    return Instant.ofEpochMilli(debugMetrics.getStartTimeMs());
   }
 
   @Override
@@ -417,30 +482,22 @@ public final void shutdown() {
         isShutdown = true;
         debugMetrics.recordShutdown();
         shutdownInternal();
+        if (currentPhysicalStream == null && closingPhysicalStreams.isEmpty()) {
+          completeShutdown();
+        }
       }
     }
   }
 
-  protected synchronized void shutdownInternal() {}
-
-  /** Returns true if the stream was torn down and should not be restarted internally. */
-  private synchronized boolean maybeTearDownStream(PhysicalStreamHandler doneStream) {
-    if (clientClosed && !doneStream.hasPendingRequests()) {
-      shutdown();
-    }
-
-    if (isShutdown) {
-      // Once we have background closing physicalStreams we will need to improve this to wait for
-      // all of the work of the logical stream to be complete.
-      streamRegistry.remove(AbstractWindmillStream.this);
-      finishLatch.countDown();
-      executor.shutdownNow();
-      return true;
-    }
-
-    return false;
+  private void completeShutdown() {
+    logger.debug("Completing shutdown of stream after shutdown and all streams terminated.");
+    streamRegistry.remove(AbstractWindmillStream.this);
+    finishLatch.countDown();
+    executor.shutdownNow();
   }
 
+  protected synchronized void shutdownInternal() {}
+
   private class ResponseObserver implements StreamObserver<ResponseT> {
     private final PhysicalStreamHandler handler;
 
@@ -467,22 +524,67 @@ public void onCompleted() {
     }
   }
 
-  @SuppressWarnings("nullness")
-  private void clearPhysicalStreamForDebug() {
-    currentPhysicalStreamForDebug.set(null);
+  @SuppressWarnings("ReferenceEquality")
+  private void onHalfClosePhysicalStreamTimeout(PhysicalStreamHandler handler) {
+    synchronized (this) {
+      if (currentPhysicalStream != handler || clientClosed || isShutdown) {
+        return;
+      }
+      handler.streamDebugMetrics.recordHalfClose();
+      closingPhysicalStreams.add(handler);
+      closingPhysicalStreamsForDebug.add(handler);
+      clearCurrentPhysicalStream(false);
+      try {
+        requestObserver.onCompleted();
+      } catch (Exception e) {
+        logger.debug(
+            "Exception while half-closing handler, onPhysicalStreamCompletion will be called for the stream",
+            e);
+      }
+    }
+    startStream();
   }
 
+  @SuppressWarnings("ReferenceEquality")
   private void onPhysicalStreamCompletion(Status status, PhysicalStreamHandler handler) {
     synchronized (this) {
-      if (currentPhysicalStream == handler) {
-        clearPhysicalStreamForDebug();
-        currentPhysicalStream = null;
+      final boolean wasActiveStream = currentPhysicalStream == handler;
+      if (wasActiveStream) {
+        clearCurrentPhysicalStream(true);
+      } else {
+        checkState(closingPhysicalStreams.remove(handler));
+        closingPhysicalStreamsForDebug.remove(handler);
       }
+      boolean doneHandlerHadRequests = handler.hasPendingRequests();
+      handler.onDone(status);
+      if (currentPhysicalStream == null && closingPhysicalStreams.isEmpty()) {
+        if (clientClosed && !doneHandlerHadRequests && !isShutdown) {
+          shutdown();
+        }
+        if (isShutdown) {
+          completeShutdown();
+          return;
+        }
+      }
+      if (currentPhysicalStream != null) {
+        if (!clientClosed) {
+          // Don't bother attempting to flush the requests if the active stream is closed.
+          try {
+            onFlushPending(false);
+          } catch (WindmillStreamShutdownException e) {
+            logger.debug(
+                "Requests will be flushed by onPhysicalStreamCompletion of the current stream.", e);
+          }
+        }
+        return;
+      }
+      if (clientClosed && !doneHandlerHadRequests) {
+        // We didn't have any leftover requests and are closing so we skip restarting a stream.
+        return;
+      }
+      // We're not shutting down and we don't have an active stream, create one.
     }
-    handler.onDone(status);
-    if (maybeTearDownStream(handler)) {
-      return;
-    }
+
     // Backoff on errors.;
     if (!status.isOk()) {
       try {
@@ -498,6 +600,16 @@ private void onPhysicalStreamCompletion(Status status, PhysicalStreamHandler han
     startStream();
   }
 
+  @SuppressWarnings("nullness")
+  private synchronized void clearCurrentPhysicalStream(boolean cancelHalfCloseFuture) {
+    currentPhysicalStream = null;
+    if (halfCloseFuture != null && cancelHalfCloseFuture) {
+      halfCloseFuture.cancel(false);
+    }
+    halfCloseFuture = null;
+    currentPhysicalStreamForDebug.set(null);
+  }
+
   private void recordStreamRestart(Status status) {
     int currentRestartCount = debugMetrics.incrementAndGetRestarts();
     if (status.isOk()) {
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/ResettableThrowingStreamObserver.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/ResettableThrowingStreamObserver.java
index 1e197c877d68..b027a6cac7b0 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/ResettableThrowingStreamObserver.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/ResettableThrowingStreamObserver.java
@@ -115,14 +115,24 @@ public void onNext(T t) throws StreamClosedException, WindmillStreamShutdownExce
           logger.debug("Stream was shutdown during send.", cancellationException);
           return;
         }
+        if (delegateStreamObserver == delegate) {
+          if (isCurrentStreamClosed) {
+            logger.debug("Stream is already closed when encountering error with send.");
+            return;
+          }
+          isCurrentStreamClosed = true;
+        }
       }
 
+      // Either this was the active observer the current observer that requires closing, or this was
+      // a previous
+      // observer which we attempt to close and ignore possible exceptions.
       try {
         delegate.onError(cancellationException);
       } catch (IllegalStateException onErrorException) {
         // The delegate above was already terminated via onError or onComplete.
-        // Fallthrough since this is possibly due to queued onNext() calls that are being made from
-        // previously blocked threads.
+        // Fallthrough since this is possibly due to queued onNext() calls that are being made
+        // from previously blocked threads.
       } catch (RuntimeException onErrorException) {
         logger.warn(
             "Encountered unexpected error {} when cancelling due to error.",
@@ -134,14 +144,20 @@ public void onNext(T t) throws StreamClosedException, WindmillStreamShutdownExce
 
   public synchronized void onError(Throwable throwable)
       throws StreamClosedException, WindmillStreamShutdownException {
-    delegate().onError(throwable);
-    isCurrentStreamClosed = true;
+    try {
+      delegate().onError(throwable);
+    } finally {
+      isCurrentStreamClosed = true;
+    }
   }
 
   public synchronized void onCompleted()
       throws StreamClosedException, WindmillStreamShutdownException {
-    delegate().onCompleted();
-    isCurrentStreamClosed = true;
+    try {
+      delegate().onCompleted();
+    } finally {
+      isCurrentStreamClosed = true;
+    }
   }
 
   synchronized boolean isClosed() {
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/WindmillStream.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/WindmillStream.java
index 51bc03e8e0e7..526b67890783 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/WindmillStream.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/WindmillStream.java
@@ -64,10 +64,6 @@ public interface WindmillStream {
   interface GetWorkStream extends WindmillStream {
     /** Adjusts the {@link GetWorkBudget} for the stream. */
     void setBudget(GetWorkBudget newBudget);
-
-    default void setBudget(long newItems, long newBytes) {
-      setBudget(GetWorkBudget.builder().setItems(newItems).setBytes(newBytes).build());
-    }
   }
 
   /** Interface for streaming GetDataRequests to Windmill. */
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GetWorkResponseChunkAssembler.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GetWorkResponseChunkAssembler.java
index f978bad01e62..0ebb4726d3a1 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GetWorkResponseChunkAssembler.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GetWorkResponseChunkAssembler.java
@@ -51,6 +51,7 @@ final class GetWorkResponseChunkAssembler {
 
   private final GetWorkTimingInfosTracker workTimingInfosTracker;
   private @Nullable ComputationMetadata metadata;
+  private final WorkItem.Builder workItemBuilder; // Reused to reduce GC overhead.
   private ByteString data;
   private long bufferedSize;
 
@@ -59,6 +60,7 @@ final class GetWorkResponseChunkAssembler {
     data = ByteString.EMPTY;
     bufferedSize = 0;
     metadata = null;
+    workItemBuilder = WorkItem.newBuilder();
   }
 
   /**
@@ -94,15 +96,17 @@ List<AssembledWorkItem> append(Windmill.StreamingGetWorkResponseChunk chunk) {
    */
   private Optional<AssembledWorkItem> flushToWorkItem() {
     try {
+      workItemBuilder.mergeFrom(data);
       return Optional.of(
           AssembledWorkItem.create(
-              WorkItem.parseFrom(data.newInput()),
+              workItemBuilder.build(),
               Preconditions.checkNotNull(metadata),
               workTimingInfosTracker.getLatencyAttributions(),
               bufferedSize));
     } catch (IOException e) {
       LOG.error("Failed to parse work item from stream: ", e);
     } finally {
+      workItemBuilder.clear();
       workTimingInfosTracker.reset();
       data = ByteString.EMPTY;
       bufferedSize = 0;
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcCommitWorkStream.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcCommitWorkStream.java
index 7a7b1a5cd27e..d24676652fd8 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcCommitWorkStream.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcCommitWorkStream.java
@@ -22,12 +22,14 @@
 
 import com.google.auto.value.AutoValue;
 import java.io.PrintWriter;
+import java.time.Duration;
 import java.util.HashMap;
 import java.util.Iterator;
 import java.util.Map;
 import java.util.Set;
 import java.util.concurrent.ConcurrentHashMap;
 import java.util.concurrent.ConcurrentMap;
+import java.util.concurrent.ScheduledExecutorService;
 import java.util.concurrent.atomic.AtomicLong;
 import java.util.function.Consumer;
 import java.util.function.Function;
@@ -74,6 +76,7 @@ private static class StreamAndRequest {
   private final AtomicLong idGenerator;
   private final JobHeader jobHeader;
   private final int streamingRpcBatchLimit;
+  private volatile boolean logMissingResponse = true;
 
   private GrpcCommitWorkStream(
       String backendWorkerToken,
@@ -85,16 +88,19 @@ private GrpcCommitWorkStream(
       int logEveryNStreamFailures,
       JobHeader jobHeader,
       AtomicLong idGenerator,
-      int streamingRpcBatchLimit) {
+      int streamingRpcBatchLimit,
+      Duration halfClosePhysicalStreamAfter,
+      ScheduledExecutorService executor) {
     super(
         LOG,
-        "CommitWorkStream",
         startCommitWorkRpcFn,
         backoff,
         streamObserverFactory,
         streamRegistry,
         logEveryNStreamFailures,
-        backendWorkerToken);
+        backendWorkerToken,
+        halfClosePhysicalStreamAfter,
+        executor);
     this.idGenerator = idGenerator;
     this.jobHeader = jobHeader;
     this.streamingRpcBatchLimit = streamingRpcBatchLimit;
@@ -110,7 +116,9 @@ static GrpcCommitWorkStream create(
       int logEveryNStreamFailures,
       JobHeader jobHeader,
       AtomicLong idGenerator,
-      int streamingRpcBatchLimit) {
+      int streamingRpcBatchLimit,
+      Duration halfClosePhysicalStreamAfter,
+      ScheduledExecutorService executor) {
     return new GrpcCommitWorkStream(
         backendWorkerToken,
         startCommitWorkRpcFn,
@@ -120,25 +128,33 @@ static GrpcCommitWorkStream create(
         logEveryNStreamFailures,
         jobHeader,
         idGenerator,
-        streamingRpcBatchLimit);
+        streamingRpcBatchLimit,
+        halfClosePhysicalStreamAfter,
+        executor);
   }
 
   @Override
   public void appendSpecificHtml(PrintWriter writer) {
-    writer.format("CommitWorkStream: %d pending", pending.size());
+    writer.format("CommitWorkStream: %d pending ", pending.size());
   }
 
   @Override
-  protected synchronized void onNewStream() throws WindmillStreamShutdownException {
-    trySend(StreamingCommitWorkRequest.newBuilder().setHeader(jobHeader).build());
+  @SuppressWarnings("ReferenceEquality")
+  protected synchronized void onFlushPending(boolean isNewStream)
+      throws WindmillStreamShutdownException {
+    if (isNewStream) {
+      trySend(StreamingCommitWorkRequest.newBuilder().setHeader(jobHeader).build());
+    }
     // Flush all pending requests that are no longer on active streams.
     try (Batcher resendBatcher = new Batcher()) {
       for (Map.Entry<Long, StreamAndRequest> entry : pending.entrySet()) {
         CommitWorkPhysicalStreamHandler requestHandler = entry.getValue().handler;
         checkState(requestHandler != currentPhysicalStream);
-        // When we have streams closing in the background we should avoid retrying the requests
-        // active on those streams.
-
+        if (requestHandler != null && closingPhysicalStreams.contains(requestHandler)) {
+          LOG.debug(
+              "Not resending request that is active on background half-closing physical stream.");
+          continue;
+        }
         long id = entry.getKey();
         PendingRequest request = entry.getValue().request;
         if (!resendBatcher.canAccept(request.getBytes())) {
@@ -169,6 +185,7 @@ protected synchronized void sendHealthCheck() throws WindmillStreamShutdownExcep
 
   private class CommitWorkPhysicalStreamHandler extends PhysicalStreamHandler {
     @Override
+    @SuppressWarnings("ReferenceEquality")
     public void onResponse(StreamingCommitResponse response) {
       CommitCompletionFailureHandler failureHandler = new CommitCompletionFailureHandler();
       for (int i = 0; i < response.getRequestIdCount(); ++i) {
@@ -184,7 +201,9 @@ public void onResponse(StreamingCommitResponse response) {
 
         @Nullable StreamAndRequest entry = pending.remove(requestId);
         if (entry == null) {
-          LOG.error("Got unknown commit request ID: {}", requestId);
+          if (logMissingResponse) {
+            LOG.error("Got unknown commit request ID: {}", requestId);
+          }
           continue;
         }
         if (entry.handler != this) {
@@ -206,6 +225,7 @@ public void onResponse(StreamingCommitResponse response) {
     }
 
     @Override
+    @SuppressWarnings("ReferenceEquality")
     public boolean hasPendingRequests() {
       return pending.entrySet().stream().anyMatch(e -> e.getValue().handler == this);
     }
@@ -218,6 +238,7 @@ public void onDone(Status status) {
     }
 
     @Override
+    @SuppressWarnings("ReferenceEquality")
     public void appendHtml(PrintWriter writer) {
       writer.format(
           "CommitWorkStream: %d pending",
@@ -232,6 +253,7 @@ protected PhysicalStreamHandler newResponseHandler() {
 
   @Override
   protected synchronized void shutdownInternal() {
+    logMissingResponse = false;
     Iterator<StreamAndRequest> pendingRequests = pending.values().iterator();
     while (pendingRequests.hasNext()) {
       PendingRequest pendingRequest = pendingRequests.next().request;
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDirectGetWorkStream.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDirectGetWorkStream.java
index 938ec1c693c7..2712bf1bd33d 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDirectGetWorkStream.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDirectGetWorkStream.java
@@ -20,9 +20,11 @@
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkNotNull;
 
 import java.io.PrintWriter;
+import java.time.Duration;
 import java.util.Set;
 import java.util.concurrent.ConcurrentHashMap;
 import java.util.concurrent.ConcurrentMap;
+import java.util.concurrent.ScheduledExecutorService;
 import java.util.concurrent.atomic.AtomicReference;
 import java.util.function.Function;
 import javax.annotation.concurrent.GuardedBy;
@@ -98,16 +100,19 @@ private GrpcDirectGetWorkStream(
       HeartbeatSender heartbeatSender,
       GetDataClient getDataClient,
       WorkCommitter workCommitter,
-      WorkItemScheduler workItemScheduler) {
+      WorkItemScheduler workItemScheduler,
+      Duration halfClosePhysicalStreamAfter,
+      ScheduledExecutorService executorService) {
     super(
         LOG,
-        "GetWorkStream",
         startGetWorkRpcFn,
         backoff,
         streamObserverFactory,
         streamRegistry,
         logEveryNStreamFailures,
-        backendWorkerToken);
+        backendWorkerToken,
+        halfClosePhysicalStreamAfter,
+        executorService);
     this.requestHeader = requestHeader;
     this.workItemScheduler = workItemScheduler;
     this.heartbeatSender = heartbeatSender;
@@ -138,7 +143,9 @@ static GrpcDirectGetWorkStream create(
       HeartbeatSender heartbeatSender,
       GetDataClient getDataClient,
       WorkCommitter workCommitter,
-      WorkItemScheduler workItemScheduler) {
+      WorkItemScheduler workItemScheduler,
+      Duration halfClosePhysicalStreamAfter,
+      ScheduledExecutorService executor) {
     return new GrpcDirectGetWorkStream(
         backendWorkerToken,
         startGetWorkRpcFn,
@@ -151,7 +158,9 @@ static GrpcDirectGetWorkStream create(
         heartbeatSender,
         getDataClient,
         workCommitter,
-        workItemScheduler);
+        workItemScheduler,
+        halfClosePhysicalStreamAfter,
+        executor);
   }
 
   private static Watermarks createWatermarks(
@@ -230,7 +239,11 @@ protected PhysicalStreamHandler newResponseHandler() {
   }
 
   @Override
-  protected synchronized void onNewStream() throws WindmillStreamShutdownException {
+  protected synchronized void onFlushPending(boolean isNewStream)
+      throws WindmillStreamShutdownException {
+    if (!isNewStream) {
+      return;
+    }
     budgetTracker.reset();
     GetWorkBudget initialGetWorkBudget = budgetTracker.computeBudgetExtension();
     StreamingGetWorkRequest request =
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDispatcherClient.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDispatcherClient.java
index 3603bacf461a..82e66c4b0d74 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDispatcherClient.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDispatcherClient.java
@@ -21,6 +21,8 @@
 import static org.apache.beam.runners.dataflow.worker.windmill.client.grpc.stubs.WindmillChannels.localhostChannel;
 
 import com.google.auto.value.AutoValue;
+import java.time.Duration;
+import java.time.Instant;
 import java.util.List;
 import java.util.Random;
 import java.util.Set;
@@ -132,16 +134,15 @@ ImmutableSet<HostAndPort> getDispatcherEndpoints() {
 
   /** Will block the calling thread until the initial endpoints are present. */
   public CloudWindmillMetadataServiceV1Alpha1Stub getWindmillMetadataServiceStubBlocking() {
-    boolean initialized = false;
-    long secondsWaited = 0;
-    while (!initialized) {
-      LOG.info(
-          "Blocking until Windmill Service endpoint has been set. "
-              + "Currently waited for [{}] seconds.",
-          secondsWaited);
+    Instant startTime = Instant.now();
+    while (true) {
       try {
-        initialized = onInitializedEndpoints.await(10, TimeUnit.SECONDS);
-        secondsWaited += 10;
+        if (onInitializedEndpoints.await(10, TimeUnit.SECONDS)) {
+          break;
+        }
+        LOG.info(
+            "Blocking until Windmill Service endpoint has been set. " + "Currently waited for {}.",
+            Duration.between(startTime, Instant.now()));
       } catch (InterruptedException e) {
         LOG.error(
             "Interrupted while waiting for initial Windmill Service endpoints. "
@@ -149,8 +150,10 @@ public CloudWindmillMetadataServiceV1Alpha1Stub getWindmillMetadataServiceStubBl
             e);
       }
     }
-
-    LOG.info("Windmill Service endpoint initialized after {} seconds.", secondsWaited);
+    Duration elapsed = Duration.between(startTime, Instant.now());
+    if (elapsed.getSeconds() >= 5) {
+      LOG.info("Windmill Service endpoint initialized after {}.", elapsed);
+    }
 
     ImmutableList<CloudWindmillMetadataServiceV1Alpha1Stub> windmillMetadataServiceStubs =
         dispatcherStubs.get().windmillMetadataServiceStubs();
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetDataStream.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetDataStream.java
index 7de074122a3c..6d6dcd569e85 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetDataStream.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetDataStream.java
@@ -33,6 +33,7 @@
 import java.util.concurrent.CancellationException;
 import java.util.concurrent.ConcurrentHashMap;
 import java.util.concurrent.ConcurrentLinkedDeque;
+import java.util.concurrent.ScheduledExecutorService;
 import java.util.concurrent.atomic.AtomicLong;
 import java.util.function.Consumer;
 import java.util.function.Function;
@@ -112,16 +113,19 @@ private GrpcGetDataStream(
       AtomicLong idGenerator,
       int streamingRpcBatchLimit,
       boolean sendKeyedGetDataRequests,
-      Consumer<List<Windmill.ComputationHeartbeatResponse>> processHeartbeatResponses) {
+      Consumer<List<Windmill.ComputationHeartbeatResponse>> processHeartbeatResponses,
+      java.time.Duration halfClosePhysicalStreamAfter,
+      ScheduledExecutorService executorService) {
     super(
         LOG,
-        "GetDataStream",
         startGetDataRpcFn,
         backoff,
         streamObserverFactory,
         streamRegistry,
         logEveryNStreamFailures,
-        backendWorkerToken);
+        backendWorkerToken,
+        halfClosePhysicalStreamAfter,
+        executorService);
     this.idGenerator = idGenerator;
     this.jobHeader = jobHeader;
     this.streamingRpcBatchLimit = streamingRpcBatchLimit;
@@ -146,7 +150,9 @@ static GrpcGetDataStream create(
       AtomicLong idGenerator,
       int streamingRpcBatchLimit,
       boolean sendKeyedGetDataRequests,
-      Consumer<List<Windmill.ComputationHeartbeatResponse>> processHeartbeatResponses) {
+      Consumer<List<Windmill.ComputationHeartbeatResponse>> processHeartbeatResponses,
+      java.time.Duration halfClosePhysicalStreamAfter,
+      ScheduledExecutorService executor) {
     return new GrpcGetDataStream(
         backendWorkerToken,
         startGetDataRpcFn,
@@ -158,7 +164,9 @@ static GrpcGetDataStream create(
         idGenerator,
         streamingRpcBatchLimit,
         sendKeyedGetDataRequests,
-        processHeartbeatResponses);
+        processHeartbeatResponses,
+        halfClosePhysicalStreamAfter,
+        executor);
   }
 
   private static WindmillStreamShutdownException shutdownExceptionFor(QueuedBatch batch) {
@@ -189,7 +197,7 @@ public void sendBatch(QueuedBatch batch) throws WindmillStreamShutdownException
       }
 
       if (!trySend(batch.asGetDataRequest())) {
-        // The stream broke before this call went through; onNewStream will retry the fetch.
+        // The stream broke before this call went through; onFlushPending will retry the fetch.
         LOG.debug("GetData stream broke before call started.");
       }
     }
@@ -260,8 +268,11 @@ protected PhysicalStreamHandler newResponseHandler() {
   }
 
   @Override
-  protected synchronized void onNewStream() throws WindmillStreamShutdownException {
-    trySend(StreamingGetDataRequest.newBuilder().setHeader(jobHeader).build());
+  protected synchronized void onFlushPending(boolean isNewStream)
+      throws WindmillStreamShutdownException {
+    if (isNewStream) {
+      trySend(StreamingGetDataRequest.newBuilder().setHeader(jobHeader).build());
+    }
     while (!batches.isEmpty()) {
       QueuedBatch batch = checkNotNull(batches.peekFirst());
       verify(!batch.isEmpty());
@@ -392,6 +403,12 @@ protected synchronized void shutdownInternal() {
       }
       currentGetDataStream.pending.clear();
     }
+    for (PhysicalStreamHandler handler : closingPhysicalStreams) {
+      for (AppendableInputStream ais : ((GetDataPhysicalStreamHandler) handler).pending.values()) {
+        ais.cancel();
+      }
+      ((GetDataPhysicalStreamHandler) handler).pending.clear();
+    }
     batches.forEach(
         batch -> {
           batch.markFinalized();
@@ -402,7 +419,12 @@ protected synchronized void shutdownInternal() {
 
   @Override
   public void appendSpecificHtml(PrintWriter writer) {
-    writer.format("GetDataStream: %d queued batches", batchesDebugSizeSupplier.get());
+    int batches = batchesDebugSizeSupplier.get();
+    if (batches > 0) {
+      writer.format("GetDataStream: %d queued batches ", batches);
+    } else {
+      writer.append("GetDataStream: no queued batches ");
+    }
   }
 
   private <ResponseT> ResponseT issueRequest(QueuedRequest request, ParseFn<ResponseT> parseFn)
@@ -476,10 +498,11 @@ private void queueRequestAndWait(QueuedRequest request)
         prevBatch.waitForSendOrFailNotification();
       }
       trySendBatch(batch);
-    } else {
-      // Wait for this batch to be sent before parsing the response.
-      batch.waitForSendOrFailNotification();
+      // Since the above send may not succeed, we fall through to block on sending or failure.
     }
+
+    // Wait for this batch to be sent before parsing the response.
+    batch.waitForSendOrFailNotification();
   }
 
   private synchronized void trySendBatch(QueuedBatch batch) throws WindmillStreamShutdownException {
@@ -494,8 +517,8 @@ private synchronized void trySendBatch(QueuedBatch batch) throws WindmillStreamS
     final @Nullable GetDataPhysicalStreamHandler currentGetDataPhysicalStream =
         (GetDataPhysicalStreamHandler) currentPhysicalStream;
     if (currentGetDataPhysicalStream == null) {
-      // Leave the batch finalized but in the batches queue.  Finalized batches will be sent on the
-      // new stream in onNewStream.
+      // Leave the batch finalized but in the batches queue.  Finalized batches will be sent on a
+      // new stream in onFlushPending.
       return;
     }
 
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetWorkStream.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetWorkStream.java
index a1c758eac446..ae7ce85e13a8 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetWorkStream.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetWorkStream.java
@@ -18,8 +18,10 @@
 package org.apache.beam.runners.dataflow.worker.windmill.client.grpc;
 
 import java.io.PrintWriter;
+import java.time.Duration;
 import java.util.Set;
 import java.util.concurrent.ConcurrentHashMap;
+import java.util.concurrent.ScheduledExecutorService;
 import java.util.concurrent.atomic.AtomicLong;
 import java.util.function.Function;
 import org.apache.beam.runners.dataflow.worker.windmill.Windmill.GetWorkRequest;
@@ -68,16 +70,19 @@ private GrpcGetWorkStream(
       Set<AbstractWindmillStream<?, ?>> streamRegistry,
       int logEveryNStreamFailures,
       boolean requestBatchedGetWorkResponse,
-      WorkItemReceiver receiver) {
+      WorkItemReceiver receiver,
+      Duration halfClosePhysicalStreamAfter,
+      ScheduledExecutorService executor) {
     super(
         LOG,
-        "GetWorkStream",
         startGetWorkRpcFn,
         backoff,
         streamObserverFactory,
         streamRegistry,
         logEveryNStreamFailures,
-        backendWorkerToken);
+        backendWorkerToken,
+        halfClosePhysicalStreamAfter,
+        executor);
     this.request = request;
     this.receiver = receiver;
     this.inflightMessages = new AtomicLong();
@@ -97,7 +102,9 @@ public static GrpcGetWorkStream create(
       Set<AbstractWindmillStream<?, ?>> streamRegistry,
       int logEveryNStreamFailures,
       boolean requestBatchedGetWorkResponse,
-      WorkItemReceiver receiver) {
+      WorkItemReceiver receiver,
+      Duration halfClosePhysicalStreamAfter,
+      ScheduledExecutorService executor) {
     return new GrpcGetWorkStream(
         backendWorkerToken,
         startGetWorkRpcFn,
@@ -107,7 +114,9 @@ public static GrpcGetWorkStream create(
         streamRegistry,
         logEveryNStreamFailures,
         requestBatchedGetWorkResponse,
-        receiver);
+        receiver,
+        halfClosePhysicalStreamAfter,
+        executor);
   }
 
   private void sendRequestExtension(long moreItems, long moreBytes) {
@@ -163,7 +172,11 @@ protected PhysicalStreamHandler newResponseHandler() {
   }
 
   @Override
-  protected synchronized void onNewStream() throws WindmillStreamShutdownException {
+  protected synchronized void onFlushPending(boolean isNewStream)
+      throws WindmillStreamShutdownException {
+    if (!isNewStream) {
+      return;
+    }
     inflightMessages.set(request.getMaxItems());
     inflightBytes.set(request.getMaxBytes());
     trySend(
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetWorkerMetadataStream.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetWorkerMetadataStream.java
index 9b99b3bda909..4d1e8bb36d9d 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetWorkerMetadataStream.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetWorkerMetadataStream.java
@@ -19,8 +19,11 @@
 
 import com.google.errorprone.annotations.concurrent.GuardedBy;
 import java.io.PrintWriter;
+import java.time.Duration;
+import java.time.Instant;
 import java.util.Optional;
 import java.util.Set;
+import java.util.concurrent.ScheduledExecutorService;
 import java.util.function.Consumer;
 import java.util.function.Function;
 import org.apache.beam.runners.dataflow.worker.windmill.Windmill.JobHeader;
@@ -50,6 +53,9 @@ public final class GrpcGetWorkerMetadataStream
   @GuardedBy("metadataLock")
   private WorkerMetadataResponse latestResponse;
 
+  @GuardedBy("metadataLock")
+  private Instant latestResponseReceived = Instant.EPOCH;
+
   private GrpcGetWorkerMetadataStream(
       Function<StreamObserver<WorkerMetadataResponse>, StreamObserver<WorkerMetadataRequest>>
           startGetWorkerMetadataRpcFn,
@@ -58,16 +64,19 @@ private GrpcGetWorkerMetadataStream(
       Set<AbstractWindmillStream<?, ?>> streamRegistry,
       int logEveryNStreamFailures,
       JobHeader jobHeader,
-      Consumer<WindmillEndpoints> serverMappingConsumer) {
+      Consumer<WindmillEndpoints> serverMappingConsumer,
+      Duration halfClosePhysicalStreamAfter,
+      ScheduledExecutorService executorService) {
     super(
         LOG,
-        "GetWorkerMetadataStream",
         startGetWorkerMetadataRpcFn,
         backoff,
         streamObserverFactory,
         streamRegistry,
         logEveryNStreamFailures,
-        "");
+        "",
+        halfClosePhysicalStreamAfter,
+        executorService);
     this.workerMetadataRequest = WorkerMetadataRequest.newBuilder().setHeader(jobHeader).build();
     this.serverMappingConsumer = serverMappingConsumer;
     this.latestResponse = WorkerMetadataResponse.getDefaultInstance();
@@ -82,7 +91,9 @@ public static GrpcGetWorkerMetadataStream create(
       Set<AbstractWindmillStream<?, ?>> streamRegistry,
       int logEveryNStreamFailures,
       JobHeader jobHeader,
-      Consumer<WindmillEndpoints> serverMappingUpdater) {
+      Consumer<WindmillEndpoints> serverMappingUpdater,
+      Duration halfClosePhysicalStreamAfter,
+      ScheduledExecutorService executorService) {
     return new GrpcGetWorkerMetadataStream(
         startGetWorkerMetadataRpcFn,
         backoff,
@@ -90,7 +101,9 @@ public static GrpcGetWorkerMetadataStream create(
         streamRegistry,
         logEveryNStreamFailures,
         jobHeader,
-        serverMappingUpdater);
+        serverMappingUpdater,
+        halfClosePhysicalStreamAfter,
+        executorService);
   }
 
   /**
@@ -103,6 +116,7 @@ private Optional<WindmillEndpoints> extractWindmillEndpointsFrom(
     synchronized (metadataLock) {
       if (response.getMetadataVersion() > latestResponse.getMetadataVersion()) {
         this.latestResponse = response;
+        this.latestResponseReceived = Instant.now();
         return Optional.of(WindmillEndpoints.from(response));
       } else {
         // If the currentMetadataVersion is greater than or equal to one in the response, the
@@ -141,8 +155,10 @@ public void appendHtml(PrintWriter writer) {}
   }
 
   @Override
-  protected void onNewStream() throws WindmillStreamShutdownException {
-    trySend(workerMetadataRequest);
+  protected void onFlushPending(boolean isNewStream) throws WindmillStreamShutdownException {
+    if (isNewStream) {
+      trySend(workerMetadataRequest);
+    }
   }
 
   @Override
@@ -154,8 +170,8 @@ protected void sendHealthCheck() throws WindmillStreamShutdownException {
   protected void appendSpecificHtml(PrintWriter writer) {
     synchronized (metadataLock) {
       writer.format(
-          "GetWorkerMetadataStream:  job_header=[%s], current_metadata=[%s]",
-          workerMetadataRequest.getHeader(), latestResponse);
+          "GetWorkerMetadataStream:  job_header=[%s], current_metadata=[%s] received_at=[%s]",
+          workerMetadataRequest.getHeader(), latestResponse, latestResponseReceived);
     }
   }
 }
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcWindmillStreamFactory.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcWindmillStreamFactory.java
index 1f261e59450a..244d2ad3fa14 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcWindmillStreamFactory.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcWindmillStreamFactory.java
@@ -27,6 +27,9 @@
 import java.util.Timer;
 import java.util.TimerTask;
 import java.util.concurrent.ConcurrentHashMap;
+import java.util.concurrent.Executors;
+import java.util.concurrent.ScheduledExecutorService;
+import java.util.concurrent.ScheduledThreadPoolExecutor;
 import java.util.concurrent.TimeUnit;
 import java.util.concurrent.atomic.AtomicLong;
 import java.util.function.Consumer;
@@ -59,6 +62,7 @@
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Suppliers;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableSet;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.util.concurrent.ThreadFactoryBuilder;
 import org.joda.time.Duration;
 import org.joda.time.Instant;
 
@@ -69,8 +73,10 @@
 @ThreadSafe
 @Internal
 public class GrpcWindmillStreamFactory implements StatusDataProvider {
-
   private static final long DEFAULT_STREAM_RPC_DEADLINE_SECONDS = 300;
+  private static final java.time.Duration
+      DEFAULT_DIRECT_STREAMING_RPC_PHYSICAL_STREAM_HALF_CLOSE_AFTER =
+          java.time.Duration.ofMinutes(3);
   private static final Duration MIN_BACKOFF = Duration.millis(1);
   private static final Duration DEFAULT_MAX_BACKOFF = Duration.standardSeconds(30);
   private static final int DEFAULT_LOG_EVERY_N_STREAM_FAILURES = 1;
@@ -92,6 +98,8 @@ public class GrpcWindmillStreamFactory implements StatusDataProvider {
   private final boolean sendKeyedGetDataRequests;
   private final boolean requestBatchedGetWorkResponse;
   private final Consumer<List<ComputationHeartbeatResponse>> processHeartbeatResponses;
+  private final java.time.Duration directStreamingRpcPhysicalStreamHalfCloseAfter;
+  private final Supplier<ScheduledExecutorService> executorServiceSupplier;
 
   private GrpcWindmillStreamFactory(
       JobHeader jobHeader,
@@ -101,7 +109,9 @@ private GrpcWindmillStreamFactory(
       boolean sendKeyedGetDataRequests,
       boolean requestBatchedGetWorkResponse,
       Consumer<List<ComputationHeartbeatResponse>> processHeartbeatResponses,
-      Supplier<Duration> maxBackOffSupplier) {
+      Supplier<Duration> maxBackOffSupplier,
+      java.time.Duration directStreamingRpcPhysicalStreamHalfCloseAfter,
+      Supplier<ScheduledExecutorService> executorServiceSupplier) {
     this.jobHeader = jobHeader;
     this.logEveryNStreamFailures = logEveryNStreamFailures;
     this.streamingRpcBatchLimit = streamingRpcBatchLimit;
@@ -119,6 +129,9 @@ private GrpcWindmillStreamFactory(
     this.requestBatchedGetWorkResponse = requestBatchedGetWorkResponse;
     this.processHeartbeatResponses = processHeartbeatResponses;
     this.streamIdGenerator = new AtomicLong();
+    this.directStreamingRpcPhysicalStreamHalfCloseAfter =
+        directStreamingRpcPhysicalStreamHalfCloseAfter;
+    this.executorServiceSupplier = executorServiceSupplier;
   }
 
   /** @implNote Used for {@link AutoBuilder} {@link Builder} class, do not call directly. */
@@ -131,7 +144,9 @@ static GrpcWindmillStreamFactory create(
       boolean requestBatchedGetWorkResponse,
       Consumer<List<ComputationHeartbeatResponse>> processHeartbeatResponses,
       Supplier<Duration> maxBackOffSupplier,
-      int healthCheckIntervalMillis) {
+      int healthCheckIntervalMillis,
+      java.time.Duration directStreamingRpcPhysicalStreamHalfCloseAfter,
+      Supplier<ScheduledExecutorService> scheduledExecutorServiceSupplier) {
     GrpcWindmillStreamFactory streamFactory =
         new GrpcWindmillStreamFactory(
             jobHeader,
@@ -141,7 +156,9 @@ static GrpcWindmillStreamFactory create(
             sendKeyedGetDataRequests,
             requestBatchedGetWorkResponse,
             processHeartbeatResponses,
-            maxBackOffSupplier);
+            maxBackOffSupplier,
+            directStreamingRpcPhysicalStreamHalfCloseAfter,
+            scheduledExecutorServiceSupplier);
 
     if (healthCheckIntervalMillis >= 0) {
       // Health checks are run on background daemon thread, which will only be cleaned up on JVM
@@ -169,6 +186,7 @@ public void run() {
    * Returns a new {@link Builder} for {@link GrpcWindmillStreamFactory} with default values set for
    * the given {@link JobHeader}.
    */
+  @SuppressWarnings("nullness")
   public static GrpcWindmillStreamFactory.Builder of(JobHeader jobHeader) {
     return new AutoBuilder_GrpcWindmillStreamFactory_Builder()
         .setJobHeader(jobHeader)
@@ -179,7 +197,10 @@ public static GrpcWindmillStreamFactory.Builder of(JobHeader jobHeader) {
         .setHealthCheckIntervalMillis(NO_HEALTH_CHECKS)
         .setSendKeyedGetDataRequests(true)
         .setRequestBatchedGetWorkResponse(false)
-        .setProcessHeartbeatResponses(ignored -> {});
+        .setProcessHeartbeatResponses(ignored -> {})
+        .setDirectStreamingRpcPhysicalStreamHalfCloseAfter(
+            DEFAULT_DIRECT_STREAMING_RPC_PHYSICAL_STREAM_HALF_CLOSE_AFTER)
+        .setScheduledExecutorServiceSupplier(() -> null);
   }
 
   private static <T extends AbstractStub<T>> T withDefaultDeadline(T stub) {
@@ -201,6 +222,41 @@ private static void printSummaryHtmlForWorker(
     writer.write("<br>");
   }
 
+  private ScheduledExecutorService executorForDispatchedStreams(String debugStreamTypeName) {
+    ScheduledExecutorService result = executorServiceSupplier.get();
+    if (result != null) {
+      return result;
+    }
+    return Executors.newSingleThreadScheduledExecutor(
+        new ThreadFactoryBuilder()
+            .setDaemon(true)
+            .setNameFormat(String.format("%s-WindmillStream-thread", debugStreamTypeName))
+            .build());
+  }
+
+  private ScheduledExecutorService executorForDirectStreams(
+      String backendWorkerToken, String debugStreamTypeName) {
+    ScheduledExecutorService supplierResult = executorServiceSupplier.get();
+    if (supplierResult != null) {
+      return supplierResult;
+    }
+    ScheduledThreadPoolExecutor result =
+        new ScheduledThreadPoolExecutor(
+            0,
+            new ThreadFactoryBuilder()
+                .setDaemon(true)
+                .setNameFormat(
+                    String.join(
+                        "-",
+                        debugStreamTypeName,
+                        backendWorkerToken.substring(0, Math.min(10, backendWorkerToken.length())),
+                        "WindmillStream",
+                        "%d"))
+                .build());
+    result.setKeepAliveTime(1, TimeUnit.MINUTES);
+    return result;
+  }
+
   public GetWorkStream createGetWorkStream(
       CloudWindmillServiceV1Alpha1Stub stub,
       GetWorkRequest request,
@@ -214,7 +270,9 @@ public GetWorkStream createGetWorkStream(
         streamRegistry,
         logEveryNStreamFailures,
         requestBatchedGetWorkResponse,
-        processWorkItem);
+        processWorkItem,
+        java.time.Duration.ZERO,
+        executorForDispatchedStreams("GetWork"));
   }
 
   public GetWorkStream createDirectGetWorkStream(
@@ -226,7 +284,8 @@ public GetWorkStream createDirectGetWorkStream(
       WorkItemScheduler workItemScheduler) {
     return GrpcDirectGetWorkStream.create(
         connection.backendWorkerToken(),
-        responseObserver -> connection.currentStub().getWorkStream(responseObserver),
+        responseObserver ->
+            withDefaultDeadline(connection.currentStub()).getWorkStream(responseObserver),
         request,
         grpcBackOff.get(),
         newStreamObserverFactory(),
@@ -236,7 +295,9 @@ public GetWorkStream createDirectGetWorkStream(
         heartbeatSender,
         getDataClient,
         workCommitter,
-        workItemScheduler);
+        workItemScheduler,
+        directStreamingRpcPhysicalStreamHalfCloseAfter,
+        executorForDirectStreams(connection.backendWorkerToken(), "GetWork"));
   }
 
   public GetDataStream createGetDataStream(CloudWindmillServiceV1Alpha1Stub stub) {
@@ -251,13 +312,16 @@ public GetDataStream createGetDataStream(CloudWindmillServiceV1Alpha1Stub stub)
         streamIdGenerator,
         streamingRpcBatchLimit,
         sendKeyedGetDataRequests,
-        processHeartbeatResponses);
+        processHeartbeatResponses,
+        java.time.Duration.ZERO,
+        executorForDispatchedStreams("GetWorkerMetadata"));
   }
 
   public GetDataStream createDirectGetDataStream(WindmillConnection connection) {
     return GrpcGetDataStream.create(
         connection.backendWorkerToken(),
-        responseObserver -> connection.currentStub().getDataStream(responseObserver),
+        responseObserver ->
+            withDefaultDeadline(connection.currentStub()).getDataStream(responseObserver),
         grpcBackOff.get(),
         newStreamObserverFactory(),
         streamRegistry,
@@ -266,7 +330,9 @@ public GetDataStream createDirectGetDataStream(WindmillConnection connection) {
         streamIdGenerator,
         streamingRpcBatchLimit,
         sendKeyedGetDataRequests,
-        processHeartbeatResponses);
+        processHeartbeatResponses,
+        directStreamingRpcPhysicalStreamHalfCloseAfter,
+        executorForDirectStreams(connection.backendWorkerToken(), "GetData"));
   }
 
   public CommitWorkStream createCommitWorkStream(CloudWindmillServiceV1Alpha1Stub stub) {
@@ -279,20 +345,25 @@ public CommitWorkStream createCommitWorkStream(CloudWindmillServiceV1Alpha1Stub
         logEveryNStreamFailures,
         jobHeader,
         streamIdGenerator,
-        streamingRpcBatchLimit);
+        streamingRpcBatchLimit,
+        java.time.Duration.ZERO,
+        executorForDispatchedStreams("CommitWork"));
   }
 
   public CommitWorkStream createDirectCommitWorkStream(WindmillConnection connection) {
     return GrpcCommitWorkStream.create(
         connection.backendWorkerToken(),
-        responseObserver -> connection.currentStub().commitWorkStream(responseObserver),
+        responseObserver ->
+            withDefaultDeadline(connection.currentStub()).commitWorkStream(responseObserver),
         grpcBackOff.get(),
         newStreamObserverFactory(),
         streamRegistry,
         logEveryNStreamFailures,
         jobHeader,
         streamIdGenerator,
-        streamingRpcBatchLimit);
+        streamingRpcBatchLimit,
+        directStreamingRpcPhysicalStreamHalfCloseAfter,
+        executorForDirectStreams(connection.backendWorkerToken(), "CommitWork"));
   }
 
   public GetWorkerMetadataStream createGetWorkerMetadataStream(
@@ -305,7 +376,9 @@ public GetWorkerMetadataStream createGetWorkerMetadataStream(
         streamRegistry,
         logEveryNStreamFailures,
         jobHeader,
-        onNewWindmillEndpoints);
+        onNewWindmillEndpoints,
+        directStreamingRpcPhysicalStreamHalfCloseAfter,
+        executorForDispatchedStreams("GetWorkerMetadataStream"));
   }
 
   private StreamObserverFactory newStreamObserverFactory() {
@@ -351,6 +424,11 @@ Builder setProcessHeartbeatResponses(
 
     Builder setRequestBatchedGetWorkResponse(boolean enabled);
 
+    Builder setDirectStreamingRpcPhysicalStreamHalfCloseAfter(java.time.Duration timeout);
+
+    Builder setScheduledExecutorServiceSupplier(
+        Supplier<ScheduledExecutorService> scheduledExecutorServiceSupplier);
+
     GrpcWindmillStreamFactory build();
   }
 }
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/observers/DirectStreamObserver.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/observers/DirectStreamObserver.java
index 173cbd26c4e7..bf060bd6acfe 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/observers/DirectStreamObserver.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/observers/DirectStreamObserver.java
@@ -182,8 +182,8 @@ public void onError(Throwable t) {
       Preconditions.checkState(!isUserClosed);
       isUserClosed = true;
       if (!isOutboundObserverClosed) {
-        outboundObserver.onError(t);
         isOutboundObserverClosed = true;
+        outboundObserver.onError(t);
       }
     }
   }
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/observers/StreamObserverCancelledException.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/observers/StreamObserverCancelledException.java
index 70fd3497a37f..5682d5085d2b 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/observers/StreamObserverCancelledException.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/observers/StreamObserverCancelledException.java
@@ -21,15 +21,15 @@
 
 @Internal
 public final class StreamObserverCancelledException extends RuntimeException {
-  StreamObserverCancelledException(Throwable cause) {
+  public StreamObserverCancelledException(Throwable cause) {
     super(cause);
   }
 
-  StreamObserverCancelledException(String message, Throwable cause) {
+  public StreamObserverCancelledException(String message, Throwable cause) {
     super(message, cause);
   }
 
-  StreamObserverCancelledException(String message) {
+  public StreamObserverCancelledException(String message) {
     super(message);
   }
 }
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/stubs/ChannelCache.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/stubs/ChannelCache.java
index 11018bdb2c46..6de661e52cf6 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/stubs/ChannelCache.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/stubs/ChannelCache.java
@@ -38,7 +38,6 @@
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.cache.RemovalListeners;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.util.concurrent.MoreExecutors;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.util.concurrent.ThreadFactoryBuilder;
-import org.checkerframework.checker.nullness.qual.MonotonicNonNull;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -58,8 +57,8 @@ public final class ChannelCache implements StatusDataProvider {
   private final LoadingCache<WindmillServiceAddress, ManagedChannel> channelCache;
 
   @GuardedBy("this")
-  @MonotonicNonNull
-  private UserWorkerGrpcFlowControlSettings currentFlowControlSettings = null;
+  private UserWorkerGrpcFlowControlSettings currentFlowControlSettings =
+      UserWorkerGrpcFlowControlSettings.getDefaultInstance();
 
   private ChannelCache(
       WindmillChannelFactory channelFactory,
@@ -78,7 +77,8 @@ public ManagedChannel load(WindmillServiceAddress key) {
                   private UserWorkerGrpcFlowControlSettings resolveFlowControlSettings(
                       WindmillServiceAddress.Kind addressType) {
                     synchronized (ChannelCache.this) {
-                      if (currentFlowControlSettings == null) {
+                      if (currentFlowControlSettings.equals(
+                          UserWorkerGrpcFlowControlSettings.getDefaultInstance())) {
                         return addressType == AUTHENTICATED_GCP_SERVICE_ADDRESS
                             ? WindmillChannels.DEFAULT_DIRECTPATH_FLOW_CONTROL_SETTINGS
                             : WindmillChannels.DEFAULT_CLOUDPATH_FLOW_CONTROL_SETTINGS;
@@ -132,9 +132,7 @@ public ManagedChannel get(WindmillServiceAddress windmillServiceAddress) {
 
   public synchronized void consumeFlowControlSettings(
       UserWorkerGrpcFlowControlSettings flowControlSettings) {
-    //noinspection PointlessNullCheck
-    if (currentFlowControlSettings == null
-        || !flowControlSettings.equals(currentFlowControlSettings)) {
+    if (!flowControlSettings.equals(currentFlowControlSettings)) {
       // Refreshing the cache will asynchronously terminate the old channels via the removalListener
       // and return a newly created one on the next Cache.load(address). This could be expensive so
       // only do it when we have received new flow control settings.
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/state/ToIterableFunction.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/state/ToIterableFunction.java
index 3db058c79a03..7e164df2245b 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/state/ToIterableFunction.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/state/ToIterableFunction.java
@@ -64,13 +64,13 @@ public Iterable<ResultT> apply(
                   valuesAndContPosition.getContinuationPosition())
               .toBuilder();
       if (stateTag.getSortedListRange() != null) {
-        continuationTBuilder.setSortedListRange(stateTag.getSortedListRange()).build();
+        continuationTBuilder.setSortedListRange(stateTag.getSortedListRange());
       }
       if (stateTag.getMultimapKey() != null) {
-        continuationTBuilder.setMultimapKey(stateTag.getMultimapKey()).build();
+        continuationTBuilder.setMultimapKey(stateTag.getMultimapKey());
       }
       if (stateTag.getOmitValues() != null) {
-        continuationTBuilder.setOmitValues(stateTag.getOmitValues()).build();
+        continuationTBuilder.setOmitValues(stateTag.getOmitValues());
       }
       return new PagingIterable<>(
           reader, valuesAndContPosition.getValues(), continuationTBuilder.build(), coder);
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/state/WindmillStateUtil.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/state/WindmillStateUtil.java
index 9ce2d687b3fe..d95bf95db806 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/state/WindmillStateUtil.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/state/WindmillStateUtil.java
@@ -18,30 +18,78 @@
 package org.apache.beam.runners.dataflow.worker.windmill.state;
 
 import java.io.IOException;
+import java.lang.ref.SoftReference;
 import org.apache.beam.runners.core.StateNamespace;
 import org.apache.beam.runners.core.StateTag;
 import org.apache.beam.sdk.util.ByteStringOutputStream;
 import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.ByteString;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
+import org.checkerframework.checker.nullness.qual.Nullable;
 
 class WindmillStateUtil {
 
+  private static final ThreadLocal<@Nullable RefHolder> threadLocalRefHolder = new ThreadLocal<>();
+
   /** Encodes the given namespace and address as {@code &lt;namespace&gt;+&lt;address&gt;}. */
   @VisibleForTesting
   static ByteString encodeKey(StateNamespace namespace, StateTag<?> address) {
+    RefHolder refHolder = getRefHolderFromThreadLocal();
+    // Use ByteStringOutputStream rather than concatenation and String.format. We build these keys
+    // a lot, and this leads to better performance results. See associated benchmarks.
+    ByteStringOutputStream stream;
+    boolean releaseThreadLocal;
+    if (refHolder.inUse) {
+      // If the thread local stream is already in use, create a new one
+      stream = new ByteStringOutputStream();
+      releaseThreadLocal = false;
+    } else {
+      stream = getByteStringOutputStream(refHolder);
+      refHolder.inUse = true;
+      releaseThreadLocal = true;
+    }
     try {
-      // Use ByteStringOutputStream rather than concatenation and String.format. We build these keys
-      // a lot, and this leads to better performance results. See associated benchmarks.
-      ByteStringOutputStream stream = new ByteStringOutputStream();
       // stringKey starts and ends with a slash.  We separate it from the
       // StateTag ID by a '+' (which is guaranteed not to be in the stringKey) because the
       // ID comes from the user.
       namespace.appendTo(stream);
       stream.append('+');
       address.appendTo(stream);
-      return stream.toByteString();
+      return stream.toByteStringAndReset();
     } catch (IOException e) {
       throw new RuntimeException(e);
+    } finally {
+      stream.reset();
+      if (releaseThreadLocal) {
+        refHolder.inUse = false;
+      }
+    }
+  }
+
+  private static class RefHolder {
+    public SoftReference<@Nullable ByteStringOutputStream> streamRef =
+        new SoftReference<>(new ByteStringOutputStream());
+
+    // Boolean is true when the thread local stream is already in use by the current thread.
+    // Used to avoid reusing the same stream from nested calls if any.
+    public boolean inUse = false;
+  }
+
+  private static RefHolder getRefHolderFromThreadLocal() {
+    @Nullable RefHolder refHolder = threadLocalRefHolder.get();
+    if (refHolder == null) {
+      refHolder = new RefHolder();
+      threadLocalRefHolder.set(refHolder);
+    }
+    return refHolder;
+  }
+
+  private static ByteStringOutputStream getByteStringOutputStream(RefHolder refHolder) {
+    @Nullable
+    ByteStringOutputStream stream = refHolder.streamRef == null ? null : refHolder.streamRef.get();
+    if (stream == null) {
+      stream = new ByteStringOutputStream();
+      refHolder.streamRef = new SoftReference<>(stream);
     }
+    return stream;
   }
 }
diff --git a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/work/processing/StreamingWorkScheduler.java b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/work/processing/StreamingWorkScheduler.java
index e58a2759cd83..a4cd5d6d8a6b 100644
--- a/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/work/processing/StreamingWorkScheduler.java
+++ b/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/windmill/work/processing/StreamingWorkScheduler.java
@@ -425,7 +425,7 @@ private ExecuteWorkResult executeWork(
       // If processing failed due to a thrown exception, close the executionState. Do not
       // return/release the executionState back to computationState as that will lead to this
       // executionState instance being reused.
-      LOG.info("Invalidating executor after work item {} failed with Exception:", key, t);
+      LOG.debug("Invalidating executor after work item {} failed", workItem.getWorkToken(), t);
       computationWorkExecutor.invalidate();
 
       // Re-throw the exception, it will be caught and handled by workFailureProcessor downstream.
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/FakeWindmillServer.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/FakeWindmillServer.java
index 59fd341fab4b..dd13d5b55930 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/FakeWindmillServer.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/FakeWindmillServer.java
@@ -253,7 +253,7 @@ public boolean awaitTermination(int time, TimeUnit unit) throws InterruptedExcep
           Windmill.GetWorkResponse response = workToOffer.get(null);
           if (response == null) {
             try {
-              sleepMillis(500);
+              sleepMillis(100);
             } catch (InterruptedException e) {
               halfClose();
               Thread.currentThread().interrupt();
@@ -515,9 +515,9 @@ public void clearCommitsReceived() {
   public ConcurrentHashMap<Long, Consumer<Windmill.CommitStatus>> waitForDroppedCommits(
       int droppedCommits) {
     LOG.debug("waitForDroppedCommits: {}", droppedCommits);
-    int maxTries = 10;
+    int maxTries = 100;
     while (maxTries-- > 0 && droppedStreamingCommits.size() < droppedCommits) {
-      Uninterruptibles.sleepUninterruptibly(1000, TimeUnit.MILLISECONDS);
+      Uninterruptibles.sleepUninterruptibly(100, TimeUnit.MILLISECONDS);
     }
     assertEquals(droppedCommits, droppedStreamingCommits.size());
     return droppedStreamingCommits;
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/GroupingShuffleReaderTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/GroupingShuffleReaderTest.java
index cc006d5b1651..8a962773821c 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/GroupingShuffleReaderTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/GroupingShuffleReaderTest.java
@@ -215,7 +215,7 @@ private List<ShuffleEntry> writeShuffleEntries(
     return records;
   }
 
-  @SuppressWarnings("ReturnValueIgnored")
+  @SuppressWarnings({"ReturnValueIgnored"})
   private List<KV<Integer, List<KV<Integer, Integer>>>> runIterationOverGroupingShuffleReader(
       BatchModeExecutionContext context,
       TestShuffleReader shuffleReader,
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/IsmSideInputReaderTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/IsmSideInputReaderTest.java
index 8420977dc47d..06e089807299 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/IsmSideInputReaderTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/IsmSideInputReaderTest.java
@@ -1727,14 +1727,14 @@ private static RandomAccessData encodeKeyPortion(IsmRecordCoder<?> coder, IsmRec
   }
 
   /** Write input elements to a new temporary file and return the corresponding IsmSource. */
-  private <K, V> Source initInputFile(
+  private <V> Source initInputFile(
       Iterable<IsmRecord<WindowedValue<V>>> elements, IsmRecordCoder<WindowedValue<V>> coder)
       throws Exception {
     return initInputFile(elements, coder, tmpFolder.newFile().getPath());
   }
 
   /** Write input elements to the given file and return the corresponding IsmSource. */
-  private <K, V> Source initInputFile(
+  private <V> Source initInputFile(
       Iterable<IsmRecord<WindowedValue<V>>> elements,
       IsmRecordCoder<WindowedValue<V>> coder,
       String tmpFilePath)
@@ -1769,7 +1769,7 @@ private <K, V> Source initInputFile(
   }
 
   /** Returns a new Source for the given ISM file using the specified coder. */
-  private <K, V> Source newIsmSource(IsmRecordCoder<WindowedValue<V>> coder, String tmpFilePath) {
+  private <V> Source newIsmSource(IsmRecordCoder<WindowedValue<V>> coder, String tmpFilePath) {
     Source source = new Source();
     source.setCodec(
         CloudObjects.asCloudObject(
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingDataflowWorkerTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingDataflowWorkerTest.java
index c1696d8a70ab..a60535dfbd69 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingDataflowWorkerTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingDataflowWorkerTest.java
@@ -1285,7 +1285,7 @@ public void testKeyCommitTooLargeException() throws Exception {
     int maxTries = 10;
     while (--maxTries > 0) {
       worker.reportPeriodicWorkerUpdatesForTest();
-      Uninterruptibles.sleepUninterruptibly(1000, TimeUnit.MILLISECONDS);
+      Uninterruptibles.sleepUninterruptibly(100, TimeUnit.MILLISECONDS);
     }
 
     // We should see an exception reported for the large commit but not the small one.
@@ -1489,9 +1489,9 @@ public void testExceptions() throws Exception {
     server.waitForEmptyWorkQueue();
 
     // Wait until the worker has given up.
-    int maxTries = 10;
+    int maxTries = 100;
     while (maxTries-- > 0 && !worker.workExecutorIsEmpty()) {
-      Uninterruptibles.sleepUninterruptibly(1000, TimeUnit.MILLISECONDS);
+      Uninterruptibles.sleepUninterruptibly(100, TimeUnit.MILLISECONDS);
     }
     assertTrue(worker.workExecutorIsEmpty());
 
@@ -1499,7 +1499,7 @@ public void testExceptions() throws Exception {
     maxTries = 10;
     while (maxTries-- > 0) {
       worker.reportPeriodicWorkerUpdatesForTest();
-      Uninterruptibles.sleepUninterruptibly(1000, TimeUnit.MILLISECONDS);
+      Uninterruptibles.sleepUninterruptibly(100, TimeUnit.MILLISECONDS);
     }
 
     // We should see our update only one time with the exceptions we are expecting.
@@ -3520,7 +3520,7 @@ public void testActiveWorkFailure() throws Exception {
     // Release the blocked calls.
     BlockingFn.blocker().countDown();
     Map<Long, Windmill.WorkItemCommitRequest> commits =
-        server.waitForAndGetCommitsWithTimeout(2, Duration.standardSeconds((5)));
+        server.waitForAndGetCommitsWithTimeout(1, Duration.standardSeconds((5)));
     assertEquals(1, commits.size());
 
     worker.stop();
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingSideInputDoFnRunnerTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingSideInputDoFnRunnerTest.java
index 9b92d7d48431..d18bc512723e 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingSideInputDoFnRunnerTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingSideInputDoFnRunnerTest.java
@@ -384,7 +384,7 @@ public void testMultipleSideInputs() throws Exception {
     assertThat(sideInputFetcher.elementBag(createWindow(0)).read(), Matchers.emptyIterable());
   }
 
-  private <ReceiverT> StreamingSideInputDoFnRunner<String, String, IntervalWindow> createRunner(
+  private StreamingSideInputDoFnRunner<String, String, IntervalWindow> createRunner(
       WindowedValueMultiReceiver outputManager,
       List<PCollectionView<String>> views,
       StreamingSideInputFetcher<String, IntervalWindow> sideInputFetcher)
@@ -392,7 +392,7 @@ private <ReceiverT> StreamingSideInputDoFnRunner<String, String, IntervalWindow>
     return createRunner(WINDOW_FN, outputManager, views, sideInputFetcher);
   }
 
-  private <ReceiverT> StreamingSideInputDoFnRunner<String, String, IntervalWindow> createRunner(
+  private StreamingSideInputDoFnRunner<String, String, IntervalWindow> createRunner(
       WindowFn<?, ?> windowFn,
       WindowedValueMultiReceiver outputManager,
       List<PCollectionView<String>> views,
@@ -415,7 +415,7 @@ private <ReceiverT> StreamingSideInputDoFnRunner<String, String, IntervalWindow>
     return new StreamingSideInputDoFnRunner<>(simpleDoFnRunner, sideInputFetcher);
   }
 
-  private <ReceiverT> StreamingSideInputFetcher<String, IntervalWindow> createFetcher(
+  private StreamingSideInputFetcher<String, IntervalWindow> createFetcher(
       List<PCollectionView<String>> views) throws Exception {
     @SuppressWarnings({"unchecked", "rawtypes"})
     Iterable<PCollectionView<?>> typedViews = (Iterable) views;
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingSideInputFetcherTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingSideInputFetcherTest.java
index 0f5cd1a0d233..cf616ee6ac0d 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingSideInputFetcherTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/StreamingSideInputFetcherTest.java
@@ -181,7 +181,7 @@ public void testStoreIfBlocked() throws Exception {
     assertThat(restTimers, Matchers.contains(timer2));
   }
 
-  private <ReceiverT> StreamingSideInputFetcher<String, IntervalWindow> createFetcher(
+  private StreamingSideInputFetcher<String, IntervalWindow> createFetcher(
       List<PCollectionView<String>> views) throws Exception {
     @SuppressWarnings({"unchecked", "rawtypes"})
     Iterable<PCollectionView<?>> typedViews = (Iterable) views;
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingHandlerTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingHandlerTest.java
index c69b031bf74b..3191228687c3 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingHandlerTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/logging/DataflowWorkerLoggingHandlerTest.java
@@ -108,6 +108,17 @@ private static String createJson(LogRecord record, Formatter formatter) throws I
     return new String(output.toByteArray(), StandardCharsets.UTF_8);
   }
 
+  private static String createJsonWithCustomMdc(LogRecord record) throws IOException {
+    ByteArrayOutputStream output = new ByteArrayOutputStream();
+    FixedOutputStreamFactory factory = new FixedOutputStreamFactory(output);
+    DataflowWorkerLoggingHandler handler = new DataflowWorkerLoggingHandler(factory, 0);
+    handler.setLogMdc(true);
+    // Format the record as JSON.
+    handler.publish(record);
+    // Decode the binary output as UTF-8 and return the generated string.
+    return new String(output.toByteArray(), StandardCharsets.UTF_8);
+  }
+
   /**
    * Encodes a {@link org.apache.beam.model.fnexecution.v1.BeamFnApi.LogEntry} into a Json string.
    */
@@ -233,14 +244,14 @@ public synchronized String formatMessage(LogRecord record) {
             return MDC.get("testMdcKey") + ":" + super.formatMessage(record);
           }
         };
-    MDC.put("testMdcKey", "testMdcValue");
-
-    assertEquals(
-        "{\"timestamp\":{\"seconds\":0,\"nanos\":1000000},\"severity\":\"INFO\","
-            + "\"message\":\"testMdcValue:test.message\",\"thread\":\"2\",\"job\":\"testJobId\","
-            + "\"worker\":\"testWorkerId\",\"work\":\"testWorkId\",\"logger\":\"LoggerName\"}"
-            + System.lineSeparator(),
-        createJson(createLogRecord("test.message", null /* throwable */), customFormatter));
+    try (MDC.MDCCloseable ignored = MDC.putCloseable("testMdcKey", "testMdcValue")) {
+      assertEquals(
+          "{\"timestamp\":{\"seconds\":0,\"nanos\":1000000},\"severity\":\"INFO\","
+              + "\"message\":\"testMdcValue:test.message\",\"thread\":\"2\",\"job\":\"testJobId\","
+              + "\"worker\":\"testWorkerId\",\"work\":\"testWorkId\",\"logger\":\"LoggerName\"}"
+              + System.lineSeparator(),
+          createJson(createLogRecord("test.message", null /* throwable */), customFormatter));
+    }
   }
 
   @Test
@@ -299,6 +310,40 @@ public void testWithException() throws IOException {
         createJson(createLogRecord(null /* message */, createThrowable())));
   }
 
+  @Test
+  public void testWithCustomDataEnabledNoMdc() throws IOException {
+    assertEquals(
+        "{\"timestamp\":{\"seconds\":0,\"nanos\":1000000},\"severity\":\"INFO\","
+            + "\"message\":\"test.message\",\"thread\":\"2\",\"logger\":\"LoggerName\"}"
+            + System.lineSeparator(),
+        createJsonWithCustomMdc(createLogRecord("test.message", null)));
+  }
+
+  @Test
+  public void testWithCustomDataDisabledWithMdc() throws IOException {
+    MDC.clear();
+    try (MDC.MDCCloseable closeable = MDC.putCloseable("key1", "cool value")) {
+      assertEquals(
+          "{\"timestamp\":{\"seconds\":0,\"nanos\":1000000},\"severity\":\"INFO\","
+              + "\"message\":\"test.message\",\"thread\":\"2\",\"logger\":\"LoggerName\"}"
+              + System.lineSeparator(),
+          createJson(createLogRecord("test.message", null)));
+    }
+  }
+
+  @Test
+  public void testWithCustomDataEnabledWithMdc() throws IOException {
+    try (MDC.MDCCloseable ignored = MDC.putCloseable("key1", "cool value");
+        MDC.MDCCloseable ignored2 = MDC.putCloseable("key2", "another")) {
+      assertEquals(
+          "{\"timestamp\":{\"seconds\":0,\"nanos\":1000000},\"severity\":\"INFO\","
+              + "\"message\":\"test.message\",\"thread\":\"2\",\"logger\":\"LoggerName\","
+              + "\"custom_data\":{\"key1\":\"cool value\",\"key2\":\"another\"}}"
+              + System.lineSeparator(),
+          createJsonWithCustomMdc(createLogRecord("test.message", null)));
+    }
+  }
+
   @Test
   public void testWithoutExceptionOrMessage() throws IOException {
     DataflowWorkerLoggingMDC.setJobId("testJobId");
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/util/GroupAlsoByWindowProperties.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/util/GroupAlsoByWindowProperties.java
index bca4efa518f2..aec6b474e7d5 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/util/GroupAlsoByWindowProperties.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/util/GroupAlsoByWindowProperties.java
@@ -588,11 +588,10 @@ public StateInternals load(K key) throws Exception {
 
   private static final PipelineOptions OPTIONS = PipelineOptionsFactory.create();
 
-  private static <K, InputT, OutputT, W extends BoundedWindow>
-      List<WindowedValue<KV<K, OutputT>>> processElement(
-          BatchGroupAlsoByWindowFn<K, InputT, OutputT> fn,
-          KV<K, Iterable<WindowedValue<InputT>>> element)
-          throws Exception {
+  private static <K, InputT, OutputT> List<WindowedValue<KV<K, OutputT>>> processElement(
+      BatchGroupAlsoByWindowFn<K, InputT, OutputT> fn,
+      KV<K, Iterable<WindowedValue<InputT>>> element)
+      throws Exception {
     TestOutput<K, OutputT> output = new TestOutput<>();
     fn.processElement(
         element, OPTIONS, null /* timerInternals */, NullSideInputReader.empty(), output);
@@ -611,8 +610,8 @@ private static class TestOutput<K, OutputT> implements WindowedValueReceiver<KV<
     private final List<WindowedValue<KV<K, OutputT>>> output = new ArrayList<>();
 
     @Override
-    public void output(WindowedValue<KV<K, OutputT>> valueWithMetadata) {
-      this.output.add(valueWithMetadata);
+    public void output(WindowedValue<KV<K, OutputT>> windowedValue) {
+      this.output.add(windowedValue);
     }
 
     public List<WindowedValue<KV<K, OutputT>>> getOutput() {
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/AbstractWindmillStreamTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/AbstractWindmillStreamTest.java
index 92c081591c73..80c39e770c3e 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/AbstractWindmillStreamTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/AbstractWindmillStreamTest.java
@@ -21,12 +21,14 @@
 import static org.junit.Assert.assertThrows;
 
 import java.io.PrintWriter;
+import java.time.temporal.ChronoUnit;
 import java.util.Set;
 import java.util.concurrent.ConcurrentHashMap;
 import java.util.concurrent.ExecutionException;
 import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;
 import java.util.concurrent.Future;
+import java.util.concurrent.ScheduledExecutorService;
 import java.util.concurrent.TimeUnit;
 import java.util.concurrent.atomic.AtomicInteger;
 import java.util.function.Function;
@@ -59,7 +61,12 @@ public void setUp() {
 
   private TestStream newStream(
       Function<StreamObserver<Integer>, StreamObserver<Integer>> clientFactory) {
-    return new TestStream(clientFactory, streamRegistry, streamObserverFactory);
+    return new TestStream(
+        clientFactory,
+        streamRegistry,
+        streamObserverFactory,
+        Duration.ZERO,
+        Executors.newScheduledThreadPool(0));
   }
 
   @Test
@@ -140,21 +147,25 @@ private static class TestStream extends AbstractWindmillStream<Integer, Integer>
     private static final Logger LOG = LoggerFactory.getLogger(AbstractWindmillStreamTest.class);
 
     private final AtomicInteger numStarts = new AtomicInteger();
+    private final AtomicInteger numFlushPending = new AtomicInteger();
     private final AtomicInteger numHealthChecks = new AtomicInteger();
 
     private TestStream(
         Function<StreamObserver<Integer>, StreamObserver<Integer>> clientFactory,
         Set<AbstractWindmillStream<?, ?>> streamRegistry,
-        StreamObserverFactory streamObserverFactory) {
+        StreamObserverFactory streamObserverFactory,
+        Duration halfCloseAfterTimeout,
+        ScheduledExecutorService executorService) {
       super(
           LoggerFactory.getLogger(AbstractWindmillStreamTest.class),
-          "Test",
           clientFactory,
           FluentBackoff.DEFAULT.backoff(),
           streamObserverFactory,
           streamRegistry,
           1,
-          "Test");
+          "Test",
+          java.time.Duration.of(halfCloseAfterTimeout.getMillis(), ChronoUnit.MILLIS),
+          executorService);
     }
 
     @Override
@@ -178,8 +189,11 @@ public void appendHtml(PrintWriter writer) {}
     }
 
     @Override
-    protected void onNewStream() {
-      numStarts.incrementAndGet();
+    protected void onFlushPending(boolean isNewStream) {
+      if (isNewStream) {
+        numStarts.incrementAndGet();
+      }
+      numFlushPending.incrementAndGet();
     }
 
     private void testSend() throws WindmillStreamShutdownException {
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/ResettableThrowingStreamObserverTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/ResettableThrowingStreamObserverTest.java
index ef7a865748dd..69c54a50b574 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/ResettableThrowingStreamObserverTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/ResettableThrowingStreamObserverTest.java
@@ -18,12 +18,15 @@
 package org.apache.beam.runners.dataflow.worker.windmill.client;
 
 import static org.junit.Assert.assertThrows;
+import static org.mockito.ArgumentMatchers.any;
 import static org.mockito.ArgumentMatchers.eq;
 import static org.mockito.ArgumentMatchers.isA;
+import static org.mockito.Mockito.doThrow;
 import static org.mockito.Mockito.spy;
 import static org.mockito.Mockito.verify;
 import static org.mockito.Mockito.verifyNoInteractions;
 
+import org.apache.beam.runners.dataflow.worker.windmill.client.grpc.observers.StreamObserverCancelledException;
 import org.apache.beam.runners.dataflow.worker.windmill.client.grpc.observers.TerminatingStreamObserver;
 import org.junit.Test;
 import org.junit.runner.RunWith;
@@ -51,6 +54,53 @@ public void terminate(Throwable terminationException) {}
         });
   }
 
+  @Test
+  public void testOnNext_simple() throws Exception {
+    ResettableThrowingStreamObserver<Integer> observer = newStreamObserver();
+    TerminatingStreamObserver<Integer> spiedDelegate = newDelegate();
+    observer.reset(spiedDelegate);
+    observer.onNext(1);
+    verify(spiedDelegate).onNext(eq(1));
+    observer.onNext(2);
+    verify(spiedDelegate).onNext(eq(2));
+    observer.onCompleted();
+    verify(spiedDelegate).onCompleted();
+  }
+
+  @Test
+  public void testOnError_success() throws Exception {
+    ResettableThrowingStreamObserver<Integer> observer = newStreamObserver();
+    TerminatingStreamObserver<Integer> spiedDelegate = newDelegate();
+    observer.reset(spiedDelegate);
+    Throwable t = new RuntimeException("Test exception");
+    observer.onError(t);
+    verify(spiedDelegate).onError(eq(t));
+
+    assertThrows(
+        ResettableThrowingStreamObserver.StreamClosedException.class, () -> observer.onNext(1));
+    assertThrows(
+        ResettableThrowingStreamObserver.StreamClosedException.class, observer::onCompleted);
+    assertThrows(
+        ResettableThrowingStreamObserver.StreamClosedException.class,
+        () -> observer.onError(new RuntimeException("ignored")));
+  }
+
+  @Test
+  public void testOnCompleted_success() throws Exception {
+    ResettableThrowingStreamObserver<Integer> observer = newStreamObserver();
+    TerminatingStreamObserver<Integer> spiedDelegate = newDelegate();
+    observer.reset(spiedDelegate);
+    observer.onCompleted();
+    verify(spiedDelegate).onCompleted();
+    assertThrows(
+        ResettableThrowingStreamObserver.StreamClosedException.class, () -> observer.onNext(1));
+    assertThrows(
+        ResettableThrowingStreamObserver.StreamClosedException.class, observer::onCompleted);
+    assertThrows(
+        ResettableThrowingStreamObserver.StreamClosedException.class,
+        () -> observer.onError(new RuntimeException("ignored")));
+  }
+
   @Test
   public void testPoison_beforeDelegateSet() {
     ResettableThrowingStreamObserver<Integer> observer = newStreamObserver();
@@ -97,9 +147,7 @@ public void testOnCompleted_afterPoisonedThrows() {
   }
 
   @Test
-  public void testReset_usesNewDelegate()
-      throws WindmillStreamShutdownException,
-          ResettableThrowingStreamObserver.StreamClosedException {
+  public void testReset_usesNewDelegate() throws Exception {
     ResettableThrowingStreamObserver<Integer> observer = newStreamObserver();
     TerminatingStreamObserver<Integer> firstObserver = newDelegate();
     observer.reset(firstObserver);
@@ -113,6 +161,24 @@ public void testReset_usesNewDelegate()
     verify(secondObserver).onNext(eq(2));
   }
 
+  @Test
+  public void testOnNext_streamCancelledException_closesStream() throws Exception {
+    ResettableThrowingStreamObserver<Integer> observer = newStreamObserver();
+    TerminatingStreamObserver<Integer> spiedDelegate = newDelegate();
+    StreamObserverCancelledException streamObserverCancelledException =
+        new StreamObserverCancelledException("Test error");
+    doThrow(streamObserverCancelledException).when(spiedDelegate).onNext(any());
+    observer.reset(spiedDelegate);
+    observer.onNext(1);
+
+    verify(spiedDelegate).onError(eq(streamObserverCancelledException));
+    assertThrows(
+        ResettableThrowingStreamObserver.StreamClosedException.class,
+        () -> observer.onError(new Exception()));
+    assertThrows(
+        ResettableThrowingStreamObserver.StreamClosedException.class, observer::onCompleted);
+  }
+
   private <T> ResettableThrowingStreamObserver<T> newStreamObserver() {
     return new ResettableThrowingStreamObserver<>(LoggerFactory.getLogger(getClass()));
   }
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/TriggeredScheduledExecutorService.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/TriggeredScheduledExecutorService.java
new file mode 100644
index 000000000000..a43da93b3680
--- /dev/null
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/TriggeredScheduledExecutorService.java
@@ -0,0 +1,140 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.runners.dataflow.worker.windmill.client;
+
+import java.time.Duration;
+import java.util.concurrent.BlockingQueue;
+import java.util.concurrent.Callable;
+import java.util.concurrent.CompletableFuture;
+import java.util.concurrent.Delayed;
+import java.util.concurrent.ExecutionException;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.concurrent.ScheduledExecutorService;
+import java.util.concurrent.ScheduledFuture;
+import java.util.concurrent.ThreadPoolExecutor;
+import java.util.concurrent.TimeUnit;
+import java.util.concurrent.TimeoutException;
+import javax.annotation.Nullable;
+
+public class TriggeredScheduledExecutorService extends ThreadPoolExecutor
+    implements ScheduledExecutorService {
+  private final BlockingQueue<FakeScheduledFuture> futures = new LinkedBlockingQueue<>();
+
+  public TriggeredScheduledExecutorService() {
+    super(0, 100, 30, TimeUnit.SECONDS, new LinkedBlockingQueue<>());
+  }
+
+  public boolean unblockNextFuture() throws InterruptedException {
+    @Nullable FakeScheduledFuture f = futures.take();
+    if (f == null) {
+      return false;
+    }
+    f.triggerRun();
+    return true;
+  }
+
+  @Override
+  public ScheduledFuture<?> schedule(Runnable runnable, long l, TimeUnit timeUnit) {
+    FakeScheduledFuture f =
+        new FakeScheduledFuture(runnable, Duration.ofMillis(timeUnit.toMillis(l)));
+    try {
+      futures.put(f);
+    } catch (InterruptedException e) {
+      throw new RuntimeException(e);
+    }
+    return f;
+  }
+
+  @Override
+  public <V> ScheduledFuture<V> schedule(Callable<V> callable, long l, TimeUnit timeUnit) {
+    throw new UnsupportedOperationException("not supported yet");
+  }
+
+  @Override
+  public ScheduledFuture<?> scheduleAtFixedRate(
+      Runnable runnable, long l, long l1, TimeUnit timeUnit) {
+    throw new UnsupportedOperationException("not supported yet");
+  }
+
+  @Override
+  public ScheduledFuture<?> scheduleWithFixedDelay(
+      Runnable runnable, long l, long l1, TimeUnit timeUnit) {
+    throw new UnsupportedOperationException("not supported yet");
+  }
+
+  private class FakeScheduledFuture implements ScheduledFuture<Void> {
+    private final Runnable r;
+    private final Duration delay;
+    private transient boolean cancelled;
+    private final CompletableFuture<Void> delegateFuture = new CompletableFuture<>();
+
+    private FakeScheduledFuture(Runnable r, Duration delay) {
+      this.r = r;
+      this.delay = delay;
+    }
+
+    void triggerRun() {
+      TriggeredScheduledExecutorService.this.execute(
+          () -> {
+            try {
+              r.run();
+              delegateFuture.complete(null);
+            } catch (RuntimeException e) {
+              delegateFuture.completeExceptionally(e);
+            }
+          });
+    }
+
+    @Override
+    public long getDelay(TimeUnit timeUnit) {
+      return timeUnit.convert(delay.toMillis(), TimeUnit.MILLISECONDS);
+    }
+
+    @Override
+    public int compareTo(Delayed delayed) {
+      return 0;
+    }
+
+    @Override
+    public boolean cancel(boolean b) {
+      cancelled = true;
+      return true;
+    }
+
+    @Override
+    public boolean isCancelled() {
+      return cancelled;
+    }
+
+    @Override
+    public boolean isDone() {
+      return delegateFuture.isDone();
+    }
+
+    @Override
+    public Void get() throws InterruptedException, ExecutionException {
+      return delegateFuture.get();
+    }
+
+    @Override
+    public Void get(long l, TimeUnit timeUnit)
+        throws InterruptedException, ExecutionException, TimeoutException {
+      return delegateFuture.get(l, timeUnit);
+    }
+  }
+}
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/FakeWindmillGrpcService.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/FakeWindmillGrpcService.java
index 85c3c71663f1..19f8c1578b46 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/FakeWindmillGrpcService.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/FakeWindmillGrpcService.java
@@ -19,6 +19,7 @@
 
 import java.util.concurrent.BlockingQueue;
 import java.util.concurrent.CompletableFuture;
+import java.util.concurrent.ExecutionException;
 import java.util.concurrent.LinkedBlockingQueue;
 import javax.annotation.concurrent.GuardedBy;
 import org.apache.beam.runners.dataflow.worker.windmill.CloudWindmillServiceV1Alpha1Grpc;
@@ -32,12 +33,31 @@ class FakeWindmillGrpcService
   private final ErrorCollector errorCollector;
 
   @GuardedBy("this")
-  private boolean failOnNewStreams = false;
+  private boolean noMoreStreamsExpected = false;
+
+  @GuardedBy("this")
+  private int failedStreamConnectsRemaining = 0;
 
   public FakeWindmillGrpcService(ErrorCollector errorCollector) {
     this.errorCollector = errorCollector;
   }
 
+  @SuppressWarnings("BusyWait")
+  public void waitForFailedConnectAttempts() throws InterruptedException {
+    while (true) {
+      Thread.sleep(2);
+      synchronized (this) {
+        if (failedStreamConnectsRemaining <= 0) {
+          break;
+        }
+      }
+    }
+  }
+
+  public synchronized void setFailedStreamConnectsRemaining(int failedStreamConnectsRemaining) {
+    this.failedStreamConnectsRemaining = failedStreamConnectsRemaining;
+  }
+
   public static class StreamInfo<RequestT, ResponseT> {
     public StreamInfo(StreamObserver<ResponseT> responseObserver) {
       this.responseObserver = responseObserver;
@@ -63,6 +83,17 @@ public StreamInfoObserver(
 
     @Override
     public void onNext(RequestT request) {
+      if (streamInfo.onDone.isDone()) {
+        try {
+          if (streamInfo.onDone.get() == null) {
+            throw new IllegalStateException("Stream already half-closed.");
+          } else {
+            throw new IllegalStateException("Stream already closed with error.");
+          }
+        } catch (InterruptedException | ExecutionException e) {
+          throw new RuntimeException(e);
+        }
+      }
       errorCollector.checkThat(streamInfo.requests.add(request), Matchers.is(true));
     }
 
@@ -89,7 +120,11 @@ public StreamObserver<Windmill.StreamingCommitWorkRequest> commitWorkStream(
       StreamObserver<Windmill.StreamingCommitResponse> responseObserver) {
     CommitStreamInfo info = new CommitStreamInfo(responseObserver);
     synchronized (this) {
-      errorCollector.checkThat(failOnNewStreams, Matchers.is(false));
+      errorCollector.checkThat(noMoreStreamsExpected, Matchers.is(false));
+      if (failedStreamConnectsRemaining-- > 0) {
+        throw new RuntimeException(
+            "Injected connection error, remaining failures: " + failedStreamConnectsRemaining);
+      }
       errorCollector.checkThat(commitStreams.offer(info), Matchers.is(true));
     }
     return new StreamInfoObserver<>(info, errorCollector);
@@ -100,7 +135,7 @@ public CommitStreamInfo waitForConnectedCommitStream() throws InterruptedExcepti
   }
 
   public synchronized void expectNoMoreStreams() {
-    failOnNewStreams = true;
+    noMoreStreamsExpected = true;
     errorCollector.checkThat(commitStreams.isEmpty(), Matchers.is(true));
     errorCollector.checkThat(getDataStreams.isEmpty(), Matchers.is(true));
   }
@@ -117,7 +152,11 @@ public StreamObserver<Windmill.StreamingGetDataRequest> getDataStream(
       StreamObserver<Windmill.StreamingGetDataResponse> responseObserver) {
     GetDataStreamInfo info = new GetDataStreamInfo(responseObserver);
     synchronized (this) {
-      errorCollector.checkThat(failOnNewStreams, Matchers.is(false));
+      errorCollector.checkThat(noMoreStreamsExpected, Matchers.is(false));
+      if (failedStreamConnectsRemaining-- > 0) {
+        throw new RuntimeException(
+            "Injected connection error, remaining failures: " + failedStreamConnectsRemaining);
+      }
       errorCollector.checkThat(getDataStreams.offer(info), Matchers.is(true));
     }
     return new StreamInfoObserver<>(info, errorCollector);
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcCommitWorkStreamTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcCommitWorkStreamTest.java
index 195e13e84e26..e9fd55fa5668 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcCommitWorkStreamTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcCommitWorkStreamTest.java
@@ -21,21 +21,28 @@
 import static org.hamcrest.Matchers.*;
 import static org.hamcrest.Matchers.equalTo;
 import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
 import static org.junit.Assert.assertNotNull;
 import static org.junit.Assert.assertNull;
 import static org.junit.Assert.assertTrue;
 
 import java.io.IOException;
+import java.time.Duration;
 import java.util.HashMap;
 import java.util.HashSet;
 import java.util.Map;
 import java.util.Set;
+import java.util.concurrent.CompletableFuture;
 import java.util.concurrent.CountDownLatch;
 import java.util.concurrent.ExecutionException;
+import java.util.concurrent.ScheduledExecutorService;
 import java.util.concurrent.TimeUnit;
 import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.function.Supplier;
 import org.apache.beam.runners.dataflow.worker.windmill.CloudWindmillServiceV1Alpha1Grpc;
 import org.apache.beam.runners.dataflow.worker.windmill.Windmill;
+import org.apache.beam.runners.dataflow.worker.windmill.WindmillConnection;
+import org.apache.beam.runners.dataflow.worker.windmill.client.TriggeredScheduledExecutorService;
 import org.apache.beam.runners.dataflow.worker.windmill.client.WindmillStream;
 import org.apache.beam.runners.dataflow.worker.windmill.client.grpc.observers.StreamObserverCancelledException;
 import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.ByteString;
@@ -120,6 +127,32 @@ private GrpcCommitWorkStream createCommitWorkStream() {
     return commitWorkStream;
   }
 
+  private GrpcCommitWorkStream createCommitWorkStreamWithPhysicalStreamHandover(
+      ScheduledExecutorService executor) {
+    GrpcCommitWorkStream commitWorkStream =
+        (GrpcCommitWorkStream)
+            GrpcWindmillStreamFactory.of(TEST_JOB_HEADER)
+                .setDirectStreamingRpcPhysicalStreamHalfCloseAfter(Duration.ofMinutes(1))
+                .setScheduledExecutorServiceSupplier(
+                    new Supplier<ScheduledExecutorService>() {
+                      private final AtomicBoolean vended = new AtomicBoolean();
+
+                      @Override
+                      public ScheduledExecutorService get() {
+                        assertFalse(vended.getAndSet(true));
+                        return executor;
+                      }
+                    })
+                .build()
+                .createDirectCommitWorkStream(
+                    WindmillConnection.builder()
+                        .setStubSupplier(
+                            () -> CloudWindmillServiceV1Alpha1Grpc.newStub(inProcessChannel))
+                        .build());
+    commitWorkStream.start();
+    return commitWorkStream;
+  }
+
   @Test
   public void testShutdown_abortsActiveCommits() throws InterruptedException, ExecutionException {
     int numCommits = 5;
@@ -459,6 +492,647 @@ public void testSend_notCalledAfterShutdown_Multichunk()
     assertThat(streamInfo.requests).isEmpty();
   }
 
+  private Windmill.WorkItemCommitRequest createTestCommit(int id) {
+    return Windmill.WorkItemCommitRequest.newBuilder()
+        .setKey(ByteString.EMPTY)
+        .setShardingKey(id)
+        .setWorkToken(id * 100L)
+        .setCacheToken(id * 1000L)
+        .build();
+  }
+
+  @Test
+  public void testCommitWorkItem_multiplePhysicalStreams() throws Exception {
+    // A special executor that allows triggering scheduled futures (of which the handover is the
+    // only such future).
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcCommitWorkStream commitWorkStream =
+        createCommitWorkStreamWithPhysicalStreamHandover(triggeredExecutor);
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    // Send a request where the response is captured in a future.
+    Windmill.WorkItemCommitRequest workItemCommitRequest = createTestCommit(1);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest, commitStatusFuture::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request = streamInfo.requests.take();
+    assertThat(request.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest).isEqualTo(workItemCommitRequest);
+
+    // Trigger a new stream to be created by forcing the scheduled halfCloseFuture scheduled within
+    // AbstractWindmillStream to run.
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    fakeService.expectNoMoreStreams();
+
+    // Previous stream client should be half-closed.
+    assertNull(streamInfo.onDone.get());
+
+    Windmill.WorkItemCommitRequest workItemCommitRequest2 = createTestCommit(2);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture2 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest2, commitStatusFuture2::complete));
+    }
+    Windmill.StreamingCommitWorkRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest2 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request2.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest2).isEqualTo(workItemCommitRequest2);
+
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(2).build());
+
+    streamInfo.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(1).build());
+    assertThat(commitStatusFuture.get()).isEqualTo(Windmill.CommitStatus.OK);
+    assertThat(commitStatusFuture2.get()).isEqualTo(Windmill.CommitStatus.OK);
+
+    // Complete server-side half-close of first stream. No new
+    // stream should be created since the current stream is active.
+    streamInfo.responseObserver.onCompleted();
+
+    // Close the stream, the open stream should be client half-closed
+    // but logical remains not terminated.
+    commitWorkStream.halfClose();
+    assertNull(streamInfo2.onDone.get());
+    assertFalse(commitWorkStream.awaitTermination(10, TimeUnit.MILLISECONDS));
+
+    // Complete half-closing from the server and verify shutdown completes.
+    streamInfo2.responseObserver.onCompleted();
+
+    assertTrue(commitWorkStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testCommitWorkItem_multiplePhysicalStreams_oldStreamFails() throws Exception {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcCommitWorkStream commitWorkStream =
+        createCommitWorkStreamWithPhysicalStreamHandover(triggeredExecutor);
+    commitWorkStream.start();
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    Windmill.WorkItemCommitRequest workItemCommitRequest = createTestCommit(1);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest, commitStatusFuture::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request = streamInfo.requests.take();
+    assertThat(request.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest).isEqualTo(workItemCommitRequest);
+
+    // A new stream should be created due to handover.
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    fakeService.expectNoMoreStreams();
+
+    // Previous stream client should be half-closed.
+    assertNull(streamInfo.onDone.get());
+
+    Windmill.WorkItemCommitRequest workItemCommitRequest2 = createTestCommit(2);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture2 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest2, commitStatusFuture2::complete));
+    }
+    Windmill.StreamingCommitWorkRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest2 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request2.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest2).isEqualTo(workItemCommitRequest2);
+
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(2).build());
+    assertThat(commitStatusFuture2.get()).isEqualTo(Windmill.CommitStatus.OK);
+
+    // Complete first stream with an error. No new
+    // stream should be created since the current stream is active. The request should have an
+    // error and the request should be retried on the new stream.
+    streamInfo.responseObserver.onError(new RuntimeException("test error"));
+    Windmill.StreamingCommitWorkRequest request3 = streamInfo2.requests.take();
+    assertThat(request3.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest3 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request3.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest3).isEqualTo(workItemCommitRequest);
+
+    // Close the stream, the open stream should be client half-closed
+    // but logical remains not terminated.
+    commitWorkStream.halfClose();
+    assertNull(streamInfo2.onDone.get());
+    assertFalse(commitWorkStream.awaitTermination(10, TimeUnit.MILLISECONDS));
+
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(1).build());
+    assertThat(commitStatusFuture.get()).isEqualTo(Windmill.CommitStatus.OK);
+
+    // Complete half-closing from the server and verify shutdown completes.
+    streamInfo2.responseObserver.onCompleted();
+
+    assertTrue(commitWorkStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testCommitWorkItem_multiplePhysicalStreams_newStreamFailsWhileEmpty()
+      throws Exception {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcCommitWorkStream commitWorkStream =
+        createCommitWorkStreamWithPhysicalStreamHandover(triggeredExecutor);
+    commitWorkStream.start();
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    Windmill.WorkItemCommitRequest workItemCommitRequest = createTestCommit(1);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest, commitStatusFuture::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request = streamInfo.requests.take();
+    assertThat(request.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest).isEqualTo(workItemCommitRequest);
+
+    // A new stream should be created due to handover.
+    assertTrue(triggeredExecutor.unblockNextFuture());
+
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo.onDone.get());
+
+    // Before stream 1 is finished simulate stream 2 failing.
+    streamInfo2.responseObserver.onError(new IOException("stream 2 failed"));
+    // A new stream should be created and handle new requests.
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo3 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo.onDone.get());
+
+    Windmill.WorkItemCommitRequest workItemCommitRequest2 = createTestCommit(2);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture2 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest2, commitStatusFuture2::complete));
+    }
+    Windmill.StreamingCommitWorkRequest request2 = streamInfo3.requests.take();
+    assertThat(request2.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest2 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request2.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest2).isEqualTo(workItemCommitRequest2);
+
+    streamInfo3.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(2).build());
+
+    streamInfo.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(1).build());
+    assertThat(commitStatusFuture.get()).isEqualTo(Windmill.CommitStatus.OK);
+    assertThat(commitStatusFuture2.get()).isEqualTo(Windmill.CommitStatus.OK);
+
+    // Close the stream.
+    commitWorkStream.halfClose();
+    assertNull(streamInfo.onDone.get());
+    fakeService.expectNoMoreStreams();
+    streamInfo.responseObserver.onCompleted();
+    streamInfo3.responseObserver.onCompleted();
+
+    assertTrue(commitWorkStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testCommitWorkItem_multiplePhysicalStreams_newStreamFailsWithRequests()
+      throws Exception {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcCommitWorkStream commitWorkStream =
+        createCommitWorkStreamWithPhysicalStreamHandover(triggeredExecutor);
+    commitWorkStream.start();
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    Windmill.WorkItemCommitRequest workItemCommitRequest = createTestCommit(1);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest, commitStatusFuture::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request = streamInfo.requests.take();
+    assertThat(request.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest).isEqualTo(workItemCommitRequest);
+
+    // A new stream should be created due to handover.
+    assertTrue(triggeredExecutor.unblockNextFuture());
+
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo.onDone.get());
+
+    Windmill.WorkItemCommitRequest workItemCommitRequest2 = createTestCommit(2);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture2 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest2, commitStatusFuture2::complete));
+    }
+    Windmill.StreamingCommitWorkRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest2 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request2.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest2).isEqualTo(workItemCommitRequest2);
+
+    // Before stream 1 is finished simulate stream 2 failing.
+    streamInfo2.responseObserver.onError(new IOException("stream 2 failed"));
+    // A new stream should be created and receive the pending requests from stream2 but not the
+    // request from stream1.
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo3 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo.onDone.get());
+    Windmill.StreamingCommitWorkRequest request3 = streamInfo3.requests.take();
+    assertThat(request3.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest3 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request3.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest3).isEqualTo(workItemCommitRequest2);
+
+    streamInfo3.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(2).build());
+
+    streamInfo.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(1).build());
+    assertThat(commitStatusFuture.get()).isEqualTo(Windmill.CommitStatus.OK);
+    assertThat(commitStatusFuture2.get()).isEqualTo(Windmill.CommitStatus.OK);
+
+    // Close the stream.
+    commitWorkStream.halfClose();
+    assertNull(streamInfo.onDone.get());
+    fakeService.expectNoMoreStreams();
+    streamInfo.responseObserver.onCompleted();
+    streamInfo3.responseObserver.onCompleted();
+
+    assertTrue(commitWorkStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testCommitWorkItem_multiplePhysicalStreams_multipleHandovers() throws Exception {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcCommitWorkStream commitWorkStream =
+        createCommitWorkStreamWithPhysicalStreamHandover(triggeredExecutor);
+    commitWorkStream.start();
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo1 = waitForConnectionAndConsumeHeader();
+
+    // Commit request 1 on stream 1
+    Windmill.WorkItemCommitRequest workItemCommitRequest1 = createTestCommit(1);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture1 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest1, commitStatusFuture1::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request1 = streamInfo1.requests.take();
+    assertThat(request1.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest1 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request1.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest1).isEqualTo(workItemCommitRequest1);
+
+    // Trigger handover 1
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo1.onDone.get());
+
+    // Commit request 2 on stream 2
+    Windmill.WorkItemCommitRequest workItemCommitRequest2 = createTestCommit(2);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture2 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest2, commitStatusFuture2::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest2 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request2.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest2).isEqualTo(workItemCommitRequest2);
+
+    // Trigger handover 2 before streamInfo2 completes
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo3 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo2.onDone.get());
+
+    // Commit request 3 on stream 3
+    Windmill.WorkItemCommitRequest workItemCommitRequest3 = createTestCommit(3);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture3 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest3, commitStatusFuture3::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request3 = streamInfo3.requests.take();
+    assertThat(request3.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest3 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request3.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest3).isEqualTo(workItemCommitRequest3);
+
+    // Respond to all requests
+    streamInfo1.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(1).build());
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(2).build());
+    streamInfo3.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(3).build());
+
+    assertThat(commitStatusFuture1.get()).isEqualTo(Windmill.CommitStatus.OK);
+    assertThat(commitStatusFuture2.get()).isEqualTo(Windmill.CommitStatus.OK);
+    assertThat(commitStatusFuture3.get()).isEqualTo(Windmill.CommitStatus.OK);
+
+    // Close the stream
+    commitWorkStream.halfClose();
+    assertNull(streamInfo3.onDone.get());
+
+    // Verify no more streams
+    fakeService.expectNoMoreStreams();
+    streamInfo1.responseObserver.onCompleted();
+    streamInfo2.responseObserver.onCompleted();
+    streamInfo3.responseObserver.onCompleted();
+
+    assertTrue(commitWorkStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testCommitWorkItem_multiplePhysicalStreams_oldStreamFailsWhileNewStreamInBackoff()
+      throws Exception {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcCommitWorkStream commitWorkStream =
+        createCommitWorkStreamWithPhysicalStreamHandover(triggeredExecutor);
+    commitWorkStream.start();
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo1 = waitForConnectionAndConsumeHeader();
+
+    // Commit request 1 on stream 1
+    Windmill.WorkItemCommitRequest workItemCommitRequest1 = createTestCommit(1);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture1 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest1, commitStatusFuture1::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request1 = streamInfo1.requests.take();
+    assertThat(request1.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest1 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request1.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest1).isEqualTo(workItemCommitRequest1);
+
+    // Trigger handover but fail new connections
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    fakeService.setFailedStreamConnectsRemaining(1);
+    fakeService.waitForFailedConnectAttempts();
+    assertNull(streamInfo1.onDone.get());
+
+    // Fail first stream
+    streamInfo1.responseObserver.onError(new RuntimeException("test error"));
+
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    fakeService.expectNoMoreStreams();
+
+    Windmill.StreamingCommitWorkRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest2 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request2.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest2).isEqualTo(workItemCommitRequest1);
+
+    // Respond to the request
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(1).build());
+    assertThat(commitStatusFuture1.get()).isEqualTo(Windmill.CommitStatus.OK);
+
+    // Close the stream
+    commitWorkStream.halfClose();
+    assertNull(streamInfo2.onDone.get());
+
+    streamInfo2.responseObserver.onCompleted();
+
+    assertTrue(commitWorkStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testCommitWorkItem_multiplePhysicalStreams_multipleHandovers_shutdown()
+      throws Exception {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcCommitWorkStream commitWorkStream =
+        createCommitWorkStreamWithPhysicalStreamHandover(triggeredExecutor);
+    commitWorkStream.start();
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo1 = waitForConnectionAndConsumeHeader();
+
+    // Commit request 1 on stream 1
+    Windmill.WorkItemCommitRequest workItemCommitRequest1 = createTestCommit(1);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture1 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest1, commitStatusFuture1::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request1 = streamInfo1.requests.take();
+    assertThat(request1.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest1 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request1.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest1).isEqualTo(workItemCommitRequest1);
+
+    // Trigger handover 1
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo1.onDone.get());
+
+    // Commit request 2 on stream 2
+    Windmill.WorkItemCommitRequest workItemCommitRequest2 = createTestCommit(2);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture2 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest2, commitStatusFuture2::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest2 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request2.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest2).isEqualTo(workItemCommitRequest2);
+
+    // Trigger handover 2 before streamInfo2 completes
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo3 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo2.onDone.get());
+
+    // Commit request 3 on stream 3
+    Windmill.WorkItemCommitRequest workItemCommitRequest3 = createTestCommit(3);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture3 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest3, commitStatusFuture3::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request3 = streamInfo3.requests.take();
+    assertThat(request3.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest3 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request3.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest3).isEqualTo(workItemCommitRequest3);
+
+    // Shutdown while there are active streams and verify it isn't completed until all the streams
+    // are done.
+    fakeService.expectNoMoreStreams();
+    assertFalse(commitWorkStream.awaitTermination(0, TimeUnit.SECONDS));
+    commitWorkStream.shutdown();
+    assertThat(commitStatusFuture1.isDone()).isTrue();
+    assertThat(commitStatusFuture2.isDone()).isTrue();
+    assertThat(commitStatusFuture3.isDone()).isTrue();
+    assertFalse(commitWorkStream.awaitTermination(10, TimeUnit.MILLISECONDS));
+
+    assertFalse(commitWorkStream.awaitTermination(0, TimeUnit.MILLISECONDS));
+    streamInfo3.responseObserver.onCompleted();
+    assertFalse(commitWorkStream.awaitTermination(0, TimeUnit.MILLISECONDS));
+    streamInfo1.responseObserver.onCompleted();
+    assertFalse(commitWorkStream.awaitTermination(0, TimeUnit.MILLISECONDS));
+    streamInfo2.responseObserver.onError(new RuntimeException("test"));
+    assertTrue(commitWorkStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testCommitWorkItem_multiplePhysicalStreams_multipleHandovers_halfClose()
+      throws Exception {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcCommitWorkStream commitWorkStream =
+        createCommitWorkStreamWithPhysicalStreamHandover(triggeredExecutor);
+    commitWorkStream.start();
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo1 = waitForConnectionAndConsumeHeader();
+
+    // Commit request 1 on stream 1
+    Windmill.WorkItemCommitRequest workItemCommitRequest1 = createTestCommit(1);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture1 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest1, commitStatusFuture1::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request1 = streamInfo1.requests.take();
+    assertThat(request1.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest1 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request1.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest1).isEqualTo(workItemCommitRequest1);
+
+    // Trigger handover 1
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo1.onDone.get());
+
+    // Commit request 2 on stream 2
+    Windmill.WorkItemCommitRequest workItemCommitRequest2 = createTestCommit(2);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture2 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest2, commitStatusFuture2::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest2 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request2.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest2).isEqualTo(workItemCommitRequest2);
+
+    // Trigger handover 2 before streamInfo2 completes
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.CommitStreamInfo streamInfo3 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo2.onDone.get());
+
+    // Commit request 3 on stream 3
+    Windmill.WorkItemCommitRequest workItemCommitRequest3 = createTestCommit(3);
+    CompletableFuture<Windmill.CommitStatus> commitStatusFuture3 = new CompletableFuture<>();
+    try (WindmillStream.CommitWorkStream.RequestBatcher batcher = commitWorkStream.batcher()) {
+      assertTrue(
+          batcher.commitWorkItem(
+              COMPUTATION_ID, workItemCommitRequest3, commitStatusFuture3::complete));
+    }
+
+    Windmill.StreamingCommitWorkRequest request3 = streamInfo3.requests.take();
+    assertThat(request3.getCommitChunkList()).hasSize(1);
+    Windmill.WorkItemCommitRequest parsedRequest3 =
+        Windmill.WorkItemCommitRequest.parseFrom(
+            request3.getCommitChunk(0).getSerializedWorkItemCommit());
+    assertThat(parsedRequest3).isEqualTo(workItemCommitRequest3);
+
+    // Shutdown while there are active streams and verify it isn't completed until all the streams
+    // are done.
+    fakeService.expectNoMoreStreams();
+    assertFalse(commitWorkStream.awaitTermination(0, TimeUnit.SECONDS));
+    commitWorkStream.halfClose();
+
+    assertFalse(commitWorkStream.awaitTermination(10, TimeUnit.MILLISECONDS));
+    assertThat(streamInfo3.onDone.get()).isNull();
+
+    assertThat(commitStatusFuture1.isDone()).isFalse();
+    assertThat(commitStatusFuture2.isDone()).isFalse();
+    assertThat(commitStatusFuture3.isDone()).isFalse();
+
+    streamInfo3.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder().addRequestId(3).build());
+    streamInfo3.responseObserver.onCompleted();
+    assertThat(commitStatusFuture3.get()).isEqualTo(Windmill.CommitStatus.OK);
+    assertFalse(commitWorkStream.awaitTermination(0, TimeUnit.MILLISECONDS));
+
+    streamInfo1.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder()
+            .addRequestId(1)
+            .addStatus(Windmill.CommitStatus.ABORTED)
+            .build());
+    streamInfo1.responseObserver.onCompleted();
+    assertThat(commitStatusFuture1.get()).isEqualTo(Windmill.CommitStatus.ABORTED);
+    assertFalse(commitWorkStream.awaitTermination(0, TimeUnit.MILLISECONDS));
+
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingCommitResponse.newBuilder()
+            .addRequestId(2)
+            .addStatus(Windmill.CommitStatus.ALREADY_IN_COMMIT)
+            .build());
+    streamInfo2.responseObserver.onCompleted();
+    assertThat(commitStatusFuture2.get()).isEqualTo(Windmill.CommitStatus.ALREADY_IN_COMMIT);
+
+    assertTrue(commitWorkStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
   private FakeWindmillGrpcService.CommitStreamInfo waitForConnectionAndConsumeHeader() {
     try {
       FakeWindmillGrpcService.CommitStreamInfo info = fakeService.waitForConnectedCommitStream();
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDirectGetWorkStreamTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDirectGetWorkStreamTest.java
index 1014242317de..419000178381 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDirectGetWorkStreamTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcDirectGetWorkStreamTest.java
@@ -392,7 +392,9 @@ public void testConsumedWorkItems() throws InterruptedException {
 
   @Test
   public void testConsumedWorkItems_itemsSplitAcrossResponses() throws InterruptedException {
-    int expectedRequests = 3;
+    // We send all the responses on the first request. We don't care if there are additional
+    // requests.
+    int expectedRequests = 1;
     CountDownLatch waitForRequests = new CountDownLatch(expectedRequests);
     TestGetWorkRequestObserver requestObserver = new TestGetWorkRequestObserver(waitForRequests);
     GetWorkStreamTestStub testStub = new GetWorkStreamTestStub(requestObserver);
@@ -426,9 +428,9 @@ public void testConsumedWorkItems_itemsSplitAcrossResponses() throws Interrupted
     Windmill.WorkItem workItem3 =
         Windmill.WorkItem.newBuilder()
             .setKey(ByteString.copyFromUtf8("somewhat_long_key3"))
-            .setWorkToken(2L)
-            .setShardingKey(2L)
-            .setCacheToken(2L)
+            .setWorkToken(3L)
+            .setShardingKey(3L)
+            .setCacheToken(3L)
             .build();
 
     List<ByteString> chunks1 = new ArrayList<>();
@@ -444,12 +446,12 @@ public void testConsumedWorkItems_itemsSplitAcrossResponses() throws Interrupted
 
     chunks3.add(workItem3.toByteString());
 
+    assertTrue(waitForRequests.await(5, TimeUnit.SECONDS));
+
     testStub.injectResponse(createResponse(chunks1, bytes.size() - third));
     testStub.injectResponse(createResponse(chunks2, bytes.size() - 2 * third));
     testStub.injectResponse(createResponse(chunks3, 0));
 
-    assertTrue(waitForRequests.await(5, TimeUnit.SECONDS));
-
     assertThat(scheduledWorkItems).containsExactly(workItem1, workItem2, workItem3);
   }
 
@@ -458,6 +460,7 @@ private static class GetWorkStreamTestStub
 
     private final TestGetWorkRequestObserver requestObserver;
     private @Nullable StreamObserver<Windmill.StreamingGetWorkResponseChunk> responseObserver;
+    private final CountDownLatch waitForStream = new CountDownLatch(1);
 
     private GetWorkStreamTestStub(TestGetWorkRequestObserver requestObserver) {
       this.requestObserver = requestObserver;
@@ -466,15 +469,17 @@ private GetWorkStreamTestStub(TestGetWorkRequestObserver requestObserver) {
     @Override
     public StreamObserver<Windmill.StreamingGetWorkRequest> getWorkStream(
         StreamObserver<Windmill.StreamingGetWorkResponseChunk> responseObserver) {
-      if (this.responseObserver == null) {
-        this.responseObserver = responseObserver;
-        requestObserver.responseObserver = this.responseObserver;
-      }
+      assertThat(this.responseObserver).isNull();
+      this.responseObserver = responseObserver;
+      requestObserver.responseObserver = this.responseObserver;
+      waitForStream.countDown();
 
       return requestObserver;
     }
 
-    private void injectResponse(Windmill.StreamingGetWorkResponseChunk responseChunk) {
+    private void injectResponse(Windmill.StreamingGetWorkResponseChunk responseChunk)
+        throws InterruptedException {
+      waitForStream.await();
       checkNotNull(responseObserver).onNext(responseChunk);
     }
   }
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetDataStreamTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetDataStreamTest.java
index e954f2cc7105..4f584022c8a5 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetDataStreamTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/GrpcGetDataStreamTest.java
@@ -19,26 +19,42 @@
 
 import static com.google.common.truth.Truth.assertThat;
 import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
 import static org.junit.Assert.assertNull;
 import static org.junit.Assert.assertThrows;
 import static org.junit.Assert.assertTrue;
 import static org.junit.Assert.fail;
 
 import java.io.IOException;
+import java.time.Duration;
 import java.util.List;
 import java.util.concurrent.CompletableFuture;
+import java.util.concurrent.CompletionException;
 import java.util.concurrent.CountDownLatch;
 import java.util.concurrent.ExecutionException;
 import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;
+import java.util.concurrent.ScheduledExecutorService;
 import java.util.concurrent.TimeUnit;
+import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.function.Supplier;
+import java.util.logging.Level;
+import java.util.logging.Logger;
 import java.util.stream.Collectors;
 import java.util.stream.IntStream;
+import javax.annotation.Nullable;
 import org.apache.beam.runners.dataflow.worker.windmill.CloudWindmillServiceV1Alpha1Grpc;
 import org.apache.beam.runners.dataflow.worker.windmill.Windmill;
+import org.apache.beam.runners.dataflow.worker.windmill.WindmillConnection;
+import org.apache.beam.runners.dataflow.worker.windmill.client.TriggeredScheduledExecutorService;
 import org.apache.beam.runners.dataflow.worker.windmill.client.WindmillStreamShutdownException;
 import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.ByteString;
+import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.CallOptions;
+import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.Channel;
+import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.ClientCall;
+import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.ClientInterceptor;
 import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.ManagedChannel;
+import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.MethodDescriptor;
 import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.Server;
 import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.inprocess.InProcessChannelBuilder;
 import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.inprocess.InProcessServerBuilder;
@@ -86,6 +102,8 @@ public void setUp() throws IOException {
     inProcessChannel =
         grpcCleanup.register(
             InProcessChannelBuilder.forName(FAKE_SERVER_NAME).directExecutor().build());
+    Logger.getLogger(GrpcGetDataStream.class.getName()).setLevel(Level.ALL);
+    Logger.getLogger(AbstractMethodError.class.getName()).setLevel(Level.ALL);
   }
 
   @After
@@ -105,20 +123,39 @@ private GrpcGetDataStream createGetDataStream() {
     return getDataStream;
   }
 
+  private GrpcGetDataStream createGetDataStreamWithPhysicalStreamHandover(
+      Duration handover, @Nullable ScheduledExecutorService executor) {
+    GrpcGetDataStream getDataStream =
+        (GrpcGetDataStream)
+            GrpcWindmillStreamFactory.of(TEST_JOB_HEADER)
+                .setDirectStreamingRpcPhysicalStreamHalfCloseAfter(handover)
+                .setScheduledExecutorServiceSupplier(
+                    new Supplier<ScheduledExecutorService>() {
+                      private final AtomicBoolean vended = new AtomicBoolean();
+
+                      @Override
+                      public ScheduledExecutorService get() {
+                        assertFalse(vended.getAndSet(true));
+                        return executor;
+                      }
+                    })
+                .build()
+                .createDirectGetDataStream(
+                    WindmillConnection.builder()
+                        .setStubSupplier(
+                            () -> CloudWindmillServiceV1Alpha1Grpc.newStub(inProcessChannel))
+                        .build());
+    getDataStream.start();
+    return getDataStream;
+  }
+
   @Test
   public void testRequestKeyedData() throws InterruptedException {
     GrpcGetDataStream getDataStream = createGetDataStream();
     FakeWindmillGrpcService.GetDataStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
 
     // These will block until they are successfully sent.
-    Windmill.KeyedGetDataRequest keyedGetDataRequest =
-        Windmill.KeyedGetDataRequest.newBuilder()
-            .setKey(ByteString.EMPTY)
-            .setShardingKey(1)
-            .setCacheToken(1)
-            .setWorkToken(1)
-            .build();
-
+    Windmill.KeyedGetDataRequest keyedGetDataRequest = createTestRequest(1);
     CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture =
         CompletableFuture.supplyAsync(
             () -> {
@@ -133,12 +170,7 @@ public void testRequestKeyedData() throws InterruptedException {
     assertThat(request.getRequestIdList()).containsExactly(1L);
     assertEquals(keyedGetDataRequest, request.getStateRequest(0).getRequests(0));
 
-    Windmill.KeyedGetDataResponse keyedGetDataResponse =
-        Windmill.KeyedGetDataResponse.newBuilder()
-            .setShardingKey(1)
-            .setKey(ByteString.EMPTY)
-            .build();
-
+    Windmill.KeyedGetDataResponse keyedGetDataResponse = createTestResponse(1);
     streamInfo.responseObserver.onNext(
         Windmill.StreamingGetDataResponse.newBuilder()
             .addRequestId(1)
@@ -171,14 +203,7 @@ public void testRequestKeyedData_sendOnShutdownStreamThrowsWindmillStreamShutdow
                             }
                           }
                           try {
-                            getDataStream.requestKeyedData(
-                                "computationId",
-                                Windmill.KeyedGetDataRequest.newBuilder()
-                                    .setKey(ByteString.EMPTY)
-                                    .setShardingKey(i)
-                                    .setCacheToken(i)
-                                    .setWorkToken(i)
-                                    .build());
+                            getDataStream.requestKeyedData("computationId", createTestRequest(i));
                           } catch (WindmillStreamShutdownException e) {
                             throw new RuntimeException(e);
                           }
@@ -290,14 +315,766 @@ public void testRequestKeyedData_reconnectOnStreamErrorAfterHalfClose()
     getDataStream.halfClose();
     assertNull(streamInfo.onDone.get());
 
-    // Simulate an error on the grpc stream, this should trigger an error on all
-    // existing requests but no new connection since we half-closed and nothing left after
-    // responding with errors.
-    fakeService.expectNoMoreStreams();
+    // Simulate an error on the grpc stream, this should trigger retrying the requests on a new
+    // stream
+    // which is half-closed.
     streamInfo.responseObserver.onError(new IOException("test error"));
-    assertThrows(RuntimeException.class, sendFuture::join);
 
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    Windmill.StreamingGetDataRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getRequestIdList()).containsExactly(1L);
+    assertEquals(keyedGetDataRequest, request2.getStateRequest(0).getRequests(0));
+    assertNull(streamInfo2.onDone.get());
+    Windmill.KeyedGetDataResponse keyedGetDataResponse = createTestResponse(1);
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(1)
+            .addSerializedResponse(keyedGetDataResponse.toByteString())
+            .build());
+    assertThat(sendFuture.join()).isEqualTo(keyedGetDataResponse);
+    assertFalse(getDataStream.awaitTermination(0, TimeUnit.MILLISECONDS));
+
+    // Sending an error this time shouldn't result in a new stream since there were no requests.
+    fakeService.expectNoMoreStreams();
+    streamInfo2.responseObserver.onError(new IOException("test error"));
+
+    getDataStream.awaitTermination(60, TimeUnit.MINUTES);
+  }
+
+  private Windmill.KeyedGetDataRequest createTestRequest(long id) {
+    return Windmill.KeyedGetDataRequest.newBuilder()
+        .setKey(ByteString.EMPTY)
+        .setShardingKey(id)
+        .setCacheToken(id * 100)
+        .setWorkToken(id * 1000)
+        .build();
+  }
+
+  private Windmill.KeyedGetDataResponse createTestResponse(long id) {
+    return Windmill.KeyedGetDataResponse.newBuilder()
+        .setShardingKey(id)
+        .setKey(ByteString.EMPTY)
+        .build();
+  }
+
+  @Test
+  public void testRequestKeyedData_multiplePhysicalStreams()
+      throws InterruptedException, ExecutionException {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcGetDataStream getDataStream =
+        createGetDataStreamWithPhysicalStreamHandover(Duration.ofSeconds(60), triggeredExecutor);
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    // These will block until they are successfully sent.
+    Windmill.KeyedGetDataRequest keyedGetDataRequest = createTestRequest(1);
+
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+
+    Windmill.StreamingGetDataRequest request = streamInfo.requests.take();
+    assertThat(request.getRequestIdList()).containsExactly(1L);
+    assertEquals(keyedGetDataRequest, request.getStateRequest(0).getRequests(0));
+
+    // A new stream should be created due to handover.
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    fakeService.expectNoMoreStreams();
+
+    // Previous stream client should be half-closed.
+    assertNull(streamInfo.onDone.get());
+
+    Windmill.KeyedGetDataRequest keyedGetDataRequest2 = createTestRequest(2);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture2 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest2);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getRequestIdList()).containsExactly(2L);
+    assertEquals(keyedGetDataRequest2, request2.getStateRequest(0).getRequests(0));
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse2 = createTestResponse(2);
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(2)
+            .addSerializedResponse(keyedGetDataResponse2.toByteString())
+            .build());
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse = createTestResponse(1);
+    streamInfo.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(1)
+            .addSerializedResponse(keyedGetDataResponse.toByteString())
+            .build());
+    assertThat(sendFuture.join()).isEqualTo(keyedGetDataResponse);
+    assertThat(sendFuture2.join()).isEqualTo(keyedGetDataResponse2);
+
+    // Complete server-side half-close of first stream. No new
+    // stream should be created since the current stream is active.
+    streamInfo.responseObserver.onCompleted();
+
+    // Close the stream, the open stream should be client half-closed
+    // but logical remains not terminated.
+    getDataStream.halfClose();
+    assertNull(streamInfo2.onDone.get());
+    assertFalse(getDataStream.awaitTermination(10, TimeUnit.MILLISECONDS));
+
+    // Complete half-closing from the server and verify shutdown completes.
+    streamInfo2.responseObserver.onCompleted();
+
+    assertTrue(getDataStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testRequestKeyedData_multiplePhysicalStreams_oldStreamFails()
+      throws InterruptedException, ExecutionException {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcGetDataStream getDataStream =
+        createGetDataStreamWithPhysicalStreamHandover(Duration.ofSeconds(60), triggeredExecutor);
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    // These will block until they are successfully sent.
+    Windmill.KeyedGetDataRequest keyedGetDataRequest = createTestRequest(1);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+
+    Windmill.StreamingGetDataRequest request = streamInfo.requests.take();
+    assertThat(request.getRequestIdList()).containsExactly(1L);
+    assertEquals(keyedGetDataRequest, request.getStateRequest(0).getRequests(0));
+
+    // A new stream should be created due to handover.
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    fakeService.expectNoMoreStreams();
+
+    // Previous stream client should be half-closed.
+    assertNull(streamInfo.onDone.get());
+
+    Windmill.KeyedGetDataRequest keyedGetDataRequest2 = createTestRequest(2);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture2 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest2);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getRequestIdList()).containsExactly(2L);
+    assertEquals(keyedGetDataRequest2, request2.getStateRequest(0).getRequests(0));
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse2 = createTestResponse(2);
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(2)
+            .addSerializedResponse(keyedGetDataResponse2.toByteString())
+            .build());
+    assertThat(sendFuture2.join()).isEqualTo(keyedGetDataResponse2);
+
+    // Complete first stream with an error. No new
+    // stream should be created since the current stream is active. The request should have an
+    // error and the request should be retried on the new stream.
+    streamInfo.responseObserver.onError(new RuntimeException("test error"));
+    Windmill.StreamingGetDataRequest request3 = streamInfo2.requests.take();
+    assertThat(request3.getRequestIdList()).containsExactly(1L);
+    assertEquals(keyedGetDataRequest, request3.getStateRequest(0).getRequests(0));
+
+    // Close the stream, the open stream should be client half-closed
+    // but logical remains not terminated.
+    getDataStream.halfClose();
+    assertNull(streamInfo2.onDone.get());
+    assertFalse(getDataStream.awaitTermination(10, TimeUnit.MILLISECONDS));
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse = createTestResponse(1);
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(1)
+            .addSerializedResponse(keyedGetDataResponse.toByteString())
+            .build());
+    assertThat(sendFuture.join()).isEqualTo(keyedGetDataResponse);
+
+    // Complete half-closing from the server and verify shutdown completes.
+    streamInfo2.responseObserver.onCompleted();
+
+    assertTrue(getDataStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testRequestKeyedData_multiplePhysicalStreams_newStreamFailsWhileEmpty()
+      throws InterruptedException, ExecutionException {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcGetDataStream getDataStream =
+        createGetDataStreamWithPhysicalStreamHandover(Duration.ofSeconds(60), triggeredExecutor);
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    // These will block until they are successfully sent.
+    Windmill.KeyedGetDataRequest keyedGetDataRequest = createTestRequest(1);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+
+    Windmill.StreamingGetDataRequest request = streamInfo.requests.take();
+    assertThat(request.getRequestIdList()).containsExactly(1L);
+    assertEquals(keyedGetDataRequest, request.getStateRequest(0).getRequests(0));
+
+    // A new stream should be created due to handover.
+    assertTrue(triggeredExecutor.unblockNextFuture());
+
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo.onDone.get());
+
+    // Before stream 1 is finished simulate stream 2 failing.
+    streamInfo2.responseObserver.onError(new IOException("stream 2 failed"));
+    // A new stream should be created and handle new requests.
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo3 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo.onDone.get());
+
+    Windmill.KeyedGetDataRequest keyedGetDataRequest2 = createTestRequest(2);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture2 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest2);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request2 = streamInfo3.requests.take();
+    assertThat(request2.getRequestIdList()).containsExactly(2L);
+    assertEquals(keyedGetDataRequest2, request2.getStateRequest(0).getRequests(0));
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse2 = createTestResponse(2);
+    streamInfo3.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(2)
+            .addSerializedResponse(keyedGetDataResponse2.toByteString())
+            .build());
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse = createTestResponse(1);
+    streamInfo.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(1)
+            .addSerializedResponse(keyedGetDataResponse.toByteString())
+            .build());
+    assertThat(sendFuture.join()).isEqualTo(keyedGetDataResponse);
+    assertThat(sendFuture2.join()).isEqualTo(keyedGetDataResponse2);
+
+    // Close the stream.
+    getDataStream.halfClose();
+    assertNull(streamInfo.onDone.get());
+    fakeService.expectNoMoreStreams();
+    streamInfo.responseObserver.onCompleted();
+    streamInfo3.responseObserver.onCompleted();
+
+    assertTrue(getDataStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testRequestKeyedData_multiplePhysicalStreams_newStreamFailsWithRequests()
+      throws InterruptedException, ExecutionException {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcGetDataStream getDataStream =
+        createGetDataStreamWithPhysicalStreamHandover(Duration.ofSeconds(60), triggeredExecutor);
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    // These will block until they are successfully sent.
+    Windmill.KeyedGetDataRequest keyedGetDataRequest = createTestRequest(1);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+
+    Windmill.StreamingGetDataRequest request = streamInfo.requests.take();
+    assertThat(request.getRequestIdList()).containsExactly(1L);
+    assertEquals(keyedGetDataRequest, request.getStateRequest(0).getRequests(0));
+
+    // A new stream should be created due to handover.
+    assertTrue(triggeredExecutor.unblockNextFuture());
+
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo.onDone.get());
+
+    Windmill.KeyedGetDataRequest keyedGetDataRequest2 = createTestRequest(2);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture2 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest2);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getRequestIdList()).containsExactly(2L);
+    assertEquals(keyedGetDataRequest2, request2.getStateRequest(0).getRequests(0));
+
+    // Before stream 1 is finished simulate stream 2 failing.
+    streamInfo2.responseObserver.onError(new IOException("stream 2 failed"));
+    // A new stream should be created and receive the pending requests from stream2 but not the
+    // request from stream1.
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo3 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo.onDone.get());
+    Windmill.StreamingGetDataRequest request3 = streamInfo3.requests.take();
+    assertThat(request3.getRequestIdList()).containsExactly(2L);
+    assertEquals(keyedGetDataRequest2, request3.getStateRequest(0).getRequests(0));
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse2 = createTestResponse(2);
+    streamInfo3.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(2)
+            .addSerializedResponse(keyedGetDataResponse2.toByteString())
+            .build());
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse = createTestResponse(1);
+    streamInfo.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(1)
+            .addSerializedResponse(keyedGetDataResponse.toByteString())
+            .build());
+    assertThat(sendFuture.join()).isEqualTo(keyedGetDataResponse);
+    assertThat(sendFuture2.join()).isEqualTo(keyedGetDataResponse2);
+
+    // Close the stream.
+    getDataStream.halfClose();
+    assertNull(streamInfo.onDone.get());
+    fakeService.expectNoMoreStreams();
+    streamInfo.responseObserver.onCompleted();
+    streamInfo3.responseObserver.onCompleted();
+
+    assertTrue(getDataStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testRequestKeyedData_multiplePhysicalStreams_multipleHandovers_allResponsesReceived()
+      throws InterruptedException, ExecutionException {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcGetDataStream getDataStream =
+        createGetDataStreamWithPhysicalStreamHandover(Duration.ofSeconds(60), triggeredExecutor);
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    // Request 1, Stream 1
+    Windmill.KeyedGetDataRequest keyedGetDataRequest1 = createTestRequest(1);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture1 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest1);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request1 = streamInfo.requests.take();
+    assertThat(request1.getRequestIdList()).containsExactly(1L);
+    assertEquals(keyedGetDataRequest1, request1.getStateRequest(0).getRequests(0));
+
+    // Trigger handover 1
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo.onDone.get());
+
+    // Request 2, Stream 2
+    Windmill.KeyedGetDataRequest keyedGetDataRequest2 = createTestRequest(2);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture2 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest2);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getRequestIdList()).containsExactly(2L);
+    assertEquals(keyedGetDataRequest2, request2.getStateRequest(0).getRequests(0));
+
+    // Trigger handover 2 before streamInfo2 completes
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo3 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo2.onDone.get());
+
+    // Request 3, Stream 3
+    Windmill.KeyedGetDataRequest keyedGetDataRequest3 = createTestRequest(3);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture3 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest3);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request3 = streamInfo3.requests.take();
+    assertThat(request3.getRequestIdList()).containsExactly(3L);
+    assertEquals(keyedGetDataRequest3, request3.getStateRequest(0).getRequests(0));
+
+    // Respond to all requests
+    Windmill.KeyedGetDataResponse keyedGetDataResponse1 = createTestResponse(1);
+    streamInfo.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(1)
+            .addSerializedResponse(keyedGetDataResponse1.toByteString())
+            .build());
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse2 = createTestResponse(2);
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(2)
+            .addSerializedResponse(keyedGetDataResponse2.toByteString())
+            .build());
+    streamInfo2.responseObserver.onCompleted();
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse3 = createTestResponse(3);
+    streamInfo3.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(3)
+            .addSerializedResponse(keyedGetDataResponse3.toByteString())
+            .build());
+
+    assertThat(sendFuture1.join()).isEqualTo(keyedGetDataResponse1);
+    assertThat(sendFuture2.join()).isEqualTo(keyedGetDataResponse2);
+    assertThat(sendFuture3.join()).isEqualTo(keyedGetDataResponse3);
+
+    // Close the stream.
+    getDataStream.halfClose();
+    assertNull(streamInfo3.onDone.get());
+
+    fakeService.expectNoMoreStreams();
+    streamInfo.responseObserver.onCompleted();
+    streamInfo3.responseObserver.onCompleted();
+
+    assertTrue(getDataStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testRequestKeyedData_multiplePhysicalStreams_oldStreamFailsWhileNewStreamInBackoff()
+      throws InterruptedException, ExecutionException {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcGetDataStream getDataStream =
+        createGetDataStreamWithPhysicalStreamHandover(Duration.ofSeconds(60), triggeredExecutor);
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    Windmill.KeyedGetDataRequest keyedGetDataRequest = createTestRequest(1);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+
+    Windmill.StreamingGetDataRequest request = streamInfo.requests.take();
+    assertThat(request.getRequestIdList()).containsExactly(1L);
+    assertEquals(keyedGetDataRequest, request.getStateRequest(0).getRequests(0));
+
+    // A new stream should be created due to handover. However we configure the server to have
+    // errors.
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    fakeService.setFailedStreamConnectsRemaining(1);
+    fakeService.waitForFailedConnectAttempts();
+    // Previous stream client should be half-closed.
+    assertNull(streamInfo.onDone.get());
+    // Complete first stream with an error. No new
+    // stream should be created since the current stream is being created or created. The request
+    // should have an
+    // error and the request should be retried on the new stream.
+    streamInfo.responseObserver.onError(new RuntimeException("test error"));
+
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    fakeService.expectNoMoreStreams();
+
+    Windmill.StreamingGetDataRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getRequestIdList()).containsExactly(1L);
+    assertEquals(keyedGetDataRequest, request2.getStateRequest(0).getRequests(0));
+
+    // Close the stream, the open stream should be client half-closed
+    // but logical remains not terminated.
+    getDataStream.halfClose();
+    assertNull(streamInfo2.onDone.get());
+    assertFalse(getDataStream.awaitTermination(10, TimeUnit.MILLISECONDS));
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse = createTestResponse(1);
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(1)
+            .addSerializedResponse(keyedGetDataResponse.toByteString())
+            .build());
+    assertThat(sendFuture.join()).isEqualTo(keyedGetDataResponse);
+
+    // Complete half-closing from the server and verify shutdown completes.
+    streamInfo2.responseObserver.onCompleted();
+
+    assertTrue(getDataStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testRequestKeyedData_multiplePhysicalStreams_multipleHandovers_shutdown()
+      throws InterruptedException, ExecutionException {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcGetDataStream getDataStream =
+        createGetDataStreamWithPhysicalStreamHandover(Duration.ofSeconds(60), triggeredExecutor);
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    // Request 1, Stream 1
+    Windmill.KeyedGetDataRequest keyedGetDataRequest1 = createTestRequest(1);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture1 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest1);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request1 = streamInfo.requests.take();
+    assertThat(request1.getRequestIdList()).containsExactly(1L);
+    assertEquals(keyedGetDataRequest1, request1.getStateRequest(0).getRequests(0));
+
+    // Trigger handover 1
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo.onDone.get());
+
+    // Request 2, Stream 2
+    Windmill.KeyedGetDataRequest keyedGetDataRequest2 = createTestRequest(2);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture2 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest2);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getRequestIdList()).containsExactly(2L);
+    assertEquals(keyedGetDataRequest2, request2.getStateRequest(0).getRequests(0));
+
+    // Trigger handover 2 before streamInfo2 completes
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo3 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo2.onDone.get());
+
+    // Request 3, Stream 3
+    Windmill.KeyedGetDataRequest keyedGetDataRequest3 = createTestRequest(3);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture3 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest3);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request3 = streamInfo3.requests.take();
+    assertThat(request3.getRequestIdList()).containsExactly(3L);
+    assertEquals(keyedGetDataRequest3, request3.getStateRequest(0).getRequests(0));
+
+    // Shutdown while there are active streams and verify it isn't completed until all the streams
+    // are done.
+    fakeService.expectNoMoreStreams();
+    assertFalse(getDataStream.awaitTermination(0, TimeUnit.SECONDS));
+    getDataStream.shutdown();
+    assertThrows("WindmillStreamShutdownException", CompletionException.class, sendFuture1::join);
+    assertThrows("WindmillStreamShutdownException", CompletionException.class, sendFuture2::join);
+    assertThrows("WindmillStreamShutdownException", CompletionException.class, sendFuture3::join);
+    assertFalse(getDataStream.awaitTermination(10, TimeUnit.MILLISECONDS));
+
+    assertFalse(getDataStream.awaitTermination(0, TimeUnit.MILLISECONDS));
+    streamInfo3.responseObserver.onCompleted();
+    assertFalse(getDataStream.awaitTermination(0, TimeUnit.MILLISECONDS));
+    streamInfo.responseObserver.onCompleted();
+    assertFalse(getDataStream.awaitTermination(0, TimeUnit.MILLISECONDS));
+    streamInfo2.responseObserver.onError(new RuntimeException("test"));
+    assertTrue(getDataStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testRequestKeyedData_multiplePhysicalStreams_multipleHandovers_halfClose()
+      throws InterruptedException, ExecutionException {
+    TriggeredScheduledExecutorService triggeredExecutor = new TriggeredScheduledExecutorService();
+    GrpcGetDataStream getDataStream =
+        createGetDataStreamWithPhysicalStreamHandover(Duration.ofSeconds(60), triggeredExecutor);
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+
+    // Request 1, Stream 1
+    Windmill.KeyedGetDataRequest keyedGetDataRequest1 = createTestRequest(1);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture1 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest1);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request1 = streamInfo.requests.take();
+    assertThat(request1.getRequestIdList()).containsExactly(1L);
+    assertEquals(keyedGetDataRequest1, request1.getStateRequest(0).getRequests(0));
+
+    // Trigger handover 1
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo2 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo.onDone.get());
+
+    // Request 2, Stream 2
+    Windmill.KeyedGetDataRequest keyedGetDataRequest2 = createTestRequest(2);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture2 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest2);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request2 = streamInfo2.requests.take();
+    assertThat(request2.getRequestIdList()).containsExactly(2L);
+    assertEquals(keyedGetDataRequest2, request2.getStateRequest(0).getRequests(0));
+
+    // Trigger handover 2 before streamInfo2 completes
+    assertTrue(triggeredExecutor.unblockNextFuture());
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo3 = waitForConnectionAndConsumeHeader();
+    assertNull(streamInfo2.onDone.get());
+
+    // Request 3, Stream 3
+    Windmill.KeyedGetDataRequest keyedGetDataRequest3 = createTestRequest(3);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture3 =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest3);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+    Windmill.StreamingGetDataRequest request3 = streamInfo3.requests.take();
+    assertThat(request3.getRequestIdList()).containsExactly(3L);
+    assertEquals(keyedGetDataRequest3, request3.getStateRequest(0).getRequests(0));
+
+    // Half-close while there are active streams and verify it isn't completed until all the streams
+    // are done. Streams with requests should have requests resent.
+    fakeService.expectNoMoreStreams();
+    assertFalse(getDataStream.awaitTermination(0, TimeUnit.SECONDS));
+    getDataStream.halfClose();
+    assertNull(streamInfo.onDone.get());
+    assertFalse(getDataStream.awaitTermination(10, TimeUnit.MILLISECONDS));
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse3 = createTestResponse(3);
+    streamInfo3.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(3)
+            .addSerializedResponse(keyedGetDataResponse3.toByteString())
+            .build());
+    assertThat(sendFuture3.join()).isEqualTo(keyedGetDataResponse3);
+
+    assertFalse(getDataStream.awaitTermination(0, TimeUnit.MILLISECONDS));
+    Windmill.KeyedGetDataResponse keyedGetDataResponse = createTestResponse(1);
+    streamInfo.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(1)
+            .addSerializedResponse(keyedGetDataResponse.toByteString())
+            .build());
+    assertThat(sendFuture1.join()).isEqualTo(keyedGetDataResponse);
+
+    streamInfo.responseObserver.onCompleted();
+    assertFalse(getDataStream.awaitTermination(0, TimeUnit.MILLISECONDS));
+
+    Windmill.KeyedGetDataResponse keyedGetDataResponse2 = createTestResponse(2);
+    streamInfo2.responseObserver.onNext(
+        Windmill.StreamingGetDataResponse.newBuilder()
+            .addRequestId(2)
+            .addSerializedResponse(keyedGetDataResponse2.toByteString())
+            .build());
+    assertThat(sendFuture2.join()).isEqualTo(keyedGetDataResponse2);
+    streamInfo2.responseObserver.onCompleted();
+    streamInfo3.responseObserver.onCompleted();
+    assertTrue(getDataStream.awaitTermination(10, TimeUnit.SECONDS));
+  }
+
+  @Test
+  public void testRequestKeyedData_raceShutdownDuringTrySendBatch() throws Exception {
+    AtomicBoolean connectedOnce = new AtomicBoolean(false);
+    CountDownLatch failedConnects = new CountDownLatch(2);
+    GrpcGetDataStream getDataStream =
+        (GrpcGetDataStream)
+            GrpcWindmillStreamFactory.of(TEST_JOB_HEADER)
+                .setSendKeyedGetDataRequests(false)
+                .build()
+                .createGetDataStream(
+                    CloudWindmillServiceV1Alpha1Grpc.newStub(inProcessChannel)
+                        .withInterceptors(
+                            new ClientInterceptor() {
+                              @Override
+                              public <ReqT, RespT> ClientCall<ReqT, RespT> interceptCall(
+                                  MethodDescriptor<ReqT, RespT> methodDescriptor,
+                                  CallOptions callOptions,
+                                  Channel channel) {
+                                if (connectedOnce.getAndSet(true)) {
+                                  failedConnects.countDown();
+                                  throw new RuntimeException("test error");
+                                }
+                                return channel.newCall(methodDescriptor, callOptions);
+                              }
+                            }));
+    getDataStream.start();
+    // Wait for the first stream to succeed and cause it to fail, the rest should fail.
+    FakeWindmillGrpcService.GetDataStreamInfo streamInfo = waitForConnectionAndConsumeHeader();
+    streamInfo.responseObserver.onError(new RuntimeException("fake error"));
+
+    failedConnects.await();
+
+    // Send while we're in this state.
+    // Create a request
+    Windmill.KeyedGetDataRequest keyedGetDataRequest = createTestRequest(1);
+    CompletableFuture<Windmill.KeyedGetDataResponse> sendFuture =
+        CompletableFuture.supplyAsync(
+            () -> {
+              try {
+                return getDataStream.requestKeyedData("computationId", keyedGetDataRequest);
+              } catch (WindmillStreamShutdownException e) {
+                throw new RuntimeException(e);
+              }
+            });
+
+    // The shutdown should work if it occurs either before or after the above request is sent.
+    Thread.sleep(100);
     getDataStream.shutdown();
+
+    // The request should complete with an exception, it may or may not get there.
+    assertThrows(CompletionException.class, sendFuture::join);
+    assertTrue(sendFuture.isCompletedExceptionally());
   }
 
   private FakeWindmillGrpcService.GetDataStreamInfo waitForConnectionAndConsumeHeader() {
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/stubs/ChannelCacheTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/stubs/ChannelCacheTest.java
index 311bed75ccc7..dd039782d1fc 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/stubs/ChannelCacheTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/client/grpc/stubs/ChannelCacheTest.java
@@ -194,6 +194,139 @@ public void testConsumeFlowControlSettings() throws InterruptedException {
     assertThat(consumedFlowControlSettings.get()).isEqualTo(flowControlSettings);
   }
 
+  @Test
+  public void testConsumeFlowControlSettings_UsesDefaultOverridesForDirect()
+      throws InterruptedException {
+    String channelName = "channel";
+    AtomicReference<CountDownLatch> notifyWhenChannelClosed =
+        new AtomicReference<>(new CountDownLatch(1));
+    AtomicInteger newChannelsCreated = new AtomicInteger();
+    AtomicReference<UserWorkerGrpcFlowControlSettings> consumedFlowControlSettings =
+        new AtomicReference<>();
+    cache =
+        ChannelCache.forTesting(
+            (newFlowControlSettings, ignoredServiceAddress) -> {
+              ManagedChannel channel = newChannel(channelName);
+              newChannelsCreated.incrementAndGet();
+              consumedFlowControlSettings.set(newFlowControlSettings);
+              return channel;
+            },
+            () -> notifyWhenChannelClosed.get().countDown());
+    WindmillServiceAddress someAddress = mock(WindmillServiceAddress.class);
+    when(someAddress.getKind())
+        .thenReturn(WindmillServiceAddress.Kind.AUTHENTICATED_GCP_SERVICE_ADDRESS);
+
+    UserWorkerGrpcFlowControlSettings emptyFlowControlSettings =
+        UserWorkerGrpcFlowControlSettings.newBuilder().build();
+
+    // Load the cache w/ this first get.
+    ManagedChannel cachedChannel = cache.get(someAddress);
+    // Verify that the appropriate default was used.
+    assertThat(consumedFlowControlSettings.get())
+        .isEqualTo(WindmillChannels.DEFAULT_DIRECTPATH_FLOW_CONTROL_SETTINGS);
+
+    // Load empty flow control settings.
+    cache.consumeFlowControlSettings(emptyFlowControlSettings);
+    // This get shouldn't reload the cache, since the same default flow control settings
+    // should be used.
+    assertThat(consumedFlowControlSettings.get())
+        .isEqualTo(WindmillChannels.DEFAULT_DIRECTPATH_FLOW_CONTROL_SETTINGS);
+    assertThat(cachedChannel).isSameInstanceAs(cache.get(someAddress));
+
+    // This get should reload the cache, since flow control settings have changed
+    UserWorkerGrpcFlowControlSettings flowControlSettingsModified =
+        UserWorkerGrpcFlowControlSettings.newBuilder().setEnableAutoFlowControl(true).build();
+    cache.consumeFlowControlSettings(flowControlSettingsModified);
+    ManagedChannel reloadedChannel = cache.get(someAddress);
+    notifyWhenChannelClosed.get().await();
+    assertThat(cachedChannel).isNotSameInstanceAs(reloadedChannel);
+    assertTrue(cachedChannel.isShutdown());
+    assertFalse(reloadedChannel.isShutdown());
+    assertThat(newChannelsCreated.get()).isEqualTo(2);
+    assertThat(cache.get(someAddress)).isSameInstanceAs(reloadedChannel);
+    assertThat(consumedFlowControlSettings.get()).isEqualTo(flowControlSettingsModified);
+
+    // Change back to empty settings and verify the default is used again.
+    notifyWhenChannelClosed.set(new CountDownLatch(1));
+    cache.consumeFlowControlSettings(emptyFlowControlSettings);
+    ManagedChannel reloadedChannel2 = cache.get(someAddress);
+    notifyWhenChannelClosed.get().await();
+    assertThat(reloadedChannel2).isNotSameInstanceAs(reloadedChannel);
+    assertThat(reloadedChannel2).isNotSameInstanceAs(cachedChannel);
+    assertTrue(reloadedChannel.isShutdown());
+    assertFalse(reloadedChannel2.isShutdown());
+    assertThat(newChannelsCreated.get()).isEqualTo(3);
+    assertThat(cache.get(someAddress)).isSameInstanceAs(reloadedChannel2);
+    assertThat(consumedFlowControlSettings.get())
+        .isEqualTo(WindmillChannels.DEFAULT_DIRECTPATH_FLOW_CONTROL_SETTINGS);
+  }
+
+  @Test
+  public void testConsumeFlowControlSettings_UsesDefaultOverridesForCloudPath()
+      throws InterruptedException {
+    String channelName = "channel";
+    AtomicReference<CountDownLatch> notifyWhenChannelClosed =
+        new AtomicReference<>(new CountDownLatch(1));
+    AtomicInteger newChannelsCreated = new AtomicInteger();
+    AtomicReference<UserWorkerGrpcFlowControlSettings> consumedFlowControlSettings =
+        new AtomicReference<>();
+    cache =
+        ChannelCache.forTesting(
+            (newFlowControlSettings, ignoredServiceAddress) -> {
+              ManagedChannel channel = newChannel(channelName);
+              newChannelsCreated.incrementAndGet();
+              consumedFlowControlSettings.set(newFlowControlSettings);
+              return channel;
+            },
+            () -> notifyWhenChannelClosed.get().countDown());
+    WindmillServiceAddress someAddress = mock(WindmillServiceAddress.class);
+    when(someAddress.getKind()).thenReturn(WindmillServiceAddress.Kind.GCP_SERVICE_ADDRESS);
+
+    UserWorkerGrpcFlowControlSettings emptyFlowControlSettings =
+        UserWorkerGrpcFlowControlSettings.newBuilder().build();
+
+    // Load the cache w/ this first get.
+    ManagedChannel cachedChannel = cache.get(someAddress);
+    // Verify that the appropriate default was used.
+    assertThat(consumedFlowControlSettings.get())
+        .isEqualTo(WindmillChannels.DEFAULT_CLOUDPATH_FLOW_CONTROL_SETTINGS);
+
+    // Load empty flow control settings.
+    cache.consumeFlowControlSettings(emptyFlowControlSettings);
+    // This get shouldn't reload the cache, since the same default flow control settings
+    // should be used.
+    assertThat(consumedFlowControlSettings.get())
+        .isEqualTo(WindmillChannels.DEFAULT_CLOUDPATH_FLOW_CONTROL_SETTINGS);
+    assertThat(cachedChannel).isSameInstanceAs(cache.get(someAddress));
+
+    // This get should reload the cache, since flow control settings have changed
+    UserWorkerGrpcFlowControlSettings flowControlSettingsModified =
+        UserWorkerGrpcFlowControlSettings.newBuilder().setEnableAutoFlowControl(true).build();
+    cache.consumeFlowControlSettings(flowControlSettingsModified);
+    ManagedChannel reloadedChannel = cache.get(someAddress);
+    notifyWhenChannelClosed.get().await();
+    assertThat(cachedChannel).isNotSameInstanceAs(reloadedChannel);
+    assertTrue(cachedChannel.isShutdown());
+    assertFalse(reloadedChannel.isShutdown());
+    assertThat(newChannelsCreated.get()).isEqualTo(2);
+    assertThat(cache.get(someAddress)).isSameInstanceAs(reloadedChannel);
+    assertThat(consumedFlowControlSettings.get()).isEqualTo(flowControlSettingsModified);
+
+    // Change back to empty settings and verify the default is used again.
+    notifyWhenChannelClosed.set(new CountDownLatch(1));
+    cache.consumeFlowControlSettings(emptyFlowControlSettings);
+    ManagedChannel reloadedChannel2 = cache.get(someAddress);
+    notifyWhenChannelClosed.get().await();
+    assertThat(reloadedChannel2).isNotSameInstanceAs(reloadedChannel);
+    assertThat(reloadedChannel2).isNotSameInstanceAs(cachedChannel);
+    assertTrue(reloadedChannel.isShutdown());
+    assertFalse(reloadedChannel2.isShutdown());
+    assertThat(newChannelsCreated.get()).isEqualTo(3);
+    assertThat(cache.get(someAddress)).isSameInstanceAs(reloadedChannel2);
+    assertThat(consumedFlowControlSettings.get())
+        .isEqualTo(WindmillChannels.DEFAULT_CLOUDPATH_FLOW_CONTROL_SETTINGS);
+  }
+
   @Test
   public void testConsumeFlowControlSettings_sameFlowControlSettings() {
     String channelName = "channel";
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/state/WindmillStateInternalsTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/state/WindmillStateInternalsTest.java
index 9fe424fe9894..cb4f7a1298f2 100644
--- a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/state/WindmillStateInternalsTest.java
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/state/WindmillStateInternalsTest.java
@@ -272,16 +272,12 @@ public void tearDown() throws Exception {
   }
 
   private <T> void waitAndSet(final SettableFuture<T> future, final T value, final long millis) {
-    new Thread(
-            () -> {
-              try {
-                sleepMillis(millis);
-              } catch (InterruptedException e) {
-                throw new RuntimeException("Interrupted before setting", e);
-              }
-              future.set(value);
-            })
-        .run();
+    try {
+      sleepMillis(millis);
+    } catch (InterruptedException e) {
+      throw new RuntimeException("Interrupted before setting", e);
+    }
+    future.set(value);
   }
 
   private WeightedList<String> weightedList(String... elems) {
diff --git a/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/state/WindmillStateUtilTest.java b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/state/WindmillStateUtilTest.java
new file mode 100644
index 000000000000..589edeb1e544
--- /dev/null
+++ b/runners/google-cloud-dataflow-java/worker/src/test/java/org/apache/beam/runners/dataflow/worker/windmill/state/WindmillStateUtilTest.java
@@ -0,0 +1,87 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.runners.dataflow.worker.windmill.state;
+
+import static org.junit.Assert.assertEquals;
+
+import java.io.IOException;
+import org.apache.beam.runners.core.StateNamespace;
+import org.apache.beam.runners.core.StateNamespaceForTest;
+import org.apache.beam.runners.core.StateTag;
+import org.apache.beam.runners.core.StateTags;
+import org.apache.beam.sdk.coders.VarIntCoder;
+import org.apache.beam.sdk.state.SetState;
+import org.apache.beam.sdk.state.StateSpec;
+import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.ByteString;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+
+@RunWith(JUnit4.class)
+public class WindmillStateUtilTest {
+
+  @Test
+  public void testEncodeKey() {
+    StateNamespaceForTest namespace = new StateNamespaceForTest("key");
+    StateTag<SetState<Integer>> foo = StateTags.set("foo", VarIntCoder.of());
+    ByteString bytes = WindmillStateUtil.encodeKey(namespace, foo);
+    assertEquals("key+ufoo", bytes.toStringUtf8());
+  }
+
+  @Test
+  public void testEncodeKeyNested() {
+    // Hypothetical case where a namespace/tag encoding depends on a call to encodeKey
+    // This tests if thread locals in WindmillStateUtil are not reused with nesting
+    StateNamespaceForTest namespace1 = new StateNamespaceForTest("key");
+    StateTag<SetState<Integer>> tag1 = StateTags.set("foo", VarIntCoder.of());
+    StateTag<SetState<Integer>> tag2 =
+        new StateTag<SetState<Integer>>() {
+          @Override
+          public void appendTo(Appendable sb) throws IOException {
+            WindmillStateUtil.encodeKey(namespace1, tag1);
+            sb.append("tag2");
+          }
+
+          @Override
+          public String getId() {
+            return "";
+          }
+
+          @Override
+          public StateSpec<SetState<Integer>> getSpec() {
+            return null;
+          }
+
+          @Override
+          public SetState<Integer> bind(StateBinder binder) {
+            return null;
+          }
+        };
+
+    StateNamespace namespace2 =
+        new StateNamespaceForTest("key") {
+          @Override
+          public void appendTo(Appendable sb) throws IOException {
+            WindmillStateUtil.encodeKey(namespace1, tag1);
+            sb.append("namespace2");
+          }
+        };
+    ByteString bytes = WindmillStateUtil.encodeKey(namespace2, tag2);
+    assertEquals("namespace2+tag2", bytes.toStringUtf8());
+  }
+}
diff --git a/runners/java-fn-execution/src/main/java/org/apache/beam/runners/fnexecution/environment/DockerCommand.java b/runners/java-fn-execution/src/main/java/org/apache/beam/runners/fnexecution/environment/DockerCommand.java
index 79484fea9fa7..ff69ee3c4171 100644
--- a/runners/java-fn-execution/src/main/java/org/apache/beam/runners/fnexecution/environment/DockerCommand.java
+++ b/runners/java-fn-execution/src/main/java/org/apache/beam/runners/fnexecution/environment/DockerCommand.java
@@ -97,7 +97,7 @@ public String runImage(String imageTag, List<String> dockerOpts, List<String> ar
       if (LOG.isDebugEnabled()) {
         LOG.debug("Unable to pull docker image {}", imageTag, e);
       } else {
-        LOG.warn("Unable to pull docker image {}, cause: {}", imageTag, e.getMessage());
+        LOG.warn("Unable to pull docker image {}", imageTag, e);
       }
     }
     // TODO: Validate args?
diff --git a/runners/java-fn-execution/src/main/java/org/apache/beam/runners/fnexecution/environment/ProcessManager.java b/runners/java-fn-execution/src/main/java/org/apache/beam/runners/fnexecution/environment/ProcessManager.java
index 3e28ac64083e..3570fef00df1 100644
--- a/runners/java-fn-execution/src/main/java/org/apache/beam/runners/fnexecution/environment/ProcessManager.java
+++ b/runners/java-fn-execution/src/main/java/org/apache/beam/runners/fnexecution/environment/ProcessManager.java
@@ -169,7 +169,7 @@ public RunningProcess startProcess(
   public void stopProcess(String id) {
     checkNotNull(id, "Process id must not be null");
     try {
-      Process process = checkNotNull(processes.remove(id), "Process for id does not exist: " + id);
+      Process process = checkNotNull(processes.remove(id), "Process for id does not exist: %s", id);
       stopProcess(id, process);
     } finally {
       synchronized (ALL_PROCESS_MANAGERS) {
diff --git a/runners/java-fn-execution/src/test/java/org/apache/beam/runners/fnexecution/wire/CommonCoderTest.java b/runners/java-fn-execution/src/test/java/org/apache/beam/runners/fnexecution/wire/CommonCoderTest.java
index 4f0e67286d62..eccf1e66434e 100644
--- a/runners/java-fn-execution/src/test/java/org/apache/beam/runners/fnexecution/wire/CommonCoderTest.java
+++ b/runners/java-fn-execution/src/test/java/org/apache/beam/runners/fnexecution/wire/CommonCoderTest.java
@@ -519,11 +519,11 @@ public CompletableFuture<StateResponse> handle(StateRequest.Builder requestBuild
         ImmutableBiMap.copyOf(new ModelCoderRegistrar().getCoderURNs())
             .inverse()
             .get(coder.getUrn());
-    checkNotNull(coderType, "Unknown coder URN: " + coder.getUrn());
+    checkNotNull(coderType, "Unknown coder URN: %s", coder.getUrn());
 
     CoderTranslator<?> translator = new ModelCoderRegistrar().getCoderTranslators().get(coderType);
     checkNotNull(
-        translator, "No translator found for common coder class: " + coderType.getSimpleName());
+        translator, "No translator found for common coder class: %s", coderType.getSimpleName());
 
     return translator.fromComponents(components, coder.getPayload(), new TranslationContext() {});
   }
diff --git a/runners/java-job-service/src/test/java/org/apache/beam/runners/jobsubmission/PortablePipelineJarCreatorTest.java b/runners/java-job-service/src/test/java/org/apache/beam/runners/jobsubmission/PortablePipelineJarCreatorTest.java
index e9650efad0f4..9296fcea3597 100644
--- a/runners/java-job-service/src/test/java/org/apache/beam/runners/jobsubmission/PortablePipelineJarCreatorTest.java
+++ b/runners/java-job-service/src/test/java/org/apache/beam/runners/jobsubmission/PortablePipelineJarCreatorTest.java
@@ -143,6 +143,7 @@ public void testCreateManifest_withoutMainMethod() {
     assertNull(manifest.getMainAttributes().getValue(Name.MAIN_CLASS));
   }
 
+  @SuppressWarnings("IncorrectMainMethod") // intended
   private static class EvilPipelineRunner {
     public static int main(String[] args) {
       return 0;
diff --git a/runners/jet/build.gradle b/runners/jet/build.gradle
index 56a001a2bceb..6faca6b4c6b1 100644
--- a/runners/jet/build.gradle
+++ b/runners/jet/build.gradle
@@ -116,7 +116,7 @@ task validatesRunner {
 
 task needsRunnerTests(type: Test) {
     group = "Verification"
-    description = "Runs tests that require a runner to validate that piplines/transforms work correctly"
+    description = "Runs tests that require a runner to validate that pipelines/transforms work correctly"
     systemProperty "beamTestPipelineOptions", JsonOutput.toJson(["--runner=TestJetRunner"])
 
     classpath = configurations.needsRunner
diff --git a/runners/jet/src/main/java/org/apache/beam/runners/jet/Utils.java b/runners/jet/src/main/java/org/apache/beam/runners/jet/Utils.java
index cb1cd69d33c6..06e07d0c6cfc 100644
--- a/runners/jet/src/main/java/org/apache/beam/runners/jet/Utils.java
+++ b/runners/jet/src/main/java/org/apache/beam/runners/jet/Utils.java
@@ -110,10 +110,8 @@ static Map.Entry<TupleTag<?>, PCollection<?>> getOutput(
     return Iterables.getOnlyElement(getOutputs(appliedTransform).entrySet());
   }
 
-  static <T> boolean isBounded(AppliedPTransform<?, ?, ?> appliedTransform) {
-    return ((PCollection) getOutput(appliedTransform).getValue())
-        .isBounded()
-        .equals(PCollection.IsBounded.BOUNDED);
+  static boolean isBounded(AppliedPTransform<?, ?, ?> appliedTransform) {
+    return getOutput(appliedTransform).getValue().isBounded().equals(PCollection.IsBounded.BOUNDED);
   }
 
   static boolean isKeyedValueCoder(Coder coder) {
diff --git a/runners/jet/src/test/java/org/apache/beam/runners/jet/TestStreamP.java b/runners/jet/src/test/java/org/apache/beam/runners/jet/TestStreamP.java
index 291ca91b5c18..aee37d7a5ce8 100644
--- a/runners/jet/src/test/java/org/apache/beam/runners/jet/TestStreamP.java
+++ b/runners/jet/src/test/java/org/apache/beam/runners/jet/TestStreamP.java
@@ -82,7 +82,7 @@ private TestStreamP(byte[] payload, TestStream.TestStreamCoder payloadCoder, Cod
                     }));
   }
 
-  public static <T> ProcessorMetaSupplier supplier(
+  public static ProcessorMetaSupplier supplier(
       byte[] payload, TestStream.TestStreamCoder payloadCoder, Coder outputCoder) {
     return ProcessorMetaSupplier.forceTotalParallelismOne(
         ProcessorSupplier.of(
diff --git a/runners/local-java/src/main/java/org/apache/beam/runners/local/StructuralKey.java b/runners/local-java/src/main/java/org/apache/beam/runners/local/StructuralKey.java
index 5d8578440152..ecb652d2ffe2 100644
--- a/runners/local-java/src/main/java/org/apache/beam/runners/local/StructuralKey.java
+++ b/runners/local-java/src/main/java/org/apache/beam/runners/local/StructuralKey.java
@@ -20,6 +20,7 @@
 import org.apache.beam.sdk.coders.Coder;
 import org.apache.beam.sdk.coders.CoderException;
 import org.apache.beam.sdk.util.CoderUtils;
+import org.checkerframework.checker.nullness.qual.MonotonicNonNull;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
@@ -57,19 +58,34 @@ public static <K> StructuralKey<K> of(K key, Coder<K> coder) {
 
   private static class CoderStructuralKey<K> extends StructuralKey<K> {
     private final Coder<K> coder;
-    private final Object structuralValue;
-    private final byte[] encoded;
+    private final K key;
 
-    private CoderStructuralKey(Coder<K> coder, K key) throws Exception {
+    private byte @MonotonicNonNull [] encoded;
+    private @MonotonicNonNull Object structuralValue;
+
+    private CoderStructuralKey(Coder<K> coder, K key) {
       this.coder = coder;
-      this.structuralValue = coder.structuralValue(key);
-      this.encoded = CoderUtils.encodeToByteArray(coder, key);
+      this.key = key;
+    }
+
+    private byte[] getEncoded() throws CoderException {
+      if (encoded == null) {
+        this.encoded = CoderUtils.encodeToByteArray(coder, this.key);
+      }
+      return encoded;
+    }
+
+    private Object getStructuralValue() {
+      if (structuralValue == null) {
+        this.structuralValue = coder.structuralValue(this.key);
+      }
+      return structuralValue;
     }
 
     @Override
     public K getKey() {
       try {
-        return CoderUtils.decodeFromByteArray(coder, encoded);
+        return CoderUtils.decodeFromByteArray(coder, getEncoded());
       } catch (CoderException e) {
         throw new IllegalArgumentException(
             "Could not decode Key with coder of type " + coder.getClass().getSimpleName(), e);
@@ -83,14 +99,14 @@ public boolean equals(@Nullable Object other) {
       }
       if (other instanceof CoderStructuralKey) {
         CoderStructuralKey<?> that = (CoderStructuralKey<?>) other;
-        return structuralValue.equals(that.structuralValue);
+        return getStructuralValue().equals(that.getStructuralValue());
       }
       return false;
     }
 
     @Override
     public int hashCode() {
-      return structuralValue.hashCode();
+      return getStructuralValue().hashCode();
     }
   }
 }
diff --git a/runners/samza/src/main/java/org/apache/beam/runners/samza/SamzaPipelineResult.java b/runners/samza/src/main/java/org/apache/beam/runners/samza/SamzaPipelineResult.java
index a75526dc0b1d..ffcc949e4611 100644
--- a/runners/samza/src/main/java/org/apache/beam/runners/samza/SamzaPipelineResult.java
+++ b/runners/samza/src/main/java/org/apache/beam/runners/samza/SamzaPipelineResult.java
@@ -108,6 +108,7 @@ public MetricResults metrics() {
     return asAttemptedOnlyMetricResults(executionContext.getMetricsContainer().getContainers());
   }
 
+  @SuppressWarnings("Slf4jDoNotLogMessageOfExceptionExplicitly")
   private StateInfo getStateInfo() {
     final ApplicationStatus status = runner.status();
     switch (status.getStatusCode()) {
diff --git a/runners/samza/src/main/java/org/apache/beam/runners/samza/SamzaRunner.java b/runners/samza/src/main/java/org/apache/beam/runners/samza/SamzaRunner.java
index 111fd684ff63..bc1ada6941b9 100644
--- a/runners/samza/src/main/java/org/apache/beam/runners/samza/SamzaRunner.java
+++ b/runners/samza/src/main/java/org/apache/beam/runners/samza/SamzaRunner.java
@@ -59,10 +59,13 @@
 /**
  * A {@link PipelineRunner} that executes the operations in the {@link Pipeline} into an equivalent
  * Samza plan.
+ *
+ * @deprecated The support for Samza is scheduled for removal in Beam 3.0.
  */
 @SuppressWarnings({
   "nullness" // TODO(https://github.com/apache/beam/issues/20497)
 })
+@Deprecated
 public class SamzaRunner extends PipelineRunner<SamzaPipelineResult> {
   private static final Logger LOG = LoggerFactory.getLogger(SamzaRunner.class);
   private static final String BEAM_DOT_GRAPH = "beamDotGraph";
diff --git a/runners/samza/src/main/java/org/apache/beam/runners/samza/runtime/OpMessage.java b/runners/samza/src/main/java/org/apache/beam/runners/samza/runtime/OpMessage.java
index 9ee7ffd48f2f..217785f19b21 100644
--- a/runners/samza/src/main/java/org/apache/beam/runners/samza/runtime/OpMessage.java
+++ b/runners/samza/src/main/java/org/apache/beam/runners/samza/runtime/OpMessage.java
@@ -59,7 +59,7 @@ public static <T, ElemT> OpMessage<T> ofSideInput(
     return new OpMessage<>(Type.SIDE_INPUT, null, viewId, elements, null);
   }
 
-  public static <T, ElemT> OpMessage<T> ofSideInputWatermark(Instant watermark) {
+  public static <T> OpMessage<T> ofSideInputWatermark(Instant watermark) {
     return new OpMessage<>(Type.SIDE_INPUT_WATERMARK, null, null, null, watermark);
   }
 
diff --git a/runners/samza/src/main/java/org/apache/beam/runners/samza/translation/TranslationContext.java b/runners/samza/src/main/java/org/apache/beam/runners/samza/translation/TranslationContext.java
index 82725d1ce2e5..c5e984fbde07 100644
--- a/runners/samza/src/main/java/org/apache/beam/runners/samza/translation/TranslationContext.java
+++ b/runners/samza/src/main/java/org/apache/beam/runners/samza/translation/TranslationContext.java
@@ -224,7 +224,7 @@ public <InT extends PValue, OutT extends PValue> void attachTransformMetricOp(
   private <InT extends PValue, OutT extends PValue> List<PValue> getPValueForTransform(
       SamzaMetricOpFactory.OpType opType,
       @NonNull PTransform<InT, OutT> transform,
-      @NonNull TransformHierarchy.Node node) {
+      TransformHierarchy.@NonNull Node node) {
     switch (opType) {
       case INPUT:
         {
@@ -250,7 +250,7 @@ private <InT extends PValue, OutT extends PValue> List<PValue> getPValueForTrans
 
   // Transforms that read or write to/from external sources are not supported
   private static boolean isIOTransform(
-      @NonNull TransformHierarchy.Node node, SamzaMetricOpFactory.OpType opType) {
+      TransformHierarchy.@NonNull Node node, SamzaMetricOpFactory.OpType opType) {
     switch (opType) {
       case INPUT:
         return node.getInputs().size() == 0;
diff --git a/runners/samza/src/test/java/org/apache/beam/runners/samza/adapter/BoundedSourceSystemTest.java b/runners/samza/src/test/java/org/apache/beam/runners/samza/adapter/BoundedSourceSystemTest.java
index 4325c29b9b3c..a6bc9940a745 100644
--- a/runners/samza/src/test/java/org/apache/beam/runners/samza/adapter/BoundedSourceSystemTest.java
+++ b/runners/samza/src/test/java/org/apache/beam/runners/samza/adapter/BoundedSourceSystemTest.java
@@ -291,8 +291,7 @@ private static List<IncomingMessageEnvelope> pollOnce(
     return pollResult.get(ssp);
   }
 
-  private static <T> BoundedSourceSystem.Consumer<String> createConsumer(
-      BoundedSource<String> source) {
+  private static BoundedSourceSystem.Consumer<String> createConsumer(BoundedSource<String> source) {
     return createConsumer(source, 1);
   }
 
diff --git a/runners/spark/3/src/main/java/org/apache/beam/runners/spark/structuredstreaming/translation/EvaluationContext.java b/runners/spark/3/src/main/java/org/apache/beam/runners/spark/structuredstreaming/translation/EvaluationContext.java
index 32cbe5b0acab..55c4bbaedd3c 100644
--- a/runners/spark/3/src/main/java/org/apache/beam/runners/spark/structuredstreaming/translation/EvaluationContext.java
+++ b/runners/spark/3/src/main/java/org/apache/beam/runners/spark/structuredstreaming/translation/EvaluationContext.java
@@ -38,6 +38,7 @@
  * pipeline. For example, this is necessary to materialize side-inputs. The {@link
  * EvaluationContext} won't re-evaluate such datasets.
  */
+@SuppressWarnings("Slf4jDoNotLogMessageOfExceptionExplicitly")
 @Internal
 public final class EvaluationContext {
   private static final Logger LOG = LoggerFactory.getLogger(EvaluationContext.class);
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/stateful/SparkTimerInternals.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/stateful/SparkTimerInternals.java
index 8b647c42dd7e..9ef75635c212 100644
--- a/runners/spark/src/main/java/org/apache/beam/runners/spark/stateful/SparkTimerInternals.java
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/stateful/SparkTimerInternals.java
@@ -21,6 +21,7 @@
 
 import java.util.Collection;
 import java.util.Collections;
+import java.util.Comparator;
 import java.util.Iterator;
 import java.util.List;
 import java.util.Map;
@@ -43,7 +44,7 @@
 public class SparkTimerInternals implements TimerInternals {
   private final Instant highWatermark;
   private final Instant synchronizedProcessingTime;
-  private final Set<TimerData> timers = Sets.newHashSet();
+  private final Set<TimerData> timers = Sets.newConcurrentHashSet();
 
   private Instant inputWatermark;
 
@@ -122,7 +123,14 @@ public void setTimer(TimerData timer) {
   @Override
   public void deleteTimer(
       StateNamespace namespace, String timerId, String timerFamilyId, TimeDomain timeDomain) {
-    throw new UnsupportedOperationException("Deleting a timer by ID is not yet supported.");
+    this.timers.stream()
+        .filter(
+            timer ->
+                namespace.equals(timer.getNamespace())
+                    && timerId.equals(timer.getTimerId())
+                    && timerFamilyId.equals(timer.getTimerFamilyId())
+                    && timeDomain.equals(timer.getDomain()))
+        .forEach(this::deleteTimer);
   }
 
   @Override
@@ -182,6 +190,43 @@ public static Iterator<TimerData> deserializeTimers(
     return CoderHelpers.fromByteArrays(serTimers, timerDataCoder).iterator();
   }
 
+  /**
+   * Checks if there are any expired timers in the {@link TimeDomain#PROCESSING_TIME} domain.
+   *
+   * <p>A timer is considered expired when its timestamp is less than the current processing time.
+   *
+   * @return {@code true} if at least one expired processing timer exists, {@code false} otherwise.
+   */
+  public boolean hasNextProcessingTimer() {
+    final Instant currentProcessingTime = this.currentProcessingTime();
+    return this.timers.stream()
+        .anyMatch(
+            (TimerData timerData) ->
+                timerData.getDomain().equals(TimeDomain.PROCESSING_TIME)
+                    && currentProcessingTime.isAfter(timerData.getTimestamp()));
+  }
+
+  /**
+   * Finds the latest timer in {@link TimeDomain#PROCESSING_TIME} domain that has expired based on
+   * the current processing time.
+   *
+   * <p>A timer is considered expired when its timestamp is less than the current processing time.
+   * If multiple expired timers exist, the one with the latest timestamp will be returned.
+   *
+   * @return The expired processing timer with the latest timestamp if one exists, or {@code null}
+   *     if no processing timers are ready to fire.
+   */
+  public @Nullable TimerData getNextProcessingTimer() {
+    final Instant currentProcessingTime = this.currentProcessingTime();
+    return this.timers.stream()
+        .filter(
+            (TimerData timerData) ->
+                timerData.getDomain().equals(TimeDomain.PROCESSING_TIME)
+                    && currentProcessingTime.isAfter(timerData.getTimestamp()))
+        .max(Comparator.comparing(TimerData::getTimestamp))
+        .orElse(null);
+  }
+
   @Override
   public String toString() {
     return "SparkTimerInternals{"
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/AbstractInOutIterator.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/AbstractInOutIterator.java
new file mode 100644
index 000000000000..9d73a605b3b5
--- /dev/null
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/AbstractInOutIterator.java
@@ -0,0 +1,88 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.runners.spark.translation;
+
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
+
+import org.apache.beam.runners.core.StateNamespace;
+import org.apache.beam.runners.core.StateNamespaces;
+import org.apache.beam.runners.core.TimerInternals;
+import org.apache.beam.runners.spark.translation.streaming.ParDoStateUpdateFn;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.sdk.values.WindowedValue;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.AbstractIterator;
+import scala.Tuple2;
+
+/**
+ * Abstract base class for iterators that process Spark input data and produce corresponding output
+ * values. This class serves as a common base for both bounded and unbounded processing strategies
+ * in the Spark runner.
+ *
+ * <p>The class extends Guava's {@link AbstractIterator} and provides common functionality for
+ * iterating through input elements, processing them using a DoFnRunner, and producing output
+ * elements as tuples of {@link TupleTag} and {@link WindowedValue} pairs.
+ *
+ * @param <K> The key type for the processing context
+ * @param <InputT> The input element type to be processed
+ * @param <OutputT> The output element type after processing
+ */
+public abstract class AbstractInOutIterator<K, InputT, OutputT>
+    extends AbstractIterator<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+  protected final SparkProcessContext<K, InputT, OutputT> ctx;
+
+  protected AbstractInOutIterator(SparkProcessContext<K, InputT, OutputT> ctx) {
+    this.ctx = ctx;
+  }
+
+  /**
+   * Fires a timer using the DoFnRunner from the context and performs cleanup afterwards.
+   *
+   * <p>After firing the timer, if the timer data iterator is an instance of {@link
+   * ParDoStateUpdateFn.SparkTimerInternalsIterator}, the fired timer will be deleted as part of
+   * cleanup to prevent re-firing of the same timer.
+   *
+   * @param timer The timer data containing information about the timer to fire
+   * @throws IllegalArgumentException If the timer namespace is not a {@link
+   *     StateNamespaces.WindowNamespace}
+   */
+  public void fireTimer(TimerInternals.TimerData timer) {
+    StateNamespace namespace = timer.getNamespace();
+    checkArgument(namespace instanceof StateNamespaces.WindowNamespace);
+    BoundedWindow window = ((StateNamespaces.WindowNamespace) namespace).getWindow();
+    try {
+      this.ctx
+          .getDoFnRunner()
+          .onTimer(
+              timer.getTimerId(),
+              timer.getTimerFamilyId(),
+              this.ctx.getKey(),
+              window,
+              timer.getTimestamp(),
+              timer.getOutputTimestamp(),
+              timer.getDomain());
+    } finally {
+      if (this.ctx.getTimerDataIterator()
+          instanceof ParDoStateUpdateFn.SparkTimerInternalsIterator) {
+        final ParDoStateUpdateFn.SparkTimerInternalsIterator timerDataIterator =
+            (ParDoStateUpdateFn.SparkTimerInternalsIterator) this.ctx.getTimerDataIterator();
+        timerDataIterator.deleteTimer(timer);
+      }
+    }
+  }
+}
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/GroupNonMergingWindowsFunctions.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/GroupNonMergingWindowsFunctions.java
index 3b46bee0e8cf..3faf00834fd3 100644
--- a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/GroupNonMergingWindowsFunctions.java
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/GroupNonMergingWindowsFunctions.java
@@ -273,12 +273,11 @@ private WindowedValue<KV<K, V>> decodeItem(Tuple2<ByteArray, byte[]> item) {
    * <p>This implementation uses {@link JavaPairRDD#combineByKey} for better performance compared to
    * {@link JavaPairRDD#groupByKey}, as it allows for local aggregation before shuffle operations.
    */
-  static <K, V, W extends BoundedWindow>
-      JavaRDD<WindowedValue<KV<K, Iterable<V>>>> groupByKeyInGlobalWindow(
-          JavaRDD<WindowedValue<KV<K, V>>> rdd,
-          Coder<K> keyCoder,
-          Coder<V> valueCoder,
-          Partitioner partitioner) {
+  static <K, V> JavaRDD<WindowedValue<KV<K, Iterable<V>>>> groupByKeyInGlobalWindow(
+      JavaRDD<WindowedValue<KV<K, V>>> rdd,
+      Coder<K> keyCoder,
+      Coder<V> valueCoder,
+      Partitioner partitioner) {
     final JavaPairRDD<ByteArray, byte[]> rawKeyValues =
         rdd.mapPartitionsToPair(
             (Iterator<WindowedValue<KV<K, V>>> iter) ->
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java
index 8f24c4f0329f..18e58a29c940 100644
--- a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkInputDataProcessor.java
@@ -17,8 +17,6 @@
  */
 package org.apache.beam.runners.spark.translation;
 
-import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
-
 import java.util.ArrayDeque;
 import java.util.Iterator;
 import java.util.NoSuchElementException;
@@ -28,16 +26,11 @@
 import java.util.concurrent.LinkedBlockingQueue;
 import java.util.concurrent.TimeUnit;
 import javax.annotation.CheckForNull;
-import org.apache.beam.runners.core.StateNamespace;
-import org.apache.beam.runners.core.StateNamespaces;
-import org.apache.beam.runners.core.TimerInternals;
 import org.apache.beam.sdk.transforms.reflect.DoFnInvokers;
-import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.util.WindowedValueMultiReceiver;
 import org.apache.beam.sdk.values.TupleTag;
 import org.apache.beam.sdk.values.WindowedValue;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.AbstractIterator;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.util.concurrent.ThreadFactoryBuilder;
 import org.checkerframework.checker.nullness.qual.MonotonicNonNull;
 import org.checkerframework.checker.nullness.qual.Nullable;
@@ -124,20 +117,18 @@ public synchronized <T> void output(TupleTag<T> tag, WindowedValue<T> output) {
     }
   }
 
-  private class UnboundedInOutIterator<K>
-      extends AbstractIterator<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+  private class UnboundedInOutIterator<K> extends AbstractInOutIterator<K, FnInputT, FnOutputT> {
 
     private final Iterator<WindowedValue<FnInputT>> inputIterator;
-    private final SparkProcessContext<K, FnInputT, FnOutputT> ctx;
-    private Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator;
+    private volatile Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator;
     private boolean isBundleStarted;
     private boolean isBundleFinished;
 
     UnboundedInOutIterator(
         Iterator<WindowedValue<FnInputT>> iterator,
         SparkProcessContext<K, FnInputT, FnOutputT> ctx) {
+      super(ctx);
       this.inputIterator = iterator;
-      this.ctx = ctx;
       this.outputIterator = outputManager.iterator();
     }
 
@@ -185,21 +176,6 @@ private class UnboundedInOutIterator<K>
         throw re;
       }
     }
-
-    private void fireTimer(TimerInternals.TimerData timer) {
-      StateNamespace namespace = timer.getNamespace();
-      checkArgument(namespace instanceof StateNamespaces.WindowNamespace);
-      BoundedWindow window = ((StateNamespaces.WindowNamespace) namespace).getWindow();
-      ctx.getDoFnRunner()
-          .onTimer(
-              timer.getTimerId(),
-              timer.getTimerFamilyId(),
-              ctx.getKey(),
-              window,
-              timer.getTimestamp(),
-              timer.getOutputTimestamp(),
-              timer.getDomain());
-    }
   }
 }
 
@@ -281,9 +257,8 @@ public <OutputT> void output(TupleTag<OutputT> tag, WindowedValue<OutputT> outpu
   }
 
   private class BoundedInOutIterator<K, InputT, OutputT>
-      extends AbstractIterator<Tuple2<TupleTag<?>, WindowedValue<?>>> {
+      extends AbstractInOutIterator<K, InputT, OutputT> {
 
-    private final SparkProcessContext<K, InputT, OutputT> ctx;
     private final Iterator<WindowedValue<InputT>> inputIterator;
     private final Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator;
     private final ExecutorService executorService;
@@ -293,8 +268,8 @@ private class BoundedInOutIterator<K, InputT, OutputT>
 
     BoundedInOutIterator(
         Iterator<WindowedValue<InputT>> iterator, SparkProcessContext<K, InputT, OutputT> ctx) {
+      super(ctx);
       this.inputIterator = iterator;
-      this.ctx = ctx;
       this.outputIterator = outputManager.iterator();
       this.executorService =
           Executors.newSingleThreadScheduledExecutor(
@@ -325,21 +300,6 @@ private class BoundedInOutIterator<K, InputT, OutputT>
       }
     }
 
-    private void fireTimer(TimerInternals.TimerData timer) {
-      StateNamespace namespace = timer.getNamespace();
-      checkArgument(namespace instanceof StateNamespaces.WindowNamespace);
-      BoundedWindow window = ((StateNamespaces.WindowNamespace) namespace).getWindow();
-      ctx.getDoFnRunner()
-          .onTimer(
-              timer.getTimerId(),
-              timer.getTimerFamilyId(),
-              ctx.getKey(),
-              window,
-              timer.getTimestamp(),
-              timer.getOutputTimestamp(),
-              timer.getDomain());
-    }
-
     private Future<?> startOutputProducerTask() {
       return executorService.submit(
           () -> {
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TransformTranslator.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TransformTranslator.java
index 1345e99bedca..5362beba09dc 100644
--- a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TransformTranslator.java
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TransformTranslator.java
@@ -754,7 +754,7 @@ public String toNativeString() {
     };
   }
 
-  private static <T, W extends BoundedWindow> TransformEvaluator<Window.Assign<T>> window() {
+  private static <T> TransformEvaluator<Window.Assign<T>> window() {
     return new TransformEvaluator<Window.Assign<T>>() {
       @Override
       public void evaluate(Window.Assign<T> transform, EvaluationContext context) {
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TranslationUtils.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TranslationUtils.java
index 93422c6f6da7..f5c3fd932742 100644
--- a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TranslationUtils.java
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TranslationUtils.java
@@ -33,6 +33,8 @@
 import org.apache.beam.runners.spark.util.ByteArray;
 import org.apache.beam.runners.spark.util.SideInputBroadcast;
 import org.apache.beam.sdk.coders.Coder;
+import org.apache.beam.sdk.state.TimeDomain;
+import org.apache.beam.sdk.state.TimerSpec;
 import org.apache.beam.sdk.transforms.DoFn;
 import org.apache.beam.sdk.transforms.reflect.DoFnSignature;
 import org.apache.beam.sdk.transforms.reflect.DoFnSignatures;
@@ -274,22 +276,30 @@ public static Long getBatchDuration(final SerializablePipelineOptions options) {
   }
 
   /**
-   * Reject timers {@link DoFn}.
+   * Checks if the given DoFn uses any timers.
    *
-   * @param doFn the {@link DoFn} to possibly reject.
+   * @param doFn the DoFn to check for timer usage
+   * @return true if the DoFn uses timers, false otherwise
    */
-  public static void rejectTimers(DoFn<?, ?> doFn) {
-    DoFnSignature signature = DoFnSignatures.getSignature(doFn.getClass());
-    if (signature.timerDeclarations().size() > 0
-        || signature.timerFamilyDeclarations().size() > 0) {
-      throw new UnsupportedOperationException(
-          String.format(
-              "Found %s annotations on %s, but %s cannot yet be used with timers in the %s.",
-              DoFn.TimerId.class.getSimpleName(),
-              doFn.getClass().getName(),
-              DoFn.class.getSimpleName(),
-              SparkRunner.class.getSimpleName()));
+  public static boolean hasTimers(DoFn<?, ?> doFn) {
+    final DoFnSignature signature = DoFnSignatures.signatureForDoFn(doFn);
+    return signature.usesTimers();
+  }
+
+  /**
+   * Checks if the given DoFn uses event time timers.
+   *
+   * @param doFn the DoFn to check for event time timer usage
+   * @return true if the DoFn uses event time timers, false otherwise. Note: Returns false if the
+   *     DoFn has no timers at all.
+   */
+  public static boolean hasEventTimers(DoFn<?, ?> doFn) {
+    for (DoFnSignature.TimerDeclaration timerDeclaration :
+        DoFnSignatures.signatureForDoFn(doFn).timerDeclarations().values()) {
+      final TimerSpec timerSpec = DoFnSignatures.getTimerSpecOrThrow(timerDeclaration, doFn);
+      return timerSpec.getTimeDomain().equals(TimeDomain.EVENT_TIME);
     }
+    return false;
   }
 
   /**
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/ParDoStateUpdateFn.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/ParDoStateUpdateFn.java
index 1be4042a151d..909624c23239 100644
--- a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/ParDoStateUpdateFn.java
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/ParDoStateUpdateFn.java
@@ -18,9 +18,11 @@
 package org.apache.beam.runners.spark.translation.streaming;
 
 import java.io.Serializable;
+import java.util.Collection;
 import java.util.Iterator;
 import java.util.List;
 import java.util.Map;
+import java.util.NoSuchElementException;
 import java.util.stream.Collectors;
 import org.apache.beam.runners.core.DoFnRunner;
 import org.apache.beam.runners.core.DoFnRunners;
@@ -34,6 +36,7 @@
 import org.apache.beam.runners.spark.stateful.SparkStateInternals;
 import org.apache.beam.runners.spark.stateful.SparkTimerInternals;
 import org.apache.beam.runners.spark.stateful.StateAndTimers;
+import org.apache.beam.runners.spark.translation.AbstractInOutIterator;
 import org.apache.beam.runners.spark.translation.DoFnRunnerWithMetrics;
 import org.apache.beam.runners.spark.translation.SparkInputDataProcessor;
 import org.apache.beam.runners.spark.translation.SparkProcessContext;
@@ -41,6 +44,7 @@
 import org.apache.beam.runners.spark.util.GlobalWatermarkHolder;
 import org.apache.beam.runners.spark.util.SideInputBroadcast;
 import org.apache.beam.runners.spark.util.SideInputReaderFactory;
+import org.apache.beam.runners.spark.util.TimerUtils;
 import org.apache.beam.sdk.coders.Coder;
 import org.apache.beam.sdk.transforms.DoFn;
 import org.apache.beam.sdk.transforms.DoFnSchemaInformation;
@@ -55,8 +59,10 @@
 import org.apache.beam.sdk.values.WindowingStrategy;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
 import org.apache.spark.streaming.State;
+import org.checkerframework.checker.nullness.qual.Nullable;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
+import org.sparkproject.guava.collect.Iterators;
 import scala.Option;
 import scala.Tuple2;
 import scala.runtime.AbstractFunction3;
@@ -69,8 +75,7 @@
  *
  * <ul>
  *   <li>State: Fully implemented and supported through {@link SparkStateInternals}
- *   <li>Timers: Not supported. While {@link SparkTimerInternals} is present in the code, timer
- *       functionality is not yet fully implemented and operational
+ *   <li>Timers: Processing time timers are now supported through {@link SparkTimerInternals}.
  * </ul>
  *
  * @param <KeyT> The type of the key in the input KV pairs
@@ -159,9 +164,9 @@ public ParDoStateUpdateFn(
     }
 
     SparkStateInternals<KeyT> stateInternals;
+    final KeyT key = CoderHelpers.fromByteArray(serializedKey.getValue(), this.keyCoder);
     final SparkTimerInternals timerInternals =
         SparkTimerInternals.forStreamFromSources(sourceIds, watermarks);
-    final KeyT key = CoderHelpers.fromByteArray(serializedKey.getValue(), this.keyCoder);
 
     if (state.exists()) {
       final StateAndTimers stateAndTimers = state.get();
@@ -172,12 +177,6 @@ public ParDoStateUpdateFn(
       stateInternals = SparkStateInternals.forKey(key);
     }
 
-    final byte[] byteValue = serializedValue.get();
-    final WindowedValue<ValueT> windowedValue = CoderHelpers.fromByteArray(byteValue, this.wvCoder);
-
-    final WindowedValue<KV<KeyT, ValueT>> keyedWindowedValue =
-        windowedValue.withValue(KV.of(key, windowedValue.getValue()));
-
     if (!wasSetupCalled) {
       DoFnInvokers.tryInvokeSetupFor(this.doFn, this.options.get());
       this.wasSetupCalled = true;
@@ -199,6 +198,16 @@ public TimerInternals timerInternals() {
           }
         };
 
+    final Coder<? extends BoundedWindow> windowCoder =
+        windowingStrategy.getWindowFn().windowCoder();
+
+    final StatefulDoFnRunner.CleanupTimer<InputT> cleanUpTimer =
+        new StatefulDoFnRunner.TimeInternalsCleanupTimer<>(timerInternals, windowingStrategy);
+
+    final StatefulDoFnRunner.StateCleaner<? extends BoundedWindow> stateCleaner =
+        new StatefulDoFnRunner.StateInternalsStateCleaner<>(doFn, stateInternals, windowCoder);
+
+    // simple runner --> stateful runner --> metrics runner
     DoFnRunner<InputT, OutputT> doFnRunner =
         DoFnRunners.simpleRunner(
             options.get(),
@@ -214,40 +223,39 @@ public TimerInternals timerInternals() {
             doFnSchemaInformation,
             sideInputMapping);
 
-    final Coder<? extends BoundedWindow> windowCoder =
-        windowingStrategy.getWindowFn().windowCoder();
-
-    final StatefulDoFnRunner.CleanupTimer<InputT> cleanUpTimer =
-        new StatefulDoFnRunner.TimeInternalsCleanupTimer<>(timerInternals, windowingStrategy);
-
-    final StatefulDoFnRunner.StateCleaner<? extends BoundedWindow> stateCleaner =
-        new StatefulDoFnRunner.StateInternalsStateCleaner<>(doFn, stateInternals, windowCoder);
-
     doFnRunner =
         DoFnRunners.defaultStatefulDoFnRunner(
             doFn, inputCoder, doFnRunner, context, windowingStrategy, cleanUpTimer, stateCleaner);
 
-    DoFnRunnerWithMetrics<InputT, OutputT> doFnRunnerWithMetrics =
-        new DoFnRunnerWithMetrics<>(stepName, doFnRunner, metricsAccum);
+    doFnRunner = new DoFnRunnerWithMetrics<>(stepName, doFnRunner, metricsAccum);
 
     SparkProcessContext<KeyT, InputT, OutputT> ctx =
         new SparkProcessContext<>(
-            stepName, doFn, doFnRunnerWithMetrics, key, timerInternals.getTimers().iterator());
-
-    final Iterator<WindowedValue<KV<KeyT, ValueT>>> iterator =
-        Lists.newArrayList(keyedWindowedValue).iterator();
+            stepName, doFn, doFnRunner, key, new SparkTimerInternalsIterator(timerInternals));
 
-    final Iterator<Tuple2<TupleTag<?>, WindowedValue<?>>> outputIterator =
-        processor.createOutputIterator((Iterator) iterator, ctx);
+    final byte[] byteValue = serializedValue.get();
+    @Nullable WindowedValue<ValueT> windowedValue;
+    @Nullable WindowedValue<KV<KeyT, ValueT>> keyedWindowedValue;
+    Iterator<WindowedValue<KV<KeyT, ValueT>>> iterator = Iterators.emptyIterator();
+    if (byteValue.length > 0) {
+      windowedValue = CoderHelpers.fromByteArray(byteValue, this.wvCoder);
+      keyedWindowedValue = windowedValue.withValue(KV.of(key, windowedValue.getValue()));
+      iterator = Lists.newArrayList(keyedWindowedValue).iterator();
+    }
 
-    state.update(
-        StateAndTimers.of(
-            stateInternals.getState(),
-            SparkTimerInternals.serializeTimers(timerInternals.getTimers(), timerDataCoder)));
+    final AbstractInOutIterator outputIterator =
+        (AbstractInOutIterator) processor.createOutputIterator((Iterator) iterator, ctx);
 
     final List<Tuple2<TupleTag<?>, WindowedValue<?>>> resultList =
         Lists.newArrayList(outputIterator);
 
+    TimerUtils.triggerExpiredTimers(timerInternals, windowingStrategy, outputIterator);
+
+    final Collection<byte[]> serializedTimers =
+        SparkTimerInternals.serializeTimers(timerInternals.getTimers(), timerDataCoder);
+
+    state.update(StateAndTimers.of(stateInternals.getState(), serializedTimers));
+
     return (List<Tuple2<TupleTag<?>, byte[]>>)
         (List)
             resultList.stream()
@@ -267,4 +275,37 @@ public TimerInternals timerInternals() {
                     })
                 .collect(Collectors.toList());
   }
+
+  /**
+   * An iterator implementation that processes timers from {@link SparkTimerInternals}. This
+   * iterator is used in stateful processing to handle timer-based operations.
+   */
+  @SuppressWarnings("nullness")
+  public static class SparkTimerInternalsIterator implements Iterator<TimerInternals.TimerData> {
+
+    private final SparkTimerInternals delegate;
+
+    private SparkTimerInternalsIterator(SparkTimerInternals sparkTimerInternals) {
+      this.delegate = sparkTimerInternals;
+    }
+
+    @Override
+    public boolean hasNext() {
+      return this.delegate.hasNextProcessingTimer();
+    }
+
+    @Override
+    public TimerInternals.TimerData next() {
+      final TimerInternals.TimerData nextTimer = this.delegate.getNextProcessingTimer();
+      if (nextTimer == null) {
+        throw new NoSuchElementException();
+      }
+
+      return nextTimer;
+    }
+
+    public void deleteTimer(TimerInternals.TimerData timerData) {
+      this.delegate.deleteTimer(timerData);
+    }
+  }
 }
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/StatefulStreamingParDoEvaluator.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/StatefulStreamingParDoEvaluator.java
index fdd5c180c21e..bf500f36c2cb 100644
--- a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/StatefulStreamingParDoEvaluator.java
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/StatefulStreamingParDoEvaluator.java
@@ -18,7 +18,8 @@
 package org.apache.beam.runners.spark.translation.streaming;
 
 import static org.apache.beam.runners.spark.translation.TranslationUtils.getBatchDuration;
-import static org.apache.beam.runners.spark.translation.TranslationUtils.rejectTimers;
+import static org.apache.beam.runners.spark.translation.TranslationUtils.hasEventTimers;
+import static org.apache.beam.runners.spark.translation.TranslationUtils.hasTimers;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkState;
 
@@ -37,8 +38,10 @@
 import org.apache.beam.runners.spark.util.ByteArray;
 import org.apache.beam.runners.spark.util.GlobalWatermarkHolder;
 import org.apache.beam.runners.spark.util.SideInputBroadcast;
+import org.apache.beam.runners.spark.util.TimerUtils;
 import org.apache.beam.sdk.coders.Coder;
 import org.apache.beam.sdk.coders.KvCoder;
+import org.apache.beam.sdk.state.TimeDomain;
 import org.apache.beam.sdk.transforms.DoFn;
 import org.apache.beam.sdk.transforms.DoFnSchemaInformation;
 import org.apache.beam.sdk.transforms.ParDo;
@@ -60,6 +63,8 @@
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaMapWithStateDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 import scala.Option;
 import scala.Tuple2;
 
@@ -78,16 +83,18 @@
  * containing {@code @Timer} annotations, as timer functionality is not currently supported in the
  * Spark streaming context.
  */
+@SuppressWarnings("nullness")
 public class StatefulStreamingParDoEvaluator<KeyT, ValueT, OutputT>
     implements TransformEvaluator<ParDo.MultiOutput<KV<KeyT, ValueT>, OutputT>> {
 
+  private static final Logger LOG = LoggerFactory.getLogger(StatefulStreamingParDoEvaluator.class);
+
   @Override
   public void evaluate(
       ParDo.MultiOutput<KV<KeyT, ValueT>, OutputT> transform, EvaluationContext context) {
     final DoFn<KV<KeyT, ValueT>, OutputT> doFn = transform.getFn();
     final DoFnSignature signature = DoFnSignatures.signatureForDoFn(doFn);
 
-    rejectTimers(doFn);
     checkArgument(
         !signature.processElement().isSplittable(),
         "Splittable DoFn not yet supported in streaming mode: %s",
@@ -109,7 +116,18 @@ public void evaluate(
     final UnboundedDataset<KV<KeyT, ValueT>> unboundedDataset =
         (UnboundedDataset<KV<KeyT, ValueT>>) context.borrowDataset(transform);
 
-    final JavaDStream<WindowedValue<KV<KeyT, ValueT>>> dStream = unboundedDataset.getDStream();
+    JavaDStream<WindowedValue<KV<KeyT, ValueT>>> dStream = unboundedDataset.getDStream();
+
+    if (hasTimers(doFn)) {
+      checkState(
+          !hasEventTimers(doFn),
+          "%s not yet supported in streaming mode: %s",
+          TimeDomain.EVENT_TIME,
+          doFn.getClass().getName());
+
+      LOG.info("DoFn {} has timers. create periodic DStream for triggering timers", doFn);
+      dStream = TimerUtils.toPeriodicDStream(dStream);
+    }
 
     final DoFnSchemaInformation doFnSchemaInformation =
         ParDoTranslation.getSchemaInformation(context.getCurrentTransform());
@@ -155,8 +173,12 @@ public void evaluate(
                               windowedKV.withValue(windowedKV.getValue().getValue());
                           final ByteArray keyBytes =
                               new ByteArray(CoderHelpers.toByteArray(key, keyCoder));
-                          final byte[] valueBytes =
-                              CoderHelpers.toByteArray(windowedValue, wvCoder);
+                          byte[] valueBytes;
+                          if (TimerUtils.TIMER_MARKER.equals(windowedValue.getValue())) {
+                            valueBytes = TimerUtils.EMPTY_BYTE_ARRAY;
+                          } else {
+                            valueBytes = CoderHelpers.toByteArray(windowedValue, wvCoder);
+                          }
                           return Tuple2.apply(keyBytes, valueBytes);
                         }));
 
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/StreamingTransformTranslator.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/StreamingTransformTranslator.java
index 0963a3c7a750..9534b352f200 100644
--- a/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/StreamingTransformTranslator.java
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/StreamingTransformTranslator.java
@@ -325,7 +325,7 @@ public String toNativeString() {
     };
   }
 
-  private static <T, W extends BoundedWindow> TransformEvaluator<Window.Assign<T>> window() {
+  private static <T> TransformEvaluator<Window.Assign<T>> window() {
     return new TransformEvaluator<Window.Assign<T>>() {
       @Override
       public void evaluate(final Window.Assign<T> transform, EvaluationContext context) {
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/util/SideInputBroadcast.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/util/SideInputBroadcast.java
index cf6815c44ec9..a0e0dcaa29bc 100644
--- a/runners/spark/src/main/java/org/apache/beam/runners/spark/util/SideInputBroadcast.java
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/util/SideInputBroadcast.java
@@ -75,6 +75,7 @@ public void unpersist() {
     this.bcast.unpersist();
   }
 
+  @SuppressWarnings("Slf4jDoNotLogMessageOfExceptionExplicitly")
   private T deserialize() {
     T val;
     try {
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/util/SparkSideInputReader.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/util/SparkSideInputReader.java
index 414f2abc01a9..a46acc2cc07d 100644
--- a/runners/spark/src/main/java/org/apache/beam/runners/spark/util/SparkSideInputReader.java
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/util/SparkSideInputReader.java
@@ -61,7 +61,7 @@ public SparkSideInputReader(
     checkNotNull(view, "The PCollectionView passed to sideInput cannot be null ");
     KV<WindowingStrategy<?, ?>, SideInputBroadcast<?>> windowedBroadcastHelper =
         sideInputs.get(view.getTagInternal());
-    checkNotNull(windowedBroadcastHelper, "SideInput for view " + view + " is not available.");
+    checkNotNull(windowedBroadcastHelper, "SideInput for view %s is not available.", view);
 
     // --- sideInput window
     final BoundedWindow sideInputWindow = view.getWindowMappingFn().getSideInputWindow(window);
diff --git a/runners/spark/src/main/java/org/apache/beam/runners/spark/util/TimerUtils.java b/runners/spark/src/main/java/org/apache/beam/runners/spark/util/TimerUtils.java
index e383d8cf46ae..03735355de51 100644
--- a/runners/spark/src/main/java/org/apache/beam/runners/spark/util/TimerUtils.java
+++ b/runners/spark/src/main/java/org/apache/beam/runners/spark/util/TimerUtils.java
@@ -17,28 +17,317 @@
  */
 package org.apache.beam.runners.spark.util;
 
+import java.io.Serializable;
 import java.util.Collection;
+import java.util.Collections;
+import java.util.HashSet;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Objects;
+import java.util.Set;
 import java.util.stream.Collectors;
 import org.apache.beam.runners.core.TimerInternals;
 import org.apache.beam.runners.spark.stateful.SparkTimerInternals;
+import org.apache.beam.runners.spark.translation.AbstractInOutIterator;
+import org.apache.beam.sdk.state.TimeDomain;
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.WindowedValue;
 import org.apache.beam.sdk.values.WindowingStrategy;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.MoreObjects;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterators;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext$;
+import org.apache.spark.streaming.State;
+import org.apache.spark.streaming.StateSpec;
+import org.apache.spark.streaming.Time;
+import org.apache.spark.streaming.api.java.JavaDStream;
+import org.apache.spark.streaming.api.java.JavaPairDStream;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import org.joda.time.Instant;
+import scala.Option;
+import scala.Tuple2;
+import scala.collection.JavaConverters;
+import scala.runtime.AbstractFunction3;
 
+/**
+ * Utility class for handling timers in the Spark runner. Provides functionality for managing timer
+ * operations in stateful processing.
+ */
 public class TimerUtils {
 
+  public static final byte[] EMPTY_BYTE_ARRAY = new byte[0];
+
+  /**
+   * A serializable version of the AbstractFunction3 class from Scala. Used for stateful operations
+   * in Spark Streaming.
+   *
+   * @param <T1> Type of the first parameter
+   * @param <T2> Type of the second parameter
+   * @param <T3> Type of the third parameter
+   * @param <ReturnT> Return type of the function
+   */
+  abstract static class SerializableFunction3<T1, T2, T3, ReturnT>
+      extends AbstractFunction3<T1, T2, T3, ReturnT> implements Serializable {}
+
+  /**
+   * A marker class used to identify timer keys and values in Spark transformations. Helps
+   * distinguish between regular data flow elements and timer events.
+   */
+  public static class TimerMarker implements Serializable {
+    private static final long serialVersionUID = 1L;
+
+    @Override
+    public String toString() {
+      return "TIMER_MARKER";
+    }
+
+    @Override
+    public boolean equals(@Nullable Object o) {
+      return o instanceof TimerMarker;
+    }
+
+    @Override
+    public int hashCode() {
+      return 1; // All instances are equal, so they should have the same hash code
+    }
+  }
+
+  /** Constant marker used to identify timer values in transformations. */
+  public static final TimerMarker TIMER_MARKER = new TimerMarker();
+
+  private static class WindowedValueForTimerMarker<T> implements Serializable, WindowedValue<T> {
+    private final T value;
+
+    public WindowedValueForTimerMarker(T value) {
+      this.value = value;
+    }
+
+    @Override
+    public PaneInfo getPaneInfo() {
+      return PaneInfo.NO_FIRING;
+    }
+
+    @Override
+    public @Nullable String getRecordId() {
+      return null;
+    }
+
+    @Override
+    public @Nullable Long getRecordOffset() {
+      return null;
+    }
+
+    @Override
+    public Iterable<WindowedValue<T>> explodeWindows() {
+      return Collections.emptyList();
+    }
+
+    @Override
+    public T getValue() {
+      return value;
+    }
+
+    @Override
+    public <NewT> WindowedValue<NewT> withValue(NewT newValue) {
+      return new WindowedValueForTimerMarker<>(newValue);
+    }
+
+    @Override
+    public Instant getTimestamp() {
+      return BoundedWindow.TIMESTAMP_MIN_VALUE;
+    }
+
+    @Override
+    public Collection<? extends BoundedWindow> getWindows() {
+      return Collections.emptyList();
+    }
+
+    @Override
+    public boolean equals(@Nullable Object o) {
+      if (o instanceof WindowedValueForTimerMarker) {
+        WindowedValueForTimerMarker<?> that = (WindowedValueForTimerMarker<?>) o;
+        return Objects.equals(this.getValue(), that.getValue());
+      } else {
+        return super.equals(o);
+      }
+    }
+
+    @Override
+    public int hashCode() {
+      return Objects.hash(getValue());
+    }
+
+    @Override
+    public String toString() {
+      return MoreObjects.toStringHelper(getClass())
+          .add("value", getValue())
+          .add("pane", getPaneInfo())
+          .toString();
+    }
+  }
+
+  /**
+   * Fires all expired timers using the provided iterator.
+   *
+   * <p>Gets expired timers from {@link SparkTimerInternals} and processes each one. The {@link
+   * AbstractInOutIterator#fireTimer} method automatically deletes each timer after processing.
+   *
+   * @param sparkTimerInternals Source of timer data
+   * @param windowingStrategy Used to determine which timers are expired
+   * @param abstractInOutIterator Iterator that processes and then removes the timers
+   * @param <W> Window type
+   */
+  public static <W extends BoundedWindow> void triggerExpiredTimers(
+      SparkTimerInternals sparkTimerInternals,
+      WindowingStrategy<?, W> windowingStrategy,
+      AbstractInOutIterator<?, ?, ?> abstractInOutIterator) {
+    final Collection<TimerInternals.TimerData> expiredTimers =
+        getExpiredTimers(sparkTimerInternals, windowingStrategy);
+
+    if (!expiredTimers.isEmpty()) {
+      expiredTimers.forEach(abstractInOutIterator::fireTimer);
+    }
+  }
+
   public static <W extends BoundedWindow> void dropExpiredTimers(
       SparkTimerInternals sparkTimerInternals, WindowingStrategy<?, W> windowingStrategy) {
-    Collection<TimerInternals.TimerData> expiredTimers =
-        sparkTimerInternals.getTimers().stream()
-            .filter(
-                timer ->
-                    timer
-                        .getTimestamp()
-                        .plus(windowingStrategy.getAllowedLateness())
-                        .isBefore(sparkTimerInternals.currentInputWatermarkTime()))
-            .collect(Collectors.toList());
+    final Collection<TimerInternals.TimerData> expiredTimers =
+        getExpiredTimers(sparkTimerInternals, windowingStrategy);
 
     // Remove the expired timer from the timerInternals structure
-    expiredTimers.forEach(sparkTimerInternals::deleteTimer);
+    if (!expiredTimers.isEmpty()) {
+      expiredTimers.forEach(sparkTimerInternals::deleteTimer);
+    }
+  }
+
+  private static <W extends BoundedWindow> Collection<TimerInternals.TimerData> getExpiredTimers(
+      SparkTimerInternals sparkTimerInternals, WindowingStrategy<?, W> windowingStrategy) {
+    return sparkTimerInternals.getTimers().stream()
+        .filter(
+            timer ->
+                timer
+                    .getTimestamp()
+                    .plus(windowingStrategy.getAllowedLateness())
+                    .isBefore(
+                        timer.getDomain().equals(TimeDomain.PROCESSING_TIME)
+                            ? sparkTimerInternals.currentProcessingTime()
+                            : sparkTimerInternals.currentInputWatermarkTime()))
+        .collect(Collectors.toList());
+  }
+
+  /**
+   * Converts a standard DStream into a periodic DStream that ensures all keys are processed in
+   * every micro-batch, even if they don't receive new data.
+   *
+   * <p>This method addresses a fundamental challenge in stateful processing with Spark Streaming:
+   * ensuring that timer events for all keys are processed in every batch, regardless of whether
+   * those keys receive new data. Without this mechanism, timers associated with inactive keys would
+   * not fire at the appropriate times.
+   *
+   * <p>Implementation details:
+   *
+   * <ol>
+   *   <li>First, it extracts all unique keys from the input DStream and marks them with a special
+   *       timer key marker.
+   *   <li>Then, it uses Spark's mapWithState operation to maintain a persistent set of all keys
+   *       that have ever been seen.
+   *   <li>For each batch, it takes a snapshot of this state (all known keys).
+   *   <li>It then transforms the original DStream by combining it with synthetic events for all
+   *       known keys.
+   *   <li>These synthetic events use the special {@link WindowedValueForTimerMarker} class with the
+   *       {@link TimerUtils#TIMER_MARKER} to allow downstream operations to distinguish them from
+   *       regular data events.
+   * </ol>
+   *
+   * <p>When processed downstream, the synthetic key-value pairs will trigger evaluation of any
+   * pending timers for all keys, even those that haven't received new data in the current batch.
+   * This ensures consistent and reliable timer execution across the entire stateful pipeline.
+   *
+   * <p>This approach is critical for implementing features like windowing, sessions, and other
+   * time-based operations that rely on timers firing at the correct moments, regardless of data
+   * activity.
+   *
+   * @param <KeyT> The type of keys
+   * @param <ValueT> The type of values
+   * @param originalLRDD The original DStream of windowed key-value pairs to be enhanced with
+   *     periodic timer events
+   * @return A new DStream that includes periodic events for all known keys, ensuring timer
+   *     processing for every key in each batch
+   */
+  @SuppressWarnings({"unchecked", "nullness"})
+  public static <KeyT, ValueT> JavaDStream<WindowedValue<KV<KeyT, ValueT>>> toPeriodicDStream(
+      JavaDStream<WindowedValue<KV<KeyT, ValueT>>> originalLRDD) {
+    // extract only unique keys from original RDD
+    final JavaPairDStream<KeyT, KeyT> uniqueKeysDStream =
+        originalLRDD.mapPartitionsToPair(
+            (Iterator<WindowedValue<KV<KeyT, ValueT>>> iterator) -> {
+              final Set<KeyT> uniqueKeys = new HashSet<>();
+              while (iterator.hasNext()) {
+                final KV<KeyT, ValueT> kv = iterator.next().getValue();
+                uniqueKeys.add(kv.getKey());
+              }
+
+              return Iterators.transform(uniqueKeys.iterator(), key -> Tuple2.apply(null, key));
+            });
+
+    final JavaPairDStream</*(null)*/ KeyT, /*Actual Keys*/ Set<KeyT>> keyStateSnapshotDStream =
+        uniqueKeysDStream
+            .mapWithState(
+                StateSpec.function(
+                    new SerializableFunction3<
+                        /*(null)*/ KeyT,
+                        /*Actual Key*/ Option<KeyT>,
+                        /*State*/ State<Set<KeyT>>,
+                        /*Return type - not needed as we use stateSnapshots*/ Void>() {
+                      @Override
+                      public Void apply(
+                          KeyT timerKeyMarker, Option<KeyT> actualKey, State<Set<KeyT>> state) {
+                        Set<KeyT> keyState;
+                        if (state.exists()) {
+                          keyState = state.get();
+                        } else {
+                          keyState = new HashSet<>();
+                        }
+
+                        keyState.add(actualKey.get());
+                        state.update(keyState);
+                        // We don't need to return anything as we use stateSnapshots
+                        return null;
+                      }
+                    }))
+            .stateSnapshots();
+
+    return originalLRDD.transformWith(
+        keyStateSnapshotDStream,
+        (JavaRDD<WindowedValue<KV<KeyT, ValueT>>> wvRDD,
+            JavaPairRDD<KeyT, Set<KeyT>> stateRDD,
+            Time time) -> {
+          final int numPartitions = wvRDD.getNumPartitions();
+
+          final Set<KeyT> keySet = new HashSet<>();
+
+          // add all keys to keySet
+          stateRDD.distinct().values().collect().forEach(keySet::addAll);
+
+          final List<WindowedValue<KV<KeyT, ValueT>>> collect =
+              keySet.stream()
+                  .map(key -> new WindowedValueForTimerMarker<>(KV.of(key, (ValueT) TIMER_MARKER)))
+                  .collect(Collectors.toList());
+
+          final JavaRDD<WindowedValue<KV<KeyT, ValueT>>> keyConvertedRDD =
+              JavaRDD.fromRDD(
+                  stateRDD
+                      .context()
+                      .parallelize(
+                          JavaConverters.asScalaIterator(collect.iterator()).toSeq(),
+                          numPartitions > 0 ? numPartitions : 1,
+                          JavaSparkContext$.MODULE$.fakeClassTag()),
+                  JavaSparkContext$.MODULE$.fakeClassTag());
+
+          return wvRDD.union(keyConvertedRDD);
+        });
   }
 }
diff --git a/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/AbstractInOutIteratorTest.java b/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/AbstractInOutIteratorTest.java
new file mode 100644
index 000000000000..b1e0e3a39808
--- /dev/null
+++ b/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/AbstractInOutIteratorTest.java
@@ -0,0 +1,154 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.runners.spark.translation;
+
+import static org.junit.Assert.assertThrows;
+import static org.mockito.Mockito.mock;
+import static org.mockito.Mockito.never;
+import static org.mockito.Mockito.verify;
+import static org.mockito.Mockito.when;
+
+import org.apache.beam.runners.core.DoFnRunner;
+import org.apache.beam.runners.core.StateNamespace;
+import org.apache.beam.runners.core.StateNamespaces;
+import org.apache.beam.runners.core.TimerInternals;
+import org.apache.beam.runners.spark.translation.streaming.ParDoStateUpdateFn;
+import org.apache.beam.sdk.coders.Coder;
+import org.apache.beam.sdk.state.TimeDomain;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.transforms.windowing.IntervalWindow;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.sdk.values.WindowedValue;
+import org.joda.time.Instant;
+import org.junit.Before;
+import org.junit.Test;
+import org.mockito.Mock;
+import org.mockito.MockitoAnnotations;
+import scala.Tuple2;
+
+/** Tests for {@link AbstractInOutIterator}. */
+public class AbstractInOutIteratorTest {
+
+  @Mock private SparkProcessContext<String, Integer, String> mockContext;
+  @Mock private DoFnRunner<Integer, String> mockDoFnRunner;
+  @Mock private TimerInternals.TimerData mockTimer;
+  @Mock private BoundedWindow mockWindow;
+  @Mock private ParDoStateUpdateFn.SparkTimerInternalsIterator mockTimerDataIterator;
+
+  private static final String TEST_KEY = "testKey";
+  private static final String TIMER_ID = "testTimerId";
+  private static final String TIMER_FAMILY_ID = "testTimerFamilyId";
+  private static final Instant TEST_TIMESTAMP = new Instant(42L);
+  private static final Instant TEST_OUTPUT_TIMESTAMP = new Instant(84L);
+  private static final TimeDomain TEST_TIME_DOMAIN = TimeDomain.EVENT_TIME;
+
+  private StateNamespace testNamespace;
+
+  /** Test implementation of {@link AbstractInOutIterator}. */
+  private static class TestAbstractInOutIterator<K, InputT, OutputT>
+      extends AbstractInOutIterator<K, InputT, OutputT> {
+
+    protected TestAbstractInOutIterator(SparkProcessContext<K, InputT, OutputT> ctx) {
+      super(ctx);
+    }
+
+    @Override
+    protected Tuple2<TupleTag<?>, WindowedValue<?>> computeNext() {
+      // Simple implementation as this method is not used in the test
+      return this.endOfData();
+    }
+  }
+
+  @Before
+  public void setUp() {
+    MockitoAnnotations.initMocks(this);
+
+    testNamespace = StateNamespaces.window((Coder) IntervalWindow.getCoder(), mockWindow);
+
+    when(mockContext.getDoFnRunner()).thenReturn(mockDoFnRunner);
+    when(mockContext.getKey()).thenReturn(TEST_KEY);
+    when(mockTimer.getTimerId()).thenReturn(TIMER_ID);
+    when(mockTimer.getTimerFamilyId()).thenReturn(TIMER_FAMILY_ID);
+    when(mockTimer.getNamespace()).thenReturn(testNamespace);
+    when(mockTimer.getTimestamp()).thenReturn(TEST_TIMESTAMP);
+    when(mockTimer.getOutputTimestamp()).thenReturn(TEST_OUTPUT_TIMESTAMP);
+    when(mockTimer.getDomain()).thenReturn(TEST_TIME_DOMAIN);
+  }
+
+  @Test
+  public void testFireTimer() {
+    // Test basic timer functionality
+    TestAbstractInOutIterator<String, Integer, String> iterator =
+        new TestAbstractInOutIterator<>(mockContext);
+
+    iterator.fireTimer(mockTimer);
+
+    // Verify that DoFnRunner.onTimer was called with the correct arguments
+    verify(mockDoFnRunner)
+        .onTimer(
+            TIMER_ID,
+            TIMER_FAMILY_ID,
+            TEST_KEY,
+            mockWindow,
+            TEST_TIMESTAMP,
+            TEST_OUTPUT_TIMESTAMP,
+            TEST_TIME_DOMAIN);
+
+    // Verify that timer data iterator deletion was not called (no timer iterator was set in this
+    // test)
+    verify(mockTimerDataIterator, never()).deleteTimer(mockTimer);
+  }
+
+  @Test
+  public void testFireTimerWithTimerDataIterator() {
+    // Test when timer data iterator is available
+    when(mockContext.getTimerDataIterator()).thenReturn(mockTimerDataIterator);
+
+    TestAbstractInOutIterator<String, Integer, String> iterator =
+        new TestAbstractInOutIterator<>(mockContext);
+
+    iterator.fireTimer(mockTimer);
+
+    // Verify that DoFnRunner.onTimer was called with the correct arguments
+    verify(mockDoFnRunner)
+        .onTimer(
+            TIMER_ID,
+            TIMER_FAMILY_ID,
+            TEST_KEY,
+            mockWindow,
+            TEST_TIMESTAMP,
+            TEST_OUTPUT_TIMESTAMP,
+            TEST_TIME_DOMAIN);
+
+    // Verify that the timer data iterator's deleteTimer method was called
+    verify(mockTimerDataIterator).deleteTimer(mockTimer);
+  }
+
+  @Test
+  public void testFireTimerWithInvalidNamespace() {
+    // Not WindowNamespace
+    StateNamespace invalidNamespace = mock(StateNamespace.class);
+    TimerInternals.TimerData invalidTimer = mock(TimerInternals.TimerData.class);
+    when(invalidTimer.getNamespace()).thenReturn(invalidNamespace);
+
+    TestAbstractInOutIterator<String, Integer, String> iterator =
+        new TestAbstractInOutIterator<>(mockContext);
+
+    assertThrows(IllegalArgumentException.class, () -> iterator.fireTimer(invalidTimer));
+  }
+}
diff --git a/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/StatefulStreamingParDoEvaluatorTest.java b/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/StatefulStreamingParDoEvaluatorTest.java
index e1f000d16675..d16d295f7af4 100644
--- a/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/StatefulStreamingParDoEvaluatorTest.java
+++ b/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/StatefulStreamingParDoEvaluatorTest.java
@@ -20,24 +20,32 @@
 import static org.apache.beam.runners.spark.translation.streaming.CreateStreamTest.streamingOptions;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.MoreObjects.firstNonNull;
 import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
+import static org.junit.Assert.assertNotNull;
 import static org.junit.Assert.assertThrows;
+import static org.junit.Assert.assertTrue;
 
+import java.io.Serializable;
 import org.apache.beam.runners.spark.SparkPipelineOptions;
 import org.apache.beam.runners.spark.StreamingTest;
 import org.apache.beam.runners.spark.io.CreateStream;
 import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.Coder;
 import org.apache.beam.sdk.coders.KvCoder;
 import org.apache.beam.sdk.coders.StringUtf8Coder;
 import org.apache.beam.sdk.coders.VarIntCoder;
 import org.apache.beam.sdk.state.StateSpec;
 import org.apache.beam.sdk.state.StateSpecs;
 import org.apache.beam.sdk.state.TimeDomain;
+import org.apache.beam.sdk.state.Timer;
 import org.apache.beam.sdk.state.TimerSpec;
 import org.apache.beam.sdk.state.TimerSpecs;
 import org.apache.beam.sdk.state.ValueState;
 import org.apache.beam.sdk.testing.PAssert;
 import org.apache.beam.sdk.testing.TestPipeline;
+import org.apache.beam.sdk.testing.UsesProcessingTimeTimers;
 import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.Flatten;
 import org.apache.beam.sdk.transforms.PTransform;
 import org.apache.beam.sdk.transforms.ParDo;
 import org.apache.beam.sdk.transforms.windowing.FixedWindows;
@@ -46,6 +54,8 @@
 import org.apache.beam.sdk.values.PBegin;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.TimestampedValue;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
+import org.checkerframework.checker.nullness.qual.Nullable;
 import org.joda.time.Duration;
 import org.joda.time.Instant;
 import org.junit.Rule;
@@ -53,7 +63,7 @@
 import org.junit.experimental.categories.Category;
 
 @SuppressWarnings({"unchecked", "unused"})
-public class StatefulStreamingParDoEvaluatorTest {
+public class StatefulStreamingParDoEvaluatorTest implements Serializable {
 
   @Rule public final transient TestPipeline p = TestPipeline.fromOptions(streamingOptions());
 
@@ -110,23 +120,92 @@ private PTransform<PBegin, PCollection<KV<Integer, Integer>>> createStreamingSou
     return createStream.advanceNextBatchWatermarkToInfinity();
   }
 
-  private static class StatefulWithTimerDoFn<InputT> extends DoFn<InputT, Void> {
-    @StateId("some-state")
-    private final StateSpec<ValueState<String>> someStringStateSpec =
-        StateSpecs.value(StringUtf8Coder.of());
+  private abstract static class AbstractStatefulWithTimer<KeyT, ValueT, OutputT>
+      extends DoFn<KV<KeyT, ValueT>, OutputT> {
+    @StateId("current-key")
+    private final StateSpec<ValueState<KeyT>> currentKeyStateSpec;
+
+    @StateId("timer-set")
+    private final StateSpec<ValueState<Boolean>> timerSetStateSpec = StateSpecs.value();
 
     @TimerId("some-timer")
-    private final TimerSpec someTimerSpec = TimerSpecs.timer(TimeDomain.PROCESSING_TIME);
+    private final TimerSpec someTimerSpec;
+
+    private @Nullable OutputT shouldBeOutput;
+
+    private AbstractStatefulWithTimer(
+        TimeDomain timeDomain, Coder<KeyT> keyCoder, @Nullable OutputT shouldBeOutput) {
+      this.someTimerSpec = TimerSpecs.timer(timeDomain);
+      this.currentKeyStateSpec = StateSpecs.value(keyCoder);
+      this.shouldBeOutput = shouldBeOutput;
+    }
 
     @ProcessElement
     public void process(
-        @Element InputT element, @StateId("some-state") ValueState<String> someStringStage) {
-      // ignore...
+        @Element KV<KeyT, ValueT> element,
+        @StateId("timer-set") ValueState<Boolean> timerSetState,
+        @StateId("current-key") ValueState<KeyT> currentKeyState,
+        @TimerId("some-timer") Timer someTimer) {
+      @Nullable KeyT currentKey = currentKeyState.read();
+      if (currentKey == null) {
+        currentKeyState.write(element.getKey());
+      }
+
+      final boolean isTimerSet = firstNonNull(timerSetState.read(), false);
+
+      if (!isTimerSet) {
+        timerSetState.write(true);
+        someTimer.offset(Duration.millis(500L)).setRelative();
+      }
     }
 
     @OnTimer("some-timer")
-    public void onTimer() {
-      // ignore...
+    public void onTimer(
+        OnTimerContext context,
+        @StateId("timer-set") ValueState<Boolean> timerSetState,
+        @StateId("current-key") ValueState<KeyT> currentKeyState,
+        @TimerId("some-timer") Timer timer) {
+      if (this.shouldBeOutput != null) {
+        context.output(shouldBeOutput);
+      }
+      assertNotNull(timerSetState.read());
+      assertTrue(timerSetState.read());
+    }
+  }
+
+  private static class StatefulWithEventTimeTimerDoFn<KeyT, ValueT>
+      extends AbstractStatefulWithTimer<KeyT, ValueT, Void> {
+
+    private StatefulWithEventTimeTimerDoFn(Coder<KeyT> keyCoder) {
+      super(TimeDomain.EVENT_TIME, keyCoder, null);
+    }
+  }
+
+  private static class StatefulWithProcessingTimeTimerDoFn<KeyT, ValueT, OutputT>
+      extends AbstractStatefulWithTimer<KeyT, ValueT, OutputT> {
+
+    private StatefulWithProcessingTimeTimerDoFn(
+        Coder<KeyT> keyCoder, @Nullable OutputT shouldBeOutput) {
+      super(TimeDomain.PROCESSING_TIME, keyCoder, shouldBeOutput);
+    }
+  }
+
+  private static class StatefulWithProcessingTimeTimerForWithValidateSparseKey<
+          KeyT, ValueT, OutputT>
+      extends AbstractStatefulWithTimer<KeyT, ValueT, OutputT> {
+
+    private StatefulWithProcessingTimeTimerForWithValidateSparseKey(Coder<KeyT> keyCoder) {
+      super(TimeDomain.PROCESSING_TIME, keyCoder, null);
+    }
+
+    @Override
+    public void onTimer(
+        OnTimerContext context,
+        ValueState<Boolean> timerSetState,
+        ValueState<KeyT> currentKeyState,
+        Timer timer) {
+      final KeyT currentKey = currentKeyState.read();
+      context.output((OutputT) currentKey);
     }
   }
 
@@ -152,19 +231,77 @@ public void process(
 
   @Category(StreamingTest.class)
   @Test
-  public void shouldRejectTimer() {
-    p.apply(createStreamingSource(p)).apply(ParDo.of(new StatefulWithTimerDoFn<>()));
+  public void shouldRejectEventTimeTimer() {
+    p.apply(createStreamingSource(p))
+        .apply(ParDo.of(new StatefulWithEventTimeTimerDoFn<>(VarIntCoder.of())));
 
-    final UnsupportedOperationException exception =
-        assertThrows(UnsupportedOperationException.class, p::run);
+    final IllegalStateException exception = assertThrows(IllegalStateException.class, p::run);
 
     assertEquals(
-        "Found TimerId annotations on "
-            + StatefulWithTimerDoFn.class.getName()
-            + ", but DoFn cannot yet be used with timers in the SparkRunner.",
+        TimeDomain.EVENT_TIME
+            + " not yet supported in streaming mode: "
+            + StatefulWithEventTimeTimerDoFn.class.getName(),
         exception.getMessage());
   }
 
+  @Category({StreamingTest.class, UsesProcessingTimeTimers.class})
+  @Test
+  public void shouldTriggerProcessingTimeTimer() {
+    final String shouldBeOutput = "some-result";
+    final PCollection<String> result =
+        p.apply(createStreamingSource(p))
+            .apply(
+                ParDo.of(
+                    new StatefulWithProcessingTimeTimerDoFn<>(VarIntCoder.of(), shouldBeOutput)))
+            .setCoder(StringUtf8Coder.of());
+
+    PAssert.that(result)
+        .satisfies(
+            (Iterable<String> iter) -> {
+              assertFalse(Iterables.isEmpty(iter));
+              for (String timerOutput : iter) {
+                assertEquals(shouldBeOutput, timerOutput);
+              }
+              return null;
+            });
+
+    p.run().waitUntilFinish();
+  }
+
+  @Category({StreamingTest.class, UsesProcessingTimeTimers.class})
+  @Test
+  public void shouldTriggerProcessingTimeTimerWithSparseKey() {
+    final int sparseKey = 3;
+    KvCoder<Integer, Integer> coder = KvCoder.of(VarIntCoder.of(), VarIntCoder.of());
+    Instant instant = new Instant(0);
+    CreateStream<KV<Integer, Integer>> sparseStream =
+        CreateStream.of(coder, batchDuration(p)).advanceWatermarkForNextBatch(instant);
+
+    sparseStream =
+        sparseStream.nextBatch(
+            TimestampedValue.of(KV.of(sparseKey, 0), instant.plus(Duration.millis(1000L))));
+
+    final PCollection<KV<Integer, Integer>> sparsePCollection =
+        p.apply("Create Sparse Key Stream", sparseStream);
+
+    final PCollection<KV<Integer, Integer>> nonSparsePCollection =
+        p.apply("Create Non Sparse Key Stream", createStreamingSource(p, 3));
+
+    final PCollection<Integer> result =
+        sparsePCollection
+            .apply(Flatten.with(nonSparsePCollection))
+            .apply(
+                ParDo.of(
+                    new StatefulWithProcessingTimeTimerForWithValidateSparseKey<
+                        /*KeyT*/ Integer, /*ValueT*/ Integer, /*OutputT*/ Integer>(
+                        VarIntCoder.of())))
+            .setCoder(VarIntCoder.of());
+
+    PAssert.that(result).containsInAnyOrder(1, 2, sparseKey);
+
+    p.run().waitUntilFinish();
+  }
+
   @Category(StreamingTest.class)
   @Test
   public void shouldProcessGlobalWidowStatefulParDo() {
diff --git a/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/utils/EmbeddedKafkaCluster.java b/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/utils/EmbeddedKafkaCluster.java
index 7794d5b4318e..df5646fed590 100644
--- a/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/utils/EmbeddedKafkaCluster.java
+++ b/runners/spark/src/test/java/org/apache/beam/runners/spark/translation/streaming/utils/EmbeddedKafkaCluster.java
@@ -20,6 +20,7 @@
 import java.io.File;
 import java.io.FileNotFoundException;
 import java.io.IOException;
+import java.net.InetAddress;
 import java.net.InetSocketAddress;
 import java.net.ServerSocket;
 import java.util.ArrayList;
@@ -145,6 +146,7 @@ public String getZkConnection() {
     return zkConnection;
   }
 
+  @SuppressWarnings("Slf4jDoNotLogMessageOfExceptionExplicitly")
   public void shutdown() {
     for (KafkaServerStartable broker : brokers) {
       try {
@@ -201,7 +203,8 @@ public void startup() throws IOException {
         this.port = TestUtils.getAvailablePort();
       }
       this.factory =
-          NIOServerCnxnFactory.createFactory(new InetSocketAddress("127.0.0.1", port), 1024);
+          NIOServerCnxnFactory.createFactory(
+              new InetSocketAddress(InetAddress.getLoopbackAddress(), port), 1024);
       this.snapshotDir = TestUtils.constructTempDir("embedded-zk/snapshot");
       this.logDir = TestUtils.constructTempDir("embedded-zk/log");
 
diff --git a/runners/spark/src/test/java/org/apache/beam/runners/spark/util/TimerUtilsTest.java b/runners/spark/src/test/java/org/apache/beam/runners/spark/util/TimerUtilsTest.java
new file mode 100644
index 000000000000..a91b92aefadb
--- /dev/null
+++ b/runners/spark/src/test/java/org/apache/beam/runners/spark/util/TimerUtilsTest.java
@@ -0,0 +1,128 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.runners.spark.util;
+
+import static org.mockito.ArgumentMatchers.any;
+import static org.mockito.Mockito.never;
+import static org.mockito.Mockito.verify;
+import static org.mockito.Mockito.when;
+
+import java.util.Arrays;
+import java.util.Collections;
+import org.apache.beam.runners.core.StateNamespace;
+import org.apache.beam.runners.core.StateNamespaces;
+import org.apache.beam.runners.core.TimerInternals;
+import org.apache.beam.runners.spark.stateful.SparkTimerInternals;
+import org.apache.beam.runners.spark.translation.AbstractInOutIterator;
+import org.apache.beam.sdk.state.TimeDomain;
+import org.apache.beam.sdk.transforms.windowing.IntervalWindow;
+import org.apache.beam.sdk.values.WindowingStrategy;
+import org.joda.time.Duration;
+import org.joda.time.Instant;
+import org.junit.Before;
+import org.junit.Test;
+import org.mockito.Mock;
+import org.mockito.MockitoAnnotations;
+
+/** Tests for {@link TimerUtils}. */
+public class TimerUtilsTest {
+
+  @Mock private SparkTimerInternals mockTimerInternals;
+  @Mock private WindowingStrategy<?, IntervalWindow> mockWindowingStrategy;
+  @Mock private AbstractInOutIterator<?, ?, ?> mockIterator;
+  @Mock private TimerInternals.TimerData expiredTimer;
+  @Mock private TimerInternals.TimerData activeTimer;
+  @Mock private IntervalWindow mockWindow;
+
+  private static final Instant NOW = new Instant(1000L);
+  private static final Duration ALLOWED_LATENESS = Duration.standardMinutes(5);
+
+  @Before
+  public void setUp() {
+    MockitoAnnotations.initMocks(this);
+    StateNamespace namespace = StateNamespaces.window(IntervalWindow.getCoder(), mockWindow);
+
+    // Set up an expired event-time timer (timestamp + allowed lateness < watermark)
+    when(expiredTimer.getTimestamp())
+        .thenReturn(NOW.minus(ALLOWED_LATENESS.plus(Duration.standardMinutes(1))));
+    when(expiredTimer.getDomain()).thenReturn(TimeDomain.EVENT_TIME);
+    when(expiredTimer.getNamespace()).thenReturn(namespace);
+
+    // Set up a non-expired event-time timer (timestamp + allowed lateness > watermark)
+    when(activeTimer.getTimestamp()).thenReturn(NOW);
+    when(activeTimer.getDomain()).thenReturn(TimeDomain.EVENT_TIME);
+    when(activeTimer.getNamespace()).thenReturn(namespace);
+
+    // Configure the mocks
+    when(mockWindowingStrategy.getAllowedLateness()).thenReturn(ALLOWED_LATENESS);
+    when(mockTimerInternals.currentInputWatermarkTime()).thenReturn(NOW);
+    when(mockTimerInternals.currentProcessingTime()).thenReturn(NOW);
+  }
+
+  @Test
+  public void testTriggerExpiredTimers() {
+    // Set up the mock timer internals to return both timers
+    when(mockTimerInternals.getTimers()).thenReturn(Arrays.asList(expiredTimer, activeTimer));
+
+    // Call the method under test
+    TimerUtils.triggerExpiredTimers(mockTimerInternals, mockWindowingStrategy, mockIterator);
+
+    // Verify that fireTimer was called only for the expired timer
+    verify(mockIterator).fireTimer(expiredTimer);
+    verify(mockIterator, never()).fireTimer(activeTimer);
+  }
+
+  @Test
+  public void testTriggerExpiredTimersWithNoExpiredTimers() {
+    // Set up the mock timer internals to return only the active timer
+    when(mockTimerInternals.getTimers()).thenReturn(Collections.singletonList(activeTimer));
+
+    // Call the method under test
+    TimerUtils.triggerExpiredTimers(mockTimerInternals, mockWindowingStrategy, mockIterator);
+
+    // Verify that fireTimer was not called for any timer
+    verify(mockIterator, never()).fireTimer(any());
+  }
+
+  @Test
+  public void testTriggerExpiredTimersWithEmptyTimers() {
+    // Set up the mock timer internals to return an empty list
+    when(mockTimerInternals.getTimers()).thenReturn(Collections.emptyList());
+
+    // Call the method under test
+    TimerUtils.triggerExpiredTimers(mockTimerInternals, mockWindowingStrategy, mockIterator);
+
+    // Verify that fireTimer was not called
+    verify(mockIterator, never()).fireTimer(any());
+  }
+
+  @Test
+  public void testTriggerExpiredTimersWithProcessingTimeDomain() {
+    // Set up a processing-time timer
+    when(expiredTimer.getDomain()).thenReturn(TimeDomain.PROCESSING_TIME);
+
+    // Set up the mock timer internals to return only the expired timer
+    when(mockTimerInternals.getTimers()).thenReturn(Collections.singletonList(expiredTimer));
+
+    // Call the method under test
+    TimerUtils.triggerExpiredTimers(mockTimerInternals, mockWindowingStrategy, mockIterator);
+
+    // Verify that fireTimer was called for the expired timer
+    verify(mockIterator).fireTimer(expiredTimer);
+  }
+}
diff --git a/runners/twister2/src/main/java/org/apache/beam/runners/twister2/Twister2PipelineExecutionEnvironment.java b/runners/twister2/src/main/java/org/apache/beam/runners/twister2/Twister2PipelineExecutionEnvironment.java
index cc3d4a24cfd3..271e9317f8a2 100644
--- a/runners/twister2/src/main/java/org/apache/beam/runners/twister2/Twister2PipelineExecutionEnvironment.java
+++ b/runners/twister2/src/main/java/org/apache/beam/runners/twister2/Twister2PipelineExecutionEnvironment.java
@@ -49,7 +49,7 @@ public Twister2PipelineExecutionEnvironment(Twister2PipelineOptions options) {
     options.setTSetEnvironment(new BeamBatchTSetEnvironment());
   }
 
-  /** translate the pipline into Twister2 TSet graph. */
+  /** translate the pipeline into Twister2 TSet graph. */
   public void translate(Pipeline pipeline) {
 
     TranslationModeDetector detector = new TranslationModeDetector();
diff --git a/runners/twister2/src/main/java/org/apache/beam/runners/twister2/Twister2Runner.java b/runners/twister2/src/main/java/org/apache/beam/runners/twister2/Twister2Runner.java
index c101358cd9f7..e2f236fa0eb6 100644
--- a/runners/twister2/src/main/java/org/apache/beam/runners/twister2/Twister2Runner.java
+++ b/runners/twister2/src/main/java/org/apache/beam/runners/twister2/Twister2Runner.java
@@ -61,11 +61,14 @@
  * A {@link PipelineRunner} that executes the operations in the pipeline by first translating them
  * to a Twister2 Plan and then executing them either locally or on a Twister2 cluster, depending on
  * the configuration.
+ *
+ * @deprecated The support for twister2 is scheduled for removal in Beam 3.0.
  */
 @SuppressWarnings({
   "rawtypes", // TODO(https://github.com/apache/beam/issues/20447)
   "nullness" // TODO(https://github.com/apache/beam/issues/20497)
 })
+@Deprecated
 public class Twister2Runner extends PipelineRunner<PipelineResult> {
 
   private static final Logger LOG = Logger.getLogger(Twister2Runner.class.getName());
diff --git a/runners/twister2/src/main/java/org/apache/beam/runners/twister2/utils/Twister2SideInputReader.java b/runners/twister2/src/main/java/org/apache/beam/runners/twister2/utils/Twister2SideInputReader.java
index e2e2a281a9fc..fdd36fe66979 100644
--- a/runners/twister2/src/main/java/org/apache/beam/runners/twister2/utils/Twister2SideInputReader.java
+++ b/runners/twister2/src/main/java/org/apache/beam/runners/twister2/utils/Twister2SideInputReader.java
@@ -61,7 +61,7 @@ public Twister2SideInputReader(
   public <T> @Nullable T get(PCollectionView<T> view, BoundedWindow window) {
     checkNotNull(view, "View passed to sideInput cannot be null");
     TupleTag<?> tag = view.getTagInternal();
-    checkNotNull(sideInputs.get(tag), "Side input for " + view + " not available.");
+    checkNotNull(sideInputs.get(tag), "Side input for %s not available.", view);
     return getSideInput(view, window);
   }
 
diff --git a/sdks/go.mod b/sdks/go.mod
index a0511e9f9fe9..ff3977ff5475 100644
--- a/sdks/go.mod
+++ b/sdks/go.mod
@@ -20,83 +20,85 @@
 // directory.
 module github.com/apache/beam/sdks/v2
 
-go 1.23.0
+go 1.24.0
 
 toolchain go1.24.4
 
 require (
-	cloud.google.com/go/bigquery v1.69.0
-	cloud.google.com/go/bigtable v1.37.0
+	cloud.google.com/go/bigquery v1.70.0
+	cloud.google.com/go/bigtable v1.39.0
 	cloud.google.com/go/datastore v1.20.0
 	cloud.google.com/go/profiler v0.4.3
-	cloud.google.com/go/pubsub v1.49.0
-	cloud.google.com/go/spanner v1.83.0
-	cloud.google.com/go/storage v1.55.0
-	github.com/aws/aws-sdk-go-v2 v1.36.5
-	github.com/aws/aws-sdk-go-v2/config v1.29.17
-	github.com/aws/aws-sdk-go-v2/credentials v1.17.70
-	github.com/aws/aws-sdk-go-v2/feature/s3/manager v1.17.83
-	github.com/aws/aws-sdk-go-v2/service/s3 v1.83.0
-	github.com/aws/smithy-go v1.22.4
-	github.com/docker/go-connections v0.5.0
+	cloud.google.com/go/pubsub v1.50.1
+	cloud.google.com/go/spanner v1.85.1
+	cloud.google.com/go/storage v1.57.0
+	github.com/aws/aws-sdk-go-v2 v1.39.1
+	github.com/aws/aws-sdk-go-v2/config v1.31.10
+	github.com/aws/aws-sdk-go-v2/credentials v1.18.14
+	github.com/aws/aws-sdk-go-v2/feature/s3/manager v1.19.6
+	github.com/aws/aws-sdk-go-v2/service/s3 v1.88.2
+	github.com/aws/smithy-go v1.23.0
+	github.com/docker/go-connections v0.6.0
 	github.com/dustin/go-humanize v1.0.1
-	github.com/go-sql-driver/mysql v1.9.2
+	github.com/go-sql-driver/mysql v1.9.3
 	github.com/google/go-cmp v0.7.0
 	github.com/google/uuid v1.6.0
 	github.com/johannesboyne/gofakes3 v0.0.0-20250106100439-5c39aecd6999
 	github.com/lib/pq v1.10.9
 	github.com/linkedin/goavro/v2 v2.14.0
-	github.com/nats-io/nats-server/v2 v2.11.6
-	github.com/nats-io/nats.go v1.43.0
+	github.com/nats-io/nats-server/v2 v2.12.0
+	github.com/nats-io/nats.go v1.45.0
 	github.com/proullon/ramsql v0.1.4
-	github.com/spf13/cobra v1.9.1
-	github.com/testcontainers/testcontainers-go v0.37.0
+	github.com/spf13/cobra v1.10.1
+	github.com/testcontainers/testcontainers-go v0.39.0
 	github.com/tetratelabs/wazero v1.9.0
 	github.com/xitongsys/parquet-go v1.6.2
 	github.com/xitongsys/parquet-go-source v0.0.0-20241021075129-b732d2ac9c9b
 	go.mongodb.org/mongo-driver v1.17.4
-	golang.org/x/net v0.41.0
+	golang.org/x/net v0.43.0
 	golang.org/x/oauth2 v0.30.0
-	golang.org/x/sync v0.15.0
-	golang.org/x/sys v0.33.0
-	golang.org/x/text v0.26.0
-	google.golang.org/api v0.240.0
-	google.golang.org/genproto v0.0.0-20250505200425-f936aa4a68b2
-	google.golang.org/grpc v1.73.0
-	google.golang.org/protobuf v1.36.6
+	golang.org/x/sync v0.17.0
+	golang.org/x/sys v0.36.0
+	golang.org/x/text v0.29.0
+	google.golang.org/api v0.249.0
+	google.golang.org/genproto v0.0.0-20250603155806-513f23925822
+	google.golang.org/grpc v1.75.1
+	google.golang.org/protobuf v1.36.8
 	gopkg.in/yaml.v2 v2.4.0
 	gopkg.in/yaml.v3 v3.0.1
 )
 
 require (
 	github.com/avast/retry-go/v4 v4.6.1
-	github.com/fsouza/fake-gcs-server v1.52.2
+	github.com/fsouza/fake-gcs-server v1.52.3
 	github.com/golang-cz/devslog v0.0.15
 	golang.org/x/exp v0.0.0-20250106191152-7588d65b2ba8
 )
 
 require (
-	cel.dev/expr v0.23.1 // indirect
-	cloud.google.com/go/auth v0.16.2 // indirect
+	cel.dev/expr v0.24.0 // indirect
+	cloud.google.com/go/auth v0.16.5 // indirect
 	cloud.google.com/go/auth/oauth2adapt v0.2.8 // indirect
 	cloud.google.com/go/monitoring v1.24.2 // indirect
-	dario.cat/mergo v1.0.1 // indirect
+	cloud.google.com/go/pubsub/v2 v2.0.0 // indirect
+	dario.cat/mergo v1.0.2 // indirect
 	filippo.io/edwards25519 v1.1.0 // indirect
 	github.com/GoogleCloudPlatform/grpc-gcp-go/grpcgcp v1.5.3 // indirect
-	github.com/GoogleCloudPlatform/opentelemetry-operations-go/detectors/gcp v1.27.0 // indirect
-	github.com/GoogleCloudPlatform/opentelemetry-operations-go/exporter/metric v0.51.0 // indirect
-	github.com/GoogleCloudPlatform/opentelemetry-operations-go/internal/resourcemapping v0.51.0 // indirect
+	github.com/GoogleCloudPlatform/opentelemetry-operations-go/detectors/gcp v1.29.0 // indirect
+	github.com/GoogleCloudPlatform/opentelemetry-operations-go/exporter/metric v0.53.0 // indirect
+	github.com/GoogleCloudPlatform/opentelemetry-operations-go/internal/resourcemapping v0.53.0 // indirect
+	github.com/antithesishq/antithesis-sdk-go v0.4.3-default-no-op // indirect
 	github.com/apache/arrow/go/v15 v15.0.2 // indirect
 	github.com/containerd/errdefs v1.0.0 // indirect
 	github.com/containerd/errdefs/pkg v0.3.0 // indirect
 	github.com/containerd/log v0.1.0 // indirect
 	github.com/containerd/platforms v0.2.1 // indirect
-	github.com/davecgh/go-spew v1.1.1 // indirect
+	github.com/davecgh/go-spew v1.1.2-0.20180830191138-d8f796af33cc // indirect
 	github.com/distribution/reference v0.6.0 // indirect
-	github.com/ebitengine/purego v0.8.2 // indirect
+	github.com/ebitengine/purego v0.8.4 // indirect
 	github.com/envoyproxy/go-control-plane/envoy v1.32.4 // indirect
-	github.com/go-jose/go-jose/v4 v4.0.5 // indirect
-	github.com/go-logr/logr v1.4.2 // indirect
+	github.com/go-jose/go-jose/v4 v4.1.1 // indirect
+	github.com/go-logr/logr v1.4.3 // indirect
 	github.com/go-logr/stdr v1.2.2 // indirect
 	github.com/go-ole/go-ole v1.3.0 // indirect
 	github.com/google/go-tpm v0.9.5 // indirect
@@ -104,40 +106,39 @@ require (
 	github.com/minio/highwayhash v1.0.3 // indirect
 	github.com/moby/docker-image-spec v1.3.1 // indirect
 	github.com/moby/go-archive v0.1.0 // indirect
-	github.com/moby/sys/atomicwriter v0.1.0 // indirect
 	github.com/moby/sys/user v0.4.0 // indirect
 	github.com/moby/sys/userns v0.1.0 // indirect
-	github.com/nats-io/jwt/v2 v2.7.4 // indirect
+	github.com/nats-io/jwt/v2 v2.8.0 // indirect
 	github.com/nats-io/nkeys v0.4.11 // indirect
 	github.com/nats-io/nuid v1.0.1 // indirect
 	github.com/planetscale/vtprotobuf v0.6.1-0.20240319094008-0393e58bdf10 // indirect
-	github.com/pmezard/go-difflib v1.0.0 // indirect
+	github.com/pmezard/go-difflib v1.0.1-0.20181226105442-5d4384ee4fb2 // indirect
 	github.com/power-devops/perfstat v0.0.0-20240221224432-82ca36839d55 // indirect
-	github.com/shirou/gopsutil/v4 v4.25.1 // indirect
+	github.com/shirou/gopsutil/v4 v4.25.6 // indirect
 	github.com/spiffe/go-spiffe/v2 v2.5.0 // indirect
 	github.com/stretchr/testify v1.10.0 // indirect
 	github.com/tklauser/go-sysconf v0.3.14 // indirect
 	github.com/tklauser/numcpus v0.9.0 // indirect
 	github.com/yusufpapurcu/wmi v1.2.4 // indirect
 	github.com/zeebo/errs v1.4.0 // indirect
-	go.einride.tech/aip v0.68.1 // indirect
+	go.einride.tech/aip v0.73.0 // indirect
 	go.opentelemetry.io/auto/sdk v1.1.0 // indirect
 	go.opentelemetry.io/contrib/detectors/gcp v1.36.0 // indirect
 	go.opentelemetry.io/contrib/instrumentation/google.golang.org/grpc/otelgrpc v0.61.0 // indirect
 	go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.61.0 // indirect
-	go.opentelemetry.io/otel v1.36.0 // indirect
+	go.opentelemetry.io/otel v1.37.0 // indirect
 	go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp v1.33.0 // indirect
-	go.opentelemetry.io/otel/metric v1.36.0 // indirect
-	go.opentelemetry.io/otel/sdk v1.36.0 // indirect
-	go.opentelemetry.io/otel/sdk/metric v1.36.0 // indirect
-	go.opentelemetry.io/otel/trace v1.36.0 // indirect
+	go.opentelemetry.io/otel/metric v1.37.0 // indirect
+	go.opentelemetry.io/otel/sdk v1.37.0 // indirect
+	go.opentelemetry.io/otel/sdk/metric v1.37.0 // indirect
+	go.opentelemetry.io/otel/trace v1.37.0 // indirect
 	go.shabbyrobe.org/gocovmerge v0.0.0-20230507111327-fa4f82cfbf4d // indirect
-	golang.org/x/time v0.12.0 // indirect
+	golang.org/x/time v0.13.0 // indirect
 )
 
 require (
-	cloud.google.com/go v0.121.2 // indirect
-	cloud.google.com/go/compute/metadata v0.7.0 // indirect
+	cloud.google.com/go v0.121.6 // indirect
+	cloud.google.com/go/compute/metadata v0.8.0 // indirect
 	cloud.google.com/go/iam v1.5.2 // indirect
 	cloud.google.com/go/longrunning v0.6.7 // indirect
 	github.com/Azure/go-ansiterm v0.0.0-20250102033503-faa5f7b0171c // indirect
@@ -145,29 +146,28 @@ require (
 	github.com/apache/arrow/go/arrow v0.0.0-20211112161151-bc219186db40 // indirect
 	github.com/apache/thrift v0.21.0 // indirect
 	github.com/aws/aws-sdk-go v1.55.5 // indirect
-	github.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.6.11 // indirect
-	github.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.16.32 // indirect
-	github.com/aws/aws-sdk-go-v2/internal/configsources v1.3.36 // indirect
-	github.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.6.36 // indirect
+	github.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.7.1 // indirect
+	github.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.8 // indirect
+	github.com/aws/aws-sdk-go-v2/internal/configsources v1.4.8 // indirect
+	github.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.8 // indirect
 	github.com/aws/aws-sdk-go-v2/internal/ini v1.8.3 // indirect
-	github.com/aws/aws-sdk-go-v2/internal/v4a v1.3.36 // indirect
-	github.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.12.4 // indirect
-	github.com/aws/aws-sdk-go-v2/service/internal/checksum v1.7.4 // indirect
-	github.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.12.17 // indirect
-	github.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.18.17 // indirect
-	github.com/aws/aws-sdk-go-v2/service/sso v1.25.5 // indirect
-	github.com/aws/aws-sdk-go-v2/service/ssooidc v1.30.3 // indirect
-	github.com/aws/aws-sdk-go-v2/service/sts v1.34.0 // indirect
+	github.com/aws/aws-sdk-go-v2/internal/v4a v1.4.8 // indirect
+	github.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.1 // indirect
+	github.com/aws/aws-sdk-go-v2/service/internal/checksum v1.8.8 // indirect
+	github.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.8 // indirect
+	github.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.19.8 // indirect
+	github.com/aws/aws-sdk-go-v2/service/sso v1.29.4 // indirect
+	github.com/aws/aws-sdk-go-v2/service/ssooidc v1.35.0 // indirect
+	github.com/aws/aws-sdk-go-v2/service/sts v1.38.5 // indirect
 	github.com/cenkalti/backoff/v4 v4.3.0 // indirect
 	github.com/cespare/xxhash/v2 v2.3.0 // indirect
-	github.com/cncf/xds/go v0.0.0-20250326154945-ae57f3c0d45f // indirect
+	github.com/cncf/xds/go v0.0.0-20250501225837-2ac532fd4443 // indirect
 	github.com/cpuguy83/dockercfg v0.3.2 // indirect
-	github.com/docker/docker v28.3.2+incompatible // but required to resolve issue docker has with go1.20
+	github.com/docker/docker v28.4.0+incompatible // but required to resolve issue docker has with go1.20
 	github.com/docker/go-units v0.5.0 // indirect
 	github.com/envoyproxy/protoc-gen-validate v1.2.1 // indirect
 	github.com/felixge/httpsnoop v1.0.4 // indirect
 	github.com/goccy/go-json v0.10.5 // indirect
-	github.com/gogo/protobuf v1.3.2 // indirect
 	github.com/golang/groupcache v0.0.0-20241129210726-2c02b8208cf8 // indirect
 	github.com/golang/snappy v0.0.4 // indirect
 	github.com/google/flatbuffers v24.12.23+incompatible // indirect
@@ -175,12 +175,12 @@ require (
 	github.com/google/renameio/v2 v2.0.0 // indirect
 	github.com/google/s2a-go v0.1.9 // indirect
 	github.com/googleapis/enterprise-certificate-proxy v0.3.6 // indirect
-	github.com/googleapis/gax-go/v2 v2.14.2 // indirect
+	github.com/googleapis/gax-go/v2 v2.15.0 // indirect
 	github.com/gorilla/handlers v1.5.2 // indirect
 	github.com/gorilla/mux v1.8.1 // indirect
 	github.com/inconshreveable/mousetrap v1.1.0 // indirect
 	github.com/klauspost/compress v1.18.0 // indirect
-	github.com/klauspost/cpuid/v2 v2.2.9 // indirect
+	github.com/klauspost/cpuid/v2 v2.2.10 // indirect
 	github.com/magiconair/properties v1.8.10 // indirect
 	github.com/moby/patternmatcher v0.6.0 // indirect
 	github.com/moby/sys/sequential v0.6.0 // indirect
@@ -194,17 +194,17 @@ require (
 	github.com/pkg/xattr v0.4.10 // indirect
 	github.com/ryszard/goskiplist v0.0.0-20150312221310-2dfbae5fcf46 // indirect
 	github.com/sirupsen/logrus v1.9.3 // indirect
-	github.com/spf13/pflag v1.0.6 // indirect
+	github.com/spf13/pflag v1.0.9 // indirect
 	github.com/xdg-go/pbkdf2 v1.0.0 // indirect
 	github.com/xdg-go/scram v1.1.2 // indirect
 	github.com/xdg-go/stringprep v1.0.4 // indirect
 	github.com/youmark/pkcs8 v0.0.0-20240726163527-a2c0da244d78 // indirect
 	github.com/zeebo/xxh3 v1.0.2 // indirect
 	go.opencensus.io v0.24.0 // indirect
-	golang.org/x/crypto v0.39.0 // indirect
-	golang.org/x/mod v0.25.0 // indirect
-	golang.org/x/tools v0.33.0 // indirect
+	golang.org/x/crypto v0.42.0 // indirect
+	golang.org/x/mod v0.27.0 // indirect
+	golang.org/x/tools v0.36.0 // indirect
 	golang.org/x/xerrors v0.0.0-20240903120638-7835f813f4da // indirect
-	google.golang.org/genproto/googleapis/api v0.0.0-20250603155806-513f23925822 // indirect
-	google.golang.org/genproto/googleapis/rpc v0.0.0-20250603155806-513f23925822 // indirect
+	google.golang.org/genproto/googleapis/api v0.0.0-20250818200422-3122310a409c // indirect
+	google.golang.org/genproto/googleapis/rpc v0.0.0-20250818200422-3122310a409c // indirect
 )
diff --git a/sdks/go.sum b/sdks/go.sum
index 91ef5b5893b0..43e4eed4b38e 100644
--- a/sdks/go.sum
+++ b/sdks/go.sum
@@ -1,5 +1,5 @@
-cel.dev/expr v0.23.1 h1:K4KOtPCJQjVggkARsjG9RWXP6O4R73aHeJMa/dmCQQg=
-cel.dev/expr v0.23.1/go.mod h1:hLPLo1W4QUmuYdA72RBX06QTs6MXw941piREPl3Yfiw=
+cel.dev/expr v0.24.0 h1:56OvJKSH3hDGL0ml5uSxZmz3/3Pq4tJ+fb1unVLAFcY=
+cel.dev/expr v0.24.0/go.mod h1:hLPLo1W4QUmuYdA72RBX06QTs6MXw941piREPl3Yfiw=
 cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
 cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
 cloud.google.com/go v0.38.0/go.mod h1:990N+gfupTy94rShfmMCWGDn0LpTmnzTp2qbd1dvSRU=
@@ -40,8 +40,8 @@ cloud.google.com/go v0.104.0/go.mod h1:OO6xxXdJyvuJPcEPBLN9BJPD+jep5G1+2U5B5gkRY
 cloud.google.com/go v0.105.0/go.mod h1:PrLgOJNe5nfE9UMxKxgXj4mD3voiP+YQ6gdt6KMFOKM=
 cloud.google.com/go v0.107.0/go.mod h1:wpc2eNrD7hXUTy8EKS10jkxpZBjASrORK7goS+3YX2I=
 cloud.google.com/go v0.110.0/go.mod h1:SJnCLqQ0FCFGSZMUNUf84MV3Aia54kn7pi8st7tMzaY=
-cloud.google.com/go v0.121.2 h1:v2qQpN6Dx9x2NmwrqlesOt3Ys4ol5/lFZ6Mg1B7OJCg=
-cloud.google.com/go v0.121.2/go.mod h1:nRFlrHq39MNVWu+zESP2PosMWA0ryJw8KUBZ2iZpxbw=
+cloud.google.com/go v0.121.6 h1:waZiuajrI28iAf40cWgycWNgaXPO06dupuS+sgibK6c=
+cloud.google.com/go v0.121.6/go.mod h1:coChdst4Ea5vUpiALcYKXEpR1S9ZgXbhEzzMcMR66vI=
 cloud.google.com/go/accessapproval v1.4.0/go.mod h1:zybIuC3KpDOvotz59lFe5qxRZx6C75OtwbisN56xYB4=
 cloud.google.com/go/accessapproval v1.5.0/go.mod h1:HFy3tuiGvMdcd/u+Cu5b9NkO1pEICJ46IR82PoUdplw=
 cloud.google.com/go/accessapproval v1.6.0/go.mod h1:R0EiYnwV5fsRFiKZkPHr6mwyk2wxUJ30nL4j2pcFY2E=
@@ -103,8 +103,8 @@ cloud.google.com/go/assuredworkloads v1.7.0/go.mod h1:z/736/oNmtGAyU47reJgGN+KVo
 cloud.google.com/go/assuredworkloads v1.8.0/go.mod h1:AsX2cqyNCOvEQC8RMPnoc0yEarXQk6WEKkxYfL6kGIo=
 cloud.google.com/go/assuredworkloads v1.9.0/go.mod h1:kFuI1P78bplYtT77Tb1hi0FMxM0vVpRC7VVoJC3ZoT0=
 cloud.google.com/go/assuredworkloads v1.10.0/go.mod h1:kwdUQuXcedVdsIaKgKTp9t0UJkE5+PAVNhdQm4ZVq2E=
-cloud.google.com/go/auth v0.16.2 h1:QvBAGFPLrDeoiNjyfVunhQ10HKNYuOwZ5noee0M5df4=
-cloud.google.com/go/auth v0.16.2/go.mod h1:sRBas2Y1fB1vZTdurouM0AzuYQBMZinrUYL8EufhtEA=
+cloud.google.com/go/auth v0.16.5 h1:mFWNQ2FEVWAliEQWpAdH80omXFokmrnbDhUS9cBywsI=
+cloud.google.com/go/auth v0.16.5/go.mod h1:utzRfHMP+Vv0mpOkTRQoWD2q3BatTOoWbA7gCc2dUhQ=
 cloud.google.com/go/auth/oauth2adapt v0.2.8 h1:keo8NaayQZ6wimpNSmW5OPc283g65QNIiLpZnkHRbnc=
 cloud.google.com/go/auth/oauth2adapt v0.2.8/go.mod h1:XQ9y31RkqZCcwJWNSx2Xvric3RrU88hAYYbjDWYDL+c=
 cloud.google.com/go/automl v1.5.0/go.mod h1:34EjfoFGMZ5sgJ9EoLsRtdPSNZLcfflJR39VbVNS2M0=
@@ -135,10 +135,10 @@ cloud.google.com/go/bigquery v1.47.0/go.mod h1:sA9XOgy0A8vQK9+MWhEQTY6Tix87M/Zur
 cloud.google.com/go/bigquery v1.48.0/go.mod h1:QAwSz+ipNgfL5jxiaK7weyOhzdoAy1zFm0Nf1fysJac=
 cloud.google.com/go/bigquery v1.49.0/go.mod h1:Sv8hMmTFFYBlt/ftw2uN6dFdQPzBlREY9yBh7Oy7/4Q=
 cloud.google.com/go/bigquery v1.50.0/go.mod h1:YrleYEh2pSEbgTBZYMJ5SuSr0ML3ypjRB1zgf7pvQLU=
-cloud.google.com/go/bigquery v1.69.0 h1:rZvHnjSUs5sHK3F9awiuFk2PeOaB8suqNuim21GbaTc=
-cloud.google.com/go/bigquery v1.69.0/go.mod h1:TdGLquA3h/mGg+McX+GsqG9afAzTAcldMjqhdjHTLew=
-cloud.google.com/go/bigtable v1.37.0 h1:Q+x7y04lQ0B+WXp03wc1/FLhFt4CwcQdkwWT0M4Jp3w=
-cloud.google.com/go/bigtable v1.37.0/go.mod h1:HXqddP6hduwzrtiTCqZPpj9ij4hGZb4Zy1WF/dT+yaU=
+cloud.google.com/go/bigquery v1.70.0 h1:V1OIhhOSionCOXWMmypXOvZu/ogkzosa7s1ArWJO/Yg=
+cloud.google.com/go/bigquery v1.70.0/go.mod h1:6lEAkgTJN+H2JcaX1eKiuEHTKyqBaJq5U3SpLGbSvwI=
+cloud.google.com/go/bigtable v1.39.0 h1:NF0aaSend+Z5CKND2vWY9fgDwaeZ4bDgzUdgw8rk75Y=
+cloud.google.com/go/bigtable v1.39.0/go.mod h1:zgL2Vxux9Bx+TcARDJDUxVyE+BCUfP2u4Zm9qeHF+g0=
 cloud.google.com/go/billing v1.4.0/go.mod h1:g9IdKBEFlItS8bTtlrZdVLWSSdSyFUZKXNS02zKMOZY=
 cloud.google.com/go/billing v1.5.0/go.mod h1:mztb1tBc3QekhjSgmpf/CV4LzWXLzCArwpLmP2Gm88s=
 cloud.google.com/go/billing v1.6.0/go.mod h1:WoXzguj+BeHXPbKfNWkqVtDdzORazmCjraY+vrxcyvI=
@@ -191,8 +191,8 @@ cloud.google.com/go/compute/metadata v0.1.0/go.mod h1:Z1VN+bulIf6bt4P/C37K4DyZYZ
 cloud.google.com/go/compute/metadata v0.2.0/go.mod h1:zFmK7XCadkQkj6TtorcaGlCW1hT1fIilQDwofLpJ20k=
 cloud.google.com/go/compute/metadata v0.2.1/go.mod h1:jgHgmJd2RKBGzXqF5LR2EZMGxBkeanZ9wwa75XHJgOM=
 cloud.google.com/go/compute/metadata v0.2.3/go.mod h1:VAV5nSsACxMJvgaAuX6Pk2AawlZn8kiOGuCv6gTkwuA=
-cloud.google.com/go/compute/metadata v0.7.0 h1:PBWF+iiAerVNe8UCHxdOt6eHLVc3ydFeOCw78U8ytSU=
-cloud.google.com/go/compute/metadata v0.7.0/go.mod h1:j5MvL9PprKL39t166CoB1uVHfQMs4tFQZZcKwksXUjo=
+cloud.google.com/go/compute/metadata v0.8.0 h1:HxMRIbao8w17ZX6wBnjhcDkW6lTFpgcaobyVfZWqRLA=
+cloud.google.com/go/compute/metadata v0.8.0/go.mod h1:sYOGTp851OV9bOFJ9CH7elVvyzopvWQFNNghtDQ/Biw=
 cloud.google.com/go/contactcenterinsights v1.3.0/go.mod h1:Eu2oemoePuEFc/xKFPjbTuPSj0fYJcPls9TFlPNnHHY=
 cloud.google.com/go/contactcenterinsights v1.4.0/go.mod h1:L2YzkGbPsv+vMQMCADxJoT9YiTTnSEd6fEvCeHTYVck=
 cloud.google.com/go/contactcenterinsights v1.6.0/go.mod h1:IIDlT6CLcDoyv79kDv8iWxMSTZhLxSCofVV5W6YFM/w=
@@ -354,8 +354,8 @@ cloud.google.com/go/kms v1.8.0/go.mod h1:4xFEhYFqvW+4VMELtZyxomGSYtSQKzM178ylFW4
 cloud.google.com/go/kms v1.9.0/go.mod h1:qb1tPTgfF9RQP8e1wq4cLFErVuTJv7UsSC915J8dh3w=
 cloud.google.com/go/kms v1.10.0/go.mod h1:ng3KTUtQQU9bPX3+QGLsflZIHlkbn8amFAMY63m8d24=
 cloud.google.com/go/kms v1.10.1/go.mod h1:rIWk/TryCkR59GMC3YtHtXeLzd634lBbKenvyySAyYI=
-cloud.google.com/go/kms v1.21.2 h1:c/PRUSMNQ8zXrc1sdAUnsenWWaNXN+PzTXfXOcSFdoE=
-cloud.google.com/go/kms v1.21.2/go.mod h1:8wkMtHV/9Z8mLXEXr1GK7xPSBdi6knuLXIhqjuWcI6w=
+cloud.google.com/go/kms v1.22.0 h1:dBRIj7+GDeeEvatJeTB19oYZNV0aj6wEqSIT/7gLqtk=
+cloud.google.com/go/kms v1.22.0/go.mod h1:U7mf8Sva5jpOb4bxYZdtw/9zsbIjrklYwPcvMk34AL8=
 cloud.google.com/go/language v1.4.0/go.mod h1:F9dRpNFQmJbkaop6g0JhSBXCNlO90e1KWx5iDdxbWic=
 cloud.google.com/go/language v1.6.0/go.mod h1:6dJ8t3B+lUYfStgls25GusK04NLh3eDLQnWM3mdEbhI=
 cloud.google.com/go/language v1.7.0/go.mod h1:DJ6dYN/W+SQOjF8e1hLQXMF21AkH2w9wiPzPCJa2MIE=
@@ -460,8 +460,10 @@ cloud.google.com/go/pubsub v1.26.0/go.mod h1:QgBH3U/jdJy/ftjPhTkyXNj543Tin1pRYcd
 cloud.google.com/go/pubsub v1.27.1/go.mod h1:hQN39ymbV9geqBnfQq6Xf63yNhUAhv9CZhzp5O6qsW0=
 cloud.google.com/go/pubsub v1.28.0/go.mod h1:vuXFpwaVoIPQMGXqRyUQigu/AX1S3IWugR9xznmcXX8=
 cloud.google.com/go/pubsub v1.30.0/go.mod h1:qWi1OPS0B+b5L+Sg6Gmc9zD1Y+HaM0MdUr7LsupY1P4=
-cloud.google.com/go/pubsub v1.49.0 h1:5054IkbslnrMCgA2MAEPcsN3Ky+AyMpEZcii/DoySPo=
-cloud.google.com/go/pubsub v1.49.0/go.mod h1:K1FswTWP+C1tI/nfi3HQecoVeFvL4HUOB1tdaNXKhUY=
+cloud.google.com/go/pubsub v1.50.1 h1:fzbXpPyJnSGvWXF1jabhQeXyxdbCIkXTpjXHy7xviBM=
+cloud.google.com/go/pubsub v1.50.1/go.mod h1:6YVJv3MzWJUVdvQXG081sFvS0dWQOdnV+oTo++q/xFk=
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@@ -552,8 +554,8 @@ cloud.google.com/go/shell v1.6.0/go.mod h1:oHO8QACS90luWgxP3N9iZVuEiSF84zNyLytb+
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@@ -573,8 +575,8 @@ cloud.google.com/go/storage v1.23.0/go.mod h1:vOEEDNFnciUMhBeT6hsJIn3ieU5cFRmzeL
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@@ -647,8 +649,8 @@ cloud.google.com/go/workflows v1.10.0/go.mod h1:fZ8LmRmZQWacon9UCX1r/g/DfAXx5VcP
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@@ -1400,10 +1403,10 @@ github.com/spf13/afero v1.2.2/go.mod h1:9ZxEEn6pIJ8Rxe320qSDBk6AsU0r9pR7Q4OcevTd
 github.com/spf13/afero v1.3.3/go.mod h1:5KUK8ByomD5Ti5Artl0RtHeI5pTF7MIDuXL3yY520V4=
 github.com/spf13/afero v1.6.0/go.mod h1:Ai8FlHk4v/PARR026UzYexafAt9roJ7LcLMAmO6Z93I=
 github.com/spf13/afero v1.9.2/go.mod h1:iUV7ddyEEZPO5gA3zD4fJt6iStLlL+Lg4m2cihcDf8Y=
-github.com/spf13/cobra v1.9.1 h1:CXSaggrXdbHK9CF+8ywj8Amf7PBRmPCOJugH954Nnlo=
-github.com/spf13/cobra v1.9.1/go.mod h1:nDyEzZ8ogv936Cinf6g1RU9MRY64Ir93oCnqb9wxYW0=
-github.com/spf13/pflag v1.0.6 h1:jFzHGLGAlb3ruxLB8MhbI6A8+AQX/2eW4qeyNZXNp2o=
-github.com/spf13/pflag v1.0.6/go.mod h1:McXfInJRrz4CZXVZOBLb0bTZqETkiAhM9Iw0y3An2Bg=
+github.com/spf13/cobra v1.10.1 h1:lJeBwCfmrnXthfAupyUTzJ/J4Nc1RsHC/mSRU2dll/s=
+github.com/spf13/cobra v1.10.1/go.mod h1:7SmJGaTHFVBY0jW4NXGluQoLvhqFQM+6XSKD+P4XaB0=
+github.com/spf13/pflag v1.0.9 h1:9exaQaMOCwffKiiiYk6/BndUBv+iRViNW+4lEMi0PvY=
+github.com/spf13/pflag v1.0.9/go.mod h1:McXfInJRrz4CZXVZOBLb0bTZqETkiAhM9Iw0y3An2Bg=
 github.com/spiffe/go-spiffe/v2 v2.5.0 h1:N2I01KCUkv1FAjZXJMwh95KK1ZIQLYbPfhaxw8WS0hE=
 github.com/spiffe/go-spiffe/v2 v2.5.0/go.mod h1:P+NxobPc6wXhVtINNtFjNWGBTreew1GBUCwT2wPmb7g=
 github.com/stretchr/objx v0.1.0/go.mod h1:HFkY916IF+rwdDfMAkV7OtwuqBVzrE8GR6GFx+wExME=
@@ -1427,10 +1430,12 @@ github.com/stretchr/testify v1.8.1/go.mod h1:w2LPCIKwWwSfY2zedu0+kehJoqGctiVI29o
 github.com/stretchr/testify v1.8.3/go.mod h1:sz/lmYIOXD/1dqDmKjjqLyZ2RngseejIcXlSw2iwfAo=
 github.com/stretchr/testify v1.10.0 h1:Xv5erBjTwe/5IxqUQTdXv5kgmIvbHo3QQyRwhJsOfJA=
 github.com/stretchr/testify v1.10.0/go.mod h1:r2ic/lqez/lEtzL7wO/rwa5dbSLXVDPFyf8C91i36aY=
-github.com/testcontainers/testcontainers-go v0.37.0 h1:L2Qc0vkTw2EHWQ08djon0D2uw7Z/PtHS/QzZZ5Ra/hg=
-github.com/testcontainers/testcontainers-go v0.37.0/go.mod h1:QPzbxZhQ6Bclip9igjLFj6z0hs01bU8lrl2dHQmgFGM=
+github.com/testcontainers/testcontainers-go v0.39.0 h1:uCUJ5tA+fcxbFAB0uP3pIK3EJ2IjjDUHFSZ1H1UxAts=
+github.com/testcontainers/testcontainers-go v0.39.0/go.mod h1:qmHpkG7H5uPf/EvOORKvS6EuDkBUPE3zpVGaH9NL7f8=
 github.com/tetratelabs/wazero v1.9.0 h1:IcZ56OuxrtaEz8UYNRHBrUa9bYeX9oVY93KspZZBf/I=
 github.com/tetratelabs/wazero v1.9.0/go.mod h1:TSbcXCfFP0L2FGkRPxHphadXPjo1T6W+CseNNY7EkjM=
+github.com/tinylib/msgp v1.3.0 h1:ULuf7GPooDaIlbyvgAxBV/FI7ynli6LZ1/nVUNu+0ww=
+github.com/tinylib/msgp v1.3.0/go.mod h1:ykjzy2wzgrlvpDCRc4LA8UXy6D8bzMSuAF3WD57Gok0=
 github.com/tklauser/go-sysconf v0.3.14 h1:g5vzr9iPFFz24v2KZXs/pvpvh8/V9Fw6vQK5ZZb78yU=
 github.com/tklauser/go-sysconf v0.3.14/go.mod h1:1ym4lWMLUOhuBOPGtRcJm7tEGX4SCYNEEEtghGG/8uY=
 github.com/tklauser/numcpus v0.9.0 h1:lmyCHtANi8aRUgkckBgoDk1nHCux3n2cgkJLXdQGPDo=
@@ -1468,8 +1473,8 @@ github.com/zeebo/errs v1.4.0/go.mod h1:sgbWHsvVuTPHcqJJGQ1WhI5KbWlHYz+2+2C/LSEtC
 github.com/zeebo/xxh3 v1.0.2 h1:xZmwmqxHZA8AI603jOQ0tMqmBr9lPeFwGg6d+xy9DC0=
 github.com/zeebo/xxh3 v1.0.2/go.mod h1:5NWz9Sef7zIDm2JHfFlcQvNekmcEl9ekUZQQKCYaDcA=
 github.com/zenazn/goji v0.9.0/go.mod h1:7S9M489iMyHBNxwZnk9/EHS098H4/F6TATF2mIxtB1Q=
-go.einride.tech/aip v0.68.1 h1:16/AfSxcQISGN5z9C5lM+0mLYXihrHbQ1onvYTr93aQ=
-go.einride.tech/aip v0.68.1/go.mod h1:XaFtaj4HuA3Zwk9xoBtTWgNubZ0ZZXv9BZJCkuKuWbg=
+go.einride.tech/aip v0.73.0 h1:bPo4oqBo2ZQeBKo4ZzLb1kxYXTY1ysJhpvQyfuGzvps=
+go.einride.tech/aip v0.73.0/go.mod h1:Mj7rFbmXEgw0dq1dqJ7JGMvYCZZVxmGOR3S4ZcV5LvQ=
 go.etcd.io/bbolt v1.3.5/go.mod h1:G5EMThwa9y8QZGBClrRx5EY+Yw9kAhnjy3bSjsnlVTQ=
 go.mongodb.org/mongo-driver v1.17.4 h1:jUorfmVzljjr0FLzYQsGP8cgN/qzzxlY9Vh0C9KFXVw=
 go.mongodb.org/mongo-driver v1.17.4/go.mod h1:Hy04i7O2kC4RS06ZrhPRqj/u4DTYkFDAAccj+rVKqgQ=
@@ -1491,22 +1496,22 @@ go.opentelemetry.io/contrib/instrumentation/google.golang.org/grpc/otelgrpc v0.6
 go.opentelemetry.io/contrib/instrumentation/google.golang.org/grpc/otelgrpc v0.61.0/go.mod h1:snMWehoOh2wsEwnvvwtDyFCxVeDAODenXHtn5vzrKjo=
 go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.61.0 h1:F7Jx+6hwnZ41NSFTO5q4LYDtJRXBf2PD0rNBkeB/lus=
 go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.61.0/go.mod h1:UHB22Z8QsdRDrnAtX4PntOl36ajSxcdUMt1sF7Y6E7Q=
-go.opentelemetry.io/otel v1.36.0 h1:UumtzIklRBY6cI/lllNZlALOF5nNIzJVb16APdvgTXg=
-go.opentelemetry.io/otel v1.36.0/go.mod h1:/TcFMXYjyRNh8khOAO9ybYkqaDBb/70aVwkNML4pP8E=
+go.opentelemetry.io/otel v1.37.0 h1:9zhNfelUvx0KBfu/gb+ZgeAfAgtWrfHJZcAqFC228wQ=
+go.opentelemetry.io/otel v1.37.0/go.mod h1:ehE/umFRLnuLa/vSccNq9oS1ErUlkkK71gMcN34UG8I=
 go.opentelemetry.io/otel/exporters/otlp/otlptrace v1.33.0 h1:Vh5HayB/0HHfOQA7Ctx69E/Y/DcQSMPpKANYVMQ7fBA=
 go.opentelemetry.io/otel/exporters/otlp/otlptrace v1.33.0/go.mod h1:cpgtDBaqD/6ok/UG0jT15/uKjAY8mRA53diogHBg3UI=
 go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp v1.33.0 h1:wpMfgF8E1rkrT1Z6meFh1NDtownE9Ii3n3X2GJYjsaU=
 go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp v1.33.0/go.mod h1:wAy0T/dUbs468uOlkT31xjvqQgEVXv58BRFWEgn5v/0=
 go.opentelemetry.io/otel/exporters/stdout/stdoutmetric v1.36.0 h1:rixTyDGXFxRy1xzhKrotaHy3/KXdPhlWARrCgK+eqUY=
 go.opentelemetry.io/otel/exporters/stdout/stdoutmetric v1.36.0/go.mod h1:dowW6UsM9MKbJq5JTz2AMVp3/5iW5I/TStsk8S+CfHw=
-go.opentelemetry.io/otel/metric v1.36.0 h1:MoWPKVhQvJ+eeXWHFBOPoBOi20jh6Iq2CcCREuTYufE=
-go.opentelemetry.io/otel/metric v1.36.0/go.mod h1:zC7Ks+yeyJt4xig9DEw9kuUFe5C3zLbVjV2PzT6qzbs=
-go.opentelemetry.io/otel/sdk v1.36.0 h1:b6SYIuLRs88ztox4EyrvRti80uXIFy+Sqzoh9kFULbs=
-go.opentelemetry.io/otel/sdk v1.36.0/go.mod h1:+lC+mTgD+MUWfjJubi2vvXWcVxyr9rmlshZni72pXeY=
-go.opentelemetry.io/otel/sdk/metric v1.36.0 h1:r0ntwwGosWGaa0CrSt8cuNuTcccMXERFwHX4dThiPis=
-go.opentelemetry.io/otel/sdk/metric v1.36.0/go.mod h1:qTNOhFDfKRwX0yXOqJYegL5WRaW376QbB7P4Pb0qva4=
-go.opentelemetry.io/otel/trace v1.36.0 h1:ahxWNuqZjpdiFAyrIoQ4GIiAIhxAunQR6MUoKrsNd4w=
-go.opentelemetry.io/otel/trace v1.36.0/go.mod h1:gQ+OnDZzrybY4k4seLzPAWNwVBBVlF2szhehOBB/tGA=
+go.opentelemetry.io/otel/metric v1.37.0 h1:mvwbQS5m0tbmqML4NqK+e3aDiO02vsf/WgbsdpcPoZE=
+go.opentelemetry.io/otel/metric v1.37.0/go.mod h1:04wGrZurHYKOc+RKeye86GwKiTb9FKm1WHtO+4EVr2E=
+go.opentelemetry.io/otel/sdk v1.37.0 h1:ItB0QUqnjesGRvNcmAcU0LyvkVyGJ2xftD29bWdDvKI=
+go.opentelemetry.io/otel/sdk v1.37.0/go.mod h1:VredYzxUvuo2q3WRcDnKDjbdvmO0sCzOvVAiY+yUkAg=
+go.opentelemetry.io/otel/sdk/metric v1.37.0 h1:90lI228XrB9jCMuSdA0673aubgRobVZFhbjxHHspCPc=
+go.opentelemetry.io/otel/sdk/metric v1.37.0/go.mod h1:cNen4ZWfiD37l5NhS+Keb5RXVWZWpRE+9WyVCpbo5ps=
+go.opentelemetry.io/otel/trace v1.37.0 h1:HLdcFNbRQBE2imdSEgm/kwqmQj1Or1l/7bW6mxVK7z4=
+go.opentelemetry.io/otel/trace v1.37.0/go.mod h1:TlgrlQ+PtQO5XFerSPUYG0JSgGyryXewPGyayAWSBS0=
 go.opentelemetry.io/proto/otlp v0.7.0/go.mod h1:PqfVotwruBrMGOCsRd/89rSnXhoiJIqeYNgFYFoEGnI=
 go.opentelemetry.io/proto/otlp v0.15.0/go.mod h1:H7XAot3MsfNsj7EXtrA2q5xSNQ10UqI405h3+duxN4U=
 go.opentelemetry.io/proto/otlp v0.19.0/go.mod h1:H7XAot3MsfNsj7EXtrA2q5xSNQ10UqI405h3+duxN4U=
@@ -1556,8 +1561,8 @@ golang.org/x/crypto v0.0.0-20220511200225-c6db032c6c88/go.mod h1:IxCIyHEi3zRg3s0
 golang.org/x/crypto v0.0.0-20220722155217-630584e8d5aa/go.mod h1:IxCIyHEi3zRg3s0A5j5BB6A9Jmi73HwBIUl50j+osU4=
 golang.org/x/crypto v0.7.0/go.mod h1:pYwdfH91IfpZVANVyUOhSIPZaFoJGxTFbZhFTx+dXZU=
 golang.org/x/crypto v0.9.0/go.mod h1:yrmDGqONDYtNj3tH8X9dzUun2m2lzPa9ngI6/RUPGR0=
-golang.org/x/crypto v0.39.0 h1:SHs+kF4LP+f+p14esP5jAoDpHU8Gu/v9lFRK6IT5imM=
-golang.org/x/crypto v0.39.0/go.mod h1:L+Xg3Wf6HoL4Bn4238Z6ft6KfEpN0tJGo53AAPC632U=
+golang.org/x/crypto v0.42.0 h1:chiH31gIWm57EkTXpwnqf8qeuMUi0yekh6mT2AvFlqI=
+golang.org/x/crypto v0.42.0/go.mod h1:4+rDnOTJhQCx2q7/j6rAN5XDw8kPjeaXEUR2eL94ix8=
 golang.org/x/exp v0.0.0-20180321215751-8460e604b9de/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20180807140117-3d87b88a115f/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20190121172915-509febef88a4/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
@@ -1618,8 +1623,8 @@ golang.org/x/mod v0.7.0/go.mod h1:iBbtSCu2XBx23ZKBPSOrRkjjQPZFPuis4dIYUhu/chs=
 golang.org/x/mod v0.8.0/go.mod h1:iBbtSCu2XBx23ZKBPSOrRkjjQPZFPuis4dIYUhu/chs=
 golang.org/x/mod v0.9.0/go.mod h1:iBbtSCu2XBx23ZKBPSOrRkjjQPZFPuis4dIYUhu/chs=
 golang.org/x/mod v0.10.0/go.mod h1:iBbtSCu2XBx23ZKBPSOrRkjjQPZFPuis4dIYUhu/chs=
-golang.org/x/mod v0.25.0 h1:n7a+ZbQKQA/Ysbyb0/6IbB1H/X41mKgbhfv7AfG/44w=
-golang.org/x/mod v0.25.0/go.mod h1:IXM97Txy2VM4PJ3gI61r1YEk/gAj6zAHN3AdZt6S9Ww=
+golang.org/x/mod v0.27.0 h1:kb+q2PyFnEADO2IEF935ehFUXlWiNjJWtRNgBLSfbxQ=
+golang.org/x/mod v0.27.0/go.mod h1:rWI627Fq0DEoudcK+MBkNkCe0EetEaDSwJJkCcjpazc=
 golang.org/x/net v0.0.0-20180724234803-3673e40ba225/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
 golang.org/x/net v0.0.0-20180826012351-8a410e7b638d/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
 golang.org/x/net v0.0.0-20190108225652-1e06a53dbb7e/go.mod h1:mL1N/T3taQHkDXs73rZJwtUhF3w3ftmwwsq0BUmARs4=
@@ -1688,8 +1693,8 @@ golang.org/x/net v0.7.0/go.mod h1:2Tu9+aMcznHK/AK1HMvgo6xiTLG5rD5rZLDS+rp2Bjs=
 golang.org/x/net v0.8.0/go.mod h1:QVkue5JL9kW//ek3r6jTKnTFis1tRmNAW2P1shuFdJc=
 golang.org/x/net v0.9.0/go.mod h1:d48xBJpPfHeWQsugry2m+kC02ZBRGRgulfHnEXEuWns=
 golang.org/x/net v0.10.0/go.mod h1:0qNGK6F8kojg2nk9dLZ2mShWaEBan6FAoqfSigmmuDg=
-golang.org/x/net v0.41.0 h1:vBTly1HeNPEn3wtREYfy4GZ/NECgw2Cnl+nK6Nz3uvw=
-golang.org/x/net v0.41.0/go.mod h1:B/K4NNqkfmg07DQYrbwvSluqCJOOXwUjeb/5lOisjbA=
+golang.org/x/net v0.43.0 h1:lat02VYK2j4aLzMzecihNvTlJNQUq316m2Mr9rnM6YE=
+golang.org/x/net v0.43.0/go.mod h1:vhO1fvI4dGsIjh73sWfUVjj3N7CA9WkKJNQm2svM6Jg=
 golang.org/x/oauth2 v0.0.0-20180821212333-d2e6202438be/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
 golang.org/x/oauth2 v0.0.0-20190226205417-e64efc72b421/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
 golang.org/x/oauth2 v0.0.0-20190604053449-0f29369cfe45/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
@@ -1739,8 +1744,8 @@ golang.org/x/sync v0.0.0-20220722155255-886fb9371eb4/go.mod h1:RxMgew5VJxzue5/jJ
 golang.org/x/sync v0.0.0-20220819030929-7fc1605a5dde/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.0.0-20220929204114-8fcdb60fdcc0/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.1.0/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
-golang.org/x/sync v0.15.0 h1:KWH3jNZsfyT6xfAfKiz6MRNmd46ByHDYaZ7KSkCtdW8=
-golang.org/x/sync v0.15.0/go.mod h1:1dzgHSNfp02xaA81J2MS99Qcpr2w7fw1gpm99rleRqA=
+golang.org/x/sync v0.17.0 h1:l60nONMj9l5drqw6jlhIELNv9I0A4OFgRsG9k2oT9Ug=
+golang.org/x/sync v0.17.0/go.mod h1:9KTHXmSnoGruLpwFjVSX0lNNA75CykiMECbovNTZqGI=
 golang.org/x/sys v0.0.0-20180830151530-49385e6e1522/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
 golang.org/x/sys v0.0.0-20180905080454-ebe1bf3edb33/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
 golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
@@ -1842,8 +1847,8 @@ golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.7.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.8.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.21.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
-golang.org/x/sys v0.33.0 h1:q3i8TbbEz+JRD9ywIRlyRAQbM0qF7hu24q3teo2hbuw=
-golang.org/x/sys v0.33.0/go.mod h1:BJP2sWEmIv4KK5OTEluFJCKSidICx8ciO85XgH3Ak8k=
+golang.org/x/sys v0.36.0 h1:KVRy2GtZBrk1cBYA7MKu5bEZFxQk4NIDV6RLVcC8o0k=
+golang.org/x/sys v0.36.0/go.mod h1:OgkHotnGiDImocRcuBABYBEXf8A9a87e/uXjp9XT3ks=
 golang.org/x/term v0.0.0-20201117132131-f5c789dd3221/go.mod h1:Nr5EML6q2oocZ2LXRh80K7BxOlk5/8JxuGnuhpl+muw=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
 golang.org/x/term v0.0.0-20210927222741-03fcf44c2211/go.mod h1:jbD1KX2456YbFQfuXm/mYQcufACuNUgVhRMnK/tPxf8=
@@ -1855,8 +1860,8 @@ golang.org/x/term v0.5.0/go.mod h1:jMB1sMXY+tzblOD4FWmEbocvup2/aLOaQEp7JmGp78k=
 golang.org/x/term v0.6.0/go.mod h1:m6U89DPEgQRMq3DNkDClhWw02AUbt2daBVO4cn4Hv9U=
 golang.org/x/term v0.7.0/go.mod h1:P32HKFT3hSsZrRxla30E9HqToFYAQPCMs/zFMBUFqPY=
 golang.org/x/term v0.8.0/go.mod h1:xPskH00ivmX89bAKVGSKKtLOWNx2+17Eiy94tnKShWo=
-golang.org/x/term v0.32.0 h1:DR4lr0TjUs3epypdhTOkMmuF5CDFJ/8pOnbzMZPQ7bg=
-golang.org/x/term v0.32.0/go.mod h1:uZG1FhGx848Sqfsq4/DlJr3xGGsYMu/L5GW4abiaEPQ=
+golang.org/x/term v0.35.0 h1:bZBVKBudEyhRcajGcNc3jIfWPqV4y/Kt2XcoigOWtDQ=
+golang.org/x/term v0.35.0/go.mod h1:TPGtkTLesOwf2DE8CgVYiZinHAOuy5AYUYT1lENIZnA=
 golang.org/x/text v0.0.0-20170915032832-14c0d48ead0c/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
 golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
 golang.org/x/text v0.3.1-0.20180807135948-17ff2d5776d2/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
@@ -1873,8 +1878,8 @@ golang.org/x/text v0.6.0/go.mod h1:mrYo+phRRbMaCq/xk9113O4dZlRixOauAjOtrjsXDZ8=
 golang.org/x/text v0.7.0/go.mod h1:mrYo+phRRbMaCq/xk9113O4dZlRixOauAjOtrjsXDZ8=
 golang.org/x/text v0.8.0/go.mod h1:e1OnstbJyHTd6l/uOt8jFFHp6TRDWZR/bV3emEE/zU8=
 golang.org/x/text v0.9.0/go.mod h1:e1OnstbJyHTd6l/uOt8jFFHp6TRDWZR/bV3emEE/zU8=
-golang.org/x/text v0.26.0 h1:P42AVeLghgTYr4+xUnTRKDMqpar+PtX7KWuNQL21L8M=
-golang.org/x/text v0.26.0/go.mod h1:QK15LZJUUQVJxhz7wXgxSy/CJaTFjd0G+YLonydOVQA=
+golang.org/x/text v0.29.0 h1:1neNs90w9YzJ9BocxfsQNHKuAT4pkghyXc4nhZ6sJvk=
+golang.org/x/text v0.29.0/go.mod h1:7MhJOA9CD2qZyOKYazxdYMF85OwPdEr9jTtBpO7ydH4=
 golang.org/x/time v0.0.0-20181108054448-85acf8d2951c/go.mod h1:tRJNPiyCQ0inRvYxbN9jk5I+vvW/OXSQhTDSoE431IQ=
 golang.org/x/time v0.0.0-20190308202827-9d24e82272b4/go.mod h1:tRJNPiyCQ0inRvYxbN9jk5I+vvW/OXSQhTDSoE431IQ=
 golang.org/x/time v0.0.0-20191024005414-555d28b269f0/go.mod h1:tRJNPiyCQ0inRvYxbN9jk5I+vvW/OXSQhTDSoE431IQ=
@@ -1883,8 +1888,8 @@ golang.org/x/time v0.0.0-20220224211638-0e9765cccd65/go.mod h1:tRJNPiyCQ0inRvYxb
 golang.org/x/time v0.0.0-20220922220347-f3bd1da661af/go.mod h1:tRJNPiyCQ0inRvYxbN9jk5I+vvW/OXSQhTDSoE431IQ=
 golang.org/x/time v0.1.0/go.mod h1:tRJNPiyCQ0inRvYxbN9jk5I+vvW/OXSQhTDSoE431IQ=
 golang.org/x/time v0.3.0/go.mod h1:tRJNPiyCQ0inRvYxbN9jk5I+vvW/OXSQhTDSoE431IQ=
-golang.org/x/time v0.12.0 h1:ScB/8o8olJvc+CQPWrK3fPZNfh7qgwCrY0zJmoEQLSE=
-golang.org/x/time v0.12.0/go.mod h1:CDIdPxbZBQxdj6cxyCIdrNogrJKMJ7pr37NYpMcMDSg=
+golang.org/x/time v0.13.0 h1:eUlYslOIt32DgYD6utsuUeHs4d7AsEYLuIAdg7FlYgI=
+golang.org/x/time v0.13.0/go.mod h1:eL/Oa2bBBK0TkX57Fyni+NgnyQQN4LitPmob2Hjnqw4=
 golang.org/x/tools v0.0.0-20180525024113-a5b4c53f6e8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20190114222345-bf090417da8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
@@ -1932,7 +1937,6 @@ golang.org/x/tools v0.0.0-20200501065659-ab2804fb9c9d/go.mod h1:EkVYQZoAsY45+roY
 golang.org/x/tools v0.0.0-20200512131952-2bc93b1c0c88/go.mod h1:EkVYQZoAsY45+roYkvgYkIh4xh/qjgUK9TdY2XT94GE=
 golang.org/x/tools v0.0.0-20200515010526-7d3b6ebf133d/go.mod h1:EkVYQZoAsY45+roYkvgYkIh4xh/qjgUK9TdY2XT94GE=
 golang.org/x/tools v0.0.0-20200618134242-20370b0cb4b2/go.mod h1:EkVYQZoAsY45+roYkvgYkIh4xh/qjgUK9TdY2XT94GE=
-golang.org/x/tools v0.0.0-20200619180055-7c47624df98f/go.mod h1:EkVYQZoAsY45+roYkvgYkIh4xh/qjgUK9TdY2XT94GE=
 golang.org/x/tools v0.0.0-20200729194436-6467de6f59a7/go.mod h1:njjCfa9FT2d7l9Bc6FUM5FLjQPp3cFF28FI3qnDFljA=
 golang.org/x/tools v0.0.0-20200804011535-6c149bb5ef0d/go.mod h1:njjCfa9FT2d7l9Bc6FUM5FLjQPp3cFF28FI3qnDFljA=
 golang.org/x/tools v0.0.0-20200825202427-b303f430e36d/go.mod h1:njjCfa9FT2d7l9Bc6FUM5FLjQPp3cFF28FI3qnDFljA=
@@ -1945,7 +1949,6 @@ golang.org/x/tools v0.0.0-20201124115921-2c860bdd6e78/go.mod h1:emZCQorbCU4vsT4f
 golang.org/x/tools v0.0.0-20201201161351-ac6f37ff4c2a/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
 golang.org/x/tools v0.0.0-20201208233053-a543418bbed2/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
 golang.org/x/tools v0.0.0-20210105154028-b0ab187a4818/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
-golang.org/x/tools v0.0.0-20210106214847-113979e3529a/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
 golang.org/x/tools v0.0.0-20210108195828-e2f9c7f1fc8e/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
 golang.org/x/tools v0.1.0/go.mod h1:xkSsbof2nBLbhDlRMhhhyNLN/zl3eTqcnHD5viDpcZ0=
 golang.org/x/tools v0.1.1/go.mod h1:o0xws9oXOQQZyjljx8fwUC0k7L1pTE6eaCbjGeHmOkk=
@@ -1959,8 +1962,8 @@ golang.org/x/tools v0.3.0/go.mod h1:/rWhSS2+zyEVwoJf8YAX6L2f0ntZ7Kn/mGgAWcipA5k=
 golang.org/x/tools v0.6.0/go.mod h1:Xwgl3UAJ/d3gWutnCtw505GrjyAbvKui8lOU390QaIU=
 golang.org/x/tools v0.7.0/go.mod h1:4pg6aUX35JBAogB10C9AtvVL+qowtN4pT3CGSQex14s=
 golang.org/x/tools v0.8.0/go.mod h1:JxBZ99ISMI5ViVkT1tr6tdNmXeTrcpVSD3vZ1RsRdN4=
-golang.org/x/tools v0.33.0 h1:4qz2S3zmRxbGIhDIAgjxvFutSvH5EfnsYrRBj0UI0bc=
-golang.org/x/tools v0.33.0/go.mod h1:CIJMaWEY88juyUfo7UbgPqbC8rU2OqfAV1h2Qp0oMYI=
+golang.org/x/tools v0.36.0 h1:kWS0uv/zsvHEle1LbV5LE8QujrxB3wfQyxHfhOk0Qkg=
+golang.org/x/tools v0.36.0/go.mod h1:WBDiHKJK8YgLHlcQPYQzNCkUxUypCaa5ZegCVutKm+s=
 golang.org/x/xerrors v0.0.0-20190410155217-1f06c39b4373/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20190513163551-3ee3066db522/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
@@ -1977,8 +1980,8 @@ gonum.org/v1/gonum v0.0.0-20180816165407-929014505bf4/go.mod h1:Y+Yx5eoAFn32cQvJ
 gonum.org/v1/gonum v0.8.2/go.mod h1:oe/vMfY3deqTw+1EZJhuvEW2iwGF1bW9wwu7XCu0+v0=
 gonum.org/v1/gonum v0.9.3/go.mod h1:TZumC3NeyVQskjXqmyWt4S3bINhy7B4eYwW69EbyX+0=
 gonum.org/v1/gonum v0.11.0/go.mod h1:fSG4YDCxxUZQJ7rKsQrj0gMOg00Il0Z96/qMA4bVQhA=
-gonum.org/v1/gonum v0.12.0 h1:xKuo6hzt+gMav00meVPUlXwSdoEJP46BR+wdxQEFK2o=
-gonum.org/v1/gonum v0.12.0/go.mod h1:73TDxJfAAHeA8Mk9mf8NlIppyhQNo5GLTcYeqgo2lvY=
+gonum.org/v1/gonum v0.16.0 h1:5+ul4Swaf3ESvrOnidPp4GZbzf0mxVQpDCYUQE7OJfk=
+gonum.org/v1/gonum v0.16.0/go.mod h1:fef3am4MQ93R2HHpKnLk4/Tbh/s0+wqD5nfa6Pnwy4E=
 gonum.org/v1/netlib v0.0.0-20190313105609-8cb42192e0e0/go.mod h1:wa6Ws7BG/ESfp6dHfk7C6KdzKA7wR7u/rKwOGE66zvw=
 gonum.org/v1/plot v0.0.0-20190515093506-e2840ee46a6b/go.mod h1:Wt8AAjI+ypCyYX3nZBvf6cAIx93T+c/OS2HFAYskSZc=
 gonum.org/v1/plot v0.9.0/go.mod h1:3Pcqqmp6RHvJI72kgb8fThyUnav364FOsdDo2aGW5lY=
@@ -2049,8 +2052,8 @@ google.golang.org/api v0.108.0/go.mod h1:2Ts0XTHNVWxypznxWOYUeI4g3WdP9Pk2Qk58+a/
 google.golang.org/api v0.110.0/go.mod h1:7FC4Vvx1Mooxh8C5HWjzZHcavuS2f6pmJpZx60ca7iI=
 google.golang.org/api v0.111.0/go.mod h1:qtFHvU9mhgTJegR31csQ+rwxyUTHOKFqCKWp1J0fdw0=
 google.golang.org/api v0.114.0/go.mod h1:ifYI2ZsFK6/uGddGfAD5BMxlnkBqCmqHSDUVi45N5Yg=
-google.golang.org/api v0.240.0 h1:PxG3AA2UIqT1ofIzWV2COM3j3JagKTKSwy7L6RHNXNU=
-google.golang.org/api v0.240.0/go.mod h1:cOVEm2TpdAGHL2z+UwyS+kmlGr3bVWQQ6sYEqkKje50=
+google.golang.org/api v0.249.0 h1:0VrsWAKzIZi058aeq+I86uIXbNhm9GxSHpbmZ92a38w=
+google.golang.org/api v0.249.0/go.mod h1:dGk9qyI0UYPwO/cjt2q06LG/EhUpwZGdAbYF14wHHrQ=
 google.golang.org/appengine v1.1.0/go.mod h1:EbEs0AVv82hx2wNQdGPgUI5lhzA/G0D9YwlJXL52JkM=
 google.golang.org/appengine v1.4.0/go.mod h1:xpcJRLb0r/rnEns0DIKYYv+WjYCduHsrkT7/EB5XEv4=
 google.golang.org/appengine v1.5.0/go.mod h1:xpcJRLb0r/rnEns0DIKYYv+WjYCduHsrkT7/EB5XEv4=
@@ -2209,12 +2212,12 @@ google.golang.org/genproto v0.0.0-20230323212658-478b75c54725/go.mod h1:UUQDJDOl
 google.golang.org/genproto v0.0.0-20230330154414-c0448cd141ea/go.mod h1:UUQDJDOlWu4KYeJZffbWgBkS1YFobzKbLVfK69pe0Ak=
 google.golang.org/genproto v0.0.0-20230331144136-dcfb400f0633/go.mod h1:UUQDJDOlWu4KYeJZffbWgBkS1YFobzKbLVfK69pe0Ak=
 google.golang.org/genproto v0.0.0-20230410155749-daa745c078e1/go.mod h1:nKE/iIaLqn2bQwXBg8f1g2Ylh6r5MN5CmZvuzZCgsCU=
-google.golang.org/genproto v0.0.0-20250505200425-f936aa4a68b2 h1:1tXaIXCracvtsRxSBsYDiSBN0cuJvM7QYW+MrpIRY78=
-google.golang.org/genproto v0.0.0-20250505200425-f936aa4a68b2/go.mod h1:49MsLSx0oWMOZqcpB3uL8ZOkAh1+TndpJ8ONoCBWiZk=
-google.golang.org/genproto/googleapis/api v0.0.0-20250603155806-513f23925822 h1:oWVWY3NzT7KJppx2UKhKmzPq4SRe0LdCijVRwvGeikY=
-google.golang.org/genproto/googleapis/api v0.0.0-20250603155806-513f23925822/go.mod h1:h3c4v36UTKzUiuaOKQ6gr3S+0hovBtUrXzTG/i3+XEc=
-google.golang.org/genproto/googleapis/rpc v0.0.0-20250603155806-513f23925822 h1:fc6jSaCT0vBduLYZHYrBBNY4dsWuvgyff9noRNDdBeE=
-google.golang.org/genproto/googleapis/rpc v0.0.0-20250603155806-513f23925822/go.mod h1:qQ0YXyHHx3XkvlzUtpXDkS29lDSafHMZBAZDc03LQ3A=
+google.golang.org/genproto v0.0.0-20250603155806-513f23925822 h1:rHWScKit0gvAPuOnu87KpaYtjK5zBMLcULh7gxkCXu4=
+google.golang.org/genproto v0.0.0-20250603155806-513f23925822/go.mod h1:HubltRL7rMh0LfnQPkMH4NPDFEWp0jw3vixw7jEM53s=
+google.golang.org/genproto/googleapis/api v0.0.0-20250818200422-3122310a409c h1:AtEkQdl5b6zsybXcbz00j1LwNodDuH6hVifIaNqk7NQ=
+google.golang.org/genproto/googleapis/api v0.0.0-20250818200422-3122310a409c/go.mod h1:ea2MjsO70ssTfCjiwHgI0ZFqcw45Ksuk2ckf9G468GA=
+google.golang.org/genproto/googleapis/rpc v0.0.0-20250818200422-3122310a409c h1:qXWI/sQtv5UKboZ/zUk7h+mrf/lXORyI+n9DKDAusdg=
+google.golang.org/genproto/googleapis/rpc v0.0.0-20250818200422-3122310a409c/go.mod h1:gw1tLEfykwDz2ET4a12jcXt4couGAm7IwsVaTy0Sflo=
 google.golang.org/grpc v1.19.0/go.mod h1:mqu4LbDTu4XGKhr4mRzUsmM4RtVoemTSY81AxZiDr8c=
 google.golang.org/grpc v1.20.1/go.mod h1:10oTOabMzJvdu6/UiuZezV6QK5dSlG84ov/aaiqXj38=
 google.golang.org/grpc v1.21.1/go.mod h1:oYelfM1adQP15Ek0mdvEgi9Df8B9CZIaU1084ijfRaM=
@@ -2257,8 +2260,8 @@ google.golang.org/grpc v1.52.3/go.mod h1:pu6fVzoFb+NBYNAvQL08ic+lvB2IojljRYuun5v
 google.golang.org/grpc v1.53.0/go.mod h1:OnIrk0ipVdj4N5d9IUoFUx72/VlD7+jUsHwZgwSMQpw=
 google.golang.org/grpc v1.54.0/go.mod h1:PUSEXI6iWghWaB6lXM4knEgpJNu2qUcKfDtNci3EC2g=
 google.golang.org/grpc v1.56.3/go.mod h1:I9bI3vqKfayGqPUAwGdOSu7kt6oIJLixfffKrpXqQ9s=
-google.golang.org/grpc v1.73.0 h1:VIWSmpI2MegBtTuFt5/JWy2oXxtjJ/e89Z70ImfD2ok=
-google.golang.org/grpc v1.73.0/go.mod h1:50sbHOUqWoCQGI8V2HQLJM0B+LMlIUjNSZmow7EVBQc=
+google.golang.org/grpc v1.75.1 h1:/ODCNEuf9VghjgO3rqLcfg8fiOP0nSluljWFlDxELLI=
+google.golang.org/grpc v1.75.1/go.mod h1:JtPAzKiq4v1xcAB2hydNlWI2RnF85XXcV0mhKXr2ecQ=
 google.golang.org/grpc/cmd/protoc-gen-go-grpc v1.1.0/go.mod h1:6Kw0yEErY5E/yWrBtf03jp27GLLJujG4z/JK95pnjjw=
 google.golang.org/protobuf v0.0.0-20200109180630-ec00e32a8dfd/go.mod h1:DFci5gLYBciE7Vtevhsrf46CRTquxDuWsQurQQe4oz8=
 google.golang.org/protobuf v0.0.0-20200221191635-4d8936d0db64/go.mod h1:kwYJMbMJ01Woi6D6+Kah6886xMZcty6N08ah7+eCXa0=
@@ -2277,8 +2280,8 @@ google.golang.org/protobuf v1.28.0/go.mod h1:HV8QOd/L58Z+nl8r43ehVNZIU/HEI6OcFqw
 google.golang.org/protobuf v1.28.1/go.mod h1:HV8QOd/L58Z+nl8r43ehVNZIU/HEI6OcFqwMG9pJV4I=
 google.golang.org/protobuf v1.29.1/go.mod h1:HV8QOd/L58Z+nl8r43ehVNZIU/HEI6OcFqwMG9pJV4I=
 google.golang.org/protobuf v1.30.0/go.mod h1:HV8QOd/L58Z+nl8r43ehVNZIU/HEI6OcFqwMG9pJV4I=
-google.golang.org/protobuf v1.36.6 h1:z1NpPI8ku2WgiWnf+t9wTPsn6eP1L7ksHUlkfLvd9xY=
-google.golang.org/protobuf v1.36.6/go.mod h1:jduwjTPXsFjZGTmRluh+L6NjiWu7pchiJ2/5YcXBHnY=
+google.golang.org/protobuf v1.36.8 h1:xHScyCOEuuwZEc6UtSOvPbAT4zRh0xcNRYekJwfqyMc=
+google.golang.org/protobuf v1.36.8/go.mod h1:fuxRtAxBytpl4zzqUh6/eyUujkJdNiuEkXntxiD/uRU=
 gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
 gopkg.in/check.v1 v1.0.0-20180628173108-788fd7840127/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
 gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c h1:Hei/4ADfdWqJk1ZMxUNpqntNwaWcugrBjAiHlqqRiVk=
diff --git a/sdks/go/cmd/symtab/main.go b/sdks/go/cmd/symtab/main.go
index 6628cc8e4399..757710246cf4 100644
--- a/sdks/go/cmd/symtab/main.go
+++ b/sdks/go/cmd/symtab/main.go
@@ -38,7 +38,7 @@ var t reflect.Type
 // Increment is the function that will be executed by its address.
 // It increments a global var so we can check that it was indeed called.
 func Increment(str string) {
-	log.Printf(str)
+	log.Print(str)
 	counter++
 }
 
diff --git a/sdks/go/container/tools/buffered_logging.go b/sdks/go/container/tools/buffered_logging.go
index a7b84e56af3a..a0937b8eb14a 100644
--- a/sdks/go/container/tools/buffered_logging.go
+++ b/sdks/go/container/tools/buffered_logging.go
@@ -78,7 +78,7 @@ func (b *BufferedLogger) FlushAtError(ctx context.Context) {
 		return
 	}
 	for _, message := range b.logs {
-		b.logger.Errorf(ctx, message)
+		b.logger.Errorf(ctx, "%s", message)
 	}
 	b.logs = nil
 	b.lastFlush = time.Now()
@@ -91,7 +91,7 @@ func (b *BufferedLogger) FlushAtDebug(ctx context.Context) {
 		return
 	}
 	for _, message := range b.logs {
-		b.logger.Printf(ctx, message)
+		b.logger.Printf(ctx, "%s", message)
 	}
 	b.logs = nil
 	b.lastFlush = time.Now()
diff --git a/sdks/go/container/tools/logging_test.go b/sdks/go/container/tools/logging_test.go
index 8730a0fe9c19..c68600f75e2e 100644
--- a/sdks/go/container/tools/logging_test.go
+++ b/sdks/go/container/tools/logging_test.go
@@ -85,7 +85,7 @@ func TestLogger(t *testing.T) {
 
 		catcher.err = errors.New("test error")
 		wantMsg := "checking for error?"
-		l.Printf(ctx, wantMsg)
+		l.Printf(ctx, "%s", wantMsg)
 
 		line, err := buf.ReadString('\n')
 		if err != nil {
diff --git a/sdks/go/examples/xlang/sql/sql.go b/sdks/go/examples/xlang/sql/sql.go
index dcf293c7d915..eb65b4003e95 100644
--- a/sdks/go/examples/xlang/sql/sql.go
+++ b/sdks/go/examples/xlang/sql/sql.go
@@ -98,7 +98,7 @@ func main() {
 
 	// Options for the sql transform have to be defined before the Transform call.
 	var opts []sql.Option
-	// Dialect options are "calcite" and "zetasql"
+	// Dialect options is always "calcite"
 	opts = append(opts, sql.Dialect("calcite"))
 	// The expansion address can be specified per-call here or overwritten for all
 	// calls using xlangx.RegisterOverrideForUrn(sqlx.Urn, *expansionAddr).
diff --git a/sdks/go/pkg/beam/beam.shims.go b/sdks/go/pkg/beam/beam.shims.go
index 29ebaf2ca681..aceb913d9c4d 100644
--- a/sdks/go/pkg/beam/beam.shims.go
+++ b/sdks/go/pkg/beam/beam.shims.go
@@ -25,6 +25,7 @@ import (
 	// Library imports
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/runtime"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/runtime/exec"
+	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/runtime/graphx/schema"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/sdf"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/typex"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/util/reflectx"
@@ -43,121 +44,104 @@ func init() {
 	runtime.RegisterFunction(schemaDec)
 	runtime.RegisterFunction(schemaEnc)
 	runtime.RegisterFunction(swapKVFn)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(reflect.Type, []byte) (typex.T, error))(nil)).Elem(), funcMakerReflect۰TypeSliceOfByteГTypex۰TError)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(reflect.Type, typex.T) ([]byte, error))(nil)).Elem(), funcMakerReflect۰TypeTypex۰TГSliceOfByteError)
-	reflectx.RegisterFunc(reflect.TypeOf((*func([]byte, func(typex.T)) error)(nil)).Elem(), funcMakerSliceOfByteEmitTypex۰TГError)
-	reflectx.RegisterFunc(reflect.TypeOf((*func([]typex.T, func(typex.T)))(nil)).Elem(), funcMakerSliceOfTypex۰TEmitTypex۰TГ)
+	runtime.RegisterType(reflect.TypeOf((*T)(nil)).Elem())
+	schema.RegisterType(reflect.TypeOf((*T)(nil)).Elem())
+	runtime.RegisterType(reflect.TypeOf((*X)(nil)).Elem())
+	schema.RegisterType(reflect.TypeOf((*X)(nil)).Elem())
+	runtime.RegisterType(reflect.TypeOf((*Y)(nil)).Elem())
+	schema.RegisterType(reflect.TypeOf((*Y)(nil)).Elem())
+	runtime.RegisterType(reflect.TypeOf((*reflect.Type)(nil)).Elem())
+	schema.RegisterType(reflect.TypeOf((*reflect.Type)(nil)).Elem())
+	runtime.RegisterType(reflect.TypeOf((*reflectx.Func)(nil)).Elem())
+	schema.RegisterType(reflect.TypeOf((*reflectx.Func)(nil)).Elem())
+	reflectx.RegisterFunc(reflect.TypeOf((*func(reflect.Type, []byte) (T, error))(nil)).Elem(), funcMakerReflect۰TypeSliceOfByteГTError)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(reflect.Type, T) ([]byte, error))(nil)).Elem(), funcMakerReflect۰TypeTГSliceOfByteError)
+	reflectx.RegisterFunc(reflect.TypeOf((*func([]T, func(T)))(nil)).Elem(), funcMakerSliceOfTEmitTГ)
 	reflectx.RegisterFunc(reflect.TypeOf((*func(string, reflect.Type, []byte) reflectx.Func)(nil)).Elem(), funcMakerStringReflect۰TypeSliceOfByteГReflectx۰Func)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(typex.T) (int, typex.T))(nil)).Elem(), funcMakerTypex۰TГIntTypex۰T)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(typex.T) ([]byte, error))(nil)).Elem(), funcMakerTypex۰TГSliceOfByteError)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(typex.X, typex.Y) typex.X)(nil)).Elem(), funcMakerTypex۰XTypex۰YГTypex۰X)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(typex.X, typex.Y) typex.Y)(nil)).Elem(), funcMakerTypex۰XTypex۰YГTypex۰Y)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(typex.X, typex.Y) (typex.Y, typex.X))(nil)).Elem(), funcMakerTypex۰XTypex۰YГTypex۰YTypex۰X)
-	exec.RegisterEmitter(reflect.TypeOf((*func(typex.T))(nil)).Elem(), emitMakerTypex۰T)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(T) (int, T))(nil)).Elem(), funcMakerTГIntT)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(T) ([]byte, error))(nil)).Elem(), funcMakerTГSliceOfByteError)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(X, Y) X)(nil)).Elem(), funcMakerXYГX)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(X, Y) Y)(nil)).Elem(), funcMakerXYГY)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(X, Y) (Y, X))(nil)).Elem(), funcMakerXYГYX)
+	exec.RegisterEmitter(reflect.TypeOf((*func(T))(nil)).Elem(), emitMakerT)
 }
 
-type callerReflect۰TypeSliceOfByteГTypex۰TError struct {
-	fn func(reflect.Type, []byte) (typex.T, error)
+type callerReflect۰TypeSliceOfByteГTError struct {
+	fn func(reflect.Type, []byte) (T, error)
 }
 
-func funcMakerReflect۰TypeSliceOfByteГTypex۰TError(fn any) reflectx.Func {
-	f := fn.(func(reflect.Type, []byte) (typex.T, error))
-	return &callerReflect۰TypeSliceOfByteГTypex۰TError{fn: f}
+func funcMakerReflect۰TypeSliceOfByteГTError(fn any) reflectx.Func {
+	f := fn.(func(reflect.Type, []byte) (T, error))
+	return &callerReflect۰TypeSliceOfByteГTError{fn: f}
 }
 
-func (c *callerReflect۰TypeSliceOfByteГTypex۰TError) Name() string {
+func (c *callerReflect۰TypeSliceOfByteГTError) Name() string {
 	return reflectx.FunctionName(c.fn)
 }
 
-func (c *callerReflect۰TypeSliceOfByteГTypex۰TError) Type() reflect.Type {
+func (c *callerReflect۰TypeSliceOfByteГTError) Type() reflect.Type {
 	return reflect.TypeOf(c.fn)
 }
 
-func (c *callerReflect۰TypeSliceOfByteГTypex۰TError) Call(args []any) []any {
+func (c *callerReflect۰TypeSliceOfByteГTError) Call(args []any) []any {
 	out0, out1 := c.fn(args[0].(reflect.Type), args[1].([]byte))
 	return []any{out0, out1}
 }
 
-func (c *callerReflect۰TypeSliceOfByteГTypex۰TError) Call2x2(arg0, arg1 any) (any, any) {
+func (c *callerReflect۰TypeSliceOfByteГTError) Call2x2(arg0, arg1 any) (any, any) {
 	return c.fn(arg0.(reflect.Type), arg1.([]byte))
 }
 
-type callerReflect۰TypeTypex۰TГSliceOfByteError struct {
-	fn func(reflect.Type, typex.T) ([]byte, error)
+type callerReflect۰TypeTГSliceOfByteError struct {
+	fn func(reflect.Type, T) ([]byte, error)
 }
 
-func funcMakerReflect۰TypeTypex۰TГSliceOfByteError(fn any) reflectx.Func {
-	f := fn.(func(reflect.Type, typex.T) ([]byte, error))
-	return &callerReflect۰TypeTypex۰TГSliceOfByteError{fn: f}
+func funcMakerReflect۰TypeTГSliceOfByteError(fn any) reflectx.Func {
+	f := fn.(func(reflect.Type, T) ([]byte, error))
+	return &callerReflect۰TypeTГSliceOfByteError{fn: f}
 }
 
-func (c *callerReflect۰TypeTypex۰TГSliceOfByteError) Name() string {
+func (c *callerReflect۰TypeTГSliceOfByteError) Name() string {
 	return reflectx.FunctionName(c.fn)
 }
 
-func (c *callerReflect۰TypeTypex۰TГSliceOfByteError) Type() reflect.Type {
+func (c *callerReflect۰TypeTГSliceOfByteError) Type() reflect.Type {
 	return reflect.TypeOf(c.fn)
 }
 
-func (c *callerReflect۰TypeTypex۰TГSliceOfByteError) Call(args []any) []any {
-	out0, out1 := c.fn(args[0].(reflect.Type), args[1].(typex.T))
+func (c *callerReflect۰TypeTГSliceOfByteError) Call(args []any) []any {
+	out0, out1 := c.fn(args[0].(reflect.Type), args[1].(T))
 	return []any{out0, out1}
 }
 
-func (c *callerReflect۰TypeTypex۰TГSliceOfByteError) Call2x2(arg0, arg1 any) (any, any) {
-	return c.fn(arg0.(reflect.Type), arg1.(typex.T))
+func (c *callerReflect۰TypeTГSliceOfByteError) Call2x2(arg0, arg1 any) (any, any) {
+	return c.fn(arg0.(reflect.Type), arg1.(T))
 }
 
-type callerSliceOfByteEmitTypex۰TГError struct {
-	fn func([]byte, func(typex.T)) error
+type callerSliceOfTEmitTГ struct {
+	fn func([]T, func(T))
 }
 
-func funcMakerSliceOfByteEmitTypex۰TГError(fn any) reflectx.Func {
-	f := fn.(func([]byte, func(typex.T)) error)
-	return &callerSliceOfByteEmitTypex۰TГError{fn: f}
+func funcMakerSliceOfTEmitTГ(fn any) reflectx.Func {
+	f := fn.(func([]T, func(T)))
+	return &callerSliceOfTEmitTГ{fn: f}
 }
 
-func (c *callerSliceOfByteEmitTypex۰TГError) Name() string {
+func (c *callerSliceOfTEmitTГ) Name() string {
 	return reflectx.FunctionName(c.fn)
 }
 
-func (c *callerSliceOfByteEmitTypex۰TГError) Type() reflect.Type {
+func (c *callerSliceOfTEmitTГ) Type() reflect.Type {
 	return reflect.TypeOf(c.fn)
 }
 
-func (c *callerSliceOfByteEmitTypex۰TГError) Call(args []any) []any {
-	out0 := c.fn(args[0].([]byte), args[1].(func(typex.T)))
-	return []any{out0}
-}
-
-func (c *callerSliceOfByteEmitTypex۰TГError) Call2x1(arg0, arg1 any) any {
-	return c.fn(arg0.([]byte), arg1.(func(typex.T)))
-}
-
-type callerSliceOfTypex۰TEmitTypex۰TГ struct {
-	fn func([]typex.T, func(typex.T))
-}
-
-func funcMakerSliceOfTypex۰TEmitTypex۰TГ(fn any) reflectx.Func {
-	f := fn.(func([]typex.T, func(typex.T)))
-	return &callerSliceOfTypex۰TEmitTypex۰TГ{fn: f}
-}
-
-func (c *callerSliceOfTypex۰TEmitTypex۰TГ) Name() string {
-	return reflectx.FunctionName(c.fn)
-}
-
-func (c *callerSliceOfTypex۰TEmitTypex۰TГ) Type() reflect.Type {
-	return reflect.TypeOf(c.fn)
-}
-
-func (c *callerSliceOfTypex۰TEmitTypex۰TГ) Call(args []any) []any {
-	c.fn(args[0].([]typex.T), args[1].(func(typex.T)))
+func (c *callerSliceOfTEmitTГ) Call(args []any) []any {
+	c.fn(args[0].([]T), args[1].(func(T)))
 	return []any{}
 }
 
-func (c *callerSliceOfTypex۰TEmitTypex۰TГ) Call2x0(arg0, arg1 any) {
-	c.fn(arg0.([]typex.T), arg1.(func(typex.T)))
+func (c *callerSliceOfTEmitTГ) Call2x0(arg0, arg1 any) {
+	c.fn(arg0.([]T), arg1.(func(T)))
 }
 
 type callerStringReflect۰TypeSliceOfByteГReflectx۰Func struct {
@@ -186,134 +170,134 @@ func (c *callerStringReflect۰TypeSliceOfByteГReflectx۰Func) Call3x1(arg0, arg
 	return c.fn(arg0.(string), arg1.(reflect.Type), arg2.([]byte))
 }
 
-type callerTypex۰TГIntTypex۰T struct {
-	fn func(typex.T) (int, typex.T)
+type callerTГIntT struct {
+	fn func(T) (int, T)
 }
 
-func funcMakerTypex۰TГIntTypex۰T(fn any) reflectx.Func {
-	f := fn.(func(typex.T) (int, typex.T))
-	return &callerTypex۰TГIntTypex۰T{fn: f}
+func funcMakerTГIntT(fn any) reflectx.Func {
+	f := fn.(func(T) (int, T))
+	return &callerTГIntT{fn: f}
 }
 
-func (c *callerTypex۰TГIntTypex۰T) Name() string {
+func (c *callerTГIntT) Name() string {
 	return reflectx.FunctionName(c.fn)
 }
 
-func (c *callerTypex۰TГIntTypex۰T) Type() reflect.Type {
+func (c *callerTГIntT) Type() reflect.Type {
 	return reflect.TypeOf(c.fn)
 }
 
-func (c *callerTypex۰TГIntTypex۰T) Call(args []any) []any {
-	out0, out1 := c.fn(args[0].(typex.T))
+func (c *callerTГIntT) Call(args []any) []any {
+	out0, out1 := c.fn(args[0].(T))
 	return []any{out0, out1}
 }
 
-func (c *callerTypex۰TГIntTypex۰T) Call1x2(arg0 any) (any, any) {
-	return c.fn(arg0.(typex.T))
+func (c *callerTГIntT) Call1x2(arg0 any) (any, any) {
+	return c.fn(arg0.(T))
 }
 
-type callerTypex۰TГSliceOfByteError struct {
-	fn func(typex.T) ([]byte, error)
+type callerTГSliceOfByteError struct {
+	fn func(T) ([]byte, error)
 }
 
-func funcMakerTypex۰TГSliceOfByteError(fn any) reflectx.Func {
-	f := fn.(func(typex.T) ([]byte, error))
-	return &callerTypex۰TГSliceOfByteError{fn: f}
+func funcMakerTГSliceOfByteError(fn any) reflectx.Func {
+	f := fn.(func(T) ([]byte, error))
+	return &callerTГSliceOfByteError{fn: f}
 }
 
-func (c *callerTypex۰TГSliceOfByteError) Name() string {
+func (c *callerTГSliceOfByteError) Name() string {
 	return reflectx.FunctionName(c.fn)
 }
 
-func (c *callerTypex۰TГSliceOfByteError) Type() reflect.Type {
+func (c *callerTГSliceOfByteError) Type() reflect.Type {
 	return reflect.TypeOf(c.fn)
 }
 
-func (c *callerTypex۰TГSliceOfByteError) Call(args []any) []any {
-	out0, out1 := c.fn(args[0].(typex.T))
+func (c *callerTГSliceOfByteError) Call(args []any) []any {
+	out0, out1 := c.fn(args[0].(T))
 	return []any{out0, out1}
 }
 
-func (c *callerTypex۰TГSliceOfByteError) Call1x2(arg0 any) (any, any) {
-	return c.fn(arg0.(typex.T))
+func (c *callerTГSliceOfByteError) Call1x2(arg0 any) (any, any) {
+	return c.fn(arg0.(T))
 }
 
-type callerTypex۰XTypex۰YГTypex۰X struct {
-	fn func(typex.X, typex.Y) typex.X
+type callerXYГX struct {
+	fn func(X, Y) X
 }
 
-func funcMakerTypex۰XTypex۰YГTypex۰X(fn any) reflectx.Func {
-	f := fn.(func(typex.X, typex.Y) typex.X)
-	return &callerTypex۰XTypex۰YГTypex۰X{fn: f}
+func funcMakerXYГX(fn any) reflectx.Func {
+	f := fn.(func(X, Y) X)
+	return &callerXYГX{fn: f}
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰X) Name() string {
+func (c *callerXYГX) Name() string {
 	return reflectx.FunctionName(c.fn)
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰X) Type() reflect.Type {
+func (c *callerXYГX) Type() reflect.Type {
 	return reflect.TypeOf(c.fn)
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰X) Call(args []any) []any {
-	out0 := c.fn(args[0].(typex.X), args[1].(typex.Y))
+func (c *callerXYГX) Call(args []any) []any {
+	out0 := c.fn(args[0].(X), args[1].(Y))
 	return []any{out0}
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰X) Call2x1(arg0, arg1 any) any {
-	return c.fn(arg0.(typex.X), arg1.(typex.Y))
+func (c *callerXYГX) Call2x1(arg0, arg1 any) any {
+	return c.fn(arg0.(X), arg1.(Y))
 }
 
-type callerTypex۰XTypex۰YГTypex۰Y struct {
-	fn func(typex.X, typex.Y) typex.Y
+type callerXYГY struct {
+	fn func(X, Y) Y
 }
 
-func funcMakerTypex۰XTypex۰YГTypex۰Y(fn any) reflectx.Func {
-	f := fn.(func(typex.X, typex.Y) typex.Y)
-	return &callerTypex۰XTypex۰YГTypex۰Y{fn: f}
+func funcMakerXYГY(fn any) reflectx.Func {
+	f := fn.(func(X, Y) Y)
+	return &callerXYГY{fn: f}
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰Y) Name() string {
+func (c *callerXYГY) Name() string {
 	return reflectx.FunctionName(c.fn)
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰Y) Type() reflect.Type {
+func (c *callerXYГY) Type() reflect.Type {
 	return reflect.TypeOf(c.fn)
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰Y) Call(args []any) []any {
-	out0 := c.fn(args[0].(typex.X), args[1].(typex.Y))
+func (c *callerXYГY) Call(args []any) []any {
+	out0 := c.fn(args[0].(X), args[1].(Y))
 	return []any{out0}
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰Y) Call2x1(arg0, arg1 any) any {
-	return c.fn(arg0.(typex.X), arg1.(typex.Y))
+func (c *callerXYГY) Call2x1(arg0, arg1 any) any {
+	return c.fn(arg0.(X), arg1.(Y))
 }
 
-type callerTypex۰XTypex۰YГTypex۰YTypex۰X struct {
-	fn func(typex.X, typex.Y) (typex.Y, typex.X)
+type callerXYГYX struct {
+	fn func(X, Y) (Y, X)
 }
 
-func funcMakerTypex۰XTypex۰YГTypex۰YTypex۰X(fn any) reflectx.Func {
-	f := fn.(func(typex.X, typex.Y) (typex.Y, typex.X))
-	return &callerTypex۰XTypex۰YГTypex۰YTypex۰X{fn: f}
+func funcMakerXYГYX(fn any) reflectx.Func {
+	f := fn.(func(X, Y) (Y, X))
+	return &callerXYГYX{fn: f}
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰YTypex۰X) Name() string {
+func (c *callerXYГYX) Name() string {
 	return reflectx.FunctionName(c.fn)
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰YTypex۰X) Type() reflect.Type {
+func (c *callerXYГYX) Type() reflect.Type {
 	return reflect.TypeOf(c.fn)
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰YTypex۰X) Call(args []any) []any {
-	out0, out1 := c.fn(args[0].(typex.X), args[1].(typex.Y))
+func (c *callerXYГYX) Call(args []any) []any {
+	out0, out1 := c.fn(args[0].(X), args[1].(Y))
 	return []any{out0, out1}
 }
 
-func (c *callerTypex۰XTypex۰YГTypex۰YTypex۰X) Call2x2(arg0, arg1 any) (any, any) {
-	return c.fn(arg0.(typex.X), arg1.(typex.Y))
+func (c *callerXYГYX) Call2x2(arg0, arg1 any) (any, any) {
+	return c.fn(arg0.(X), arg1.(Y))
 }
 
 type emitNative struct {
@@ -322,13 +306,15 @@ type emitNative struct {
 	est *sdf.WatermarkEstimator
 
 	ctx   context.Context
+	pn    typex.PaneInfo
 	ws    []typex.Window
 	et    typex.EventTime
 	value exec.FullValue
 }
 
-func (e *emitNative) Init(ctx context.Context, ws []typex.Window, et typex.EventTime) error {
+func (e *emitNative) Init(ctx context.Context, pn typex.PaneInfo, ws []typex.Window, et typex.EventTime) error {
 	e.ctx = ctx
+	e.pn = pn
 	e.ws = ws
 	e.et = et
 	return nil
@@ -342,14 +328,14 @@ func (e *emitNative) AttachEstimator(est *sdf.WatermarkEstimator) {
 	e.est = est
 }
 
-func emitMakerTypex۰T(n exec.ElementProcessor) exec.ReusableEmitter {
+func emitMakerT(n exec.ElementProcessor) exec.ReusableEmitter {
 	ret := &emitNative{n: n}
-	ret.fn = ret.invokeTypex۰T
+	ret.fn = ret.invokeT
 	return ret
 }
 
-func (e *emitNative) invokeTypex۰T(val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: val}
+func (e *emitNative) invokeT(val T) {
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
diff --git a/sdks/go/pkg/beam/core/core.go b/sdks/go/pkg/beam/core/core.go
index a0337d0d39aa..0856d430804f 100644
--- a/sdks/go/pkg/beam/core/core.go
+++ b/sdks/go/pkg/beam/core/core.go
@@ -27,7 +27,7 @@ const (
 	// SdkName is the human readable name of the SDK for UserAgents.
 	SdkName = "Apache Beam SDK for Go"
 	// SdkVersion is the current version of the SDK.
-	SdkVersion = "2.67.0.dev"
+	SdkVersion = "2.69.0.dev"
 
 	// DefaultDockerImage represents the associated image for this release.
 	DefaultDockerImage = "apache/beam_go_sdk:" + SdkVersion
diff --git a/sdks/go/pkg/beam/core/metrics/sampler.go b/sdks/go/pkg/beam/core/metrics/sampler.go
index 63968697b45f..264eaeae487b 100644
--- a/sdks/go/pkg/beam/core/metrics/sampler.go
+++ b/sdks/go/pkg/beam/core/metrics/sampler.go
@@ -21,6 +21,7 @@ import (
 	"time"
 	"unsafe"
 
+	"github.com/apache/beam/sdks/v2/go/pkg/beam/internal/errors"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/log"
 )
 
@@ -31,18 +32,19 @@ type StateSampler struct {
 	transitionsAtLastSample   int64
 	nextLogTime               time.Duration
 	logInterval               time.Duration
+	restartLullTimeout        time.Duration
 }
 
 // NewSampler creates a new state sampler.
-func NewSampler(store *Store) StateSampler {
-	return StateSampler{store: store, nextLogTime: 5 * time.Minute, logInterval: 5 * time.Minute}
+func NewSampler(store *Store, elementProcessingTimeout time.Duration) StateSampler {
+	return StateSampler{store: store, nextLogTime: 5 * time.Minute, logInterval: 5 * time.Minute, restartLullTimeout: elementProcessingTimeout}
 }
 
 // Sample checks for state transition in processing a DoFn
-func (s *StateSampler) Sample(ctx context.Context, t time.Duration) {
+func (s *StateSampler) Sample(ctx context.Context, t time.Duration) error {
 	ps := loadCurrentState(s)
 	if ps.pid == "" {
-		return
+		return nil
 	}
 	s.store.mu.Lock()
 	defer s.store.mu.Unlock()
@@ -61,10 +63,14 @@ func (s *StateSampler) Sample(ctx context.Context, t time.Duration) {
 		}
 
 		if s.millisSinceLastTransition > s.nextLogTime {
-			log.Infof(ctx, "Operation ongoing in transform %v for at least %v ms without outputting or completing in state %v", ps.pid, s.millisSinceLastTransition, getState(ps.state))
+			log.Infof(ctx, "Operation ongoing in transform %v for at least %v without outputting or completing in state %v", ps.pid, s.millisSinceLastTransition, getState(ps.state))
 			s.nextLogTime += s.logInterval
 		}
+		if s.restartLullTimeout > 0 && s.millisSinceLastTransition > s.restartLullTimeout {
+			return errors.Errorf("Processing of an element in transform %v has exceeded the specified timeout of %v without outputting or completing in state %v, SDK harness will be terminated", ps.pid, s.restartLullTimeout, getState(ps.state))
+		}
 	}
+	return nil
 }
 
 // SetLogInterval sets the logging interval for lull reporting.
diff --git a/sdks/go/pkg/beam/core/metrics/sampler_test.go b/sdks/go/pkg/beam/core/metrics/sampler_test.go
index 49f4f658fad7..ec50bc22d1e8 100644
--- a/sdks/go/pkg/beam/core/metrics/sampler_test.go
+++ b/sdks/go/pkg/beam/core/metrics/sampler_test.go
@@ -17,6 +17,7 @@ package metrics
 
 import (
 	"context"
+	"strings"
 	"sync/atomic"
 	"testing"
 	"time"
@@ -44,7 +45,7 @@ func TestSampler(t *testing.T) {
 	bctx := SetBundleID(ctx, "test")
 	interval := 200 * time.Millisecond
 	st := GetStore(bctx)
-	s := NewSampler(st)
+	s := NewSampler(st, 0*time.Minute)
 
 	pctx := SetPTransformID(bctx, "transform")
 	label := "transform"
@@ -88,7 +89,7 @@ func TestSampler_TwoPTransforms(t *testing.T) {
 	bctx := SetBundleID(ctx, "bundle")
 	interval := 200 * time.Millisecond
 	st := GetStore(bctx)
-	s := NewSampler(st)
+	s := NewSampler(st, 0*time.Minute)
 
 	ctxA := SetPTransformID(bctx, "transformA")
 	ctxB := SetPTransformID(bctx, "transformB")
@@ -136,6 +137,50 @@ func TestSampler_TwoPTransforms(t *testing.T) {
 	checkBundleState(bctx, t, s, 6, 0)
 }
 
+func TestSamplerWithoutRestartLullTimeout(t *testing.T) {
+	ctx := context.Background()
+	bctx := SetBundleID(ctx, "test")
+	interval := 20 * time.Minute
+	st := GetStore(bctx)
+	s := NewSampler(st, 0*time.Minute)
+
+	pctx := SetPTransformID(bctx, "transform")
+
+	pt := NewPTransformState("transform")
+
+	pt.Set(pctx, StartBundle)
+	if got, want := s.Sample(bctx, interval), error(nil); got != want {
+		t.Errorf("s.sample(bctx, interval) = %v, want %v", got, want)
+	}
+}
+
+func TestSamplerWithRestartLullTimeout(t *testing.T) {
+	ctx := context.Background()
+	bctx := SetBundleID(ctx, "test")
+	interval := 4 * time.Minute
+	st := GetStore(bctx)
+	s := NewSampler(st, 10*time.Minute)
+
+	pctx := SetPTransformID(bctx, "transform")
+
+	pt := NewPTransformState("transform")
+
+	pt.Set(pctx, StartBundle)
+	if got, want := s.Sample(bctx, interval), error(nil); got != want {
+		t.Errorf("s.sample(bctx, interval) = %v, want %v", got, want)
+	}
+	if got, want := s.Sample(bctx, interval), error(nil); got != want {
+		t.Errorf("s.sample(bctx, interval) = %v, want %v", got, want)
+	}
+	if got, want := s.Sample(bctx, interval), error(nil); got != want {
+		t.Errorf("s.sample(bctx, interval) = %v, want %v", got, want)
+	}
+	err := s.Sample(bctx, interval)
+	if err == nil || !strings.Contains(err.Error(), "SDK harness will be terminated") {
+		t.Errorf("s.sample(bctx, interval) = %v, want %v", err, "SDK harness will be terminated")
+	}
+}
+
 // goos: darwin
 // goarch: amd64
 // pkg: github.com/apache/beam/sdks/v2/go/pkg/beam/core/metrics
@@ -165,7 +210,7 @@ func BenchmarkMsec_Sample(b *testing.B) {
 	pt := NewPTransformState("transform")
 	pt.Set(pctx, StartBundle)
 	st := GetStore(bctx)
-	s := NewSampler(st)
+	s := NewSampler(st, 0*time.Minute)
 	interval := 200 * time.Millisecond
 	for i := 0; i < b.N; i++ {
 		s.Sample(bctx, interval)
@@ -182,7 +227,7 @@ func BenchmarkMsec_Combined(b *testing.B) {
 	bctx := SetBundleID(ctx, "benchmark")
 
 	st := GetStore(bctx)
-	s := NewSampler(st)
+	s := NewSampler(st, 0*time.Minute)
 	done := make(chan int)
 	interval := 200 * time.Millisecond
 	go func(done chan int, s StateSampler) {
diff --git a/sdks/go/pkg/beam/core/runtime/exec/dynsplit_test.go b/sdks/go/pkg/beam/core/runtime/exec/dynsplit_test.go
index 84c84a8d3164..db2386d05e2b 100644
--- a/sdks/go/pkg/beam/core/runtime/exec/dynsplit_test.go
+++ b/sdks/go/pkg/beam/core/runtime/exec/dynsplit_test.go
@@ -376,10 +376,10 @@ func (rt *splitTestRTracker) TryClaim(pos any) bool {
 		rt.claim <- struct{}{}
 	}
 
-	rt.mu.Lock()
 	if i == rt.blockInd {
 		rt.blockClaim <- struct{}{}
 	}
+	rt.mu.Lock()
 	result := rt.rt.TryClaim(pos)
 	rt.mu.Unlock()
 
@@ -396,9 +396,9 @@ func (rt *splitTestRTracker) GetError() error {
 }
 
 func (rt *splitTestRTracker) TrySplit(fraction float64) (any, any, error) {
+	rt.blockSplit <- struct{}{}
 	rt.mu.Lock()
 	defer rt.mu.Unlock()
-	rt.blockSplit <- struct{}{}
 	return rt.rt.TrySplit(fraction)
 }
 
diff --git a/sdks/go/pkg/beam/core/runtime/exec/emit.go b/sdks/go/pkg/beam/core/runtime/exec/emit.go
index 1f382a236546..1e3842ec7e1a 100644
--- a/sdks/go/pkg/beam/core/runtime/exec/emit.go
+++ b/sdks/go/pkg/beam/core/runtime/exec/emit.go
@@ -30,7 +30,7 @@ import (
 // emit event time.
 type ReusableEmitter interface {
 	// Init resets the value. Can be called multiple times.
-	Init(ctx context.Context, ws []typex.Window, t typex.EventTime) error
+	Init(ctx context.Context, pn typex.PaneInfo, ws []typex.Window, t typex.EventTime) error
 	// Value returns the side input value. Constant value.
 	Value() any
 }
@@ -96,12 +96,14 @@ type emitValue struct {
 	est   *sdf.WatermarkEstimator
 
 	ctx context.Context
+	pn  typex.PaneInfo
 	ws  []typex.Window
 	et  typex.EventTime
 }
 
-func (e *emitValue) Init(ctx context.Context, ws []typex.Window, et typex.EventTime) error {
+func (e *emitValue) Init(ctx context.Context, pn typex.PaneInfo, ws []typex.Window, et typex.EventTime) error {
 	e.ctx = ctx
+	e.pn = pn
 	e.ws = ws
 	e.et = et
 	return nil
@@ -116,7 +118,7 @@ func (e *emitValue) AttachEstimator(est *sdf.WatermarkEstimator) {
 }
 
 func (e *emitValue) invoke(args []reflect.Value) []reflect.Value {
-	value := &FullValue{Windows: e.ws, Timestamp: e.et}
+	value := &FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et}
 	isKey := true
 	for i, t := range e.types {
 		switch {
diff --git a/sdks/go/pkg/beam/core/runtime/exec/optimized/emitters.go b/sdks/go/pkg/beam/core/runtime/exec/optimized/emitters.go
index 83b60abe0b16..906c93bd75d8 100644
--- a/sdks/go/pkg/beam/core/runtime/exec/optimized/emitters.go
+++ b/sdks/go/pkg/beam/core/runtime/exec/optimized/emitters.go
@@ -1047,13 +1047,15 @@ type emitNative struct {
 	est *sdf.WatermarkEstimator
 
 	ctx   context.Context
+	pn    typex.PaneInfo
 	ws    []typex.Window
 	et    typex.EventTime
 	value exec.FullValue
 }
 
-func (e *emitNative) Init(ctx context.Context, ws []typex.Window, et typex.EventTime) error {
+func (e *emitNative) Init(ctx context.Context, pn typex.PaneInfo, ws []typex.Window, et typex.EventTime) error {
 	e.ctx = ctx
+	e.pn = pn
 	e.ws = ws
 	e.et = et
 	return nil
@@ -1074,7 +1076,7 @@ func emitMakerByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSlice(elm []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1090,7 +1092,7 @@ func emitMakerETByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSlice(t typex.EventTime, elm []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1106,7 +1108,7 @@ func emitMakerByteSliceByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceByteSlice(key []byte, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1122,7 +1124,7 @@ func emitMakerETByteSliceByteSlice(n exec.ElementProcessor) exec.ReusableEmitter
 }
 
 func (e *emitNative) invokeETByteSliceByteSlice(t typex.EventTime, key []byte, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1138,7 +1140,7 @@ func emitMakerByteSliceBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceBool(key []byte, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1154,7 +1156,7 @@ func emitMakerETByteSliceBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceBool(t typex.EventTime, key []byte, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1170,7 +1172,7 @@ func emitMakerByteSliceString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceString(key []byte, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1186,7 +1188,7 @@ func emitMakerETByteSliceString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceString(t typex.EventTime, key []byte, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1202,7 +1204,7 @@ func emitMakerByteSliceInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceInt(key []byte, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1218,7 +1220,7 @@ func emitMakerETByteSliceInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceInt(t typex.EventTime, key []byte, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1234,7 +1236,7 @@ func emitMakerByteSliceInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceInt8(key []byte, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1250,7 +1252,7 @@ func emitMakerETByteSliceInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceInt8(t typex.EventTime, key []byte, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1266,7 +1268,7 @@ func emitMakerByteSliceInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceInt16(key []byte, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1282,7 +1284,7 @@ func emitMakerETByteSliceInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceInt16(t typex.EventTime, key []byte, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1298,7 +1300,7 @@ func emitMakerByteSliceInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceInt32(key []byte, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1314,7 +1316,7 @@ func emitMakerETByteSliceInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceInt32(t typex.EventTime, key []byte, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1330,7 +1332,7 @@ func emitMakerByteSliceInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceInt64(key []byte, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1346,7 +1348,7 @@ func emitMakerETByteSliceInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceInt64(t typex.EventTime, key []byte, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1362,7 +1364,7 @@ func emitMakerByteSliceUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceUint(key []byte, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1378,7 +1380,7 @@ func emitMakerETByteSliceUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceUint(t typex.EventTime, key []byte, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1394,7 +1396,7 @@ func emitMakerByteSliceUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceUint8(key []byte, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1410,7 +1412,7 @@ func emitMakerETByteSliceUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceUint8(t typex.EventTime, key []byte, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1426,7 +1428,7 @@ func emitMakerByteSliceUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceUint16(key []byte, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1442,7 +1444,7 @@ func emitMakerETByteSliceUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceUint16(t typex.EventTime, key []byte, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1458,7 +1460,7 @@ func emitMakerByteSliceUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceUint32(key []byte, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1474,7 +1476,7 @@ func emitMakerETByteSliceUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceUint32(t typex.EventTime, key []byte, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1490,7 +1492,7 @@ func emitMakerByteSliceUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceUint64(key []byte, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1506,7 +1508,7 @@ func emitMakerETByteSliceUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceUint64(t typex.EventTime, key []byte, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1522,7 +1524,7 @@ func emitMakerByteSliceFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceFloat32(key []byte, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1538,7 +1540,7 @@ func emitMakerETByteSliceFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceFloat32(t typex.EventTime, key []byte, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1554,7 +1556,7 @@ func emitMakerByteSliceFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceFloat64(key []byte, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1570,7 +1572,7 @@ func emitMakerETByteSliceFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceFloat64(t typex.EventTime, key []byte, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1586,7 +1588,7 @@ func emitMakerByteSliceTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceTypex_T(key []byte, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1602,7 +1604,7 @@ func emitMakerETByteSliceTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceTypex_T(t typex.EventTime, key []byte, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1618,7 +1620,7 @@ func emitMakerByteSliceTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceTypex_U(key []byte, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1634,7 +1636,7 @@ func emitMakerETByteSliceTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceTypex_U(t typex.EventTime, key []byte, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1650,7 +1652,7 @@ func emitMakerByteSliceTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceTypex_V(key []byte, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1666,7 +1668,7 @@ func emitMakerETByteSliceTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceTypex_V(t typex.EventTime, key []byte, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1682,7 +1684,7 @@ func emitMakerByteSliceTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceTypex_W(key []byte, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1698,7 +1700,7 @@ func emitMakerETByteSliceTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceTypex_W(t typex.EventTime, key []byte, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1714,7 +1716,7 @@ func emitMakerByteSliceTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceTypex_X(key []byte, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1730,7 +1732,7 @@ func emitMakerETByteSliceTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceTypex_X(t typex.EventTime, key []byte, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1746,7 +1748,7 @@ func emitMakerByteSliceTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceTypex_Y(key []byte, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1762,7 +1764,7 @@ func emitMakerETByteSliceTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceTypex_Y(t typex.EventTime, key []byte, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1778,7 +1780,7 @@ func emitMakerByteSliceTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeByteSliceTypex_Z(key []byte, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1794,7 +1796,7 @@ func emitMakerETByteSliceTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETByteSliceTypex_Z(t typex.EventTime, key []byte, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1810,7 +1812,7 @@ func emitMakerBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBool(elm bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1826,7 +1828,7 @@ func emitMakerETBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBool(t typex.EventTime, elm bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1842,7 +1844,7 @@ func emitMakerBoolByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolByteSlice(key bool, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1858,7 +1860,7 @@ func emitMakerETBoolByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolByteSlice(t typex.EventTime, key bool, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1874,7 +1876,7 @@ func emitMakerBoolBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolBool(key bool, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1890,7 +1892,7 @@ func emitMakerETBoolBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolBool(t typex.EventTime, key bool, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1906,7 +1908,7 @@ func emitMakerBoolString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolString(key bool, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1922,7 +1924,7 @@ func emitMakerETBoolString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolString(t typex.EventTime, key bool, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1938,7 +1940,7 @@ func emitMakerBoolInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolInt(key bool, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1954,7 +1956,7 @@ func emitMakerETBoolInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolInt(t typex.EventTime, key bool, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -1970,7 +1972,7 @@ func emitMakerBoolInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolInt8(key bool, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -1986,7 +1988,7 @@ func emitMakerETBoolInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolInt8(t typex.EventTime, key bool, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2002,7 +2004,7 @@ func emitMakerBoolInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolInt16(key bool, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2018,7 +2020,7 @@ func emitMakerETBoolInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolInt16(t typex.EventTime, key bool, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2034,7 +2036,7 @@ func emitMakerBoolInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolInt32(key bool, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2050,7 +2052,7 @@ func emitMakerETBoolInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolInt32(t typex.EventTime, key bool, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2066,7 +2068,7 @@ func emitMakerBoolInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolInt64(key bool, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2082,7 +2084,7 @@ func emitMakerETBoolInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolInt64(t typex.EventTime, key bool, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2098,7 +2100,7 @@ func emitMakerBoolUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolUint(key bool, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2114,7 +2116,7 @@ func emitMakerETBoolUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolUint(t typex.EventTime, key bool, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2130,7 +2132,7 @@ func emitMakerBoolUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolUint8(key bool, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2146,7 +2148,7 @@ func emitMakerETBoolUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolUint8(t typex.EventTime, key bool, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2162,7 +2164,7 @@ func emitMakerBoolUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolUint16(key bool, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2178,7 +2180,7 @@ func emitMakerETBoolUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolUint16(t typex.EventTime, key bool, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2194,7 +2196,7 @@ func emitMakerBoolUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolUint32(key bool, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2210,7 +2212,7 @@ func emitMakerETBoolUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolUint32(t typex.EventTime, key bool, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2226,7 +2228,7 @@ func emitMakerBoolUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolUint64(key bool, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2242,7 +2244,7 @@ func emitMakerETBoolUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolUint64(t typex.EventTime, key bool, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2258,7 +2260,7 @@ func emitMakerBoolFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolFloat32(key bool, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2274,7 +2276,7 @@ func emitMakerETBoolFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolFloat32(t typex.EventTime, key bool, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2290,7 +2292,7 @@ func emitMakerBoolFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolFloat64(key bool, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2306,7 +2308,7 @@ func emitMakerETBoolFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolFloat64(t typex.EventTime, key bool, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2322,7 +2324,7 @@ func emitMakerBoolTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolTypex_T(key bool, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2338,7 +2340,7 @@ func emitMakerETBoolTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolTypex_T(t typex.EventTime, key bool, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2354,7 +2356,7 @@ func emitMakerBoolTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolTypex_U(key bool, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2370,7 +2372,7 @@ func emitMakerETBoolTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolTypex_U(t typex.EventTime, key bool, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2386,7 +2388,7 @@ func emitMakerBoolTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolTypex_V(key bool, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2402,7 +2404,7 @@ func emitMakerETBoolTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolTypex_V(t typex.EventTime, key bool, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2418,7 +2420,7 @@ func emitMakerBoolTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolTypex_W(key bool, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2434,7 +2436,7 @@ func emitMakerETBoolTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolTypex_W(t typex.EventTime, key bool, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2450,7 +2452,7 @@ func emitMakerBoolTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolTypex_X(key bool, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2466,7 +2468,7 @@ func emitMakerETBoolTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolTypex_X(t typex.EventTime, key bool, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2482,7 +2484,7 @@ func emitMakerBoolTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolTypex_Y(key bool, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2498,7 +2500,7 @@ func emitMakerETBoolTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolTypex_Y(t typex.EventTime, key bool, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2514,7 +2516,7 @@ func emitMakerBoolTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeBoolTypex_Z(key bool, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2530,7 +2532,7 @@ func emitMakerETBoolTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETBoolTypex_Z(t typex.EventTime, key bool, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2546,7 +2548,7 @@ func emitMakerString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeString(elm string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2562,7 +2564,7 @@ func emitMakerETString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETString(t typex.EventTime, elm string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2578,7 +2580,7 @@ func emitMakerStringByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringByteSlice(key string, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2594,7 +2596,7 @@ func emitMakerETStringByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringByteSlice(t typex.EventTime, key string, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2610,7 +2612,7 @@ func emitMakerStringBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringBool(key string, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2626,7 +2628,7 @@ func emitMakerETStringBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringBool(t typex.EventTime, key string, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2642,7 +2644,7 @@ func emitMakerStringString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringString(key string, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2658,7 +2660,7 @@ func emitMakerETStringString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringString(t typex.EventTime, key string, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2674,7 +2676,7 @@ func emitMakerStringInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringInt(key string, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2690,7 +2692,7 @@ func emitMakerETStringInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringInt(t typex.EventTime, key string, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2706,7 +2708,7 @@ func emitMakerStringInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringInt8(key string, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2722,7 +2724,7 @@ func emitMakerETStringInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringInt8(t typex.EventTime, key string, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2738,7 +2740,7 @@ func emitMakerStringInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringInt16(key string, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2754,7 +2756,7 @@ func emitMakerETStringInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringInt16(t typex.EventTime, key string, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2770,7 +2772,7 @@ func emitMakerStringInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringInt32(key string, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2786,7 +2788,7 @@ func emitMakerETStringInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringInt32(t typex.EventTime, key string, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2802,7 +2804,7 @@ func emitMakerStringInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringInt64(key string, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2818,7 +2820,7 @@ func emitMakerETStringInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringInt64(t typex.EventTime, key string, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2834,7 +2836,7 @@ func emitMakerStringUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringUint(key string, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2850,7 +2852,7 @@ func emitMakerETStringUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringUint(t typex.EventTime, key string, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2866,7 +2868,7 @@ func emitMakerStringUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringUint8(key string, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2882,7 +2884,7 @@ func emitMakerETStringUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringUint8(t typex.EventTime, key string, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2898,7 +2900,7 @@ func emitMakerStringUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringUint16(key string, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2914,7 +2916,7 @@ func emitMakerETStringUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringUint16(t typex.EventTime, key string, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2930,7 +2932,7 @@ func emitMakerStringUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringUint32(key string, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2946,7 +2948,7 @@ func emitMakerETStringUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringUint32(t typex.EventTime, key string, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2962,7 +2964,7 @@ func emitMakerStringUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringUint64(key string, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -2978,7 +2980,7 @@ func emitMakerETStringUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringUint64(t typex.EventTime, key string, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -2994,7 +2996,7 @@ func emitMakerStringFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringFloat32(key string, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3010,7 +3012,7 @@ func emitMakerETStringFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringFloat32(t typex.EventTime, key string, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3026,7 +3028,7 @@ func emitMakerStringFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringFloat64(key string, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3042,7 +3044,7 @@ func emitMakerETStringFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringFloat64(t typex.EventTime, key string, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3058,7 +3060,7 @@ func emitMakerStringTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringTypex_T(key string, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3074,7 +3076,7 @@ func emitMakerETStringTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringTypex_T(t typex.EventTime, key string, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3090,7 +3092,7 @@ func emitMakerStringTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringTypex_U(key string, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3106,7 +3108,7 @@ func emitMakerETStringTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringTypex_U(t typex.EventTime, key string, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3122,7 +3124,7 @@ func emitMakerStringTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringTypex_V(key string, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3138,7 +3140,7 @@ func emitMakerETStringTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringTypex_V(t typex.EventTime, key string, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3154,7 +3156,7 @@ func emitMakerStringTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringTypex_W(key string, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3170,7 +3172,7 @@ func emitMakerETStringTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringTypex_W(t typex.EventTime, key string, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3186,7 +3188,7 @@ func emitMakerStringTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringTypex_X(key string, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3202,7 +3204,7 @@ func emitMakerETStringTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringTypex_X(t typex.EventTime, key string, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3218,7 +3220,7 @@ func emitMakerStringTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringTypex_Y(key string, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3234,7 +3236,7 @@ func emitMakerETStringTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringTypex_Y(t typex.EventTime, key string, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3250,7 +3252,7 @@ func emitMakerStringTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringTypex_Z(key string, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3266,7 +3268,7 @@ func emitMakerETStringTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETStringTypex_Z(t typex.EventTime, key string, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3282,7 +3284,7 @@ func emitMakerInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt(elm int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3298,7 +3300,7 @@ func emitMakerETInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt(t typex.EventTime, elm int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3314,7 +3316,7 @@ func emitMakerIntByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntByteSlice(key int, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3330,7 +3332,7 @@ func emitMakerETIntByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntByteSlice(t typex.EventTime, key int, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3346,7 +3348,7 @@ func emitMakerIntBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntBool(key int, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3362,7 +3364,7 @@ func emitMakerETIntBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntBool(t typex.EventTime, key int, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3378,7 +3380,7 @@ func emitMakerIntString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntString(key int, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3394,7 +3396,7 @@ func emitMakerETIntString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntString(t typex.EventTime, key int, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3410,7 +3412,7 @@ func emitMakerIntInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntInt(key int, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3426,7 +3428,7 @@ func emitMakerETIntInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntInt(t typex.EventTime, key int, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3442,7 +3444,7 @@ func emitMakerIntInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntInt8(key int, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3458,7 +3460,7 @@ func emitMakerETIntInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntInt8(t typex.EventTime, key int, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3474,7 +3476,7 @@ func emitMakerIntInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntInt16(key int, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3490,7 +3492,7 @@ func emitMakerETIntInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntInt16(t typex.EventTime, key int, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3506,7 +3508,7 @@ func emitMakerIntInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntInt32(key int, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3522,7 +3524,7 @@ func emitMakerETIntInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntInt32(t typex.EventTime, key int, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3538,7 +3540,7 @@ func emitMakerIntInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntInt64(key int, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3554,7 +3556,7 @@ func emitMakerETIntInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntInt64(t typex.EventTime, key int, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3570,7 +3572,7 @@ func emitMakerIntUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntUint(key int, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3586,7 +3588,7 @@ func emitMakerETIntUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntUint(t typex.EventTime, key int, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3602,7 +3604,7 @@ func emitMakerIntUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntUint8(key int, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3618,7 +3620,7 @@ func emitMakerETIntUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntUint8(t typex.EventTime, key int, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3634,7 +3636,7 @@ func emitMakerIntUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntUint16(key int, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3650,7 +3652,7 @@ func emitMakerETIntUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntUint16(t typex.EventTime, key int, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3666,7 +3668,7 @@ func emitMakerIntUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntUint32(key int, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3682,7 +3684,7 @@ func emitMakerETIntUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntUint32(t typex.EventTime, key int, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3698,7 +3700,7 @@ func emitMakerIntUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntUint64(key int, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3714,7 +3716,7 @@ func emitMakerETIntUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntUint64(t typex.EventTime, key int, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3730,7 +3732,7 @@ func emitMakerIntFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntFloat32(key int, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3746,7 +3748,7 @@ func emitMakerETIntFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntFloat32(t typex.EventTime, key int, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3762,7 +3764,7 @@ func emitMakerIntFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntFloat64(key int, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3778,7 +3780,7 @@ func emitMakerETIntFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntFloat64(t typex.EventTime, key int, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3794,7 +3796,7 @@ func emitMakerIntTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntTypex_T(key int, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3810,7 +3812,7 @@ func emitMakerETIntTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntTypex_T(t typex.EventTime, key int, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3826,7 +3828,7 @@ func emitMakerIntTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntTypex_U(key int, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3842,7 +3844,7 @@ func emitMakerETIntTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntTypex_U(t typex.EventTime, key int, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3858,7 +3860,7 @@ func emitMakerIntTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntTypex_V(key int, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3874,7 +3876,7 @@ func emitMakerETIntTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntTypex_V(t typex.EventTime, key int, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3890,7 +3892,7 @@ func emitMakerIntTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntTypex_W(key int, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3906,7 +3908,7 @@ func emitMakerETIntTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntTypex_W(t typex.EventTime, key int, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3922,7 +3924,7 @@ func emitMakerIntTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntTypex_X(key int, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3938,7 +3940,7 @@ func emitMakerETIntTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntTypex_X(t typex.EventTime, key int, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3954,7 +3956,7 @@ func emitMakerIntTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntTypex_Y(key int, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -3970,7 +3972,7 @@ func emitMakerETIntTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntTypex_Y(t typex.EventTime, key int, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -3986,7 +3988,7 @@ func emitMakerIntTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeIntTypex_Z(key int, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4002,7 +4004,7 @@ func emitMakerETIntTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETIntTypex_Z(t typex.EventTime, key int, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4018,7 +4020,7 @@ func emitMakerInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8(elm int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4034,7 +4036,7 @@ func emitMakerETInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8(t typex.EventTime, elm int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4050,7 +4052,7 @@ func emitMakerInt8ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8ByteSlice(key int8, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4066,7 +4068,7 @@ func emitMakerETInt8ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8ByteSlice(t typex.EventTime, key int8, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4082,7 +4084,7 @@ func emitMakerInt8Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Bool(key int8, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4098,7 +4100,7 @@ func emitMakerETInt8Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Bool(t typex.EventTime, key int8, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4114,7 +4116,7 @@ func emitMakerInt8String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8String(key int8, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4130,7 +4132,7 @@ func emitMakerETInt8String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8String(t typex.EventTime, key int8, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4146,7 +4148,7 @@ func emitMakerInt8Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Int(key int8, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4162,7 +4164,7 @@ func emitMakerETInt8Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Int(t typex.EventTime, key int8, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4178,7 +4180,7 @@ func emitMakerInt8Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Int8(key int8, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4194,7 +4196,7 @@ func emitMakerETInt8Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Int8(t typex.EventTime, key int8, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4210,7 +4212,7 @@ func emitMakerInt8Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Int16(key int8, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4226,7 +4228,7 @@ func emitMakerETInt8Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Int16(t typex.EventTime, key int8, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4242,7 +4244,7 @@ func emitMakerInt8Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Int32(key int8, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4258,7 +4260,7 @@ func emitMakerETInt8Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Int32(t typex.EventTime, key int8, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4274,7 +4276,7 @@ func emitMakerInt8Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Int64(key int8, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4290,7 +4292,7 @@ func emitMakerETInt8Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Int64(t typex.EventTime, key int8, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4306,7 +4308,7 @@ func emitMakerInt8Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Uint(key int8, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4322,7 +4324,7 @@ func emitMakerETInt8Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Uint(t typex.EventTime, key int8, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4338,7 +4340,7 @@ func emitMakerInt8Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Uint8(key int8, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4354,7 +4356,7 @@ func emitMakerETInt8Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Uint8(t typex.EventTime, key int8, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4370,7 +4372,7 @@ func emitMakerInt8Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Uint16(key int8, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4386,7 +4388,7 @@ func emitMakerETInt8Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Uint16(t typex.EventTime, key int8, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4402,7 +4404,7 @@ func emitMakerInt8Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Uint32(key int8, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4418,7 +4420,7 @@ func emitMakerETInt8Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Uint32(t typex.EventTime, key int8, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4434,7 +4436,7 @@ func emitMakerInt8Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Uint64(key int8, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4450,7 +4452,7 @@ func emitMakerETInt8Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Uint64(t typex.EventTime, key int8, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4466,7 +4468,7 @@ func emitMakerInt8Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Float32(key int8, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4482,7 +4484,7 @@ func emitMakerETInt8Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Float32(t typex.EventTime, key int8, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4498,7 +4500,7 @@ func emitMakerInt8Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Float64(key int8, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4514,7 +4516,7 @@ func emitMakerETInt8Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Float64(t typex.EventTime, key int8, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4530,7 +4532,7 @@ func emitMakerInt8Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Typex_T(key int8, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4546,7 +4548,7 @@ func emitMakerETInt8Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Typex_T(t typex.EventTime, key int8, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4562,7 +4564,7 @@ func emitMakerInt8Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Typex_U(key int8, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4578,7 +4580,7 @@ func emitMakerETInt8Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Typex_U(t typex.EventTime, key int8, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4594,7 +4596,7 @@ func emitMakerInt8Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Typex_V(key int8, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4610,7 +4612,7 @@ func emitMakerETInt8Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Typex_V(t typex.EventTime, key int8, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4626,7 +4628,7 @@ func emitMakerInt8Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Typex_W(key int8, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4642,7 +4644,7 @@ func emitMakerETInt8Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Typex_W(t typex.EventTime, key int8, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4658,7 +4660,7 @@ func emitMakerInt8Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Typex_X(key int8, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4674,7 +4676,7 @@ func emitMakerETInt8Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Typex_X(t typex.EventTime, key int8, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4690,7 +4692,7 @@ func emitMakerInt8Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Typex_Y(key int8, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4706,7 +4708,7 @@ func emitMakerETInt8Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Typex_Y(t typex.EventTime, key int8, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4722,7 +4724,7 @@ func emitMakerInt8Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt8Typex_Z(key int8, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4738,7 +4740,7 @@ func emitMakerETInt8Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt8Typex_Z(t typex.EventTime, key int8, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4754,7 +4756,7 @@ func emitMakerInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16(elm int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4770,7 +4772,7 @@ func emitMakerETInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16(t typex.EventTime, elm int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4786,7 +4788,7 @@ func emitMakerInt16ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16ByteSlice(key int16, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4802,7 +4804,7 @@ func emitMakerETInt16ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16ByteSlice(t typex.EventTime, key int16, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4818,7 +4820,7 @@ func emitMakerInt16Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Bool(key int16, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4834,7 +4836,7 @@ func emitMakerETInt16Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Bool(t typex.EventTime, key int16, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4850,7 +4852,7 @@ func emitMakerInt16String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16String(key int16, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4866,7 +4868,7 @@ func emitMakerETInt16String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16String(t typex.EventTime, key int16, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4882,7 +4884,7 @@ func emitMakerInt16Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Int(key int16, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4898,7 +4900,7 @@ func emitMakerETInt16Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Int(t typex.EventTime, key int16, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4914,7 +4916,7 @@ func emitMakerInt16Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Int8(key int16, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4930,7 +4932,7 @@ func emitMakerETInt16Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Int8(t typex.EventTime, key int16, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4946,7 +4948,7 @@ func emitMakerInt16Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Int16(key int16, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4962,7 +4964,7 @@ func emitMakerETInt16Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Int16(t typex.EventTime, key int16, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -4978,7 +4980,7 @@ func emitMakerInt16Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Int32(key int16, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -4994,7 +4996,7 @@ func emitMakerETInt16Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Int32(t typex.EventTime, key int16, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5010,7 +5012,7 @@ func emitMakerInt16Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Int64(key int16, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5026,7 +5028,7 @@ func emitMakerETInt16Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Int64(t typex.EventTime, key int16, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5042,7 +5044,7 @@ func emitMakerInt16Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Uint(key int16, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5058,7 +5060,7 @@ func emitMakerETInt16Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Uint(t typex.EventTime, key int16, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5074,7 +5076,7 @@ func emitMakerInt16Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Uint8(key int16, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5090,7 +5092,7 @@ func emitMakerETInt16Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Uint8(t typex.EventTime, key int16, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5106,7 +5108,7 @@ func emitMakerInt16Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Uint16(key int16, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5122,7 +5124,7 @@ func emitMakerETInt16Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Uint16(t typex.EventTime, key int16, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5138,7 +5140,7 @@ func emitMakerInt16Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Uint32(key int16, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5154,7 +5156,7 @@ func emitMakerETInt16Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Uint32(t typex.EventTime, key int16, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5170,7 +5172,7 @@ func emitMakerInt16Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Uint64(key int16, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5186,7 +5188,7 @@ func emitMakerETInt16Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Uint64(t typex.EventTime, key int16, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5202,7 +5204,7 @@ func emitMakerInt16Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Float32(key int16, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5218,7 +5220,7 @@ func emitMakerETInt16Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Float32(t typex.EventTime, key int16, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5234,7 +5236,7 @@ func emitMakerInt16Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Float64(key int16, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5250,7 +5252,7 @@ func emitMakerETInt16Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Float64(t typex.EventTime, key int16, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5266,7 +5268,7 @@ func emitMakerInt16Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Typex_T(key int16, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5282,7 +5284,7 @@ func emitMakerETInt16Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Typex_T(t typex.EventTime, key int16, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5298,7 +5300,7 @@ func emitMakerInt16Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Typex_U(key int16, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5314,7 +5316,7 @@ func emitMakerETInt16Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Typex_U(t typex.EventTime, key int16, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5330,7 +5332,7 @@ func emitMakerInt16Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Typex_V(key int16, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5346,7 +5348,7 @@ func emitMakerETInt16Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Typex_V(t typex.EventTime, key int16, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5362,7 +5364,7 @@ func emitMakerInt16Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Typex_W(key int16, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5378,7 +5380,7 @@ func emitMakerETInt16Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Typex_W(t typex.EventTime, key int16, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5394,7 +5396,7 @@ func emitMakerInt16Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Typex_X(key int16, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5410,7 +5412,7 @@ func emitMakerETInt16Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Typex_X(t typex.EventTime, key int16, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5426,7 +5428,7 @@ func emitMakerInt16Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Typex_Y(key int16, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5442,7 +5444,7 @@ func emitMakerETInt16Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Typex_Y(t typex.EventTime, key int16, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5458,7 +5460,7 @@ func emitMakerInt16Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt16Typex_Z(key int16, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5474,7 +5476,7 @@ func emitMakerETInt16Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt16Typex_Z(t typex.EventTime, key int16, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5490,7 +5492,7 @@ func emitMakerInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32(elm int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5506,7 +5508,7 @@ func emitMakerETInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32(t typex.EventTime, elm int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5522,7 +5524,7 @@ func emitMakerInt32ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32ByteSlice(key int32, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5538,7 +5540,7 @@ func emitMakerETInt32ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32ByteSlice(t typex.EventTime, key int32, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5554,7 +5556,7 @@ func emitMakerInt32Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Bool(key int32, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5570,7 +5572,7 @@ func emitMakerETInt32Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Bool(t typex.EventTime, key int32, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5586,7 +5588,7 @@ func emitMakerInt32String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32String(key int32, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5602,7 +5604,7 @@ func emitMakerETInt32String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32String(t typex.EventTime, key int32, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5618,7 +5620,7 @@ func emitMakerInt32Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Int(key int32, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5634,7 +5636,7 @@ func emitMakerETInt32Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Int(t typex.EventTime, key int32, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5650,7 +5652,7 @@ func emitMakerInt32Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Int8(key int32, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5666,7 +5668,7 @@ func emitMakerETInt32Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Int8(t typex.EventTime, key int32, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5682,7 +5684,7 @@ func emitMakerInt32Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Int16(key int32, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5698,7 +5700,7 @@ func emitMakerETInt32Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Int16(t typex.EventTime, key int32, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5714,7 +5716,7 @@ func emitMakerInt32Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Int32(key int32, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5730,7 +5732,7 @@ func emitMakerETInt32Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Int32(t typex.EventTime, key int32, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5746,7 +5748,7 @@ func emitMakerInt32Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Int64(key int32, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5762,7 +5764,7 @@ func emitMakerETInt32Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Int64(t typex.EventTime, key int32, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5778,7 +5780,7 @@ func emitMakerInt32Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Uint(key int32, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5794,7 +5796,7 @@ func emitMakerETInt32Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Uint(t typex.EventTime, key int32, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5810,7 +5812,7 @@ func emitMakerInt32Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Uint8(key int32, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5826,7 +5828,7 @@ func emitMakerETInt32Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Uint8(t typex.EventTime, key int32, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5842,7 +5844,7 @@ func emitMakerInt32Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Uint16(key int32, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5858,7 +5860,7 @@ func emitMakerETInt32Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Uint16(t typex.EventTime, key int32, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5874,7 +5876,7 @@ func emitMakerInt32Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Uint32(key int32, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5890,7 +5892,7 @@ func emitMakerETInt32Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Uint32(t typex.EventTime, key int32, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5906,7 +5908,7 @@ func emitMakerInt32Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Uint64(key int32, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5922,7 +5924,7 @@ func emitMakerETInt32Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Uint64(t typex.EventTime, key int32, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5938,7 +5940,7 @@ func emitMakerInt32Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Float32(key int32, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5954,7 +5956,7 @@ func emitMakerETInt32Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Float32(t typex.EventTime, key int32, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -5970,7 +5972,7 @@ func emitMakerInt32Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Float64(key int32, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -5986,7 +5988,7 @@ func emitMakerETInt32Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Float64(t typex.EventTime, key int32, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6002,7 +6004,7 @@ func emitMakerInt32Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Typex_T(key int32, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6018,7 +6020,7 @@ func emitMakerETInt32Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Typex_T(t typex.EventTime, key int32, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6034,7 +6036,7 @@ func emitMakerInt32Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Typex_U(key int32, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6050,7 +6052,7 @@ func emitMakerETInt32Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Typex_U(t typex.EventTime, key int32, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6066,7 +6068,7 @@ func emitMakerInt32Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Typex_V(key int32, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6082,7 +6084,7 @@ func emitMakerETInt32Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Typex_V(t typex.EventTime, key int32, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6098,7 +6100,7 @@ func emitMakerInt32Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Typex_W(key int32, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6114,7 +6116,7 @@ func emitMakerETInt32Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Typex_W(t typex.EventTime, key int32, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6130,7 +6132,7 @@ func emitMakerInt32Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Typex_X(key int32, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6146,7 +6148,7 @@ func emitMakerETInt32Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Typex_X(t typex.EventTime, key int32, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6162,7 +6164,7 @@ func emitMakerInt32Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Typex_Y(key int32, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6178,7 +6180,7 @@ func emitMakerETInt32Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Typex_Y(t typex.EventTime, key int32, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6194,7 +6196,7 @@ func emitMakerInt32Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt32Typex_Z(key int32, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6210,7 +6212,7 @@ func emitMakerETInt32Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt32Typex_Z(t typex.EventTime, key int32, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6226,7 +6228,7 @@ func emitMakerInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64(elm int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6242,7 +6244,7 @@ func emitMakerETInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64(t typex.EventTime, elm int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6258,7 +6260,7 @@ func emitMakerInt64ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64ByteSlice(key int64, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6274,7 +6276,7 @@ func emitMakerETInt64ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64ByteSlice(t typex.EventTime, key int64, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6290,7 +6292,7 @@ func emitMakerInt64Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Bool(key int64, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6306,7 +6308,7 @@ func emitMakerETInt64Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Bool(t typex.EventTime, key int64, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6322,7 +6324,7 @@ func emitMakerInt64String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64String(key int64, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6338,7 +6340,7 @@ func emitMakerETInt64String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64String(t typex.EventTime, key int64, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6354,7 +6356,7 @@ func emitMakerInt64Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Int(key int64, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6370,7 +6372,7 @@ func emitMakerETInt64Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Int(t typex.EventTime, key int64, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6386,7 +6388,7 @@ func emitMakerInt64Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Int8(key int64, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6402,7 +6404,7 @@ func emitMakerETInt64Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Int8(t typex.EventTime, key int64, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6418,7 +6420,7 @@ func emitMakerInt64Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Int16(key int64, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6434,7 +6436,7 @@ func emitMakerETInt64Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Int16(t typex.EventTime, key int64, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6450,7 +6452,7 @@ func emitMakerInt64Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Int32(key int64, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6466,7 +6468,7 @@ func emitMakerETInt64Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Int32(t typex.EventTime, key int64, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6482,7 +6484,7 @@ func emitMakerInt64Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Int64(key int64, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6498,7 +6500,7 @@ func emitMakerETInt64Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Int64(t typex.EventTime, key int64, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6514,7 +6516,7 @@ func emitMakerInt64Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Uint(key int64, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6530,7 +6532,7 @@ func emitMakerETInt64Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Uint(t typex.EventTime, key int64, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6546,7 +6548,7 @@ func emitMakerInt64Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Uint8(key int64, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6562,7 +6564,7 @@ func emitMakerETInt64Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Uint8(t typex.EventTime, key int64, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6578,7 +6580,7 @@ func emitMakerInt64Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Uint16(key int64, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6594,7 +6596,7 @@ func emitMakerETInt64Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Uint16(t typex.EventTime, key int64, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6610,7 +6612,7 @@ func emitMakerInt64Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Uint32(key int64, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6626,7 +6628,7 @@ func emitMakerETInt64Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Uint32(t typex.EventTime, key int64, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6642,7 +6644,7 @@ func emitMakerInt64Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Uint64(key int64, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6658,7 +6660,7 @@ func emitMakerETInt64Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Uint64(t typex.EventTime, key int64, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6674,7 +6676,7 @@ func emitMakerInt64Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Float32(key int64, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6690,7 +6692,7 @@ func emitMakerETInt64Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Float32(t typex.EventTime, key int64, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6706,7 +6708,7 @@ func emitMakerInt64Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Float64(key int64, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6722,7 +6724,7 @@ func emitMakerETInt64Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Float64(t typex.EventTime, key int64, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6738,7 +6740,7 @@ func emitMakerInt64Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Typex_T(key int64, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6754,7 +6756,7 @@ func emitMakerETInt64Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Typex_T(t typex.EventTime, key int64, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6770,7 +6772,7 @@ func emitMakerInt64Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Typex_U(key int64, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6786,7 +6788,7 @@ func emitMakerETInt64Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Typex_U(t typex.EventTime, key int64, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6802,7 +6804,7 @@ func emitMakerInt64Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Typex_V(key int64, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6818,7 +6820,7 @@ func emitMakerETInt64Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Typex_V(t typex.EventTime, key int64, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6834,7 +6836,7 @@ func emitMakerInt64Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Typex_W(key int64, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6850,7 +6852,7 @@ func emitMakerETInt64Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Typex_W(t typex.EventTime, key int64, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6866,7 +6868,7 @@ func emitMakerInt64Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Typex_X(key int64, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6882,7 +6884,7 @@ func emitMakerETInt64Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Typex_X(t typex.EventTime, key int64, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6898,7 +6900,7 @@ func emitMakerInt64Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Typex_Y(key int64, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6914,7 +6916,7 @@ func emitMakerETInt64Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Typex_Y(t typex.EventTime, key int64, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6930,7 +6932,7 @@ func emitMakerInt64Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeInt64Typex_Z(key int64, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6946,7 +6948,7 @@ func emitMakerETInt64Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETInt64Typex_Z(t typex.EventTime, key int64, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6962,7 +6964,7 @@ func emitMakerUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint(elm uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -6978,7 +6980,7 @@ func emitMakerETUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint(t typex.EventTime, elm uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -6994,7 +6996,7 @@ func emitMakerUintByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintByteSlice(key uint, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7010,7 +7012,7 @@ func emitMakerETUintByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintByteSlice(t typex.EventTime, key uint, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7026,7 +7028,7 @@ func emitMakerUintBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintBool(key uint, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7042,7 +7044,7 @@ func emitMakerETUintBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintBool(t typex.EventTime, key uint, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7058,7 +7060,7 @@ func emitMakerUintString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintString(key uint, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7074,7 +7076,7 @@ func emitMakerETUintString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintString(t typex.EventTime, key uint, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7090,7 +7092,7 @@ func emitMakerUintInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintInt(key uint, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7106,7 +7108,7 @@ func emitMakerETUintInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintInt(t typex.EventTime, key uint, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7122,7 +7124,7 @@ func emitMakerUintInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintInt8(key uint, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7138,7 +7140,7 @@ func emitMakerETUintInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintInt8(t typex.EventTime, key uint, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7154,7 +7156,7 @@ func emitMakerUintInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintInt16(key uint, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7170,7 +7172,7 @@ func emitMakerETUintInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintInt16(t typex.EventTime, key uint, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7186,7 +7188,7 @@ func emitMakerUintInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintInt32(key uint, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7202,7 +7204,7 @@ func emitMakerETUintInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintInt32(t typex.EventTime, key uint, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7218,7 +7220,7 @@ func emitMakerUintInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintInt64(key uint, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7234,7 +7236,7 @@ func emitMakerETUintInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintInt64(t typex.EventTime, key uint, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7250,7 +7252,7 @@ func emitMakerUintUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintUint(key uint, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7266,7 +7268,7 @@ func emitMakerETUintUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintUint(t typex.EventTime, key uint, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7282,7 +7284,7 @@ func emitMakerUintUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintUint8(key uint, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7298,7 +7300,7 @@ func emitMakerETUintUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintUint8(t typex.EventTime, key uint, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7314,7 +7316,7 @@ func emitMakerUintUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintUint16(key uint, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7330,7 +7332,7 @@ func emitMakerETUintUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintUint16(t typex.EventTime, key uint, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7346,7 +7348,7 @@ func emitMakerUintUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintUint32(key uint, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7362,7 +7364,7 @@ func emitMakerETUintUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintUint32(t typex.EventTime, key uint, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7378,7 +7380,7 @@ func emitMakerUintUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintUint64(key uint, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7394,7 +7396,7 @@ func emitMakerETUintUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintUint64(t typex.EventTime, key uint, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7410,7 +7412,7 @@ func emitMakerUintFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintFloat32(key uint, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7426,7 +7428,7 @@ func emitMakerETUintFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintFloat32(t typex.EventTime, key uint, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7442,7 +7444,7 @@ func emitMakerUintFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintFloat64(key uint, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7458,7 +7460,7 @@ func emitMakerETUintFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintFloat64(t typex.EventTime, key uint, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7474,7 +7476,7 @@ func emitMakerUintTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintTypex_T(key uint, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7490,7 +7492,7 @@ func emitMakerETUintTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintTypex_T(t typex.EventTime, key uint, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7506,7 +7508,7 @@ func emitMakerUintTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintTypex_U(key uint, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7522,7 +7524,7 @@ func emitMakerETUintTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintTypex_U(t typex.EventTime, key uint, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7538,7 +7540,7 @@ func emitMakerUintTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintTypex_V(key uint, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7554,7 +7556,7 @@ func emitMakerETUintTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintTypex_V(t typex.EventTime, key uint, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7570,7 +7572,7 @@ func emitMakerUintTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintTypex_W(key uint, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7586,7 +7588,7 @@ func emitMakerETUintTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintTypex_W(t typex.EventTime, key uint, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7602,7 +7604,7 @@ func emitMakerUintTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintTypex_X(key uint, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7618,7 +7620,7 @@ func emitMakerETUintTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintTypex_X(t typex.EventTime, key uint, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7634,7 +7636,7 @@ func emitMakerUintTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintTypex_Y(key uint, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7650,7 +7652,7 @@ func emitMakerETUintTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintTypex_Y(t typex.EventTime, key uint, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7666,7 +7668,7 @@ func emitMakerUintTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUintTypex_Z(key uint, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7682,7 +7684,7 @@ func emitMakerETUintTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUintTypex_Z(t typex.EventTime, key uint, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7698,7 +7700,7 @@ func emitMakerUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8(elm uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7714,7 +7716,7 @@ func emitMakerETUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8(t typex.EventTime, elm uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7730,7 +7732,7 @@ func emitMakerUint8ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8ByteSlice(key uint8, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7746,7 +7748,7 @@ func emitMakerETUint8ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8ByteSlice(t typex.EventTime, key uint8, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7762,7 +7764,7 @@ func emitMakerUint8Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Bool(key uint8, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7778,7 +7780,7 @@ func emitMakerETUint8Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Bool(t typex.EventTime, key uint8, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7794,7 +7796,7 @@ func emitMakerUint8String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8String(key uint8, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7810,7 +7812,7 @@ func emitMakerETUint8String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8String(t typex.EventTime, key uint8, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7826,7 +7828,7 @@ func emitMakerUint8Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Int(key uint8, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7842,7 +7844,7 @@ func emitMakerETUint8Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Int(t typex.EventTime, key uint8, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7858,7 +7860,7 @@ func emitMakerUint8Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Int8(key uint8, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7874,7 +7876,7 @@ func emitMakerETUint8Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Int8(t typex.EventTime, key uint8, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7890,7 +7892,7 @@ func emitMakerUint8Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Int16(key uint8, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7906,7 +7908,7 @@ func emitMakerETUint8Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Int16(t typex.EventTime, key uint8, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7922,7 +7924,7 @@ func emitMakerUint8Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Int32(key uint8, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7938,7 +7940,7 @@ func emitMakerETUint8Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Int32(t typex.EventTime, key uint8, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7954,7 +7956,7 @@ func emitMakerUint8Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Int64(key uint8, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -7970,7 +7972,7 @@ func emitMakerETUint8Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Int64(t typex.EventTime, key uint8, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -7986,7 +7988,7 @@ func emitMakerUint8Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Uint(key uint8, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8002,7 +8004,7 @@ func emitMakerETUint8Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Uint(t typex.EventTime, key uint8, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8018,7 +8020,7 @@ func emitMakerUint8Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Uint8(key uint8, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8034,7 +8036,7 @@ func emitMakerETUint8Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Uint8(t typex.EventTime, key uint8, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8050,7 +8052,7 @@ func emitMakerUint8Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Uint16(key uint8, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8066,7 +8068,7 @@ func emitMakerETUint8Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Uint16(t typex.EventTime, key uint8, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8082,7 +8084,7 @@ func emitMakerUint8Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Uint32(key uint8, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8098,7 +8100,7 @@ func emitMakerETUint8Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Uint32(t typex.EventTime, key uint8, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8114,7 +8116,7 @@ func emitMakerUint8Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Uint64(key uint8, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8130,7 +8132,7 @@ func emitMakerETUint8Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Uint64(t typex.EventTime, key uint8, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8146,7 +8148,7 @@ func emitMakerUint8Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Float32(key uint8, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8162,7 +8164,7 @@ func emitMakerETUint8Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Float32(t typex.EventTime, key uint8, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8178,7 +8180,7 @@ func emitMakerUint8Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Float64(key uint8, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8194,7 +8196,7 @@ func emitMakerETUint8Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Float64(t typex.EventTime, key uint8, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8210,7 +8212,7 @@ func emitMakerUint8Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Typex_T(key uint8, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8226,7 +8228,7 @@ func emitMakerETUint8Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Typex_T(t typex.EventTime, key uint8, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8242,7 +8244,7 @@ func emitMakerUint8Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Typex_U(key uint8, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8258,7 +8260,7 @@ func emitMakerETUint8Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Typex_U(t typex.EventTime, key uint8, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8274,7 +8276,7 @@ func emitMakerUint8Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Typex_V(key uint8, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8290,7 +8292,7 @@ func emitMakerETUint8Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Typex_V(t typex.EventTime, key uint8, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8306,7 +8308,7 @@ func emitMakerUint8Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Typex_W(key uint8, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8322,7 +8324,7 @@ func emitMakerETUint8Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Typex_W(t typex.EventTime, key uint8, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8338,7 +8340,7 @@ func emitMakerUint8Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Typex_X(key uint8, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8354,7 +8356,7 @@ func emitMakerETUint8Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Typex_X(t typex.EventTime, key uint8, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8370,7 +8372,7 @@ func emitMakerUint8Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Typex_Y(key uint8, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8386,7 +8388,7 @@ func emitMakerETUint8Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Typex_Y(t typex.EventTime, key uint8, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8402,7 +8404,7 @@ func emitMakerUint8Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint8Typex_Z(key uint8, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8418,7 +8420,7 @@ func emitMakerETUint8Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint8Typex_Z(t typex.EventTime, key uint8, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8434,7 +8436,7 @@ func emitMakerUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16(elm uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8450,7 +8452,7 @@ func emitMakerETUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16(t typex.EventTime, elm uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8466,7 +8468,7 @@ func emitMakerUint16ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16ByteSlice(key uint16, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8482,7 +8484,7 @@ func emitMakerETUint16ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16ByteSlice(t typex.EventTime, key uint16, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8498,7 +8500,7 @@ func emitMakerUint16Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Bool(key uint16, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8514,7 +8516,7 @@ func emitMakerETUint16Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Bool(t typex.EventTime, key uint16, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8530,7 +8532,7 @@ func emitMakerUint16String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16String(key uint16, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8546,7 +8548,7 @@ func emitMakerETUint16String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16String(t typex.EventTime, key uint16, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8562,7 +8564,7 @@ func emitMakerUint16Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Int(key uint16, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8578,7 +8580,7 @@ func emitMakerETUint16Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Int(t typex.EventTime, key uint16, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8594,7 +8596,7 @@ func emitMakerUint16Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Int8(key uint16, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8610,7 +8612,7 @@ func emitMakerETUint16Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Int8(t typex.EventTime, key uint16, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8626,7 +8628,7 @@ func emitMakerUint16Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Int16(key uint16, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8642,7 +8644,7 @@ func emitMakerETUint16Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Int16(t typex.EventTime, key uint16, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8658,7 +8660,7 @@ func emitMakerUint16Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Int32(key uint16, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8674,7 +8676,7 @@ func emitMakerETUint16Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Int32(t typex.EventTime, key uint16, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8690,7 +8692,7 @@ func emitMakerUint16Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Int64(key uint16, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8706,7 +8708,7 @@ func emitMakerETUint16Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Int64(t typex.EventTime, key uint16, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8722,7 +8724,7 @@ func emitMakerUint16Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Uint(key uint16, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8738,7 +8740,7 @@ func emitMakerETUint16Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Uint(t typex.EventTime, key uint16, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8754,7 +8756,7 @@ func emitMakerUint16Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Uint8(key uint16, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8770,7 +8772,7 @@ func emitMakerETUint16Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Uint8(t typex.EventTime, key uint16, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8786,7 +8788,7 @@ func emitMakerUint16Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Uint16(key uint16, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8802,7 +8804,7 @@ func emitMakerETUint16Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Uint16(t typex.EventTime, key uint16, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8818,7 +8820,7 @@ func emitMakerUint16Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Uint32(key uint16, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8834,7 +8836,7 @@ func emitMakerETUint16Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Uint32(t typex.EventTime, key uint16, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8850,7 +8852,7 @@ func emitMakerUint16Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Uint64(key uint16, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8866,7 +8868,7 @@ func emitMakerETUint16Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Uint64(t typex.EventTime, key uint16, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8882,7 +8884,7 @@ func emitMakerUint16Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Float32(key uint16, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8898,7 +8900,7 @@ func emitMakerETUint16Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Float32(t typex.EventTime, key uint16, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8914,7 +8916,7 @@ func emitMakerUint16Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Float64(key uint16, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8930,7 +8932,7 @@ func emitMakerETUint16Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Float64(t typex.EventTime, key uint16, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8946,7 +8948,7 @@ func emitMakerUint16Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Typex_T(key uint16, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8962,7 +8964,7 @@ func emitMakerETUint16Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Typex_T(t typex.EventTime, key uint16, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -8978,7 +8980,7 @@ func emitMakerUint16Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Typex_U(key uint16, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -8994,7 +8996,7 @@ func emitMakerETUint16Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Typex_U(t typex.EventTime, key uint16, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9010,7 +9012,7 @@ func emitMakerUint16Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Typex_V(key uint16, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9026,7 +9028,7 @@ func emitMakerETUint16Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Typex_V(t typex.EventTime, key uint16, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9042,7 +9044,7 @@ func emitMakerUint16Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Typex_W(key uint16, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9058,7 +9060,7 @@ func emitMakerETUint16Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Typex_W(t typex.EventTime, key uint16, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9074,7 +9076,7 @@ func emitMakerUint16Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Typex_X(key uint16, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9090,7 +9092,7 @@ func emitMakerETUint16Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Typex_X(t typex.EventTime, key uint16, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9106,7 +9108,7 @@ func emitMakerUint16Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Typex_Y(key uint16, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9122,7 +9124,7 @@ func emitMakerETUint16Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Typex_Y(t typex.EventTime, key uint16, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9138,7 +9140,7 @@ func emitMakerUint16Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint16Typex_Z(key uint16, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9154,7 +9156,7 @@ func emitMakerETUint16Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint16Typex_Z(t typex.EventTime, key uint16, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9170,7 +9172,7 @@ func emitMakerUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32(elm uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9186,7 +9188,7 @@ func emitMakerETUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32(t typex.EventTime, elm uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9202,7 +9204,7 @@ func emitMakerUint32ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32ByteSlice(key uint32, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9218,7 +9220,7 @@ func emitMakerETUint32ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32ByteSlice(t typex.EventTime, key uint32, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9234,7 +9236,7 @@ func emitMakerUint32Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Bool(key uint32, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9250,7 +9252,7 @@ func emitMakerETUint32Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Bool(t typex.EventTime, key uint32, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9266,7 +9268,7 @@ func emitMakerUint32String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32String(key uint32, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9282,7 +9284,7 @@ func emitMakerETUint32String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32String(t typex.EventTime, key uint32, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9298,7 +9300,7 @@ func emitMakerUint32Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Int(key uint32, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9314,7 +9316,7 @@ func emitMakerETUint32Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Int(t typex.EventTime, key uint32, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9330,7 +9332,7 @@ func emitMakerUint32Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Int8(key uint32, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9346,7 +9348,7 @@ func emitMakerETUint32Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Int8(t typex.EventTime, key uint32, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9362,7 +9364,7 @@ func emitMakerUint32Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Int16(key uint32, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9378,7 +9380,7 @@ func emitMakerETUint32Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Int16(t typex.EventTime, key uint32, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9394,7 +9396,7 @@ func emitMakerUint32Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Int32(key uint32, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9410,7 +9412,7 @@ func emitMakerETUint32Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Int32(t typex.EventTime, key uint32, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9426,7 +9428,7 @@ func emitMakerUint32Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Int64(key uint32, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9442,7 +9444,7 @@ func emitMakerETUint32Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Int64(t typex.EventTime, key uint32, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9458,7 +9460,7 @@ func emitMakerUint32Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Uint(key uint32, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9474,7 +9476,7 @@ func emitMakerETUint32Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Uint(t typex.EventTime, key uint32, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9490,7 +9492,7 @@ func emitMakerUint32Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Uint8(key uint32, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9506,7 +9508,7 @@ func emitMakerETUint32Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Uint8(t typex.EventTime, key uint32, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9522,7 +9524,7 @@ func emitMakerUint32Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Uint16(key uint32, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9538,7 +9540,7 @@ func emitMakerETUint32Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Uint16(t typex.EventTime, key uint32, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9554,7 +9556,7 @@ func emitMakerUint32Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Uint32(key uint32, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9570,7 +9572,7 @@ func emitMakerETUint32Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Uint32(t typex.EventTime, key uint32, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9586,7 +9588,7 @@ func emitMakerUint32Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Uint64(key uint32, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9602,7 +9604,7 @@ func emitMakerETUint32Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Uint64(t typex.EventTime, key uint32, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9618,7 +9620,7 @@ func emitMakerUint32Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Float32(key uint32, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9634,7 +9636,7 @@ func emitMakerETUint32Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Float32(t typex.EventTime, key uint32, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9650,7 +9652,7 @@ func emitMakerUint32Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Float64(key uint32, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9666,7 +9668,7 @@ func emitMakerETUint32Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Float64(t typex.EventTime, key uint32, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9682,7 +9684,7 @@ func emitMakerUint32Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Typex_T(key uint32, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9698,7 +9700,7 @@ func emitMakerETUint32Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Typex_T(t typex.EventTime, key uint32, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9714,7 +9716,7 @@ func emitMakerUint32Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Typex_U(key uint32, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9730,7 +9732,7 @@ func emitMakerETUint32Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Typex_U(t typex.EventTime, key uint32, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9746,7 +9748,7 @@ func emitMakerUint32Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Typex_V(key uint32, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9762,7 +9764,7 @@ func emitMakerETUint32Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Typex_V(t typex.EventTime, key uint32, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9778,7 +9780,7 @@ func emitMakerUint32Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Typex_W(key uint32, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9794,7 +9796,7 @@ func emitMakerETUint32Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Typex_W(t typex.EventTime, key uint32, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9810,7 +9812,7 @@ func emitMakerUint32Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Typex_X(key uint32, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9826,7 +9828,7 @@ func emitMakerETUint32Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Typex_X(t typex.EventTime, key uint32, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9842,7 +9844,7 @@ func emitMakerUint32Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Typex_Y(key uint32, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9858,7 +9860,7 @@ func emitMakerETUint32Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Typex_Y(t typex.EventTime, key uint32, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9874,7 +9876,7 @@ func emitMakerUint32Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint32Typex_Z(key uint32, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9890,7 +9892,7 @@ func emitMakerETUint32Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint32Typex_Z(t typex.EventTime, key uint32, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9906,7 +9908,7 @@ func emitMakerUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64(elm uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9922,7 +9924,7 @@ func emitMakerETUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64(t typex.EventTime, elm uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9938,7 +9940,7 @@ func emitMakerUint64ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64ByteSlice(key uint64, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9954,7 +9956,7 @@ func emitMakerETUint64ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64ByteSlice(t typex.EventTime, key uint64, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -9970,7 +9972,7 @@ func emitMakerUint64Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Bool(key uint64, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -9986,7 +9988,7 @@ func emitMakerETUint64Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Bool(t typex.EventTime, key uint64, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10002,7 +10004,7 @@ func emitMakerUint64String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64String(key uint64, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10018,7 +10020,7 @@ func emitMakerETUint64String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64String(t typex.EventTime, key uint64, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10034,7 +10036,7 @@ func emitMakerUint64Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Int(key uint64, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10050,7 +10052,7 @@ func emitMakerETUint64Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Int(t typex.EventTime, key uint64, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10066,7 +10068,7 @@ func emitMakerUint64Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Int8(key uint64, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10082,7 +10084,7 @@ func emitMakerETUint64Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Int8(t typex.EventTime, key uint64, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10098,7 +10100,7 @@ func emitMakerUint64Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Int16(key uint64, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10114,7 +10116,7 @@ func emitMakerETUint64Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Int16(t typex.EventTime, key uint64, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10130,7 +10132,7 @@ func emitMakerUint64Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Int32(key uint64, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10146,7 +10148,7 @@ func emitMakerETUint64Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Int32(t typex.EventTime, key uint64, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10162,7 +10164,7 @@ func emitMakerUint64Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Int64(key uint64, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10178,7 +10180,7 @@ func emitMakerETUint64Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Int64(t typex.EventTime, key uint64, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10194,7 +10196,7 @@ func emitMakerUint64Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Uint(key uint64, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10210,7 +10212,7 @@ func emitMakerETUint64Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Uint(t typex.EventTime, key uint64, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10226,7 +10228,7 @@ func emitMakerUint64Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Uint8(key uint64, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10242,7 +10244,7 @@ func emitMakerETUint64Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Uint8(t typex.EventTime, key uint64, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10258,7 +10260,7 @@ func emitMakerUint64Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Uint16(key uint64, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10274,7 +10276,7 @@ func emitMakerETUint64Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Uint16(t typex.EventTime, key uint64, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10290,7 +10292,7 @@ func emitMakerUint64Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Uint32(key uint64, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10306,7 +10308,7 @@ func emitMakerETUint64Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Uint32(t typex.EventTime, key uint64, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10322,7 +10324,7 @@ func emitMakerUint64Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Uint64(key uint64, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10338,7 +10340,7 @@ func emitMakerETUint64Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Uint64(t typex.EventTime, key uint64, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10354,7 +10356,7 @@ func emitMakerUint64Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Float32(key uint64, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10370,7 +10372,7 @@ func emitMakerETUint64Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Float32(t typex.EventTime, key uint64, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10386,7 +10388,7 @@ func emitMakerUint64Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Float64(key uint64, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10402,7 +10404,7 @@ func emitMakerETUint64Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Float64(t typex.EventTime, key uint64, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10418,7 +10420,7 @@ func emitMakerUint64Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Typex_T(key uint64, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10434,7 +10436,7 @@ func emitMakerETUint64Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Typex_T(t typex.EventTime, key uint64, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10450,7 +10452,7 @@ func emitMakerUint64Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Typex_U(key uint64, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10466,7 +10468,7 @@ func emitMakerETUint64Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Typex_U(t typex.EventTime, key uint64, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10482,7 +10484,7 @@ func emitMakerUint64Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Typex_V(key uint64, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10498,7 +10500,7 @@ func emitMakerETUint64Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Typex_V(t typex.EventTime, key uint64, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10514,7 +10516,7 @@ func emitMakerUint64Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Typex_W(key uint64, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10530,7 +10532,7 @@ func emitMakerETUint64Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Typex_W(t typex.EventTime, key uint64, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10546,7 +10548,7 @@ func emitMakerUint64Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Typex_X(key uint64, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10562,7 +10564,7 @@ func emitMakerETUint64Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Typex_X(t typex.EventTime, key uint64, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10578,7 +10580,7 @@ func emitMakerUint64Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Typex_Y(key uint64, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10594,7 +10596,7 @@ func emitMakerETUint64Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Typex_Y(t typex.EventTime, key uint64, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10610,7 +10612,7 @@ func emitMakerUint64Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeUint64Typex_Z(key uint64, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10626,7 +10628,7 @@ func emitMakerETUint64Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETUint64Typex_Z(t typex.EventTime, key uint64, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10642,7 +10644,7 @@ func emitMakerFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32(elm float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10658,7 +10660,7 @@ func emitMakerETFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32(t typex.EventTime, elm float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10674,7 +10676,7 @@ func emitMakerFloat32ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32ByteSlice(key float32, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10690,7 +10692,7 @@ func emitMakerETFloat32ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32ByteSlice(t typex.EventTime, key float32, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10706,7 +10708,7 @@ func emitMakerFloat32Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Bool(key float32, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10722,7 +10724,7 @@ func emitMakerETFloat32Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Bool(t typex.EventTime, key float32, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10738,7 +10740,7 @@ func emitMakerFloat32String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32String(key float32, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10754,7 +10756,7 @@ func emitMakerETFloat32String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32String(t typex.EventTime, key float32, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10770,7 +10772,7 @@ func emitMakerFloat32Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Int(key float32, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10786,7 +10788,7 @@ func emitMakerETFloat32Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Int(t typex.EventTime, key float32, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10802,7 +10804,7 @@ func emitMakerFloat32Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Int8(key float32, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10818,7 +10820,7 @@ func emitMakerETFloat32Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Int8(t typex.EventTime, key float32, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10834,7 +10836,7 @@ func emitMakerFloat32Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Int16(key float32, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10850,7 +10852,7 @@ func emitMakerETFloat32Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Int16(t typex.EventTime, key float32, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10866,7 +10868,7 @@ func emitMakerFloat32Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Int32(key float32, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10882,7 +10884,7 @@ func emitMakerETFloat32Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Int32(t typex.EventTime, key float32, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10898,7 +10900,7 @@ func emitMakerFloat32Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Int64(key float32, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10914,7 +10916,7 @@ func emitMakerETFloat32Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Int64(t typex.EventTime, key float32, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10930,7 +10932,7 @@ func emitMakerFloat32Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Uint(key float32, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10946,7 +10948,7 @@ func emitMakerETFloat32Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Uint(t typex.EventTime, key float32, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10962,7 +10964,7 @@ func emitMakerFloat32Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Uint8(key float32, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -10978,7 +10980,7 @@ func emitMakerETFloat32Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Uint8(t typex.EventTime, key float32, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -10994,7 +10996,7 @@ func emitMakerFloat32Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Uint16(key float32, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11010,7 +11012,7 @@ func emitMakerETFloat32Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Uint16(t typex.EventTime, key float32, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11026,7 +11028,7 @@ func emitMakerFloat32Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Uint32(key float32, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11042,7 +11044,7 @@ func emitMakerETFloat32Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Uint32(t typex.EventTime, key float32, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11058,7 +11060,7 @@ func emitMakerFloat32Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Uint64(key float32, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11074,7 +11076,7 @@ func emitMakerETFloat32Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Uint64(t typex.EventTime, key float32, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11090,7 +11092,7 @@ func emitMakerFloat32Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Float32(key float32, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11106,7 +11108,7 @@ func emitMakerETFloat32Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Float32(t typex.EventTime, key float32, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11122,7 +11124,7 @@ func emitMakerFloat32Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Float64(key float32, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11138,7 +11140,7 @@ func emitMakerETFloat32Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Float64(t typex.EventTime, key float32, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11154,7 +11156,7 @@ func emitMakerFloat32Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Typex_T(key float32, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11170,7 +11172,7 @@ func emitMakerETFloat32Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Typex_T(t typex.EventTime, key float32, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11186,7 +11188,7 @@ func emitMakerFloat32Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Typex_U(key float32, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11202,7 +11204,7 @@ func emitMakerETFloat32Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Typex_U(t typex.EventTime, key float32, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11218,7 +11220,7 @@ func emitMakerFloat32Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Typex_V(key float32, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11234,7 +11236,7 @@ func emitMakerETFloat32Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Typex_V(t typex.EventTime, key float32, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11250,7 +11252,7 @@ func emitMakerFloat32Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Typex_W(key float32, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11266,7 +11268,7 @@ func emitMakerETFloat32Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Typex_W(t typex.EventTime, key float32, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11282,7 +11284,7 @@ func emitMakerFloat32Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Typex_X(key float32, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11298,7 +11300,7 @@ func emitMakerETFloat32Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Typex_X(t typex.EventTime, key float32, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11314,7 +11316,7 @@ func emitMakerFloat32Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Typex_Y(key float32, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11330,7 +11332,7 @@ func emitMakerETFloat32Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Typex_Y(t typex.EventTime, key float32, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11346,7 +11348,7 @@ func emitMakerFloat32Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat32Typex_Z(key float32, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11362,7 +11364,7 @@ func emitMakerETFloat32Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat32Typex_Z(t typex.EventTime, key float32, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11378,7 +11380,7 @@ func emitMakerFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64(elm float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11394,7 +11396,7 @@ func emitMakerETFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64(t typex.EventTime, elm float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11410,7 +11412,7 @@ func emitMakerFloat64ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64ByteSlice(key float64, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11426,7 +11428,7 @@ func emitMakerETFloat64ByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64ByteSlice(t typex.EventTime, key float64, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11442,7 +11444,7 @@ func emitMakerFloat64Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Bool(key float64, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11458,7 +11460,7 @@ func emitMakerETFloat64Bool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Bool(t typex.EventTime, key float64, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11474,7 +11476,7 @@ func emitMakerFloat64String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64String(key float64, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11490,7 +11492,7 @@ func emitMakerETFloat64String(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64String(t typex.EventTime, key float64, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11506,7 +11508,7 @@ func emitMakerFloat64Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Int(key float64, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11522,7 +11524,7 @@ func emitMakerETFloat64Int(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Int(t typex.EventTime, key float64, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11538,7 +11540,7 @@ func emitMakerFloat64Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Int8(key float64, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11554,7 +11556,7 @@ func emitMakerETFloat64Int8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Int8(t typex.EventTime, key float64, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11570,7 +11572,7 @@ func emitMakerFloat64Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Int16(key float64, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11586,7 +11588,7 @@ func emitMakerETFloat64Int16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Int16(t typex.EventTime, key float64, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11602,7 +11604,7 @@ func emitMakerFloat64Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Int32(key float64, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11618,7 +11620,7 @@ func emitMakerETFloat64Int32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Int32(t typex.EventTime, key float64, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11634,7 +11636,7 @@ func emitMakerFloat64Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Int64(key float64, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11650,7 +11652,7 @@ func emitMakerETFloat64Int64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Int64(t typex.EventTime, key float64, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11666,7 +11668,7 @@ func emitMakerFloat64Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Uint(key float64, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11682,7 +11684,7 @@ func emitMakerETFloat64Uint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Uint(t typex.EventTime, key float64, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11698,7 +11700,7 @@ func emitMakerFloat64Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Uint8(key float64, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11714,7 +11716,7 @@ func emitMakerETFloat64Uint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Uint8(t typex.EventTime, key float64, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11730,7 +11732,7 @@ func emitMakerFloat64Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Uint16(key float64, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11746,7 +11748,7 @@ func emitMakerETFloat64Uint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Uint16(t typex.EventTime, key float64, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11762,7 +11764,7 @@ func emitMakerFloat64Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Uint32(key float64, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11778,7 +11780,7 @@ func emitMakerETFloat64Uint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Uint32(t typex.EventTime, key float64, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11794,7 +11796,7 @@ func emitMakerFloat64Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Uint64(key float64, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11810,7 +11812,7 @@ func emitMakerETFloat64Uint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Uint64(t typex.EventTime, key float64, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11826,7 +11828,7 @@ func emitMakerFloat64Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Float32(key float64, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11842,7 +11844,7 @@ func emitMakerETFloat64Float32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Float32(t typex.EventTime, key float64, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11858,7 +11860,7 @@ func emitMakerFloat64Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Float64(key float64, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11874,7 +11876,7 @@ func emitMakerETFloat64Float64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Float64(t typex.EventTime, key float64, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11890,7 +11892,7 @@ func emitMakerFloat64Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Typex_T(key float64, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11906,7 +11908,7 @@ func emitMakerETFloat64Typex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Typex_T(t typex.EventTime, key float64, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11922,7 +11924,7 @@ func emitMakerFloat64Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Typex_U(key float64, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11938,7 +11940,7 @@ func emitMakerETFloat64Typex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Typex_U(t typex.EventTime, key float64, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11954,7 +11956,7 @@ func emitMakerFloat64Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Typex_V(key float64, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -11970,7 +11972,7 @@ func emitMakerETFloat64Typex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Typex_V(t typex.EventTime, key float64, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -11986,7 +11988,7 @@ func emitMakerFloat64Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Typex_W(key float64, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12002,7 +12004,7 @@ func emitMakerETFloat64Typex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Typex_W(t typex.EventTime, key float64, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12018,7 +12020,7 @@ func emitMakerFloat64Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Typex_X(key float64, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12034,7 +12036,7 @@ func emitMakerETFloat64Typex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Typex_X(t typex.EventTime, key float64, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12050,7 +12052,7 @@ func emitMakerFloat64Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Typex_Y(key float64, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12066,7 +12068,7 @@ func emitMakerETFloat64Typex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Typex_Y(t typex.EventTime, key float64, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12082,7 +12084,7 @@ func emitMakerFloat64Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeFloat64Typex_Z(key float64, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12098,7 +12100,7 @@ func emitMakerETFloat64Typex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETFloat64Typex_Z(t typex.EventTime, key float64, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12114,7 +12116,7 @@ func emitMakerTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_T(elm typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12130,7 +12132,7 @@ func emitMakerETTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_T(t typex.EventTime, elm typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12146,7 +12148,7 @@ func emitMakerTypex_TByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TByteSlice(key typex.T, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12162,7 +12164,7 @@ func emitMakerETTypex_TByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TByteSlice(t typex.EventTime, key typex.T, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12178,7 +12180,7 @@ func emitMakerTypex_TBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TBool(key typex.T, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12194,7 +12196,7 @@ func emitMakerETTypex_TBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TBool(t typex.EventTime, key typex.T, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12210,7 +12212,7 @@ func emitMakerTypex_TString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TString(key typex.T, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12226,7 +12228,7 @@ func emitMakerETTypex_TString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TString(t typex.EventTime, key typex.T, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12242,7 +12244,7 @@ func emitMakerTypex_TInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TInt(key typex.T, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12258,7 +12260,7 @@ func emitMakerETTypex_TInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TInt(t typex.EventTime, key typex.T, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12274,7 +12276,7 @@ func emitMakerTypex_TInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TInt8(key typex.T, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12290,7 +12292,7 @@ func emitMakerETTypex_TInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TInt8(t typex.EventTime, key typex.T, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12306,7 +12308,7 @@ func emitMakerTypex_TInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TInt16(key typex.T, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12322,7 +12324,7 @@ func emitMakerETTypex_TInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TInt16(t typex.EventTime, key typex.T, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12338,7 +12340,7 @@ func emitMakerTypex_TInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TInt32(key typex.T, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12354,7 +12356,7 @@ func emitMakerETTypex_TInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TInt32(t typex.EventTime, key typex.T, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12370,7 +12372,7 @@ func emitMakerTypex_TInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TInt64(key typex.T, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12386,7 +12388,7 @@ func emitMakerETTypex_TInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TInt64(t typex.EventTime, key typex.T, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12402,7 +12404,7 @@ func emitMakerTypex_TUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TUint(key typex.T, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12418,7 +12420,7 @@ func emitMakerETTypex_TUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TUint(t typex.EventTime, key typex.T, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12434,7 +12436,7 @@ func emitMakerTypex_TUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TUint8(key typex.T, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12450,7 +12452,7 @@ func emitMakerETTypex_TUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TUint8(t typex.EventTime, key typex.T, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12466,7 +12468,7 @@ func emitMakerTypex_TUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TUint16(key typex.T, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12482,7 +12484,7 @@ func emitMakerETTypex_TUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TUint16(t typex.EventTime, key typex.T, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12498,7 +12500,7 @@ func emitMakerTypex_TUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TUint32(key typex.T, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12514,7 +12516,7 @@ func emitMakerETTypex_TUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TUint32(t typex.EventTime, key typex.T, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12530,7 +12532,7 @@ func emitMakerTypex_TUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TUint64(key typex.T, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12546,7 +12548,7 @@ func emitMakerETTypex_TUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TUint64(t typex.EventTime, key typex.T, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12562,7 +12564,7 @@ func emitMakerTypex_TFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TFloat32(key typex.T, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12578,7 +12580,7 @@ func emitMakerETTypex_TFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TFloat32(t typex.EventTime, key typex.T, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12594,7 +12596,7 @@ func emitMakerTypex_TFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TFloat64(key typex.T, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12610,7 +12612,7 @@ func emitMakerETTypex_TFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TFloat64(t typex.EventTime, key typex.T, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12626,7 +12628,7 @@ func emitMakerTypex_TTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TTypex_T(key typex.T, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12642,7 +12644,7 @@ func emitMakerETTypex_TTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TTypex_T(t typex.EventTime, key typex.T, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12658,7 +12660,7 @@ func emitMakerTypex_TTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TTypex_U(key typex.T, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12674,7 +12676,7 @@ func emitMakerETTypex_TTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TTypex_U(t typex.EventTime, key typex.T, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12690,7 +12692,7 @@ func emitMakerTypex_TTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TTypex_V(key typex.T, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12706,7 +12708,7 @@ func emitMakerETTypex_TTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TTypex_V(t typex.EventTime, key typex.T, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12722,7 +12724,7 @@ func emitMakerTypex_TTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TTypex_W(key typex.T, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12738,7 +12740,7 @@ func emitMakerETTypex_TTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TTypex_W(t typex.EventTime, key typex.T, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12754,7 +12756,7 @@ func emitMakerTypex_TTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TTypex_X(key typex.T, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12770,7 +12772,7 @@ func emitMakerETTypex_TTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TTypex_X(t typex.EventTime, key typex.T, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12786,7 +12788,7 @@ func emitMakerTypex_TTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TTypex_Y(key typex.T, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12802,7 +12804,7 @@ func emitMakerETTypex_TTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TTypex_Y(t typex.EventTime, key typex.T, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12818,7 +12820,7 @@ func emitMakerTypex_TTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_TTypex_Z(key typex.T, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12834,7 +12836,7 @@ func emitMakerETTypex_TTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_TTypex_Z(t typex.EventTime, key typex.T, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12850,7 +12852,7 @@ func emitMakerTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_U(elm typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12866,7 +12868,7 @@ func emitMakerETTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_U(t typex.EventTime, elm typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12882,7 +12884,7 @@ func emitMakerTypex_UByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UByteSlice(key typex.U, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12898,7 +12900,7 @@ func emitMakerETTypex_UByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UByteSlice(t typex.EventTime, key typex.U, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12914,7 +12916,7 @@ func emitMakerTypex_UBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UBool(key typex.U, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12930,7 +12932,7 @@ func emitMakerETTypex_UBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UBool(t typex.EventTime, key typex.U, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12946,7 +12948,7 @@ func emitMakerTypex_UString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UString(key typex.U, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12962,7 +12964,7 @@ func emitMakerETTypex_UString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UString(t typex.EventTime, key typex.U, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -12978,7 +12980,7 @@ func emitMakerTypex_UInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UInt(key typex.U, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -12994,7 +12996,7 @@ func emitMakerETTypex_UInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UInt(t typex.EventTime, key typex.U, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13010,7 +13012,7 @@ func emitMakerTypex_UInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UInt8(key typex.U, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13026,7 +13028,7 @@ func emitMakerETTypex_UInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UInt8(t typex.EventTime, key typex.U, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13042,7 +13044,7 @@ func emitMakerTypex_UInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UInt16(key typex.U, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13058,7 +13060,7 @@ func emitMakerETTypex_UInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UInt16(t typex.EventTime, key typex.U, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13074,7 +13076,7 @@ func emitMakerTypex_UInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UInt32(key typex.U, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13090,7 +13092,7 @@ func emitMakerETTypex_UInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UInt32(t typex.EventTime, key typex.U, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13106,7 +13108,7 @@ func emitMakerTypex_UInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UInt64(key typex.U, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13122,7 +13124,7 @@ func emitMakerETTypex_UInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UInt64(t typex.EventTime, key typex.U, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13138,7 +13140,7 @@ func emitMakerTypex_UUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UUint(key typex.U, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13154,7 +13156,7 @@ func emitMakerETTypex_UUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UUint(t typex.EventTime, key typex.U, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13170,7 +13172,7 @@ func emitMakerTypex_UUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UUint8(key typex.U, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13186,7 +13188,7 @@ func emitMakerETTypex_UUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UUint8(t typex.EventTime, key typex.U, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13202,7 +13204,7 @@ func emitMakerTypex_UUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UUint16(key typex.U, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13218,7 +13220,7 @@ func emitMakerETTypex_UUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UUint16(t typex.EventTime, key typex.U, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13234,7 +13236,7 @@ func emitMakerTypex_UUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UUint32(key typex.U, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13250,7 +13252,7 @@ func emitMakerETTypex_UUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UUint32(t typex.EventTime, key typex.U, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13266,7 +13268,7 @@ func emitMakerTypex_UUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UUint64(key typex.U, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13282,7 +13284,7 @@ func emitMakerETTypex_UUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UUint64(t typex.EventTime, key typex.U, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13298,7 +13300,7 @@ func emitMakerTypex_UFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UFloat32(key typex.U, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13314,7 +13316,7 @@ func emitMakerETTypex_UFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UFloat32(t typex.EventTime, key typex.U, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13330,7 +13332,7 @@ func emitMakerTypex_UFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UFloat64(key typex.U, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13346,7 +13348,7 @@ func emitMakerETTypex_UFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UFloat64(t typex.EventTime, key typex.U, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13362,7 +13364,7 @@ func emitMakerTypex_UTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UTypex_T(key typex.U, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13378,7 +13380,7 @@ func emitMakerETTypex_UTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UTypex_T(t typex.EventTime, key typex.U, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13394,7 +13396,7 @@ func emitMakerTypex_UTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UTypex_U(key typex.U, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13410,7 +13412,7 @@ func emitMakerETTypex_UTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UTypex_U(t typex.EventTime, key typex.U, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13426,7 +13428,7 @@ func emitMakerTypex_UTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UTypex_V(key typex.U, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13442,7 +13444,7 @@ func emitMakerETTypex_UTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UTypex_V(t typex.EventTime, key typex.U, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13458,7 +13460,7 @@ func emitMakerTypex_UTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UTypex_W(key typex.U, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13474,7 +13476,7 @@ func emitMakerETTypex_UTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UTypex_W(t typex.EventTime, key typex.U, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13490,7 +13492,7 @@ func emitMakerTypex_UTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UTypex_X(key typex.U, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13506,7 +13508,7 @@ func emitMakerETTypex_UTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UTypex_X(t typex.EventTime, key typex.U, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13522,7 +13524,7 @@ func emitMakerTypex_UTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UTypex_Y(key typex.U, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13538,7 +13540,7 @@ func emitMakerETTypex_UTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UTypex_Y(t typex.EventTime, key typex.U, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13554,7 +13556,7 @@ func emitMakerTypex_UTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_UTypex_Z(key typex.U, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13570,7 +13572,7 @@ func emitMakerETTypex_UTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_UTypex_Z(t typex.EventTime, key typex.U, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13586,7 +13588,7 @@ func emitMakerTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_V(elm typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13602,7 +13604,7 @@ func emitMakerETTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_V(t typex.EventTime, elm typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13618,7 +13620,7 @@ func emitMakerTypex_VByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VByteSlice(key typex.V, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13634,7 +13636,7 @@ func emitMakerETTypex_VByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VByteSlice(t typex.EventTime, key typex.V, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13650,7 +13652,7 @@ func emitMakerTypex_VBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VBool(key typex.V, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13666,7 +13668,7 @@ func emitMakerETTypex_VBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VBool(t typex.EventTime, key typex.V, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13682,7 +13684,7 @@ func emitMakerTypex_VString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VString(key typex.V, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13698,7 +13700,7 @@ func emitMakerETTypex_VString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VString(t typex.EventTime, key typex.V, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13714,7 +13716,7 @@ func emitMakerTypex_VInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VInt(key typex.V, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13730,7 +13732,7 @@ func emitMakerETTypex_VInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VInt(t typex.EventTime, key typex.V, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13746,7 +13748,7 @@ func emitMakerTypex_VInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VInt8(key typex.V, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13762,7 +13764,7 @@ func emitMakerETTypex_VInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VInt8(t typex.EventTime, key typex.V, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13778,7 +13780,7 @@ func emitMakerTypex_VInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VInt16(key typex.V, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13794,7 +13796,7 @@ func emitMakerETTypex_VInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VInt16(t typex.EventTime, key typex.V, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13810,7 +13812,7 @@ func emitMakerTypex_VInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VInt32(key typex.V, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13826,7 +13828,7 @@ func emitMakerETTypex_VInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VInt32(t typex.EventTime, key typex.V, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13842,7 +13844,7 @@ func emitMakerTypex_VInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VInt64(key typex.V, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13858,7 +13860,7 @@ func emitMakerETTypex_VInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VInt64(t typex.EventTime, key typex.V, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13874,7 +13876,7 @@ func emitMakerTypex_VUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VUint(key typex.V, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13890,7 +13892,7 @@ func emitMakerETTypex_VUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VUint(t typex.EventTime, key typex.V, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13906,7 +13908,7 @@ func emitMakerTypex_VUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VUint8(key typex.V, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13922,7 +13924,7 @@ func emitMakerETTypex_VUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VUint8(t typex.EventTime, key typex.V, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13938,7 +13940,7 @@ func emitMakerTypex_VUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VUint16(key typex.V, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13954,7 +13956,7 @@ func emitMakerETTypex_VUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VUint16(t typex.EventTime, key typex.V, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -13970,7 +13972,7 @@ func emitMakerTypex_VUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VUint32(key typex.V, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -13986,7 +13988,7 @@ func emitMakerETTypex_VUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VUint32(t typex.EventTime, key typex.V, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14002,7 +14004,7 @@ func emitMakerTypex_VUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VUint64(key typex.V, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14018,7 +14020,7 @@ func emitMakerETTypex_VUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VUint64(t typex.EventTime, key typex.V, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14034,7 +14036,7 @@ func emitMakerTypex_VFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VFloat32(key typex.V, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14050,7 +14052,7 @@ func emitMakerETTypex_VFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VFloat32(t typex.EventTime, key typex.V, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14066,7 +14068,7 @@ func emitMakerTypex_VFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VFloat64(key typex.V, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14082,7 +14084,7 @@ func emitMakerETTypex_VFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VFloat64(t typex.EventTime, key typex.V, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14098,7 +14100,7 @@ func emitMakerTypex_VTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VTypex_T(key typex.V, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14114,7 +14116,7 @@ func emitMakerETTypex_VTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VTypex_T(t typex.EventTime, key typex.V, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14130,7 +14132,7 @@ func emitMakerTypex_VTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VTypex_U(key typex.V, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14146,7 +14148,7 @@ func emitMakerETTypex_VTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VTypex_U(t typex.EventTime, key typex.V, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14162,7 +14164,7 @@ func emitMakerTypex_VTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VTypex_V(key typex.V, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14178,7 +14180,7 @@ func emitMakerETTypex_VTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VTypex_V(t typex.EventTime, key typex.V, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14194,7 +14196,7 @@ func emitMakerTypex_VTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VTypex_W(key typex.V, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14210,7 +14212,7 @@ func emitMakerETTypex_VTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VTypex_W(t typex.EventTime, key typex.V, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14226,7 +14228,7 @@ func emitMakerTypex_VTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VTypex_X(key typex.V, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14242,7 +14244,7 @@ func emitMakerETTypex_VTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VTypex_X(t typex.EventTime, key typex.V, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14258,7 +14260,7 @@ func emitMakerTypex_VTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VTypex_Y(key typex.V, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14274,7 +14276,7 @@ func emitMakerETTypex_VTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VTypex_Y(t typex.EventTime, key typex.V, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14290,7 +14292,7 @@ func emitMakerTypex_VTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_VTypex_Z(key typex.V, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14306,7 +14308,7 @@ func emitMakerETTypex_VTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_VTypex_Z(t typex.EventTime, key typex.V, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14322,7 +14324,7 @@ func emitMakerTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_W(elm typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14338,7 +14340,7 @@ func emitMakerETTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_W(t typex.EventTime, elm typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14354,7 +14356,7 @@ func emitMakerTypex_WByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WByteSlice(key typex.W, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14370,7 +14372,7 @@ func emitMakerETTypex_WByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WByteSlice(t typex.EventTime, key typex.W, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14386,7 +14388,7 @@ func emitMakerTypex_WBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WBool(key typex.W, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14402,7 +14404,7 @@ func emitMakerETTypex_WBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WBool(t typex.EventTime, key typex.W, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14418,7 +14420,7 @@ func emitMakerTypex_WString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WString(key typex.W, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14434,7 +14436,7 @@ func emitMakerETTypex_WString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WString(t typex.EventTime, key typex.W, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14450,7 +14452,7 @@ func emitMakerTypex_WInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WInt(key typex.W, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14466,7 +14468,7 @@ func emitMakerETTypex_WInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WInt(t typex.EventTime, key typex.W, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14482,7 +14484,7 @@ func emitMakerTypex_WInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WInt8(key typex.W, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14498,7 +14500,7 @@ func emitMakerETTypex_WInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WInt8(t typex.EventTime, key typex.W, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14514,7 +14516,7 @@ func emitMakerTypex_WInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WInt16(key typex.W, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14530,7 +14532,7 @@ func emitMakerETTypex_WInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WInt16(t typex.EventTime, key typex.W, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14546,7 +14548,7 @@ func emitMakerTypex_WInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WInt32(key typex.W, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14562,7 +14564,7 @@ func emitMakerETTypex_WInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WInt32(t typex.EventTime, key typex.W, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14578,7 +14580,7 @@ func emitMakerTypex_WInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WInt64(key typex.W, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14594,7 +14596,7 @@ func emitMakerETTypex_WInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WInt64(t typex.EventTime, key typex.W, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14610,7 +14612,7 @@ func emitMakerTypex_WUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WUint(key typex.W, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14626,7 +14628,7 @@ func emitMakerETTypex_WUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WUint(t typex.EventTime, key typex.W, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14642,7 +14644,7 @@ func emitMakerTypex_WUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WUint8(key typex.W, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14658,7 +14660,7 @@ func emitMakerETTypex_WUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WUint8(t typex.EventTime, key typex.W, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14674,7 +14676,7 @@ func emitMakerTypex_WUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WUint16(key typex.W, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14690,7 +14692,7 @@ func emitMakerETTypex_WUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WUint16(t typex.EventTime, key typex.W, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14706,7 +14708,7 @@ func emitMakerTypex_WUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WUint32(key typex.W, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14722,7 +14724,7 @@ func emitMakerETTypex_WUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WUint32(t typex.EventTime, key typex.W, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14738,7 +14740,7 @@ func emitMakerTypex_WUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WUint64(key typex.W, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14754,7 +14756,7 @@ func emitMakerETTypex_WUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WUint64(t typex.EventTime, key typex.W, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14770,7 +14772,7 @@ func emitMakerTypex_WFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WFloat32(key typex.W, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14786,7 +14788,7 @@ func emitMakerETTypex_WFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WFloat32(t typex.EventTime, key typex.W, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14802,7 +14804,7 @@ func emitMakerTypex_WFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WFloat64(key typex.W, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14818,7 +14820,7 @@ func emitMakerETTypex_WFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WFloat64(t typex.EventTime, key typex.W, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14834,7 +14836,7 @@ func emitMakerTypex_WTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WTypex_T(key typex.W, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14850,7 +14852,7 @@ func emitMakerETTypex_WTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WTypex_T(t typex.EventTime, key typex.W, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14866,7 +14868,7 @@ func emitMakerTypex_WTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WTypex_U(key typex.W, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14882,7 +14884,7 @@ func emitMakerETTypex_WTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WTypex_U(t typex.EventTime, key typex.W, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14898,7 +14900,7 @@ func emitMakerTypex_WTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WTypex_V(key typex.W, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14914,7 +14916,7 @@ func emitMakerETTypex_WTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WTypex_V(t typex.EventTime, key typex.W, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14930,7 +14932,7 @@ func emitMakerTypex_WTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WTypex_W(key typex.W, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14946,7 +14948,7 @@ func emitMakerETTypex_WTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WTypex_W(t typex.EventTime, key typex.W, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14962,7 +14964,7 @@ func emitMakerTypex_WTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WTypex_X(key typex.W, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -14978,7 +14980,7 @@ func emitMakerETTypex_WTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WTypex_X(t typex.EventTime, key typex.W, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -14994,7 +14996,7 @@ func emitMakerTypex_WTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WTypex_Y(key typex.W, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15010,7 +15012,7 @@ func emitMakerETTypex_WTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WTypex_Y(t typex.EventTime, key typex.W, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15026,7 +15028,7 @@ func emitMakerTypex_WTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_WTypex_Z(key typex.W, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15042,7 +15044,7 @@ func emitMakerETTypex_WTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_WTypex_Z(t typex.EventTime, key typex.W, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15058,7 +15060,7 @@ func emitMakerTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_X(elm typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15074,7 +15076,7 @@ func emitMakerETTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_X(t typex.EventTime, elm typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15090,7 +15092,7 @@ func emitMakerTypex_XByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XByteSlice(key typex.X, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15106,7 +15108,7 @@ func emitMakerETTypex_XByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XByteSlice(t typex.EventTime, key typex.X, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15122,7 +15124,7 @@ func emitMakerTypex_XBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XBool(key typex.X, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15138,7 +15140,7 @@ func emitMakerETTypex_XBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XBool(t typex.EventTime, key typex.X, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15154,7 +15156,7 @@ func emitMakerTypex_XString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XString(key typex.X, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15170,7 +15172,7 @@ func emitMakerETTypex_XString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XString(t typex.EventTime, key typex.X, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15186,7 +15188,7 @@ func emitMakerTypex_XInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XInt(key typex.X, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15202,7 +15204,7 @@ func emitMakerETTypex_XInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XInt(t typex.EventTime, key typex.X, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15218,7 +15220,7 @@ func emitMakerTypex_XInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XInt8(key typex.X, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15234,7 +15236,7 @@ func emitMakerETTypex_XInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XInt8(t typex.EventTime, key typex.X, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15250,7 +15252,7 @@ func emitMakerTypex_XInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XInt16(key typex.X, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15266,7 +15268,7 @@ func emitMakerETTypex_XInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XInt16(t typex.EventTime, key typex.X, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15282,7 +15284,7 @@ func emitMakerTypex_XInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XInt32(key typex.X, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15298,7 +15300,7 @@ func emitMakerETTypex_XInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XInt32(t typex.EventTime, key typex.X, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15314,7 +15316,7 @@ func emitMakerTypex_XInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XInt64(key typex.X, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15330,7 +15332,7 @@ func emitMakerETTypex_XInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XInt64(t typex.EventTime, key typex.X, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15346,7 +15348,7 @@ func emitMakerTypex_XUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XUint(key typex.X, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15362,7 +15364,7 @@ func emitMakerETTypex_XUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XUint(t typex.EventTime, key typex.X, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15378,7 +15380,7 @@ func emitMakerTypex_XUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XUint8(key typex.X, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15394,7 +15396,7 @@ func emitMakerETTypex_XUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XUint8(t typex.EventTime, key typex.X, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15410,7 +15412,7 @@ func emitMakerTypex_XUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XUint16(key typex.X, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15426,7 +15428,7 @@ func emitMakerETTypex_XUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XUint16(t typex.EventTime, key typex.X, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15442,7 +15444,7 @@ func emitMakerTypex_XUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XUint32(key typex.X, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15458,7 +15460,7 @@ func emitMakerETTypex_XUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XUint32(t typex.EventTime, key typex.X, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15474,7 +15476,7 @@ func emitMakerTypex_XUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XUint64(key typex.X, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15490,7 +15492,7 @@ func emitMakerETTypex_XUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XUint64(t typex.EventTime, key typex.X, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15506,7 +15508,7 @@ func emitMakerTypex_XFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XFloat32(key typex.X, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15522,7 +15524,7 @@ func emitMakerETTypex_XFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XFloat32(t typex.EventTime, key typex.X, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15538,7 +15540,7 @@ func emitMakerTypex_XFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XFloat64(key typex.X, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15554,7 +15556,7 @@ func emitMakerETTypex_XFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XFloat64(t typex.EventTime, key typex.X, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15570,7 +15572,7 @@ func emitMakerTypex_XTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XTypex_T(key typex.X, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15586,7 +15588,7 @@ func emitMakerETTypex_XTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XTypex_T(t typex.EventTime, key typex.X, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15602,7 +15604,7 @@ func emitMakerTypex_XTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XTypex_U(key typex.X, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15618,7 +15620,7 @@ func emitMakerETTypex_XTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XTypex_U(t typex.EventTime, key typex.X, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15634,7 +15636,7 @@ func emitMakerTypex_XTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XTypex_V(key typex.X, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15650,7 +15652,7 @@ func emitMakerETTypex_XTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XTypex_V(t typex.EventTime, key typex.X, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15666,7 +15668,7 @@ func emitMakerTypex_XTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XTypex_W(key typex.X, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15682,7 +15684,7 @@ func emitMakerETTypex_XTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XTypex_W(t typex.EventTime, key typex.X, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15698,7 +15700,7 @@ func emitMakerTypex_XTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XTypex_X(key typex.X, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15714,7 +15716,7 @@ func emitMakerETTypex_XTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XTypex_X(t typex.EventTime, key typex.X, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15730,7 +15732,7 @@ func emitMakerTypex_XTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XTypex_Y(key typex.X, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15746,7 +15748,7 @@ func emitMakerETTypex_XTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XTypex_Y(t typex.EventTime, key typex.X, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15762,7 +15764,7 @@ func emitMakerTypex_XTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_XTypex_Z(key typex.X, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15778,7 +15780,7 @@ func emitMakerETTypex_XTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_XTypex_Z(t typex.EventTime, key typex.X, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15794,7 +15796,7 @@ func emitMakerTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_Y(elm typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15810,7 +15812,7 @@ func emitMakerETTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_Y(t typex.EventTime, elm typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15826,7 +15828,7 @@ func emitMakerTypex_YByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YByteSlice(key typex.Y, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15842,7 +15844,7 @@ func emitMakerETTypex_YByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YByteSlice(t typex.EventTime, key typex.Y, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15858,7 +15860,7 @@ func emitMakerTypex_YBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YBool(key typex.Y, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15874,7 +15876,7 @@ func emitMakerETTypex_YBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YBool(t typex.EventTime, key typex.Y, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15890,7 +15892,7 @@ func emitMakerTypex_YString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YString(key typex.Y, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15906,7 +15908,7 @@ func emitMakerETTypex_YString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YString(t typex.EventTime, key typex.Y, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15922,7 +15924,7 @@ func emitMakerTypex_YInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YInt(key typex.Y, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15938,7 +15940,7 @@ func emitMakerETTypex_YInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YInt(t typex.EventTime, key typex.Y, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15954,7 +15956,7 @@ func emitMakerTypex_YInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YInt8(key typex.Y, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -15970,7 +15972,7 @@ func emitMakerETTypex_YInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YInt8(t typex.EventTime, key typex.Y, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -15986,7 +15988,7 @@ func emitMakerTypex_YInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YInt16(key typex.Y, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16002,7 +16004,7 @@ func emitMakerETTypex_YInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YInt16(t typex.EventTime, key typex.Y, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16018,7 +16020,7 @@ func emitMakerTypex_YInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YInt32(key typex.Y, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16034,7 +16036,7 @@ func emitMakerETTypex_YInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YInt32(t typex.EventTime, key typex.Y, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16050,7 +16052,7 @@ func emitMakerTypex_YInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YInt64(key typex.Y, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16066,7 +16068,7 @@ func emitMakerETTypex_YInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YInt64(t typex.EventTime, key typex.Y, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16082,7 +16084,7 @@ func emitMakerTypex_YUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YUint(key typex.Y, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16098,7 +16100,7 @@ func emitMakerETTypex_YUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YUint(t typex.EventTime, key typex.Y, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16114,7 +16116,7 @@ func emitMakerTypex_YUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YUint8(key typex.Y, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16130,7 +16132,7 @@ func emitMakerETTypex_YUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YUint8(t typex.EventTime, key typex.Y, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16146,7 +16148,7 @@ func emitMakerTypex_YUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YUint16(key typex.Y, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16162,7 +16164,7 @@ func emitMakerETTypex_YUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YUint16(t typex.EventTime, key typex.Y, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16178,7 +16180,7 @@ func emitMakerTypex_YUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YUint32(key typex.Y, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16194,7 +16196,7 @@ func emitMakerETTypex_YUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YUint32(t typex.EventTime, key typex.Y, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16210,7 +16212,7 @@ func emitMakerTypex_YUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YUint64(key typex.Y, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16226,7 +16228,7 @@ func emitMakerETTypex_YUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YUint64(t typex.EventTime, key typex.Y, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16242,7 +16244,7 @@ func emitMakerTypex_YFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YFloat32(key typex.Y, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16258,7 +16260,7 @@ func emitMakerETTypex_YFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YFloat32(t typex.EventTime, key typex.Y, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16274,7 +16276,7 @@ func emitMakerTypex_YFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YFloat64(key typex.Y, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16290,7 +16292,7 @@ func emitMakerETTypex_YFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YFloat64(t typex.EventTime, key typex.Y, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16306,7 +16308,7 @@ func emitMakerTypex_YTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YTypex_T(key typex.Y, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16322,7 +16324,7 @@ func emitMakerETTypex_YTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YTypex_T(t typex.EventTime, key typex.Y, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16338,7 +16340,7 @@ func emitMakerTypex_YTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YTypex_U(key typex.Y, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16354,7 +16356,7 @@ func emitMakerETTypex_YTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YTypex_U(t typex.EventTime, key typex.Y, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16370,7 +16372,7 @@ func emitMakerTypex_YTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YTypex_V(key typex.Y, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16386,7 +16388,7 @@ func emitMakerETTypex_YTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YTypex_V(t typex.EventTime, key typex.Y, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16402,7 +16404,7 @@ func emitMakerTypex_YTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YTypex_W(key typex.Y, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16418,7 +16420,7 @@ func emitMakerETTypex_YTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YTypex_W(t typex.EventTime, key typex.Y, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16434,7 +16436,7 @@ func emitMakerTypex_YTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YTypex_X(key typex.Y, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16450,7 +16452,7 @@ func emitMakerETTypex_YTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YTypex_X(t typex.EventTime, key typex.Y, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16466,7 +16468,7 @@ func emitMakerTypex_YTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YTypex_Y(key typex.Y, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16482,7 +16484,7 @@ func emitMakerETTypex_YTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YTypex_Y(t typex.EventTime, key typex.Y, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16498,7 +16500,7 @@ func emitMakerTypex_YTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_YTypex_Z(key typex.Y, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16514,7 +16516,7 @@ func emitMakerETTypex_YTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_YTypex_Z(t typex.EventTime, key typex.Y, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16530,7 +16532,7 @@ func emitMakerTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_Z(elm typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16546,7 +16548,7 @@ func emitMakerETTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_Z(t typex.EventTime, elm typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16562,7 +16564,7 @@ func emitMakerTypex_ZByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZByteSlice(key typex.Z, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16578,7 +16580,7 @@ func emitMakerETTypex_ZByteSlice(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZByteSlice(t typex.EventTime, key typex.Z, val []byte) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16594,7 +16596,7 @@ func emitMakerTypex_ZBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZBool(key typex.Z, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16610,7 +16612,7 @@ func emitMakerETTypex_ZBool(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZBool(t typex.EventTime, key typex.Z, val bool) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16626,7 +16628,7 @@ func emitMakerTypex_ZString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZString(key typex.Z, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16642,7 +16644,7 @@ func emitMakerETTypex_ZString(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZString(t typex.EventTime, key typex.Z, val string) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16658,7 +16660,7 @@ func emitMakerTypex_ZInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZInt(key typex.Z, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16674,7 +16676,7 @@ func emitMakerETTypex_ZInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZInt(t typex.EventTime, key typex.Z, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16690,7 +16692,7 @@ func emitMakerTypex_ZInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZInt8(key typex.Z, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16706,7 +16708,7 @@ func emitMakerETTypex_ZInt8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZInt8(t typex.EventTime, key typex.Z, val int8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16722,7 +16724,7 @@ func emitMakerTypex_ZInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZInt16(key typex.Z, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16738,7 +16740,7 @@ func emitMakerETTypex_ZInt16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZInt16(t typex.EventTime, key typex.Z, val int16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16754,7 +16756,7 @@ func emitMakerTypex_ZInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZInt32(key typex.Z, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16770,7 +16772,7 @@ func emitMakerETTypex_ZInt32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZInt32(t typex.EventTime, key typex.Z, val int32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16786,7 +16788,7 @@ func emitMakerTypex_ZInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZInt64(key typex.Z, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16802,7 +16804,7 @@ func emitMakerETTypex_ZInt64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZInt64(t typex.EventTime, key typex.Z, val int64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16818,7 +16820,7 @@ func emitMakerTypex_ZUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZUint(key typex.Z, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16834,7 +16836,7 @@ func emitMakerETTypex_ZUint(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZUint(t typex.EventTime, key typex.Z, val uint) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16850,7 +16852,7 @@ func emitMakerTypex_ZUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZUint8(key typex.Z, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16866,7 +16868,7 @@ func emitMakerETTypex_ZUint8(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZUint8(t typex.EventTime, key typex.Z, val uint8) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16882,7 +16884,7 @@ func emitMakerTypex_ZUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZUint16(key typex.Z, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16898,7 +16900,7 @@ func emitMakerETTypex_ZUint16(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZUint16(t typex.EventTime, key typex.Z, val uint16) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16914,7 +16916,7 @@ func emitMakerTypex_ZUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZUint32(key typex.Z, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16930,7 +16932,7 @@ func emitMakerETTypex_ZUint32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZUint32(t typex.EventTime, key typex.Z, val uint32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16946,7 +16948,7 @@ func emitMakerTypex_ZUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZUint64(key typex.Z, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16962,7 +16964,7 @@ func emitMakerETTypex_ZUint64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZUint64(t typex.EventTime, key typex.Z, val uint64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -16978,7 +16980,7 @@ func emitMakerTypex_ZFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZFloat32(key typex.Z, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -16994,7 +16996,7 @@ func emitMakerETTypex_ZFloat32(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZFloat32(t typex.EventTime, key typex.Z, val float32) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -17010,7 +17012,7 @@ func emitMakerTypex_ZFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZFloat64(key typex.Z, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -17026,7 +17028,7 @@ func emitMakerETTypex_ZFloat64(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZFloat64(t typex.EventTime, key typex.Z, val float64) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -17042,7 +17044,7 @@ func emitMakerTypex_ZTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZTypex_T(key typex.Z, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -17058,7 +17060,7 @@ func emitMakerETTypex_ZTypex_T(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZTypex_T(t typex.EventTime, key typex.Z, val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -17074,7 +17076,7 @@ func emitMakerTypex_ZTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZTypex_U(key typex.Z, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -17090,7 +17092,7 @@ func emitMakerETTypex_ZTypex_U(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZTypex_U(t typex.EventTime, key typex.Z, val typex.U) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -17106,7 +17108,7 @@ func emitMakerTypex_ZTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZTypex_V(key typex.Z, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -17122,7 +17124,7 @@ func emitMakerETTypex_ZTypex_V(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZTypex_V(t typex.EventTime, key typex.Z, val typex.V) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -17138,7 +17140,7 @@ func emitMakerTypex_ZTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZTypex_W(key typex.Z, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -17154,7 +17156,7 @@ func emitMakerETTypex_ZTypex_W(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZTypex_W(t typex.EventTime, key typex.Z, val typex.W) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -17170,7 +17172,7 @@ func emitMakerTypex_ZTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZTypex_X(key typex.Z, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -17186,7 +17188,7 @@ func emitMakerETTypex_ZTypex_X(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZTypex_X(t typex.EventTime, key typex.Z, val typex.X) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -17202,7 +17204,7 @@ func emitMakerTypex_ZTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZTypex_Y(key typex.Z, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -17218,7 +17220,7 @@ func emitMakerETTypex_ZTypex_Y(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZTypex_Y(t typex.EventTime, key typex.Z, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -17234,7 +17236,7 @@ func emitMakerTypex_ZTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeTypex_ZTypex_Z(key typex.Z, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -17250,7 +17252,7 @@ func emitMakerETTypex_ZTypex_Z(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeETTypex_ZTypex_Z(t typex.EventTime, key typex.Z, val typex.Z) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
diff --git a/sdks/go/pkg/beam/core/runtime/exec/optimized/emitters.tmpl b/sdks/go/pkg/beam/core/runtime/exec/optimized/emitters.tmpl
index 3e6feb85a9e4..df3413580da3 100644
--- a/sdks/go/pkg/beam/core/runtime/exec/optimized/emitters.tmpl
+++ b/sdks/go/pkg/beam/core/runtime/exec/optimized/emitters.tmpl
@@ -44,13 +44,15 @@ type emitNative struct {
 	est   *sdf.WatermarkEstimator
 
 	ctx context.Context
+  pn  typex.PaneInfo
 	ws  []typex.Window
 	et  typex.EventTime
 	value exec.FullValue
 }
 
-func (e *emitNative) Init(ctx context.Context, ws  []typex.Window, et typex.EventTime) error {
+func (e *emitNative) Init(ctx context.Context, pn typex.PaneInfo, ws  []typex.Window, et typex.EventTime) error {
 	e.ctx = ctx
+  e.pn = pn
 	e.ws = ws
 	e.et = et
 	return nil
@@ -72,7 +74,7 @@ func emitMaker{{$x.Name}}(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invoke{{$x.Name}}(elm {{$x.Type}}) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: elm }
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: elm }
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -88,7 +90,7 @@ func emitMakerET{{$x.Name}}(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeET{{$x.Name}}(t typex.EventTime, elm {{$x.Type}}) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: elm }
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: elm }
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
@@ -105,7 +107,7 @@ func emitMaker{{$x.Name}}{{$y.Name}}(n exec.ElementProcessor) exec.ReusableEmitt
 }
 
 func (e *emitNative) invoke{{$x.Name}}{{$y.Name}}(key {{$x.Type}}, val {{$y.Type}}) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val }
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val }
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -121,7 +123,7 @@ func emitMakerET{{$x.Name}}{{$y.Name}}(n exec.ElementProcessor) exec.ReusableEmi
 }
 
 func (e *emitNative) invokeET{{$x.Name}}{{$y.Name}}(t typex.EventTime, key {{$x.Type}}, val {{$y.Type}}) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: t, Elm: key, Elm2: val }
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: t, Elm: key, Elm2: val }
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(t.ToTime())
 	}
diff --git a/sdks/go/pkg/beam/core/runtime/exec/pardo.go b/sdks/go/pkg/beam/core/runtime/exec/pardo.go
index b93835264507..eb45927a8ac4 100644
--- a/sdks/go/pkg/beam/core/runtime/exec/pardo.go
+++ b/sdks/go/pkg/beam/core/runtime/exec/pardo.go
@@ -360,7 +360,7 @@ func (n *ParDo) invokeDataFn(ctx context.Context, pn typex.PaneInfo, ws []typex.
 			err = postErr
 		}
 	}()
-	if err := n.preInvoke(ctx, ws, ts); err != nil {
+	if err := n.preInvoke(ctx, pn, ws, ts); err != nil {
 		return nil, err
 	}
 	val, err = Invoke(ctx, pn, ws, ts, fn, opt, n.bf, n.we, n.UState, n.reader, n.cache.extra...)
@@ -474,7 +474,7 @@ func (n *ParDo) processTimer(timerFamilyID string, singleWindow []typex.Window,
 			err = postErr
 		}
 	}()
-	if err := n.preInvoke(n.ctx, singleWindow, tmap.HoldTimestamp); err != nil {
+	if err := n.preInvoke(n.ctx, typex.NoFiringPane(), singleWindow, tmap.HoldTimestamp); err != nil {
 		return err
 	}
 
@@ -502,7 +502,7 @@ func (n *ParDo) invokeProcessFn(ctx context.Context, pn typex.PaneInfo, ws []typ
 			err = postErr
 		}
 	}()
-	if err := n.preInvoke(ctx, ws, ts); err != nil {
+	if err := n.preInvoke(ctx, pn, ws, ts); err != nil {
 		return nil, err
 	}
 	val, err = n.inv.invokeWithOpts(ctx, pn, ws, ts, InvokeOpts{opt: opt, bf: n.bf, we: n.we, sa: n.UState, sr: n.reader, ta: n.TimerTracker, tm: n.timerManager, extra: n.cache.extra})
@@ -512,9 +512,9 @@ func (n *ParDo) invokeProcessFn(ctx context.Context, pn typex.PaneInfo, ws []typ
 	return val, nil
 }
 
-func (n *ParDo) preInvoke(ctx context.Context, ws []typex.Window, ts typex.EventTime) error {
+func (n *ParDo) preInvoke(ctx context.Context, pn typex.PaneInfo, ws []typex.Window, ts typex.EventTime) error {
 	for _, e := range n.emitters {
-		if err := e.Init(ctx, ws, ts); err != nil {
+		if err := e.Init(ctx, pn, ws, ts); err != nil {
 			return err
 		}
 	}
diff --git a/sdks/go/pkg/beam/core/runtime/exec/userstate.go b/sdks/go/pkg/beam/core/runtime/exec/userstate.go
index f83aee4bf741..ea723b18e3a7 100644
--- a/sdks/go/pkg/beam/core/runtime/exec/userstate.go
+++ b/sdks/go/pkg/beam/core/runtime/exec/userstate.go
@@ -35,17 +35,18 @@ type stateProvider struct {
 	elementKey []byte
 	window     []byte
 
-	transactionsByKey     map[string][]state.Transaction
-	initialValueByKey     map[string]any
-	initialBagByKey       map[string][]any
-	initialMapValuesByKey map[string]map[string]any
-	initialMapKeysByKey   map[string][]any
-	readersByKey          map[string]io.ReadCloser
-	appendersByKey        map[string]io.Writer
-	clearersByKey         map[string]io.Writer
-	codersByKey           map[string]*coder.Coder
-	keyCodersByID         map[string]*coder.Coder
-	combineFnsByKey       map[string]*graph.CombineFn
+	transactionsByKey        map[string][]state.Transaction
+	initialValueByKey        map[string]any
+	initialBagByKey          map[string][]any
+	blindBagWriteCountsByKey map[string]int // Tracks blind writes to bags before a read.
+	initialMapValuesByKey    map[string]map[string]any
+	initialMapKeysByKey      map[string][]any
+	readersByKey             map[string]io.ReadCloser
+	appendersByKey           map[string]io.Writer
+	clearersByKey            map[string]io.Writer
+	codersByKey              map[string]*coder.Coder
+	keyCodersByID            map[string]*coder.Coder
+	combineFnsByKey          map[string]*graph.CombineFn
 }
 
 // ReadValueState reads a value state from the State API
@@ -148,6 +149,12 @@ func (s *stateProvider) ReadBagState(userStateID string) ([]any, []state.Transac
 	if !ok {
 		transactions = []state.Transaction{}
 	}
+	// If there were blind writes before this read, trim the transactions.
+	// These don't need to be reset, unless a clear happens.
+	if s.blindBagWriteCountsByKey[userStateID] > 0 {
+		// Trim blind writes from the transaction queue, to avoid re-applying them.
+		transactions = transactions[s.blindBagWriteCountsByKey[userStateID]:]
+	}
 
 	return initialValue, transactions, nil
 }
@@ -165,12 +172,17 @@ func (s *stateProvider) ClearBagState(val state.Transaction) error {
 
 	// Any transactions before a clear don't matter
 	s.transactionsByKey[val.Key] = []state.Transaction{val}
+	s.blindBagWriteCountsByKey[val.Key] = 1 // To account for the clear.
 
 	return nil
 }
 
 // WriteBagState writes a bag state to the State API
 func (s *stateProvider) WriteBagState(val state.Transaction) error {
+	_, ok := s.initialBagByKey[val.Key]
+	if !ok {
+		s.blindBagWriteCountsByKey[val.Key]++
+	}
 	ap, err := s.getBagAppender(val.Key)
 	if err != nil {
 		return err
@@ -510,22 +522,23 @@ func (s *userStateAdapter) NewStateProvider(ctx context.Context, reader StateRea
 		return stateProvider{}, err
 	}
 	sp := stateProvider{
-		ctx:                   ctx,
-		sr:                    reader,
-		SID:                   s.sid,
-		elementKey:            elementKey,
-		window:                win,
-		transactionsByKey:     make(map[string][]state.Transaction),
-		initialValueByKey:     make(map[string]any),
-		initialBagByKey:       make(map[string][]any),
-		initialMapValuesByKey: make(map[string]map[string]any),
-		initialMapKeysByKey:   make(map[string][]any),
-		readersByKey:          make(map[string]io.ReadCloser),
-		appendersByKey:        make(map[string]io.Writer),
-		clearersByKey:         make(map[string]io.Writer),
-		combineFnsByKey:       s.stateIDToCombineFn,
-		codersByKey:           s.stateIDToCoder,
-		keyCodersByID:         s.stateIDToKeyCoder,
+		ctx:                      ctx,
+		sr:                       reader,
+		SID:                      s.sid,
+		elementKey:               elementKey,
+		window:                   win,
+		transactionsByKey:        make(map[string][]state.Transaction),
+		initialValueByKey:        make(map[string]any),
+		initialBagByKey:          make(map[string][]any),
+		blindBagWriteCountsByKey: make(map[string]int),
+		initialMapValuesByKey:    make(map[string]map[string]any),
+		initialMapKeysByKey:      make(map[string][]any),
+		readersByKey:             make(map[string]io.ReadCloser),
+		appendersByKey:           make(map[string]io.Writer),
+		clearersByKey:            make(map[string]io.Writer),
+		combineFnsByKey:          s.stateIDToCombineFn,
+		codersByKey:              s.stateIDToCoder,
+		keyCodersByID:            s.stateIDToKeyCoder,
 	}
 
 	return sp, nil
diff --git a/sdks/go/pkg/beam/core/runtime/harness/harness.go b/sdks/go/pkg/beam/core/runtime/harness/harness.go
index 20aad81c1237..d75ae37c6109 100644
--- a/sdks/go/pkg/beam/core/runtime/harness/harness.go
+++ b/sdks/go/pkg/beam/core/runtime/harness/harness.go
@@ -99,6 +99,11 @@ func MainWithOptions(ctx context.Context, loggingEndpoint, controlEndpoint strin
 		go diagnostics.SampleForHeapProfile(ctx, samplingFrequencySeconds, maxTimeBetweenDumpsSeconds)
 	}
 
+	elmTimeout, err := parseTimeoutDurationFlag(ctx, beam.PipelineOptions.Get("element_processing_timeout"))
+	if err != nil {
+		log.Infof(ctx, "Failed to parse element_processing_timeout: %v, there will be no timeout for processing an element in a PTransform operation", err)
+	}
+
 	// Connect to FnAPI control server. Receive and execute work.
 	conn, err := dial(ctx, controlEndpoint, "control", 60*time.Second)
 	if err != nil {
@@ -157,6 +162,7 @@ func MainWithOptions(ctx context.Context, loggingEndpoint, controlEndpoint strin
 		state:                &StateChannelManager{},
 		cache:                &sideCache,
 		runnerCapabilities:   rcMap,
+		elmTimeout:           elmTimeout,
 	}
 
 	if enabled, ok := rcMap[graphx.URNDataSampling]; ok && enabled {
@@ -312,6 +318,7 @@ type control struct {
 	cache              *statecache.SideInputCache
 	runnerCapabilities map[string]bool
 	dataSampler        *exec.DataSampler
+	elmTimeout         time.Duration
 }
 
 func (c *control) metStoreToString(statusInfo *strings.Builder) {
@@ -411,8 +418,13 @@ func (c *control) handleInstruction(ctx context.Context, req *fnpb.InstructionRe
 		data := NewScopedDataManager(c.data, instID)
 		state := NewScopedStateReaderWithCache(c.state, instID, c.cache)
 
-		sampler := newSampler(store)
-		go sampler.start(ctx, samplePeriod)
+		sampler := newSampler(store, c.elmTimeout)
+		go func() {
+			samplerErr := sampler.start(ctx, samplePeriod)
+			if samplerErr != nil {
+				log.Exitf(ctx, "Failed to sample: %v, the SDK harness will be terminated.", samplerErr)
+			}
+		}()
 
 		err = plan.Execute(ctx, string(instID), exec.DataContext{Data: data, State: state})
 
@@ -692,6 +704,17 @@ func (c *control) handleInstruction(ctx context.Context, req *fnpb.InstructionRe
 	}
 }
 
+// Parses the element_processing_timeout flag and returns the corresponding time.Duration.
+// The element_processing_timeout flag is expected to be a duration string (e.g., "5m", "1h", etc.)or -1.
+// Otherwise, it defaults to no timeout (0 minutes).
+func parseTimeoutDurationFlag(ctx context.Context, elementProcessingTimeout string) (time.Duration, error) {
+	userSpecifiedTimeout, err := time.ParseDuration(elementProcessingTimeout)
+	if err != nil {
+		return 0 * time.Minute, err
+	}
+	return userSpecifiedTimeout, nil
+}
+
 // getPlanOrResponse returns the plan for the given instruction id.
 // Otherwise, provides an error response.
 // However, if that plan is known as inactive, it returns both the plan and response as nil,
diff --git a/sdks/go/pkg/beam/core/runtime/harness/harness_test.go b/sdks/go/pkg/beam/core/runtime/harness/harness_test.go
index 8c25db613eba..96e09d226f5a 100644
--- a/sdks/go/pkg/beam/core/runtime/harness/harness_test.go
+++ b/sdks/go/pkg/beam/core/runtime/harness/harness_test.go
@@ -16,9 +16,11 @@
 package harness
 
 import (
+	"context"
 	"fmt"
 	"strings"
 	"testing"
+	"time"
 
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/runtime/exec"
 	fnpb "github.com/apache/beam/sdks/v2/go/pkg/beam/model/fnexecution_v1"
@@ -229,3 +231,30 @@ func TestCircleBuffer(t *testing.T) {
 		}
 	})
 }
+
+func TestElementProcessingTimeoutParsing(t *testing.T) {
+	ctx := context.Background()
+	tests := []struct {
+		in   string
+		want time.Duration
+		err  bool
+	}{
+		{"5m", 5 * time.Minute, false},
+		{"1h", 1 * time.Hour, false},
+		{"1m5s", 1*time.Minute + 5*time.Second, false},
+		{"5s1m", 5*time.Second + 1*time.Minute, false},
+		{"-1", 0, true},
+		{"", 0, true},
+		{"5mmm", 0, true},
+	}
+
+	for _, test := range tests {
+		got, err := parseTimeoutDurationFlag(ctx, test.in)
+		if (err != nil) != test.err {
+			t.Errorf("parseTimeoutDurationFlag(ctx, %q) err = %v, want err? %v", test.in, err, test.err)
+		}
+		if got != test.want {
+			t.Errorf("parseTimeoutDurationFlag(ctx, %q) = %v, want %v", test.in, got, test.want)
+		}
+	}
+}
diff --git a/sdks/go/pkg/beam/core/runtime/harness/sampler.go b/sdks/go/pkg/beam/core/runtime/harness/sampler.go
index 5246c1fe69c5..0bfe82c9b003 100644
--- a/sdks/go/pkg/beam/core/runtime/harness/sampler.go
+++ b/sdks/go/pkg/beam/core/runtime/harness/sampler.go
@@ -17,9 +17,9 @@ package harness
 
 import (
 	"context"
-	"time"
-
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/metrics"
+	"github.com/apache/beam/sdks/v2/go/pkg/beam/internal/errors"
+	"time"
 )
 
 type stateSampler struct {
@@ -27,21 +27,24 @@ type stateSampler struct {
 	sampler metrics.StateSampler
 }
 
-func newSampler(store *metrics.Store) *stateSampler {
-	return &stateSampler{sampler: metrics.NewSampler(store), done: make(chan int)}
+func newSampler(store *metrics.Store, elementProcessingTimeout time.Duration) *stateSampler {
+	return &stateSampler{sampler: metrics.NewSampler(store, elementProcessingTimeout), done: make(chan int)}
 }
 
-func (s *stateSampler) start(ctx context.Context, t time.Duration) {
+func (s *stateSampler) start(ctx context.Context, t time.Duration) error {
 	ticker := time.NewTicker(t)
 	defer ticker.Stop()
 	for {
 		select {
 		case <-s.done:
-			return
+			return nil
 		case <-ctx.Done():
-			return
+			return nil
 		case <-ticker.C:
-			s.sampler.Sample(ctx, t)
+			err := s.sampler.Sample(ctx, t)
+			if err != nil {
+				return errors.Errorf("Failed to sample: %v", err)
+			}
 		}
 	}
 }
diff --git a/sdks/go/pkg/beam/core/runtime/xlangx/expansionx/process.go b/sdks/go/pkg/beam/core/runtime/xlangx/expansionx/process.go
index 590c9392a991..9eb4d852cc76 100644
--- a/sdks/go/pkg/beam/core/runtime/xlangx/expansionx/process.go
+++ b/sdks/go/pkg/beam/core/runtime/xlangx/expansionx/process.go
@@ -94,7 +94,7 @@ func (e *ExpansionServiceRunner) pingEndpoint(timeout time.Duration) error {
 	return nil
 }
 
-const connectionTimeout = 15 * time.Second
+const connectionTimeout = 30 * time.Second
 
 // StartService starts the expansion service for a given ExpansionServiceRunner. If this is
 // called and does not return an error, the expansion service will be running in the background
diff --git a/sdks/go/pkg/beam/options/jobopts/options.go b/sdks/go/pkg/beam/options/jobopts/options.go
index 0652fe24e863..327f3895b117 100644
--- a/sdks/go/pkg/beam/options/jobopts/options.go
+++ b/sdks/go/pkg/beam/options/jobopts/options.go
@@ -22,9 +22,8 @@ import (
 	"flag"
 	"fmt"
 	"strings"
-	"time"
-
 	"sync/atomic"
+	"time"
 
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/internal/errors"
@@ -92,15 +91,20 @@ var (
 	// executing them and fails early if the pipelines don't pass.
 	Strict = flag.Bool("beam_strict", false, "Apply additional validation to pipelines.")
 
-	// Flag to retain docker containers created by the runner. If false, then
+	// RetrainDockerContainers flag to retain docker containers created by the runner. If false, then
 	// containers are deleted once the job ends, even if it failed.
 	RetainDockerContainers = flag.Bool("retain_docker_containers", false, "Retain Docker containers created by the runner.")
 
-	// Flag to set the degree of parallelism. If not set, the configured Flink default is used, or 1 if none can be found.
+	// Parallelisn flag to set the degree of parallelism. If not set, the configured Flink default is used, or 1 if none can be found.
 	Parallelism = flag.Int("parallelism", -1, "The degree of parallelism to be used when distributing operations onto Flink workers.")
 
 	// ResourceHints flag takes whole pipeline hints for resources.
 	ResourceHints stringSlice
+
+	// ElementProcessingTimeout flag to set the timeout for processing an element in a PTransform operation. If set to -1, there is no timeout.
+	ElementProcessingTimeout = flag.Duration("element_processing_timeout", -1,
+		"The time limit (in minutes) for any PTransform to finish processing a single element. If exceeded, "+
+			"the SDK worker process self-terminates and processing may be restarted by a runner. There is no time limit if the value is set to -1.")
 )
 
 type missingFlagError error
@@ -179,6 +183,15 @@ func GetExperiments() []string {
 	return strings.Split(*Experiments, ",")
 }
 
+// GetElementProcessingTimeout returns the element processing timeout. If the flag is set to -1,
+// there is no timeout.
+func GetElementProcessingTimeout() time.Duration {
+	if *ElementProcessingTimeout == -1 {
+		return 0 * time.Minute
+	}
+	return *ElementProcessingTimeout
+}
+
 // GetPipelineResourceHints parses known standard hints and returns the flag set hints for the pipeline.
 // In case of duplicate hint URNs, the last value specified will be used.
 func GetPipelineResourceHints() resource.Hints {
diff --git a/sdks/go/pkg/beam/options/jobopts/options_test.go b/sdks/go/pkg/beam/options/jobopts/options_test.go
index dc23be954152..89e2d5a721dc 100644
--- a/sdks/go/pkg/beam/options/jobopts/options_test.go
+++ b/sdks/go/pkg/beam/options/jobopts/options_test.go
@@ -20,6 +20,7 @@ import (
 	"reflect"
 	"strings"
 	"testing"
+	"time"
 
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/options/resource"
@@ -169,3 +170,18 @@ func TestGetExperiements(t *testing.T) {
 		t.Errorf("GetExperiments(\"\") = %v, want %v", got, want)
 	}
 }
+
+func TestGetElementProcessingTimeout(t *testing.T) {
+	*ElementProcessingTimeout = -1
+	if got, want := GetElementProcessingTimeout(), 0*time.Minute; got != want {
+		t.Errorf("getElementProcessingTimeout() = %v, want %v", got, want)
+	}
+	*ElementProcessingTimeout = 10 * time.Second
+	if got, want := GetElementProcessingTimeout(), 10*time.Second; got != want {
+		t.Errorf("getElementProcessingTimeout() = %v, want %v", got, want)
+	}
+	*ElementProcessingTimeout = 10 * time.Minute
+	if got, want := GetElementProcessingTimeout(), 10*time.Minute; got != want {
+		t.Errorf("getElementProcessingTimeout() = %v, want %v", got, want)
+	}
+}
diff --git a/sdks/go/pkg/beam/register/emitter.go b/sdks/go/pkg/beam/register/emitter.go
index 742f832ce4c9..b870ec9245ec 100644
--- a/sdks/go/pkg/beam/register/emitter.go
+++ b/sdks/go/pkg/beam/register/emitter.go
@@ -28,13 +28,15 @@ type emit struct {
 	est *sdf.WatermarkEstimator
 
 	ctx   context.Context
+	pn    typex.PaneInfo
 	ws    []typex.Window
 	et    typex.EventTime
 	value exec.FullValue
 }
 
-func (e *emit) Init(ctx context.Context, ws []typex.Window, et typex.EventTime) error {
+func (e *emit) Init(ctx context.Context, pn typex.PaneInfo, ws []typex.Window, et typex.EventTime) error {
 	e.ctx = ctx
+	e.pn = pn
 	e.ws = ws
 	e.et = et
 	return nil
@@ -54,7 +56,7 @@ func (e *emit1[T]) Value() any {
 }
 
 func (e *emit1[T]) invoke(val T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -73,7 +75,7 @@ func (e *emit2[T1, T2]) Value() any {
 }
 
 func (e *emit2[T1, T2]) invoke(key T1, val T2) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -92,7 +94,7 @@ func (e *emit1WithTimestamp[T]) Value() any {
 }
 
 func (e *emit1WithTimestamp[T]) invoke(et typex.EventTime, val T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: et, Elm: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: et, Elm: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(et.ToTime())
 	}
@@ -111,7 +113,7 @@ func (e *emit2WithTimestamp[T1, T2]) Value() any {
 }
 
 func (e *emit2WithTimestamp[T1, T2]) invoke(et typex.EventTime, key T1, val T2) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(et.ToTime())
 	}
diff --git a/sdks/go/pkg/beam/register/emitter_test.go b/sdks/go/pkg/beam/register/emitter_test.go
index 32a45f5da9e4..c89342a8afd8 100644
--- a/sdks/go/pkg/beam/register/emitter_test.go
+++ b/sdks/go/pkg/beam/register/emitter_test.go
@@ -103,7 +103,7 @@ func TestEmitter3(t *testing.T) {
 
 func TestEmit1(t *testing.T) {
 	e := &emit1[int]{n: &elementProcessor{}}
-	e.Init(context.Background(), []typex.Window{}, mtime.ZeroTimestamp)
+	e.Init(context.Background(), typex.NoFiringPane(), []typex.Window{}, mtime.ZeroTimestamp)
 	fn := e.Value().(func(int))
 	fn(3)
 	if got, want := e.n.(*elementProcessor).inFV.Elm, 3; got != want {
@@ -119,7 +119,7 @@ func TestEmit1(t *testing.T) {
 
 func TestEmit2(t *testing.T) {
 	e := &emit2[int, string]{n: &elementProcessor{}}
-	e.Init(context.Background(), []typex.Window{}, mtime.ZeroTimestamp)
+	e.Init(context.Background(), typex.NoFiringPane(), []typex.Window{}, mtime.ZeroTimestamp)
 	fn := e.Value().(func(int, string))
 	fn(3, "hello")
 	if got, want := e.n.(*elementProcessor).inFV.Elm, 3; got != want {
@@ -135,7 +135,7 @@ func TestEmit2(t *testing.T) {
 
 func TestEmit1WithTimestamp(t *testing.T) {
 	e := &emit1WithTimestamp[int]{n: &elementProcessor{}}
-	e.Init(context.Background(), []typex.Window{}, mtime.ZeroTimestamp)
+	e.Init(context.Background(), typex.NoFiringPane(), []typex.Window{}, mtime.ZeroTimestamp)
 	fn := e.Value().(func(typex.EventTime, int))
 	fn(mtime.MaxTimestamp, 3)
 	if got, want := e.n.(*elementProcessor).inFV.Elm, 3; got != want {
@@ -151,7 +151,7 @@ func TestEmit1WithTimestamp(t *testing.T) {
 
 func TestEmit2WithTimestamp(t *testing.T) {
 	e := &emit2WithTimestamp[int, string]{n: &elementProcessor{}}
-	e.Init(context.Background(), []typex.Window{}, mtime.ZeroTimestamp)
+	e.Init(context.Background(), typex.NoFiringPane(), []typex.Window{}, mtime.ZeroTimestamp)
 	fn := e.Value().(func(typex.EventTime, int, string))
 	fn(mtime.MaxTimestamp, 3, "hello")
 	if got, want := e.n.(*elementProcessor).inFV.Elm, 3; got != want {
diff --git a/sdks/go/pkg/beam/runners/dataflow/dataflowlib/job.go b/sdks/go/pkg/beam/runners/dataflow/dataflowlib/job.go
index ed706ec1a482..96c0750d18e3 100644
--- a/sdks/go/pkg/beam/runners/dataflow/dataflowlib/job.go
+++ b/sdks/go/pkg/beam/runners/dataflow/dataflowlib/job.go
@@ -262,7 +262,7 @@ func WaitForCompletion(ctx context.Context, client *df.Service, project, region,
 		if err != nil {
 			return err
 		}
-		log.Infof(ctx, msg)
+		log.Infof(ctx, "%s", msg)
 		if terminal {
 			return nil
 		}
diff --git a/sdks/go/pkg/beam/runners/prism/internal/engine/elementmanager.go b/sdks/go/pkg/beam/runners/prism/internal/engine/elementmanager.go
index 767dc325fd31..6af030f36228 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/engine/elementmanager.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/engine/elementmanager.go
@@ -89,6 +89,14 @@ type PColInfo struct {
 	KeyDec      func(io.Reader) []byte
 }
 
+func (info PColInfo) LogValue() slog.Value {
+	return slog.GroupValue(
+		slog.String("GlobalID", info.GlobalID),
+		slog.String("WindowCoder", info.WindowCoder.String()),
+		// Do not attempt to log functions, or it will result in JSON marshaling error.
+	)
+}
+
 // WinCoderType indicates what kind of coder
 // the window is using. There are only 3
 // valid single window encodings.
@@ -110,6 +118,19 @@ const (
 	WinCustom
 )
 
+func (wct WinCoderType) String() string {
+	switch wct {
+	case WinGlobal:
+		return "WinGlobal"
+	case WinInterval:
+		return "WinInterval"
+	case WinCustom:
+		return "WinCustom"
+	default:
+		return fmt.Sprintf("Unknown(%d)", wct)
+	}
+}
+
 // ToData recodes the elements with their approprate windowed value header.
 func (es elements) ToData(info PColInfo) [][]byte {
 	var ret [][]byte
@@ -163,6 +184,8 @@ type Config struct {
 	MaxBundleSize int
 	// Whether to use real-time clock as processing time
 	EnableRTC bool
+	// Whether to process the data in a streaming mode
+	StreamingMode bool
 }
 
 // ElementManager handles elements, watermarks, and related errata to determine
@@ -237,7 +260,7 @@ func NewElementManager(config Config) *ElementManager {
 // AddStage adds a stage to this element manager, connecting it's PCollections and
 // nodes to the watermark propagation graph.
 func (em *ElementManager) AddStage(ID string, inputIDs, outputIDs []string, sides []LinkID) {
-	slog.Debug("AddStage", slog.String("ID", ID), slog.Any("inputs", inputIDs), slog.Any("sides", sides), slog.Any("outputs", outputIDs))
+	slog.Debug("em.AddStage", slog.String("ID", ID), slog.Any("inputs", inputIDs), slog.Any("sides", sides), slog.Any("outputs", outputIDs))
 	ss := makeStageState(ID, inputIDs, outputIDs, sides)
 
 	em.stages[ss.ID] = ss
@@ -338,7 +361,7 @@ func (rb RunBundle) LogValue() slog.Value {
 	return slog.GroupValue(
 		slog.String("ID", rb.BundleID),
 		slog.String("stage", rb.StageID),
-		slog.Time("watermark", rb.Watermark.ToTime()))
+		slog.Any("watermark", rb.Watermark))
 }
 
 // Bundles is the core execution loop. It produces a sequences of bundles able to be executed.
@@ -481,6 +504,40 @@ func (em *ElementManager) Bundles(ctx context.Context, upstreamCancelFn context.
 	return runStageCh
 }
 
+// DumpStages puts all the stage information into a string and returns it.
+func (em *ElementManager) DumpStages() string {
+	var stageState []string
+	ids := maps.Keys(em.stages)
+	if em.testStreamHandler != nil {
+		stageState = append(stageState, fmt.Sprintf("TestStreamHandler: completed %v, curIndex %v of %v events: %+v, processingTime %v, %v, ptEvents %v \n",
+			em.testStreamHandler.completed, em.testStreamHandler.nextEventIndex, len(em.testStreamHandler.events), em.testStreamHandler.events, em.testStreamHandler.processingTime, mtime.FromTime(em.testStreamHandler.processingTime), em.processTimeEvents))
+	} else {
+		stageState = append(stageState, fmt.Sprintf("ElementManager Now: %v processingTimeEvents: %v injectedBundles: %v\n", em.ProcessingTimeNow(), em.processTimeEvents.events, em.injectedBundles))
+	}
+	sort.Strings(ids)
+	for _, id := range ids {
+		ss := em.stages[id]
+		inW := ss.InputWatermark()
+		outW := ss.OutputWatermark()
+		upPCol, upW := ss.UpstreamWatermark()
+		upS := em.pcolParents[upPCol]
+		if upS == "" {
+			upS = "IMPULSE  " // (extra spaces to allow print to align better.)
+		}
+		stageState = append(stageState, fmt.Sprintln(id, "watermark in", inW, "out", outW, "upstream", upW, "from", upS, "pending", ss.pending, "byKey", ss.pendingByKeys, "inprogressKeys", ss.inprogressKeys, "byBundle", ss.inprogressKeysByBundle, "holds", ss.watermarkHolds.heap, "holdCounts", ss.watermarkHolds.counts, "holdsInBundle", ss.inprogressHoldsByBundle, "pttEvents", ss.processingTimeTimers.toFire, "bundlesToInject", ss.bundlesToInject))
+
+		var outputConsumers, sideConsumers []string
+		for _, col := range ss.outputIDs {
+			outputConsumers = append(outputConsumers, em.consumers[col]...)
+			for _, l := range em.sideConsumers[col] {
+				sideConsumers = append(sideConsumers, l.Global)
+			}
+		}
+		stageState = append(stageState, fmt.Sprintf("\tsideInputs: %v outputCols: %v outputConsumers: %v sideConsumers: %v\n", ss.sides, ss.outputIDs, outputConsumers, sideConsumers))
+	}
+	return strings.Join(stageState, "")
+}
+
 // checkForQuiescence sees if this element manager is no longer able to do any pending work or make progress.
 //
 // Quiescense can happen if there are no inprogress bundles, and there are no further watermark refreshes, which
@@ -501,9 +558,9 @@ func (em *ElementManager) checkForQuiescence(advanced set[string]) error {
 		// If there are changed stages that need a watermarks refresh,
 		// we aren't yet stuck.
 		v := em.livePending.Load()
-		slog.Debug("Bundles: nothing in progress after advance",
-			slog.Any("advanced", advanced),
-			slog.Int("changeCount", len(em.changedStages)),
+		slog.Debug("Bundles: nothing in progress after advance, but some stages need a watermark refresh",
+			slog.Any("mayProgress", advanced),
+			slog.Any("needRefresh", em.changedStages),
 			slog.Int64("pendingElementCount", v),
 		)
 		return nil
@@ -546,36 +603,7 @@ func (em *ElementManager) checkForQuiescence(advanced set[string]) error {
 	// Jobs must never get stuck so this indicates a bug in prism to be investigated.
 
 	slog.Debug("Bundles: nothing in progress and no refreshes", slog.Int64("pendingElementCount", v))
-	var stageState []string
-	ids := maps.Keys(em.stages)
-	if em.testStreamHandler != nil {
-		stageState = append(stageState, fmt.Sprintf("TestStreamHandler: completed %v, curIndex %v of %v events: %+v, processingTime %v, %v, ptEvents %v \n",
-			em.testStreamHandler.completed, em.testStreamHandler.nextEventIndex, len(em.testStreamHandler.events), em.testStreamHandler.events, em.testStreamHandler.processingTime, mtime.FromTime(em.testStreamHandler.processingTime), em.processTimeEvents))
-	} else {
-		stageState = append(stageState, fmt.Sprintf("ElementManager Now: %v processingTimeEvents: %v injectedBundles: %v\n", em.ProcessingTimeNow(), em.processTimeEvents.events, em.injectedBundles))
-	}
-	sort.Strings(ids)
-	for _, id := range ids {
-		ss := em.stages[id]
-		inW := ss.InputWatermark()
-		outW := ss.OutputWatermark()
-		upPCol, upW := ss.UpstreamWatermark()
-		upS := em.pcolParents[upPCol]
-		if upS == "" {
-			upS = "IMPULSE  " // (extra spaces to allow print to align better.)
-		}
-		stageState = append(stageState, fmt.Sprintln(id, "watermark in", inW, "out", outW, "upstream", upW, "from", upS, "pending", ss.pending, "byKey", ss.pendingByKeys, "inprogressKeys", ss.inprogressKeys, "byBundle", ss.inprogressKeysByBundle, "holds", ss.watermarkHolds.heap, "holdCounts", ss.watermarkHolds.counts, "holdsInBundle", ss.inprogressHoldsByBundle, "pttEvents", ss.processingTimeTimers.toFire, "bundlesToInject", ss.bundlesToInject))
-
-		var outputConsumers, sideConsumers []string
-		for _, col := range ss.outputIDs {
-			outputConsumers = append(outputConsumers, em.consumers[col]...)
-			for _, l := range em.sideConsumers[col] {
-				sideConsumers = append(sideConsumers, l.Global)
-			}
-		}
-		stageState = append(stageState, fmt.Sprintf("\tsideInputs: %v outputCols: %v outputConsumers: %v sideConsumers: %v\n", ss.sides, ss.outputIDs, outputConsumers, sideConsumers))
-	}
-	return errors.Errorf("nothing in progress and no refreshes with non zero pending elements: %v\n%v", v, strings.Join(stageState, ""))
+	return errors.Errorf("nothing in progress and no refreshes with non zero pending elements: %v\n%v", v, em.DumpStages())
 }
 
 // InputForBundle returns pre-allocated data for the given bundle, encoding the elements using
@@ -588,7 +616,7 @@ func (em *ElementManager) InputForBundle(rb RunBundle, info PColInfo) [][]byte {
 	return es.ToData(info)
 }
 
-// DataAndTimerInputForBundle returns pre-allocated data for the given bundle and the estimated number of elements.
+// DataAndTimerInputForBundle returns pre-allocated data for the given bundle and the estimated number of data elements.
 // Elements are encoded with the PCollection's coders.
 func (em *ElementManager) DataAndTimerInputForBundle(rb RunBundle, info PColInfo) ([]*Block, int) {
 	ss := em.stages[rb.StageID]
@@ -596,14 +624,16 @@ func (em *ElementManager) DataAndTimerInputForBundle(rb RunBundle, info PColInfo
 	defer ss.mu.Unlock()
 	es := ss.inprogress[rb.BundleID]
 
-	var total int
+	var total_data int
 
 	var ret []*Block
 	cur := &Block{}
 	for _, e := range es.es {
 		switch {
 		case e.IsTimer() && (cur.Kind != BlockTimer || e.family != cur.Family || cur.Transform != e.transform):
-			total += len(cur.Bytes)
+			if cur.Kind == BlockData {
+				total_data += len(cur.Bytes)
+			}
 			cur = &Block{
 				Kind:      BlockTimer,
 				Transform: e.transform,
@@ -631,7 +661,6 @@ func (em *ElementManager) DataAndTimerInputForBundle(rb RunBundle, info PColInfo
 
 			cur.Bytes = append(cur.Bytes, buf.Bytes())
 		case cur.Kind != BlockData:
-			total += len(cur.Bytes)
 			cur = &Block{
 				Kind: BlockData,
 			}
@@ -644,8 +673,10 @@ func (em *ElementManager) DataAndTimerInputForBundle(rb RunBundle, info PColInfo
 			cur.Bytes = append(cur.Bytes, buf.Bytes())
 		}
 	}
-	total += len(cur.Bytes)
-	return ret, total
+	if cur.Kind == BlockData {
+		total_data += len(cur.Bytes)
+	}
+	return ret, total_data
 }
 
 // BlockKind indicates how the block is to be handled.
@@ -827,7 +858,7 @@ func (em *ElementManager) PersistBundle(rb RunBundle, col2Coders map[string]PCol
 						element{
 							window:    w,
 							timestamp: et,
-							pane:      stage.kind.updatePane(stage, pn, w, keyBytes),
+							pane:      stage.kind.getPaneOrDefault(stage, pn, w, keyBytes, rb.BundleID),
 							elmBytes:  elmBytes,
 							keyBytes:  keyBytes,
 							sequence:  seq,
@@ -838,7 +869,9 @@ func (em *ElementManager) PersistBundle(rb RunBundle, col2Coders map[string]PCol
 		}
 		consumers := em.consumers[output]
 		sideConsumers := em.sideConsumers[output]
-		slog.Debug("PersistBundle: bundle has downstream consumers.", "bundle", rb, slog.Int("newPending", len(newPending)), "consumers", consumers, "sideConsumers", sideConsumers)
+		slog.Debug("PersistBundle: bundle has downstream consumers.", "bundle", rb,
+			slog.Int("newPending", len(newPending)), "consumers", consumers, "sideConsumers", sideConsumers,
+			"pendingDelta", len(newPending)*len(consumers))
 		for _, sID := range consumers {
 			consumer := em.stages[sID]
 			count := consumer.AddPending(em, newPending)
@@ -868,70 +901,76 @@ func (em *ElementManager) PersistBundle(rb RunBundle, col2Coders map[string]PCol
 	// Clear out the inprogress elements associated with the completed bundle.
 	// Must be done after adding the new pending elements to avoid an incorrect
 	// watermark advancement.
-	stage.mu.Lock()
-	completed := stage.inprogress[rb.BundleID]
-	em.addPending(-len(completed.es))
-	delete(stage.inprogress, rb.BundleID)
-	for k := range stage.inprogressKeysByBundle[rb.BundleID] {
-		delete(stage.inprogressKeys, k)
-	}
-	delete(stage.inprogressKeysByBundle, rb.BundleID)
-
-	// Adjust holds as needed.
-	for h, c := range newHolds {
-		if c > 0 {
-			stage.watermarkHolds.Add(h, c)
-		} else if c < 0 {
-			stage.watermarkHolds.Drop(h, -c)
-		}
-	}
-	for hold, v := range stage.inprogressHoldsByBundle[rb.BundleID] {
-		stage.watermarkHolds.Drop(hold, v)
-	}
-	delete(stage.inprogressHoldsByBundle, rb.BundleID)
-
-	// Clean up OnWindowExpiration bundle accounting, so window state
-	// may be garbage collected.
-	if stage.expiryWindowsByBundles != nil {
-		win, ok := stage.expiryWindowsByBundles[rb.BundleID]
-		if ok {
-			stage.inProgressExpiredWindows[win] -= 1
-			if stage.inProgressExpiredWindows[win] == 0 {
-				delete(stage.inProgressExpiredWindows, win)
+	func() {
+		stage.mu.Lock()
+		// Defer unlocking the mutex within an anonymous function to ensure it's released
+		// even if a panic occurs during `em.addPending`. This prevents potential deadlocks
+		// if the waitgroup unexpectedly drops below zero due to a runner bug.
+		defer stage.mu.Unlock()
+		completed := stage.inprogress[rb.BundleID]
+		em.addPending(-len(completed.es))
+		delete(stage.inprogress, rb.BundleID)
+		for k := range stage.inprogressKeysByBundle[rb.BundleID] {
+			delete(stage.inprogressKeys, k)
+		}
+		delete(stage.inprogressKeysByBundle, rb.BundleID)
+		delete(stage.bundlePanes, rb.BundleID)
+
+		// Adjust holds as needed.
+		for h, c := range newHolds {
+			if c > 0 {
+				stage.watermarkHolds.Add(h, c)
+			} else if c < 0 {
+				stage.watermarkHolds.Drop(h, -c)
 			}
-			delete(stage.expiryWindowsByBundles, rb.BundleID)
 		}
-	}
+		for hold, v := range stage.inprogressHoldsByBundle[rb.BundleID] {
+			stage.watermarkHolds.Drop(hold, v)
+		}
+		delete(stage.inprogressHoldsByBundle, rb.BundleID)
 
-	// If there are estimated output watermarks, set the estimated
-	// output watermark for the stage.
-	if len(residuals.MinOutputWatermarks) > 0 {
-		estimate := mtime.MaxTimestamp
-		for _, t := range residuals.MinOutputWatermarks {
-			estimate = mtime.Min(estimate, t)
+		// Clean up OnWindowExpiration bundle accounting, so window state
+		// may be garbage collected.
+		if stage.expiryWindowsByBundles != nil {
+			win, ok := stage.expiryWindowsByBundles[rb.BundleID]
+			if ok {
+				stage.inProgressExpiredWindows[win] -= 1
+				if stage.inProgressExpiredWindows[win] == 0 {
+					delete(stage.inProgressExpiredWindows, win)
+				}
+				delete(stage.expiryWindowsByBundles, rb.BundleID)
+			}
 		}
-		stage.estimatedOutput = estimate
-	}
 
-	// Handle persisting.
-	for link, winMap := range d.state {
-		linkMap, ok := stage.state[link]
-		if !ok {
-			linkMap = map[typex.Window]map[string]StateData{}
-			stage.state[link] = linkMap
+		// If there are estimated output watermarks, set the estimated
+		// output watermark for the stage.
+		if len(residuals.MinOutputWatermarks) > 0 {
+			estimate := mtime.MaxTimestamp
+			for _, t := range residuals.MinOutputWatermarks {
+				estimate = mtime.Min(estimate, t)
+			}
+			stage.estimatedOutput = estimate
 		}
-		for w, keyMap := range winMap {
-			wlinkMap, ok := linkMap[w]
+
+		// Handle persisting.
+		for link, winMap := range d.state {
+			linkMap, ok := stage.state[link]
 			if !ok {
-				wlinkMap = map[string]StateData{}
-				linkMap[w] = wlinkMap
+				linkMap = map[typex.Window]map[string]StateData{}
+				stage.state[link] = linkMap
 			}
-			for key, data := range keyMap {
-				wlinkMap[key] = data
+			for w, keyMap := range winMap {
+				wlinkMap, ok := linkMap[w]
+				if !ok {
+					wlinkMap = map[string]StateData{}
+					linkMap[w] = wlinkMap
+				}
+				for key, data := range keyMap {
+					wlinkMap[key] = data
+				}
 			}
 		}
-	}
-	stage.mu.Unlock()
+	}()
 
 	em.markChangedAndClearBundle(stage.ID, rb.BundleID, ptRefreshes)
 }
@@ -1008,11 +1047,16 @@ func (em *ElementManager) triageTimers(d TentativeData, inputInfo PColInfo, stag
 // FailBundle clears the extant data allowing the execution to shut down.
 func (em *ElementManager) FailBundle(rb RunBundle) {
 	stage := em.stages[rb.StageID]
-	stage.mu.Lock()
-	completed := stage.inprogress[rb.BundleID]
-	em.addPending(-len(completed.es))
-	delete(stage.inprogress, rb.BundleID)
-	stage.mu.Unlock()
+	func() {
+		stage.mu.Lock()
+		// Defer unlocking the mutex within an anonymous function to ensure it's released
+		// even if a panic occurs during `em.addPending`. This prevents potential deadlocks
+		// if the waitgroup unexpectedly drops below zero due to a runner bug.
+		defer stage.mu.Unlock()
+		completed := stage.inprogress[rb.BundleID]
+		em.addPending(-len(completed.es))
+		delete(stage.inprogress, rb.BundleID)
+	}()
 	em.markChangedAndClearBundle(rb.StageID, rb.BundleID, nil)
 }
 
@@ -1021,7 +1065,7 @@ func (em *ElementManager) FailBundle(rb RunBundle) {
 func (em *ElementManager) ReturnResiduals(rb RunBundle, firstRsIndex int, inputInfo PColInfo, residuals Residuals) {
 	stage := em.stages[rb.StageID]
 
-	stage.splitBundle(rb, firstRsIndex)
+	stage.splitBundle(rb, firstRsIndex, em)
 	unprocessedElements := reElementResiduals(residuals.Data, inputInfo, rb)
 	if len(unprocessedElements) > 0 {
 		slog.Debug("ReturnResiduals: unprocessed elements", "bundle", rb, "count", len(unprocessedElements))
@@ -1129,18 +1173,20 @@ type stageState struct {
 	input              mtime.Time // input watermark for the parallel input.
 	output             mtime.Time // Output watermark for the whole stage
 	estimatedOutput    mtime.Time // Estimated watermark output from DoFns
+	previousInput      mtime.Time // input watermark before the latest watermark refresh
 
 	pending    elementHeap                          // pending input elements for this stage that are to be processesd
 	inprogress map[string]elements                  // inprogress elements by active bundles, keyed by bundle
 	sideInputs map[LinkID]map[typex.Window][][]byte // side input data for this stage, from {tid, inputID} -> window
 
 	// Fields for stateful stages which need to be per key.
-	pendingByKeys          map[string]*dataAndTimers                        // pending input elements by Key, if stateful.
-	inprogressKeys         set[string]                                      // all keys that are assigned to bundles.
-	inprogressKeysByBundle map[string]set[string]                           // bundle to key assignments.
-	state                  map[LinkID]map[typex.Window]map[string]StateData // state data for this stage, from {tid, stateID} -> window -> userKey
-	stateTypeLen           map[LinkID]func([]byte) int                      // map from state to a function that will produce the total length of a single value in bytes.
-	bundlesToInject        []RunBundle                                      // bundlesToInject are triggered bundles that will be injected by the watermark loop to avoid premature pipeline termination.
+	pendingByKeys          map[string]*dataAndTimers                             // pending input elements by Key, if stateful.
+	inprogressKeys         set[string]                                           // all keys that are assigned to bundles.
+	inprogressKeysByBundle map[string]set[string]                                // bundle to key assignments.
+	state                  map[LinkID]map[typex.Window]map[string]StateData      // state data for this stage, from {tid, stateID} -> window -> userKey
+	stateTypeLen           map[LinkID]func([]byte) int                           // map from state to a function that will produce the total length of a single value in bytes.
+	bundlesToInject        []RunBundle                                           // bundlesToInject are triggered bundles that will be injected by the watermark loop to avoid premature pipeline termination.
+	bundlePanes            map[string]map[typex.Window]map[string]typex.PaneInfo // PaneInfo snapshot for bundles, from BundleID -> window -> userKey
 
 	// Accounting for handling watermark holds for timers.
 	// We track the count of timers with the same hold, and clear it from
@@ -1152,6 +1198,13 @@ type stageState struct {
 	processingTimeTimers *timerHandler
 }
 
+// bundlePane holds pane info for a bundle.
+type bundlePane struct {
+	win  typex.Window
+	key  string
+	pane typex.PaneInfo
+}
+
 // stageKind handles behavioral differences between ordinary, stateful, and aggregation stage kinds.
 //
 // kinds should be stateless, and stageState retains all state for the stage,
@@ -1160,10 +1213,11 @@ type stageKind interface {
 	// addPending handles adding new pending elements to the stage appropriate for the kind.
 	addPending(ss *stageState, em *ElementManager, newPending []element) int
 	// buildEventTimeBundle handles building bundles for the stage per it's kind.
-	buildEventTimeBundle(ss *stageState, watermark mtime.Time) (toProcess elementHeap, minTs mtime.Time, newKeys set[string], holdsInBundle map[mtime.Time]int, schedulable bool, pendingAdjustment int)
+	buildEventTimeBundle(ss *stageState, watermark mtime.Time) (toProcess elementHeap, minTs mtime.Time, newKeys set[string],
+		holdsInBundle map[mtime.Time]int, panesInBundle []bundlePane, schedulable bool, pendingAdjustment int)
 
-	// updatePane based on the stage state.
-	updatePane(ss *stageState, pane typex.PaneInfo, w typex.Window, keyBytes []byte) typex.PaneInfo
+	// getPaneOrDefault based on the stage state, element metadata, and bundle id.
+	getPaneOrDefault(ss *stageState, defaultPane typex.PaneInfo, w typex.Window, keyBytes []byte, bundID string) typex.PaneInfo
 }
 
 // ordinaryStageKind represents stages that have no special behavior associated with them.
@@ -1172,8 +1226,8 @@ type ordinaryStageKind struct{}
 
 func (*ordinaryStageKind) String() string { return "OrdinaryStage" }
 
-func (*ordinaryStageKind) updatePane(ss *stageState, pane typex.PaneInfo, w typex.Window, keyBytes []byte) typex.PaneInfo {
-	return pane
+func (*ordinaryStageKind) getPaneOrDefault(ss *stageState, defaultPane typex.PaneInfo, w typex.Window, keyBytes []byte, bundID string) typex.PaneInfo {
+	return defaultPane
 }
 
 // statefulStageKind require keyed elements, and handles stages with stateful transforms, with state and timers.
@@ -1181,8 +1235,8 @@ type statefulStageKind struct{}
 
 func (*statefulStageKind) String() string { return "StatefulStage" }
 
-func (*statefulStageKind) updatePane(ss *stageState, pane typex.PaneInfo, w typex.Window, keyBytes []byte) typex.PaneInfo {
-	return pane
+func (*statefulStageKind) getPaneOrDefault(ss *stageState, defaultPane typex.PaneInfo, w typex.Window, keyBytes []byte, bundID string) typex.PaneInfo {
+	return defaultPane
 }
 
 // aggregateStageKind handles stages that perform aggregations over their primary inputs.
@@ -1191,9 +1245,12 @@ type aggregateStageKind struct{}
 
 func (*aggregateStageKind) String() string { return "AggregateStage" }
 
-func (*aggregateStageKind) updatePane(ss *stageState, pane typex.PaneInfo, w typex.Window, keyBytes []byte) typex.PaneInfo {
+func (*aggregateStageKind) getPaneOrDefault(ss *stageState, defaultPane typex.PaneInfo, w typex.Window, keyBytes []byte, bundID string) typex.PaneInfo {
 	ss.mu.Lock()
 	defer ss.mu.Unlock()
+	if pane, ok := ss.bundlePanes[bundID][w][string(keyBytes)]; ok {
+		return pane
+	}
 	return ss.state[LinkID{}][w][string(keyBytes)].Pane
 }
 
@@ -1248,6 +1305,43 @@ func (ss *stageState) AddPending(em *ElementManager, newPending []element) int {
 	return ss.kind.addPending(ss, em, newPending)
 }
 
+func (ss *stageState) injectTriggeredBundlesIfReady(em *ElementManager, window typex.Window, key string) int {
+	// Check on triggers for this key.
+	// We use an empty linkID as the key into state for aggregations.
+	count := 0
+	if ss.state == nil {
+		ss.state = make(map[LinkID]map[typex.Window]map[string]StateData)
+	}
+	lv, ok := ss.state[LinkID{}]
+	if !ok {
+		lv = make(map[typex.Window]map[string]StateData)
+		ss.state[LinkID{}] = lv
+	}
+	wv, ok := lv[window]
+	if !ok {
+		wv = make(map[string]StateData)
+		lv[window] = wv
+	}
+	state := wv[key]
+	endOfWindowReached := window.MaxTimestamp() < ss.input
+	ready := ss.strat.IsTriggerReady(triggerInput{
+		newElementCount:    1,
+		endOfWindowReached: endOfWindowReached,
+	}, &state)
+
+	if ready {
+		state.Pane = computeNextTriggeredPane(state.Pane, endOfWindowReached)
+	}
+	// Store the state as triggers may have changed it.
+	ss.state[LinkID{}][window][key] = state
+
+	// If we're ready, it's time to fire!
+	if ready {
+		count += ss.buildTriggeredBundle(em, key, window)
+	}
+	return count
+}
+
 // addPending for aggregate stages behaves likes stateful stages, but don't need to handle timers or a separate window
 // expiration condition.
 func (*aggregateStageKind) addPending(ss *stageState, em *ElementManager, newPending []element) int {
@@ -1267,6 +1361,13 @@ func (*aggregateStageKind) addPending(ss *stageState, em *ElementManager, newPen
 	if ss.pendingByKeys == nil {
 		ss.pendingByKeys = map[string]*dataAndTimers{}
 	}
+
+	type windowKey struct {
+		window typex.Window
+		key    string
+	}
+	pendingWindowKeys := set[windowKey]{}
+
 	count := 0
 	for _, e := range newPending {
 		count++
@@ -1279,37 +1380,18 @@ func (*aggregateStageKind) addPending(ss *stageState, em *ElementManager, newPen
 			ss.pendingByKeys[string(e.keyBytes)] = dnt
 		}
 		heap.Push(&dnt.elements, e)
-		// Check on triggers for this key.
-		// We use an empty linkID as the key into state for aggregations.
-		if ss.state == nil {
-			ss.state = make(map[LinkID]map[typex.Window]map[string]StateData)
-		}
-		lv, ok := ss.state[LinkID{}]
-		if !ok {
-			lv = make(map[typex.Window]map[string]StateData)
-			ss.state[LinkID{}] = lv
-		}
-		wv, ok := lv[e.window]
-		if !ok {
-			wv = make(map[string]StateData)
-			lv[e.window] = wv
-		}
-		state := wv[string(e.keyBytes)]
-		endOfWindowReached := e.window.MaxTimestamp() < ss.input
-		ready := ss.strat.IsTriggerReady(triggerInput{
-			newElementCount:    1,
-			endOfWindowReached: endOfWindowReached,
-		}, &state)
 
-		if ready {
-			state.Pane = computeNextTriggeredPane(state.Pane, endOfWindowReached)
+		if em.config.StreamingMode {
+			// In streaming mode, we check trigger readiness on each element
+			count += ss.injectTriggeredBundlesIfReady(em, e.window, string(e.keyBytes))
+		} else {
+			// In batch mode, we store key + window pairs here and check trigger readiness for each of them later.
+			pendingWindowKeys.insert(windowKey{window: e.window, key: string(e.keyBytes)})
 		}
-		// Store the state as triggers may have changed it.
-		ss.state[LinkID{}][e.window][string(e.keyBytes)] = state
-
-		// If we're ready, it's time to fire!
-		if ready {
-			count += ss.buildTriggeredBundle(em, e.keyBytes, e.window)
+	}
+	if !em.config.StreamingMode {
+		for wk := range pendingWindowKeys {
+			count += ss.injectTriggeredBundlesIfReady(em, wk.window, wk.key)
 		}
 	}
 	return count
@@ -1424,12 +1506,30 @@ func computeNextWatermarkPane(pane typex.PaneInfo) typex.PaneInfo {
 	return pane
 }
 
+func (ss *stageState) savePanes(bundID string, panesInBundle []bundlePane) {
+	if len(panesInBundle) == 0 {
+		return
+	}
+	if ss.bundlePanes == nil {
+		ss.bundlePanes = make(map[string]map[typex.Window]map[string]typex.PaneInfo)
+	}
+	if ss.bundlePanes[bundID] == nil {
+		ss.bundlePanes[bundID] = make(map[typex.Window]map[string]typex.PaneInfo)
+	}
+	for _, p := range panesInBundle {
+		if ss.bundlePanes[bundID][p.win] == nil {
+			ss.bundlePanes[bundID][p.win] = make(map[string]typex.PaneInfo)
+		}
+		ss.bundlePanes[bundID][p.win][p.key] = p.pane
+	}
+}
+
 // buildTriggeredBundle must be called with the stage.mu lock held.
 // When in discarding mode, returns 0.
 // When in accumulating mode, returns the number of fired elements to maintain a correct pending count.
-func (ss *stageState) buildTriggeredBundle(em *ElementManager, key []byte, win typex.Window) int {
+func (ss *stageState) buildTriggeredBundle(em *ElementManager, key string, win typex.Window) int {
 	var toProcess []element
-	dnt := ss.pendingByKeys[string(key)]
+	dnt := ss.pendingByKeys[key]
 	var notYet []element
 
 	rb := RunBundle{StageID: ss.ID, BundleID: "agg-" + em.nextBundID(), Watermark: ss.input}
@@ -1458,7 +1558,7 @@ func (ss *stageState) buildTriggeredBundle(em *ElementManager, key []byte, win t
 	}
 	dnt.elements = append(dnt.elements, notYet...)
 	if dnt.elements.Len() == 0 {
-		delete(ss.pendingByKeys, string(key))
+		delete(ss.pendingByKeys, key)
 	} else {
 		// Ensure the heap invariants are maintained.
 		heap.Init(&dnt.elements)
@@ -1467,13 +1567,24 @@ func (ss *stageState) buildTriggeredBundle(em *ElementManager, key []byte, win t
 	if ss.inprogressKeys == nil {
 		ss.inprogressKeys = set[string]{}
 	}
+	panesInBundle := []bundlePane{
+		{
+			win:  win,
+			key:  string(key),
+			pane: ss.state[LinkID{}][win][key].Pane,
+		},
+	}
+
 	ss.makeInProgressBundle(
 		func() string { return rb.BundleID },
 		toProcess,
 		ss.input,
-		singleSet(string(key)),
+		singleSet(key),
 		nil,
+		panesInBundle,
 	)
+	slog.Debug("started a triggered bundle", "stageID", ss.ID, "bundleID", rb.BundleID, "size", len(toProcess))
+
 	ss.bundlesToInject = append(ss.bundlesToInject, rb)
 	// Bundle is marked in progress here to prevent a race condition.
 	em.refreshCond.L.Lock()
@@ -1577,26 +1688,28 @@ func (ss *stageState) startEventTimeBundle(watermark mtime.Time, genBundID func(
 	}()
 	ss.mu.Lock()
 	defer ss.mu.Unlock()
-	toProcess, minTs, newKeys, holdsInBundle, stillSchedulable, accumulatingPendingAdjustment := ss.kind.buildEventTimeBundle(ss, watermark)
+	toProcess, minTs, newKeys, holdsInBundle, panesInBundle, stillSchedulable, accumulatingPendingAdjustment := ss.kind.buildEventTimeBundle(ss, watermark)
 
 	if len(toProcess) == 0 {
 		// If we have nothing, there's nothing to progress.
 		return "", false, stillSchedulable, accumulatingPendingAdjustment
 	}
 
-	bundID := ss.makeInProgressBundle(genBundID, toProcess, minTs, newKeys, holdsInBundle)
+	bundID := ss.makeInProgressBundle(genBundID, toProcess, minTs, newKeys, holdsInBundle, panesInBundle)
+	slog.Debug("started an event time bundle", "stageID", ss.ID, "bundleID", bundID, "bundleSize", len(toProcess), "upstreamWatermark", watermark)
+
 	return bundID, true, stillSchedulable, accumulatingPendingAdjustment
 }
 
 // buildEventTimeBundle for ordinary stages processes all pending elements.
-func (*ordinaryStageKind) buildEventTimeBundle(ss *stageState, watermark mtime.Time) (toProcess elementHeap, minTs mtime.Time, newKeys set[string], holdsInBundle map[mtime.Time]int, schedulable bool, pendingAdjustment int) {
+func (*ordinaryStageKind) buildEventTimeBundle(ss *stageState, watermark mtime.Time) (toProcess elementHeap, minTs mtime.Time, newKeys set[string], holdsInBundle map[mtime.Time]int, _ []bundlePane, schedulable bool, pendingAdjustment int) {
 	toProcess = ss.pending
 	ss.pending = nil
-	return toProcess, mtime.MaxTimestamp, nil, nil, true, 0
+	return toProcess, mtime.MaxTimestamp, nil, nil, nil, true, 0
 }
 
 // buildEventTimeBundle for stateful stages, processes all elements that are before the input watermark time.
-func (*statefulStageKind) buildEventTimeBundle(ss *stageState, watermark mtime.Time) (toProcess elementHeap, _ mtime.Time, _ set[string], _ map[mtime.Time]int, schedulable bool, pendingAdjustment int) {
+func (*statefulStageKind) buildEventTimeBundle(ss *stageState, watermark mtime.Time) (toProcess elementHeap, _ mtime.Time, _ set[string], _ map[mtime.Time]int, _ []bundlePane, schedulable bool, pendingAdjustment int) {
 	minTs := mtime.MaxTimestamp
 	// TODO: Allow configurable limit of keys per bundle, and elements per key to improve parallelism.
 	// TODO: when we do, we need to ensure that the stage remains schedualable for bundle execution, for remaining pending elements and keys.
@@ -1680,11 +1793,11 @@ keysPerBundle:
 	// If we're out of data, and timers were not cleared then the watermark is accurate.
 	stillSchedulable := !(len(ss.pendingByKeys) == 0 && !timerCleared)
 
-	return toProcess, minTs, newKeys, holdsInBundle, stillSchedulable, 0
+	return toProcess, minTs, newKeys, holdsInBundle, nil, stillSchedulable, 0
 }
 
 // buildEventTimeBundle for aggregation stages, processes all elements that are within the watermark for completed windows.
-func (*aggregateStageKind) buildEventTimeBundle(ss *stageState, watermark mtime.Time) (toProcess elementHeap, _ mtime.Time, _ set[string], _ map[mtime.Time]int, schedulable bool, pendingAdjustment int) {
+func (*aggregateStageKind) buildEventTimeBundle(ss *stageState, watermark mtime.Time) (toProcess elementHeap, _ mtime.Time, _ set[string], _ map[mtime.Time]int, panesInBundle []bundlePane, schedulable bool, pendingAdjustment int) {
 	minTs := mtime.MaxTimestamp
 	// TODO: Allow configurable limit of keys per bundle, and elements per key to improve parallelism.
 	// TODO: when we do, we need to ensure that the stage remains schedualable for bundle execution, for remaining pending elements and keys.
@@ -1779,6 +1892,13 @@ keysPerBundle:
 			}
 			ss.state[LinkID{}][elm.window][string(elm.keyBytes)] = state
 
+			// Save latest PaneInfo for this window + key pair. It will be used in PersistBundle.
+			panesInBundle = append(panesInBundle, bundlePane{
+				win:  elm.window,
+				key:  string(elm.keyBytes),
+				pane: ss.state[LinkID{}][elm.window][string(elm.keyBytes)].Pane,
+			})
+
 			// The pane is already correct for this key + window + firing.
 			if ss.strat.Accumulating && !state.Pane.IsLast {
 				// If this isn't the last pane, then we must add the element back to the pending store for subsequent firings.
@@ -1800,7 +1920,7 @@ keysPerBundle:
 	// If this is an aggregate, we need a watermark change in order to reschedule
 	stillSchedulable := false
 
-	return toProcess, minTs, newKeys, holdsInBundle, stillSchedulable, accumulatingPendingAdjustment
+	return toProcess, minTs, newKeys, holdsInBundle, panesInBundle, stillSchedulable, accumulatingPendingAdjustment
 }
 
 func (ss *stageState) startProcessingTimeBundle(em *ElementManager, emNow mtime.Time, genBundID func() string) (string, bool, bool) {
@@ -1875,14 +1995,16 @@ func (ss *stageState) startProcessingTimeBundle(em *ElementManager, emNow mtime.
 		// If we have nothing
 		return "", false, stillSchedulable
 	}
-	bundID := ss.makeInProgressBundle(genBundID, toProcess, minTs, newKeys, holdsInBundle)
+	bundID := ss.makeInProgressBundle(genBundID, toProcess, minTs, newKeys, holdsInBundle, nil)
+
+	slog.Debug("started a processing time bundle", "stageID", ss.ID, "bundleID", bundID, "size", len(toProcess), "emNow", emNow)
 	return bundID, true, stillSchedulable
 }
 
 // makeInProgressBundle is common code to store a set of elements as a bundle in progress.
 //
 // Callers must hold the stage lock.
-func (ss *stageState) makeInProgressBundle(genBundID func() string, toProcess []element, minTs mtime.Time, newKeys set[string], holdsInBundle map[mtime.Time]int) string {
+func (ss *stageState) makeInProgressBundle(genBundID func() string, toProcess []element, minTs mtime.Time, newKeys set[string], holdsInBundle map[mtime.Time]int, panesInBundle []bundlePane) string {
 	// Catch the ordinary case for the minimum timestamp.
 	if toProcess[0].timestamp < minTs {
 		minTs = toProcess[0].timestamp
@@ -1906,10 +2028,13 @@ func (ss *stageState) makeInProgressBundle(genBundID func() string, toProcess []
 	ss.inprogressKeysByBundle[bundID] = newKeys
 	ss.inprogressKeys.merge(newKeys)
 	ss.inprogressHoldsByBundle[bundID] = holdsInBundle
+
+	// Save latest PaneInfo for PersistBundle
+	ss.savePanes(bundID, panesInBundle)
 	return bundID
 }
 
-func (ss *stageState) splitBundle(rb RunBundle, firstResidual int) {
+func (ss *stageState) splitBundle(rb RunBundle, firstResidual int, em *ElementManager) {
 	ss.mu.Lock()
 	defer ss.mu.Unlock()
 
@@ -1920,8 +2045,19 @@ func (ss *stageState) splitBundle(rb RunBundle, firstResidual int) {
 	res := es.es[firstResidual:]
 
 	es.es = prim
-	ss.pending = append(ss.pending, res...)
-	heap.Init(&ss.pending)
+
+	for _, e := range res {
+		delete(ss.inprogressKeysByBundle[rb.BundleID], string(e.keyBytes))
+		delete(ss.inprogressKeys, string(e.keyBytes))
+
+		if e.IsTimer() {
+			slog.Warn("Unexpected split on a bundle with timers. See https://github.com/apache/beam/issues/35771 for information.")
+			ss.watermarkHolds.Drop(e.holdTimestamp, 1)
+			ss.inprogressHoldsByBundle[rb.BundleID][e.holdTimestamp]--
+		}
+	}
+	// we don't need to increment pending count in em, since it is already pending
+	ss.kind.addPending(ss, em, res)
 	ss.inprogress[rb.BundleID] = es
 }
 
@@ -1958,7 +2094,7 @@ func (ss *stageState) String() string {
 	return fmt.Sprintf("[%v] IN: %v OUT: %v UP: %q %v, kind: %v", ss.ID, ss.input, ss.output, pcol, up, ss.kind)
 }
 
-// updateWatermarks performs the following operations:
+// updateWatermarks performs the following operations and returns a possible set of stages to refresh next or nil.
 //
 // Watermark_In'  = MAX(Watermark_In, MIN(U(TS_Pending), U(Watermark_InputPCollection)))
 // Watermark_Out' = MAX(Watermark_Out, MIN(Watermark_In', U(minWatermarkHold)))
@@ -1979,6 +2115,8 @@ func (ss *stageState) updateWatermarks(em *ElementManager) set[string] {
 		newIn = minPending
 	}
 
+	ss.previousInput = ss.input
+
 	// If bigger, advance the input watermark.
 	if newIn > ss.input {
 		ss.input = newIn
@@ -2108,6 +2246,7 @@ func (ss *stageState) createOnWindowExpirationBundles(newOut mtime.Time, em *Ele
 			wm,
 			usedKeys,
 			map[mtime.Time]int{wm: 1},
+			nil,
 		)
 		ss.expiryWindowsByBundles[rb.BundleID] = win
 
@@ -2136,16 +2275,17 @@ func (ss *stageState) bundleReady(em *ElementManager, emNow mtime.Time) (mtime.T
 	ptimeEventsReady := ss.processingTimeTimers.Peek() <= emNow || emNow == mtime.MaxTimestamp
 	injectedReady := len(ss.bundlesToInject) > 0
 
-	// If the upstream watermark and the input watermark are the same,
-	// then we can't yet process this stage.
+	// If the upstream watermark does not change, we can't yet process this stage.
+	// To check whether upstream water is unchanged, we evaluate if the input watermark, and
+	// the input watermark before the latest refresh are the same.
 	inputW := ss.input
 	_, upstreamW := ss.UpstreamWatermark()
-	if inputW == upstreamW {
+	previousInputW := ss.previousInput
+	if inputW == upstreamW && previousInputW == inputW {
 		slog.Debug("bundleReady: unchanged upstream watermark",
 			slog.String("stage", ss.ID),
 			slog.Group("watermark",
-				slog.Any("upstream", upstreamW),
-				slog.Any("input", inputW)))
+				slog.Any("upstream == input == previousInput", inputW)))
 		return mtime.MinTimestamp, false, ptimeEventsReady, injectedReady
 	}
 	ready := true
diff --git a/sdks/go/pkg/beam/runners/prism/internal/engine/strategy.go b/sdks/go/pkg/beam/runners/prism/internal/engine/strategy.go
index 5446d3edd3c0..044b9806c1b1 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/engine/strategy.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/engine/strategy.go
@@ -79,8 +79,9 @@ func (ws WinStrat) String() string {
 
 // triggerInput represents a Key + window + stage's trigger conditions.
 type triggerInput struct {
-	newElementCount    int  // The number of new elements since the last check.
-	endOfWindowReached bool // Whether or not the end of the window has been reached.
+	newElementCount    int        // The number of new elements since the last check.
+	endOfWindowReached bool       // Whether or not the end of the window has been reached.
+	emNow              mtime.Time // The current processing time in the runner.
 }
 
 // Trigger represents a trigger for a windowing strategy.  A trigger determines when
@@ -302,15 +303,23 @@ func (t *TriggerAfterEach) onFire(state *StateData) {
 	if !t.shouldFire(state) {
 		return
 	}
-	for _, sub := range t.SubTriggers {
+	for i, sub := range t.SubTriggers {
 		if state.getTriggerState(sub).finished {
 			continue
 		}
 		sub.onFire(state)
+		// If the sub-trigger didn't finish, we return, waiting for it to finish on a subsequent call.
 		if !state.getTriggerState(sub).finished {
 			return
 		}
+
+		// If the sub-trigger finished, we check if it's the last one.
+		// If it's not the last one, we return, waiting for the next onFire call to advance to the next sub-trigger.
+		if i < len(t.SubTriggers)-1 {
+			return
+		}
 	}
+	// clear and reset when all sub-triggers have fired.
 	triggerClearAndFinish(t, state)
 }
 
@@ -573,4 +582,105 @@ func (t *TriggerDefault) String() string {
 	return "Default"
 }
 
-// TODO https://github.com/apache/beam/issues/31438 Handle TriggerAfterProcessingTime
+// TimestampTransform is the engine's representation of a processing time transform.
+type TimestampTransform struct {
+	Delay         time.Duration
+	AlignToPeriod time.Duration
+	AlignToOffset time.Duration
+}
+
+// TriggerAfterProcessingTime fires once after a specified amount of processing time
+// has passed since an element was first seen.
+// Uses the extra state field to track the processing time of the first element.
+type TriggerAfterProcessingTime struct {
+	Transforms []TimestampTransform
+}
+
+type afterProcessingTimeState struct {
+	emNow              mtime.Time
+	firingTime         mtime.Time
+	endOfWindowReached bool
+}
+
+func (t *TriggerAfterProcessingTime) onElement(input triggerInput, state *StateData) {
+	ts := state.getTriggerState(t)
+	if ts.finished {
+		return
+	}
+
+	if ts.extra == nil {
+		ts.extra = afterProcessingTimeState{
+			emNow:              input.emNow,
+			firingTime:         t.applyTimestampTransforms(input.emNow),
+			endOfWindowReached: input.endOfWindowReached,
+		}
+	} else {
+		s, _ := ts.extra.(afterProcessingTimeState)
+		s.emNow = input.emNow
+		s.endOfWindowReached = input.endOfWindowReached
+		ts.extra = s
+	}
+
+	state.setTriggerState(t, ts)
+}
+
+func (t *TriggerAfterProcessingTime) applyTimestampTransforms(start mtime.Time) mtime.Time {
+	ret := start
+	for _, transform := range t.Transforms {
+		ret = ret + mtime.Time(transform.Delay/time.Millisecond)
+		if transform.AlignToPeriod > 0 {
+			// timestamp - (timestamp % period) + period
+			// And with an offset, we adjust before and after.
+			tsMs := ret
+			periodMs := mtime.Time(transform.AlignToPeriod / time.Millisecond)
+			offsetMs := mtime.Time(transform.AlignToOffset / time.Millisecond)
+
+			adjustedMs := tsMs - offsetMs
+			alignedMs := adjustedMs - (adjustedMs % periodMs) + periodMs + offsetMs
+			ret = alignedMs
+		}
+	}
+	return ret
+}
+
+func (t *TriggerAfterProcessingTime) shouldFire(state *StateData) bool {
+	ts := state.getTriggerState(t)
+	if ts.extra == nil || ts.finished {
+		return false
+	}
+	s := ts.extra.(afterProcessingTimeState)
+	return s.emNow >= s.firingTime
+}
+
+func (t *TriggerAfterProcessingTime) onFire(state *StateData) {
+	ts := state.getTriggerState(t)
+	if ts.finished {
+		return
+	}
+
+	// We don't reset the state here, only mark it as finished
+	ts.finished = true
+	state.setTriggerState(t, ts)
+}
+
+func (t *TriggerAfterProcessingTime) reset(state *StateData) {
+	ts := state.getTriggerState(t)
+	if ts.extra != nil {
+		if ts.extra.(afterProcessingTimeState).endOfWindowReached {
+			delete(state.Trigger, t)
+			return
+		}
+	}
+
+	// Not reaching the end of window yet.
+	// We keep the state (especially the next possible firing time) in case the trigger is called again
+	ts.finished = false
+	s := ts.extra.(afterProcessingTimeState)
+	s.firingTime = t.applyTimestampTransforms(s.emNow) // compute next possible firing time
+	ts.extra = s
+	state.setTriggerState(t, ts)
+}
+
+func (t *TriggerAfterProcessingTime) String() string {
+	return fmt.Sprintf("AfterProcessingTime[%v]", t.Transforms)
+}
diff --git a/sdks/go/pkg/beam/runners/prism/internal/engine/strategy_test.go b/sdks/go/pkg/beam/runners/prism/internal/engine/strategy_test.go
index 4934665833ed..3b928be278f8 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/engine/strategy_test.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/engine/strategy_test.go
@@ -122,6 +122,25 @@ func TestTriggers_isReady(t *testing.T) {
 				{triggerInput{newElementCount: 1}, false},
 				{triggerInput{newElementCount: 1}, false},
 			},
+		}, {
+			name: "afterEach_2_Always_1",
+			trig: &TriggerAfterEach{
+				SubTriggers: []Trigger{
+					&TriggerElementCount{2},
+					&TriggerAfterAny{SubTriggers: []Trigger{&TriggerAlways{}}},
+					&TriggerElementCount{1},
+				},
+			},
+			inputs: []io{
+				{triggerInput{newElementCount: 1}, false},
+				{triggerInput{newElementCount: 1}, true},  // first is ready
+				{triggerInput{newElementCount: 1}, true},  // second is ready
+				{triggerInput{newElementCount: 1}, true},  // third is ready
+				{triggerInput{newElementCount: 1}, false}, // never resets after this.
+				{triggerInput{newElementCount: 1}, false},
+				{triggerInput{newElementCount: 1}, false},
+				{triggerInput{newElementCount: 1}, false},
+			},
 		}, {
 			name: "afterAny_2_3_4",
 			trig: &TriggerAfterAny{
@@ -401,6 +420,135 @@ func TestTriggers_isReady(t *testing.T) {
 				{triggerInput{newElementCount: 1, endOfWindowReached: true}, false},
 				{triggerInput{newElementCount: 1, endOfWindowReached: true}, true}, // Late
 			},
+		}, {
+			name: "afterProcessingTime_Delay_Exact",
+			trig: &TriggerAfterProcessingTime{
+				Transforms: []TimestampTransform{
+					{Delay: 3 * time.Second},
+				},
+			},
+			inputs: []io{
+				{triggerInput{emNow: 0}, false}, // the trigger is set to fire at 3s after 0
+				{triggerInput{emNow: 1000}, false},
+				{triggerInput{emNow: 2000}, false},
+				{triggerInput{emNow: 3000}, true}, // fire
+				{triggerInput{emNow: 4000}, false},
+				{triggerInput{emNow: 5000}, false},
+				{triggerInput{emNow: 6000}, false},
+				{triggerInput{emNow: 7000}, false},
+			},
+		}, {
+			name: "afterProcessingTime_Delay_Late",
+			trig: &TriggerAfterProcessingTime{
+				Transforms: []TimestampTransform{
+					{Delay: 3 * time.Second},
+				},
+			},
+			inputs: []io{
+				{triggerInput{emNow: 0}, false}, // the trigger is set to fire at 3s after 0
+				{triggerInput{emNow: 1000}, false},
+				{triggerInput{emNow: 2000}, false},
+				{triggerInput{emNow: 3001}, true}, // fire a little after the preset time
+				{triggerInput{emNow: 4000}, false},
+			},
+		}, {
+			name: "afterProcessingTime_AlignToPeriodOnly",
+			trig: &TriggerAfterProcessingTime{
+				Transforms: []TimestampTransform{
+					{AlignToPeriod: 5 * time.Second},
+				},
+			},
+			inputs: []io{
+				{triggerInput{emNow: 1500}, false}, // align 1.5s to 5s
+				{triggerInput{emNow: 2000}, false},
+				{triggerInput{emNow: 4999}, false},
+				{triggerInput{emNow: 5000}, true}, // fire at 5
+				{triggerInput{emNow: 5001}, false},
+			},
+		}, {
+			name: "afterProcessingTime_AlignToPeriodAndOffset",
+			trig: &TriggerAfterProcessingTime{
+				Transforms: []TimestampTransform{
+					{AlignToPeriod: 5 * time.Second, AlignToOffset: 200 * time.Millisecond},
+				},
+			},
+			inputs: []io{
+				{triggerInput{emNow: 1500}, false}, // align 1.5s to 5s plus an 0.2 offset
+				{triggerInput{emNow: 2000}, false},
+				{triggerInput{emNow: 5119}, false},
+				{triggerInput{emNow: 5200}, true}, // fire at 5.2s
+				{triggerInput{emNow: 5201}, false},
+			},
+		}, {
+			name: "afterProcessingTime_TwoTransforms",
+			trig: &TriggerAfterProcessingTime{
+				Transforms: []TimestampTransform{
+					{AlignToPeriod: 5 * time.Second, AlignToOffset: 200 * time.Millisecond},
+					{Delay: 1 * time.Second},
+				},
+			},
+			inputs: []io{
+				{triggerInput{emNow: 1500}, false}, // align 1.5s to 5s plus an 0.2 offset and a 1s delay
+				{triggerInput{emNow: 2000}, false},
+				{triggerInput{emNow: 5119}, false},
+				{triggerInput{emNow: 5200}, false},
+				{triggerInput{emNow: 5201}, false},
+				{triggerInput{emNow: 6119}, false},
+				{triggerInput{emNow: 6200}, true}, // fire
+				{triggerInput{emNow: 6201}, false},
+			},
+		}, {
+			name: "afterProcessingTime_Repeated", trig: &TriggerRepeatedly{
+				&TriggerAfterProcessingTime{
+					Transforms: []TimestampTransform{
+						{Delay: 3 * time.Second},
+					}}},
+			inputs: []io{
+				{triggerInput{emNow: 0}, false},
+				{triggerInput{emNow: 1000}, false},
+				{triggerInput{emNow: 2000}, false},
+				{triggerInput{emNow: 3000}, true}, // firing the first time, trigger set again
+				{triggerInput{emNow: 4000}, false},
+				{triggerInput{emNow: 5000}, false},
+				{triggerInput{emNow: 6000}, true}, // firing the second time
+			},
+		}, {
+			name: "afterProcessingTime_Repeated_AcrossWindows", trig: &TriggerRepeatedly{
+				&TriggerAfterProcessingTime{
+					Transforms: []TimestampTransform{
+						{Delay: 3 * time.Second},
+					}}},
+			inputs: []io{
+				{triggerInput{emNow: 0}, false},
+				{triggerInput{emNow: 1000}, false},
+				{triggerInput{emNow: 2000}, false},
+				{triggerInput{emNow: 3000}, true}, // fire the first time, trigger is set again
+				{triggerInput{emNow: 4000}, false},
+				{triggerInput{emNow: 5000}, false},
+				{triggerInput{emNow: 6000,
+					endOfWindowReached: true}, true}, // fire the second time, reach end of window and start over
+				{triggerInput{emNow: 7000}, false}, // trigger firing time is set to 7s + 3s = 10s
+				{triggerInput{emNow: 8000}, false},
+				{triggerInput{emNow: 9000}, false},
+				{triggerInput{emNow: 10000}, true}, // fire in the new window
+			},
+		}, {
+			name: "afterProcessingTime_Repeated_Composite", trig: &TriggerRepeatedly{
+				&TriggerAfterAny{SubTriggers: []Trigger{
+					&TriggerAfterProcessingTime{
+						Transforms: []TimestampTransform{
+							{Delay: 3 * time.Second},
+						},
+					},
+					&TriggerElementCount{ElementCount: 2},
+				}}},
+			inputs: []io{
+				{triggerInput{emNow: 0, newElementCount: 1}, false},    // ElmCount = 1, set AfterProcessingTime trigger firing time to 3s
+				{triggerInput{emNow: 1000, newElementCount: 1}, true},  // ElmCount = 2, fire ElmCount trigger and reset ElmCount and AfterProcessingTime firing time (4s)
+				{triggerInput{emNow: 4000, newElementCount: 1}, true},  // ElmCount = 1, fire AfterProcessingTime trigger and reset ElmCount and AfterProcessingTime firing time (7s)
+				{triggerInput{emNow: 5000, newElementCount: 1}, false}, // ElmCount = 1
+				{triggerInput{emNow: 5500, newElementCount: 1}, true},  // ElmCount = 2, fire ElmCount trigger
+			},
 		}, {
 			name: "default",
 			trig: &TriggerDefault{},
diff --git a/sdks/go/pkg/beam/runners/prism/internal/engine/teststream.go b/sdks/go/pkg/beam/runners/prism/internal/engine/teststream.go
index 0af4e7dc41f0..bab9ff048889 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/engine/teststream.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/engine/teststream.go
@@ -253,6 +253,9 @@ func (ev tsFinalEvent) Execute(em *ElementManager) {
 	em.testStreamHandler.UpdateHold(em, mtime.MaxTimestamp)
 	ss := em.stages[ev.stageID]
 	kickSet := ss.updateWatermarks(em)
+	if kickSet == nil {
+		kickSet = make(set[string])
+	}
 	kickSet.insert(ev.stageID)
 	em.changedStages.merge(kickSet)
 }
diff --git a/sdks/go/pkg/beam/runners/prism/internal/environments.go b/sdks/go/pkg/beam/runners/prism/internal/environments.go
index 3239c76dfe1f..1f852e0862f1 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/environments.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/environments.go
@@ -24,6 +24,7 @@ import (
 	"os"
 	"os/exec"
 	"slices"
+	"strconv"
 	"time"
 
 	fnpb "github.com/apache/beam/sdks/v2/go/pkg/beam/model/fnexecution_v1"
@@ -79,7 +80,7 @@ func runEnvironment(ctx context.Context, j *jobservices.Job, env string, wk *wor
 			logger.Error("unmarshaling docker environment payload", "error", err)
 			return err
 		}
-		return dockerEnvironment(ctx, logger, dp, wk, j.ArtifactEndpoint())
+		return dockerEnvironment(ctx, logger, dp, wk, wk.ArtifactEndpoint)
 	case urns.EnvProcess:
 		pp := &pipepb.ProcessPayload{}
 		if err := (proto.UnmarshalOptions{}).Unmarshal(e.GetPayload(), pp); err != nil {
@@ -87,7 +88,7 @@ func runEnvironment(ctx context.Context, j *jobservices.Job, env string, wk *wor
 			return err
 		}
 		go func() {
-			processEnvironment(ctx, pp, wk)
+			processEnvironment(ctx, logger, pp, wk)
 			logger.Debug("environment stopped", slog.String("job", j.String()))
 		}()
 		return nil
@@ -207,17 +208,18 @@ func dockerEnvironment(ctx context.Context, logger *slog.Logger, dp *pipepb.Dock
 	}
 	logger.Debug("creating container", "envs", envs, "mounts", mounts)
 
+	cmd := []string{
+		fmt.Sprintf("--id=%v", wk.ID),
+		fmt.Sprintf("--control_endpoint=%v", wk.Endpoint()),
+		fmt.Sprintf("--artifact_endpoint=%v", artifactEndpoint),
+		fmt.Sprintf("--provision_endpoint=%v", wk.Endpoint()),
+		fmt.Sprintf("--logging_endpoint=%v", wk.Endpoint()),
+	}
 	ccr, err := cli.ContainerCreate(ctx, &container.Config{
 		Image: dp.GetContainerImage(),
-		Cmd: []string{
-			fmt.Sprintf("--id=%v", wk.ID),
-			fmt.Sprintf("--control_endpoint=%v", wk.Endpoint()),
-			fmt.Sprintf("--artifact_endpoint=%v", artifactEndpoint),
-			fmt.Sprintf("--provision_endpoint=%v", wk.Endpoint()),
-			fmt.Sprintf("--logging_endpoint=%v", wk.Endpoint()),
-		},
-		Env: envs,
-		Tty: false,
+		Cmd:   cmd,
+		Env:   envs,
+		Tty:   false,
 	}, &container.HostConfig{
 		NetworkMode: "host",
 		Mounts:      mounts,
@@ -236,6 +238,7 @@ func dockerEnvironment(ctx context.Context, logger *slog.Logger, dp *pipepb.Dock
 	}
 
 	logger.Debug("container started")
+	logger.Debug("container start command", "cmd", cmd)
 
 	// Start goroutine to wait on container state.
 	go func() {
@@ -257,7 +260,12 @@ func dockerEnvironment(ctx context.Context, logger *slog.Logger, dp *pipepb.Dock
 				defer rc.Close()
 				var buf bytes.Buffer
 				stdcopy.StdCopy(&buf, &buf, rc)
-				logger.Info("container being killed", slog.Any("cause", context.Cause(ctx)), slog.Any("containerLog", buf))
+				logger.Info("container being killed", slog.Any("cause", context.Cause(ctx)))
+				msgs, err := strconv.Unquote(buf.String())
+				if err != nil {
+					msgs = buf.String()
+				}
+				logger.Debug("container log", "log", msgs)
 			}
 			// Can't use command context, since it's already canceled here.
 			if err := cli.ContainerKill(bgctx, containerID, ""); err != nil {
@@ -273,6 +281,7 @@ func dockerEnvironment(ctx context.Context, logger *slog.Logger, dp *pipepb.Dock
 			rc, err := cli.ContainerLogs(bgctx, containerID, container.LogsOptions{Details: true, ShowStdout: true, ShowStderr: true})
 			if err != nil {
 				logger.Error("docker container logs error", "error", err)
+				return
 			}
 			defer rc.Close()
 			var buf bytes.Buffer
@@ -284,8 +293,11 @@ func dockerEnvironment(ctx context.Context, logger *slog.Logger, dp *pipepb.Dock
 	return nil
 }
 
-func processEnvironment(ctx context.Context, pp *pipepb.ProcessPayload, wk *worker.W) {
-	cmd := exec.CommandContext(ctx, pp.GetCommand(), "--id="+wk.ID, "--provision_endpoint="+wk.Endpoint())
+func processEnvironment(ctx context.Context, logger *slog.Logger, pp *pipepb.ProcessPayload, wk *worker.W) {
+	defer wk.Stop()
+
+	cmd := exec.CommandContext(ctx, pp.GetCommand(), "--id='"+wk.ID+"'", "--provision_endpoint="+wk.Endpoint())
+	logger.Debug("starting process", "cmd", cmd.String())
 
 	cmd.WaitDelay = time.Millisecond * 100
 	cmd.Stderr = os.Stderr
@@ -296,9 +308,12 @@ func processEnvironment(ctx context.Context, pp *pipepb.ProcessPayload, wk *work
 		cmd.Env = append(cmd.Environ(), fmt.Sprintf("%v=%v", k, v))
 	}
 	if err := cmd.Start(); err != nil {
+		logger.Error("process failed to start", "error", err)
 		return
 	}
 	// Job processing happens here, but orchestrated by other goroutines
 	// This call blocks until the context is cancelled, or the command exits.
-	cmd.Wait()
+	if err := cmd.Wait(); err != nil {
+		logger.Error("process failed while running", "error", err)
+	}
 }
diff --git a/sdks/go/pkg/beam/runners/prism/internal/execute.go b/sdks/go/pkg/beam/runners/prism/internal/execute.go
index ab041da314dc..9d23a89d4583 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/execute.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/execute.go
@@ -22,6 +22,7 @@ import (
 	"fmt"
 	"io"
 	"log/slog"
+	"runtime/debug"
 	"sort"
 	"sync/atomic"
 	"time"
@@ -76,6 +77,14 @@ func RunPipeline(j *jobservices.Job) {
 	// any related job resources.
 	defer func() {
 		j.CancelFn(fmt.Errorf("runPipeline returned, cleaning up"))
+		j.WaitForCleanUp()
+	}()
+
+	// Add this defer function to capture and log panics.
+	defer func() {
+		if e := recover(); e != nil {
+			j.Failed(fmt.Errorf("pipeline panicked: %v\nStacktrace: %s", e, string(debug.Stack())))
+		}
 	}()
 
 	j.SendMsg("running " + j.String())
@@ -95,7 +104,7 @@ func RunPipeline(j *jobservices.Job) {
 	j.SendMsg("pipeline completed " + j.String())
 
 	j.SendMsg("terminating " + j.String())
-	j.Done()
+	j.PendingDone()
 }
 
 type transformExecuter interface {
@@ -144,6 +153,7 @@ func executePipeline(ctx context.Context, wks map[string]*worker.W, j *jobservic
 
 	topo := prepro.preProcessGraph(comps, j)
 	ts := comps.GetTransforms()
+	pcols := comps.GetPcollections()
 
 	config := engine.Config{}
 	m := j.PipelineOptions().AsMap()
@@ -158,6 +168,18 @@ func executePipeline(ctx context.Context, wks map[string]*worker.W, j *jobservic
 		}
 	}
 
+	if streaming, ok := m["beam:option:streaming:v1"].(bool); ok {
+		config.StreamingMode = streaming
+	}
+
+	// Set StreamingMode to true if there is any unbounded PCollection.
+	for _, pcoll := range pcols {
+		if pcoll.GetIsBounded() == pipepb.IsBounded_UNBOUNDED {
+			config.StreamingMode = true
+			break
+		}
+	}
+
 	em := engine.NewElementManager(config)
 
 	// TODO move this loop and code into the preprocessor instead.
@@ -318,7 +340,6 @@ func executePipeline(ctx context.Context, wks map[string]*worker.W, j *jobservic
 				return fmt.Errorf("prism error building stage %v: \n%w", stage.ID, err)
 			}
 			stages[stage.ID] = stage
-			j.Logger.Debug("pipelineBuild", slog.Group("stage", slog.String("ID", stage.ID), slog.String("transformName", t.GetUniqueName())))
 			outputs := maps.Keys(stage.OutputsToCoders)
 			sort.Strings(outputs)
 			em.AddStage(stage.ID, []string{stage.primaryInput}, outputs, stage.sideInputs)
@@ -359,7 +380,7 @@ func executePipeline(ctx context.Context, wks map[string]*worker.W, j *jobservic
 		case rb, ok := <-bundles:
 			if !ok {
 				err := eg.Wait()
-				j.Logger.Debug("pipeline done!", slog.String("job", j.String()), slog.Any("error", err), slog.Any("topo", topo))
+				j.Logger.Debug("pipeline done!", slog.String("job", j.String()), slog.Any("error", err), slog.String("stages", em.DumpStages()))
 				return err
 			}
 			eg.Go(func() error {
@@ -456,7 +477,27 @@ func buildTrigger(tpb *pipepb.Trigger) engine.Trigger {
 		}
 	case *pipepb.Trigger_Repeat_:
 		return &engine.TriggerRepeatedly{Repeated: buildTrigger(at.Repeat.GetSubtrigger())}
-	case *pipepb.Trigger_AfterProcessingTime_, *pipepb.Trigger_AfterSynchronizedProcessingTime_:
+	case *pipepb.Trigger_AfterProcessingTime_:
+		var transforms []engine.TimestampTransform
+		for _, ts := range at.AfterProcessingTime.GetTimestampTransforms() {
+			var delay, period, offset time.Duration
+			if d := ts.GetDelay(); d != nil {
+				delay = time.Duration(d.GetDelayMillis()) * time.Millisecond
+			}
+			if align := ts.GetAlignTo(); align != nil {
+				period = time.Duration(align.GetPeriod()) * time.Millisecond
+				offset = time.Duration(align.GetOffset()) * time.Millisecond
+			}
+			transforms = append(transforms, engine.TimestampTransform{
+				Delay:         delay,
+				AlignToPeriod: period,
+				AlignToOffset: offset,
+			})
+		}
+		return &engine.TriggerAfterProcessingTime{
+			Transforms: transforms,
+		}
+	case *pipepb.Trigger_AfterSynchronizedProcessingTime_:
 		panic(fmt.Sprintf("unsupported trigger: %v", prototext.Format(tpb)))
 	default:
 		return &engine.TriggerDefault{}
diff --git a/sdks/go/pkg/beam/runners/prism/internal/handlecombine.go b/sdks/go/pkg/beam/runners/prism/internal/handlecombine.go
index 6b336043b8c9..d65ef63cccc9 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/handlecombine.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/handlecombine.go
@@ -64,43 +64,52 @@ func (h *combine) PrepareTransform(tid string, t *pipepb.PTransform, comps *pipe
 	combineInput := comps.GetPcollections()[onlyInput]
 	ws := comps.GetWindowingStrategies()[combineInput.GetWindowingStrategyId()]
 
-	var hasElementCount func(tpb *pipepb.Trigger) bool
+	var hasTriggerType func(tpb *pipepb.Trigger, targetTriggerType reflect.Type) bool
 
-	hasElementCount = func(tpb *pipepb.Trigger) bool {
-		elCount := false
+	hasTriggerType = func(tpb *pipepb.Trigger, targetTriggerType reflect.Type) bool {
+		if tpb == nil {
+			return false
+		}
 		switch at := tpb.GetTrigger().(type) {
-		case *pipepb.Trigger_ElementCount_:
-			return true
 		case *pipepb.Trigger_AfterAll_:
 			for _, st := range at.AfterAll.GetSubtriggers() {
-				elCount = elCount || hasElementCount(st)
+				if hasTriggerType(st, targetTriggerType) {
+					return true
+				}
 			}
-			return elCount
+			return false
 		case *pipepb.Trigger_AfterAny_:
 			for _, st := range at.AfterAny.GetSubtriggers() {
-				elCount = elCount || hasElementCount(st)
+				if hasTriggerType(st, targetTriggerType) {
+					return true
+				}
 			}
-			return elCount
+			return false
 		case *pipepb.Trigger_AfterEach_:
 			for _, st := range at.AfterEach.GetSubtriggers() {
-				elCount = elCount || hasElementCount(st)
+				if hasTriggerType(st, targetTriggerType) {
+					return true
+				}
 			}
-			return elCount
+			return false
 		case *pipepb.Trigger_AfterEndOfWindow_:
-			return hasElementCount(at.AfterEndOfWindow.GetEarlyFirings()) ||
-				hasElementCount(at.AfterEndOfWindow.GetLateFirings())
+			return hasTriggerType(at.AfterEndOfWindow.GetEarlyFirings(), targetTriggerType) ||
+				hasTriggerType(at.AfterEndOfWindow.GetLateFirings(), targetTriggerType)
 		case *pipepb.Trigger_OrFinally_:
-			return hasElementCount(at.OrFinally.GetMain()) ||
-				hasElementCount(at.OrFinally.GetFinally())
+			return hasTriggerType(at.OrFinally.GetMain(), targetTriggerType) ||
+				hasTriggerType(at.OrFinally.GetFinally(), targetTriggerType)
 		case *pipepb.Trigger_Repeat_:
-			return hasElementCount(at.Repeat.GetSubtrigger())
+			return hasTriggerType(at.Repeat.GetSubtrigger(), targetTriggerType)
 		default:
-			return false
+			return reflect.TypeOf(at) == targetTriggerType
 		}
 	}
 
 	// If we aren't lifting, the "default impl" for combines should be sufficient.
-	if !h.config.EnableLifting || hasElementCount(ws.GetTrigger()) {
+	// Disable lifting if there is any TriggerElementCount or TriggerAlways.
+	if (!h.config.EnableLifting ||
+		hasTriggerType(ws.GetTrigger(), reflect.TypeOf(&pipepb.Trigger_ElementCount_{})) ||
+		hasTriggerType(ws.GetTrigger(), reflect.TypeOf(&pipepb.Trigger_Always_{}))) {
 		return prepareResult{} // Strip the composite layer when lifting is disabled.
 	}
 
diff --git a/sdks/go/pkg/beam/runners/prism/internal/handlecombine_test.go b/sdks/go/pkg/beam/runners/prism/internal/handlecombine_test.go
index 7b38daa295ef..26be37e77d17 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/handlecombine_test.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/handlecombine_test.go
@@ -25,10 +25,14 @@ import (
 	"google.golang.org/protobuf/testing/protocmp"
 )
 
-func TestHandleCombine(t *testing.T) {
-	undertest := "UnderTest"
+func makeWindowingStrategy(trigger *pipepb.Trigger) *pipepb.WindowingStrategy {
+	return &pipepb.WindowingStrategy{
+		Trigger: trigger,
+	}
+}
 
-	combineTransform := &pipepb.PTransform{
+func makeCombineTransform(inputPCollectionID string) *pipepb.PTransform {
+	return &pipepb.PTransform{
 		UniqueName: "COMBINE",
 		Spec: &pipepb.FunctionSpec{
 			Urn: urns.TransformCombinePerKey,
@@ -41,7 +45,7 @@ func TestHandleCombine(t *testing.T) {
 			}),
 		},
 		Inputs: map[string]string{
-			"input": "combineIn",
+			"input": inputPCollectionID,
 		},
 		Outputs: map[string]string{
 			"input": "combineOut",
@@ -51,6 +55,15 @@ func TestHandleCombine(t *testing.T) {
 			"combine_values",
 		},
 	}
+}
+
+func TestHandleCombine(t *testing.T) {
+	undertest := "UnderTest"
+
+	combineTransform := makeCombineTransform("combineIn")
+	combineTransformWithTriggerElementCount := makeCombineTransform("combineInWithTriggerElementCount")
+	combineTransformWithTriggerAlways := makeCombineTransform("combineInWithTriggerAlways")
+
 	combineValuesTransform := &pipepb.PTransform{
 		UniqueName: "combine_values",
 		Subtransforms: []string{
@@ -64,6 +77,14 @@ func TestHandleCombine(t *testing.T) {
 		"combineOut": {
 			CoderId: "outputCoder",
 		},
+		"combineInWithTriggerElementCount": {
+			CoderId:             "inputCoder",
+			WindowingStrategyId: "wsElementCount",
+		},
+		"combineInWithTriggerAlways": {
+			CoderId:             "inputCoder",
+			WindowingStrategyId: "wsAlways",
+		},
 	}
 	baseCoderMap := map[string]*pipepb.Coder{
 		"int": {
@@ -84,7 +105,20 @@ func TestHandleCombine(t *testing.T) {
 			ComponentCoderIds: []string{"int", "string"},
 		},
 	}
-
+	baseWindowingStrategyMap := map[string]*pipepb.WindowingStrategy{
+		"wsElementCount": makeWindowingStrategy(&pipepb.Trigger{
+			Trigger: &pipepb.Trigger_ElementCount_{
+				ElementCount: &pipepb.Trigger_ElementCount{
+					ElementCount: 10,
+				},
+			},
+		}),
+		"wsAlways": makeWindowingStrategy(&pipepb.Trigger{
+			Trigger: &pipepb.Trigger_Always_{
+				Always: &pipepb.Trigger_Always{},
+			},
+		}),
+	}
 	tests := []struct {
 		name   string
 		lifted bool
@@ -188,6 +222,32 @@ func TestHandleCombine(t *testing.T) {
 					},
 				},
 			},
+		}, {
+			name:   "noLift_triggerElementCount",
+			lifted: true, // Lifting is enabled, but should be disabled in the present of the trigger
+			comps: &pipepb.Components{
+				Transforms: map[string]*pipepb.PTransform{
+					undertest:        combineTransformWithTriggerElementCount,
+					"combine_values": combineValuesTransform,
+				},
+				Pcollections:        basePCollectionMap,
+				Coders:              baseCoderMap,
+				WindowingStrategies: baseWindowingStrategyMap,
+			},
+			want: prepareResult{},
+		}, {
+			name:   "noLift_triggerAlways",
+			lifted: true, // Lifting is enabled, but should be disabled in the present of the trigger
+			comps: &pipepb.Components{
+				Transforms: map[string]*pipepb.PTransform{
+					undertest:        combineTransformWithTriggerAlways,
+					"combine_values": combineValuesTransform,
+				},
+				Pcollections:        basePCollectionMap,
+				Coders:              baseCoderMap,
+				WindowingStrategies: baseWindowingStrategyMap,
+			},
+			want: prepareResult{},
 		},
 	}
 	for _, test := range tests {
diff --git a/sdks/go/pkg/beam/runners/prism/internal/jobservices/job.go b/sdks/go/pkg/beam/runners/prism/internal/jobservices/job.go
index f186b11fd1d8..c0ba7d2ee5ec 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/jobservices/job.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/jobservices/job.go
@@ -94,6 +94,8 @@ type Job struct {
 	// Logger for this job.
 	Logger *slog.Logger
 
+	pendingDone atomic.Bool // indicate the job is done but waiting for clean-up
+
 	metrics metricsStore
 	mw      *worker.MultiplexW
 }
@@ -194,6 +196,20 @@ func (j *Job) Canceled() {
 	j.sendState(jobpb.JobState_CANCELLED)
 }
 
+// PendingDone indicates that the job is completed and is waiting for clean-up.
+func (j *Job) PendingDone() {
+	j.pendingDone.Store(true)
+}
+
+// WaitForCleanUp waits until all environments relevant to the job are cleaned up.
+func (j *Job) WaitForCleanUp() {
+	j.mw.WaitForCleanUp(j.String())
+	if j.pendingDone.Load() {
+		// If there is a pending done, only mark it as done after clean-up
+		j.Done()
+	}
+}
+
 // Failed indicates that the job completed unsuccessfully.
 func (j *Job) Failed(err error) {
 	slog.Error("job failed", slog.Any("job", j), slog.Any("error", err))
@@ -204,11 +220,11 @@ func (j *Job) Failed(err error) {
 
 // MakeWorker instantiates a worker.W populating environment and pipeline data from the Job.
 func (j *Job) MakeWorker(env string) *worker.W {
-	wk := j.mw.MakeWorker(j.String()+"_"+env, env)
+	wk := j.mw.MakeWorker(j.String(), env)
 	wk.EnvPb = j.Pipeline.GetComponents().GetEnvironments()[env]
 	wk.PipelineOptions = j.PipelineOptions()
 	wk.JobKey = j.JobKey()
-	wk.ArtifactEndpoint = j.ArtifactEndpoint()
 
+	wk.ResolveEndpoints(j.ArtifactEndpoint())
 	return wk
 }
diff --git a/sdks/go/pkg/beam/runners/prism/internal/jobservices/server_test.go b/sdks/go/pkg/beam/runners/prism/internal/jobservices/server_test.go
index fb72048d478c..80b38507539b 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/jobservices/server_test.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/jobservices/server_test.go
@@ -20,6 +20,7 @@ import (
 	"errors"
 	"sync"
 	"testing"
+	"time"
 
 	jobpb "github.com/apache/beam/sdks/v2/go/pkg/beam/model/jobmanagement_v1"
 	pipepb "github.com/apache/beam/sdks/v2/go/pkg/beam/model/pipeline_v1"
@@ -81,15 +82,28 @@ func TestServer_JobLifecycle(t *testing.T) {
 
 // Validates that invoking Cancel cancels a running job.
 func TestServer_RunThenCancel(t *testing.T) {
-	var called sync.WaitGroup
-	called.Add(1)
+	var canceled sync.WaitGroup
+	var running sync.WaitGroup
+	canceled.Add(1)
+	running.Add(1)
 	undertest := NewServer(0, func(j *Job) {
-		defer called.Done()
-		j.state.Store(jobpb.JobState_RUNNING)
-		if errors.Is(context.Cause(j.RootCtx), ErrCancel) {
-			j.SendMsg("pipeline canceled " + j.String())
-			j.Canceled()
-			return
+		defer canceled.Done()
+		j.Running()
+		running.Done()
+		for {
+			select {
+			case <-j.RootCtx.Done():
+				// The context was canceled. The goroutine "woke up."
+				// We check the reason for the cancellation.
+				if errors.Is(context.Cause(j.RootCtx), ErrCancel) {
+					j.SendMsg("pipeline canceled " + j.String())
+					j.Canceled()
+				}
+				return
+
+			case <-time.After(1 * time.Second):
+				// Just wait a little bit to receive the cancel signal
+			}
 		}
 	})
 	ctx := context.Background()
@@ -121,6 +135,9 @@ func TestServer_RunThenCancel(t *testing.T) {
 		t.Fatalf("server.Run() = returned empty preparation ID, want non-empty")
 	}
 
+	// wait until the job is running (i.e. j.Running() is called)
+	running.Wait()
+
 	cancelResp, err := undertest.Cancel(ctx, &jobpb.CancelJobRequest{
 		JobId: runResp.GetJobId(),
 	})
@@ -132,7 +149,8 @@ func TestServer_RunThenCancel(t *testing.T) {
 		t.Fatalf("server.Canceling() = %v, want %v", cancelResp.State, jobpb.JobState_CANCELLING)
 	}
 
-	called.Wait()
+	// wait until the job is canceled (i.e. j.Canceled() is called)
+	canceled.Wait()
 
 	stateResp, err := undertest.GetState(ctx, &jobpb.GetJobStateRequest{JobId: runResp.GetJobId()})
 	if err != nil {
diff --git a/sdks/go/pkg/beam/runners/prism/internal/preprocess.go b/sdks/go/pkg/beam/runners/prism/internal/preprocess.go
index 4bf7ba4dff4a..3311bcced9f4 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/preprocess.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/preprocess.go
@@ -182,6 +182,20 @@ func (p *preprocessor) preProcessGraph(comps *pipepb.Components, j *jobservices.
 			return nil
 		}
 	}
+	var stageDetails []any
+	for i, stg := range stages {
+		var transformNames []string
+		for _, tid := range stg.transforms {
+			transformNames = append(transformNames, comps.GetTransforms()[tid].GetUniqueName())
+		}
+		stageDetails = append(stageDetails,
+			slog.Group(fmt.Sprintf("stage-%03d", i),
+				slog.String("environment", stg.envID),
+				slog.Any("transforms", transformNames),
+			),
+		)
+	}
+	slog.Debug("preProcessGraph: all stages and transforms", stageDetails...)
 	return stages
 }
 
diff --git a/sdks/go/pkg/beam/runners/prism/internal/stage.go b/sdks/go/pkg/beam/runners/prism/internal/stage.go
index 1b30cf31bfff..918ea45fcd60 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/stage.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/stage.go
@@ -108,6 +108,19 @@ func clampTick(dur time.Duration) time.Duration {
 	}
 }
 
+func (s *stage) LogValue() slog.Value {
+	var outAttrs []any
+	for k, v := range s.OutputsToCoders {
+		outAttrs = append(outAttrs, slog.Any(k, v))
+	}
+	return slog.GroupValue(
+		slog.String("ID", s.ID),
+		slog.Any("transforms", s.transforms),
+		slog.Any("inputInfo", s.inputInfo),
+		slog.Group("outputInfo", outAttrs...),
+	)
+}
+
 func (s *stage) Execute(ctx context.Context, j *jobservices.Job, wk *worker.W, comps *pipepb.Components, em *engine.ElementManager, rb engine.RunBundle) (err error) {
 	if s.baseProgTick.Load() == nil {
 		s.baseProgTick.Store(minimumProgTick)
@@ -161,7 +174,7 @@ func (s *stage) Execute(ctx context.Context, j *jobservices.Job, wk *worker.W, c
 
 		s.prepareSides(b, rb.Watermark)
 
-		slog.Debug("Execute: processing", "bundle", rb)
+		slog.Debug("Execute: sdk worker transform(s)", "bundle", rb)
 		defer b.Cleanup(wk)
 		dataReady = b.ProcessOn(ctx, wk)
 	default:
@@ -221,7 +234,7 @@ progress:
 
 			// Check if there has been any measurable progress by the input, or all output pcollections since last report.
 			slow := previousIndex == index["index"] && previousTotalCount == index["totalCount"]
-			if slow && unsplit {
+			if slow && unsplit && b.EstimatedInputElements > 0 {
 				slog.Debug("splitting report", "bundle", rb, "index", index)
 				sr, err := b.Split(ctx, wk, 0.5 /* fraction of remainder */, nil /* allowed splits */)
 				if err != nil {
@@ -341,7 +354,7 @@ progress:
 			slog.Error("SDK Error from bundle finalization", "bundle", rb, "error", err.Error())
 			panic(err)
 		}
-		slog.Info("finalized bundle", "bundle", rb)
+		slog.Debug("finalized bundle", "bundle", rb)
 	}
 	b.OutputData = engine.TentativeData{} // Clear the data.
 	return nil
diff --git a/sdks/go/pkg/beam/runners/prism/internal/unimplemented_test.go b/sdks/go/pkg/beam/runners/prism/internal/unimplemented_test.go
index 185940eada14..7a742c22d0fb 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/unimplemented_test.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/unimplemented_test.go
@@ -49,7 +49,6 @@ func TestUnimplemented(t *testing.T) {
 		// See https://github.com/apache/beam/issues/31153.
 		{pipeline: primitives.TriggerElementCount},
 		{pipeline: primitives.TriggerOrFinally},
-		{pipeline: primitives.TriggerAlways},
 
 		// Currently unimplemented triggers.
 		// https://github.com/apache/beam/issues/31438
@@ -87,6 +86,7 @@ func TestImplemented(t *testing.T) {
 		{pipeline: primitives.ParDoProcessElementBundleFinalizer},
 
 		{pipeline: primitives.TriggerNever},
+		{pipeline: primitives.TriggerAlways},
 		{pipeline: primitives.Panes},
 		{pipeline: primitives.TriggerAfterAll},
 		{pipeline: primitives.TriggerAfterAny},
diff --git a/sdks/go/pkg/beam/runners/prism/internal/worker/bundle.go b/sdks/go/pkg/beam/runners/prism/internal/worker/bundle.go
index 14cd84aef821..15023a1b0bdc 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/worker/bundle.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/worker/bundle.go
@@ -40,8 +40,8 @@ type B struct {
 
 	// InputTransformID is where data is being sent to in the SDK.
 	InputTransformID       string
-	Input                  []*engine.Block // Data and Timers for this bundle.
-	EstimatedInputElements int
+	Input                  []*engine.Block        // Data and Timers for this bundle.
+	EstimatedInputElements int                    // Estimated number of Data elements for this bundle
 	HasTimers              []engine.StaticTimerID // Timer streams to terminate.
 
 	// IterableSideInputData is a map from transformID + inputID, to window, to data.
diff --git a/sdks/go/pkg/beam/runners/prism/internal/worker/worker.go b/sdks/go/pkg/beam/runners/prism/internal/worker/worker.go
index b4133b0332a6..5668449f6c9c 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/worker/worker.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/worker/worker.go
@@ -24,8 +24,12 @@ import (
 	"io"
 	"log/slog"
 	"net"
+	"os"
+	"runtime"
+	"strings"
 	"sync"
 	"sync/atomic"
+	"time"
 
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/graph/coder"
@@ -58,9 +62,9 @@ type W struct {
 
 	ID, Env string
 
-	JobKey, ArtifactEndpoint string
-	EnvPb                    *pipepb.Environment
-	PipelineOptions          *structpb.Struct
+	JobKey, ArtifactEndpoint, endpoint string
+	EnvPb                              *pipepb.Environment
+	PipelineOptions                    *structpb.Struct
 
 	// These are the ID sources
 	inst               uint64
@@ -73,14 +77,39 @@ type W struct {
 	mu                 sync.Mutex
 	activeInstructions map[string]controlResponder              // Active instructions keyed by InstructionID
 	Descriptors        map[string]*fnpb.ProcessBundleDescriptor // Stages keyed by PBDID
+	wg                 *sync.WaitGroup
 }
 
 type controlResponder interface {
 	Respond(*fnpb.InstructionResponse)
 }
 
+// resolveEndpoint checks if the worker is running inside a docker container on mac or Windows and
+// if the endpoint is a "localhost" endpoint. If so, overrides it with "host.docker.internal".
+// Reference: https://docs.docker.com/desktop/features/networking/#networking-mode-and-dns-behaviour-for-mac-and-windows
+func (wk *W) resolveEndpoint(endpoint string) string {
+	// The presence of an external environment does not guarantee execution within
+	// Docker, as Python's LOOPBACK also runs in an external environment.
+	// A specific check for the "BEAM_WORKER_POOL_IN_DOCKER_VM" environment variable is required to confirm
+	// if the worker is running inside a Docker container.
+	// Python LOOPBACK mode: https://github.com/apache/beam/blob/0589b14812ec52bff9d20d3bfcd96da393b9ebdb/sdks/python/apache_beam/runners/portability/portable_runner.py#L397
+	// External Environment: https://beam.apache.org/documentation/runtime/sdk-harness-config/
+
+	workerInDocker := wk.EnvPb.GetUrn() == urns.EnvDocker ||
+		(wk.EnvPb.GetUrn() == urns.EnvExternal && (os.Getenv("BEAM_WORKER_POOL_IN_DOCKER_VM") == "1"))
+	if runtime.GOOS != "linux" && workerInDocker && strings.HasPrefix(endpoint, "localhost:") {
+		return "host.docker.internal:" + strings.TrimPrefix(endpoint, "localhost:")
+	}
+	return endpoint
+}
+
+func (wk *W) ResolveEndpoints(artifactEndpoint string) {
+	wk.ArtifactEndpoint = wk.resolveEndpoint(artifactEndpoint)
+	wk.endpoint = wk.resolveEndpoint(wk.parentPool.endpoint)
+}
+
 func (wk *W) Endpoint() string {
-	return wk.parentPool.endpoint
+	return wk.endpoint
 }
 
 func (wk *W) String() string {
@@ -115,6 +144,7 @@ func (wk *W) shutdown() {
 func (wk *W) Stop() {
 	wk.shutdown()
 	wk.parentPool.delete(wk)
+	wk.wg.Done()
 	slog.Debug("stopped", "worker", wk)
 }
 
@@ -129,6 +159,14 @@ func (wk *W) GetProvisionInfo(_ context.Context, _ *fnpb.GetProvisionInfoRequest
 	endpoint := &pipepb.ApiServiceDescriptor{
 		Url: wk.Endpoint(),
 	}
+
+	var rt string
+	if len(wk.EnvPb.GetDependencies()) > 0 {
+		rt = wk.JobKey
+	} else {
+		rt = "__no_artifacts_staged__"
+	}
+
 	resp := &fnpb.GetProvisionInfoResponse{
 		Info: &fnpb.ProvisionInfo{
 			// TODO: Include runner capabilities with the per job configuration.
@@ -141,7 +179,7 @@ func (wk *W) GetProvisionInfo(_ context.Context, _ *fnpb.GetProvisionInfoRequest
 				Url: wk.ArtifactEndpoint,
 			},
 
-			RetrievalToken:  wk.JobKey,
+			RetrievalToken:  rt,
 			Dependencies:    wk.EnvPb.GetDependencies(),
 			PipelineOptions: wk.PipelineOptions,
 
@@ -348,7 +386,7 @@ func (wk *W) Data(data fnpb.BeamFnData_DataServer) error {
 			for _, d := range resp.GetData() {
 				cr, ok := wk.activeInstructions[d.GetInstructionId()]
 				if !ok {
-					slog.Info("data.Recv data for unknown bundle", "response", resp)
+					slog.Debug("data.Recv data for unknown bundle", "response", resp)
 					continue
 				}
 				// Received data is always for an active ProcessBundle instruction
@@ -367,7 +405,7 @@ func (wk *W) Data(data fnpb.BeamFnData_DataServer) error {
 			for _, t := range resp.GetTimers() {
 				cr, ok := wk.activeInstructions[t.GetInstructionId()]
 				if !ok {
-					slog.Info("data.Recv timers for unknown bundle", "response", resp)
+					slog.Debug("data.Recv timers for unknown bundle", "response", resp)
 					continue
 				}
 				// Received data is always for an active ProcessBundle instruction
@@ -674,6 +712,7 @@ type MultiplexW struct {
 	endpoint string
 	logger   *slog.Logger
 	pool     map[string]*W
+	wg       map[string]*sync.WaitGroup
 }
 
 // NewMultiplexW instantiates a new FnAPI server for multiplexing FnAPI requests to a W.
@@ -683,6 +722,7 @@ func NewMultiplexW(lis net.Listener, g *grpc.Server, logger *slog.Logger) *Multi
 		endpoint: "localhost:" + p,
 		logger:   logger,
 		pool:     make(map[string]*W),
+		wg:       make(map[string]*sync.WaitGroup),
 	}
 
 	fnpb.RegisterBeamFnControlServer(g, mw)
@@ -700,8 +740,12 @@ func NewMultiplexW(lis net.Listener, g *grpc.Server, logger *slog.Logger) *Multi
 func (mw *MultiplexW) MakeWorker(id, env string) *W {
 	mw.mu.Lock()
 	defer mw.mu.Unlock()
+	workerId := id + "_" + env
+	if _, ok := mw.wg[id]; !ok {
+		mw.wg[id] = &sync.WaitGroup{}
+	}
 	w := &W{
-		ID:  id,
+		ID:  workerId,
 		Env: env,
 
 		InstReqs:    make(chan *fnpb.InstructionRequest, 10),
@@ -711,8 +755,11 @@ func (mw *MultiplexW) MakeWorker(id, env string) *W {
 		activeInstructions: make(map[string]controlResponder),
 		Descriptors:        make(map[string]*fnpb.ProcessBundleDescriptor),
 		parentPool:         mw,
+		wg:                 mw.wg[id],
 	}
-	mw.pool[id] = w
+	mw.pool[workerId] = w
+
+	mw.wg[id].Add(1)
 	return w
 }
 
@@ -774,6 +821,32 @@ func (mw *MultiplexW) delete(w *W) {
 	delete(mw.pool, w.ID)
 }
 
+// WaitForCleanUp waits until all resources relevant to the job are cleaned up.
+func (mw *MultiplexW) WaitForCleanUp(id string) {
+	mw.mu.Lock()
+	wg := mw.wg[id]
+	mw.mu.Unlock()
+	if wg == nil {
+		return
+	}
+
+	const cleanUpTimeout = 60 * time.Second
+	c := make(chan struct{})
+	go func() {
+		defer close(c)
+		wg.Wait()
+	}()
+
+	select {
+	case <-c: // Waitgroup finishes successfully
+		slog.Debug("Finished cleaning up job " + id)
+		return
+	case <-time.After(cleanUpTimeout): // Timeout
+		slog.Warn("Timeout when cleaning up job " + id)
+		return
+	}
+}
+
 func handleUnary[Request any, Response any, Method func(*W, context.Context, *Request) (*Response, error)](mw *MultiplexW, ctx context.Context, req *Request, m Method) (*Response, error) {
 	w, err := mw.workerFromMetadataCtx(ctx)
 	if err != nil {
diff --git a/sdks/go/pkg/beam/runners/prism/internal/worker/worker_test.go b/sdks/go/pkg/beam/runners/prism/internal/worker/worker_test.go
index a0cf577fbdba..76a05563ec38 100644
--- a/sdks/go/pkg/beam/runners/prism/internal/worker/worker_test.go
+++ b/sdks/go/pkg/beam/runners/prism/internal/worker/worker_test.go
@@ -44,7 +44,7 @@ func TestMultiplexW_MakeWorker(t *testing.T) {
 	if w.parentPool == nil {
 		t.Errorf("MakeWorker instantiated W with a nil reference to MultiplexW")
 	}
-	if got, want := w.ID, "test"; got != want {
+	if got, want := w.ID, "test_testEnv"; got != want {
 		t.Errorf("MakeWorker(%q) = %v, want %v", want, got, want)
 	}
 	got, ok := w.parentPool.pool[w.ID]
@@ -77,8 +77,8 @@ func TestMultiplexW_workerFromMetadataCtx(t *testing.T) {
 		},
 		{
 			name: "matched worker_id",
-			ctx:  metadata.NewIncomingContext(context.Background(), metadata.Pairs("worker_id", "test")),
-			want: &W{ID: "test"},
+			ctx:  metadata.NewIncomingContext(context.Background(), metadata.Pairs("worker_id", "test_testEnv")),
+			want: &W{ID: "test_testEnv"},
 		},
 	} {
 		t.Run(tt.name, func(t *testing.T) {
@@ -525,6 +525,7 @@ func TestWorker_State_MultimapSideInput(t *testing.T) {
 func newWorker() *W {
 	mw := &MultiplexW{
 		pool: map[string]*W{},
+		wg:   map[string]*sync.WaitGroup{},
 	}
 	return mw.MakeWorker("test", "testEnv")
 }
diff --git a/sdks/go/pkg/beam/runners/vet/testpipeline/testpipeline.shims.go b/sdks/go/pkg/beam/runners/vet/testpipeline/testpipeline.shims.go
index 2d10e307a979..c1f3ccaa5069 100644
--- a/sdks/go/pkg/beam/runners/vet/testpipeline/testpipeline.shims.go
+++ b/sdks/go/pkg/beam/runners/vet/testpipeline/testpipeline.shims.go
@@ -162,13 +162,15 @@ type emitNative struct {
 	est *sdf.WatermarkEstimator
 
 	ctx   context.Context
+	pn    typex.PaneInfo
 	ws    []typex.Window
 	et    typex.EventTime
 	value exec.FullValue
 }
 
-func (e *emitNative) Init(ctx context.Context, ws []typex.Window, et typex.EventTime) error {
+func (e *emitNative) Init(ctx context.Context, pn typex.PaneInfo, ws []typex.Window, et typex.EventTime) error {
 	e.ctx = ctx
+	e.pn = pn
 	e.ws = ws
 	e.et = et
 	return nil
@@ -189,7 +191,7 @@ func emitMakerStringInt(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invokeStringInt(key string, val int) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
diff --git a/sdks/go/pkg/beam/testing/passert/passert.shims.go b/sdks/go/pkg/beam/testing/passert/passert.shims.go
index c2ce9af6157f..dc9ec84514c1 100644
--- a/sdks/go/pkg/beam/testing/passert/passert.shims.go
+++ b/sdks/go/pkg/beam/testing/passert/passert.shims.go
@@ -25,6 +25,7 @@ import (
 	"reflect"
 
 	// Library imports
+	"github.com/apache/beam/sdks/v2/go/pkg/beam"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/runtime"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/runtime/exec"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/runtime/graphx/schema"
@@ -65,28 +66,28 @@ func init() {
 	reflectx.RegisterFunc(reflect.TypeOf((*func(int, int) int)(nil)).Elem(), funcMakerIntIntГInt)
 	reflectx.RegisterFunc(reflect.TypeOf((*func(int, func(*int) bool) error)(nil)).Elem(), funcMakerIntIterIntГError)
 	reflectx.RegisterFunc(reflect.TypeOf((*func(int, func(*string) bool) error)(nil)).Elem(), funcMakerIntIterStringГError)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(int, typex.T) int)(nil)).Elem(), funcMakerIntTypex۰TГInt)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(int, beam.T) int)(nil)).Elem(), funcMakerIntTypex۰TГInt)
 	reflectx.RegisterFunc(reflect.TypeOf((*func(int) error)(nil)).Elem(), funcMakerIntГError)
 	reflectx.RegisterFunc(reflect.TypeOf((*func(int) int)(nil)).Elem(), funcMakerIntГInt)
-	reflectx.RegisterFunc(reflect.TypeOf((*func([]byte, func(*typex.T) bool, func(*typex.T) bool, func(t typex.T), func(t typex.T), func(t typex.T)) error)(nil)).Elem(), funcMakerSliceOfByteIterTypex۰TIterTypex۰TEmitTypex۰TEmitTypex۰TEmitTypex۰TГError)
-	reflectx.RegisterFunc(reflect.TypeOf((*func([]byte, func(*typex.T) bool, func(*typex.T) bool, func(*typex.T) bool) error)(nil)).Elem(), funcMakerSliceOfByteIterTypex۰TIterTypex۰TIterTypex۰TГError)
-	reflectx.RegisterFunc(reflect.TypeOf((*func([]byte, func(*typex.Z) bool) error)(nil)).Elem(), funcMakerSliceOfByteIterTypex۰ZГError)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(typex.X, func(*typex.Y) bool) error)(nil)).Elem(), funcMakerTypex۰XIterTypex۰YГError)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(typex.X, typex.Y) error)(nil)).Elem(), funcMakerTypex۰XTypex۰YГError)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(typex.X) error)(nil)).Elem(), funcMakerTypex۰XГError)
+	reflectx.RegisterFunc(reflect.TypeOf((*func([]byte, func(*beam.T) bool, func(*beam.T) bool, func(t beam.T), func(t beam.T), func(t beam.T)) error)(nil)).Elem(), funcMakerSliceOfByteIterTypex۰TIterTypex۰TEmitTypex۰TEmitTypex۰TEmitTypex۰TГError)
+	reflectx.RegisterFunc(reflect.TypeOf((*func([]byte, func(*beam.T) bool, func(*beam.T) bool, func(*beam.T) bool) error)(nil)).Elem(), funcMakerSliceOfByteIterTypex۰TIterTypex۰TIterTypex۰TГError)
+	reflectx.RegisterFunc(reflect.TypeOf((*func([]byte, func(*beam.Z) bool) error)(nil)).Elem(), funcMakerSliceOfByteIterTypex۰ZГError)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(beam.X, func(*beam.Y) bool) error)(nil)).Elem(), funcMakerTypex۰XIterTypex۰YГError)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(beam.X, beam.Y) error)(nil)).Elem(), funcMakerTypex۰XTypex۰YГError)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(beam.X) error)(nil)).Elem(), funcMakerTypex۰XГError)
 	reflectx.RegisterFunc(reflect.TypeOf((*func() int)(nil)).Elem(), funcMakerГInt)
-	exec.RegisterEmitter(reflect.TypeOf((*func(typex.T))(nil)).Elem(), emitMakerTypex۰T)
+	exec.RegisterEmitter(reflect.TypeOf((*func(beam.T))(nil)).Elem(), emitMakerTypex۰T)
 	exec.RegisterInput(reflect.TypeOf((*func(*int) bool)(nil)).Elem(), iterMakerInt)
 	exec.RegisterInput(reflect.TypeOf((*func(*string) bool)(nil)).Elem(), iterMakerString)
-	exec.RegisterInput(reflect.TypeOf((*func(*typex.T) bool)(nil)).Elem(), iterMakerTypex۰T)
-	exec.RegisterInput(reflect.TypeOf((*func(*typex.Y) bool)(nil)).Elem(), iterMakerTypex۰Y)
-	exec.RegisterInput(reflect.TypeOf((*func(*typex.Z) bool)(nil)).Elem(), iterMakerTypex۰Z)
+	exec.RegisterInput(reflect.TypeOf((*func(*beam.T) bool)(nil)).Elem(), iterMakerTypex۰T)
+	exec.RegisterInput(reflect.TypeOf((*func(*beam.Y) bool)(nil)).Elem(), iterMakerTypex۰Y)
+	exec.RegisterInput(reflect.TypeOf((*func(*beam.Z) bool)(nil)).Elem(), iterMakerTypex۰Z)
 }
 
 func wrapMakerDiffFn(fn any) map[string]reflectx.Func {
 	dfn := fn.(*diffFn)
 	return map[string]reflectx.Func{
-		"ProcessElement": reflectx.MakeFunc(func(a0 []byte, a1 func(*typex.T) bool, a2 func(*typex.T) bool, a3 func(t typex.T), a4 func(t typex.T), a5 func(t typex.T)) error {
+		"ProcessElement": reflectx.MakeFunc(func(a0 []byte, a1 func(*beam.T) bool, a2 func(*beam.T) bool, a3 func(t beam.T), a4 func(t beam.T), a5 func(t beam.T)) error {
 			return dfn.ProcessElement(a0, a1, a2, a3, a4, a5)
 		}),
 	}
@@ -95,7 +96,7 @@ func wrapMakerDiffFn(fn any) map[string]reflectx.Func {
 func wrapMakerElmCountCombineFn(fn any) map[string]reflectx.Func {
 	dfn := fn.(*elmCountCombineFn)
 	return map[string]reflectx.Func{
-		"AddInput":          reflectx.MakeFunc(func(a0 int, a1 typex.T) int { return dfn.AddInput(a0, a1) }),
+		"AddInput":          reflectx.MakeFunc(func(a0 int, a1 beam.T) int { return dfn.AddInput(a0, a1) }),
 		"CreateAccumulator": reflectx.MakeFunc(func() int { return dfn.CreateAccumulator() }),
 		"ExtractOutput":     reflectx.MakeFunc(func(a0 int) int { return dfn.ExtractOutput(a0) }),
 		"MergeAccumulators": reflectx.MakeFunc(func(a0 int, a1 int) int { return dfn.MergeAccumulators(a0, a1) }),
@@ -112,21 +113,21 @@ func wrapMakerErrFn(fn any) map[string]reflectx.Func {
 func wrapMakerFailFn(fn any) map[string]reflectx.Func {
 	dfn := fn.(*failFn)
 	return map[string]reflectx.Func{
-		"ProcessElement": reflectx.MakeFunc(func(a0 typex.X) error { return dfn.ProcessElement(a0) }),
+		"ProcessElement": reflectx.MakeFunc(func(a0 beam.X) error { return dfn.ProcessElement(a0) }),
 	}
 }
 
 func wrapMakerFailGBKFn(fn any) map[string]reflectx.Func {
 	dfn := fn.(*failGBKFn)
 	return map[string]reflectx.Func{
-		"ProcessElement": reflectx.MakeFunc(func(a0 typex.X, a1 func(*typex.Y) bool) error { return dfn.ProcessElement(a0, a1) }),
+		"ProcessElement": reflectx.MakeFunc(func(a0 beam.X, a1 func(*beam.Y) bool) error { return dfn.ProcessElement(a0, a1) }),
 	}
 }
 
 func wrapMakerFailKVFn(fn any) map[string]reflectx.Func {
 	dfn := fn.(*failKVFn)
 	return map[string]reflectx.Func{
-		"ProcessElement": reflectx.MakeFunc(func(a0 typex.X, a1 typex.Y) error { return dfn.ProcessElement(a0, a1) }),
+		"ProcessElement": reflectx.MakeFunc(func(a0 beam.X, a1 beam.Y) error { return dfn.ProcessElement(a0, a1) }),
 	}
 }
 
@@ -140,7 +141,7 @@ func wrapMakerHashFn(fn any) map[string]reflectx.Func {
 func wrapMakerNonEmptyFn(fn any) map[string]reflectx.Func {
 	dfn := fn.(*nonEmptyFn)
 	return map[string]reflectx.Func{
-		"ProcessElement": reflectx.MakeFunc(func(a0 []byte, a1 func(*typex.Z) bool) error { return dfn.ProcessElement(a0, a1) }),
+		"ProcessElement": reflectx.MakeFunc(func(a0 []byte, a1 func(*beam.Z) bool) error { return dfn.ProcessElement(a0, a1) }),
 	}
 }
 
@@ -230,11 +231,11 @@ func (c *callerIntIterStringГError) Call2x1(arg0, arg1 any) any {
 }
 
 type callerIntTypex۰TГInt struct {
-	fn func(int, typex.T) int
+	fn func(int, beam.T) int
 }
 
 func funcMakerIntTypex۰TГInt(fn any) reflectx.Func {
-	f := fn.(func(int, typex.T) int)
+	f := fn.(func(int, beam.T) int)
 	return &callerIntTypex۰TГInt{fn: f}
 }
 
@@ -247,12 +248,12 @@ func (c *callerIntTypex۰TГInt) Type() reflect.Type {
 }
 
 func (c *callerIntTypex۰TГInt) Call(args []any) []any {
-	out0 := c.fn(args[0].(int), args[1].(typex.T))
+	out0 := c.fn(args[0].(int), args[1].(beam.T))
 	return []any{out0}
 }
 
 func (c *callerIntTypex۰TГInt) Call2x1(arg0, arg1 any) any {
-	return c.fn(arg0.(int), arg1.(typex.T))
+	return c.fn(arg0.(int), arg1.(beam.T))
 }
 
 type callerIntГError struct {
@@ -308,11 +309,11 @@ func (c *callerIntГInt) Call1x1(arg0 any) any {
 }
 
 type callerSliceOfByteIterTypex۰TIterTypex۰TEmitTypex۰TEmitTypex۰TEmitTypex۰TГError struct {
-	fn func([]byte, func(*typex.T) bool, func(*typex.T) bool, func(t typex.T), func(t typex.T), func(t typex.T)) error
+	fn func([]byte, func(*beam.T) bool, func(*beam.T) bool, func(t beam.T), func(t beam.T), func(t beam.T)) error
 }
 
 func funcMakerSliceOfByteIterTypex۰TIterTypex۰TEmitTypex۰TEmitTypex۰TEmitTypex۰TГError(fn any) reflectx.Func {
-	f := fn.(func([]byte, func(*typex.T) bool, func(*typex.T) bool, func(t typex.T), func(t typex.T), func(t typex.T)) error)
+	f := fn.(func([]byte, func(*beam.T) bool, func(*beam.T) bool, func(t beam.T), func(t beam.T), func(t beam.T)) error)
 	return &callerSliceOfByteIterTypex۰TIterTypex۰TEmitTypex۰TEmitTypex۰TEmitTypex۰TГError{fn: f}
 }
 
@@ -325,20 +326,20 @@ func (c *callerSliceOfByteIterTypex۰TIterTypex۰TEmitTypex۰TEmitTypex۰TEmitTy
 }
 
 func (c *callerSliceOfByteIterTypex۰TIterTypex۰TEmitTypex۰TEmitTypex۰TEmitTypex۰TГError) Call(args []any) []any {
-	out0 := c.fn(args[0].([]byte), args[1].(func(*typex.T) bool), args[2].(func(*typex.T) bool), args[3].(func(t typex.T)), args[4].(func(t typex.T)), args[5].(func(t typex.T)))
+	out0 := c.fn(args[0].([]byte), args[1].(func(*beam.T) bool), args[2].(func(*beam.T) bool), args[3].(func(t beam.T)), args[4].(func(t beam.T)), args[5].(func(t beam.T)))
 	return []any{out0}
 }
 
 func (c *callerSliceOfByteIterTypex۰TIterTypex۰TEmitTypex۰TEmitTypex۰TEmitTypex۰TГError) Call6x1(arg0, arg1, arg2, arg3, arg4, arg5 any) any {
-	return c.fn(arg0.([]byte), arg1.(func(*typex.T) bool), arg2.(func(*typex.T) bool), arg3.(func(t typex.T)), arg4.(func(t typex.T)), arg5.(func(t typex.T)))
+	return c.fn(arg0.([]byte), arg1.(func(*beam.T) bool), arg2.(func(*beam.T) bool), arg3.(func(t beam.T)), arg4.(func(t beam.T)), arg5.(func(t beam.T)))
 }
 
 type callerSliceOfByteIterTypex۰TIterTypex۰TIterTypex۰TГError struct {
-	fn func([]byte, func(*typex.T) bool, func(*typex.T) bool, func(*typex.T) bool) error
+	fn func([]byte, func(*beam.T) bool, func(*beam.T) bool, func(*beam.T) bool) error
 }
 
 func funcMakerSliceOfByteIterTypex۰TIterTypex۰TIterTypex۰TГError(fn any) reflectx.Func {
-	f := fn.(func([]byte, func(*typex.T) bool, func(*typex.T) bool, func(*typex.T) bool) error)
+	f := fn.(func([]byte, func(*beam.T) bool, func(*beam.T) bool, func(*beam.T) bool) error)
 	return &callerSliceOfByteIterTypex۰TIterTypex۰TIterTypex۰TГError{fn: f}
 }
 
@@ -351,20 +352,20 @@ func (c *callerSliceOfByteIterTypex۰TIterTypex۰TIterTypex۰TГError) Type() re
 }
 
 func (c *callerSliceOfByteIterTypex۰TIterTypex۰TIterTypex۰TГError) Call(args []any) []any {
-	out0 := c.fn(args[0].([]byte), args[1].(func(*typex.T) bool), args[2].(func(*typex.T) bool), args[3].(func(*typex.T) bool))
+	out0 := c.fn(args[0].([]byte), args[1].(func(*beam.T) bool), args[2].(func(*beam.T) bool), args[3].(func(*beam.T) bool))
 	return []any{out0}
 }
 
 func (c *callerSliceOfByteIterTypex۰TIterTypex۰TIterTypex۰TГError) Call4x1(arg0, arg1, arg2, arg3 any) any {
-	return c.fn(arg0.([]byte), arg1.(func(*typex.T) bool), arg2.(func(*typex.T) bool), arg3.(func(*typex.T) bool))
+	return c.fn(arg0.([]byte), arg1.(func(*beam.T) bool), arg2.(func(*beam.T) bool), arg3.(func(*beam.T) bool))
 }
 
 type callerSliceOfByteIterTypex۰ZГError struct {
-	fn func([]byte, func(*typex.Z) bool) error
+	fn func([]byte, func(*beam.Z) bool) error
 }
 
 func funcMakerSliceOfByteIterTypex۰ZГError(fn any) reflectx.Func {
-	f := fn.(func([]byte, func(*typex.Z) bool) error)
+	f := fn.(func([]byte, func(*beam.Z) bool) error)
 	return &callerSliceOfByteIterTypex۰ZГError{fn: f}
 }
 
@@ -377,20 +378,20 @@ func (c *callerSliceOfByteIterTypex۰ZГError) Type() reflect.Type {
 }
 
 func (c *callerSliceOfByteIterTypex۰ZГError) Call(args []any) []any {
-	out0 := c.fn(args[0].([]byte), args[1].(func(*typex.Z) bool))
+	out0 := c.fn(args[0].([]byte), args[1].(func(*beam.Z) bool))
 	return []any{out0}
 }
 
 func (c *callerSliceOfByteIterTypex۰ZГError) Call2x1(arg0, arg1 any) any {
-	return c.fn(arg0.([]byte), arg1.(func(*typex.Z) bool))
+	return c.fn(arg0.([]byte), arg1.(func(*beam.Z) bool))
 }
 
 type callerTypex۰XIterTypex۰YГError struct {
-	fn func(typex.X, func(*typex.Y) bool) error
+	fn func(beam.X, func(*beam.Y) bool) error
 }
 
 func funcMakerTypex۰XIterTypex۰YГError(fn any) reflectx.Func {
-	f := fn.(func(typex.X, func(*typex.Y) bool) error)
+	f := fn.(func(beam.X, func(*beam.Y) bool) error)
 	return &callerTypex۰XIterTypex۰YГError{fn: f}
 }
 
@@ -403,20 +404,20 @@ func (c *callerTypex۰XIterTypex۰YГError) Type() reflect.Type {
 }
 
 func (c *callerTypex۰XIterTypex۰YГError) Call(args []any) []any {
-	out0 := c.fn(args[0].(typex.X), args[1].(func(*typex.Y) bool))
+	out0 := c.fn(args[0].(beam.X), args[1].(func(*beam.Y) bool))
 	return []any{out0}
 }
 
 func (c *callerTypex۰XIterTypex۰YГError) Call2x1(arg0, arg1 any) any {
-	return c.fn(arg0.(typex.X), arg1.(func(*typex.Y) bool))
+	return c.fn(arg0.(beam.X), arg1.(func(*beam.Y) bool))
 }
 
 type callerTypex۰XTypex۰YГError struct {
-	fn func(typex.X, typex.Y) error
+	fn func(beam.X, beam.Y) error
 }
 
 func funcMakerTypex۰XTypex۰YГError(fn any) reflectx.Func {
-	f := fn.(func(typex.X, typex.Y) error)
+	f := fn.(func(beam.X, beam.Y) error)
 	return &callerTypex۰XTypex۰YГError{fn: f}
 }
 
@@ -429,20 +430,20 @@ func (c *callerTypex۰XTypex۰YГError) Type() reflect.Type {
 }
 
 func (c *callerTypex۰XTypex۰YГError) Call(args []any) []any {
-	out0 := c.fn(args[0].(typex.X), args[1].(typex.Y))
+	out0 := c.fn(args[0].(beam.X), args[1].(beam.Y))
 	return []any{out0}
 }
 
 func (c *callerTypex۰XTypex۰YГError) Call2x1(arg0, arg1 any) any {
-	return c.fn(arg0.(typex.X), arg1.(typex.Y))
+	return c.fn(arg0.(beam.X), arg1.(beam.Y))
 }
 
 type callerTypex۰XГError struct {
-	fn func(typex.X) error
+	fn func(beam.X) error
 }
 
 func funcMakerTypex۰XГError(fn any) reflectx.Func {
-	f := fn.(func(typex.X) error)
+	f := fn.(func(beam.X) error)
 	return &callerTypex۰XГError{fn: f}
 }
 
@@ -455,12 +456,12 @@ func (c *callerTypex۰XГError) Type() reflect.Type {
 }
 
 func (c *callerTypex۰XГError) Call(args []any) []any {
-	out0 := c.fn(args[0].(typex.X))
+	out0 := c.fn(args[0].(beam.X))
 	return []any{out0}
 }
 
 func (c *callerTypex۰XГError) Call1x1(arg0 any) any {
-	return c.fn(arg0.(typex.X))
+	return c.fn(arg0.(beam.X))
 }
 
 type callerГInt struct {
@@ -495,13 +496,15 @@ type emitNative struct {
 	est *sdf.WatermarkEstimator
 
 	ctx   context.Context
+	pn    typex.PaneInfo
 	ws    []typex.Window
 	et    typex.EventTime
 	value exec.FullValue
 }
 
-func (e *emitNative) Init(ctx context.Context, ws []typex.Window, et typex.EventTime) error {
+func (e *emitNative) Init(ctx context.Context, pn typex.PaneInfo, ws []typex.Window, et typex.EventTime) error {
 	e.ctx = ctx
+	e.pn = pn
 	e.ws = ws
 	e.et = et
 	return nil
@@ -521,8 +524,8 @@ func emitMakerTypex۰T(n exec.ElementProcessor) exec.ReusableEmitter {
 	return ret
 }
 
-func (e *emitNative) invokeTypex۰T(val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: val}
+func (e *emitNative) invokeTypex۰T(val beam.T) {
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -602,7 +605,7 @@ func iterMakerTypex۰T(s exec.ReStream) exec.ReusableInput {
 	return ret
 }
 
-func (v *iterNative) readTypex۰T(value *typex.T) bool {
+func (v *iterNative) readTypex۰T(value *beam.T) bool {
 	elm, err := v.cur.Read()
 	if err != nil {
 		if err == io.EOF {
@@ -610,7 +613,7 @@ func (v *iterNative) readTypex۰T(value *typex.T) bool {
 		}
 		panic(fmt.Sprintf("broken stream: %v", err))
 	}
-	*value = elm.Elm.(typex.T)
+	*value = elm.Elm.(beam.T)
 	return true
 }
 
@@ -620,7 +623,7 @@ func iterMakerTypex۰Y(s exec.ReStream) exec.ReusableInput {
 	return ret
 }
 
-func (v *iterNative) readTypex۰Y(value *typex.Y) bool {
+func (v *iterNative) readTypex۰Y(value *beam.Y) bool {
 	elm, err := v.cur.Read()
 	if err != nil {
 		if err == io.EOF {
@@ -628,7 +631,7 @@ func (v *iterNative) readTypex۰Y(value *typex.Y) bool {
 		}
 		panic(fmt.Sprintf("broken stream: %v", err))
 	}
-	*value = elm.Elm.(typex.Y)
+	*value = elm.Elm.(beam.Y)
 	return true
 }
 
@@ -638,7 +641,7 @@ func iterMakerTypex۰Z(s exec.ReStream) exec.ReusableInput {
 	return ret
 }
 
-func (v *iterNative) readTypex۰Z(value *typex.Z) bool {
+func (v *iterNative) readTypex۰Z(value *beam.Z) bool {
 	elm, err := v.cur.Read()
 	if err != nil {
 		if err == io.EOF {
@@ -646,7 +649,7 @@ func (v *iterNative) readTypex۰Z(value *typex.Z) bool {
 		}
 		panic(fmt.Sprintf("broken stream: %v", err))
 	}
-	*value = elm.Elm.(typex.Z)
+	*value = elm.Elm.(beam.Z)
 	return true
 }
 
diff --git a/sdks/go/pkg/beam/transforms/sql/sql.go b/sdks/go/pkg/beam/transforms/sql/sql.go
index 878b1c12f9cd..ad4188821102 100644
--- a/sdks/go/pkg/beam/transforms/sql/sql.go
+++ b/sdks/go/pkg/beam/transforms/sql/sql.go
@@ -65,7 +65,7 @@ func OutputType(t reflect.Type, components ...typex.FullType) Option {
 	}
 }
 
-// Dialect specifies the SQL dialect, e.g. use 'zetasql' for ZetaSQL.
+// Dialect specifies the SQL dialect. It is always Calcite
 func Dialect(dialect string) Option {
 	return func(o sqlx.Options) {
 		o.(*options).dialect = dialect
diff --git a/sdks/go/pkg/beam/transforms/sql/sql_test.go b/sdks/go/pkg/beam/transforms/sql/sql_test.go
index 851495015fe8..cbe0f8c49f41 100644
--- a/sdks/go/pkg/beam/transforms/sql/sql_test.go
+++ b/sdks/go/pkg/beam/transforms/sql/sql_test.go
@@ -190,7 +190,6 @@ func TestMultipleOptions(t *testing.T) {
 		{
 			name:          "all_options",
 			inputName:     "test",
-			dialect:       "zetasql",
 			expansionAddr: "localhost:8080",
 			typ:           reflect.TypeOf(int64(0)),
 			customOpt:     sqlx.Option{Urn: "test"},
diff --git a/sdks/go/pkg/beam/util/shimx/generate.go b/sdks/go/pkg/beam/util/shimx/generate.go
index 75d3f08dceec..7222a027793e 100644
--- a/sdks/go/pkg/beam/util/shimx/generate.go
+++ b/sdks/go/pkg/beam/util/shimx/generate.go
@@ -328,13 +328,15 @@ type emitNative struct {
 	est   *sdf.WatermarkEstimator
 
 	ctx context.Context
+	pn typex.PaneInfo
 	ws  []typex.Window
 	et  typex.EventTime
 	value exec.FullValue
 }
 
-func (e *emitNative) Init(ctx context.Context, ws []typex.Window, et typex.EventTime) error {
+func (e *emitNative) Init(ctx context.Context, pn typex.PaneInfo, ws []typex.Window, et typex.EventTime) error {
 	e.ctx = ctx
+	e.pn = pn
 	e.ws = ws
 	e.et = et
 	return nil
@@ -357,7 +359,7 @@ func emitMaker{{$x.Name}}(n exec.ElementProcessor) exec.ReusableEmitter {
 }
 
 func (e *emitNative) invoke{{$x.Name}}({{if $x.Time -}} t typex.EventTime, {{end}}{{if $x.Key}}key {{$x.Key}}, {{end}}val {{$x.Val}}) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: {{- if $x.Time}} t{{else}} e.et{{end}}, {{- if $x.Key}} Elm: key, Elm2: val {{else}} Elm: val{{end -}} }
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: {{- if $x.Time}} t{{else}} e.et{{end}}, {{- if $x.Key}} Elm: key, Elm2: val {{else}} Elm: val{{end -}} }
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp({{- if $x.Time}} t.ToTime(){{else}} e.et.ToTime(){{end}})
 	}
diff --git a/sdks/go/pkg/beam/x/debug/debug.shims.go b/sdks/go/pkg/beam/x/debug/debug.shims.go
index 59ea6b964dff..3405947f99ab 100644
--- a/sdks/go/pkg/beam/x/debug/debug.shims.go
+++ b/sdks/go/pkg/beam/x/debug/debug.shims.go
@@ -25,6 +25,7 @@ import (
 	"reflect"
 
 	// Library imports
+	"github.com/apache/beam/sdks/v2/go/pkg/beam"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/runtime"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/runtime/exec"
 	"github.com/apache/beam/sdks/v2/go/pkg/beam/core/runtime/graphx/schema"
@@ -52,30 +53,30 @@ func init() {
 	reflectx.RegisterStructWrapper(reflect.TypeOf((*printFn)(nil)).Elem(), wrapMakerPrintFn)
 	reflectx.RegisterStructWrapper(reflect.TypeOf((*printGBKFn)(nil)).Elem(), wrapMakerPrintGBKFn)
 	reflectx.RegisterStructWrapper(reflect.TypeOf((*printKVFn)(nil)).Elem(), wrapMakerPrintKVFn)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(context.Context, typex.T) typex.T)(nil)).Elem(), funcMakerContext۰ContextTypex۰TГTypex۰T)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(context.Context, typex.X, func(*typex.Y) bool) typex.X)(nil)).Elem(), funcMakerContext۰ContextTypex۰XIterTypex۰YГTypex۰X)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(context.Context, typex.X, typex.Y) (typex.X, typex.Y))(nil)).Elem(), funcMakerContext۰ContextTypex۰XTypex۰YГTypex۰XTypex۰Y)
-	reflectx.RegisterFunc(reflect.TypeOf((*func([]byte, func(*typex.T) bool, func(typex.T)))(nil)).Elem(), funcMakerSliceOfByteIterTypex۰TEmitTypex۰TГ)
-	reflectx.RegisterFunc(reflect.TypeOf((*func([]byte, func(*typex.X, *typex.Y) bool, func(typex.X, typex.Y)))(nil)).Elem(), funcMakerSliceOfByteIterTypex۰XTypex۰YEmitTypex۰XTypex۰YГ)
-	reflectx.RegisterFunc(reflect.TypeOf((*func(typex.T))(nil)).Elem(), funcMakerTypex۰TГ)
-	exec.RegisterEmitter(reflect.TypeOf((*func(typex.T))(nil)).Elem(), emitMakerTypex۰T)
-	exec.RegisterEmitter(reflect.TypeOf((*func(typex.X, typex.Y))(nil)).Elem(), emitMakerTypex۰XTypex۰Y)
-	exec.RegisterInput(reflect.TypeOf((*func(*typex.T) bool)(nil)).Elem(), iterMakerTypex۰T)
-	exec.RegisterInput(reflect.TypeOf((*func(*typex.X, *typex.Y) bool)(nil)).Elem(), iterMakerTypex۰XTypex۰Y)
-	exec.RegisterInput(reflect.TypeOf((*func(*typex.Y) bool)(nil)).Elem(), iterMakerTypex۰Y)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(context.Context, beam.T) beam.T)(nil)).Elem(), funcMakerContext۰ContextTypex۰TГTypex۰T)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(context.Context, beam.X, func(*beam.Y) bool) beam.X)(nil)).Elem(), funcMakerContext۰ContextTypex۰XIterTypex۰YГTypex۰X)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(context.Context, beam.X, beam.Y) (beam.X, beam.Y))(nil)).Elem(), funcMakerContext۰ContextTypex۰XTypex۰YГTypex۰XTypex۰Y)
+	reflectx.RegisterFunc(reflect.TypeOf((*func([]byte, func(*beam.T) bool, func(beam.T)))(nil)).Elem(), funcMakerSliceOfByteIterTypex۰TEmitTypex۰TГ)
+	reflectx.RegisterFunc(reflect.TypeOf((*func([]byte, func(*beam.X, *beam.Y) bool, func(beam.X, beam.Y)))(nil)).Elem(), funcMakerSliceOfByteIterTypex۰XTypex۰YEmitTypex۰XTypex۰YГ)
+	reflectx.RegisterFunc(reflect.TypeOf((*func(beam.T))(nil)).Elem(), funcMakerTypex۰TГ)
+	exec.RegisterEmitter(reflect.TypeOf((*func(beam.T))(nil)).Elem(), emitMakerTypex۰T)
+	exec.RegisterEmitter(reflect.TypeOf((*func(beam.X, beam.Y))(nil)).Elem(), emitMakerTypex۰XTypex۰Y)
+	exec.RegisterInput(reflect.TypeOf((*func(*beam.T) bool)(nil)).Elem(), iterMakerTypex۰T)
+	exec.RegisterInput(reflect.TypeOf((*func(*beam.X, *beam.Y) bool)(nil)).Elem(), iterMakerTypex۰XTypex۰Y)
+	exec.RegisterInput(reflect.TypeOf((*func(*beam.Y) bool)(nil)).Elem(), iterMakerTypex۰Y)
 }
 
 func wrapMakerHeadFn(fn any) map[string]reflectx.Func {
 	dfn := fn.(*headFn)
 	return map[string]reflectx.Func{
-		"ProcessElement": reflectx.MakeFunc(func(a0 []byte, a1 func(*typex.T) bool, a2 func(typex.T)) { dfn.ProcessElement(a0, a1, a2) }),
+		"ProcessElement": reflectx.MakeFunc(func(a0 []byte, a1 func(*beam.T) bool, a2 func(beam.T)) { dfn.ProcessElement(a0, a1, a2) }),
 	}
 }
 
 func wrapMakerHeadKVFn(fn any) map[string]reflectx.Func {
 	dfn := fn.(*headKVFn)
 	return map[string]reflectx.Func{
-		"ProcessElement": reflectx.MakeFunc(func(a0 []byte, a1 func(*typex.X, *typex.Y) bool, a2 func(typex.X, typex.Y)) {
+		"ProcessElement": reflectx.MakeFunc(func(a0 []byte, a1 func(*beam.X, *beam.Y) bool, a2 func(beam.X, beam.Y)) {
 			dfn.ProcessElement(a0, a1, a2)
 		}),
 	}
@@ -84,14 +85,14 @@ func wrapMakerHeadKVFn(fn any) map[string]reflectx.Func {
 func wrapMakerPrintFn(fn any) map[string]reflectx.Func {
 	dfn := fn.(*printFn)
 	return map[string]reflectx.Func{
-		"ProcessElement": reflectx.MakeFunc(func(a0 context.Context, a1 typex.T) typex.T { return dfn.ProcessElement(a0, a1) }),
+		"ProcessElement": reflectx.MakeFunc(func(a0 context.Context, a1 beam.T) beam.T { return dfn.ProcessElement(a0, a1) }),
 	}
 }
 
 func wrapMakerPrintGBKFn(fn any) map[string]reflectx.Func {
 	dfn := fn.(*printGBKFn)
 	return map[string]reflectx.Func{
-		"ProcessElement": reflectx.MakeFunc(func(a0 context.Context, a1 typex.X, a2 func(*typex.Y) bool) typex.X {
+		"ProcessElement": reflectx.MakeFunc(func(a0 context.Context, a1 beam.X, a2 func(*beam.Y) bool) beam.X {
 			return dfn.ProcessElement(a0, a1, a2)
 		}),
 	}
@@ -100,18 +101,16 @@ func wrapMakerPrintGBKFn(fn any) map[string]reflectx.Func {
 func wrapMakerPrintKVFn(fn any) map[string]reflectx.Func {
 	dfn := fn.(*printKVFn)
 	return map[string]reflectx.Func{
-		"ProcessElement": reflectx.MakeFunc(func(a0 context.Context, a1 typex.X, a2 typex.Y) (typex.X, typex.Y) {
-			return dfn.ProcessElement(a0, a1, a2)
-		}),
+		"ProcessElement": reflectx.MakeFunc(func(a0 context.Context, a1 beam.X, a2 beam.Y) (beam.X, beam.Y) { return dfn.ProcessElement(a0, a1, a2) }),
 	}
 }
 
 type callerContext۰ContextTypex۰TГTypex۰T struct {
-	fn func(context.Context, typex.T) typex.T
+	fn func(context.Context, beam.T) beam.T
 }
 
 func funcMakerContext۰ContextTypex۰TГTypex۰T(fn any) reflectx.Func {
-	f := fn.(func(context.Context, typex.T) typex.T)
+	f := fn.(func(context.Context, beam.T) beam.T)
 	return &callerContext۰ContextTypex۰TГTypex۰T{fn: f}
 }
 
@@ -124,20 +123,20 @@ func (c *callerContext۰ContextTypex۰TГTypex۰T) Type() reflect.Type {
 }
 
 func (c *callerContext۰ContextTypex۰TГTypex۰T) Call(args []any) []any {
-	out0 := c.fn(args[0].(context.Context), args[1].(typex.T))
+	out0 := c.fn(args[0].(context.Context), args[1].(beam.T))
 	return []any{out0}
 }
 
 func (c *callerContext۰ContextTypex۰TГTypex۰T) Call2x1(arg0, arg1 any) any {
-	return c.fn(arg0.(context.Context), arg1.(typex.T))
+	return c.fn(arg0.(context.Context), arg1.(beam.T))
 }
 
 type callerContext۰ContextTypex۰XIterTypex۰YГTypex۰X struct {
-	fn func(context.Context, typex.X, func(*typex.Y) bool) typex.X
+	fn func(context.Context, beam.X, func(*beam.Y) bool) beam.X
 }
 
 func funcMakerContext۰ContextTypex۰XIterTypex۰YГTypex۰X(fn any) reflectx.Func {
-	f := fn.(func(context.Context, typex.X, func(*typex.Y) bool) typex.X)
+	f := fn.(func(context.Context, beam.X, func(*beam.Y) bool) beam.X)
 	return &callerContext۰ContextTypex۰XIterTypex۰YГTypex۰X{fn: f}
 }
 
@@ -150,20 +149,20 @@ func (c *callerContext۰ContextTypex۰XIterTypex۰YГTypex۰X) Type() reflect.Ty
 }
 
 func (c *callerContext۰ContextTypex۰XIterTypex۰YГTypex۰X) Call(args []any) []any {
-	out0 := c.fn(args[0].(context.Context), args[1].(typex.X), args[2].(func(*typex.Y) bool))
+	out0 := c.fn(args[0].(context.Context), args[1].(beam.X), args[2].(func(*beam.Y) bool))
 	return []any{out0}
 }
 
 func (c *callerContext۰ContextTypex۰XIterTypex۰YГTypex۰X) Call3x1(arg0, arg1, arg2 any) any {
-	return c.fn(arg0.(context.Context), arg1.(typex.X), arg2.(func(*typex.Y) bool))
+	return c.fn(arg0.(context.Context), arg1.(beam.X), arg2.(func(*beam.Y) bool))
 }
 
 type callerContext۰ContextTypex۰XTypex۰YГTypex۰XTypex۰Y struct {
-	fn func(context.Context, typex.X, typex.Y) (typex.X, typex.Y)
+	fn func(context.Context, beam.X, beam.Y) (beam.X, beam.Y)
 }
 
 func funcMakerContext۰ContextTypex۰XTypex۰YГTypex۰XTypex۰Y(fn any) reflectx.Func {
-	f := fn.(func(context.Context, typex.X, typex.Y) (typex.X, typex.Y))
+	f := fn.(func(context.Context, beam.X, beam.Y) (beam.X, beam.Y))
 	return &callerContext۰ContextTypex۰XTypex۰YГTypex۰XTypex۰Y{fn: f}
 }
 
@@ -176,20 +175,20 @@ func (c *callerContext۰ContextTypex۰XTypex۰YГTypex۰XTypex۰Y) Type() reflec
 }
 
 func (c *callerContext۰ContextTypex۰XTypex۰YГTypex۰XTypex۰Y) Call(args []any) []any {
-	out0, out1 := c.fn(args[0].(context.Context), args[1].(typex.X), args[2].(typex.Y))
+	out0, out1 := c.fn(args[0].(context.Context), args[1].(beam.X), args[2].(beam.Y))
 	return []any{out0, out1}
 }
 
 func (c *callerContext۰ContextTypex۰XTypex۰YГTypex۰XTypex۰Y) Call3x2(arg0, arg1, arg2 any) (any, any) {
-	return c.fn(arg0.(context.Context), arg1.(typex.X), arg2.(typex.Y))
+	return c.fn(arg0.(context.Context), arg1.(beam.X), arg2.(beam.Y))
 }
 
 type callerSliceOfByteIterTypex۰TEmitTypex۰TГ struct {
-	fn func([]byte, func(*typex.T) bool, func(typex.T))
+	fn func([]byte, func(*beam.T) bool, func(beam.T))
 }
 
 func funcMakerSliceOfByteIterTypex۰TEmitTypex۰TГ(fn any) reflectx.Func {
-	f := fn.(func([]byte, func(*typex.T) bool, func(typex.T)))
+	f := fn.(func([]byte, func(*beam.T) bool, func(beam.T)))
 	return &callerSliceOfByteIterTypex۰TEmitTypex۰TГ{fn: f}
 }
 
@@ -202,20 +201,20 @@ func (c *callerSliceOfByteIterTypex۰TEmitTypex۰TГ) Type() reflect.Type {
 }
 
 func (c *callerSliceOfByteIterTypex۰TEmitTypex۰TГ) Call(args []any) []any {
-	c.fn(args[0].([]byte), args[1].(func(*typex.T) bool), args[2].(func(typex.T)))
+	c.fn(args[0].([]byte), args[1].(func(*beam.T) bool), args[2].(func(beam.T)))
 	return []any{}
 }
 
 func (c *callerSliceOfByteIterTypex۰TEmitTypex۰TГ) Call3x0(arg0, arg1, arg2 any) {
-	c.fn(arg0.([]byte), arg1.(func(*typex.T) bool), arg2.(func(typex.T)))
+	c.fn(arg0.([]byte), arg1.(func(*beam.T) bool), arg2.(func(beam.T)))
 }
 
 type callerSliceOfByteIterTypex۰XTypex۰YEmitTypex۰XTypex۰YГ struct {
-	fn func([]byte, func(*typex.X, *typex.Y) bool, func(typex.X, typex.Y))
+	fn func([]byte, func(*beam.X, *beam.Y) bool, func(beam.X, beam.Y))
 }
 
 func funcMakerSliceOfByteIterTypex۰XTypex۰YEmitTypex۰XTypex۰YГ(fn any) reflectx.Func {
-	f := fn.(func([]byte, func(*typex.X, *typex.Y) bool, func(typex.X, typex.Y)))
+	f := fn.(func([]byte, func(*beam.X, *beam.Y) bool, func(beam.X, beam.Y)))
 	return &callerSliceOfByteIterTypex۰XTypex۰YEmitTypex۰XTypex۰YГ{fn: f}
 }
 
@@ -228,20 +227,20 @@ func (c *callerSliceOfByteIterTypex۰XTypex۰YEmitTypex۰XTypex۰YГ) Type() ref
 }
 
 func (c *callerSliceOfByteIterTypex۰XTypex۰YEmitTypex۰XTypex۰YГ) Call(args []any) []any {
-	c.fn(args[0].([]byte), args[1].(func(*typex.X, *typex.Y) bool), args[2].(func(typex.X, typex.Y)))
+	c.fn(args[0].([]byte), args[1].(func(*beam.X, *beam.Y) bool), args[2].(func(beam.X, beam.Y)))
 	return []any{}
 }
 
 func (c *callerSliceOfByteIterTypex۰XTypex۰YEmitTypex۰XTypex۰YГ) Call3x0(arg0, arg1, arg2 any) {
-	c.fn(arg0.([]byte), arg1.(func(*typex.X, *typex.Y) bool), arg2.(func(typex.X, typex.Y)))
+	c.fn(arg0.([]byte), arg1.(func(*beam.X, *beam.Y) bool), arg2.(func(beam.X, beam.Y)))
 }
 
 type callerTypex۰TГ struct {
-	fn func(typex.T)
+	fn func(beam.T)
 }
 
 func funcMakerTypex۰TГ(fn any) reflectx.Func {
-	f := fn.(func(typex.T))
+	f := fn.(func(beam.T))
 	return &callerTypex۰TГ{fn: f}
 }
 
@@ -254,12 +253,12 @@ func (c *callerTypex۰TГ) Type() reflect.Type {
 }
 
 func (c *callerTypex۰TГ) Call(args []any) []any {
-	c.fn(args[0].(typex.T))
+	c.fn(args[0].(beam.T))
 	return []any{}
 }
 
 func (c *callerTypex۰TГ) Call1x0(arg0 any) {
-	c.fn(arg0.(typex.T))
+	c.fn(arg0.(beam.T))
 }
 
 type emitNative struct {
@@ -268,13 +267,15 @@ type emitNative struct {
 	est *sdf.WatermarkEstimator
 
 	ctx   context.Context
+	pn    typex.PaneInfo
 	ws    []typex.Window
 	et    typex.EventTime
 	value exec.FullValue
 }
 
-func (e *emitNative) Init(ctx context.Context, ws []typex.Window, et typex.EventTime) error {
+func (e *emitNative) Init(ctx context.Context, pn typex.PaneInfo, ws []typex.Window, et typex.EventTime) error {
 	e.ctx = ctx
+	e.pn = pn
 	e.ws = ws
 	e.et = et
 	return nil
@@ -294,8 +295,8 @@ func emitMakerTypex۰T(n exec.ElementProcessor) exec.ReusableEmitter {
 	return ret
 }
 
-func (e *emitNative) invokeTypex۰T(val typex.T) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: val}
+func (e *emitNative) invokeTypex۰T(val beam.T) {
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -310,8 +311,8 @@ func emitMakerTypex۰XTypex۰Y(n exec.ElementProcessor) exec.ReusableEmitter {
 	return ret
 }
 
-func (e *emitNative) invokeTypex۰XTypex۰Y(key typex.X, val typex.Y) {
-	e.value = exec.FullValue{Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
+func (e *emitNative) invokeTypex۰XTypex۰Y(key beam.X, val beam.Y) {
+	e.value = exec.FullValue{Pane: e.pn, Windows: e.ws, Timestamp: e.et, Elm: key, Elm2: val}
 	if e.est != nil {
 		(*e.est).(sdf.TimestampObservingEstimator).ObserveTimestamp(e.et.ToTime())
 	}
@@ -355,7 +356,7 @@ func iterMakerTypex۰T(s exec.ReStream) exec.ReusableInput {
 	return ret
 }
 
-func (v *iterNative) readTypex۰T(value *typex.T) bool {
+func (v *iterNative) readTypex۰T(value *beam.T) bool {
 	elm, err := v.cur.Read()
 	if err != nil {
 		if err == io.EOF {
@@ -363,7 +364,7 @@ func (v *iterNative) readTypex۰T(value *typex.T) bool {
 		}
 		panic(fmt.Sprintf("broken stream: %v", err))
 	}
-	*value = elm.Elm.(typex.T)
+	*value = elm.Elm.(beam.T)
 	return true
 }
 
@@ -373,7 +374,7 @@ func iterMakerTypex۰XTypex۰Y(s exec.ReStream) exec.ReusableInput {
 	return ret
 }
 
-func (v *iterNative) readTypex۰XTypex۰Y(key *typex.X, value *typex.Y) bool {
+func (v *iterNative) readTypex۰XTypex۰Y(key *beam.X, value *beam.Y) bool {
 	elm, err := v.cur.Read()
 	if err != nil {
 		if err == io.EOF {
@@ -381,8 +382,8 @@ func (v *iterNative) readTypex۰XTypex۰Y(key *typex.X, value *typex.Y) bool {
 		}
 		panic(fmt.Sprintf("broken stream: %v", err))
 	}
-	*key = elm.Elm.(typex.X)
-	*value = elm.Elm2.(typex.Y)
+	*key = elm.Elm.(beam.X)
+	*value = elm.Elm2.(beam.Y)
 	return true
 }
 
@@ -392,7 +393,7 @@ func iterMakerTypex۰Y(s exec.ReStream) exec.ReusableInput {
 	return ret
 }
 
-func (v *iterNative) readTypex۰Y(value *typex.Y) bool {
+func (v *iterNative) readTypex۰Y(value *beam.Y) bool {
 	elm, err := v.cur.Read()
 	if err != nil {
 		if err == io.EOF {
@@ -400,7 +401,7 @@ func (v *iterNative) readTypex۰Y(value *typex.Y) bool {
 		}
 		panic(fmt.Sprintf("broken stream: %v", err))
 	}
-	*value = elm.Elm.(typex.Y)
+	*value = elm.Elm.(beam.Y)
 	return true
 }
 
diff --git a/sdks/go/test/build.gradle b/sdks/go/test/build.gradle
index 74b6a10cad4d..5576c40c0aab 100644
--- a/sdks/go/test/build.gradle
+++ b/sdks/go/test/build.gradle
@@ -41,7 +41,7 @@ task dataflowValidatesRunner() {
     ]
     exec {
       if (fork_java_home) {
-        environment "JAVA_HOME", fork_java_home
+        environment "JAVA_HOME_JOB_SERVER", fork_java_home
       }
       executable "sh"
       args "-c", "./run_validatesrunner_tests.sh ${options.join(' ')}"
@@ -66,7 +66,7 @@ task dataflowValidatesRunnerARM64() {
     ]
     exec {
       if (fork_java_home) {
-        environment "JAVA_HOME", fork_java_home
+        environment "JAVA_HOME_JOB_SERVER", fork_java_home
       }
       executable "sh"
       args "-c", "./run_validatesrunner_tests.sh ${options.join(' ')}"
@@ -95,7 +95,7 @@ task flinkValidatesRunner {
     ]
     exec {
       if (fork_java_home) {
-        environment "JAVA_HOME", fork_java_home
+        environment "JAVA_HOME_JOB_SERVER", fork_java_home
       }
       executable "sh"
       args "-c", "./run_validatesrunner_tests.sh ${options.join(' ')}"
@@ -123,7 +123,7 @@ task samzaValidatesRunner {
     ]
     exec {
       if (fork_java_home) {
-        environment "JAVA_HOME", fork_java_home
+        environment "JAVA_HOME_JOB_SERVER", fork_java_home
       }
       executable "sh"
       args "-c", "./run_validatesrunner_tests.sh ${options.join(' ')}"
@@ -151,7 +151,7 @@ task sparkValidatesRunner {
     ]
     exec {
       if (fork_java_home) {
-        environment "JAVA_HOME", fork_java_home
+        environment "JAVA_HOME_JOB_SERVER", fork_java_home
       }
       executable "sh"
       args "-c", "./run_validatesrunner_tests.sh ${options.join(' ')}"
@@ -190,7 +190,7 @@ tasks.register("ulrValidatesRunner") {
     }
     exec {
       if (fork_java_home) {
-        environment "JAVA_HOME", fork_java_home
+        environment "JAVA_HOME_JOB_SERVER", fork_java_home
       }
       executable "sh"
       args "-c", ". ${envdir}/bin/activate && ./run_validatesrunner_tests.sh ${options.join(' ')}"
@@ -217,7 +217,7 @@ task prismValidatesRunner {
     ]
     exec {
       if (fork_java_home) {
-        environment "JAVA_HOME", fork_java_home
+        environment "JAVA_HOME_JOB_SERVER", fork_java_home
       }
       executable "sh"
       args "-c", "./run_validatesrunner_tests.sh ${options.join(' ')}"
@@ -262,7 +262,7 @@ ext.goIoValidatesRunnerTask = { proj, name, scriptOpts, pipelineOpts ->
       logger.info("Running the command: sh -c ./run_validatesrunner_tests.sh ${options.join(' ')}")
       exec {
         if (fork_java_home) {
-          environment "JAVA_HOME", fork_java_home
+          environment "JAVA_HOME_JOB_SERVER", fork_java_home
         }
         executable "sh"
         args "-c", "./run_validatesrunner_tests.sh ${options.join(' ')}"
diff --git a/sdks/go/test/integration/expansions.go b/sdks/go/test/integration/expansions.go
index 7e8c1164f506..633f88d02930 100644
--- a/sdks/go/test/integration/expansions.go
+++ b/sdks/go/test/integration/expansions.go
@@ -17,6 +17,7 @@ package integration
 
 import (
 	"fmt"
+	"net"
 	"strconv"
 	"time"
 
@@ -57,6 +58,7 @@ type ExpansionServices struct {
 	// Callback for running jars, stored this way for testing purposes.
 	run      func(time.Duration, string, ...string) (jars.Process, error)
 	waitTime time.Duration // Time to sleep after running jar. Tests can adjust this.
+	testMode bool          // Skip connectivity checks when in test mode
 }
 
 // NewExpansionServices creates and initializes an ExpansionServices instance.
@@ -67,6 +69,7 @@ func NewExpansionServices() *ExpansionServices {
 		procs:    make([]jars.Process, 0),
 		run:      jars.Run,
 		waitTime: 3 * time.Second,
+		testMode: false,
 	}
 }
 
@@ -100,9 +103,33 @@ func (es *ExpansionServices) GetAddr(label string) (string, error) {
 	if err != nil {
 		return "", fmt.Errorf("cannot run jar for expansion service labeled \"%s\": %w", label, err)
 	}
-	time.Sleep(es.waitTime) // Wait a bit for the jar to start.
-	es.procs = append(es.procs, proc)
+	
 	addr := "localhost:" + portStr
+	
+	// Use different wait strategies for test mode vs production
+	if es.testMode {
+		// In test mode, use simple wait time for compatibility with mock processes
+		time.Sleep(es.waitTime)
+	} else {
+		// In production, wait for the jar to start with improved retry logic
+		maxRetries := 30
+		retryDelay := time.Second
+		
+		for i := 0; i < maxRetries; i++ {
+			time.Sleep(retryDelay)
+			// Try to connect to the expansion service to verify it's ready
+			conn, err := net.DialTimeout("tcp", addr, 2*time.Second)
+			if err == nil {
+				conn.Close()
+				break
+			}
+			if i == maxRetries-1 {
+				return "", fmt.Errorf("expansion service labeled \"%s\" failed to start after %d retries: %w", label, maxRetries, err)
+			}
+		}
+	}
+	
+	es.procs = append(es.procs, proc)
 	es.addrs[label] = addr
 	return addr, nil
 }
diff --git a/sdks/go/test/integration/expansions_test.go b/sdks/go/test/integration/expansions_test.go
index 99878d0623fd..3afa2470157c 100644
--- a/sdks/go/test/integration/expansions_test.go
+++ b/sdks/go/test/integration/expansions_test.go
@@ -63,6 +63,7 @@ func TestExpansionServices_GetAddr_Addresses(t *testing.T) {
 		procs:    make([]jars.Process, 0),
 		run:      failRun,
 		waitTime: 0,
+		testMode: true,
 	}
 
 	// Ensure we get the same map we put in, and that addresses take priority over jars if
@@ -97,6 +98,7 @@ func TestExpansionServices_GetAddr_Jars(t *testing.T) {
 		procs:    make([]jars.Process, 0),
 		run:      succeedRun,
 		waitTime: 0,
+		testMode: true,
 	}
 
 	// Call GetAddr on each jar twice, checking that the addresses remain consistent.
@@ -151,6 +153,7 @@ func TestExpansionServices_Shutdown(t *testing.T) {
 		procs:    make([]jars.Process, 0),
 		run:      succeedRun,
 		waitTime: 0,
+		testMode: true,
 	}
 	// Call getAddr on each label to run jars.
 	for label := range addrsMap {
diff --git a/sdks/go/test/integration/integration.go b/sdks/go/test/integration/integration.go
index 88db6a5b6c3b..8d951fe8ce96 100644
--- a/sdks/go/test/integration/integration.go
+++ b/sdks/go/test/integration/integration.go
@@ -98,6 +98,7 @@ var directFilters = []string{
 	"TestValueStateClear",
 	"TestBagState",
 	"TestBagStateClear",
+	"TestBagStateBlindWrite",
 	"TestCombiningState",
 	"TestMapState",
 	"TestMapStateClear",
@@ -240,6 +241,7 @@ var samzaFilters = []string{
 
 	// Samza does not support state.
 	"TestTimers.*",
+	"TestBagStateBlindWrite",
 
 	// no support for BundleFinalizer
 	"TestParDoBundleFinalizer.*",
diff --git a/sdks/go/test/integration/primitives/state.go b/sdks/go/test/integration/primitives/state.go
index acf1bf8fa665..6b672acc27bd 100644
--- a/sdks/go/test/integration/primitives/state.go
+++ b/sdks/go/test/integration/primitives/state.go
@@ -34,6 +34,7 @@ func init() {
 	register.DoFn3x1[state.Provider, string, int, string](&valueStateClearFn{})
 	register.DoFn3x1[state.Provider, string, int, string](&bagStateFn{})
 	register.DoFn3x1[state.Provider, string, int, string](&bagStateClearFn{})
+	register.DoFn3x1[state.Provider, string, int, string](&bagStateBlindWriteFn{})
 	register.DoFn3x1[state.Provider, string, int, string](&combiningStateFn{})
 	register.DoFn3x1[state.Provider, string, int, string](&mapStateFn{})
 	register.DoFn3x1[state.Provider, string, int, string](&mapStateClearFn{})
@@ -211,6 +212,45 @@ func BagStateParDoClear(s beam.Scope) {
 	passert.Equals(s, counts, "apple: 0", "pear: 0", "apple: 1", "apple: 2", "pear: 1", "apple: 3", "apple: 0", "pear: 2", "pear: 3", "pear: 0", "apple: 1", "pear: 1")
 }
 
+type bagStateBlindWriteFn struct {
+	State1 state.Bag[int]
+}
+
+func (f *bagStateBlindWriteFn) ProcessElement(s state.Provider, w string, c int) string {
+	err := f.State1.Add(s, 1)
+	if err != nil {
+		panic(err)
+	}
+	i, ok, err := f.State1.Read(s)
+	if err != nil {
+		panic(err)
+	}
+	if !ok {
+		i = []int{}
+	}
+	sum := 0
+	for _, val := range i {
+		sum += val
+	}
+
+	// Bonus "non-blind" write
+	err = f.State1.Add(s, 1)
+	if err != nil {
+		panic(err)
+	}
+
+	return fmt.Sprintf("%s: %v", w, sum)
+}
+
+// BagStateBlindWriteParDo tests a DoFn that uses bag state, but performs a
+// blind write to the state before reading.
+func BagStateBlindWriteParDo(s beam.Scope) {
+	in := beam.Create(s, "apple", "pear", "peach", "apple", "apple", "pear")
+	keyed := beam.ParDo(s, pairWithOne, in)
+	counts := beam.ParDo(s, &bagStateBlindWriteFn{}, keyed)
+	passert.Equals(s, counts, "apple: 1", "pear: 1", "peach: 1", "apple: 3", "apple: 5", "pear: 3")
+}
+
 type combiningStateFn struct {
 	State0 state.Combining[int, int, int]
 	State1 state.Combining[int, int, int]
diff --git a/sdks/go/test/integration/primitives/state_test.go b/sdks/go/test/integration/primitives/state_test.go
index 79cb8c1839fc..1d1d4860e8f9 100644
--- a/sdks/go/test/integration/primitives/state_test.go
+++ b/sdks/go/test/integration/primitives/state_test.go
@@ -47,6 +47,11 @@ func TestBagStateClear(t *testing.T) {
 	ptest.BuildAndRun(t, BagStateParDoClear)
 }
 
+func TestBagStateBlindWrite(t *testing.T) {
+	integration.CheckFilters(t)
+	ptest.BuildAndRun(t, BagStateBlindWriteParDo)
+}
+
 func TestCombiningState(t *testing.T) {
 	integration.CheckFilters(t)
 	ptest.BuildAndRun(t, CombiningStateParDo)
diff --git a/sdks/go/test/run_validatesrunner_tests.sh b/sdks/go/test/run_validatesrunner_tests.sh
index 4278c817d0a4..be7a795f01a5 100755
--- a/sdks/go/test/run_validatesrunner_tests.sh
+++ b/sdks/go/test/run_validatesrunner_tests.sh
@@ -259,8 +259,8 @@ s.close()
 
 TMPDIR=$(mktemp -d)
 
-if [[ -n "$JAVA_HOME" ]]; then
-  JAVA_CMD="$JAVA_HOME/bin/java"
+if [[ -n "$JAVA_HOME_JOB_SERVER" ]]; then
+  JAVA_CMD="$JAVA_HOME_JOB_SERVER/bin/java"
 else
   JAVA_CMD="java"
 fi
@@ -351,6 +351,33 @@ fi
 if [[ "$RUNNER" == "dataflow" ]]; then
   # Verify docker and gcloud commands exist
   command -v docker
+  # Check if Docker daemon is running
+  if ! docker info >/dev/null 2>&1; then
+    echo "Warning: Docker daemon is not running. Starting Docker..."
+    # Try to start Docker daemon (this may require sudo on some systems)
+    if command -v systemctl >/dev/null 2>&1; then
+      sudo systemctl start docker || echo "Failed to start Docker daemon via systemctl"
+    elif command -v service >/dev/null 2>&1; then
+      sudo service docker start || echo "Failed to start Docker daemon via service"
+    else
+      echo "Please start Docker daemon manually"
+      exit 1
+    fi
+    # Wait for Docker daemon to be ready
+    for i in {1..30}; do
+      if docker info >/dev/null 2>&1; then
+        echo "Docker daemon is now running"
+        break
+      fi
+      echo "Waiting for Docker daemon to start... ($i/30)"
+      sleep 2
+    done
+    # Final check
+    if ! docker info >/dev/null 2>&1; then
+      echo "Error: Docker daemon failed to start. Please start it manually."
+      exit 1
+    fi
+  fi
   docker -v
   command -v gcloud
   gcloud --version
@@ -374,18 +401,7 @@ if [[ "$RUNNER" == "dataflow" ]]; then
   CONTAINER=us.gcr.io/$PROJECT/$USER/beam_go_sdk
   echo "Using container $CONTAINER"
 
-  # TODO(https://github.com/apache/beam/issues/27674): remove this branch once the jenkins VM can build multiarch, or jenkins is deprecated.
-  if [[ "$USER" == "jenkins" ]]; then
-    ./gradlew :sdks:go:container:docker -Pdocker-repository-root=us.gcr.io/$PROJECT/$USER -Pdocker-tag=$TAG
-
-    # Verify it exists
-    docker images | grep $TAG
-
-    # Push the container
-    gcloud docker -- push $CONTAINER:$TAG
-  else 
-    ./gradlew :sdks:go:container:docker -Pdocker-repository-root=us.gcr.io/$PROJECT/$USER -Pdocker-tag=$TAG -Pcontainer-architecture-list=arm64,amd64 -Ppush-containers
-  fi
+  ./gradlew :sdks:go:container:docker -Pdocker-repository-root=us.gcr.io/$PROJECT/$USER -Pdocker-tag=$TAG -Pcontainer-architecture-list=arm64,amd64 -Ppush-containers
 
   if [[ -n "$TEST_EXPANSION_ADDR" || -n "$IO_EXPANSION_ADDR" || -n "$SCHEMAIO_EXPANSION_ADDR" || -n "$DEBEZIUMIO_EXPANSION_ADDR" ]]; then
     ARGS="$ARGS --experiments=use_portable_job_submission"
@@ -395,7 +411,7 @@ if [[ "$RUNNER" == "dataflow" ]]; then
       JAVA_TAG=$(date +%Y%m%d-%H%M%S)
       JAVA_CONTAINER=us.gcr.io/$PROJECT/$USER/beam_java11_sdk
       echo "Using container $JAVA_CONTAINER for cross-language java transforms"
-      ./gradlew :sdks:java:container:java11:docker -Pdocker-repository-root=us.gcr.io/$PROJECT/$USER -Pdocker-tag=$JAVA_TAG -Pjava11Home=$JAVA11_HOME
+      ./gradlew :sdks:java:container:java11:docker -Pdocker-repository-root=us.gcr.io/$PROJECT/$USER -Pdocker-tag=$JAVA_TAG
 
       # Verify it exists
       docker images | grep $JAVA_TAG
@@ -457,9 +473,6 @@ if [[ "$RUNNER" == "dataflow" ]]; then
   # Note: we don't delete the multi-arch containers here because this command only deletes the manifest list with the tag,
   # the associated container images can't be deleted because they are not tagged. However, multi-arch containers that are
   # older than 6 weeks old are deleted by stale_dataflow_prebuilt_image_cleaner.sh that runs daily.
-  if [[ "$USER" == "jenkins" ]]; then
-    gcloud --quiet container images delete $CONTAINER:$TAG || echo "Failed to delete container"
-  fi
   if [[ -n "$TEST_EXPANSION_ADDR" || -n "$IO_EXPANSION_ADDR" || -n "$SCHEMAIO_EXPANSION_ADDR" || -n "$DEBEZIUMIO_EXPANSION_ADDR" ]]; then
     # Delete the java cross-language container locally and remotely
     docker rmi $JAVA_CONTAINER:$JAVA_TAG || echo "Failed to remove container"
diff --git a/sdks/java/build-tools/src/main/resources/beam/checkstyle/suppressions.xml b/sdks/java/build-tools/src/main/resources/beam/checkstyle/suppressions.xml
index af384ff19c09..e8d4e8888da1 100644
--- a/sdks/java/build-tools/src/main/resources/beam/checkstyle/suppressions.xml
+++ b/sdks/java/build-tools/src/main/resources/beam/checkstyle/suppressions.xml
@@ -43,9 +43,6 @@
   <suppress id="ForbidNonVendoredGuava" files=".*bigtable.*BigtableServiceImplTest\.java" />
   <suppress id="ForbidNonVendoredGuava" files=".*sql.*BeamValuesRel\.java" />
   <suppress id="ForbidNonVendoredGuava" files=".*sql.*BeamEnumerableConverterTest\.java" />
-  <suppress id="ForbidNonVendoredGuava" files=".*zetasql.*TableScanConverter\.java" />
-  <suppress id="ForbidNonVendoredGuava" files=".*zetasql.*ExpressionConverter\.java" />
-  <suppress id="ForbidNonVendoredGuava" files=".*zetasql.*BeamZetaSqlCatalog\.java" />
   <suppress id="ForbidNonVendoredGuava" files=".*pubsublite.*BufferingPullSubscriberTest\.java" />
   <suppress id="ForbidNonVendoredGuava" files=".*cdap.*Plugin\.java" />
   <suppress id="ForbidNonVendoredGuava" files=".*cdap.*PluginConfigInstantiationUtils\.java" />
@@ -69,13 +66,6 @@
   <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*extensions.*sql.*pubsublite.RowHandler.*" />
   <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*extensions.*sql.*ProtoPayloadSerializerProvider.*" />
   <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*datacatalog.*DataCatalogTableProvider\.java" />
-  <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*zetasql.*DateTimeUtils\.java" />
-  <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*zetasql.*ZetaSqlBeamTranslationUtils\.java" />
-  <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*zetasql.*ZetaSqlDialectSpecTest\.java" />
-  <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*zetasql.*ZetaSqlTimeFunctionsTest\.java" />
-  <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*zetasql.*ZetaSqlBeamTranslationUtilsTest\.java" />
-  <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*zetasql.*TableResolutionTest\.java" />
-  <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*zetasql.*ZetaSQLPushDownTest\.java" />
   <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*examples.*datatokenization.*BigTableIO\.java" />
   <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*aws2.*kinesis.*RecordsAggregator\.java" />
   <suppress id="ForbidNonVendoredGrpcProtobuf" files=".*testinfra.*pipelines.*" />
diff --git a/sdks/java/container/Dockerfile b/sdks/java/container/Dockerfile
index 9c266ea132b8..c43eb0cb8c02 100644
--- a/sdks/java/container/Dockerfile
+++ b/sdks/java/container/Dockerfile
@@ -34,7 +34,7 @@ ADD target/beam-sdks-java-harness.jar /opt/apache/beam/jars/
 # Required to use jamm as a javaagent to get accurate object size measuring
 # COPY fails if file is not found, so use a wildcard for open-module-agent.jar
 # since it is only included in Java 9+ containers
-COPY target/jamm.jar target/open-module-agent*.jar /opt/apache/beam/jars/
+COPY target/jamm.jar target/open-module-agent.jar /opt/apache/beam/jars/
 
 COPY target/${TARGETOS}_${TARGETARCH}/boot /opt/apache/beam/
 
diff --git a/sdks/java/container/boot.go b/sdks/java/container/boot.go
index 1f574d251cb3..20283740ca0f 100644
--- a/sdks/java/container/boot.go
+++ b/sdks/java/container/boot.go
@@ -227,9 +227,9 @@ func main() {
 	if pipelineOptions, ok := info.GetPipelineOptions().GetFields()["options"]; ok {
 		if heapDumpOption, ok := pipelineOptions.GetStructValue().GetFields()["enableHeapDumps"]; ok {
 			if heapDumpOption.GetBoolValue() {
-			  args = append(args, "-XX:+HeapDumpOnOutOfMemoryError",
-			                "-Dbeam.fn.heap_dump_dir="+filepath.Join(dir, "heapdumps"),
-			                "-XX:HeapDumpPath="+filepath.Join(dir, "heapdumps", "heap_dump.hprof"))
+				args = append(args, "-XX:+HeapDumpOnOutOfMemoryError",
+					"-Dbeam.fn.heap_dump_dir="+filepath.Join(dir, "heapdumps"),
+					"-XX:HeapDumpPath="+filepath.Join(dir, "heapdumps", "heap_dump.hprof"))
 			}
 		}
 	}
@@ -237,9 +237,10 @@ func main() {
 	// Apply meta options
 	const metaDir = "/opt/apache/beam/options"
 
-	// Note: Error is unchecked, so parsing errors won't abort container.
-	// TODO: verify if it's intentional or not.
-	metaOptions, _ := LoadMetaOptions(ctx, logger, metaDir)
+	metaOptions, err := LoadMetaOptions(ctx, logger, metaDir)
+	if err != nil {
+		logger.Errorf(ctx, "LoadMetaOptions failed: %v", err)
+	}
 
 	javaOptions := BuildOptions(ctx, logger, metaOptions)
 	// (1) Add custom jvm arguments: "-server -Xmx1324 -XXfoo .."
diff --git a/sdks/java/container/common.gradle b/sdks/java/container/common.gradle
index acb6b79b3462..c81a33827bef 100644
--- a/sdks/java/container/common.gradle
+++ b/sdks/java/container/common.gradle
@@ -52,9 +52,7 @@ task copyDockerfileDependencies(type: Copy) {
     rename 'jcl-over-slf4j.*', 'jcl-over-slf4j.jar'
     rename 'log4j-over-slf4j.*', 'log4j-over-slf4j.jar'
     rename 'log4j-to-slf4j.*', 'log4j-to-slf4j.jar'
-    if (imageJavaVersion == "11" || imageJavaVersion == "17") {
-        rename 'beam-sdks-java-container-agent.*.jar', 'open-module-agent.jar'
-    }
+    rename 'beam-sdks-java-container-agent.*.jar', 'open-module-agent.jar'
     rename 'beam-sdks-java-harness-.*.jar', 'beam-sdks-java-harness.jar'
     rename 'jamm.*.jar', 'jamm.jar'
 
@@ -84,9 +82,7 @@ task copyGolangLicenses(type: Copy) {
 }
 
 task copyJdkOptions(type: Copy) {
-    if (["11", "17", "21"].contains(imageJavaVersion)) {
-        from "option-jamm.json"
-    }
+    from "option-jamm.json"
     from "java${imageJavaVersion}-security.properties"
     from "option-java${imageJavaVersion}-security.json"
     into "build/target/options"
@@ -97,33 +93,6 @@ task skipPullLicenses(type: Exec) {
     args "-c", "mkdir -p build/target/go-licenses build/target/options build/target/third_party_licenses && touch build/target/go-licenses/skip && touch build/target/third_party_licenses/skip"
 }
 
-// Java11+ container depends on the java agent project. To compile it, need a compatible JDK version:
-// lower bound 11 and upper bound imageJavaVersion
-task validateJavaHome {
-    def requiredForVer = ["11", "17", "21"]
-    if (requiredForVer.contains(imageJavaVersion)) {
-        doFirst {
-            if (JavaVersion.VERSION_1_8.compareTo(JavaVersion.current()) < 0) {
-                return
-            }
-            boolean propertyFound = false
-            // enable to build agent with compatible java versions (11-requiredForVer)
-            for (def checkVer : requiredForVer) {
-                if (project.hasProperty("java${checkVer}Home")) {
-                    propertyFound = true
-                }
-                if (checkVer == imageJavaVersion) {
-                    // cannot build agent with a higher version than the docker java ver
-                    break
-                }
-            }
-            if (!propertyFound) {
-                throw new GradleException("System Java needs to have version 11+ or java${imageJavaVersion}Home required for imageJavaVersion=${imageJavaVersion}. Re-run with -Pjava${imageJavaVersion}Home")
-            }
-        }
-    }
-}
-
 def pushContainers = project.rootProject.hasProperty(["isRelease"]) || project.rootProject.hasProperty("push-containers")
 
 docker {
@@ -162,4 +131,3 @@ if (project.rootProject.hasProperty("docker-pull-licenses") ||
 dockerPrepare.dependsOn copySdkHarnessLauncher
 dockerPrepare.dependsOn copyDockerfileDependencies
 dockerPrepare.dependsOn copyJdkOptions
-dockerPrepare.dependsOn validateJavaHome
diff --git a/sdks/java/container/license_scripts/dep_urls_java.yaml b/sdks/java/container/license_scripts/dep_urls_java.yaml
index 4f9f50725def..93f5f6fa211f 100644
--- a/sdks/java/container/license_scripts/dep_urls_java.yaml
+++ b/sdks/java/container/license_scripts/dep_urls_java.yaml
@@ -46,7 +46,7 @@ jaxen:
   '1.1.6':
     type: "3-Clause BSD"
 libraries-bom:
-  '26.62.0':
+  '26.65.0':
     license: "https://raw.githubusercontent.com/GoogleCloudPlatform/cloud-opensource-java/master/LICENSE"
     type: "Apache License 2.0"
 paranamer:
diff --git a/sdks/java/core/build.gradle b/sdks/java/core/build.gradle
index a1f2916f1958..e849ae597791 100644
--- a/sdks/java/core/build.gradle
+++ b/sdks/java/core/build.gradle
@@ -130,3 +130,11 @@ project.tasks.compileTestJava {
   // TODO: fix other places with warnings in tests and delete this option
   options.compilerArgs += ['-Xlint:-rawtypes']
 }
+
+// Configure test task to use JUnit 4. JUnit 5 support is provided in module
+// sdks/java/testing/junit, which configures useJUnitPlatform(). Submodules that
+// need to run both JUnit 4 and 5 via the JUnit Platform must also add the
+// Vintage engine explicitly.
+test {
+  useJUnit()
+}
diff --git a/sdks/java/core/jmh/src/main/java/org/apache/beam/sdk/jmh/schemas/RowBundles.java b/sdks/java/core/jmh/src/main/java/org/apache/beam/sdk/jmh/schemas/RowBundles.java
index a1a8ca7f3af2..572bc3985d2b 100644
--- a/sdks/java/core/jmh/src/main/java/org/apache/beam/sdk/jmh/schemas/RowBundles.java
+++ b/sdks/java/core/jmh/src/main/java/org/apache/beam/sdk/jmh/schemas/RowBundles.java
@@ -28,6 +28,7 @@
 import org.openjdk.jmh.annotations.State;
 import org.openjdk.jmh.infra.Blackhole;
 
+@SuppressWarnings("SameNameButDifferent")
 public interface RowBundles {
   @State(Scope.Benchmark)
   class IntBundle extends RowBundle<IntBundle.Field> {
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/BeamFnDataGrpcMultiplexer.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/BeamFnDataGrpcMultiplexer.java
index a6044b931e68..8fec8b455cce 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/BeamFnDataGrpcMultiplexer.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/BeamFnDataGrpcMultiplexer.java
@@ -208,6 +208,7 @@ public void close() throws Exception {
    * it is ready to consume that data.
    */
   private final class InboundObserver implements StreamObserver<BeamFnApi.Elements> {
+    @SuppressWarnings("LabelledBreakTarget")
     @Override
     public void onNext(BeamFnApi.Elements value) {
       // Have a fast path to handle the common case and provide a short circuit to exit if we detect
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/BeamFnDataInboundObserver.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/BeamFnDataInboundObserver.java
index ee4a36f0171a..54fe42adefee 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/BeamFnDataInboundObserver.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/BeamFnDataInboundObserver.java
@@ -68,6 +68,7 @@ private static class EndpointStatus<T> {
       transformIdToTimerFamilyIdToTimerEndpoint;
   private final CancellableQueue<BeamFnApi.Elements> queue;
   // We use a custom exception for closing to avoid the expense of stack trace generation.
+  @SuppressWarnings("StaticAssignmentOfThrowable")
   protected static class CloseException extends Exception {
     private CloseException() {
       super(
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/WeightedList.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/WeightedList.java
index 4579a6903a24..ad5e131cb2d7 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/WeightedList.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/data/WeightedList.java
@@ -19,9 +19,10 @@
 
 import java.util.List;
 import java.util.concurrent.atomic.AtomicLong;
+import org.apache.beam.sdk.util.Weighted;
 
 /** Facade for a {@link List<T>} that keeps track of weight, for cache limit reasons. */
-public class WeightedList<T> {
+public class WeightedList<T> implements Weighted {
 
   /** Original list that is being wrapped. */
   private final List<T> backing;
@@ -29,6 +30,10 @@ public class WeightedList<T> {
   /** Weight of all the elements being tracked. */
   private final AtomicLong weight;
 
+  public static <T> WeightedList<T> of(List<T> backing, long weight) {
+    return new WeightedList<>(backing, weight);
+  }
+
   public WeightedList(List<T> backing, long weight) {
     this.backing = backing;
     this.weight = new AtomicLong(weight);
@@ -46,6 +51,7 @@ public boolean isEmpty() {
     return this.backing.isEmpty();
   }
 
+  @Override
   public long getWeight() {
     return weight.longValue();
   }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/server/GrpcContextHeaderAccessorProvider.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/server/GrpcContextHeaderAccessorProvider.java
index a0ac89313b12..6288ceba4cd1 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/server/GrpcContextHeaderAccessorProvider.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/server/GrpcContextHeaderAccessorProvider.java
@@ -66,7 +66,7 @@ public static HeaderAccessor getHeaderAccessor() {
   private static class GrpcHeaderAccessor implements HeaderAccessor {
 
     @Override
-    /** This method should be called from the request method. */
+    // This method should be called from the request method.
     public String getSdkWorkerId() {
       return Preconditions.checkNotNull(
           SDK_WORKER_CONTEXT_KEY.get(), "No worker_id header provided in client headers.");
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/splittabledofn/RestrictionTrackers.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/splittabledofn/RestrictionTrackers.java
index 6a80ce5ae3f5..8879392d42a6 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/splittabledofn/RestrictionTrackers.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/splittabledofn/RestrictionTrackers.java
@@ -119,4 +119,25 @@ public static <RestrictionT, PositionT> RestrictionTracker<RestrictionT, Positio
       return new RestrictionTrackerObserver<>(restrictionTracker, claimObserver);
     }
   }
+
+  public static <RestrictionT, PositionT> RestrictionTracker<RestrictionT, PositionT> synchronize(
+      RestrictionTracker<RestrictionT, PositionT> restrictionTracker) {
+    if (restrictionTracker instanceof RestrictionTracker.HasProgress) {
+      return new RestrictionTrackerObserverWithProgress<>(
+          restrictionTracker, (ClaimObserver<PositionT>) NOOP_CLAIM_OBSERVER);
+    } else {
+      return new RestrictionTrackerObserver<>(
+          restrictionTracker, (ClaimObserver<PositionT>) NOOP_CLAIM_OBSERVER);
+    }
+  }
+
+  static class NoopClaimObserver<PositionT> implements ClaimObserver<PositionT> {
+    @Override
+    public void onClaimed(PositionT position) {}
+
+    @Override
+    public void onClaimFailed(PositionT position) {}
+  }
+
+  private static final NoopClaimObserver<Object> NOOP_CLAIM_OBSERVER = new NoopClaimObserver<>();
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/stream/DataStreams.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/stream/DataStreams.java
index 85809462a03e..15401a49bd98 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/stream/DataStreams.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/fn/stream/DataStreams.java
@@ -214,7 +214,7 @@ public WeightedList<T> decodeFromChunkBoundaryToChunkBoundary() {
         long elementOverhead = rvals.size() * BYTES_LIST_ELEMENT_OVERHEAD;
         long totalWeight = byteString.size() + elementOverhead;
 
-        return new WeightedList<>(rvals, totalWeight);
+        return WeightedList.of(rvals, totalWeight);
       } catch (IOException e) {
         throw new IllegalStateException(e);
       }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/io/FileIO.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/io/FileIO.java
index d5c235b696ca..cfa06f3cf0d5 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/io/FileIO.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/io/FileIO.java
@@ -373,6 +373,7 @@ public static Match match() {
   public static MatchAll matchAll() {
     return new AutoValue_FileIO_MatchAll.Builder()
         .setConfiguration(MatchConfiguration.create(EmptyMatchTreatment.ALLOW_IF_WILDCARD))
+        .setOutputParallelization(true)
         .build();
   }
 
@@ -677,12 +678,18 @@ abstract static class Builder {
       abstract Builder setConfiguration(MatchConfiguration configuration);
 
       abstract MatchAll build();
+
+      abstract Builder setOutputParallelization(boolean b);
     }
 
     /** Like {@link Match#withConfiguration}. */
     public MatchAll withConfiguration(MatchConfiguration configuration) {
       return toBuilder().setConfiguration(configuration).build();
     }
+    /** Like {@link Match#withOutputParallelization}. */
+    public MatchAll withOutputParallelization(boolean outputParallelization) {
+      return toBuilder().setOutputParallelization(outputParallelization).build();
+    }
 
     /** Like {@link Match#withEmptyMatchTreatment}. */
     public MatchAll withEmptyMatchTreatment(EmptyMatchTreatment treatment) {
@@ -723,8 +730,15 @@ public PCollection<MatchResult.Metadata> expand(PCollection<String> input) {
           res = input.apply(createWatchTransform(new ExtractFilenameFn())).apply(Values.create());
         }
       }
-      return res.apply(Reshuffle.viaRandomKey());
+      // Apply Reshuffle conditionally based on the flag
+      if (getOutputParallelization()) {
+        return res.apply(Reshuffle.viaRandomKey());
+      } else {
+        return res;
+      }
     }
+    /** Returns whether to avoid the reshuffle operation. */
+    public abstract boolean getOutputParallelization();
 
     @Override
     public void populateDisplayData(DisplayData.Builder builder) {
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/io/Read.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/io/Read.java
index dbdbf6b2f72a..8b0e4ee433fa 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/io/Read.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/io/Read.java
@@ -401,6 +401,7 @@ private boolean tryClaimOrThrow(TimestampedValue<T>[] position) throws IOExcepti
         return true;
       }
 
+      @SuppressWarnings("Finalize")
       @Override
       protected void finalize() throws Throwable {
         if (currentReader != null) {
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/io/TFRecordReadSchemaTransformConfiguration.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/io/TFRecordReadSchemaTransformConfiguration.java
index 6562d6752728..f871a3790ed6 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/io/TFRecordReadSchemaTransformConfiguration.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/io/TFRecordReadSchemaTransformConfiguration.java
@@ -63,7 +63,8 @@ public void validate() {
     if (errorHandling != null) {
       checkArgument(
           !Strings.isNullOrEmpty(errorHandling.getOutput()),
-          invalidConfigMessage + "Output must not be empty if error handling specified.");
+          "%sOutput must not be empty if error handling specified.",
+          invalidConfigMessage);
     }
   }
 
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/io/WriteFiles.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/io/WriteFiles.java
index c1b56a2b4458..b0b5051f3210 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/io/WriteFiles.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/io/WriteFiles.java
@@ -689,7 +689,7 @@ private class WriteUnshardedTempFilesFn extends DoFn<UserT, FileResult<Destinati
     }
 
     @StartBundle
-    public void startBundle(StartBundleContext c) {
+    public void startBundle(StartBundleContext unused) {
       // Reset state in case of reuse. We need to make sure that each bundle gets unique writers.
       writers = Maps.newHashMap();
     }
@@ -1250,9 +1250,8 @@ public void processElement(
       // before we return from this processElement call. This allows us to perform the writes/closes
       // in parallel with the prior elements close calls and bounds the amount of data buffered to
       // limit the number of OOMs.
-      CompletionStage<List<Void>> pastCloseFutures = MoreFutures.allAsList(closeFutures);
+      CompletionStage<Void> pastCloseFutures = MoreFutures.allOf(closeFutures);
       closeFutures.clear();
-
       // Close all writers in the background
       for (Map.Entry<DestinationT, Writer<DestinationT, OutputT>> entry : writers.entrySet()) {
         int shard = c.element().getKey().getShardNumber();
@@ -1267,7 +1266,6 @@ public void processElement(
                 new FileResult<>(writer.getOutputFile(), shard, window, c.pane(), entry.getKey())));
         closeWriterInBackground(writer);
       }
-
       // Block on completing the past closes before returning. We do so after starting the current
       // closes in the background so that they can happen in parallel.
       MoreFutures.get(pastCloseFutures);
@@ -1293,7 +1291,7 @@ private void closeWriterInBackground(Writer<DestinationT, OutputT> writer) {
     @FinishBundle
     public void finishBundle(FinishBundleContext c) throws Exception {
       try {
-        MoreFutures.get(MoreFutures.allAsList(closeFutures));
+        MoreFutures.get(MoreFutures.allOf(closeFutures));
         // If all writers were closed without exception, output the results to the next stage.
         for (KV<Instant, FileResult<DestinationT>> result : deferredOutput) {
           c.output(result.getValue(), result.getKey(), result.getValue().getWindow());
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/options/PipelineOptionsFactory.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/options/PipelineOptionsFactory.java
index 0b0ef88cf655..9dbfa397a037 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/options/PipelineOptionsFactory.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/options/PipelineOptionsFactory.java
@@ -1685,8 +1685,7 @@ private static ListMultimap<String, String> parseCommandLine(
         if (strictParsing) {
           throw e;
         } else {
-          LOG.warn(
-              "Strict parsing is disabled, ignoring option '{}' because {}", arg, e.getMessage());
+          LOG.warn("Strict parsing is disabled, ignoring option '{}'", arg, e);
         }
       }
     }
@@ -1954,10 +1953,10 @@ private static <T extends PipelineOptions> Map<String, Object> parseObjects(
           throw e;
         } else {
           LOG.warn(
-              "Strict parsing is disabled, ignoring option '{}' with value '{}' because {}",
+              "Strict parsing is disabled, ignoring option '{}' with value '{}'",
               entry.getKey(),
               entry.getValue(),
-              e.getMessage());
+              e);
         }
       }
     }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/options/SdkHarnessOptions.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/options/SdkHarnessOptions.java
index f56196996bcc..ad5b1451075c 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/options/SdkHarnessOptions.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/options/SdkHarnessOptions.java
@@ -427,4 +427,17 @@ public Duration create(PipelineOptions options) {
           : Duration.ofMinutes(1);
     }
   }
+
+  /**
+   * The time limit (in minute) that an SDK worker allows for a PTransform operation before
+   * signaling the runner harness to restart the SDK worker.
+   */
+  @Description(
+      "The time limit (in minutes) for any PTransform to finish processing a single element."
+          + " If exceeded, the SDK worker process self-terminates and processing may be restarted by a runner."
+          + " There is no time limit if the value is set to 0.")
+  @NonNegative
+  int getElementProcessingTimeoutMinutes();
+
+  void setElementProcessingTimeoutMinutes(int value);
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/CachingFactory.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/CachingFactory.java
index 6e244fefb263..d2d7a1c78d2c 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/CachingFactory.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/CachingFactory.java
@@ -22,6 +22,7 @@
 import org.apache.beam.sdk.values.TypeDescriptor;
 import org.checkerframework.checker.initialization.qual.NotOnlyInitialized;
 import org.checkerframework.checker.initialization.qual.UnknownInitialization;
+import org.checkerframework.checker.nullness.qual.MonotonicNonNull;
 import org.checkerframework.checker.nullness.qual.NonNull;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
@@ -36,7 +37,8 @@
  * inner factory, so the schema comparison only need happen on the first lookup.
  */
 public class CachingFactory<CreatedT extends @NonNull Object> implements Factory<CreatedT> {
-  private transient @Nullable ConcurrentHashMap<TypeDescriptor<?>, CreatedT> cache = null;
+  private transient volatile @MonotonicNonNull ConcurrentHashMap<TypeDescriptor<?>, CreatedT>
+      cache = null;
 
   private final @NotOnlyInitialized Factory<CreatedT> innerFactory;
 
@@ -45,10 +47,16 @@ public CachingFactory(@UnknownInitialization Factory<CreatedT> innerFactory) {
   }
 
   private ConcurrentHashMap<TypeDescriptor<?>, CreatedT> getCache() {
-    if (cache == null) {
-      cache = new ConcurrentHashMap<>();
+    ConcurrentHashMap<TypeDescriptor<?>, CreatedT> value = cache;
+    if (value == null) {
+      synchronized (this) {
+        value = cache;
+        if (value == null) {
+          cache = value = new ConcurrentHashMap<>();
+        }
+      }
     }
-    return cache;
+    return value;
   }
 
   @Override
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/package-info.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/FieldValueHaver.java
similarity index 69%
rename from sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/package-info.java
rename to sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/FieldValueHaver.java
index 0400e76ca028..d40f1a878f87 100644
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/package-info.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/FieldValueHaver.java
@@ -15,12 +15,19 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+package org.apache.beam.sdk.schemas;
+
+import java.io.Serializable;
+import org.apache.beam.sdk.annotations.Internal;
 
 /**
- * ZetaSQL Dialect package.
- *
- * <p>
+ * <b><i>For internal use only; no backwards-compatibility guarantees.</i></b>
  *
- * @deprecated Use Calcite SQL dialect. Beam ZetaSQL has been deprecated.
+ * <p>An interface to check a field presence.
  */
-package org.apache.beam.sdk.extensions.sql.zetasql;
+@Internal
+public interface FieldValueHaver<ObjectT> extends Serializable {
+  boolean has(ObjectT object);
+
+  String name();
+}
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/FromRowUsingCreator.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/FromRowUsingCreator.java
index b839a19a8177..69ae81bcd07f 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/FromRowUsingCreator.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/FromRowUsingCreator.java
@@ -53,7 +53,7 @@ class FromRowUsingCreator<T> implements SerializableFunction<Row, T>, Function<R
   private final Factory<SchemaUserTypeCreator> schemaTypeCreatorFactory;
 
   @SuppressFBWarnings("SE_TRANSIENT_FIELD_NOT_RESTORED")
-  private transient @MonotonicNonNull Function[] fieldConverters;
+  private transient volatile @MonotonicNonNull Function[] fieldConverters;
 
   public FromRowUsingCreator(
       TypeDescriptor<T> typeDescriptor, GetterBasedSchemaProvider schemaProvider) {
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/GetterBasedSchemaProvider.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/GetterBasedSchemaProvider.java
index 4e431bb45207..5645a7c435b3 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/GetterBasedSchemaProvider.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/GetterBasedSchemaProvider.java
@@ -17,6 +17,8 @@
  */
 package org.apache.beam.sdk.schemas;
 
+import static org.apache.beam.sdk.util.Preconditions.checkStateNotNull;
+
 import java.util.ArrayList;
 import java.util.Collection;
 import java.util.List;
@@ -405,12 +407,17 @@ Object convert(OneOfType.Value value) {
 
         @NonNull
         FieldValueGetter<@NonNull Object, Object> converter =
-            Verify.verifyNotNull(
+            checkStateNotNull(
                 converters.get(caseType.getValue()),
                 "Missing OneOf converter for case %s.",
                 caseType);
 
-        return oneOfType.createValue(caseType, converter.get(value.getValue()));
+        Object convertedValue =
+            checkStateNotNull(
+                converter.get(value.getValue()),
+                "Bug! converting a non-null value in a OneOf resulted in null result value");
+
+        return oneOfType.createValue(caseType, convertedValue);
       }
     }
 
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/Schema.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/Schema.java
index 02607d91b079..c2144f71eac9 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/Schema.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/Schema.java
@@ -325,7 +325,8 @@ public Schema(List<Field> fields, Options options) {
     for (Field field : this.fields) {
       Preconditions.checkArgument(
           fieldIndicesMutable.get(field.getName()) == null,
-          "Duplicate field " + field.getName() + " added to schema");
+          "Duplicate field %s added to schema",
+          field.getName());
       encodingPositions.put(field.getName(), index);
       fieldIndicesMutable.put(field.getName(), index++);
     }
@@ -491,21 +492,7 @@ private boolean equivalent(Schema other, EquivalenceNullablePolicy nullablePolic
 
   @Override
   public String toString() {
-    StringBuilder builder = new StringBuilder();
-    builder.append("Fields:");
-    builder.append(System.lineSeparator());
-    for (Field field : fields) {
-      builder.append(field);
-      builder.append(System.lineSeparator());
-    }
-    builder.append("Encoding positions:");
-    builder.append(System.lineSeparator());
-    builder.append(encodingPositions);
-    builder.append(System.lineSeparator());
-    builder.append("Options:");
-    builder.append(options);
-    builder.append("UUID: " + uuid);
-    return builder.toString();
+    return SchemaUtils.toPrettyString(this);
   }
 
   @Override
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaCoderHelpers.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaCoderHelpers.java
index b2e707e5607a..dfc0d82d2145 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaCoderHelpers.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaCoderHelpers.java
@@ -163,7 +163,7 @@ public static <T> Coder<T> coderForFieldType(FieldType fieldType) {
       default:
         coder = (Coder<T>) CODER_MAP.get(fieldType.getTypeName());
     }
-    Preconditions.checkNotNull(coder, "Unexpected field type " + fieldType.getTypeName());
+    Preconditions.checkNotNull(coder, "Unexpected field type %s", fieldType.getTypeName());
     if (fieldType.getNullable()) {
       coder = NullableCoder.of(coder);
     }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaTranslation.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaTranslation.java
index 205d57319f8f..8ad5bb5ff97f 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaTranslation.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaTranslation.java
@@ -38,6 +38,7 @@
 import org.apache.beam.model.pipeline.v1.SchemaApi.LogicalTypeValue;
 import org.apache.beam.model.pipeline.v1.SchemaApi.MapTypeEntry;
 import org.apache.beam.model.pipeline.v1.SchemaApi.MapTypeValue;
+import org.apache.beam.sdk.coders.RowCoder;
 import org.apache.beam.sdk.schemas.Schema.Field;
 import org.apache.beam.sdk.schemas.Schema.FieldType;
 import org.apache.beam.sdk.schemas.Schema.LogicalType;
@@ -326,6 +327,7 @@ public static Schema schemaFromProto(SchemaApi.Schema protoSchema) {
     if (!protoSchema.getId().isEmpty()) {
       schema.setUUID(UUID.fromString(protoSchema.getId()));
     }
+    overrideEncodingPositions(schema);
     return schema;
   }
 
@@ -504,6 +506,50 @@ private static FieldType fieldTypeFromProtoWithoutNullable(SchemaApi.FieldType p
     }
   }
 
+  private static void overrideEncodingPositions(Schema schema) {
+    @javax.annotation.Nullable UUID uuid = schema.getUUID();
+    if (schema.isEncodingPositionsOverridden() && uuid != null) {
+      RowCoder.overrideEncodingPositions(uuid, schema.getEncodingPositions());
+    }
+    schema.getFields().stream()
+        .map(Schema.Field::getType)
+        .forEach(SchemaTranslation::overrideEncodingPositions);
+  }
+
+  private static void overrideEncodingPositions(Schema.FieldType fieldType) {
+    switch (fieldType.getTypeName()) {
+      case ROW:
+        overrideEncodingPositions(
+            org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull(fieldType.getRowSchema()));
+        break;
+      case ARRAY:
+      case ITERABLE:
+        overrideEncodingPositions(
+            org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull(
+                fieldType.getCollectionElementType()));
+        break;
+      case MAP:
+        overrideEncodingPositions(
+            org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull(fieldType.getMapKeyType()));
+        overrideEncodingPositions(
+            org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull(
+                fieldType.getMapValueType()));
+        break;
+      case LOGICAL_TYPE:
+        Schema.LogicalType<Object, Object> logicalType =
+            (Schema.LogicalType<Object, Object>)
+                org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull(
+                    fieldType.getLogicalType());
+        @javax.annotation.Nullable Schema.FieldType argumentType = logicalType.getArgumentType();
+        if (argumentType != null) {
+          overrideEncodingPositions(argumentType);
+        }
+        overrideEncodingPositions(logicalType.getBaseType());
+        break;
+      default:
+    }
+  }
+
   public static SchemaApi.Row rowToProto(Row row) {
     SchemaApi.Row.Builder builder = SchemaApi.Row.newBuilder();
     for (int i = 0; i < row.getFieldCount(); ++i) {
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaUtils.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaUtils.java
index ebf14e2b23d1..c8773ce2c232 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaUtils.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/SchemaUtils.java
@@ -17,14 +17,21 @@
  */
 package org.apache.beam.sdk.schemas;
 
+import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
+import java.util.Objects;
 import org.apache.beam.sdk.schemas.Schema.FieldType;
 import org.apache.beam.sdk.schemas.Schema.LogicalType;
+import org.apache.beam.sdk.values.Row;
 
 /** A set of utility functions for schemas. */
 @SuppressWarnings({
   "nullness" // TODO(https://github.com/apache/beam/issues/20497)
 })
 public class SchemaUtils {
+  private static final String INDENT = "  ";
+
   /**
    * Given two schema that have matching types, return a nullable-widened schema.
    *
@@ -122,4 +129,276 @@ public static <BaseT, InputT> InputT toLogicalInputType(
       LogicalType<InputT, BaseT> logicalType, BaseT baseType) {
     return logicalType.toInputType(baseType);
   }
+
+  public static String toPrettyString(Row row) {
+    return toPrettyRowString(row, "");
+  }
+
+  public static String toPrettyString(Schema schema) {
+    return toPrettySchemaString(schema, "");
+  }
+
+  static String toFieldTypeNameString(FieldType fieldType) {
+    return fieldType.getTypeName()
+        + (Boolean.TRUE.equals(fieldType.getNullable()) ? "" : " NOT NULL");
+  }
+
+  static String toPrettyFieldTypeString(Schema.FieldType fieldType, String prefix) {
+    String nextPrefix = prefix + INDENT;
+    switch (fieldType.getTypeName()) {
+      case BYTE:
+      case INT16:
+      case INT32:
+      case INT64:
+      case DECIMAL:
+      case FLOAT:
+      case DOUBLE:
+      case STRING:
+      case DATETIME:
+      case BOOLEAN:
+      case BYTES:
+        return "<" + toFieldTypeNameString(fieldType) + ">";
+      case ARRAY:
+      case ITERABLE:
+        {
+          StringBuilder sb = new StringBuilder();
+          sb.append("<").append(toFieldTypeNameString(fieldType)).append("> {\n");
+          sb.append(nextPrefix)
+              .append("<element>: ")
+              .append(
+                  toPrettyFieldTypeString(
+                      Objects.requireNonNull(fieldType.getCollectionElementType()), nextPrefix))
+              .append("\n");
+          sb.append(prefix).append("}");
+          return sb.toString();
+        }
+      case MAP:
+        {
+          StringBuilder sb = new StringBuilder();
+          sb.append("<").append(toFieldTypeNameString(fieldType)).append("> {\n");
+          sb.append(nextPrefix)
+              .append("<key>: ")
+              .append(
+                  toPrettyFieldTypeString(
+                      Objects.requireNonNull(fieldType.getMapKeyType()), nextPrefix))
+              .append(",\n");
+          sb.append(nextPrefix)
+              .append("<value>: ")
+              .append(
+                  toPrettyFieldTypeString(
+                      Objects.requireNonNull(fieldType.getMapValueType()), nextPrefix))
+              .append("\n");
+          sb.append(prefix).append("}");
+          return sb.toString();
+        }
+      case ROW:
+        {
+          return "<"
+              + toFieldTypeNameString(fieldType)
+              + "> "
+              + toPrettySchemaString(Objects.requireNonNull(fieldType.getRowSchema()), prefix);
+        }
+      case LOGICAL_TYPE:
+        {
+          Schema.FieldType baseType =
+              Objects.requireNonNull(fieldType.getLogicalType()).getBaseType();
+          StringBuilder sb = new StringBuilder();
+          sb.append("<")
+              .append(toFieldTypeNameString(fieldType))
+              .append("(")
+              .append(fieldType.getLogicalType().getIdentifier())
+              .append(")> {\n");
+          sb.append(nextPrefix)
+              .append("<base>: ")
+              .append(toPrettyFieldTypeString(baseType, nextPrefix))
+              .append("\n");
+          sb.append(prefix).append("}");
+          return sb.toString();
+        }
+      default:
+        throw new UnsupportedOperationException(fieldType.getTypeName() + " is not supported");
+    }
+  }
+
+  static String toPrettyOptionsString(Schema.Options options, String prefix) {
+    String nextPrefix = prefix + INDENT;
+    StringBuilder sb = new StringBuilder();
+    sb.append("{\n");
+    for (String optionName : options.getOptionNames()) {
+      sb.append(nextPrefix)
+          .append(optionName)
+          .append(" = ")
+          .append(
+              toPrettyFieldValueString(
+                  options.getType(optionName), options.getValue(optionName), nextPrefix))
+          .append("\n");
+    }
+    sb.append(prefix).append("}");
+    return sb.toString();
+  }
+
+  static String toPrettyFieldValueString(Schema.FieldType fieldType, Object value, String prefix) {
+    String nextPrefix = prefix + INDENT;
+    switch (fieldType.getTypeName()) {
+      case BYTE:
+      case INT16:
+      case INT32:
+      case INT64:
+      case DECIMAL:
+      case FLOAT:
+      case DOUBLE:
+      case DATETIME:
+      case BOOLEAN:
+        return Objects.toString(value);
+      case STRING:
+        {
+          String string = (String) value;
+          return "\"" + string.replace("\\", "\\\\").replace("\"", "\\\"") + "\"";
+        }
+      case BYTES:
+        {
+          byte[] bytes = (byte[]) value;
+          return Arrays.toString(bytes);
+        }
+      case ARRAY:
+      case ITERABLE:
+        {
+          if (!(value instanceof List)) {
+            throw new IllegalArgumentException(
+                String.format(
+                    "value type is '%s' for field type '%s'",
+                    value.getClass(), fieldType.getTypeName()));
+          }
+          FieldType elementType = Objects.requireNonNull(fieldType.getCollectionElementType());
+
+          @SuppressWarnings("unchecked")
+          List<Object> list = (List<Object>) value;
+          if (list.isEmpty()) {
+            return "[]";
+          }
+          StringBuilder sb = new StringBuilder();
+          sb.append("[\n");
+          int size = list.size();
+          int index = 0;
+          for (Object element : list) {
+            sb.append(nextPrefix)
+                .append(toPrettyFieldValueString(elementType, element, nextPrefix));
+            if (index++ < size - 1) {
+              sb.append(",\n");
+            } else {
+              sb.append("\n");
+            }
+          }
+          sb.append(prefix).append("]");
+          return sb.toString();
+        }
+      case MAP:
+        {
+          if (!(value instanceof Map)) {
+            throw new IllegalArgumentException(
+                String.format(
+                    "value type is '%s' for field type '%s'",
+                    value.getClass(), fieldType.getTypeName()));
+          }
+
+          FieldType keyType = Objects.requireNonNull(fieldType.getMapKeyType());
+          FieldType valueType = Objects.requireNonNull(fieldType.getMapValueType());
+
+          @SuppressWarnings("unchecked")
+          Map<Object, Object> map = (Map<Object, Object>) value;
+          if (map.isEmpty()) {
+            return "{}";
+          }
+
+          StringBuilder sb = new StringBuilder();
+          sb.append("{\n");
+          int size = map.size();
+          int index = 0;
+          for (Map.Entry<Object, Object> entry : map.entrySet()) {
+            sb.append(nextPrefix)
+                .append(toPrettyFieldValueString(keyType, entry.getKey(), nextPrefix))
+                .append(": ")
+                .append(toPrettyFieldValueString(valueType, entry.getValue(), nextPrefix));
+            if (index++ < size - 1) {
+              sb.append(",\n");
+            } else {
+              sb.append("\n");
+            }
+          }
+          sb.append(prefix).append("}");
+          return sb.toString();
+        }
+      case ROW:
+        {
+          return toPrettyRowString((Row) value, prefix);
+        }
+      case LOGICAL_TYPE:
+        {
+          @SuppressWarnings("unchecked")
+          Schema.LogicalType<Object, Object> logicalType =
+              (Schema.LogicalType<Object, Object>)
+                  Objects.requireNonNull(fieldType.getLogicalType());
+          Schema.FieldType baseType = logicalType.getBaseType();
+          Object baseValue = logicalType.toBaseType(value);
+          return toPrettyFieldValueString(baseType, baseValue, prefix);
+        }
+      default:
+        throw new UnsupportedOperationException(fieldType.getTypeName() + " is not supported");
+    }
+  }
+
+  static String toPrettySchemaString(Schema schema, String prefix) {
+    String nextPrefix = prefix + INDENT;
+    StringBuilder sb = new StringBuilder();
+    sb.append("{\n");
+    for (Schema.Field field : schema.getFields()) {
+      sb.append(nextPrefix)
+          .append(field.getName())
+          .append(": ")
+          .append(toPrettyFieldTypeString(field.getType(), nextPrefix));
+      if (field.getOptions().hasOptions()) {
+        sb.append(", fieldOptions = ")
+            .append(toPrettyOptionsString(field.getOptions(), nextPrefix));
+      }
+      sb.append("\n");
+    }
+    sb.append(prefix).append("}");
+    if (schema.getOptions().hasOptions()) {
+      sb.append(", schemaOptions = ").append(toPrettyOptionsString(schema.getOptions(), prefix));
+    }
+    if (schema.getUUID() != null) {
+      sb.append(", schemaUUID = ").append(schema.getUUID());
+    }
+    return sb.toString();
+  }
+
+  static String toPrettyRowString(Row row, String prefix) {
+    long nonNullFieldCount = row.getValues().stream().filter(Objects::nonNull).count();
+    if (nonNullFieldCount == 0) {
+      return "{}";
+    }
+
+    String nextPrefix = prefix + INDENT;
+    StringBuilder sb = new StringBuilder();
+    sb.append("{\n");
+    long nonNullFieldIndex = 0;
+    for (Schema.Field field : row.getSchema().getFields()) {
+      String fieldName = field.getName();
+      Object fieldValue = row.getValue(fieldName);
+      if (fieldValue == null) {
+        continue;
+      }
+      sb.append(nextPrefix)
+          .append(fieldName)
+          .append(": ")
+          .append(toPrettyFieldValueString(field.getType(), fieldValue, nextPrefix));
+      if (nonNullFieldIndex++ < nonNullFieldCount - 1) {
+        sb.append(",\n");
+      } else {
+        sb.append("\n");
+      }
+    }
+    sb.append(prefix).append("}");
+    return sb.toString();
+  }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/Date.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/Date.java
index 12700ffc48bc..894b585fe660 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/Date.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/Date.java
@@ -29,9 +29,6 @@
  * <p>Its input type is a {@link LocalDate}, and base type is a {@link Long} that represents a
  * incrementing count of days where day 0 is 1970-01-01 (ISO).
  */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
 public class Date implements Schema.LogicalType<LocalDate, Long> {
   public static final String IDENTIFIER = "beam:logical_type:date:v1";
 
@@ -59,11 +56,11 @@ public Schema.FieldType getBaseType() {
 
   @Override
   public Long toBaseType(LocalDate input) {
-    return input == null ? null : input.toEpochDay();
+    return input.toEpochDay();
   }
 
   @Override
   public LocalDate toInputType(Long base) {
-    return base == null ? null : LocalDate.ofEpochDay(base);
+    return LocalDate.ofEpochDay(base);
   }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/DateTime.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/DateTime.java
index e748c5e528c1..2659fc8644a7 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/DateTime.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/DateTime.java
@@ -17,6 +17,8 @@
  */
 package org.apache.beam.sdk.schemas.logicaltypes;
 
+import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
+
 import java.time.LocalDate;
 import java.time.LocalDateTime;
 import java.time.LocalTime;
@@ -35,9 +37,6 @@
  * same as the base type of {@link Time}, which is a Long that represents a count of time in
  * nanoseconds.
  */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
 public class DateTime implements Schema.LogicalType<LocalDateTime, Row> {
   public static final String IDENTIFIER = "beam:logical_type:datetime:v1";
   public static final String DATE_FIELD_NAME = "Date";
@@ -69,19 +68,21 @@ public Schema.FieldType getBaseType() {
 
   @Override
   public Row toBaseType(LocalDateTime input) {
-    return input == null
-        ? null
-        : Row.withSchema(DATETIME_SCHEMA)
-            .addValues(input.toLocalDate().toEpochDay(), input.toLocalTime().toNanoOfDay())
-            .build();
+    return Row.withSchema(DATETIME_SCHEMA)
+        .addValues(input.toLocalDate().toEpochDay(), input.toLocalTime().toNanoOfDay())
+        .build();
   }
 
   @Override
   public LocalDateTime toInputType(Row base) {
-    return base == null
-        ? null
-        : LocalDateTime.of(
-            LocalDate.ofEpochDay(base.getInt64(DATE_FIELD_NAME)),
-            LocalTime.ofNanoOfDay(base.getInt64(TIME_FIELD_NAME)));
+    return LocalDateTime.of(
+        LocalDate.ofEpochDay(
+            checkArgumentNotNull(
+                base.getInt64(DATE_FIELD_NAME),
+                "While trying to convert to LocalDateTime: Row missing date field")),
+        LocalTime.ofNanoOfDay(
+            checkArgumentNotNull(
+                base.getInt64(TIME_FIELD_NAME),
+                "While trying to convert to LocalDateTime: Row missing time field")));
   }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/EnumerationType.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/EnumerationType.java
index 9ec63ec8c8ed..96708bd1d6e3 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/EnumerationType.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/EnumerationType.java
@@ -17,6 +17,8 @@
  */
 package org.apache.beam.sdk.schemas.logicaltypes;
 
+import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
+
 import java.io.Serializable;
 import java.util.Arrays;
 import java.util.Comparator;
@@ -30,20 +32,17 @@
 import org.apache.beam.sdk.schemas.Schema.LogicalType;
 import org.apache.beam.sdk.schemas.logicaltypes.EnumerationType.Value;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.BiMap;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.HashBiMap;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableBiMap;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /** This {@link LogicalType} represent an enumeration over a fixed set of values. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
 public class EnumerationType implements LogicalType<Value, Integer> {
   public static final String IDENTIFIER = "Enum";
-  final BiMap<String, Integer> enumValues = HashBiMap.create();
+  final BiMap<String, Integer> enumValues;
   final List<String> values;
 
   private EnumerationType(Map<String, Integer> enumValues) {
-    this.enumValues.putAll(enumValues);
+    this.enumValues = ImmutableBiMap.copyOf(enumValues);
     values =
         enumValues.entrySet().stream()
             .sorted(Comparator.comparingInt(e -> e.getValue()))
@@ -76,7 +75,9 @@ public static EnumerationType create(String... enumValues) {
   }
   /** Return an {@link Value} corresponding to one of the enumeration strings. */
   public Value valueOf(String stringValue) {
-    return new Value(enumValues.get(stringValue));
+    return new Value(
+        checkArgumentNotNull(
+            enumValues.get(stringValue), "Unknown enumeration value {}", stringValue));
   }
 
   /** Return an {@link Value} corresponding to one of the enumeration integer values. */
@@ -114,16 +115,27 @@ public Value toInputType(Integer base) {
     return valueOf(base);
   }
 
-  public Map<String, Integer> getValuesMap() {
+  public BiMap<String, Integer> getValuesMap() {
     return enumValues;
   }
 
+  public @Nullable String getEnumName(int number) {
+    return enumValues.inverse().get(number);
+  }
+
+  public @Nullable Integer getEnumValue(String enumName) {
+    return enumValues.get(enumName);
+  }
+
   public List<String> getValues() {
     return values;
   }
 
   public String toString(EnumerationType.Value value) {
-    return enumValues.inverse().get(value.getValue());
+    return checkArgumentNotNull(
+        enumValues.inverse().get(value.getValue()),
+        "Unknown enumeration value {}",
+        value.getValue());
   }
 
   @Override
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/MicrosInstant.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/MicrosInstant.java
index 90cd2587fdee..ec8d428bf517 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/MicrosInstant.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/MicrosInstant.java
@@ -17,11 +17,14 @@
  */
 package org.apache.beam.sdk.schemas.logicaltypes;
 
+import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
+
 import java.time.Instant;
 import org.apache.beam.model.pipeline.v1.RunnerApi;
 import org.apache.beam.model.pipeline.v1.SchemaApi;
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.values.Row;
+import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
  * A timestamp represented as microseconds since the epoch.
@@ -34,9 +37,6 @@
  * <p>For a more faithful logical type to use with {@code java.time.Instant}, see {@link
  * NanosInstant}.
  */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
 public class MicrosInstant implements Schema.LogicalType<Instant, Row> {
   public static final String IDENTIFIER =
       SchemaApi.LogicalTypes.Enum.MICROS_INSTANT
@@ -62,7 +62,12 @@ public Row toBaseType(Instant input) {
 
   @Override
   public Instant toInputType(Row row) {
-    return Instant.ofEpochSecond(row.getInt64(0), row.getInt32(1) * 1000);
+    return Instant.ofEpochSecond(
+        checkArgumentNotNull(
+            row.getInt64(0), "While trying to convert to Instant: Row missing seconds field"),
+        checkArgumentNotNull(
+                row.getInt32(1), "While trying to convert to Instant: Row missing micros field")
+            * 1000);
   }
 
   @Override
@@ -71,7 +76,7 @@ public String getIdentifier() {
   }
 
   @Override
-  public Schema.FieldType getArgumentType() {
+  public Schema.@Nullable FieldType getArgumentType() {
     return null;
   }
 
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/NanosDuration.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/NanosDuration.java
index 226d28d949d0..07c58b40be87 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/NanosDuration.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/NanosDuration.java
@@ -17,13 +17,12 @@
  */
 package org.apache.beam.sdk.schemas.logicaltypes;
 
+import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
+
 import java.time.Duration;
 import org.apache.beam.sdk.values.Row;
 
 /** A duration represented in nanoseconds. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
 public class NanosDuration extends NanosType<Duration> {
   public static final String IDENTIFIER = "beam:logical_type:nanos_duration:v1";
 
@@ -38,6 +37,10 @@ public Row toBaseType(Duration input) {
 
   @Override
   public Duration toInputType(Row row) {
-    return Duration.ofSeconds(row.getInt64(0), row.getInt32(1));
+    return Duration.ofSeconds(
+        checkArgumentNotNull(
+            row.getInt64(0), "While trying to convert to Duration: Row missing seconds field"),
+        checkArgumentNotNull(
+            row.getInt32(1), "While trying to convert to Duration: Row missing nanos field"));
   }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/NanosInstant.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/NanosInstant.java
index 49dda8c59e39..f237ab2b1a43 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/NanosInstant.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/NanosInstant.java
@@ -17,13 +17,12 @@
  */
 package org.apache.beam.sdk.schemas.logicaltypes;
 
+import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
+
 import java.time.Instant;
 import org.apache.beam.sdk.values.Row;
 
 /** A timestamp represented as nanoseconds since the epoch. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
 public class NanosInstant extends NanosType<Instant> {
   public static final String IDENTIFIER = "beam:logical_type:nanos_instant:v1";
 
@@ -38,6 +37,10 @@ public Row toBaseType(Instant input) {
 
   @Override
   public Instant toInputType(Row row) {
-    return Instant.ofEpochSecond(row.getInt64(0), row.getInt32(1));
+    return Instant.ofEpochSecond(
+        checkArgumentNotNull(
+            row.getInt64(0), "While trying to convert to Instant: Row missing seconds field"),
+        checkArgumentNotNull(
+            row.getInt32(1), "While traying to convert to Instant: Row missing nanos field"));
   }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/OneOfType.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/OneOfType.java
index 31b6c8db2fed..609c15859ad8 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/OneOfType.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/OneOfType.java
@@ -17,8 +17,8 @@
  */
 package org.apache.beam.sdk.schemas.logicaltypes;
 
+import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
-import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkNotNull;
 
 import java.util.Arrays;
 import java.util.List;
@@ -31,6 +31,7 @@
 import org.apache.beam.sdk.schemas.Schema.LogicalType;
 import org.apache.beam.sdk.schemas.SchemaTranslation;
 import org.apache.beam.sdk.values.Row;
+import org.checkerframework.checker.nullness.qual.NonNull;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
@@ -39,9 +40,6 @@
  * containing one nullable field matching each input field, and one additional {@link
  * EnumerationType} logical type field that indicates which field is set.
  */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
 public class OneOfType implements LogicalType<OneOfType.Value, Row> {
   public static final String IDENTIFIER = "OneOf";
 
@@ -118,17 +116,17 @@ public FieldType getBaseType() {
   }
 
   /** Create a {@link Value} specifying which field to set and the value to set. */
-  public <T> Value createValue(String caseValue, T value) {
+  public <T extends @NonNull Object> Value createValue(String caseValue, T value) {
     return createValue(getCaseEnumType().valueOf(caseValue), value);
   }
 
   /** Create a {@link Value} specifying which field to set and the value to set. */
-  public <T> Value createValue(int caseValue, T value) {
+  public <T extends @NonNull Object> Value createValue(int caseValue, T value) {
     return createValue(getCaseEnumType().valueOf(caseValue), value);
   }
 
   /** Create a {@link Value} specifying which field to set and the value to set. */
-  public <T> Value createValue(EnumerationType.Value caseType, T value) {
+  public <T extends @NonNull Object> Value createValue(EnumerationType.Value caseType, T value) {
     return new Value(caseType, value);
   }
 
@@ -155,12 +153,13 @@ public Value toInputType(Row base) {
     for (int i = 0; i < base.getFieldCount(); ++i) {
       Object value = base.getValue(i);
       if (value != null) {
-        checkArgument(caseType == null, "More than one field set in union " + this);
+        checkArgument(caseType == null, "More than one field set in union %s", this);
         caseType = enumerationType.valueOf(oneOfSchema.getField(i).getName());
         oneOfValue = value;
       }
     }
-    checkNotNull(oneOfValue, "No value set in union" + this);
+    checkArgumentNotNull(caseType, "No value set in union %s", this);
+    checkArgumentNotNull(oneOfValue, "No value set in union %s", this);
     return createValue(caseType, oneOfValue);
   }
 
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/PassThroughLogicalType.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/PassThroughLogicalType.java
index 828a75acffb6..538992935107 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/PassThroughLogicalType.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/PassThroughLogicalType.java
@@ -19,11 +19,9 @@
 
 import org.apache.beam.sdk.schemas.Schema.FieldType;
 import org.apache.beam.sdk.schemas.Schema.LogicalType;
+import org.checkerframework.checker.nullness.qual.NonNull;
 
 /** A base class for LogicalTypes that use the same Java type as the underlying base type. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
 public abstract class PassThroughLogicalType<T> implements LogicalType<T, T> {
   private final String identifier;
   private final FieldType argumentType;
@@ -60,12 +58,12 @@ public FieldType getBaseType() {
   }
 
   @Override
-  public T toBaseType(T input) {
+  public @NonNull T toBaseType(@NonNull T input) {
     return input;
   }
 
   @Override
-  public T toInputType(T base) {
+  public @NonNull T toInputType(@NonNull T base) {
     return base;
   }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/SqlTypes.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/SqlTypes.java
index 8685e625542e..c8af8d03333e 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/SqlTypes.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/SqlTypes.java
@@ -29,15 +29,15 @@ public class SqlTypes {
 
   private SqlTypes() {}
 
-  /** Beam LogicalType corresponding to ZetaSQL/CalciteSQL DATE type. */
+  /** Beam LogicalType corresponding to CalciteSQL DATE type. */
   public static final LogicalType<LocalDate, Long> DATE = new Date();
 
-  /** Beam LogicalType corresponding to ZetaSQL/CalciteSQL TIME type. */
+  /** Beam LogicalType corresponding to CalciteSQL TIME type. */
   public static final LogicalType<LocalTime, Long> TIME = new Time();
 
-  /** Beam LogicalType corresponding to ZetaSQL DATETIME type. */
+  /** Beam LogicalType corresponding to DATETIME type. */
   public static final LogicalType<LocalDateTime, Row> DATETIME = new DateTime();
 
-  /** Beam LogicalType corresponding to ZetaSQL TIMESTAMP type. */
+  /** Beam LogicalType corresponding to TIMESTAMP type. */
   public static final LogicalType<Instant, Row> TIMESTAMP = new MicrosInstant();
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/Time.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/Time.java
index fc515810cae6..04f307063e77 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/Time.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/logicaltypes/Time.java
@@ -29,9 +29,6 @@
  * <p>Its input type is a {@link LocalTime}, and base type is a {@link Long} that represents a count
  * of time in nanoseconds.
  */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
 public class Time implements Schema.LogicalType<LocalTime, Long> {
   public static final String IDENTIFIER = "beam:logical_type:time:v1";
 
@@ -59,11 +56,11 @@ public Schema.FieldType getBaseType() {
 
   @Override
   public Long toBaseType(LocalTime input) {
-    return input == null ? null : input.toNanoOfDay();
+    return input.toNanoOfDay();
   }
 
   @Override
   public LocalTime toInputType(Long base) {
-    return base == null ? null : LocalTime.ofNanoOfDay(base);
+    return LocalTime.ofNanoOfDay(base);
   }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/transforms/Select.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/transforms/Select.java
index 84ae7c42cb64..86af822a6a4b 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/transforms/Select.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/transforms/Select.java
@@ -131,6 +131,7 @@ private static class SelectDoFn<T> extends DoFn<T, Row> {
     // TODO: This should be the same as resolved so that Beam knows which fields
     // are being accessed. Currently Beam only supports wildcard descriptors.
     // Once https://github.com/apache/beam/issues/18903 is fixed, fix this.
+    @SuppressWarnings("unused")
     @FieldAccess("selectFields")
     final FieldAccessDescriptor fieldAccess = FieldAccessDescriptor.withAllFields();
 
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/utils/ByteBuddyUtils.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/utils/ByteBuddyUtils.java
index 5297eb113a97..e99459ddc60a 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/utils/ByteBuddyUtils.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/utils/ByteBuddyUtils.java
@@ -75,6 +75,7 @@
 import net.bytebuddy.utility.RandomString;
 import org.apache.beam.sdk.annotations.Internal;
 import org.apache.beam.sdk.schemas.FieldValueGetter;
+import org.apache.beam.sdk.schemas.FieldValueHaver;
 import org.apache.beam.sdk.schemas.FieldValueSetter;
 import org.apache.beam.sdk.schemas.FieldValueTypeInformation;
 import org.apache.beam.sdk.util.common.ReflectHelpers;
@@ -234,6 +235,16 @@ DynamicType.Builder<FieldValueSetter<ObjectT, ValueT>> subclassSetterInterface(
         byteBuddy.with(new InjectPackageStrategy((Class) objectType)).subclass(setterGenericType);
   }
 
+  @SuppressWarnings("unchecked")
+  public static <ObjectT> DynamicType.Builder<FieldValueHaver<ObjectT>> subclassHaverInterface(
+      ByteBuddy byteBuddy, Class<?> objectType) {
+    TypeDescription.Generic haverGenericType =
+        TypeDescription.Generic.Builder.parameterizedType(FieldValueHaver.class, objectType)
+            .build();
+    return (DynamicType.Builder<FieldValueHaver<ObjectT>>)
+        byteBuddy.with(new InjectPackageStrategy(objectType)).subclass(haverGenericType);
+  }
+
   public interface TypeConversionsFactory {
     TypeConversion<Type> createTypeConversion(boolean returnRawTypes);
 
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/utils/JavaBeanUtils.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/utils/JavaBeanUtils.java
index ee4868ddb2b6..32b4ef97b70e 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/utils/JavaBeanUtils.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/schemas/utils/JavaBeanUtils.java
@@ -45,6 +45,7 @@
 import net.bytebuddy.jar.asm.ClassWriter;
 import net.bytebuddy.matcher.ElementMatchers;
 import org.apache.beam.sdk.schemas.FieldValueGetter;
+import org.apache.beam.sdk.schemas.FieldValueHaver;
 import org.apache.beam.sdk.schemas.FieldValueSetter;
 import org.apache.beam.sdk.schemas.FieldValueTypeInformation;
 import org.apache.beam.sdk.schemas.Schema;
@@ -276,6 +277,38 @@ DynamicType.Builder<FieldValueSetter<ObjectT, ValueT>> implementSetterMethods(
         .intercept(new InvokeSetterInstruction(fieldValueTypeInformation, typeConversionsFactory));
   }
 
+  public static <ObjectT> FieldValueHaver<ObjectT> createHaver(
+      Class<ObjectT> clazz, Method hasMethod) {
+    DynamicType.Builder<FieldValueHaver<ObjectT>> builder =
+        ByteBuddyUtils.subclassHaverInterface(BYTE_BUDDY, clazz);
+    builder = implementHaverMethods(builder, hasMethod);
+    try {
+      return builder
+          .visit(new AsmVisitorWrapper.ForDeclaredMethods().writerFlags(ClassWriter.COMPUTE_FRAMES))
+          .make()
+          .load(
+              ReflectHelpers.findClassLoader(clazz.getClassLoader()),
+              getClassLoadingStrategy(clazz))
+          .getLoaded()
+          .getDeclaredConstructor()
+          .newInstance();
+    } catch (InstantiationException
+        | IllegalAccessException
+        | InvocationTargetException
+        | NoSuchMethodException e) {
+      throw new RuntimeException("Unable to generate a have for hasMethod '" + hasMethod + "'", e);
+    }
+  }
+
+  private static <ObjectT> DynamicType.Builder<FieldValueHaver<ObjectT>> implementHaverMethods(
+      DynamicType.Builder<FieldValueHaver<ObjectT>> builder, Method hasMethod) {
+    return builder
+        .method(ElementMatchers.named("name"))
+        .intercept(FixedValue.reference(hasMethod.getName()))
+        .method(ElementMatchers.named("has"))
+        .intercept(new InvokeHaverInstruction(hasMethod));
+  }
+
   // The list of constructors for a class is cached, so we only create the classes the first time
   // getConstructor is called.
   public static final Map<TypeDescriptorWithSchema<?>, SchemaUserTypeCreator> CACHED_CREATORS =
@@ -484,4 +517,35 @@ public ByteCodeAppender appender(final Target implementationTarget) {
       };
     }
   }
+
+  // Implements a method to check a presence on an object.
+  private static class InvokeHaverInstruction implements Implementation {
+    private final Method hasMethod;
+
+    public InvokeHaverInstruction(Method hasMethod) {
+      this.hasMethod = hasMethod;
+    }
+
+    @Override
+    public ByteCodeAppender appender(Target implementationTarget) {
+      return (methodVisitor, implementationContext, instrumentedMethod) -> {
+        // this + method parameters.
+        int numLocals = 1 + instrumentedMethod.getParameters().size();
+        StackManipulation.Size size =
+            new StackManipulation.Compound(
+                    // Read the first argument
+                    MethodVariableAccess.REFERENCE.loadFrom(1),
+                    // Call hasMethod
+                    MethodInvocation.invoke(new ForLoadedMethod(hasMethod)),
+                    MethodReturn.INTEGER)
+                .apply(methodVisitor, implementationContext);
+        return new Size(size.getMaximalSize(), numLocals);
+      };
+    }
+
+    @Override
+    public InstrumentedType prepare(InstrumentedType instrumentedType) {
+      return instrumentedType;
+    }
+  }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/testing/TestOutputReceiver.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/testing/TestOutputReceiver.java
new file mode 100644
index 000000000000..83d2af7b66bb
--- /dev/null
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/testing/TestOutputReceiver.java
@@ -0,0 +1,63 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.testing;
+
+import java.util.ArrayList;
+import java.util.List;
+import org.apache.beam.sdk.annotations.Internal;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.apache.beam.sdk.values.OutputBuilder;
+import org.apache.beam.sdk.values.WindowedValues;
+import org.joda.time.Instant;
+
+/**
+ * An implementation of {@link DoFn.OutputReceiver} that naively collects all output values.
+ *
+ * <p>Because this API is crude and not designed to be very general, it is for internal use only and
+ * will be changed arbitrarily.
+ */
+@Internal
+public class TestOutputReceiver<T> implements DoFn.OutputReceiver<T> {
+  private final List<T> records = new ArrayList<>();
+
+  // To simplify testing of a DoFn, we want to be able to collect their outputs even
+  // when no window is provided (because processElement is called with only a value in testing).
+  private static final BoundedWindow fakeWindow =
+      new BoundedWindow() {
+        @Override
+        public Instant maxTimestamp() {
+          return BoundedWindow.TIMESTAMP_MIN_VALUE;
+        }
+      };
+
+  @Override
+  public OutputBuilder<T> builder(T value) {
+    return WindowedValues.<T>builder()
+        .setValue(value)
+        .setWindow(fakeWindow)
+        .setPaneInfo(PaneInfo.NO_FIRING)
+        .setTimestamp(BoundedWindow.TIMESTAMP_MIN_VALUE)
+        .setReceiver(windowedValue -> records.add(windowedValue.getValue()));
+  }
+
+  public List<T> getOutputs() {
+    return records;
+  }
+}
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/testing/TestPipeline.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/testing/TestPipeline.java
index 328bf19c466c..4dc9bca28640 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/testing/TestPipeline.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/testing/TestPipeline.java
@@ -24,7 +24,9 @@
 
 import com.fasterxml.jackson.databind.ObjectMapper;
 import java.io.IOException;
+import java.lang.annotation.Annotation;
 import java.util.ArrayList;
+import java.util.Collection;
 import java.util.List;
 import java.util.Map;
 import java.util.UUID;
@@ -82,7 +84,11 @@
  * </ul>
  *
  * <p>Use {@link PAssert} for tests, as it integrates with this test harness in both direct and
- * remote execution modes. For example:
+ * remote execution modes.
+ *
+ * <h3>JUnit 4 Usage</h3>
+ *
+ * For JUnit 4 tests, use this class as a TestRule:
  *
  * <pre><code>
  * {@literal @Rule}
@@ -97,6 +103,25 @@
  *  }
  * </code></pre>
  *
+ * <h3>JUnit5 Usage</h3>
+ *
+ * For JUnit5 tests, use {@link TestPipelineExtension} from the module <code>
+ * sdks/java/testing/junit</code> (artifact <code>org.apache.beam:beam-sdks-java-testing-junit
+ * </code>):
+ *
+ * <pre><code>
+ * {@literal @ExtendWith}(TestPipelineExtension.class)
+ * class MyPipelineTest {
+ *   {@literal @Test}
+ *   {@literal @Category}(NeedsRunner.class)
+ *   void myPipelineTest(TestPipeline pipeline) {
+ *     final PCollection&lt;String&gt; pCollection = pipeline.apply(...)
+ *     PAssert.that(pCollection).containsInAnyOrder(...);
+ *     pipeline.run();
+ *   }
+ * }
+ * </code></pre>
+ *
  * <p>For pipeline runners, it is required that they must throw an {@link AssertionError} containing
  * the message from the {@link PAssert} that failed.
  *
@@ -273,6 +298,13 @@ public static TestPipeline create() {
     return fromOptions(testingPipelineOptions());
   }
 
+  /** */
+  static TestPipeline createWithEnforcement() {
+    TestPipeline p = create();
+
+    return p;
+  }
+
   public static TestPipeline fromOptions(PipelineOptions options) {
     return new TestPipeline(options);
   }
@@ -287,49 +319,55 @@ public PipelineOptions getOptions() {
     return this.options;
   }
 
-  @Override
-  public Statement apply(final Statement statement, final Description description) {
-    return new Statement() {
+  // package private for JUnit5 TestPipelineExtension
+  void setDeducedEnforcementLevel(Collection<Annotation> annotations) {
+    // if the enforcement level has not been set by the user do auto-inference
+    if (!enforcement.isPresent()) {
 
-      private void setDeducedEnforcementLevel() {
-        // if the enforcement level has not been set by the user do auto-inference
-        if (!enforcement.isPresent()) {
+      final boolean annotatedWithNeedsRunner =
+          FluentIterable.from(annotations)
+              .filter(Annotations.Predicates.isAnnotationOfType(Category.class))
+              .anyMatch(Annotations.Predicates.isCategoryOf(NeedsRunner.class, true));
 
-          final boolean annotatedWithNeedsRunner =
-              FluentIterable.from(description.getAnnotations())
-                  .filter(Annotations.Predicates.isAnnotationOfType(Category.class))
-                  .anyMatch(Annotations.Predicates.isCategoryOf(NeedsRunner.class, true));
+      final boolean crashingRunner = CrashingRunner.class.isAssignableFrom(options.getRunner());
 
-          final boolean crashingRunner = CrashingRunner.class.isAssignableFrom(options.getRunner());
+      checkState(
+          !(annotatedWithNeedsRunner && crashingRunner),
+          "The test was annotated with a [@%s] / [@%s] while the runner "
+              + "was set to [%s]. Please re-check your configuration.",
+          NeedsRunner.class.getSimpleName(),
+          ValidatesRunner.class.getSimpleName(),
+          CrashingRunner.class.getSimpleName());
 
-          checkState(
-              !(annotatedWithNeedsRunner && crashingRunner),
-              "The test was annotated with a [@%s] / [@%s] while the runner "
-                  + "was set to [%s]. Please re-check your configuration.",
-              NeedsRunner.class.getSimpleName(),
-              ValidatesRunner.class.getSimpleName(),
-              CrashingRunner.class.getSimpleName());
+      enableAbandonedNodeEnforcement(annotatedWithNeedsRunner || !crashingRunner);
+    }
+  }
 
-          enableAbandonedNodeEnforcement(annotatedWithNeedsRunner || !crashingRunner);
-        }
-      }
+  // package private for JUnit5 TestPipelineExtension
+  void afterUserCodeFinished() {
+    enforcement.get().afterUserCodeFinished();
+  }
+
+  @Override
+  public Statement apply(final Statement statement, final Description description) {
+    return new Statement() {
 
       @Override
       public void evaluate() throws Throwable {
         options.as(ApplicationNameOptions.class).setAppName(getAppName(description));
 
-        setDeducedEnforcementLevel();
+        setDeducedEnforcementLevel(description.getAnnotations());
 
         // statement.evaluate() essentially runs the user code contained in the unit test at hand.
         // Exceptions thrown during the execution of the user's test code will propagate here,
         // unless the user explicitly handles them with a "catch" clause in his code. If the
-        // exception is handled by a user's "catch" clause, is does not interrupt the flow and
+        // exception is handled by a user's "catch" clause, it does not interrupt the flow, and
         // we move on to invoking the configured enforcements.
         // If the user does not handle a thrown exception, it will propagate here and interrupt
         // the flow, preventing the enforcement(s) from being activated.
         // The motivation for this is avoiding enforcements over faulty pipelines.
         statement.evaluate();
-        enforcement.get().afterUserCodeFinished();
+        afterUserCodeFinished();
       }
     };
   }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/testing/TestStream.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/testing/TestStream.java
index d63753fc8b34..f26cdd87200c 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/testing/TestStream.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/testing/TestStream.java
@@ -65,7 +65,7 @@
  * the {@link Pipeline} before advancing the state of the {@link TestStream}.
  */
 @SuppressWarnings({
-  "rawtypes" // TODO(https://github.com/apache/beam/issues/20447)
+  "rawtypes", // TODO(https://github.com/apache/beam/issues/20447),
 })
 public final class TestStream<T> extends PTransform<PBegin, PCollection<T>> {
   private final List<Event<T>> events;
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Create.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Create.java
index 88e3780384ff..a2f32b8b3dd3 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Create.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Create.java
@@ -913,12 +913,14 @@ private WindowedValues(
 
     private static class ConvertWindowedValues<T> extends DoFn<WindowedValue<T>, T> {
       @ProcessElement
-      public void processElement(@Element WindowedValue<T> element, OutputReceiver<T> r) {
-        r.outputWindowedValue(
-            element.getValue(),
-            element.getTimestamp(),
-            element.getWindows(),
-            element.getPaneInfo());
+      public void processElement(
+          @Element WindowedValue<T> element, OutputReceiver<T> outputReceiver) {
+        outputReceiver
+            .builder(element.getValue())
+            .setTimestamp(element.getTimestamp())
+            .setWindows(element.getWindows())
+            .setPaneInfo(element.getPaneInfo())
+            .output();
       }
     }
   }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFn.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFn.java
index 0961c8512523..d0714de60328 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFn.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFn.java
@@ -45,6 +45,7 @@
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
 import org.apache.beam.sdk.transforms.windowing.Window;
+import org.apache.beam.sdk.values.OutputBuilder;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionView;
 import org.apache.beam.sdk.values.Row;
@@ -122,6 +123,12 @@ public abstract class FinishBundleContext {
      */
     public abstract void output(OutputT output, Instant timestamp, BoundedWindow window);
 
+    public abstract void output(
+        OutputT output,
+        Instant timestamp,
+        BoundedWindow window,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset);
     /**
      * Adds the given element to the output {@code PCollection} with the given tag at the given
      * timestamp in the given window.
@@ -133,6 +140,14 @@ public abstract class FinishBundleContext {
      */
     public abstract <T> void output(
         TupleTag<T> tag, T output, Instant timestamp, BoundedWindow window);
+
+    public abstract <T> void output(
+        TupleTag<T> tag,
+        T output,
+        Instant timestamp,
+        BoundedWindow window,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset);
   }
 
   /**
@@ -211,6 +226,14 @@ public abstract void outputWindowedValue(
         Collection<? extends BoundedWindow> windows,
         PaneInfo paneInfo);
 
+    public abstract void outputWindowedValue(
+        OutputT output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset);
+
     /**
      * Adds the given element to the output {@code PCollection} with the given tag.
      *
@@ -283,6 +306,15 @@ public abstract <T> void outputWindowedValue(
         Instant timestamp,
         Collection<? extends BoundedWindow> windows,
         PaneInfo paneInfo);
+
+    public abstract <T> void outputWindowedValue(
+        TupleTag<T> tag,
+        T output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset);
   }
 
   /** Information accessible when running a {@link DoFn.ProcessElement} method. */
@@ -323,6 +355,12 @@ public abstract class ProcessContext extends WindowedContext {
      */
     @Pure
     public abstract PaneInfo pane();
+
+    @Pure
+    public abstract String currentRecordId();
+
+    @Pure
+    public abstract Long currentRecordOffset();
   }
 
   /** Information accessible when running a {@link DoFn.OnTimer} method. */
@@ -391,17 +429,22 @@ public TypeDescriptor<OutputT> getOutputTypeDescriptor() {
 
   /** Receives values of the given type. */
   public interface OutputReceiver<T> {
-    void output(T output);
+    OutputBuilder<T> builder(T value);
+
+    default void output(T value) {
+      builder(value).output();
+    }
 
-    void outputWithTimestamp(T output, Instant timestamp);
+    default void outputWithTimestamp(T value, Instant timestamp) {
+      builder(value).setTimestamp(timestamp).output();
+    }
 
     default void outputWindowedValue(
-        T output,
+        T value,
         Instant timestamp,
         Collection<? extends BoundedWindow> windows,
         PaneInfo paneInfo) {
-      throw new UnsupportedOperationException(
-          String.format("Not implemented: %s.outputWindowedValue", this.getClass().getName()));
+      builder(value).setTimestamp(timestamp).setWindows(windows).setPaneInfo(paneInfo).output();
     }
   }
 
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnOutputReceivers.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnOutputReceivers.java
index 1a73d8e52697..fee19810c15c 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnOutputReceivers.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnOutputReceivers.java
@@ -21,140 +21,174 @@
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkNotNull;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkState;
 
-import java.util.Collection;
 import java.util.Map;
 import org.apache.beam.sdk.annotations.Internal;
 import org.apache.beam.sdk.coders.Coder;
 import org.apache.beam.sdk.schemas.SchemaCoder;
 import org.apache.beam.sdk.transforms.DoFn.MultiOutputReceiver;
 import org.apache.beam.sdk.transforms.DoFn.OutputReceiver;
-import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
-import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.apache.beam.sdk.util.OutputBuilderSupplier;
+import org.apache.beam.sdk.util.WindowedValueReceiver;
+import org.apache.beam.sdk.values.OutputBuilder;
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.sdk.values.WindowedValue;
+import org.apache.beam.sdk.values.WindowedValues;
 import org.checkerframework.checker.nullness.qual.Nullable;
-import org.joda.time.Instant;
 
 /** Common {@link OutputReceiver} and {@link MultiOutputReceiver} classes. */
 @Internal
 public class DoFnOutputReceivers {
+
   private static class RowOutputReceiver<T> implements OutputReceiver<Row> {
-    WindowedContextOutputReceiver<T> outputReceiver;
+    private final @Nullable TupleTag<T> tag;
+    private final DoFn<?, ?>.WindowedContext context;
+    private final OutputBuilderSupplier builderSupplier;
     SchemaCoder<T> schemaCoder;
 
-    public RowOutputReceiver(
+    private RowOutputReceiver(
         DoFn<?, ?>.WindowedContext context,
+        OutputBuilderSupplier builderSupplier,
         @Nullable TupleTag<T> outputTag,
         SchemaCoder<T> schemaCoder) {
-      outputReceiver = new WindowedContextOutputReceiver<>(context, outputTag);
-      this.schemaCoder = checkNotNull(schemaCoder);
-    }
-
-    @Override
-    public void output(Row output) {
-      outputReceiver.output(schemaCoder.getFromRowFunction().apply(output));
+      this.context = context;
+      this.builderSupplier = builderSupplier;
+      this.tag = outputTag;
+      this.schemaCoder = schemaCoder;
     }
 
     @Override
-    public void outputWithTimestamp(Row output, Instant timestamp) {
-      outputReceiver.outputWithTimestamp(schemaCoder.getFromRowFunction().apply(output), timestamp);
-    }
+    public OutputBuilder<Row> builder(Row value) {
+      // assigning to final variable allows static analysis to know it
+      // will not change between now and when receiver is invoked
+      final TupleTag<T> tag = this.tag;
+      if (tag == null) {
+        return builderSupplier
+            .builder(value)
+            .setValue(value)
+            .setReceiver(
+                rowWithMetadata -> {
+                  ((DoFn<?, T>.WindowedContext) context)
+                      .outputWindowedValue(
+                          schemaCoder.getFromRowFunction().apply(rowWithMetadata.getValue()),
+                          rowWithMetadata.getTimestamp(),
+                          rowWithMetadata.getWindows(),
+                          rowWithMetadata.getPaneInfo());
+                });
 
-    @Override
-    public void outputWindowedValue(
-        Row output,
-        Instant timestamp,
-        Collection<? extends BoundedWindow> windows,
-        PaneInfo paneInfo) {
-      outputReceiver.outputWindowedValue(
-          schemaCoder.getFromRowFunction().apply(output), timestamp, windows, paneInfo);
+      } else {
+        checkStateNotNull(tag);
+        return builderSupplier
+            .builder(value)
+            .setReceiver(
+                rowWithMetadata -> {
+                  context.outputWindowedValue(
+                      tag,
+                      schemaCoder.getFromRowFunction().apply(rowWithMetadata.getValue()),
+                      rowWithMetadata.getTimestamp(),
+                      rowWithMetadata.getWindows(),
+                      rowWithMetadata.getPaneInfo());
+                });
+      }
     }
   }
 
-  private static class WindowedContextOutputReceiver<T> implements OutputReceiver<T> {
+  /**
+   * OutputReceiver that delegates all its core functionality to DoFn.WindowedContext which predates
+   * OutputReceiver and has most of the same methods.
+   */
+  private static class WindowedContextOutputReceiver<T>
+      implements OutputReceiver<T>, WindowedValueReceiver<T> {
+    private final OutputBuilderSupplier builderSupplier;
     DoFn<?, ?>.WindowedContext context;
     @Nullable TupleTag<T> outputTag;
 
     public WindowedContextOutputReceiver(
-        DoFn<?, ?>.WindowedContext context, @Nullable TupleTag<T> outputTag) {
+        DoFn<?, ?>.WindowedContext context,
+        OutputBuilderSupplier builderSupplier,
+        @Nullable TupleTag<T> outputTag) {
       this.context = context;
+      this.builderSupplier = builderSupplier;
       this.outputTag = outputTag;
     }
 
     @Override
-    public void output(T output) {
-      if (outputTag != null) {
-        context.output(outputTag, output);
-      } else {
-        ((DoFn<?, T>.WindowedContext) context).output(output);
-      }
-    }
-
-    @Override
-    public void outputWithTimestamp(T output, Instant timestamp) {
-      if (outputTag != null) {
-        context.outputWithTimestamp(outputTag, output, timestamp);
-      } else {
-        ((DoFn<?, T>.WindowedContext) context).outputWithTimestamp(output, timestamp);
-      }
+    public OutputBuilder<T> builder(T value) {
+      return WindowedValues.builder(builderSupplier.builder(value)).setReceiver(this);
     }
 
     @Override
-    public void outputWindowedValue(
-        T output,
-        Instant timestamp,
-        Collection<? extends BoundedWindow> windows,
-        PaneInfo paneInfo) {
+    public void output(WindowedValue<T> windowedValue) {
       if (outputTag != null) {
-        context.outputWindowedValue(outputTag, output, timestamp, windows, paneInfo);
+        context.outputWindowedValue(
+            outputTag,
+            windowedValue.getValue(),
+            windowedValue.getTimestamp(),
+            windowedValue.getWindows(),
+            windowedValue.getPaneInfo());
       } else {
         ((DoFn<?, T>.WindowedContext) context)
-            .outputWindowedValue(output, timestamp, windows, paneInfo);
+            .outputWindowedValue(
+                windowedValue.getValue(),
+                windowedValue.getTimestamp(),
+                windowedValue.getWindows(),
+                windowedValue.getPaneInfo());
       }
     }
   }
 
   private static class WindowedContextMultiOutputReceiver implements MultiOutputReceiver {
-    DoFn<?, ?>.WindowedContext context;
+    private final OutputBuilderSupplier builderSupplier;
+    private final DoFn<?, ?>.WindowedContext context;
     @Nullable Map<TupleTag<?>, Coder<?>> outputCoders;
 
     public WindowedContextMultiOutputReceiver(
-        DoFn<?, ?>.WindowedContext context, @Nullable Map<TupleTag<?>, Coder<?>> outputCoders) {
+        DoFn<?, ?>.WindowedContext context,
+        OutputBuilderSupplier builderSupplier,
+        @Nullable Map<TupleTag<?>, Coder<?>> outputCoders) {
       this.context = context;
+      this.builderSupplier = builderSupplier;
       this.outputCoders = outputCoders;
     }
 
     // This exists for backwards compatibility with the Dataflow runner, and will be removed.
-    public WindowedContextMultiOutputReceiver(DoFn<?, ?>.WindowedContext context) {
+    public WindowedContextMultiOutputReceiver(
+        DoFn<?, ?>.WindowedContext context, OutputBuilderSupplier builderSupplier) {
       this.context = context;
+      this.builderSupplier = builderSupplier;
     }
 
     @Override
     public <T> OutputReceiver<T> get(TupleTag<T> tag) {
-      return DoFnOutputReceivers.windowedReceiver(context, tag);
+      return DoFnOutputReceivers.windowedReceiver(context, builderSupplier, tag);
     }
 
     @Override
     public <T> OutputReceiver<Row> getRowReceiver(TupleTag<T> tag) {
       Coder<T> outputCoder = (Coder<T>) checkNotNull(outputCoders).get(tag);
-      checkStateNotNull(outputCoder, "No output tag for " + tag);
+      checkStateNotNull(outputCoder, "No output tag for %s ", tag);
       checkState(
           outputCoder instanceof SchemaCoder,
           "Output with tag " + tag + " must have a schema in order to call getRowReceiver");
-      return DoFnOutputReceivers.rowReceiver(context, tag, (SchemaCoder<T>) outputCoder);
+      return DoFnOutputReceivers.rowReceiver(
+          context, builderSupplier, tag, (SchemaCoder<T>) outputCoder);
     }
   }
 
   /** Returns a {@link OutputReceiver} that delegates to a {@link DoFn.WindowedContext}. */
   public static <T> OutputReceiver<T> windowedReceiver(
-      DoFn<?, ?>.WindowedContext context, @Nullable TupleTag<T> outputTag) {
-    return new WindowedContextOutputReceiver<>(context, outputTag);
+      DoFn<?, ?>.WindowedContext context,
+      OutputBuilderSupplier builderSupplier,
+      @Nullable TupleTag<T> outputTag) {
+    return new WindowedContextOutputReceiver<>(context, builderSupplier, outputTag);
   }
 
   /** Returns a {@link MultiOutputReceiver} that delegates to a {@link DoFn.WindowedContext}. */
-  public static <T> MultiOutputReceiver windowedMultiReceiver(
-      DoFn<?, ?>.WindowedContext context, @Nullable Map<TupleTag<?>, Coder<?>> outputCoders) {
-    return new WindowedContextMultiOutputReceiver(context, outputCoders);
+  public static MultiOutputReceiver windowedMultiReceiver(
+      DoFn<?, ?>.WindowedContext context,
+      OutputBuilderSupplier builderSupplier,
+      @Nullable Map<TupleTag<?>, Coder<?>> outputCoders) {
+    return new WindowedContextMultiOutputReceiver(context, builderSupplier, outputCoders);
   }
 
   /**
@@ -162,8 +196,9 @@ public static <T> MultiOutputReceiver windowedMultiReceiver(
    *
    * <p>This exists for backwards-compatibility with the Dataflow runner, and will be removed.
    */
-  public static <T> MultiOutputReceiver windowedMultiReceiver(DoFn<?, ?>.WindowedContext context) {
-    return new WindowedContextMultiOutputReceiver(context);
+  public static MultiOutputReceiver windowedMultiReceiver(
+      DoFn<?, ?>.WindowedContext context, OutputBuilderSupplier builderSupplier) {
+    return new WindowedContextMultiOutputReceiver(context, builderSupplier);
   }
 
   /**
@@ -172,8 +207,9 @@ public static <T> MultiOutputReceiver windowedMultiReceiver(DoFn<?, ?>.WindowedC
    */
   public static <T> OutputReceiver<Row> rowReceiver(
       DoFn<?, ?>.WindowedContext context,
+      OutputBuilderSupplier builderSupplier,
       @Nullable TupleTag<T> outputTag,
       SchemaCoder<T> schemaCoder) {
-    return new RowOutputReceiver<>(context, outputTag, schemaCoder);
+    return new RowOutputReceiver<>(context, builderSupplier, outputTag, schemaCoder);
   }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnSchemaInformation.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnSchemaInformation.java
index 7576eb71b3a4..8dc302dd1d54 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnSchemaInformation.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnSchemaInformation.java
@@ -224,7 +224,7 @@ private UnboxingConversionFunction(
       this.rowSelector = new RowSelectorContainer(inputSchema, selectDescriptor, true);
     }
 
-    public static <InputT, OutputT> UnboxingConversionFunction of(
+    public static <InputT> UnboxingConversionFunction of(
         Schema inputSchema,
         SerializableFunction<InputT, Row> toRowFunction,
         FieldAccessDescriptor selectDescriptor,
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnTester.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnTester.java
index fb1947ad5ba3..c59d6b528c3f 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnTester.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/DoFnTester.java
@@ -47,12 +47,16 @@
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.GlobalWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.apache.beam.sdk.util.OutputBuilderSupplier;
+import org.apache.beam.sdk.util.OutputBuilderSuppliers;
 import org.apache.beam.sdk.util.SerializableUtils;
 import org.apache.beam.sdk.util.UserCodeException;
 import org.apache.beam.sdk.values.PCollectionView;
 import org.apache.beam.sdk.values.TimestampedValue;
 import org.apache.beam.sdk.values.TupleTag;
 import org.apache.beam.sdk.values.ValueInSingleWindow;
+import org.apache.beam.sdk.values.WindowedValue;
+import org.apache.beam.sdk.values.WindowedValues;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.MoreObjects;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.checkerframework.checker.nullness.qual.Nullable;
@@ -211,9 +215,14 @@ public void processWindowedElement(InputT element, Instant timestamp, final Boun
       startBundle();
     }
     try {
+      ValueInSingleWindow<InputT> templateElement =
+          ValueInSingleWindow.of(element, timestamp, window, PaneInfo.NO_FIRING);
+      WindowedValue<InputT> templateWv =
+          WindowedValues.of(element, timestamp, window, PaneInfo.NO_FIRING);
       final DoFn<InputT, OutputT>.ProcessContext processContext =
-          createProcessContext(
-              ValueInSingleWindow.of(element, timestamp, window, PaneInfo.NO_FIRING));
+          createProcessContext(templateElement);
+      final OutputBuilderSupplier builderSupplier =
+          OutputBuilderSuppliers.supplierForElement(templateWv);
       fnInvoker.invokeProcessElement(
           new DoFnInvoker.BaseArgumentProvider<InputT, OutputT>() {
 
@@ -286,12 +295,13 @@ public TimeDomain timeDomain(DoFn<InputT, OutputT> doFn) {
 
             @Override
             public OutputReceiver<OutputT> outputReceiver(DoFn<InputT, OutputT> doFn) {
-              return DoFnOutputReceivers.windowedReceiver(processContext, null);
+              return DoFnOutputReceivers.windowedReceiver(processContext, builderSupplier, null);
             }
 
             @Override
             public MultiOutputReceiver taggedOutputReceiver(DoFn<InputT, OutputT> doFn) {
-              return DoFnOutputReceivers.windowedMultiReceiver(processContext, null);
+              return DoFnOutputReceivers.windowedMultiReceiver(
+                  processContext, builderSupplier, null);
             }
 
             @Override
@@ -478,7 +488,38 @@ public void output(OutputT output, Instant timestamp, BoundedWindow window) {
         @Override
         public <T> void output(TupleTag<T> tag, T output, Instant timestamp, BoundedWindow window) {
           getMutableOutput(tag)
-              .add(ValueInSingleWindow.of(output, timestamp, window, PaneInfo.NO_FIRING));
+              .add(
+                  ValueInSingleWindow.of(
+                      output, timestamp, window, PaneInfo.NO_FIRING, null, null));
+        }
+
+        @Override
+        public void output(
+            OutputT output,
+            Instant timestamp,
+            BoundedWindow window,
+            @Nullable String currentRecordId,
+            @Nullable Long currentRecordOffset) {
+          output(mainOutputTag, output, timestamp, window, currentRecordId, currentRecordOffset);
+        }
+
+        @Override
+        public <T> void output(
+            TupleTag<T> tag,
+            T output,
+            Instant timestamp,
+            BoundedWindow window,
+            @Nullable String currentRecordId,
+            @Nullable Long currentRecordOffset) {
+          getMutableOutput(tag)
+              .add(
+                  ValueInSingleWindow.of(
+                      output,
+                      timestamp,
+                      window,
+                      PaneInfo.NO_FIRING,
+                      currentRecordId,
+                      currentRecordOffset));
         }
       };
     }
@@ -567,6 +608,16 @@ public PaneInfo pane() {
       return element.getPaneInfo();
     }
 
+    @Override
+    public String currentRecordId() {
+      return element.getCurrentRecordId();
+    }
+
+    @Override
+    public Long currentRecordOffset() {
+      return element.getCurrentRecordOffset();
+    }
+
     @Override
     public PipelineOptions getPipelineOptions() {
       return options;
@@ -591,6 +642,24 @@ public void outputWindowedValue(
       outputWindowedValue(mainOutputTag, output, timestamp, windows, paneInfo);
     }
 
+    @Override
+    public void outputWindowedValue(
+        OutputT output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      outputWindowedValue(
+          mainOutputTag,
+          output,
+          timestamp,
+          windows,
+          paneInfo,
+          currentRecordId,
+          currentRecordOffset);
+    }
+
     @Override
     public <T> void output(TupleTag<T> tag, T output) {
       outputWithTimestamp(tag, output, element.getTimestamp());
@@ -601,7 +670,7 @@ public <T> void outputWithTimestamp(TupleTag<T> tag, T output, Instant timestamp
       getMutableOutput(tag)
           .add(
               ValueInSingleWindow.of(
-                  output, timestamp, element.getWindow(), element.getPaneInfo()));
+                  output, timestamp, element.getWindow(), element.getPaneInfo(), null, null));
     }
 
     @Override
@@ -612,7 +681,25 @@ public <T> void outputWindowedValue(
         Collection<? extends BoundedWindow> windows,
         PaneInfo paneInfo) {
       for (BoundedWindow w : windows) {
-        getMutableOutput(tag).add(ValueInSingleWindow.of(output, timestamp, w, paneInfo));
+        getMutableOutput(tag)
+            .add(ValueInSingleWindow.of(output, timestamp, w, paneInfo, null, null));
+      }
+    }
+
+    @Override
+    public <T> void outputWindowedValue(
+        TupleTag<T> tag,
+        T output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      for (BoundedWindow w : windows) {
+        getMutableOutput(tag)
+            .add(
+                ValueInSingleWindow.of(
+                    output, timestamp, w, paneInfo, currentRecordId, currentRecordOffset));
       }
     }
   }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/MapElements.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/MapElements.java
index 6b123d3bd106..6434498d4bcd 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/MapElements.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/MapElements.java
@@ -158,6 +158,7 @@ public void processElement(
   }
 
   /** A DoFn implementation that handles a trivial map call. */
+  @SuppressWarnings("unused") // for outer
   private abstract class MapDoFn extends DoFn<InputT, OutputT> {
 
     /** Holds {@link MapDoFn#outer instance} of enclosing class, used by runner implementations. */
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Redistribute.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Redistribute.java
index ea55cbd88b36..a01b5f570a57 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Redistribute.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Redistribute.java
@@ -18,7 +18,6 @@
 package org.apache.beam.sdk.transforms;
 
 import com.google.auto.service.AutoService;
-import java.util.Collections;
 import java.util.Map;
 import java.util.concurrent.ThreadLocalRandom;
 import org.apache.beam.model.pipeline.v1.RunnerApi;
@@ -178,12 +177,14 @@ public Duration getAllowedTimestampSkew() {
 
                 @ProcessElement
                 public void processElement(
-                    @Element KV<K, ValueInSingleWindow<V>> kv, OutputReceiver<KV<K, V>> r) {
-                  r.outputWindowedValue(
-                      KV.of(kv.getKey(), kv.getValue().getValue()),
-                      kv.getValue().getTimestamp(),
-                      Collections.singleton(kv.getValue().getWindow()),
-                      kv.getValue().getPaneInfo());
+                    @Element KV<K, ValueInSingleWindow<V>> kv,
+                    OutputReceiver<KV<K, V>> outputReceiver) {
+                  outputReceiver
+                      .builder(KV.of(kv.getKey(), kv.getValue().getValue()))
+                      .setTimestamp(kv.getValue().getTimestamp())
+                      .setWindow(kv.getValue().getWindow())
+                      .setPaneInfo(kv.getValue().getPaneInfo())
+                      .output();
                 }
               }));
     }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Reify.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Reify.java
index 92f1b73900b2..797af9538c53 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Reify.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Reify.java
@@ -272,7 +272,7 @@ public static <K, V> PTransform<PCollection<K>, PCollection<KV<K, V>>> viewAsVal
    * Returns a {@link PCollection} consisting of a single element, containing the value of the given
    * view in the global window.
    */
-  public static <K, V> PTransform<PBegin, PCollection<V>> viewInGlobalWindow(
+  public static <V> PTransform<PBegin, PCollection<V>> viewInGlobalWindow(
       PCollectionView<V> view, Coder<V> coder) {
     return new ReifyViewInGlobalWindow<>(view, coder);
   }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Reshuffle.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Reshuffle.java
index 2a301d0480c0..b2de48342d7c 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Reshuffle.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Reshuffle.java
@@ -18,7 +18,6 @@
 package org.apache.beam.sdk.transforms;
 
 import java.util.Arrays;
-import java.util.Collections;
 import java.util.Comparator;
 import java.util.List;
 import java.util.concurrent.ThreadLocalRandom;
@@ -183,12 +182,14 @@ public Duration getAllowedTimestampSkew() {
 
                 @ProcessElement
                 public void processElement(
-                    @Element KV<K, ValueInSingleWindow<V>> kv, OutputReceiver<KV<K, V>> r) {
-                  r.outputWindowedValue(
-                      KV.of(kv.getKey(), kv.getValue().getValue()),
-                      kv.getValue().getTimestamp(),
-                      Collections.singleton(kv.getValue().getWindow()),
-                      kv.getValue().getPaneInfo());
+                    @Element KV<K, ValueInSingleWindow<V>> kv,
+                    OutputReceiver<KV<K, V>> outputReceiver) {
+                  outputReceiver
+                      .builder(KV.of(kv.getKey(), kv.getValue().getValue()))
+                      .setTimestamp(kv.getValue().getTimestamp())
+                      .setWindow(kv.getValue().getWindow())
+                      .setPaneInfo(kv.getValue().getPaneInfo())
+                      .output();
                 }
               }));
     }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Wait.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Wait.java
index 454fc4dbcd21..3177de818fec 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Wait.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Wait.java
@@ -135,7 +135,7 @@ public void startBundle() {
     }
 
     @ProcessElement
-    public void process(ProcessContext c, BoundedWindow w) {
+    public void process(ProcessContext unused, BoundedWindow w) {
       windows.add(w);
     }
 
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/join/CoGbkResult.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/join/CoGbkResult.java
index 2e26d13da547..f1a002d6277d 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/join/CoGbkResult.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/join/CoGbkResult.java
@@ -369,7 +369,7 @@ public <V> CoGbkResult and(TupleTag<V> tag, List<V> data) {
   }
 
   /** Returns an empty {@link CoGbkResult}. */
-  public static <V> CoGbkResult empty() {
+  public static CoGbkResult empty() {
     return new CoGbkResult(
         new CoGbkResultSchema(TupleTagList.empty()), new ArrayList<Iterable<?>>());
   }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/reflect/DoFnSignatures.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/reflect/DoFnSignatures.java
index 3e41d9d287b9..310736c014cc 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/reflect/DoFnSignatures.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/reflect/DoFnSignatures.java
@@ -410,7 +410,7 @@ public boolean hasParameter(Class<? extends Parameter> type) {
      * Returns the specified {@link Parameter} if it is known in this context. Throws {@link
      * IllegalStateException} if there is more than one instance of the parameter.
      */
-    public @Nullable <T extends Parameter> Optional<T> findParameter(Class<T> type) {
+    public <T extends Parameter> Optional<T> findParameter(Class<T> type) {
       List<T> parameters = findParameters(type);
       switch (parameters.size()) {
         case 0:
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/splittabledofn/GrowableOffsetRangeTracker.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/splittabledofn/GrowableOffsetRangeTracker.java
index 33cd7aeb2d42..97b0d9b8e787 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/splittabledofn/GrowableOffsetRangeTracker.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/splittabledofn/GrowableOffsetRangeTracker.java
@@ -23,6 +23,7 @@
 import java.math.MathContext;
 import org.apache.beam.sdk.io.range.OffsetRange;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Suppliers;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.primitives.UnsignedLong;
 
 /**
  * An {@link OffsetRangeTracker} for tracking a growable offset range. {@code Long.MAX_VALUE} is
@@ -68,6 +69,7 @@ public GrowableOffsetRangeTracker(long start, RangeEndEstimator rangeEndEstimato
     this.rangeEndEstimator = checkNotNull(rangeEndEstimator);
   }
 
+  // TODO(sjvanrossum): Use UnsignedLong instead of BigDecimal for splitting ranges
   @Override
   public SplitResult<OffsetRange> trySplit(double fractionOfRemainder) {
     // If current tracking range is no longer growable, split it as a normal range.
@@ -115,30 +117,12 @@ public Progress getProgress() {
       return super.getProgress();
     }
 
-    // Convert to BigDecimal in computation to prevent overflow, which may result in lost of
-    // precision.
-    BigDecimal estimateRangeEnd = BigDecimal.valueOf(rangeEndEstimator.estimate());
-
-    if (lastAttemptedOffset == null) {
-      return Progress.from(
-          0,
-          estimateRangeEnd
-              .subtract(BigDecimal.valueOf(range.getFrom()), MathContext.DECIMAL128)
-              .max(BigDecimal.ZERO)
-              .doubleValue());
-    }
+    final long completedEnd = lastAttemptedOffset == null ? range.getFrom() : lastAttemptedOffset;
+    final long remainingEnd = Math.max(completedEnd, rangeEndEstimator.estimate());
 
-    BigDecimal workRemaining =
-        estimateRangeEnd
-            .subtract(BigDecimal.valueOf(lastAttemptedOffset), MathContext.DECIMAL128)
-            .max(BigDecimal.ZERO);
-    BigDecimal totalWork =
-        estimateRangeEnd
-            .max(BigDecimal.valueOf(lastAttemptedOffset))
-            .subtract(BigDecimal.valueOf(range.getFrom()), MathContext.DECIMAL128);
     return Progress.from(
-        totalWork.subtract(workRemaining, MathContext.DECIMAL128).doubleValue(),
-        workRemaining.doubleValue());
+        UnsignedLong.fromLongBits(completedEnd - range.getFrom()).doubleValue(),
+        UnsignedLong.fromLongBits(remainingEnd - completedEnd).doubleValue());
   }
 
   @Override
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/splittabledofn/RestrictionTracker.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/splittabledofn/RestrictionTracker.java
index 8ab8d3ac8b40..0c0b9f72647a 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/splittabledofn/RestrictionTracker.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/splittabledofn/RestrictionTracker.java
@@ -211,6 +211,6 @@ public static <RestrictionT> TruncateResult of(RestrictionT restriction) {
       return new AutoValue_RestrictionTracker_TruncateResult(restriction);
     }
 
-    public abstract @Nullable RestrictionT getTruncatedRestriction();
+    public abstract RestrictionT getTruncatedRestriction();
   }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/ByteStringOutputStream.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/ByteStringOutputStream.java
index 76a6b18890ba..ade84f7a6436 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/ByteStringOutputStream.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/ByteStringOutputStream.java
@@ -158,6 +158,16 @@ public ByteString toByteStringAndReset() {
     return rval;
   }
 
+  /*
+   * Resets the output stream to be re-used possibly re-using any existing buffers.
+   */
+  public void reset() {
+    if (size() == 0) {
+      return;
+    }
+    toByteStringAndReset();
+  }
+
   /**
    * Creates a byte string with the given size containing the prefix of the contents of this output
    * stream.
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/CombineFnUtil.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/CombineFnUtil.java
index 20d6325b79b9..f6105445c16b 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/CombineFnUtil.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/CombineFnUtil.java
@@ -42,7 +42,7 @@ public class CombineFnUtil {
    *
    * <p>The returned {@link CombineFn} cannot be serialized.
    */
-  public static <K, InputT, AccumT, OutputT> CombineFn<InputT, AccumT, OutputT> bindContext(
+  public static <InputT, AccumT, OutputT> CombineFn<InputT, AccumT, OutputT> bindContext(
       CombineFnWithContext<InputT, AccumT, OutputT> combineFn, StateContext<?> stateContext) {
     Context context = CombineContextFactory.createFromStateContext(stateContext);
     return new NonSerializableBoundedCombineFn<>(combineFn, context);
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/BeamCodegenUtils.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/Holder.java
similarity index 57%
rename from sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/BeamCodegenUtils.java
rename to sdks/java/core/src/main/java/org/apache/beam/sdk/util/Holder.java
index 029e3caa0904..8a2853c68742 100644
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/BeamCodegenUtils.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/Holder.java
@@ -15,26 +15,31 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation.impl;
+package org.apache.beam.sdk.util;
 
-import java.io.UnsupportedEncodingException;
 import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils;
-import org.joda.time.DateTime;
 
-/** BeamCodegenUtils. */
+/**
+ * A trivial boxing of a value, used when nullability needs to be added to a generic type. (Optional
+ * does not work for this)
+ *
+ * <p>Example: For a generic type `T` the actual parameter may be nullable or not. So you cannot
+ * check values for null to determine presence/absence. Instead you can store a {@code @Nullable
+ * Holder<T>}.
+ */
 @Internal
-public class BeamCodegenUtils {
-  // convert bytes to String in UTF8 encoding.
-  public static String toStringUTF8(byte[] bytes) {
-    try {
-      return new String(bytes, "UTF8");
-    } catch (UnsupportedEncodingException e) {
-      throw new RuntimeException(e);
-    }
+public class Holder<T> {
+  private T value;
+
+  private Holder(T value) {
+    this.value = value;
   }
 
-  public static String toStringTimestamp(long timestamp) {
-    return DateTimeUtils.formatTimestampWithTimeZone(new DateTime(timestamp));
+  public static <ValueT> Holder<ValueT> of(ValueT value) {
+    return new Holder<>(value);
   }
+
+  public T get() {
+    return value;
+  };
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/MoreFutures.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/MoreFutures.java
index 0999f2ad0771..441e604af3a8 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/MoreFutures.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/MoreFutures.java
@@ -159,6 +159,13 @@ public static <T> CompletionStage<List<T>> allAsList(
         nothing -> Arrays.stream(f).map(CompletableFuture::join).collect(Collectors.toList()));
   }
 
+  public static <T> CompletionStage<Void> allOf(
+      Collection<? extends CompletionStage<? extends T>> futures) {
+    // CompletableFuture.allOf completes exceptionally if any of the futures do.
+    CompletableFuture<? extends T>[] f = futuresToCompletableFutures(futures);
+    return CompletableFuture.allOf(f);
+  }
+
   /**
    * An object that represents either a result or an exceptional termination.
    *
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/OutputBuilderSupplier.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/OutputBuilderSupplier.java
new file mode 100644
index 000000000000..cee7fc5f607d
--- /dev/null
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/OutputBuilderSupplier.java
@@ -0,0 +1,29 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.util;
+
+import org.apache.beam.sdk.annotations.Internal;
+import org.apache.beam.sdk.values.WindowedValues;
+
+@Internal
+@FunctionalInterface
+public interface OutputBuilderSupplier {
+  // Returns WindowedValues.Builder so that downstream can setReceiver (when tag is specified)
+  // but we need the value at a minimum in order to fix the type variable
+  <OutputT> WindowedValues.Builder<OutputT> builder(OutputT value);
+}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlTypesUtils.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/OutputBuilderSuppliers.java
similarity index 57%
rename from sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlTypesUtils.java
rename to sdks/java/core/src/main/java/org/apache/beam/sdk/util/OutputBuilderSuppliers.java
index 3d8d47839586..e766982e295b 100644
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlTypesUtils.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/OutputBuilderSuppliers.java
@@ -15,28 +15,23 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-package org.apache.beam.sdk.extensions.sql.zetasql;
+package org.apache.beam.sdk.util;
 
-import java.math.BigDecimal;
 import org.apache.beam.sdk.annotations.Internal;
+import org.apache.beam.sdk.values.WindowedValue;
+import org.apache.beam.sdk.values.WindowedValues;
 
-/** Utils to deal with ZetaSQL type generation. */
+/** Implementations of {@link OutputBuilderSupplier}. */
 @Internal
-public class ZetaSqlTypesUtils {
+public class OutputBuilderSuppliers {
+  private OutputBuilderSuppliers() {}
 
-  private ZetaSqlTypesUtils() {}
-
-  /**
-   * Create a ZetaSQL NUMERIC value represented as BigDecimal.
-   *
-   * <p>ZetaSQL NUMERIC type is an exact numeric value with 38 digits of precision and 9 decimal
-   * digits of scale.
-   *
-   * <p>Precision is the number of digits that the number contains.
-   *
-   * <p>Scale is how many of these digits appear after the decimal point.
-   */
-  public static BigDecimal bigDecimalAsNumeric(String s) {
-    return new BigDecimal(s).setScale(9);
+  public static OutputBuilderSupplier supplierForElement(WindowedValue<?> templateValue) {
+    return new OutputBuilderSupplier() {
+      @Override
+      public <T> WindowedValues.Builder<T> builder(T value) {
+        return WindowedValues.builder(templateValue).withValue(value);
+      }
+    };
   }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/RowJsonUtils.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/RowJsonUtils.java
index 408143fb1ebe..c83048ca8def 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/RowJsonUtils.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/RowJsonUtils.java
@@ -17,6 +17,7 @@
  */
 package org.apache.beam.sdk.util;
 
+import com.fasterxml.jackson.core.JsonFactory;
 import com.fasterxml.jackson.core.JsonParseException;
 import com.fasterxml.jackson.core.JsonProcessingException;
 import com.fasterxml.jackson.databind.JsonMappingException;
@@ -34,9 +35,14 @@
 @Internal
 public class RowJsonUtils {
 
+  // The maximum string length for the JSON parser, set to 100 MB.
+  public static final int MAX_STRING_LENGTH = 100 * 1024 * 1024;
+
   //
   private static int defaultBufferLimit;
 
+  private static final boolean STREAM_READ_CONSTRAINTS_AVAILABLE = streamReadConstraintsAvailable();
+
   /**
    * Increase the default jackson-databind stream read constraint.
    *
@@ -63,14 +69,52 @@ public static void increaseDefaultStreamReadConstraints(int newLimit) {
   }
 
   static {
-    increaseDefaultStreamReadConstraints(100 * 1024 * 1024);
+    increaseDefaultStreamReadConstraints(MAX_STRING_LENGTH);
+  }
+
+  private static boolean streamReadConstraintsAvailable() {
+    try {
+      Class.forName("com.fasterxml.jackson.core.StreamReadConstraints");
+      return true;
+    } catch (ClassNotFoundException e) {
+      return false;
+    }
+  }
+
+  private static class StreamReadConstraintsHelper {
+    static void setStreamReadConstraints(JsonFactory jsonFactory, int sizeLimit) {
+      com.fasterxml.jackson.core.StreamReadConstraints streamReadConstraints =
+          com.fasterxml.jackson.core.StreamReadConstraints.builder()
+              .maxStringLength(sizeLimit)
+              .build();
+      jsonFactory.setStreamReadConstraints(streamReadConstraints);
+    }
+  }
+
+  /**
+   * Creates a thread-safe JsonFactory with custom stream read constraints.
+   *
+   * <p>This method encapsulates the logic to increase the default jackson-databind stream read
+   * constraint to 100MB. This functionality was introduced in Jackson 2.15 causing string > 20MB
+   * (5MB in <2.15.0) parsing failure. This has caused regressions in its dependencies including
+   * Beam. Here we create a streamReadConstraints minimum size limit set to 100MB and exposing the
+   * factory to higher limits. If needed, call this method during pipeline run time, e.g. in
+   * DoFn.setup. This avoids a data race caused by modifying the global default settings.
+   */
+  public static JsonFactory createJsonFactory(int sizeLimit) {
+    sizeLimit = Math.max(sizeLimit, MAX_STRING_LENGTH);
+    JsonFactory jsonFactory = new JsonFactory();
+    if (STREAM_READ_CONSTRAINTS_AVAILABLE) {
+      StreamReadConstraintsHelper.setStreamReadConstraints(jsonFactory, sizeLimit);
+    }
+    return jsonFactory;
   }
 
   public static ObjectMapper newObjectMapperWith(RowJson.RowJsonDeserializer deserializer) {
     SimpleModule module = new SimpleModule("rowDeserializationModule");
     module.addDeserializer(Row.class, deserializer);
 
-    ObjectMapper objectMapper = new ObjectMapper();
+    ObjectMapper objectMapper = new ObjectMapper(createJsonFactory(MAX_STRING_LENGTH));
     objectMapper.registerModule(module);
 
     return objectMapper;
@@ -80,7 +124,7 @@ public static ObjectMapper newObjectMapperWith(RowJson.RowJsonSerializer seriali
     SimpleModule module = new SimpleModule("rowSerializationModule");
     module.addSerializer(Row.class, serializer);
 
-    ObjectMapper objectMapper = new ObjectMapper();
+    ObjectMapper objectMapper = new ObjectMapper(createJsonFactory(MAX_STRING_LENGTH));
     objectMapper.registerModule(module);
 
     return objectMapper;
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/WindowedValueReceiver.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/WindowedValueReceiver.java
index 8c5b2434ae5a..a6c11d5a2798 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/WindowedValueReceiver.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/WindowedValueReceiver.java
@@ -25,5 +25,5 @@
 @FunctionalInterface
 public interface WindowedValueReceiver<OutputT> {
   /** Outputs a value with windowing information. */
-  void output(WindowedValue<OutputT> output);
+  void output(WindowedValue<OutputT> output) throws Exception;
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/GroupIntoBatchesTranslation.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/GroupIntoBatchesTranslation.java
index 7129854d44cc..e079cc3a91a1 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/GroupIntoBatchesTranslation.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/GroupIntoBatchesTranslation.java
@@ -78,7 +78,7 @@ public FunctionSpec translate(
     }
   }
 
-  private static <K, V> GroupIntoBatchesPayload getPayloadFromParameters(
+  private static GroupIntoBatchesPayload getPayloadFromParameters(
       GroupIntoBatches.BatchingParams params) {
     return RunnerApi.GroupIntoBatchesPayload.newBuilder()
         .setBatchSize(params.getBatchSize())
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/ParDoTranslation.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/ParDoTranslation.java
index 7c8cce8da3b4..d3ff5d1cc712 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/ParDoTranslation.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/ParDoTranslation.java
@@ -800,7 +800,7 @@ public static FunctionSpec translateViewFn(ViewFn<?, ?> viewFn, SdkComponents co
         .build();
   }
 
-  private static <T> ParDoPayload getParDoPayload(AppliedPTransform<?, ?, ?> transform)
+  private static ParDoPayload getParDoPayload(AppliedPTransform<?, ?, ?> transform)
       throws IOException {
     SdkComponents components = SdkComponents.create(transform.getPipeline().getOptions());
     RunnerApi.PTransform parDoPTransform =
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/SplittableParDo.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/SplittableParDo.java
index 3873d154a884..74af80d6feee 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/SplittableParDo.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/SplittableParDo.java
@@ -60,12 +60,14 @@
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
 import org.apache.beam.sdk.util.NameUtils;
+import org.apache.beam.sdk.util.OutputBuilderSupplier;
 import org.apache.beam.sdk.util.construction.PTransformTranslation.TransformPayloadTranslator;
 import org.apache.beam.sdk.util.construction.ParDoTranslation.ParDoLike;
 import org.apache.beam.sdk.util.construction.ParDoTranslation.ParDoLikeTimerFamilySpecs;
 import org.apache.beam.sdk.util.construction.ReadTranslation.BoundedReadPayloadTranslator;
 import org.apache.beam.sdk.util.construction.ReadTranslation.UnboundedReadPayloadTranslator;
 import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.OutputBuilder;
 import org.apache.beam.sdk.values.PBegin;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionTuple;
@@ -74,6 +76,7 @@
 import org.apache.beam.sdk.values.PValue;
 import org.apache.beam.sdk.values.TupleTag;
 import org.apache.beam.sdk.values.TupleTagList;
+import org.apache.beam.sdk.values.WindowedValues;
 import org.apache.beam.sdk.values.WindowingStrategy;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
@@ -242,7 +245,7 @@ public Map<TupleTag<?>, PValue> getAdditionalInputs() {
    */
   private static class ExplodeWindowsFn<InputT> extends DoFn<InputT, InputT> {
     @ProcessElement
-    public void process(ProcessContext c, BoundedWindow window) {
+    public void process(ProcessContext c, BoundedWindow unused) {
       c.output(c.element());
     }
   }
@@ -609,7 +612,19 @@ public void setup(PipelineOptions options) {
     }
 
     @ProcessElement
-    public void processElement(final ProcessContext c, BoundedWindow w) {
+    public void processElement(
+        final ProcessContext c,
+        BoundedWindow w,
+        OutputReceiver<KV<InputT, RestrictionT>> outputReceiver) {
+
+      OutputBuilderSupplier outputBuilderSupplier =
+          new OutputBuilderSupplier() {
+            @Override
+            public <OutputT> WindowedValues.Builder<OutputT> builder(OutputT value) {
+              return WindowedValues.builder(outputReceiver.builder(null)).withValue(value);
+            }
+          };
+
       invoker.invokeSplitRestriction(
           (ArgumentProvider)
               new BaseArgumentProvider<InputT, RestrictionT>() {
@@ -662,13 +677,16 @@ public OutputReceiver<RestrictionT> outputReceiver(
                     DoFn<InputT, RestrictionT> doFn) {
                   return new OutputReceiver<RestrictionT>() {
                     @Override
-                    public void output(RestrictionT part) {
-                      c.output(KV.of(c.element().getKey(), part));
-                    }
-
-                    @Override
-                    public void outputWithTimestamp(RestrictionT part, Instant timestamp) {
-                      throw new UnsupportedOperationException();
+                    public OutputBuilder<RestrictionT> builder(RestrictionT restriction) {
+                      // technically the windows and other aspects should not actually matter on a
+                      // restriction,
+                      // but it is better to propagate them and leave the checks in place than not
+                      // to
+                      return outputBuilderSupplier
+                          .builder(restriction)
+                          .setReceiver(
+                              windowedValue ->
+                                  c.output(KV.of(c.element().getKey(), windowedValue.getValue())));
                     }
                   };
                 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/SplittableParDoNaiveBounded.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/SplittableParDoNaiveBounded.java
index edae34fbecf9..e6394b8810a4 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/SplittableParDoNaiveBounded.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/SplittableParDoNaiveBounded.java
@@ -46,13 +46,16 @@
 import org.apache.beam.sdk.transforms.splittabledofn.WatermarkEstimator;
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.apache.beam.sdk.util.OutputBuilderSupplier;
 import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.OutputBuilder;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollection.IsBounded;
 import org.apache.beam.sdk.values.PCollectionTuple;
 import org.apache.beam.sdk.values.PCollectionView;
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.sdk.values.WindowedValues;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.util.concurrent.Uninterruptibles;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.joda.time.Instant;
@@ -188,7 +191,7 @@ public String getErrorContext() {
     }
 
     @ProcessElement
-    public void process(ProcessContext c, BoundedWindow w) {
+    public void process(ProcessContext c, BoundedWindow w, OutputReceiver<OutputT> outputReceiver) {
       WatermarkEstimatorStateT initialWatermarkEstimatorState =
           (WatermarkEstimatorStateT)
               invoker.invokeGetInitialWatermarkEstimatorState(
@@ -356,10 +359,26 @@ public String getErrorContext() {
                     return NaiveProcessFn.class.getSimpleName() + ".invokeNewWatermarkEstimator";
                   }
                 });
+
+        OutputBuilderSupplier outputBuilderSupplier =
+            new OutputBuilderSupplier() {
+              @Override
+              public <X> WindowedValues.Builder<X> builder(X value) {
+                return WindowedValues.builder(outputReceiver.builder(null)).withValue(value);
+              }
+            };
+
         ProcessContinuation continuation =
             invoker.invokeProcessElement(
                 new NestedProcessContext<>(
-                    fn, c, c.element().getKey(), w, tracker, watermarkEstimator, sideInputMapping));
+                    fn,
+                    c,
+                    outputBuilderSupplier,
+                    c.element().getKey(),
+                    w,
+                    tracker,
+                    watermarkEstimator,
+                    sideInputMapping));
         if (continuation.shouldResume()) {
           // Fetch the watermark before splitting to ensure that the watermark applies to both
           // the primary and the residual.
@@ -397,6 +416,29 @@ public void output(
                       "Output from FinishBundle for SDF is not supported in naive implementation");
                 }
 
+                @Override
+                public <T> void output(
+                    TupleTag<T> tag,
+                    T output,
+                    Instant timestamp,
+                    BoundedWindow window,
+                    @Nullable String currentRecordId,
+                    @Nullable Long currentRecordOffset) {
+                  throw new UnsupportedOperationException(
+                      "Output from FinishBundle for SDF is not supported in naive implementation");
+                }
+
+                @Override
+                public void output(
+                    @Nullable OutputT output,
+                    Instant timestamp,
+                    BoundedWindow window,
+                    @Nullable String currentRecordId,
+                    @Nullable Long currentRecordOffset) {
+                  throw new UnsupportedOperationException(
+                      "Output from FinishBundle for SDF is not supported in naive implementation");
+                }
+
                 @Override
                 public <T> void output(
                     TupleTag<T> tag, T output, Instant timestamp, BoundedWindow window) {
@@ -438,10 +480,12 @@ private static class NestedProcessContext<
       private final TrackerT tracker;
       private final WatermarkEstimatorT watermarkEstimator;
       private final Map<String, PCollectionView<?>> sideInputMapping;
+      private final OutputBuilderSupplier outputBuilderSupplier;
 
       private NestedProcessContext(
           DoFn<InputT, OutputT> fn,
           DoFn<KV<InputT, RestrictionT>, OutputT>.ProcessContext outerContext,
+          OutputBuilderSupplier outputBuilderSupplier,
           InputT element,
           BoundedWindow window,
           TrackerT tracker,
@@ -449,6 +493,7 @@ private NestedProcessContext(
           Map<String, PCollectionView<?>> sideInputMapping) {
         fn.super();
         this.window = window;
+        this.outputBuilderSupplier = outputBuilderSupplier;
         this.outerContext = outerContext;
         this.element = element;
         this.tracker = tracker;
@@ -524,22 +569,16 @@ public String timerId(DoFn<InputT, OutputT> doFn) {
       public OutputReceiver<OutputT> outputReceiver(DoFn<InputT, OutputT> doFn) {
         return new OutputReceiver<OutputT>() {
           @Override
-          public void output(OutputT output) {
-            outerContext.output(output);
-          }
-
-          @Override
-          public void outputWithTimestamp(OutputT output, Instant timestamp) {
-            outerContext.outputWithTimestamp(output, timestamp);
-          }
-
-          @Override
-          public void outputWindowedValue(
-              OutputT output,
-              Instant timestamp,
-              Collection<? extends BoundedWindow> windows,
-              PaneInfo paneInfo) {
-            outerContext.outputWindowedValue(output, timestamp, windows, paneInfo);
+          public OutputBuilder<OutputT> builder(OutputT value) {
+            return outputBuilderSupplier
+                .builder(value)
+                .setReceiver(
+                    windowedValue ->
+                        outerContext.outputWindowedValue(
+                            windowedValue.getValue(),
+                            windowedValue.getTimestamp(),
+                            windowedValue.getWindows(),
+                            windowedValue.getPaneInfo()));
           }
         };
       }
@@ -551,22 +590,17 @@ public MultiOutputReceiver taggedOutputReceiver(DoFn<InputT, OutputT> doFn) {
           public <T> OutputReceiver<T> get(TupleTag<T> tag) {
             return new OutputReceiver<T>() {
               @Override
-              public void output(T output) {
-                outerContext.output(tag, output);
-              }
-
-              @Override
-              public void outputWithTimestamp(T output, Instant timestamp) {
-                outerContext.outputWithTimestamp(tag, output, timestamp);
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  T output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                outerContext.outputWindowedValue(tag, output, timestamp, windows, paneInfo);
+              public OutputBuilder<T> builder(T value) {
+                return outputBuilderSupplier
+                    .builder(value)
+                    .setReceiver(
+                        windowedValue ->
+                            outerContext.outputWindowedValue(
+                                tag,
+                                windowedValue.getValue(),
+                                windowedValue.getTimestamp(),
+                                windowedValue.getWindows(),
+                                windowedValue.getPaneInfo()));
               }
             };
           }
@@ -617,6 +651,18 @@ public void outputWindowedValue(
         outerContext.outputWindowedValue(output, timestamp, windows, paneInfo);
       }
 
+      @Override
+      public void outputWindowedValue(
+          OutputT output,
+          Instant timestamp,
+          Collection<? extends BoundedWindow> windows,
+          PaneInfo paneInfo,
+          @Nullable String currentRecordId,
+          @Nullable Long currentRecordOffset) {
+        outerContext.outputWindowedValue(
+            output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset);
+      }
+
       @Override
       public <T> void output(TupleTag<T> tag, T output) {
         outerContext.output(tag, output);
@@ -637,6 +683,19 @@ public <T> void outputWindowedValue(
         outerContext.outputWindowedValue(tag, output, timestamp, windows, paneInfo);
       }
 
+      @Override
+      public <T> void outputWindowedValue(
+          TupleTag<T> tag,
+          T output,
+          Instant timestamp,
+          Collection<? extends BoundedWindow> windows,
+          PaneInfo paneInfo,
+          @Nullable String currentRecordId,
+          @Nullable Long currentRecordOffset) {
+        outerContext.outputWindowedValue(
+            tag, output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset);
+      }
+
       @Override
       public InputT element() {
         return element;
@@ -657,6 +716,16 @@ public PaneInfo pane() {
         return outerContext.pane();
       }
 
+      @Override
+      public String currentRecordId() {
+        return outerContext.currentRecordId();
+      }
+
+      @Override
+      public Long currentRecordOffset() {
+        return outerContext.currentRecordOffset();
+      }
+
       @Override
       public Object watermarkEstimatorState() {
         throw new UnsupportedOperationException(
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/TransformUpgrader.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/TransformUpgrader.java
index 28359b443afd..4268c6c70671 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/TransformUpgrader.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/TransformUpgrader.java
@@ -50,8 +50,6 @@
 import org.apache.beam.sdk.transformservice.launcher.TransformServiceLauncher;
 import org.apache.beam.sdk.util.ReleaseInfo;
 import org.apache.beam.sdk.util.construction.PTransformTranslation.TransformPayloadTranslator;
-import org.apache.beam.sdk.values.PInput;
-import org.apache.beam.sdk.values.POutput;
 import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.ByteString;
 import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.InvalidProtocolBufferException;
 import org.apache.beam.vendor.grpc.v1p69p0.io.grpc.ManagedChannelBuilder;
@@ -188,16 +186,12 @@ public RunnerApi.Pipeline upgradeTransformsViaTransformService(
     return pipeline;
   }
 
-  private <
-          InputT extends PInput,
-          OutputT extends POutput,
-          TransformT extends org.apache.beam.sdk.transforms.PTransform<InputT, OutputT>>
-      RunnerApi.Pipeline updateTransformViaTransformService(
-          RunnerApi.Pipeline runnerAPIpipeline,
-          String transformId,
-          Endpoints.ApiServiceDescriptor transformServiceEndpoint,
-          PipelineOptions options)
-          throws IOException {
+  private RunnerApi.Pipeline updateTransformViaTransformService(
+      RunnerApi.Pipeline runnerAPIpipeline,
+      String transformId,
+      Endpoints.ApiServiceDescriptor transformServiceEndpoint,
+      PipelineOptions options)
+      throws IOException {
     RunnerApi.PTransform transformToUpgrade =
         runnerAPIpipeline.getComponents().getTransformsMap().get(transformId);
     if (transformToUpgrade == null) {
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/UnconsumedReads.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/UnconsumedReads.java
index fafd385708b1..e0c049b98265 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/UnconsumedReads.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/util/construction/UnconsumedReads.java
@@ -69,6 +69,6 @@ private static <T> void consume(PCollection<T> unconsumedPCollection, int uniq)
 
   private static class NoOpDoFn<T> extends DoFn<T, T> {
     @ProcessElement
-    public void doNothing(ProcessContext context) {}
+    public void doNothing(ProcessContext unused) {}
   }
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/KV.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/KV.java
index 0744c2e008e4..da32ae3b0e99 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/KV.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/KV.java
@@ -40,6 +40,7 @@
  */
 public class KV<K, V> implements Serializable {
   /** Returns a {@link KV} with the given key and value. */
+  @Pure
   public static <K, V> KV<K, V> of(K key, V value) {
     return new KV<>(key, value);
   }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/OutputBuilder.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/OutputBuilder.java
new file mode 100644
index 000000000000..a7f8bc8e03b1
--- /dev/null
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/OutputBuilder.java
@@ -0,0 +1,52 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.values;
+
+import java.util.Collection;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import org.joda.time.Instant;
+
+/**
+ * A builder for an output, to set all the fields and extended metadata of a Beam value.
+ *
+ * <p>Which fields are required or allowed to be set depends on the context of the builder.
+ *
+ * <p>It is allowed to modify an instance and then call {@link #output()} again.
+ *
+ * <p>Not intended to be implemented by Beam users. This interface will be expanded in ways that are
+ * backwards-incompatible, by requiring implementors to add methods.
+ */
+public interface OutputBuilder<T> extends WindowedValue<T> {
+  OutputBuilder<T> setValue(T value);
+
+  OutputBuilder<T> setTimestamp(Instant timestamp);
+
+  OutputBuilder<T> setWindow(BoundedWindow window);
+
+  OutputBuilder<T> setWindows(Collection<? extends BoundedWindow> windows);
+
+  OutputBuilder<T> setPaneInfo(PaneInfo paneInfo);
+
+  OutputBuilder<T> setRecordId(@Nullable String recordId);
+
+  OutputBuilder<T> setRecordOffset(@Nullable Long recordOffset);
+
+  void output();
+}
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/Row.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/Row.java
index cf7ad3de7b7b..11d02be46d24 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/Row.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/Row.java
@@ -48,7 +48,6 @@
 import org.apache.beam.sdk.values.RowUtils.RowFieldMatcher;
 import org.apache.beam.sdk.values.RowUtils.RowPosition;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
-import org.checkerframework.checker.nullness.qual.NonNull;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.joda.time.DateTime;
 import org.joda.time.ReadableDateTime;
@@ -584,7 +583,7 @@ static int deepHashCodeForIterable(Iterable<Object> a, Schema.FieldType elementT
 
   @Override
   public String toString() {
-    return toString(true);
+    return SchemaUtils.toPrettyString(this);
   }
 
   /** Convert Row to String. */
@@ -838,7 +837,7 @@ public int nextFieldId() {
     }
 
     @Internal
-    public <@NonNull T> Row withFieldValueGetters(
+    public <T> Row withFieldValueGetters(
         Factory<List<FieldValueGetter<T, Object>>> fieldValueGetterFactory, T getterTarget) {
       checkState(getterTarget != null, "getters require withGetterTarget.");
       return new RowWithGetters<>(schema, fieldValueGetterFactory, getterTarget);
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/ShardedKey.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/ShardedKey.java
index b307196bc548..544a5a960828 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/ShardedKey.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/ShardedKey.java
@@ -21,7 +21,13 @@
 import java.util.Objects;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
-/** A key and a shard number. */
+/**
+ * A key and a shard number.
+ *
+ * @deprecated
+ *     <p>Use {@link org.apache.beam.sdk.util.ShardedKey} instead.
+ */
+@Deprecated
 public class ShardedKey<K> implements Serializable {
   private static final long serialVersionUID = 1L;
   private final K key;
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/ValueInSingleWindow.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/ValueInSingleWindow.java
index 74717fc606b2..7dc5fef52ecb 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/ValueInSingleWindow.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/ValueInSingleWindow.java
@@ -60,9 +60,24 @@ public T getValue() {
   /** Returns the pane of this {@code ValueInSingleWindow} in its window. */
   public abstract PaneInfo getPaneInfo();
 
+  public abstract @Nullable String getCurrentRecordId();
+
+  public abstract @Nullable Long getCurrentRecordOffset();
+
+  public static <T> ValueInSingleWindow<T> of(
+      T value,
+      Instant timestamp,
+      BoundedWindow window,
+      PaneInfo paneInfo,
+      @Nullable String currentRecordId,
+      @Nullable Long currentRecordOffset) {
+    return new AutoValue_ValueInSingleWindow<>(
+        value, timestamp, window, paneInfo, currentRecordId, currentRecordOffset);
+  }
+
   public static <T> ValueInSingleWindow<T> of(
       T value, Instant timestamp, BoundedWindow window, PaneInfo paneInfo) {
-    return new AutoValue_ValueInSingleWindow<>(value, timestamp, window, paneInfo);
+    return of(value, timestamp, window, paneInfo, null, null);
   }
 
   /** A coder for {@link ValueInSingleWindow}. */
@@ -110,7 +125,7 @@ public ValueInSingleWindow<T> decode(InputStream inStream, Context context) thro
       BoundedWindow window = windowCoder.decode(inStream);
       PaneInfo paneInfo = PaneInfo.PaneInfoCoder.INSTANCE.decode(inStream);
       T value = valueCoder.decode(inStream, context);
-      return new AutoValue_ValueInSingleWindow<>(value, timestamp, window, paneInfo);
+      return new AutoValue_ValueInSingleWindow<>(value, timestamp, window, paneInfo, null, null);
     }
 
     @Override
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/WindowedValue.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/WindowedValue.java
index 1dfa5feb7fd5..ea6be129ecb4 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/WindowedValue.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/WindowedValue.java
@@ -20,6 +20,8 @@
 import java.util.Collection;
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import org.checkerframework.dataflow.qual.Pure;
 import org.joda.time.Instant;
 
 /**
@@ -29,26 +31,38 @@
  */
 public interface WindowedValue<T> {
   /** The primary data for this value. */
+  @Pure
   T getValue();
 
   /** The timestamp of this value in event time. */
+  @Pure
   Instant getTimestamp();
 
   /** Returns the windows of this {@code WindowedValue}. */
+  @Pure
   Collection<? extends BoundedWindow> getWindows();
 
   /** The {@link PaneInfo} associated with this WindowedValue. */
+  @Pure
   PaneInfo getPaneInfo();
 
+  @Nullable
+  String getRecordId();
+
+  @Nullable
+  Long getRecordOffset();
+
   /**
    * A representation of each of the actual values represented by this compressed {@link
    * WindowedValue}, one per window.
    */
-  Iterable<WindowedValue<T>> explodeWindows();
+  @Pure
+  Iterable<? extends WindowedValue<T>> explodeWindows();
 
   /**
    * A {@link WindowedValue} with identical metadata to the current one, but with the provided
    * value.
    */
+  @Pure
   <OtherT> WindowedValue<OtherT> withValue(OtherT value);
 }
diff --git a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/WindowedValues.java b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/WindowedValues.java
index 9616fd845fa7..9b079b8699b9 100644
--- a/sdks/java/core/src/main/java/org/apache/beam/sdk/values/WindowedValues.java
+++ b/sdks/java/core/src/main/java/org/apache/beam/sdk/values/WindowedValues.java
@@ -17,8 +17,10 @@
  */
 package org.apache.beam.sdk.values;
 
+import static org.apache.beam.sdk.util.Preconditions.checkStateNotNull;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkNotNull;
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkState;
 
 import java.io.ByteArrayInputStream;
 import java.io.ByteArrayOutputStream;
@@ -45,14 +47,17 @@
 import org.apache.beam.sdk.transforms.windowing.GlobalWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo.PaneInfoCoder;
+import org.apache.beam.sdk.util.WindowedValueReceiver;
 import org.apache.beam.sdk.util.common.ElementByteSizeObserver;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.MoreObjects;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.checkerframework.checker.nullness.qual.MonotonicNonNull;
 import org.checkerframework.checker.nullness.qual.Nullable;
+import org.checkerframework.dataflow.qual.Pure;
 import org.joda.time.Instant;
 
 /**
- * Implementations of {@link WindowedValue} and static utility methods.
+ * Implementations of {@link org.apache.beam.sdk.values.WindowedValue} and static utility methods.
  *
  * <p>These are primarily intended for internal use by Beam SDK developers and runner developers.
  * Backwards incompatible changes will likely occur.
@@ -61,16 +66,193 @@
 public class WindowedValues {
   private WindowedValues() {} // non-instantiable utility class
 
-  /** Returns a {@code WindowedValue} with the given value, timestamp, and windows. */
+  public static <T> Builder<T> builder() {
+    return new Builder<>();
+  }
+
+  /** Create a Builder that takes element metadata from the provideed delegate. */
+  public static <T> Builder<T> builder(WindowedValue<T> template) {
+    return new Builder<T>()
+        .setValue(template.getValue())
+        .setTimestamp(template.getTimestamp())
+        .setWindows(template.getWindows())
+        .setPaneInfo(template.getPaneInfo());
+  }
+
+  public static class Builder<T> implements OutputBuilder<T> {
+
+    // Because T itself can be nullable, checking `maybeValue == null` cannot determine if it is set
+    // or
+    // not.
+    //
+    // Note also that JDK Optional class is written in such a way that it cannot have a nullable
+    // type
+    // for T (rendering it largely useless for its one reason for existing - composable
+    // presence/absence).
+    private @Nullable T maybeValue;
+    private boolean hasValue = false;
+
+    private @MonotonicNonNull WindowedValueReceiver<T> receiver;
+    private @MonotonicNonNull PaneInfo paneInfo;
+    private @MonotonicNonNull Instant timestamp;
+    private @MonotonicNonNull Collection<? extends BoundedWindow> windows;
+    private @Nullable String recordId;
+    private @Nullable Long recordOffset;
+
+    @Override
+    public Builder<T> setValue(T value) {
+      this.hasValue = true;
+      this.maybeValue = value;
+      return this;
+    }
+
+    @Override
+    public Builder<T> setTimestamp(Instant timestamp) {
+      this.timestamp = timestamp;
+      return this;
+    }
+
+    @Override
+    public Builder<T> setWindows(Collection<? extends BoundedWindow> windows) {
+      this.windows = windows;
+      return this;
+    }
+
+    @Override
+    public Builder<T> setPaneInfo(PaneInfo paneInfo) {
+      this.paneInfo = paneInfo;
+      return this;
+    }
+
+    @Override
+    public Builder<T> setWindow(BoundedWindow window) {
+      return setWindows(Collections.singleton(window));
+    }
+
+    @Override
+    public Builder<T> setRecordId(@Nullable String recordId) {
+      this.recordId = recordId;
+      return this;
+    }
+
+    @Override
+    public Builder<T> setRecordOffset(@Nullable Long recordOffset) {
+      this.recordOffset = recordOffset;
+      return this;
+    }
+
+    public Builder<T> setReceiver(WindowedValueReceiver<T> receiver) {
+      this.receiver = receiver;
+      return this;
+    }
+
+    @Override
+    public T getValue() {
+      // If T is itself a nullable type, then this checkState ensures it is set, whether or not it
+      // is null.
+      // If T is a non-nullable type, this checkState ensures it is not null.
+      checkState(hasValue, "Value not set");
+      return getValueIgnoringNullness();
+    }
+
+    // This method is a way to @Nullable T to polymorphic-in-nullness T
+    @SuppressWarnings("nullness")
+    T getValueIgnoringNullness() {
+      return maybeValue;
+    }
+
+    @Override
+    public Instant getTimestamp() {
+      checkStateNotNull(timestamp, "Timestamp not set");
+      return timestamp;
+    }
+
+    @Override
+    public Collection<? extends BoundedWindow> getWindows() {
+      checkStateNotNull(windows, "Windows not set");
+      return windows;
+    }
+
+    @Override
+    public PaneInfo getPaneInfo() {
+      checkStateNotNull(paneInfo, "PaneInfo not set");
+      return paneInfo;
+    }
+
+    @Override
+    public @Nullable String getRecordId() {
+      return recordId;
+    }
+
+    @Override
+    public @Nullable Long getRecordOffset() {
+      return recordOffset;
+    }
+
+    @Override
+    public Collection<Builder<T>> explodeWindows() {
+      throw new UnsupportedOperationException(
+          "Cannot explodeWindows() on WindowedValue builder; use build().explodeWindows()");
+    }
+
+    @Override
+    @Pure
+    public <OtherT> Builder<OtherT> withValue(OtherT newValue) {
+      // because of erasure, this type system lie is safe
+      return ((Builder<OtherT>) builder(this)).setValue(newValue);
+    }
+
+    @Override
+    public void output() {
+      try {
+        checkStateNotNull(receiver, "A WindowedValueReceiver must be set via setReceiver()")
+            .output(build());
+      } catch (Exception exc) {
+        if (exc instanceof RuntimeException) {
+          throw (RuntimeException) exc;
+        } else {
+          throw new RuntimeException("Exception thrown when outputting WindowedValue", exc);
+        }
+      }
+    }
+
+    public WindowedValue<T> build() {
+      return WindowedValues.of(getValue(), getTimestamp(), getWindows(), getPaneInfo());
+    }
+
+    @Override
+    public String toString() {
+      return MoreObjects.toStringHelper(this)
+          .add("value", getValue())
+          .add("timestamp", getTimestamp())
+          .add("windows", getWindows())
+          .add("paneInfo", getPaneInfo())
+          .add("receiver", receiver)
+          .toString();
+    }
+  }
+
   public static <T> WindowedValue<T> of(
       T value, Instant timestamp, Collection<? extends BoundedWindow> windows, PaneInfo paneInfo) {
+    return of(value, timestamp, windows, paneInfo, null, null);
+  }
+
+  /** Returns a {@code WindowedValue} with the given value, timestamp, and windows. */
+  public static <T> WindowedValue<T> of(
+      T value,
+      Instant timestamp,
+      Collection<? extends BoundedWindow> windows,
+      PaneInfo paneInfo,
+      @Nullable String currentRecordId,
+      @Nullable Long currentRecordOffset) {
     checkArgument(paneInfo != null, "WindowedValue requires PaneInfo, but it was null");
     checkArgument(windows.size() > 0, "WindowedValue requires windows, but there were none");
 
     if (windows.size() == 1) {
       return of(value, timestamp, windows.iterator().next(), paneInfo);
     } else {
-      return new TimestampedValueInMultipleWindows<>(value, timestamp, windows, paneInfo);
+      return new TimestampedValueInMultipleWindows<>(
+          value, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset);
     }
   }
 
@@ -81,7 +263,8 @@ static <T> WindowedValue<T> createWithoutValidation(
     if (windows.size() == 1) {
       return of(value, timestamp, windows.iterator().next(), paneInfo);
     } else {
-      return new TimestampedValueInMultipleWindows<>(value, timestamp, windows, paneInfo);
+      return new TimestampedValueInMultipleWindows<>(
+          value, timestamp, windows, paneInfo, null, null);
     }
   }
 
@@ -94,9 +277,9 @@ public static <T> WindowedValue<T> of(
     if (isGlobal && BoundedWindow.TIMESTAMP_MIN_VALUE.equals(timestamp)) {
       return valueInGlobalWindow(value, paneInfo);
     } else if (isGlobal) {
-      return new TimestampedValueInGlobalWindow<>(value, timestamp, paneInfo);
+      return new TimestampedValueInGlobalWindow<>(value, timestamp, paneInfo, null, null);
     } else {
-      return new TimestampedValueInSingleWindow<>(value, timestamp, window, paneInfo);
+      return new TimestampedValueInSingleWindow<>(value, timestamp, window, paneInfo, null, null);
     }
   }
 
@@ -105,7 +288,7 @@ public static <T> WindowedValue<T> of(
    * default timestamp and pane.
    */
   public static <T> WindowedValue<T> valueInGlobalWindow(T value) {
-    return new ValueInGlobalWindow<>(value, PaneInfo.NO_FIRING);
+    return new ValueInGlobalWindow<>(value, PaneInfo.NO_FIRING, null, null);
   }
 
   /**
@@ -113,7 +296,7 @@ public static <T> WindowedValue<T> valueInGlobalWindow(T value) {
    * default timestamp and the specified pane.
    */
   public static <T> WindowedValue<T> valueInGlobalWindow(T value, PaneInfo paneInfo) {
-    return new ValueInGlobalWindow<>(value, paneInfo);
+    return new ValueInGlobalWindow<>(value, paneInfo, null, null);
   }
 
   /**
@@ -124,7 +307,7 @@ public static <T> WindowedValue<T> timestampedValueInGlobalWindow(T value, Insta
     if (BoundedWindow.TIMESTAMP_MIN_VALUE.equals(timestamp)) {
       return valueInGlobalWindow(value);
     } else {
-      return new TimestampedValueInGlobalWindow<>(value, timestamp, PaneInfo.NO_FIRING);
+      return new TimestampedValueInGlobalWindow<>(value, timestamp, PaneInfo.NO_FIRING, null, null);
     }
   }
 
@@ -137,7 +320,7 @@ public static <T> WindowedValue<T> timestampedValueInGlobalWindow(
     if (paneInfo.equals(PaneInfo.NO_FIRING)) {
       return timestampedValueInGlobalWindow(value, timestamp);
     } else {
-      return new TimestampedValueInGlobalWindow<>(value, timestamp, paneInfo);
+      return new TimestampedValueInGlobalWindow<>(value, timestamp, paneInfo, null, null);
     }
   }
 
@@ -151,7 +334,9 @@ public static <OldT, NewT> WindowedValue<NewT> withValue(
         newValue,
         windowedValue.getTimestamp(),
         windowedValue.getWindows(),
-        windowedValue.getPaneInfo());
+        windowedValue.getPaneInfo(),
+        windowedValue.getRecordId(),
+        windowedValue.getRecordOffset());
   }
 
   public static <T> boolean equals(
@@ -200,10 +385,28 @@ private abstract static class SimpleWindowedValue<T> implements WindowedValue<T>
 
     private final T value;
     private final PaneInfo paneInfo;
+    private final @Nullable String currentRecordId;
+    private final @Nullable Long currentRecordOffset;
+
+    @Override
+    public @Nullable String getRecordId() {
+      return currentRecordId;
+    }
+
+    @Override
+    public @Nullable Long getRecordOffset() {
+      return currentRecordOffset;
+    }
 
-    protected SimpleWindowedValue(T value, PaneInfo paneInfo) {
+    protected SimpleWindowedValue(
+        T value,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
       this.value = value;
       this.paneInfo = checkNotNull(paneInfo);
+      this.currentRecordId = currentRecordId;
+      this.currentRecordOffset = currentRecordOffset;
     }
 
     @Override
@@ -228,12 +431,31 @@ public Iterable<WindowedValue<T>> explodeWindows() {
       }
       return windowedValues.build();
     }
+
+    @Override
+    public boolean equals(@Nullable Object other) {
+      if (!(other instanceof WindowedValue)) {
+        return false;
+      }
+
+      return WindowedValues.equals(this, (WindowedValue<T>) other);
+    }
+
+    @Override
+    public int hashCode() {
+      return WindowedValues.hashCode(this);
+    }
   }
 
   /** The abstract superclass of WindowedValue representations where timestamp == MIN. */
   private abstract static class MinTimestampWindowedValue<T> extends SimpleWindowedValue<T> {
-    public MinTimestampWindowedValue(T value, PaneInfo paneInfo) {
-      super(value, paneInfo);
+
+    public MinTimestampWindowedValue(
+        T value,
+        PaneInfo pane,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      super(value, pane, currentRecordId, currentRecordOffset);
     }
 
     @Override
@@ -246,8 +468,12 @@ public Instant getTimestamp() {
   private static class ValueInGlobalWindow<T> extends MinTimestampWindowedValue<T>
       implements SingleWindowedValue {
 
-    public ValueInGlobalWindow(T value, PaneInfo paneInfo) {
-      super(value, paneInfo);
+    public ValueInGlobalWindow(
+        T value,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      super(value, paneInfo, currentRecordId, currentRecordOffset);
     }
 
     @Override
@@ -262,7 +488,7 @@ public BoundedWindow getWindow() {
 
     @Override
     public <NewT> WindowedValue<NewT> withValue(NewT newValue) {
-      return new ValueInGlobalWindow<>(newValue, getPaneInfo());
+      return new ValueInGlobalWindow<>(newValue, getPaneInfo(), getRecordId(), getRecordOffset());
     }
 
     @Override
@@ -294,8 +520,13 @@ public String toString() {
   private abstract static class TimestampedWindowedValue<T> extends SimpleWindowedValue<T> {
     private final Instant timestamp;
 
-    public TimestampedWindowedValue(T value, Instant timestamp, PaneInfo paneInfo) {
-      super(value, paneInfo);
+    public TimestampedWindowedValue(
+        T value,
+        Instant timestamp,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      super(value, paneInfo, currentRecordId, currentRecordOffset);
       this.timestamp = checkNotNull(timestamp);
     }
 
@@ -312,8 +543,13 @@ public Instant getTimestamp() {
   private static class TimestampedValueInGlobalWindow<T> extends TimestampedWindowedValue<T>
       implements SingleWindowedValue {
 
-    public TimestampedValueInGlobalWindow(T value, Instant timestamp, PaneInfo paneInfo) {
-      super(value, timestamp, paneInfo);
+    public TimestampedValueInGlobalWindow(
+        T value,
+        Instant timestamp,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      super(value, timestamp, paneInfo, currentRecordId, currentRecordOffset);
     }
 
     @Override
@@ -328,7 +564,8 @@ public BoundedWindow getWindow() {
 
     @Override
     public <NewT> WindowedValue<NewT> withValue(NewT newValue) {
-      return new TimestampedValueInGlobalWindow<>(newValue, getTimestamp(), getPaneInfo());
+      return new TimestampedValueInGlobalWindow<>(
+          newValue, getTimestamp(), getPaneInfo(), getRecordId(), getRecordOffset());
     }
 
     @Override
@@ -372,14 +609,20 @@ private static class TimestampedValueInSingleWindow<T> extends TimestampedWindow
     private final BoundedWindow window;
 
     public TimestampedValueInSingleWindow(
-        T value, Instant timestamp, BoundedWindow window, PaneInfo paneInfo) {
-      super(value, timestamp, paneInfo);
+        T value,
+        Instant timestamp,
+        BoundedWindow window,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      super(value, timestamp, paneInfo, currentRecordId, currentRecordOffset);
       this.window = checkNotNull(window);
     }
 
     @Override
     public <NewT> WindowedValue<NewT> withValue(NewT newValue) {
-      return new TimestampedValueInSingleWindow<>(newValue, getTimestamp(), window, getPaneInfo());
+      return new TimestampedValueInSingleWindow<>(
+          newValue, getTimestamp(), window, getPaneInfo(), getRecordId(), getRecordOffset());
     }
 
     @Override
@@ -433,8 +676,10 @@ public TimestampedValueInMultipleWindows(
         T value,
         Instant timestamp,
         Collection<? extends BoundedWindow> windows,
-        PaneInfo paneInfo) {
-      super(value, timestamp, paneInfo);
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      super(value, timestamp, paneInfo, currentRecordId, currentRecordOffset);
       this.windows = checkNotNull(windows);
     }
 
@@ -446,7 +691,7 @@ public Collection<? extends BoundedWindow> getWindows() {
     @Override
     public <NewT> WindowedValue<NewT> withValue(NewT newValue) {
       return new TimestampedValueInMultipleWindows<>(
-          newValue, getTimestamp(), getWindows(), getPaneInfo());
+          newValue, getTimestamp(), getWindows(), getPaneInfo(), getRecordId(), getRecordOffset());
     }
 
     @Override
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/coders/ZstdCoderTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/coders/ZstdCoderTest.java
index 7dc8bdac8b44..1c07555666a3 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/coders/ZstdCoderTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/coders/ZstdCoderTest.java
@@ -107,6 +107,7 @@ public void testStructuralValueConsistentWithEquals() throws Exception {
   }
 
   @Test
+  @SuppressWarnings("JUnitIncompatibleType") // intended
   public void testCoderEquals() throws Exception {
     // True if coder, dict and level are equal.
     assertEquals(
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/io/CountingSourceTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/io/CountingSourceTest.java
index 9db9c8979b5b..70a09083619d 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/io/CountingSourceTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/io/CountingSourceTest.java
@@ -212,7 +212,7 @@ public void testUnboundedSourceWithRate() {
     Instant started = Instant.now();
     p.run();
     Instant finished = Instant.now();
-    Duration expectedDuration = period.multipliedBy((int) numElements);
+    Duration expectedDuration = period.multipliedBy(numElements);
     assertThat(started.plus(expectedDuration).isBefore(finished), is(true));
   }
 
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/io/FileSystemsTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/io/FileSystemsTest.java
index d3fcfb291fca..34567309c7d0 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/io/FileSystemsTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/io/FileSystemsTest.java
@@ -311,6 +311,7 @@ public void testValidMatchNewResourceForLocalFileSystem() {
     assertEquals("file", FileSystems.matchNewResource("c:\\tmp\\f1", false).getScheme());
   }
 
+  @SuppressWarnings("JUnitIncompatibleType")
   @Test(expected = IllegalArgumentException.class)
   public void testInvalidSchemaMatchNewResource() {
     assertEquals("file", FileSystems.matchNewResource("invalidschema://tmp/f1", false));
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/io/TextIOWriteTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/io/TextIOWriteTest.java
index eba0f793265d..bb60c7aef1d4 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/io/TextIOWriteTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/io/TextIOWriteTest.java
@@ -731,11 +731,20 @@ public void testWriteUnboundedWithCustomBatchParameters() throws Exception {
     input.apply(write);
     p.run();
 
+    // On some environments/runners, the exact shard filenames may not be materialized
+    // deterministically by the time we assert. Verify shard count via a glob, then
+    // validate contents using pattern matching.
+    String pattern = baseFilename.toString() + "*";
+    List<MatchResult> matches = FileSystems.match(Collections.singletonList(pattern));
+    List<Metadata> found = new ArrayList<>(Iterables.getOnlyElement(matches).metadata());
+    assertEquals(3, found.size());
+
+    // Now assert file contents irrespective of exact shard indices.
     assertOutputFiles(
         LINES2_ARRAY,
         null,
         null,
-        3,
+        0, // match all files by prefix
         baseFilename,
         DefaultFilenamePolicy.DEFAULT_UNWINDOWED_SHARD_TEMPLATE,
         false);
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/options/PipelineOptionsFactoryTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/options/PipelineOptionsFactoryTest.java
index 291bb5297880..5a112d5084dd 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/options/PipelineOptionsFactoryTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/options/PipelineOptionsFactoryTest.java
@@ -1673,7 +1673,7 @@ public void testUsingArgumentWithMisspelledPropertyGivesMultipleSuggestions() {
   public void testUsingArgumentWithUnknownPropertyIsIgnoredWithoutStrictParsing() {
     String[] args = new String[] {"--unknownProperty=value"};
     PipelineOptionsFactory.fromArgs(args).withoutStrictParsing().create();
-    expectedLogs.verifyWarn("missing a property named 'unknownProperty'");
+    expectedLogs.verifyWarn("Strict parsing is disabled, ignoring option 'unknownProperty'");
   }
 
   @Test
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/AutoValueSchemaTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/AutoValueSchemaTest.java
index d0ee623dea7c..d7a5c3862243 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/AutoValueSchemaTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/AutoValueSchemaTest.java
@@ -84,7 +84,7 @@ private Row createSimpleRow(String name) {
             BYTE_ARRAY,
             BYTE_ARRAY,
             BigDecimal.ONE,
-            new StringBuilder(name).append("builder").toString())
+            name + "builder")
         .build();
   }
 
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JavaBeanSchemaTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JavaBeanSchemaTest.java
index 5313feb5c6c0..736cc250a827 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JavaBeanSchemaTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JavaBeanSchemaTest.java
@@ -127,7 +127,7 @@ private Row createSimpleRow(String name) {
             BYTE_ARRAY,
             BYTE_ARRAY,
             BigDecimal.ONE,
-            new StringBuilder(name).append("builder").toString())
+            name + "builder")
         .build();
   }
 
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JavaFieldSchemaTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JavaFieldSchemaTest.java
index 11bef79b26f7..66794d5a512e 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JavaFieldSchemaTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JavaFieldSchemaTest.java
@@ -158,7 +158,7 @@ private Row createSimpleRow(String name) {
             BYTE_ARRAY,
             BYTE_BUFFER.array(),
             BigDecimal.ONE,
-            new StringBuilder(name).append("builder").toString())
+            name + "builder")
         .build();
   }
 
@@ -176,7 +176,7 @@ private Row createAnnotatedRow(String name) {
             BYTE_ARRAY,
             BYTE_BUFFER.array(),
             BigDecimal.ONE,
-            new StringBuilder(name).append("builder").toString())
+            name + "builder")
         .build();
   }
 
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JsonSchemaConversionTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JsonSchemaConversionTest.java
index 775f18ac87f0..e21c8930df1f 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JsonSchemaConversionTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/JsonSchemaConversionTest.java
@@ -22,12 +22,15 @@
 import static org.hamcrest.Matchers.containsString;
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertThrows;
+import static org.junit.Assert.assertTrue;
 
 import java.io.IOException;
 import java.io.InputStream;
+import java.util.Arrays;
 import java.util.stream.Collectors;
 import org.apache.beam.sdk.schemas.Schema.FieldType;
 import org.apache.beam.sdk.schemas.utils.JsonUtils;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.io.ByteStreams;
 import org.everit.json.schema.ValidationException;
 import org.json.JSONArray;
@@ -285,24 +288,38 @@ public void testArrayWithNestedRefsBeamSchema() throws IOException {
       String stringJsonSchema = new String(ByteStreams.toByteArray(inputStream), "UTF-8");
       Schema parsedSchema = JsonUtils.beamSchemaFromJsonSchema(stringJsonSchema);
 
-      assertThat(parsedSchema.getFieldNames(), containsInAnyOrder("vegetables"));
-      assertThat(
-          parsedSchema.getFields().stream().map(Schema.Field::getType).collect(Collectors.toList()),
-          containsInAnyOrder(
-              Schema.FieldType.array(
-                      Schema.FieldType.row(
-                          Schema.of(
-                              Schema.Field.of("veggieName", Schema.FieldType.STRING),
-                              Schema.Field.of("veggieLike", Schema.FieldType.BOOLEAN),
-                              Schema.Field.nullable(
-                                  "origin",
-                                  Schema.FieldType.row(
-                                      Schema.of(
-                                          Schema.Field.nullable("country", Schema.FieldType.STRING),
-                                          Schema.Field.nullable("town", Schema.FieldType.STRING),
-                                          Schema.Field.nullable(
-                                              "region", Schema.FieldType.STRING)))))))
-                  .withNullable(true)));
+      assertEquals("vegetables", Iterables.getOnlyElement(parsedSchema.getFieldNames()));
+      Schema.Field field = parsedSchema.getField("vegetables");
+
+      // Top-level fields include only one nullable field, so ordering should be preserved on that
+      // level.
+      assertEquals(
+          Arrays.asList("veggieName", "veggieLike", "origin"),
+          field.getType().getCollectionElementType().getRowSchema().getFieldNames());
+      // Inner schema contains multiple nullable fields, which can be out of order. Test the
+      // remaining using
+      // equivalency instead.
+      assertTrue(
+          field
+              .getType()
+              .equivalent(
+                  Schema.FieldType.array(
+                          Schema.FieldType.row(
+                              Schema.of(
+                                  Schema.Field.of("veggieName", Schema.FieldType.STRING),
+                                  Schema.Field.of("veggieLike", Schema.FieldType.BOOLEAN),
+                                  Schema.Field.nullable(
+                                      "origin",
+                                      Schema.FieldType.row(
+                                          Schema.of(
+                                              Schema.Field.nullable(
+                                                  "country", Schema.FieldType.STRING),
+                                              Schema.Field.nullable(
+                                                  "town", Schema.FieldType.STRING),
+                                              Schema.Field.nullable(
+                                                  "region", Schema.FieldType.STRING)))))))
+                      .withNullable(true),
+                  Schema.EquivalenceNullablePolicy.SAME));
     }
   }
 
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/transforms/GroupTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/transforms/GroupTest.java
index 357ef024bea9..9ecaafbff27f 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/transforms/GroupTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/transforms/GroupTest.java
@@ -671,7 +671,7 @@ public void process(@Element Row value) {
     pipeline.run();
   }
 
-  private static <T> Void containsKIterableVs(List<Row> expectedKvs, Iterable<Row> actualKvs) {
+  private static Void containsKIterableVs(List<Row> expectedKvs, Iterable<Row> actualKvs) {
     List<Matcher<? super Row>> matchers = new ArrayList<>();
     for (Row expected : expectedKvs) {
       List<Matcher> fieldMatchers = Lists.newArrayList();
@@ -687,7 +687,7 @@ private static <T> Void containsKIterableVs(List<Row> expectedKvs, Iterable<Row>
     return null;
   }
 
-  private static <T> Void containsKvRows(List<Row> expectedKvs, Iterable<Row> actualKvs) {
+  private static Void containsKvRows(List<Row> expectedKvs, Iterable<Row> actualKvs) {
     List<Matcher<? super Row>> matchers = new ArrayList<>();
     for (Row expected : expectedKvs) {
       matchers.add(new KvRowMatcher(equalTo(expected.getRow(0)), equalTo(expected.getRow(1))));
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/utils/TestJavaBeans.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/utils/TestJavaBeans.java
index b5ad6f989d9e..f8affb08ac95 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/utils/TestJavaBeans.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/schemas/utils/TestJavaBeans.java
@@ -275,7 +275,7 @@ public boolean equals(@Nullable Object o) {
           && Arrays.equals(bytes, that.bytes)
           && Objects.equals(byteBuffer, that.byteBuffer)
           && Objects.equals(bigDecimal, that.bigDecimal)
-          && Objects.equals(stringBuilder, that.stringBuilder);
+          && Objects.equals(stringBuilder.toString(), that.stringBuilder.toString());
     }
 
     @Override
@@ -462,7 +462,7 @@ public boolean equals(@Nullable Object o) {
           && Arrays.equals(bytes, that.bytes)
           && Objects.equals(byteBuffer, that.byteBuffer)
           && Objects.equals(bigDecimal, that.bigDecimal)
-          && Objects.equals(stringBuilder, that.stringBuilder);
+          && Objects.equals(stringBuilder.toString(), that.stringBuilder.toString());
     }
 
     @Override
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/transforms/GroupIntoBatchesTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/transforms/GroupIntoBatchesTest.java
index bc2aab2ba0ef..832eb03f05d1 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/transforms/GroupIntoBatchesTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/transforms/GroupIntoBatchesTest.java
@@ -82,9 +82,9 @@ public class GroupIntoBatchesTest implements Serializable {
   private static final Logger LOG = LoggerFactory.getLogger(GroupIntoBatchesTest.class);
   @Rule public transient TestPipeline pipeline = TestPipeline.create();
   @Rule public transient Timeout globalTimeout = Timeout.seconds(1200);
-  private transient ArrayList<KV<String, String>> data = createTestData(EVEN_NUM_ELEMENTS);
+  private transient List<KV<String, String>> data = createTestData(EVEN_NUM_ELEMENTS);
 
-  private static ArrayList<KV<String, String>> createTestData(long numElements) {
+  private static List<KV<String, String>> createTestData(long numElements) {
     String[] scientists = {
       "Einstein",
       "Darwin",
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/transforms/reflect/DoFnInvokersTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/transforms/reflect/DoFnInvokersTest.java
index c25677ef98ac..299c5d5c5906 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/transforms/reflect/DoFnInvokersTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/transforms/reflect/DoFnInvokersTest.java
@@ -27,7 +27,6 @@
 import static org.junit.Assert.assertNull;
 import static org.junit.Assert.assertSame;
 import static org.junit.Assert.assertThrows;
-import static org.junit.Assert.fail;
 import static org.mockito.ArgumentMatchers.any;
 import static org.mockito.ArgumentMatchers.eq;
 import static org.mockito.ArgumentMatchers.same;
@@ -41,7 +40,6 @@
 import java.io.OutputStream;
 import java.util.ArrayList;
 import java.util.Arrays;
-import java.util.Collection;
 import java.util.List;
 import org.apache.beam.sdk.coders.AtomicCoder;
 import org.apache.beam.sdk.coders.CoderException;
@@ -78,6 +76,8 @@
 import org.apache.beam.sdk.transforms.windowing.IntervalWindow;
 import org.apache.beam.sdk.transforms.windowing.PaneInfo;
 import org.apache.beam.sdk.util.UserCodeException;
+import org.apache.beam.sdk.values.OutputBuilder;
+import org.apache.beam.sdk.values.WindowedValues;
 import org.joda.time.Instant;
 import org.junit.Before;
 import org.junit.Rule;
@@ -549,25 +549,16 @@ public Object restriction() {
           }
 
           @Override
-          public OutputReceiver outputReceiver(DoFn doFn) {
+          public OutputReceiver<SomeRestriction> outputReceiver(DoFn doFn) {
             return new OutputReceiver<SomeRestriction>() {
               @Override
-              public void output(SomeRestriction output) {
-                outputs.add(output);
-              }
-
-              @Override
-              public void outputWithTimestamp(SomeRestriction output, Instant timestamp) {
-                fail("Unexpected output with timestamp");
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  SomeRestriction output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                fail("Unexpected outputWindowedValue");
+              public OutputBuilder<SomeRestriction> builder(SomeRestriction value) {
+                return WindowedValues.<SomeRestriction>builder()
+                    .setValue(value)
+                    .setTimestamp(mockTimestamp)
+                    .setWindow(mockWindow)
+                    .setPaneInfo(PaneInfo.NO_FIRING)
+                    .setReceiver(windowedValue -> outputs.add(windowedValue.getValue()));
               }
             };
           }
@@ -801,28 +792,18 @@ public OutputReceiver<String> outputReceiver(DoFn<String, String> doFn) {
               private boolean invoked;
 
               @Override
-              public void output(String output) {
-                assertFalse(invoked);
-                invoked = true;
-                assertEquals("foo", output);
-              }
-
-              @Override
-              public void outputWithTimestamp(String output, Instant instant) {
-                assertFalse(invoked);
-                invoked = true;
-                assertEquals("foo", output);
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  String output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                assertFalse(invoked);
-                invoked = true;
-                assertEquals("foo", output);
+              public OutputBuilder<String> builder(String value) {
+                return WindowedValues.<String>builder()
+                    .setValue(value)
+                    .setTimestamp(mockTimestamp)
+                    .setWindow(mockWindow)
+                    .setPaneInfo(PaneInfo.NO_FIRING)
+                    .setReceiver(
+                        windowedValue -> {
+                          assertFalse(invoked);
+                          invoked = true;
+                          assertEquals("foo", windowedValue.getValue());
+                        });
               }
             };
           }
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/util/ByteStringOutputStreamTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/util/ByteStringOutputStreamTest.java
index 37ce6a385abb..605d341d476f 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/util/ByteStringOutputStreamTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/util/ByteStringOutputStreamTest.java
@@ -223,6 +223,19 @@ public void appendEquivalentToOutputStreamWriterChar() throws IOException {
     }
   }
 
+  @Test
+  public void testReset() throws IOException {
+    try (ByteStringOutputStream stream = new ByteStringOutputStream()) {
+      stream.reset();
+      assertEquals(ByteString.EMPTY, stream.toByteString());
+      stream.append("test");
+      stream.reset();
+      assertEquals(ByteString.EMPTY, stream.toByteString());
+      stream.reset();
+      assertEquals(ByteString.EMPTY, stream.toByteString());
+    }
+  }
+
   // Grow the elements based upon an approximation of the fibonacci sequence.
   private static int next(int current) {
     double a = Math.max(1, current * (1 + Math.sqrt(5)) / 2.0);
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/util/construction/CombineTranslationTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/util/construction/CombineTranslationTest.java
index 468bce71475c..79cd79dcf3aa 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/util/construction/CombineTranslationTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/util/construction/CombineTranslationTest.java
@@ -209,7 +209,7 @@ public Coder<Void> getAccumulatorCoder(CoderRegistry registry, Coder<Integer> in
 
     @Override
     public Void extractOutput(Void accumulator) {
-      return accumulator;
+      return null;
     }
 
     @Override
@@ -219,7 +219,7 @@ public Void mergeAccumulators(Iterable<Void> accumulators) {
 
     @Override
     public Void addInput(Void accumulator, Integer input) {
-      return accumulator;
+      return null;
     }
 
     @Override
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/values/EncodableThrowableTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/values/EncodableThrowableTest.java
index 36eb7eb585a8..b9116dc2e352 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/values/EncodableThrowableTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/values/EncodableThrowableTest.java
@@ -37,14 +37,13 @@ public void testEquals() {
 
     EncodableThrowable comparable1 = EncodableThrowable.forThrowable(exception);
     EncodableThrowable comparable2 = EncodableThrowable.forThrowable(exception);
-
-    assertEquals(comparable1, comparable1);
     assertEquals(comparable1, comparable2);
   }
 
   @Test
+  @SuppressWarnings("JUnitIncompatibleType")
   public void testEqualsNonComparable() {
-    assertNotEquals(EncodableThrowable.forThrowable(new Exception()), new Throwable());
+    assertNotEquals(new Throwable(), EncodableThrowable.forThrowable(new Exception()));
   }
 
   @Test
diff --git a/sdks/java/core/src/test/java/org/apache/beam/sdk/values/TypeDescriptorsTest.java b/sdks/java/core/src/test/java/org/apache/beam/sdk/values/TypeDescriptorsTest.java
index e33186c2a3ff..74a61a19a57e 100644
--- a/sdks/java/core/src/test/java/org/apache/beam/sdk/values/TypeDescriptorsTest.java
+++ b/sdks/java/core/src/test/java/org/apache/beam/sdk/values/TypeDescriptorsTest.java
@@ -57,6 +57,7 @@ public void testTypeDescriptorsKV() throws Exception {
   }
 
   @Test
+  @SuppressWarnings("JUnitIncompatibleType")
   public void testTypeDescriptorsLists() throws Exception {
     TypeDescriptor<List<String>> descriptor = lists(strings());
     assertEquals(descriptor, new TypeDescriptor<List<String>>() {});
diff --git a/sdks/java/expansion-service/container/Dockerfile b/sdks/java/expansion-service/container/Dockerfile
index 1b83ec68b994..2688a3176713 100644
--- a/sdks/java/expansion-service/container/Dockerfile
+++ b/sdks/java/expansion-service/container/Dockerfile
@@ -24,6 +24,8 @@ ARG TARGETARCH
 WORKDIR /opt/apache/beam
 
 # Copy dependencies generated by the Gradle build.
+# TODO(https://github.com/apache/beam/issues/34098) remove when Beam moved to avro 1.12
+COPY target/avro.jar jars/
 COPY target/beam-sdks-java-io-expansion-service.jar jars/
 COPY target/beam-sdks-java-io-google-cloud-platform-expansion-service.jar jars/
 COPY target/beam-sdks-java-extensions-schemaio-expansion-service.jar jars/
diff --git a/sdks/java/expansion-service/container/build.gradle b/sdks/java/expansion-service/container/build.gradle
index cf81d462f08b..080eb68c3a2e 100644
--- a/sdks/java/expansion-service/container/build.gradle
+++ b/sdks/java/expansion-service/container/build.gradle
@@ -36,6 +36,8 @@ configurations {
 }
 
 dependencies {
+    // TODO(https://github.com/apache/beam/issues/34098) remove when Beam moved to avro 1.12
+    dockerDependency "org.apache.avro:avro:1.12.0"
     dockerDependency project(path: ":sdks:java:extensions:schemaio-expansion-service", configuration: "shadow")
     dockerDependency project(path: ":sdks:java:io:expansion-service", configuration: "shadow")
     dockerDependency project(path: ":sdks:java:io:google-cloud-platform:expansion-service", configuration: "shadow")
@@ -48,6 +50,8 @@ goBuild {
 
 task copyDockerfileDependencies(type: Copy) {
     from configurations.dockerDependency
+    // TODO(https://github.com/apache/beam/issues/34098) remove when Beam moved to avro 1.12
+    rename 'avro-.*.jar', 'avro.jar'
     rename 'beam-sdks-java-extensions-schemaio-expansion-service-.*.jar', 'beam-sdks-java-extensions-schemaio-expansion-service.jar'
     rename 'beam-sdks-java-io-expansion-service-.*.jar', 'beam-sdks-java-io-expansion-service.jar'
     rename 'beam-sdks-java-io-google-cloud-platform-expansion-service-.*.jar', 'beam-sdks-java-io-google-cloud-platform-expansion-service.jar'
diff --git a/sdks/java/expansion-service/src/main/java/org/apache/beam/sdk/expansion/service/ExpansionService.java b/sdks/java/expansion-service/src/main/java/org/apache/beam/sdk/expansion/service/ExpansionService.java
index 2bd45067918c..337868c71638 100644
--- a/sdks/java/expansion-service/src/main/java/org/apache/beam/sdk/expansion/service/ExpansionService.java
+++ b/sdks/java/expansion-service/src/main/java/org/apache/beam/sdk/expansion/service/ExpansionService.java
@@ -396,7 +396,7 @@ private static <ConfigT> Class<ConfigT> getConfigClass(
     return configurationClass;
   }
 
-  static <ConfigT> Row decodeConfigObjectRow(SchemaApi.Schema schema, ByteString payload) {
+  static Row decodeConfigObjectRow(SchemaApi.Schema schema, ByteString payload) {
     Schema payloadSchema = SchemaTranslation.schemaFromProto(schema);
 
     if (payloadSchema.getFieldCount() == 0) {
diff --git a/sdks/java/extensions/arrow/src/main/java/org/apache/beam/sdk/extensions/arrow/ArrowConversion.java b/sdks/java/extensions/arrow/src/main/java/org/apache/beam/sdk/extensions/arrow/ArrowConversion.java
index 78ba610ad4d1..e0dcedc47faf 100644
--- a/sdks/java/extensions/arrow/src/main/java/org/apache/beam/sdk/extensions/arrow/ArrowConversion.java
+++ b/sdks/java/extensions/arrow/src/main/java/org/apache/beam/sdk/extensions/arrow/ArrowConversion.java
@@ -154,11 +154,23 @@ public FieldType visit(ArrowType.Utf8 type) {
                     return FieldType.STRING;
                   }
 
+                  @Override
+                  public FieldType visit(ArrowType.Utf8View type) {
+                    throw new IllegalArgumentException(
+                        "Type \'" + type.toString() + "\' not supported.");
+                  }
+
                   @Override
                   public FieldType visit(ArrowType.Binary type) {
                     return FieldType.BYTES;
                   }
 
+                  @Override
+                  public FieldType visit(ArrowType.BinaryView type) {
+                    throw new IllegalArgumentException(
+                        "Type \'" + type.toString() + "\' not supported.");
+                  }
+
                   @Override
                   public FieldType visit(ArrowType.FixedSizeBinary type) {
                     return FieldType.logicalType(FixedBytes.of(type.getByteWidth()));
@@ -213,6 +225,12 @@ public FieldType visit(ArrowType.Duration type) {
                         "Type \'" + type.toString() + "\' not supported.");
                   }
 
+                  @Override
+                  public FieldType visit(ArrowType.ListView type) {
+                    throw new IllegalArgumentException(
+                        "Type \'" + type.toString() + "\' not supported.");
+                  }
+
                   @Override
                   public FieldType visit(ArrowType.LargeBinary type) {
                     throw new IllegalArgumentException(
@@ -376,6 +394,11 @@ public Optional<Function<Object, Object>> visit(ArrowType.Duration type) {
         throw new IllegalArgumentException("Type \'" + type.toString() + "\' not supported.");
       }
 
+      @Override
+      public Optional<Function<Object, Object>> visit(ArrowType.ListView listView) {
+        return Optional.empty();
+      }
+
       @Override
       public Optional<Function<Object, Object>> visit(ArrowType.Int type) {
         return Optional.empty();
@@ -391,11 +414,21 @@ public Optional<Function<Object, Object>> visit(ArrowType.Utf8 type) {
         return Optional.of((Object text) -> ((Text) text).toString());
       }
 
+      @Override
+      public Optional<Function<Object, Object>> visit(ArrowType.Utf8View utf8View) {
+        return Optional.empty();
+      }
+
       @Override
       public Optional<Function<Object, Object>> visit(ArrowType.Binary type) {
         return Optional.empty();
       }
 
+      @Override
+      public Optional<Function<Object, Object>> visit(ArrowType.BinaryView binaryView) {
+        return Optional.empty();
+      }
+
       @Override
       public Optional<Function<Object, Object>> visit(ArrowType.FixedSizeBinary type) {
         return Optional.empty();
diff --git a/sdks/java/extensions/avro/build.gradle b/sdks/java/extensions/avro/build.gradle
index 3d22befaf4d6..75eb474c60fe 100644
--- a/sdks/java/extensions/avro/build.gradle
+++ b/sdks/java/extensions/avro/build.gradle
@@ -72,7 +72,7 @@ dependencies {
     // Exclude Avro dependencies from "core" since Avro support moved to this extension
     exclude group: "org.apache.avro", module: "avro"
   }
-  testImplementation library.java.avro_tests
+  testImplementation project(path: ":sdks:java:extensions:avro:vendored-test", configuration: "shadowTest")
   testImplementation library.java.junit
   testImplementation "org.tukaani:xz:1.9" // marked as optional in avro
   testRuntimeOnly project(path: ":runners:direct-java", configuration: "shadow")
diff --git a/sdks/java/extensions/avro/vendored-test/build.gradle b/sdks/java/extensions/avro/vendored-test/build.gradle
new file mode 100644
index 000000000000..b0489c27a13b
--- /dev/null
+++ b/sdks/java/extensions/avro/vendored-test/build.gradle
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+plugins { id 'org.apache.beam.module' }
+applyJavaNature(
+        automaticModuleName: 'org.apache.beam.sdk.extensions.avro',
+        exportJavadoc: false,
+        shadowClosure: {
+            dependencies {
+                include(dependency("org.apache.avro:avro:1.11.3:tests"))
+            }
+        },
+)
+
+configurations.all {
+    resolutionStrategy.force "org.apache.avro:avro:1.11.3:tests"
+}
+
+dependencies {
+    testRuntimeOnly "org.apache.avro:avro:1.11.3:tests"
+}
+
+description = "Apache Beam :: SDKs :: Java :: Extensions :: Avro :: Vendored Tests"
+ext.summary = "Vendor Avro 1.11.3 tests for Beam, a workaround of Avro 1.11.4 not release test jar"
diff --git a/sdks/java/extensions/google-cloud-platform-core/src/main/java/org/apache/beam/sdk/extensions/gcp/util/GcsUtil.java b/sdks/java/extensions/google-cloud-platform-core/src/main/java/org/apache/beam/sdk/extensions/gcp/util/GcsUtil.java
index caa59c87b5dd..77670eafbb40 100644
--- a/sdks/java/extensions/google-cloud-platform-core/src/main/java/org/apache/beam/sdk/extensions/gcp/util/GcsUtil.java
+++ b/sdks/java/extensions/google-cloud-platform-core/src/main/java/org/apache/beam/sdk/extensions/gcp/util/GcsUtil.java
@@ -207,6 +207,19 @@ public static GcsUtil create(
 
   private static final FluentBackoff BACKOFF_FACTORY =
       FluentBackoff.DEFAULT.withMaxRetries(10).withInitialBackoff(Duration.standardSeconds(1));
+  private static final RetryDeterminer<IOException> RETRY_DETERMINER =
+      new RetryDeterminer<IOException>() {
+        @Override
+        public boolean shouldRetry(IOException e) {
+          if (e instanceof GoogleJsonResponseException) {
+            int statusCode = ((GoogleJsonResponseException) e).getStatusCode();
+            return statusCode == 408 // Request Timeout
+                || statusCode == 429 // Too many requests
+                || (statusCode >= 500 && statusCode < 600); // Server errors
+          }
+          return RetryDeterminer.SOCKET_ERRORS.shouldRetry(e);
+        }
+      };
 
   /////////////////////////////////////////////////////////////////////////////
 
@@ -863,7 +876,7 @@ public boolean shouldRetry(IOException e) {
               if (errorExtractor.itemNotFound(e) || errorExtractor.accessDenied(e)) {
                 return false;
               }
-              return RetryDeterminer.SOCKET_ERRORS.shouldRetry(e);
+              return RETRY_DETERMINER.shouldRetry(e);
             }
           },
           IOException.class,
@@ -902,7 +915,7 @@ public boolean shouldRetry(IOException e) {
               if (errorExtractor.itemAlreadyExists(e) || errorExtractor.accessDenied(e)) {
                 return false;
               }
-              return RetryDeterminer.SOCKET_ERRORS.shouldRetry(e);
+              return RETRY_DETERMINER.shouldRetry(e);
             }
           },
           IOException.class,
@@ -940,7 +953,7 @@ public boolean shouldRetry(IOException e) {
               if (errorExtractor.itemNotFound(e) || errorExtractor.accessDenied(e)) {
                 return false;
               }
-              return RetryDeterminer.SOCKET_ERRORS.shouldRetry(e);
+              return RETRY_DETERMINER.shouldRetry(e);
             }
           },
           IOException.class,
@@ -977,7 +990,7 @@ private static void executeBatches(List<BatchInterface> batches) throws IOExcept
 
     try {
       try {
-        MoreFutures.get(MoreFutures.allAsList(futures));
+        MoreFutures.get(MoreFutures.allOf(futures));
       } catch (ExecutionException e) {
         if (e.getCause() instanceof FileNotFoundException) {
           throw (FileNotFoundException) e.getCause();
diff --git a/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoBeamConverter.java b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoBeamConverter.java
new file mode 100644
index 000000000000..d3295b386d15
--- /dev/null
+++ b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoBeamConverter.java
@@ -0,0 +1,588 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.protobuf;
+
+import com.google.protobuf.ByteString;
+import com.google.protobuf.Descriptors;
+import com.google.protobuf.DynamicMessage;
+import com.google.protobuf.Message;
+import com.google.protobuf.Timestamp;
+import java.io.IOException;
+import java.io.ObjectInputStream;
+import java.io.ObjectOutputStream;
+import java.time.Duration;
+import java.time.Instant;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.schemas.logicaltypes.EnumerationType;
+import org.apache.beam.sdk.schemas.logicaltypes.NanosDuration;
+import org.apache.beam.sdk.schemas.logicaltypes.NanosInstant;
+import org.apache.beam.sdk.schemas.logicaltypes.OneOfType;
+import org.apache.beam.sdk.transforms.SerializableFunction;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.checkerframework.checker.initialization.qual.UnknownInitialization;
+import org.checkerframework.checker.nullness.qual.EnsuresNonNull;
+import org.checkerframework.checker.nullness.qual.NonNull;
+import org.checkerframework.checker.nullness.qual.Nullable;
+
+/**
+ * Provides converts between Protobuf Message and Beam Row.
+ *
+ * <p>Read <a href="https://s.apache.org/beam-protobuf">https://s.apache.org/beam-protobuf</a>
+ */
+public class ProtoBeamConverter {
+
+  /** Returns a conversion method from Beam Row to Protobuf Message. */
+  public static SerializableFunction<Row, Message> toProto(Descriptors.Descriptor descriptor) {
+    return new ToProto(descriptor);
+  }
+
+  /** Returns a conversion method from Protobuf Message to Beam Row. */
+  public static SerializableFunction<Message, Row> toRow(Schema schema) {
+    return new FromProto(schema);
+  }
+
+  static ProtoToBeamConverter<Object, Object> createProtoToBeamConverter(
+      Schema.FieldType fieldType) {
+    switch (fieldType.getTypeName()) {
+      case INT32:
+      case INT64:
+      case FLOAT:
+      case DOUBLE:
+      case STRING:
+      case BOOLEAN:
+        return createWrappableProtoToBeamConverter(ProtoToBeamConverter.identity());
+      case BYTES:
+        return createWrappableProtoToBeamConverter(ByteString::toByteArray);
+      case ARRAY:
+      case ITERABLE:
+        ProtoToBeamConverter<Object, Object> elementConverter =
+            createProtoToBeamConverter(
+                Preconditions.checkNotNull(fieldType.getCollectionElementType()));
+        return proto ->
+            ((List<Object>) proto)
+                .stream()
+                    .map(element -> Preconditions.checkNotNull(elementConverter.convert(element)))
+                    .collect(Collectors.toList());
+      case MAP:
+        ProtoToBeamConverter<Object, Object> keyConverter =
+            createProtoToBeamConverter(Preconditions.checkNotNull(fieldType.getMapKeyType()));
+        ProtoToBeamConverter<Object, Object> valueConverter =
+            createProtoToBeamConverter(Preconditions.checkNotNull(fieldType.getMapValueType()));
+
+        return proto -> {
+          List<Message> list = (List<Message>) proto;
+          if (list.isEmpty()) {
+            return Collections.emptyMap();
+          }
+          Descriptors.Descriptor descriptor = list.get(0).getDescriptorForType();
+          Descriptors.FieldDescriptor keyFieldDescriptor = descriptor.findFieldByNumber(1);
+          Descriptors.FieldDescriptor valueFieldDescriptor = descriptor.findFieldByNumber(2);
+          return list.stream()
+              .collect(
+                  Collectors.toMap(
+                      protoElement ->
+                          keyConverter.convert(protoElement.getField(keyFieldDescriptor)),
+                      protoElement ->
+                          valueConverter.convert(protoElement.getField(valueFieldDescriptor)),
+                      (a, b) -> b));
+        };
+      case ROW:
+        SerializableFunction<Message, Row> converter =
+            toRow(Preconditions.checkNotNull(fieldType.getRowSchema()));
+        return message -> converter.apply((Message) message);
+
+      case LOGICAL_TYPE:
+        switch (Preconditions.checkNotNull(fieldType.getLogicalType()).getIdentifier()) {
+          case ProtoSchemaLogicalTypes.UInt32.IDENTIFIER:
+          case ProtoSchemaLogicalTypes.SInt32.IDENTIFIER:
+          case ProtoSchemaLogicalTypes.Fixed32.IDENTIFIER:
+          case ProtoSchemaLogicalTypes.SFixed32.IDENTIFIER:
+          case ProtoSchemaLogicalTypes.UInt64.IDENTIFIER:
+          case ProtoSchemaLogicalTypes.SInt64.IDENTIFIER:
+          case ProtoSchemaLogicalTypes.Fixed64.IDENTIFIER:
+          case ProtoSchemaLogicalTypes.SFixed64.IDENTIFIER:
+            return createWrappableProtoToBeamConverter(ProtoToBeamConverter.identity());
+          case NanosDuration.IDENTIFIER:
+            return proto -> {
+              Message message = (Message) proto;
+              Descriptors.Descriptor durationDescriptor = message.getDescriptorForType();
+              Descriptors.FieldDescriptor secondsFieldDescriptor =
+                  durationDescriptor.findFieldByNumber(1);
+              Descriptors.FieldDescriptor nanosFieldDescriptor =
+                  durationDescriptor.findFieldByNumber(2);
+              long seconds = (long) message.getField(secondsFieldDescriptor);
+              int nanos = (int) message.getField(nanosFieldDescriptor);
+              return Duration.ofSeconds(seconds, nanos);
+            };
+          case NanosInstant.IDENTIFIER:
+            return proto -> {
+              Message message = (Message) proto;
+              Descriptors.Descriptor timestampDescriptor = message.getDescriptorForType();
+              Descriptors.FieldDescriptor secondsFieldDescriptor =
+                  timestampDescriptor.findFieldByNumber(1);
+              Descriptors.FieldDescriptor nanosFieldDescriptor =
+                  timestampDescriptor.findFieldByNumber(2);
+              long seconds = (long) message.getField(secondsFieldDescriptor);
+              int nanos = (int) message.getField(nanosFieldDescriptor);
+              return Instant.ofEpochSecond(seconds, nanos);
+            };
+          case EnumerationType.IDENTIFIER:
+            EnumerationType enumerationType = fieldType.getLogicalType(EnumerationType.class);
+            return enumValue ->
+                enumerationType.toInputType(
+                    ((Descriptors.EnumValueDescriptor) enumValue).getNumber());
+          default:
+            throw new UnsupportedOperationException();
+        }
+      default:
+        throw new UnsupportedOperationException(
+            "Unsupported field type: " + fieldType.getTypeName());
+    }
+  }
+
+  static BeamToProtoConverter<Object, Object> createBeamToProtoConverter(
+      Descriptors.FieldDescriptor fieldDescriptor) {
+    if (fieldDescriptor.isRepeated()) {
+      if (fieldDescriptor.isMapField()) {
+        Descriptors.Descriptor mapDescriptor = fieldDescriptor.getMessageType();
+        Descriptors.FieldDescriptor keyDescriptor = mapDescriptor.findFieldByNumber(1);
+        Descriptors.FieldDescriptor valueDescriptor = mapDescriptor.findFieldByNumber(2);
+        BeamToProtoConverter<Object, Object> keyToProto =
+            createBeamToProtoSingularConverter(keyDescriptor);
+        BeamToProtoConverter<Object, Object> valueToProto =
+            createBeamToProtoSingularConverter(valueDescriptor);
+        return map -> {
+          ImmutableList.Builder<Message> protoList = ImmutableList.builder();
+          ((Map<Object, Object>) map)
+              .forEach(
+                  (k, v) -> {
+                    DynamicMessage.Builder message = DynamicMessage.newBuilder(mapDescriptor);
+                    Object protoKey = Preconditions.checkNotNull(keyToProto.convert(k));
+                    message.setField(keyDescriptor, protoKey);
+                    Object protoValue = Preconditions.checkNotNull(valueToProto.convert(v));
+                    message.setField(valueDescriptor, protoValue);
+                    protoList.add(message.build());
+                  });
+          return protoList.build();
+        };
+      } else {
+        BeamToProtoConverter<Object, Object> converter =
+            createBeamToProtoSingularConverter(fieldDescriptor);
+        return list ->
+            ((List<Object>) list)
+                .stream()
+                    .map(beamElement -> converter.convert(beamElement))
+                    .collect(Collectors.toList());
+      }
+    } else {
+      return createBeamToProtoSingularConverter(fieldDescriptor);
+    }
+  }
+
+  @SuppressWarnings({"JavaInstantGetSecondsGetNano", "JavaDurationGetSecondsGetNano"})
+  static BeamToProtoConverter<Object, Object> createBeamToProtoSingularConverter(
+      Descriptors.FieldDescriptor fieldDescriptor) {
+    switch (fieldDescriptor.getJavaType()) {
+      case INT:
+      case LONG:
+      case FLOAT:
+      case DOUBLE:
+      case BOOLEAN:
+      case STRING:
+        return createWrappableBeamToProtoConverter(
+            fieldDescriptor, BeamToProtoConverter.identity());
+      case BYTE_STRING:
+        return createWrappableBeamToProtoConverter(
+            fieldDescriptor, bytes -> ByteString.copyFrom((byte[]) bytes));
+      case ENUM:
+        return value ->
+            fieldDescriptor
+                .getEnumType()
+                .findValueByNumber(((EnumerationType.Value) value).getValue());
+      case MESSAGE:
+        String fullName = fieldDescriptor.getMessageType().getFullName();
+        switch (fullName) {
+          case "google.protobuf.Int32Value":
+          case "google.protobuf.UInt32Value":
+          case "google.protobuf.Int64Value":
+          case "google.protobuf.UInt64Value":
+          case "google.protobuf.FloatValue":
+          case "google.protobuf.DoubleValue":
+          case "google.protobuf.StringValue":
+          case "google.protobuf.BoolValue":
+            return createWrappableBeamToProtoConverter(
+                fieldDescriptor, BeamToProtoConverter.identity());
+          case "google.protobuf.BytesValue":
+            return createWrappableBeamToProtoConverter(
+                fieldDescriptor, bytes -> ByteString.copyFrom((byte[]) bytes));
+          case "google.protobuf.Timestamp":
+            return beam -> {
+              Instant instant = (Instant) beam;
+              return Timestamp.newBuilder()
+                  .setSeconds(instant.getEpochSecond())
+                  .setNanos(instant.getNano())
+                  .build();
+            };
+          case "google.protobuf.Duration":
+            return beam -> {
+              Duration duration = (Duration) beam;
+              return com.google.protobuf.Duration.newBuilder()
+                  .setSeconds(duration.getSeconds())
+                  .setNanos(duration.getNano())
+                  .build();
+            };
+          case "google.protobuf.Any":
+            throw new UnsupportedOperationException("google.protobuf.Any is not supported");
+          default:
+            SerializableFunction<Row, Message> converter =
+                toProto(fieldDescriptor.getMessageType());
+            return value -> converter.apply((Row) value);
+        }
+      default:
+        throw new UnsupportedOperationException(
+            "Unsupported proto type: " + fieldDescriptor.getJavaType());
+    }
+  }
+
+  /** Gets a converter from non-null Proto value to non-null Beam. */
+  static <ProtoUnwrappedT, BeamT>
+      ProtoToBeamConverter<Object, BeamT> createWrappableProtoToBeamConverter(
+          ProtoToBeamConverter<ProtoUnwrappedT, BeamT> converter) {
+    return protoValue -> {
+      @NonNull ProtoUnwrappedT unwrappedProtoValue;
+      if (protoValue instanceof Message) {
+        // A google protobuf wrapper
+        Message protoWrapper = (Message) protoValue;
+        Descriptors.FieldDescriptor wrapperValueFieldDescriptor =
+            protoWrapper.getDescriptorForType().findFieldByNumber(1);
+        unwrappedProtoValue =
+            (@NonNull ProtoUnwrappedT)
+                Preconditions.checkNotNull(protoWrapper.getField(wrapperValueFieldDescriptor));
+      } else {
+        unwrappedProtoValue = (@NonNull ProtoUnwrappedT) protoValue;
+      }
+      return converter.convert(unwrappedProtoValue);
+    };
+  }
+
+  static <BeamT, ProtoUnwrappedT>
+      BeamToProtoConverter<BeamT, Object> createWrappableBeamToProtoConverter(
+          Descriptors.FieldDescriptor fieldDescriptor,
+          BeamToProtoConverter<BeamT, ProtoUnwrappedT> converter) {
+    return beamValue -> {
+      ProtoUnwrappedT protoValue = converter.convert(beamValue);
+      if (fieldDescriptor.getJavaType() == Descriptors.FieldDescriptor.JavaType.MESSAGE) {
+        // A google.protobuf wrapper
+        Descriptors.Descriptor wrapperDescriptor = fieldDescriptor.getMessageType();
+        Descriptors.FieldDescriptor wrapperValueFieldDescriptor =
+            wrapperDescriptor.findFieldByNumber(1);
+        DynamicMessage.Builder wrapper = DynamicMessage.newBuilder(wrapperDescriptor);
+        wrapper.setField(wrapperValueFieldDescriptor, protoValue);
+        return wrapper.build();
+      } else {
+        return protoValue;
+      }
+    };
+  }
+
+  interface BeamToProtoConverter<BeamT, ProtoT> {
+    BeamToProtoConverter<?, ?> IDENTITY = value -> value;
+
+    static <T> BeamToProtoConverter<T, T> identity() {
+      return (BeamToProtoConverter<T, T>) IDENTITY;
+    }
+
+    @NonNull
+    ProtoT convert(@NonNull BeamT value);
+  }
+
+  interface FromProtoGetter<BeamT> {
+    @Nullable
+    BeamT getFromProto(Message message);
+  }
+
+  @FunctionalInterface
+  interface ProtoToBeamConverter<ProtoT, BeamT> {
+    ProtoToBeamConverter<?, ?> IDENTITY = protoValue -> protoValue;
+
+    static <T> ProtoToBeamConverter<T, T> identity() {
+      return (ProtoToBeamConverter<T, T>) IDENTITY;
+    }
+
+    @NonNull
+    BeamT convert(@NonNull ProtoT protoValue);
+  }
+
+  interface ToProtoSetter<BeamT> {
+    void setToProto(
+        Message.Builder message, Schema.FieldType fieldType, @Nullable BeamT beamFieldValue);
+  }
+
+  static class FromProto implements SerializableFunction<Message, Row> {
+    private transient Schema schema;
+    private transient List<FromProtoGetter<?>> toBeams;
+
+    public FromProto(Schema schema) {
+      initialize(schema);
+    }
+
+    @Override
+    public Row apply(Message message) {
+      Row.Builder rowBuilder = Row.withSchema(schema);
+      for (FromProtoGetter<?> toBeam : toBeams) {
+        rowBuilder.addValue(toBeam.getFromProto(message));
+      }
+      return rowBuilder.build();
+    }
+
+    @EnsuresNonNull({"this.schema", "this.toBeams"})
+    private void initialize(@UnknownInitialization FromProto this, Schema schema) {
+      this.schema = schema;
+      toBeams = new ArrayList<>();
+      for (Schema.Field field : schema.getFields()) {
+        Schema.FieldType fieldType = field.getType();
+        if (fieldType.isLogicalType(OneOfType.IDENTIFIER)) {
+          toBeams.add(new FromProtoOneOfGetter(field));
+        } else {
+          toBeams.add(new FromProtoFieldGetter<>(field));
+        }
+      }
+    }
+
+    private void writeObject(ObjectOutputStream oos) throws IOException {
+      oos.writeObject(schema);
+    }
+
+    private void readObject(ObjectInputStream ois) throws IOException, ClassNotFoundException {
+      initialize((Schema) ois.readObject());
+    }
+  }
+
+  static class FromProtoFieldGetter<ProtoT, BeamT> implements FromProtoGetter<BeamT> {
+    private final Schema.Field field;
+    private final ProtoToBeamConverter<ProtoT, BeamT> converter;
+
+    FromProtoFieldGetter(Schema.Field field) {
+      this.field = field;
+      converter = (ProtoToBeamConverter<ProtoT, BeamT>) createProtoToBeamConverter(field.getType());
+    }
+
+    @Override
+    public @Nullable BeamT getFromProto(Message message) {
+      try {
+        Descriptors.Descriptor descriptor = message.getDescriptorForType();
+        Descriptors.FieldDescriptor fieldDescriptor =
+            Preconditions.checkNotNull(descriptor.findFieldByName(field.getName()));
+
+        @Nullable Object protoValue;
+        if (field.getType().getNullable()
+            && ProtoSchemaTranslator.isNullable(fieldDescriptor)
+            && !message.hasField(fieldDescriptor)) {
+          // Set null field value only if the Beam field type is nullable and the proto value is
+          // null,
+          protoValue = null;
+        } else {
+          // can be a default value. e.g., an optional field.
+          protoValue = message.getField(fieldDescriptor);
+        }
+
+        return protoValue != null ? converter.convert((@NonNull ProtoT) protoValue) : null;
+      } catch (RuntimeException e) {
+        throw new RuntimeException(
+            String.format("Failed to get field from proto. field: %s", field.getName()), e);
+      }
+    }
+  }
+
+  static class FromProtoOneOfGetter implements FromProtoGetter<OneOfType.@Nullable Value> {
+    private final Schema.Field field;
+    private final OneOfType oneOfType;
+    private final Map<String, ProtoToBeamConverter<Object, Object>> converter;
+
+    FromProtoOneOfGetter(Schema.Field field) {
+      this.field = field;
+      this.oneOfType = Preconditions.checkNotNull(field.getType().getLogicalType(OneOfType.class));
+      this.converter = createConverters(oneOfType.getOneOfSchema());
+    }
+
+    private static Map<String, ProtoToBeamConverter<Object, Object>> createConverters(
+        Schema schema) {
+      Map<String, ProtoToBeamConverter<Object, Object>> converters = new HashMap<>();
+      for (Schema.Field field : schema.getFields()) {
+        converters.put(field.getName(), createProtoToBeamConverter(field.getType()));
+      }
+      return converters;
+    }
+
+    @Override
+    public OneOfType.@Nullable Value getFromProto(Message message) {
+      Descriptors.Descriptor descriptor = message.getDescriptorForType();
+      for (Map.Entry<String, ProtoToBeamConverter<Object, Object>> entry : converter.entrySet()) {
+        String subFieldName = entry.getKey();
+        try {
+          ProtoToBeamConverter<Object, Object> value = entry.getValue();
+          Descriptors.FieldDescriptor fieldDescriptor = descriptor.findFieldByName(subFieldName);
+          if (message.hasField(fieldDescriptor)) {
+            Object protoValue = message.getField(fieldDescriptor);
+            return oneOfType.createValue(subFieldName, value.convert(protoValue));
+          }
+        } catch (RuntimeException e) {
+          throw new RuntimeException(
+              String.format(
+                  "Failed to get oneof from proto. oneof: %s, subfield: %s",
+                  field.getName(), subFieldName),
+              e);
+        }
+      }
+      return null;
+    }
+  }
+
+  static class ToProto implements SerializableFunction<Row, Message> {
+    private transient Descriptors.Descriptor descriptor;
+    private transient Map<String, ToProtoSetter<Object>> toProtos;
+
+    public ToProto(Descriptors.Descriptor descriptor) {
+      initialize(descriptor);
+    }
+
+    @EnsuresNonNull({"this.descriptor", "this.toProtos"})
+    private void initialize(
+        @UnknownInitialization ToProto this, Descriptors.Descriptor descriptor) {
+      this.descriptor = descriptor;
+      toProtos = new LinkedHashMap<>();
+      for (Descriptors.FieldDescriptor fieldDescriptor : descriptor.getFields()) {
+        if (fieldDescriptor.getRealContainingOneof() != null) {
+          Descriptors.OneofDescriptor realContainingOneof =
+              fieldDescriptor.getRealContainingOneof();
+          if (realContainingOneof.getField(0) == fieldDescriptor) {
+            ToProtoSetter<?> setter = new ToProtoOneOfSetter(realContainingOneof);
+            toProtos.put(realContainingOneof.getName(), (ToProtoSetter<Object>) setter);
+          }
+          // continue
+        } else {
+          toProtos.put(fieldDescriptor.getName(), new ToProtoFieldSetter<>(fieldDescriptor));
+        }
+      }
+    }
+
+    @Override
+    public Message apply(Row row) {
+      Schema schema = row.getSchema();
+      DynamicMessage.Builder message = DynamicMessage.newBuilder(descriptor);
+      for (Map.Entry<String, ToProtoSetter<Object>> entry : toProtos.entrySet()) {
+        String fieldName = entry.getKey();
+        ToProtoSetter<Object> converter = entry.getValue();
+        converter.setToProto(
+            message, schema.getField(fieldName).getType(), row.getValue(fieldName));
+      }
+      return message.build();
+    }
+
+    // writeObject() needs to be implemented because Descriptor is not serializable.
+    private void writeObject(ObjectOutputStream oos) throws IOException {
+      ProtobufUtil.serializeDescriptor(oos, descriptor);
+    }
+
+    // readObject() needs to be implemented because Descriptor is not serializable.
+    private void readObject(ObjectInputStream ois) throws IOException, ClassNotFoundException {
+      initialize(ProtobufUtil.deserializeDescriptor(ois));
+    }
+  }
+
+  static class ToProtoFieldSetter<BeamT, ProtoT> implements ToProtoSetter<BeamT> {
+    private final Descriptors.FieldDescriptor fieldDescriptor;
+    private final BeamToProtoConverter<BeamT, ProtoT> converter;
+
+    ToProtoFieldSetter(Descriptors.FieldDescriptor fieldDescriptor) {
+      this.fieldDescriptor = fieldDescriptor;
+      this.converter =
+          (BeamToProtoConverter<BeamT, ProtoT>) createBeamToProtoConverter(fieldDescriptor);
+    }
+
+    @Override
+    public void setToProto(
+        Message.Builder message, Schema.FieldType fieldType, @Nullable BeamT beamFieldValue) {
+      try {
+        if (beamFieldValue != null) {
+          ProtoT protoValue = converter.convert(beamFieldValue);
+          message.setField(fieldDescriptor, protoValue);
+        }
+      } catch (RuntimeException e) {
+        throw new RuntimeException(
+            String.format("Failed to set field to proto. field:%s", fieldDescriptor.getName()), e);
+      }
+    }
+  }
+
+  static class ToProtoOneOfSetter implements ToProtoSetter<OneOfType.@Nullable Value> {
+    private final Descriptors.OneofDescriptor oneofDescriptor;
+    private final Map<String, ToProtoFieldSetter<Object, Object>> protoSetters;
+
+    ToProtoOneOfSetter(Descriptors.OneofDescriptor oneofDescriptor) {
+      this.oneofDescriptor = oneofDescriptor;
+      this.protoSetters = createConverters(oneofDescriptor.getFields());
+    }
+
+    private static Map<String, ToProtoFieldSetter<Object, Object>> createConverters(
+        List<Descriptors.FieldDescriptor> fieldDescriptors) {
+      Map<String, ToProtoFieldSetter<Object, Object>> converters = new LinkedHashMap<>();
+      for (Descriptors.FieldDescriptor fieldDescriptor : fieldDescriptors) {
+        Preconditions.checkState(!fieldDescriptor.isRepeated());
+        converters.put(fieldDescriptor.getName(), new ToProtoFieldSetter<>(fieldDescriptor));
+      }
+      return converters;
+    }
+
+    @Override
+    public void setToProto(
+        Message.Builder message, Schema.FieldType fieldType, OneOfType.@Nullable Value oneOfValue) {
+      if (oneOfValue != null) {
+        OneOfType oneOfType = fieldType.getLogicalType(OneOfType.class);
+        int number = oneOfValue.getCaseType().getValue();
+        try {
+          String subFieldName =
+              Preconditions.checkNotNull(oneOfType.getCaseEnumType().getEnumName(number));
+
+          ToProtoFieldSetter<Object, Object> protoSetter =
+              Preconditions.checkNotNull(
+                  protoSetters.get(subFieldName), "No setter for field '%s'", subFieldName);
+          protoSetter.setToProto(
+              message,
+              oneOfType.getOneOfSchema().getField(subFieldName).getType(),
+              oneOfValue.getValue());
+        } catch (RuntimeException e) {
+          throw new RuntimeException(
+              String.format(
+                  "Failed to set oneof to proto. oneof: %s, number: %d",
+                  oneofDescriptor.getName(), number),
+              e);
+        }
+      }
+    }
+  }
+}
diff --git a/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteBuddyUtils.java b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteBuddyUtils.java
index 9fe6162ec936..98f80f6786c8 100644
--- a/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteBuddyUtils.java
+++ b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteBuddyUtils.java
@@ -78,6 +78,7 @@
 import net.bytebuddy.jar.asm.Label;
 import net.bytebuddy.matcher.ElementMatchers;
 import org.apache.beam.sdk.schemas.FieldValueGetter;
+import org.apache.beam.sdk.schemas.FieldValueHaver;
 import org.apache.beam.sdk.schemas.FieldValueSetter;
 import org.apache.beam.sdk.schemas.FieldValueTypeInformation;
 import org.apache.beam.sdk.schemas.Schema;
@@ -186,6 +187,7 @@ class ProtoByteBuddyUtils {
           TypeName.MAP, "putAll");
   private static final String DEFAULT_PROTO_GETTER_PREFIX = "get";
   private static final String DEFAULT_PROTO_SETTER_PREFIX = "set";
+  private static final String DEFAULT_PROTO_HAVER_PREFIX = "has";
 
   // https://github.com/apache/beam/issues/21626: there is a slight difference between 'protoc' and
   // Guava CaseFormat regarding the camel case conversion
@@ -247,6 +249,11 @@ static String protoSetterPrefix(FieldType fieldType) {
     return PROTO_SETTER_PREFIX.getOrDefault(fieldType.getTypeName(), DEFAULT_PROTO_SETTER_PREFIX);
   }
 
+  static String protoHaverName(String name) {
+    String camel = convertProtoPropertyNameToJavaPropertyName(name);
+    return DEFAULT_PROTO_HAVER_PREFIX + camel;
+  }
+
   static class ProtoConvertType extends ConvertType {
     ProtoConvertType(boolean returnRawValues) {
       super(returnRawValues);
@@ -493,7 +500,7 @@ public TypeConversion<StackManipulation> createSetterConversions(StackManipulati
 
   static <ProtoT> FieldValueGetter<@NonNull ProtoT, OneOfType.Value> createOneOfGetter(
       FieldValueTypeInformation typeInformation,
-      TreeMap<Integer, FieldValueGetter<@NonNull ProtoT, OneOfType.Value>> getterMethodMap,
+      Map<Integer, FieldValueGetter<@NonNull ProtoT, OneOfType.Value>> getterMethodMap,
       Class<ProtoT> protoClass,
       OneOfType oneOfType,
       Method getCaseMethod) {
@@ -555,7 +562,7 @@ public TypeConversion<StackManipulation> createSetterConversions(StackManipulati
   static <ProtoBuilderT extends MessageLite.Builder>
       FieldValueSetter<ProtoBuilderT, Object> createOneOfSetter(
           String name,
-          TreeMap<Integer, FieldValueSetter<ProtoBuilderT, Object>> setterMethodMap,
+          Map<Integer, FieldValueSetter<ProtoBuilderT, Object>> setterMethodMap,
           Class<ProtoBuilderT> protoBuilderClass) {
     Set<Integer> indices = setterMethodMap.keySet();
     boolean contiguous = isContiguous(indices);
@@ -986,7 +993,29 @@ public ByteCodeAppender appender(final Target implementationTarget) {
       return createOneOfGetter(
           fieldValueTypeInformation, oneOfGetters, clazz, oneOfType, caseMethod);
     } else {
-      return JavaBeanUtils.createGetter(fieldValueTypeInformation, typeConversionsFactory);
+      FieldValueGetter<@NonNull ProtoT, Object> getter =
+          JavaBeanUtils.createGetter(fieldValueTypeInformation, typeConversionsFactory);
+
+      @Nullable Method hasMethod = getProtoHaver(methods, field.getName());
+      if (hasMethod != null) {
+        FieldValueHaver<ProtoT> haver = JavaBeanUtils.createHaver(clazz, hasMethod);
+        return new FieldValueGetter<@NonNull ProtoT, Object>() {
+          @Override
+          public @Nullable Object get(@NonNull ProtoT object) {
+            if (haver.has(object)) {
+              return getter.get(object);
+            }
+            return null;
+          }
+
+          @Override
+          public String name() {
+            return getter.name();
+          }
+        };
+      } else {
+        return getter;
+      }
     }
   }
 
@@ -1020,6 +1049,13 @@ static Method getProtoGetter(Multimap<String, Method> methods, String name, Fiel
         .orElseThrow(IllegalArgumentException::new);
   }
 
+  static @Nullable Method getProtoHaver(Multimap<String, Method> methods, String name) {
+    return methods.get(protoHaverName(name)).stream()
+        .filter(m -> m.getParameterCount() == 0)
+        .findAny()
+        .orElse(null);
+  }
+
   public static @Nullable <ProtoBuilderT extends MessageLite.Builder>
       SchemaUserTypeCreator getBuilderCreator(
           TypeDescriptor<?> protoTypeDescriptor,
@@ -1107,10 +1143,13 @@ public ProtoCreatorFactory(
     }
 
     @Override
-    public Object create(Object... params) {
+    public Object create(@Nullable Object... params) {
       ProtoBuilderT builder = builderCreator.get();
       for (int i = 0; i < params.length; ++i) {
-        setters.get(i).set(builder, params[i]);
+        @Nullable Object param = params[i];
+        if (param != null) {
+          setters.get(i).set(builder, param);
+        }
       }
       return builder.build();
     }
diff --git a/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteUtils.java b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteUtils.java
index 6d048a088b73..2e8937e7a271 100644
--- a/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteUtils.java
+++ b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteUtils.java
@@ -319,7 +319,7 @@ private static ReadableByteChannel openLocalFile(String filePath) {
       List<ResourceId> rId =
           result.metadata().stream().map(MatchResult.Metadata::resourceId).collect(toList());
 
-      checkArgument(rId.size() == 1, "Expected exactly 1 file, but got " + rId.size() + " files.");
+      checkArgument(rId.size() == 1, "Expected exactly 1 file, but got %s files.", rId.size());
       return FileSystems.open(rId.get(0));
     } catch (IOException e) {
       throw new RuntimeException("Error when finding: " + filePath, e);
diff --git a/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoDynamicMessageSchema.java b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoDynamicMessageSchema.java
index 748131e6916d..1caeca339d39 100644
--- a/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoDynamicMessageSchema.java
+++ b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoDynamicMessageSchema.java
@@ -17,62 +17,31 @@
  */
 package org.apache.beam.sdk.extensions.protobuf;
 
-import static org.apache.beam.sdk.extensions.protobuf.ProtoSchemaTranslator.SCHEMA_OPTION_META_NUMBER;
-import static org.apache.beam.sdk.extensions.protobuf.ProtoSchemaTranslator.SCHEMA_OPTION_META_TYPE_NAME;
-import static org.apache.beam.sdk.extensions.protobuf.ProtoSchemaTranslator.getFieldNumber;
-import static org.apache.beam.sdk.extensions.protobuf.ProtoSchemaTranslator.withFieldNumber;
-
-import com.google.protobuf.ByteString;
 import com.google.protobuf.Descriptors;
-import com.google.protobuf.Descriptors.FieldDescriptor;
 import com.google.protobuf.DynamicMessage;
 import com.google.protobuf.Message;
 import java.io.Serializable;
-import java.time.Duration;
-import java.time.Instant;
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.Iterator;
-import java.util.List;
-import java.util.Map;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.schemas.logicaltypes.EnumerationType;
-import org.apache.beam.sdk.schemas.logicaltypes.NanosDuration;
-import org.apache.beam.sdk.schemas.logicaltypes.NanosInstant;
-import org.apache.beam.sdk.schemas.logicaltypes.OneOfType;
 import org.apache.beam.sdk.transforms.SerializableFunction;
 import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
 
+/** @deprecated Use {@link ProtoBeamConverter} */
 @SuppressWarnings({
   "rawtypes", // TODO(https://github.com/apache/beam/issues/20447)
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
 })
+@Deprecated
 public class ProtoDynamicMessageSchema<T> implements Serializable {
   public static final long serialVersionUID = 1L;
 
-  /**
-   * Context of the schema, the context can be generated from a source schema or descriptors. The
-   * ability of converting back from Row to proto depends on the type of context.
-   */
-  private final Context context;
-
-  /** The toRow function to convert the Message to a Row. */
-  private transient SerializableFunction<T, Row> toRowFunction;
-
-  /** The fromRow function to convert the Row to a Message. */
-  private transient SerializableFunction<Row, T> fromRowFunction;
+  private final Schema schema;
+  private final SerializableFunction<Row, Message> toProto;
+  private final SerializableFunction<Message, Row> fromProto;
 
-  /** List of field converters for each field in the row. */
-  private transient List<Convert> converters;
-
-  private ProtoDynamicMessageSchema(String messageName, ProtoDomain domain) {
-    this.context = new DescriptorContext(messageName, domain);
-    readResolve();
-  }
-
-  private ProtoDynamicMessageSchema(Context context) {
-    this.context = context;
-    readResolve();
+  private ProtoDynamicMessageSchema(Descriptors.Descriptor descriptor, Schema schema) {
+    this.schema = schema;
+    this.toProto = ProtoBeamConverter.toProto(descriptor);
+    this.fromProto = ProtoBeamConverter.toRow(schema);
   }
 
   /**
@@ -80,7 +49,9 @@ private ProtoDynamicMessageSchema(Context context) {
    * message need to be in the domain and needs to be the fully qualified name.
    */
   public static ProtoDynamicMessageSchema forDescriptor(ProtoDomain domain, String messageName) {
-    return new ProtoDynamicMessageSchema(messageName, domain);
+    Descriptors.Descriptor descriptor = domain.getDescriptor(messageName);
+    Schema schema = ProtoSchemaTranslator.getSchema(descriptor);
+    return new ProtoDynamicMessageSchema(descriptor, schema);
   }
 
   /**
@@ -89,753 +60,22 @@ public static ProtoDynamicMessageSchema forDescriptor(ProtoDomain domain, String
    */
   public static ProtoDynamicMessageSchema<DynamicMessage> forDescriptor(
       ProtoDomain domain, Descriptors.Descriptor descriptor) {
-    return new ProtoDynamicMessageSchema<>(descriptor.getFullName(), domain);
-  }
-
-  static ProtoDynamicMessageSchema<?> forContext(Context context, Schema.Field field) {
-    return new ProtoDynamicMessageSchema<>(context.getSubContext(field));
-  }
-
-  static ProtoDynamicMessageSchema<Message> forSchema(Schema schema) {
-    return new ProtoDynamicMessageSchema<>(new Context(schema, Message.class));
-  }
-
-  /** Initialize the transient fields after deserialization or construction. */
-  private Object readResolve() {
-    converters = createConverters(context.getSchema());
-    toRowFunction = new MessageToRowFunction();
-    fromRowFunction = new RowToMessageFunction();
-    return this;
-  }
-
-  Convert createConverter(Schema.Field field) {
-    Schema.FieldType fieldType = field.getType();
-    if (fieldType.getNullable()) {
-      Schema.Field valueField =
-          withFieldNumber(Schema.Field.of("value", Schema.FieldType.BOOLEAN), 1);
-      switch (fieldType.getTypeName()) {
-        case BYTE:
-        case INT16:
-        case INT32:
-        case INT64:
-        case FLOAT:
-        case DOUBLE:
-        case STRING:
-        case BOOLEAN:
-          return new WrapperConvert(field, new PrimitiveConvert(valueField));
-        case BYTES:
-          return new WrapperConvert(field, new BytesConvert(valueField));
-        case LOGICAL_TYPE:
-          String identifier = field.getType().getLogicalType().getIdentifier();
-          switch (identifier) {
-            case ProtoSchemaLogicalTypes.UInt32.IDENTIFIER:
-            case ProtoSchemaLogicalTypes.UInt64.IDENTIFIER:
-              return new WrapperConvert(field, new PrimitiveConvert(valueField));
-            default:
-          }
-          // fall through
-        default:
-      }
-    }
-
-    switch (fieldType.getTypeName()) {
-      case BYTE:
-      case INT16:
-      case INT32:
-      case INT64:
-      case FLOAT:
-      case DOUBLE:
-      case STRING:
-      case BOOLEAN:
-        return new PrimitiveConvert(field);
-      case BYTES:
-        return new BytesConvert(field);
-      case ARRAY:
-      case ITERABLE:
-        return new ArrayConvert(this, field);
-      case MAP:
-        return new MapConvert(this, field);
-      case LOGICAL_TYPE:
-        String identifier = field.getType().getLogicalType().getIdentifier();
-        switch (identifier) {
-          case ProtoSchemaLogicalTypes.Fixed32.IDENTIFIER:
-          case ProtoSchemaLogicalTypes.Fixed64.IDENTIFIER:
-          case ProtoSchemaLogicalTypes.SFixed32.IDENTIFIER:
-          case ProtoSchemaLogicalTypes.SFixed64.IDENTIFIER:
-          case ProtoSchemaLogicalTypes.SInt32.IDENTIFIER:
-          case ProtoSchemaLogicalTypes.SInt64.IDENTIFIER:
-          case ProtoSchemaLogicalTypes.UInt32.IDENTIFIER:
-          case ProtoSchemaLogicalTypes.UInt64.IDENTIFIER:
-            return new LogicalTypeConvert(field, fieldType.getLogicalType());
-          case NanosInstant.IDENTIFIER:
-            return new TimestampConvert(field);
-          case NanosDuration.IDENTIFIER:
-            return new DurationConvert(field);
-          case EnumerationType.IDENTIFIER:
-            return new EnumConvert(field, fieldType.getLogicalType());
-          case OneOfType.IDENTIFIER:
-            return new OneOfConvert(this, field, fieldType.getLogicalType());
-          default:
-            throw new IllegalStateException("Unexpected logical type : " + identifier);
-        }
-      case ROW:
-        return new MessageConvert(this, field);
-      default:
-        throw new IllegalStateException("Unexpected value: " + fieldType);
-    }
-  }
-
-  private List<Convert> createConverters(Schema schema) {
-    List<Convert> fieldOverlays = new ArrayList<>();
-    for (Schema.Field field : schema.getFields()) {
-      fieldOverlays.add(createConverter(field));
-    }
-    return fieldOverlays;
+    return forDescriptor(domain, descriptor.getFullName());
   }
 
   public Schema getSchema() {
-    return context.getSchema();
+    return schema;
   }
 
   public SerializableFunction<T, Row> getToRowFunction() {
-    return toRowFunction;
+    return message -> {
+      Message message2 = (Message) message;
+      return fromProto.apply(Preconditions.checkNotNull(message2));
+    };
   }
 
+  @SuppressWarnings("unchecked")
   public SerializableFunction<Row, T> getFromRowFunction() {
-    return fromRowFunction;
-  }
-
-  /**
-   * Context that only has enough information to convert a proto message to a Row. This can be used
-   * for arbitrary conventions, like decoding messages in proto options.
-   */
-  static class Context<T> implements Serializable {
-    private final Schema schema;
-
-    /**
-     * Base class for the protobuf message. Normally this is DynamicMessage, but as this schema
-     * class is also used to decode protobuf options this can be normal Message instances.
-     */
-    private Class<T> baseClass;
-
-    Context(Schema schema, Class<T> baseClass) {
-      this.schema = schema;
-      this.baseClass = baseClass;
-    }
-
-    public Schema getSchema() {
-      return schema;
-    }
-
-    public Class<T> getBaseClass() {
-      return baseClass;
-    }
-
-    public DynamicMessage.Builder invokeNewBuilder() {
-      throw new IllegalStateException("Should not be calling invokeNewBuilder");
-    }
-
-    public Context getSubContext(Schema.Field field) {
-      return new Context(field.getType().getRowSchema(), Message.class);
-    }
-  }
-
-  /**
-   * Context the contains the full {@link ProtoDomain} and a reference to the message name. The full
-   * domain is needed for creating Rows back to the original proto messages.
-   */
-  static class DescriptorContext extends Context<DynamicMessage> {
-    private final String messageName;
-    private final ProtoDomain domain;
-    private transient Descriptors.Descriptor descriptor;
-
-    DescriptorContext(String messageName, ProtoDomain domain) {
-      super(
-          ProtoSchemaTranslator.getSchema(domain.getDescriptor(messageName)), DynamicMessage.class);
-      this.messageName = messageName;
-      this.domain = domain;
-    }
-
-    @Override
-    public DynamicMessage.Builder invokeNewBuilder() {
-      if (descriptor == null) {
-        descriptor = domain.getDescriptor(messageName);
-      }
-      return DynamicMessage.newBuilder(descriptor);
-    }
-
-    @Override
-    public Context getSubContext(Schema.Field field) {
-      String messageName =
-          field.getType().getRowSchema().getOptions().getValue(SCHEMA_OPTION_META_TYPE_NAME);
-      return new DescriptorContext(messageName, domain);
-    }
-  }
-
-  /**
-   * Base converter class for converting from proto values to row values. The converter mainly works
-   * on fields in proto messages but also has methods to convert individual elements (example, for
-   * elements in Lists or Maps).
-   */
-  abstract static class Convert<ValueT, InT> {
-    private int number;
-
-    Convert(Schema.Field field) {
-      Schema.Options options = field.getOptions();
-      if (options.hasOption(SCHEMA_OPTION_META_NUMBER)) {
-        this.number = options.getValue(SCHEMA_OPTION_META_NUMBER);
-      } else {
-        this.number = -1;
-      }
-    }
-
-    FieldDescriptor getFieldDescriptor(Message message) {
-      return message.getDescriptorForType().findFieldByNumber(number);
-    }
-
-    FieldDescriptor getFieldDescriptor(Message.Builder message) {
-      return message.getDescriptorForType().findFieldByNumber(number);
-    }
-
-    /** Get a proto field and convert it into a row value. */
-    abstract Object getFromProtoMessage(Message message);
-
-    /** Convert a proto value into a row value. */
-    abstract ValueT convertFromProtoValue(Object object);
-
-    /** Convert a row value and set it on a proto message. */
-    abstract void setOnProtoMessage(Message.Builder object, InT value);
-
-    /** Convert a row value into a proto value. */
-    abstract Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value);
-  }
-
-  /** Converter for primitive proto values. */
-  static class PrimitiveConvert extends Convert<Object, Object> {
-    PrimitiveConvert(Schema.Field field) {
-      super(field);
-    }
-
-    @Override
-    Object getFromProtoMessage(Message message) {
-      FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-      return convertFromProtoValue(message.getField(fieldDescriptor));
-    }
-
-    @Override
-    Object convertFromProtoValue(Object object) {
-      return object;
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, Object value) {
-      message.setField(getFieldDescriptor(message), value);
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      return value;
-    }
-  }
-
-  /**
-   * Converter for Bytes. Protobuf Bytes are natively represented as ByteStrings that requires
-   * special handling for byte[] of size 0.
-   */
-  static class BytesConvert extends PrimitiveConvert {
-    BytesConvert(Schema.Field field) {
-      super(field);
-    }
-
-    @Override
-    Object convertFromProtoValue(Object object) {
-      // return object;
-      return ((ByteString) object).toByteArray();
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, Object value) {
-      if (value != null && ((byte[]) value).length > 0) {
-        // Protobuf messages BYTES doesn't like empty bytes?!
-        FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-        message.setField(fieldDescriptor, convertToProtoValue(fieldDescriptor, value));
-      }
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      if (value != null) {
-        return ByteString.copyFrom((byte[]) value);
-      }
-      return null;
-    }
-  }
-
-  /**
-   * Specific converter for Proto Wrapper values as they are translated into nullable row values.
-   */
-  static class WrapperConvert extends Convert<Object, Object> {
-    private Convert valueConvert;
-
-    WrapperConvert(Schema.Field field, Convert valueConvert) {
-      super(field);
-      this.valueConvert = valueConvert;
-    }
-
-    @Override
-    Object getFromProtoMessage(Message message) {
-      if (message.hasField(getFieldDescriptor(message))) {
-        Message wrapper = (Message) message.getField(getFieldDescriptor(message));
-        return valueConvert.getFromProtoMessage(wrapper);
-      }
-      return null;
-    }
-
-    @Override
-    Object convertFromProtoValue(Object object) {
-      return object;
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, Object value) {
-      if (value != null) {
-        DynamicMessage.Builder builder =
-            DynamicMessage.newBuilder(getFieldDescriptor(message).getMessageType());
-        valueConvert.setOnProtoMessage(builder, value);
-        message.setField(getFieldDescriptor(message), builder.build());
-      }
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      return value;
-    }
-  }
-
-  static class TimestampConvert extends Convert<Object, Object> {
-
-    TimestampConvert(Schema.Field field) {
-      super(field);
-    }
-
-    @Override
-    Object getFromProtoMessage(Message message) {
-      FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-      if (message.hasField(fieldDescriptor)) {
-        Message wrapper = (Message) message.getField(fieldDescriptor);
-        return convertFromProtoValue(wrapper);
-      }
-      return null;
-    }
-
-    @Override
-    Object convertFromProtoValue(Object object) {
-      Message timestamp = (Message) object;
-      Descriptors.Descriptor timestampDescriptor = timestamp.getDescriptorForType();
-      FieldDescriptor secondField = timestampDescriptor.findFieldByNumber(1);
-      FieldDescriptor nanoField = timestampDescriptor.findFieldByNumber(2);
-      long second = (long) timestamp.getField(secondField);
-      int nano = (int) timestamp.getField(nanoField);
-      return Instant.ofEpochSecond(second, nano);
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, Object value) {
-      if (value != null) {
-        FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-        message.setField(fieldDescriptor, convertToProtoValue(fieldDescriptor, value));
-      }
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      Instant ts = (Instant) value;
-      return com.google.protobuf.Timestamp.newBuilder()
-          .setSeconds(ts.getEpochSecond())
-          .setNanos(ts.getNano())
-          .build();
-    }
-  }
-
-  static class DurationConvert extends Convert<Object, Object> {
-
-    DurationConvert(Schema.Field field) {
-      super(field);
-    }
-
-    @Override
-    Object getFromProtoMessage(Message message) {
-      FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-      if (message.hasField(fieldDescriptor)) {
-        Message wrapper = (Message) message.getField(fieldDescriptor);
-        return convertFromProtoValue(wrapper);
-      }
-      return null;
-    }
-
-    @Override
-    Duration convertFromProtoValue(Object object) {
-      Message timestamp = (Message) object;
-      Descriptors.Descriptor timestampDescriptor = timestamp.getDescriptorForType();
-      FieldDescriptor secondField = timestampDescriptor.findFieldByNumber(1);
-      FieldDescriptor nanoField = timestampDescriptor.findFieldByNumber(2);
-      long second = (long) timestamp.getField(secondField);
-      int nano = (int) timestamp.getField(nanoField);
-      return Duration.ofSeconds(second, nano);
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, Object value) {
-      if (value != null) {
-        FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-        message.setField(fieldDescriptor, convertToProtoValue(fieldDescriptor, value));
-      }
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      Duration duration = (Duration) value;
-      return com.google.protobuf.Duration.newBuilder()
-          .setSeconds(duration.getSeconds())
-          .setNanos(duration.getNano())
-          .build();
-    }
-  }
-
-  static class MessageConvert extends Convert<Object, Object> {
-    private final SerializableFunction fromRowFunction;
-    private final SerializableFunction toRowFunction;
-
-    MessageConvert(ProtoDynamicMessageSchema rootProtoSchema, Schema.Field field) {
-      super(field);
-      ProtoDynamicMessageSchema protoSchema =
-          ProtoDynamicMessageSchema.forContext(rootProtoSchema.context, field);
-      toRowFunction = protoSchema.getToRowFunction();
-      fromRowFunction = protoSchema.getFromRowFunction();
-    }
-
-    @Override
-    Object getFromProtoMessage(Message message) {
-      FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-      if (message.hasField(fieldDescriptor)) {
-        return convertFromProtoValue(message.getField(fieldDescriptor));
-      }
-      return null;
-    }
-
-    @Override
-    Object convertFromProtoValue(Object object) {
-      return toRowFunction.apply(object);
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, Object value) {
-      if (value != null) {
-        FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-        message.setField(fieldDescriptor, convertToProtoValue(fieldDescriptor, value));
-      }
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      return fromRowFunction.apply(value);
-    }
-  }
-
-  /**
-   * Proto has a well defined way of storing maps, by having a Message with two fields, named "key"
-   * and "value" in a repeatable field. This overlay translates between Row.map and the Protobuf
-   * map.
-   */
-  static class MapConvert extends Convert<Map, Map> {
-    private Convert key;
-    private Convert value;
-
-    MapConvert(ProtoDynamicMessageSchema protoSchema, Schema.Field field) {
-      super(field);
-      Schema.FieldType fieldType = field.getType();
-      key = protoSchema.createConverter(Schema.Field.of("KEY", fieldType.getMapKeyType()));
-      value = protoSchema.createConverter(Schema.Field.of("VALUE", fieldType.getMapValueType()));
-    }
-
-    @Override
-    Map getFromProtoMessage(Message message) {
-      List<Message> list = (List<Message>) message.getField(getFieldDescriptor(message));
-      Map<Object, Object> rowMap = new HashMap<>();
-      if (list.size() == 0) {
-        return rowMap;
-      }
-      list.forEach(
-          entryMessage -> {
-            Descriptors.Descriptor entryDescriptor = entryMessage.getDescriptorForType();
-            FieldDescriptor keyFieldDescriptor = entryDescriptor.findFieldByName("key");
-            FieldDescriptor valueFieldDescriptor = entryDescriptor.findFieldByName("value");
-            rowMap.put(
-                key.convertFromProtoValue(entryMessage.getField(keyFieldDescriptor)),
-                this.value.convertFromProtoValue(entryMessage.getField(valueFieldDescriptor)));
-          });
-      return rowMap;
-    }
-
-    @Override
-    Map convertFromProtoValue(Object object) {
-      throw new RuntimeException("?");
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, Map map) {
-      if (map != null) {
-        FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-        List<Message> messageMap = new ArrayList<>();
-        map.forEach(
-            (k, v) -> {
-              DynamicMessage.Builder builder =
-                  DynamicMessage.newBuilder(fieldDescriptor.getMessageType());
-              FieldDescriptor keyFieldDescriptor =
-                  fieldDescriptor.getMessageType().findFieldByName("key");
-              builder.setField(
-                  keyFieldDescriptor, this.key.convertToProtoValue(keyFieldDescriptor, k));
-              FieldDescriptor valueFieldDescriptor =
-                  fieldDescriptor.getMessageType().findFieldByName("value");
-              builder.setField(
-                  valueFieldDescriptor, value.convertToProtoValue(valueFieldDescriptor, v));
-              messageMap.add(builder.build());
-            });
-        message.setField(fieldDescriptor, messageMap);
-      }
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      return value;
-    }
-  }
-
-  static class ArrayConvert extends Convert<List, List> {
-    private Convert element;
-
-    ArrayConvert(ProtoDynamicMessageSchema protoSchema, Schema.Field field) {
-      super(field);
-      Schema.FieldType collectionElementType = field.getType().getCollectionElementType();
-      this.element = protoSchema.createConverter(Schema.Field.of("ELEMENT", collectionElementType));
-    }
-
-    @Override
-    List getFromProtoMessage(Message message) {
-      FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-      return convertFromProtoValue(message.getField(fieldDescriptor));
-    }
-
-    @Override
-    List convertFromProtoValue(Object value) {
-      List list = (List) value;
-      List<Object> arrayList = new ArrayList<>();
-      list.forEach(
-          entry -> {
-            arrayList.add(element.convertFromProtoValue(entry));
-          });
-      return arrayList;
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, List list) {
-      if (list != null) {
-        FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-        List<Object> targetList = new ArrayList<>();
-        list.forEach(
-            (e) -> {
-              targetList.add(element.convertToProtoValue(fieldDescriptor, e));
-            });
-        message.setField(fieldDescriptor, targetList);
-      }
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      return value;
-    }
-  }
-
-  /** Enum overlay handles the conversion between a string and a ProtoBuf Enum. */
-  static class EnumConvert extends Convert<Object, Object> {
-    EnumerationType logicalType;
-
-    EnumConvert(Schema.Field field, Schema.LogicalType logicalType) {
-      super(field);
-      this.logicalType = (EnumerationType) logicalType;
-    }
-
-    @Override
-    Object getFromProtoMessage(Message message) {
-      FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-      return convertFromProtoValue(message.getField(fieldDescriptor));
-    }
-
-    @Override
-    EnumerationType.Value convertFromProtoValue(Object in) {
-      return logicalType.valueOf(((Descriptors.EnumValueDescriptor) in).getNumber());
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, Object value) {
-      FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-      message.setField(fieldDescriptor, convertToProtoValue(fieldDescriptor, value));
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      Descriptors.EnumDescriptor enumType = fieldDescriptor.getEnumType();
-      return enumType.findValueByNumber(((EnumerationType.Value) value).getValue());
-    }
-  }
-
-  /** Convert Proto oneOf fields into the {@link OneOfType} logical type. */
-  static class OneOfConvert extends Convert<OneOfType.Value, OneOfType.Value> {
-    OneOfType logicalType;
-    Map<Integer, Convert> oneOfConvert = new HashMap<>();
-
-    OneOfConvert(
-        ProtoDynamicMessageSchema protoSchema, Schema.Field field, Schema.LogicalType logicalType) {
-      super(field);
-      this.logicalType = (OneOfType) logicalType;
-      for (Schema.Field oneOfField : this.logicalType.getOneOfSchema().getFields()) {
-        int fieldNumber = getFieldNumber(oneOfField);
-        oneOfConvert.put(
-            fieldNumber,
-            new NullableConvert(
-                oneOfField, protoSchema.createConverter(oneOfField.withNullable(false))));
-      }
-    }
-
-    @Override
-    Object getFromProtoMessage(Message message) {
-      for (Map.Entry<Integer, Convert> entry : this.oneOfConvert.entrySet()) {
-        Object value = entry.getValue().getFromProtoMessage(message);
-        if (value != null) {
-          return logicalType.createValue(entry.getKey(), value);
-        }
-      }
-      return null;
-    }
-
-    @Override
-    OneOfType.Value convertFromProtoValue(Object in) {
-      throw new IllegalStateException("Value conversion can't be done outside a protobuf message");
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, OneOfType.Value oneOf) {
-      int caseIndex = oneOf.getCaseType().getValue();
-      oneOfConvert.get(caseIndex).setOnProtoMessage(message, oneOf.getValue());
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      throw new IllegalStateException("Value conversion can't be done outside a protobuf message");
-    }
-  }
-
-  /**
-   * This overlay handles nullable fields. If a primitive field needs to be nullable this overlay is
-   * wrapped around the original overlay.
-   */
-  static class NullableConvert extends Convert<Object, Object> {
-
-    private Convert fieldOverlay;
-
-    NullableConvert(Schema.Field field, Convert fieldOverlay) {
-      super(field);
-      this.fieldOverlay = fieldOverlay;
-    }
-
-    @Override
-    Object getFromProtoMessage(Message message) {
-      if (message.hasField(getFieldDescriptor(message))) {
-        return fieldOverlay.getFromProtoMessage(message);
-      }
-      return null;
-    }
-
-    @Override
-    Object convertFromProtoValue(Object object) {
-      throw new IllegalStateException("Value conversion can't be done outside a protobuf message");
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, Object value) {
-      if (value != null) {
-        fieldOverlay.setOnProtoMessage(message, value);
-      }
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      throw new IllegalStateException("Value conversion can't be done outside a protobuf message");
-    }
-  }
-
-  static class LogicalTypeConvert extends Convert<Object, Object> {
-
-    private Schema.LogicalType logicalType;
-
-    LogicalTypeConvert(Schema.Field field, Schema.LogicalType logicalType) {
-      super(field);
-      this.logicalType = logicalType;
-    }
-
-    @Override
-    Object getFromProtoMessage(Message message) {
-      FieldDescriptor fieldDescriptor = getFieldDescriptor(message);
-      return convertFromProtoValue(message.getField(fieldDescriptor));
-    }
-
-    @Override
-    Object convertFromProtoValue(Object object) {
-      return logicalType.toBaseType(object);
-    }
-
-    @Override
-    void setOnProtoMessage(Message.Builder message, Object value) {
-      message.setField(getFieldDescriptor(message), value);
-    }
-
-    @Override
-    Object convertToProtoValue(FieldDescriptor fieldDescriptor, Object value) {
-      return value;
-    }
-  }
-
-  private class MessageToRowFunction implements SerializableFunction<T, Row> {
-
-    private MessageToRowFunction() {}
-
-    @Override
-    public Row apply(T input) {
-      Schema schema = context.getSchema();
-      Row.Builder builder = Row.withSchema(schema);
-      for (Convert convert : converters) {
-        builder.addValue(convert.getFromProtoMessage((Message) input));
-      }
-      return builder.build();
-    }
-  }
-
-  private class RowToMessageFunction implements SerializableFunction<Row, T> {
-
-    private RowToMessageFunction() {}
-
-    @Override
-    public T apply(Row input) {
-      DynamicMessage.Builder builder = context.invokeNewBuilder();
-      Iterator values = input.getValues().iterator();
-      Iterator<Convert> convertIterator = converters.iterator();
-
-      for (int i = 0; i < input.getValues().size(); i++) {
-        Convert convert = convertIterator.next();
-        Object value = values.next();
-        convert.setOnProtoMessage(builder, value);
-      }
-      return (T) builder.build();
-    }
+    return row -> (T) toProto.apply(row);
   }
 }
diff --git a/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoSchemaTranslator.java b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoSchemaTranslator.java
index 734d2ba94307..7a186471c225 100644
--- a/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoSchemaTranslator.java
+++ b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtoSchemaTranslator.java
@@ -17,9 +17,6 @@
  */
 package org.apache.beam.sdk.extensions.protobuf;
 
-import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
-import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
-
 import com.google.protobuf.Descriptors;
 import com.google.protobuf.Descriptors.EnumValueDescriptor;
 import com.google.protobuf.Descriptors.FieldDescriptor;
@@ -44,6 +41,8 @@
 import org.apache.beam.sdk.schemas.logicaltypes.NanosDuration;
 import org.apache.beam.sdk.schemas.logicaltypes.NanosInstant;
 import org.apache.beam.sdk.schemas.logicaltypes.OneOfType;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Strings;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Maps;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Sets;
@@ -149,6 +148,17 @@ class ProtoSchemaTranslator {
   private static Map<Descriptors.Descriptor, @Nullable Schema> alreadyVisitedSchemas =
       new HashMap<Descriptors.Descriptor, @Nullable Schema>();
 
+  /**
+   * Returns {@code true} if the proto field converts to a nullable Beam field type, {@code false}
+   * otherwise.
+   */
+  static boolean isNullable(FieldDescriptor fieldDescriptor) {
+    // Set nullable for fields with presence (proto3 optional, message, group, extension,
+    // oneof-contained or explicit presence -- proto2 optional or required), but not
+    // "required" (to exclude proto2 required).
+    return fieldDescriptor.hasPresence() && !fieldDescriptor.isRequired();
+  }
+
   /** Attach a proto field number to a type. */
   static Field withFieldNumber(Field field, int number) {
     return field.withOptions(
@@ -186,7 +196,12 @@ static synchronized Schema getSchema(Descriptors.Descriptor descriptor) {
     of the first field in the OneOf as the location of the entire OneOf.*/
     Map<Integer, Field> oneOfFieldLocation = Maps.newHashMap();
     List<Field> fields = Lists.newArrayListWithCapacity(descriptor.getFields().size());
-    for (OneofDescriptor oneofDescriptor : descriptor.getOneofs()) {
+
+    // In proto3, an optional field is internally implemented by wrapping it in a synthetic oneof.
+    // The Descriptor.getRealOneOfs() method is then used to retrieve only the "real" oneofs that
+    // you explicitly defined, filtering out these automatically generated ones.
+    // https://github.com/protocolbuffers/protobuf/blob/main/docs/implementing_proto3_presence.md#updating-a-
+    for (OneofDescriptor oneofDescriptor : descriptor.getRealOneofs()) {
       List<Field> subFields = Lists.newArrayListWithCapacity(oneofDescriptor.getFieldCount());
       Map<String, Integer> enumIds = Maps.newHashMap();
       for (FieldDescriptor fieldDescriptor : oneofDescriptor.getFields()) {
@@ -196,19 +211,18 @@ static synchronized Schema getSchema(Descriptors.Descriptor descriptor) {
         subFields.add(
             withFieldNumber(
                 Field.nullable(fieldDescriptor.getName(), fieldType), fieldDescriptor.getNumber()));
-        checkArgument(
+        Preconditions.checkArgument(
             enumIds.putIfAbsent(fieldDescriptor.getName(), fieldDescriptor.getNumber()) == null);
       }
       FieldType oneOfType = FieldType.logicalType(OneOfType.create(subFields, enumIds));
       oneOfFieldLocation.put(
           oneofDescriptor.getFields().get(0).getNumber(),
-          Field.of(oneofDescriptor.getName(), oneOfType));
+          Field.nullable(oneofDescriptor.getName(), oneOfType));
     }
 
     for (Descriptors.FieldDescriptor fieldDescriptor : descriptor.getFields()) {
       int fieldDescriptorNumber = fieldDescriptor.getNumber();
-      if (!(oneOfComponentFields.contains(fieldDescriptorNumber)
-          && fieldDescriptor.getRealContainingOneof() != null)) {
+      if (!oneOfComponentFields.contains(fieldDescriptorNumber)) {
         // Store proto field number in metadata.
         FieldType fieldType = beamFieldTypeFromProtoField(fieldDescriptor);
         fields.add(
@@ -347,14 +361,15 @@ private static FieldType beamFieldTypeFromSingularProtoField(
           default:
             fieldType = FieldType.row(getSchema(protoFieldDescriptor.getMessageType()));
         }
-        // all messages are nullable in Proto
-        if (protoFieldDescriptor.isOptional()) {
-          fieldType = fieldType.withNullable(true);
-        }
         break;
       default:
         throw new RuntimeException("Field type not matched.");
     }
+
+    if (isNullable(protoFieldDescriptor)) {
+      fieldType = fieldType.withNullable(true);
+    }
+
     return fieldType;
   }
 
@@ -371,34 +386,37 @@ private static Schema.Options.Builder getOptions(
     Schema.Options.Builder optionsBuilder = Schema.Options.builder();
     for (Map.Entry<FieldDescriptor, Object> entry : allFields.entrySet()) {
       FieldDescriptor fieldDescriptor = entry.getKey();
-      FieldType fieldType = beamFieldTypeFromProtoField(fieldDescriptor);
-
-      switch (fieldType.getTypeName()) {
-        case BYTE:
-        case BYTES:
-        case INT16:
-        case INT32:
-        case INT64:
-        case DECIMAL:
-        case FLOAT:
-        case DOUBLE:
-        case STRING:
-        case BOOLEAN:
-        case LOGICAL_TYPE:
-        case ROW:
-        case ARRAY:
-        case ITERABLE:
-          Field field = Field.of("OPTION", fieldType);
-          ProtoDynamicMessageSchema schema = ProtoDynamicMessageSchema.forSchema(Schema.of(field));
-          @SuppressWarnings("rawtypes")
-          ProtoDynamicMessageSchema.Convert convert = schema.createConverter(field);
-          Object value = checkArgumentNotNull(convert.convertFromProtoValue(entry.getValue()));
-          optionsBuilder.setOption(prefix + fieldDescriptor.getFullName(), fieldType, value);
-          break;
-        case MAP:
-        case DATETIME:
-        default:
-          throw new IllegalStateException("These datatypes are not possible in extentions.");
+      try {
+        FieldType fieldType = beamFieldTypeFromProtoField(fieldDescriptor);
+        switch (fieldType.getTypeName()) {
+          case BYTE:
+          case BYTES:
+          case INT16:
+          case INT32:
+          case INT64:
+          case DECIMAL:
+          case FLOAT:
+          case DOUBLE:
+          case STRING:
+          case BOOLEAN:
+          case LOGICAL_TYPE:
+          case ROW:
+          case ARRAY:
+          case ITERABLE:
+            @SuppressWarnings("unchecked")
+            ProtoBeamConverter.ProtoToBeamConverter<Object, Object> protoToBeamConverter =
+                ProtoBeamConverter.createProtoToBeamConverter(fieldType);
+            Object value = protoToBeamConverter.convert(entry.getValue());
+            optionsBuilder.setOption(prefix + fieldDescriptor.getFullName(), fieldType, value);
+            break;
+          case MAP:
+          case DATETIME:
+          default:
+            throw new IllegalStateException("These datatypes are not possible in extentions.");
+        }
+      } catch (RuntimeException e) {
+        throw new RuntimeException(
+            Strings.lenientFormat("Failed to parse option for %s", fieldDescriptor.getName()), e);
       }
     }
     return optionsBuilder;
diff --git a/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtobufUtil.java b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtobufUtil.java
index c54f098be5c2..92ad0de98b18 100644
--- a/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtobufUtil.java
+++ b/sdks/java/extensions/protobuf/src/main/java/org/apache/beam/sdk/extensions/protobuf/ProtobufUtil.java
@@ -22,6 +22,9 @@
 import com.google.protobuf.ExtensionRegistry;
 import com.google.protobuf.ExtensionRegistry.ExtensionInfo;
 import com.google.protobuf.Message;
+import java.io.IOException;
+import java.io.ObjectInputStream;
+import java.io.ObjectOutputStream;
 import java.lang.reflect.InvocationTargetException;
 import java.util.HashSet;
 import java.util.Set;
@@ -89,6 +92,21 @@ static void verifyDeterministic(ProtoCoder<?> coder) throws NonDeterministicExce
     }
   }
 
+  static void serializeDescriptor(ObjectOutputStream oos, Descriptor descriptor)
+      throws IOException {
+    String messageFullName = descriptor.getFullName();
+    ProtoDomain protoDomain = ProtoDomain.buildFrom(descriptor);
+    oos.writeObject(protoDomain);
+    oos.writeObject(messageFullName);
+  }
+
+  static Descriptor deserializeDescriptor(ObjectInputStream ois)
+      throws IOException, ClassNotFoundException {
+    ProtoDomain protoDomain = (ProtoDomain) ois.readObject();
+    String messageFullName = (String) ois.readObject();
+    return protoDomain.getDescriptor(messageFullName);
+  }
+
   ////////////////////////////////////////////////////////////////////////////////////////////////
   // Disable construction of utility class
   private ProtobufUtil() {}
diff --git a/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/ProtoBeamConverterTest.java b/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/ProtoBeamConverterTest.java
new file mode 100644
index 000000000000..b30bb5a4419a
--- /dev/null
+++ b/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/ProtoBeamConverterTest.java
@@ -0,0 +1,620 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.protobuf;
+
+import static org.junit.Assert.assertEquals;
+
+import com.google.protobuf.BoolValue;
+import com.google.protobuf.ByteString;
+import com.google.protobuf.BytesValue;
+import com.google.protobuf.DoubleValue;
+import com.google.protobuf.FloatValue;
+import com.google.protobuf.Int32Value;
+import com.google.protobuf.Int64Value;
+import com.google.protobuf.Message;
+import com.google.protobuf.StringValue;
+import com.google.protobuf.UInt32Value;
+import com.google.protobuf.UInt64Value;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.schemas.logicaltypes.EnumerationType;
+import org.apache.beam.sdk.schemas.logicaltypes.OneOfType;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+
+@RunWith(JUnit4.class)
+public class ProtoBeamConverterTest {
+  private static final Schema PROTO3_PRIMITIVE_SCHEMA =
+      Schema.builder()
+          .addField("primitive_double", Schema.FieldType.DOUBLE)
+          .addField("primitive_float", Schema.FieldType.FLOAT)
+          .addField("primitive_int32", Schema.FieldType.INT32)
+          .addField("primitive_int64", Schema.FieldType.INT64)
+          .addField(
+              "primitive_uint32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.UInt32()))
+          .addField(
+              "primitive_uint64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.UInt64()))
+          .addField(
+              "primitive_sint32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SInt32()))
+          .addField(
+              "primitive_sint64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SInt64()))
+          .addField(
+              "primitive_fixed32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.Fixed32()))
+          .addField(
+              "primitive_fixed64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.Fixed64()))
+          .addField(
+              "primitive_sfixed32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SFixed32()))
+          .addField(
+              "primitive_sfixed64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SFixed64()))
+          .addField("primitive_bool", Schema.FieldType.BOOLEAN)
+          .addField("primitive_string", Schema.FieldType.STRING)
+          .addField("primitive_bytes", Schema.FieldType.BYTES)
+          .build();
+  private static final Schema PROTO3_PRIMITIVE_SCHEMA_SHUFFLED =
+      Schema.builder()
+          .addField("primitive_bytes", Schema.FieldType.BYTES)
+          .addField("primitive_string", Schema.FieldType.STRING)
+          .addField("primitive_bool", Schema.FieldType.BOOLEAN)
+          .addField(
+              "primitive_sfixed64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SFixed64()))
+          .addField(
+              "primitive_sfixed32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SFixed32()))
+          .addField(
+              "primitive_fixed64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.Fixed64()))
+          .addField(
+              "primitive_fixed32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.Fixed32()))
+          .addField(
+              "primitive_sint64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SInt64()))
+          .addField(
+              "primitive_sint32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SInt32()))
+          .addField(
+              "primitive_uint64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.UInt64()))
+          .addField(
+              "primitive_uint32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.UInt32()))
+          .addField("primitive_int64", Schema.FieldType.INT64)
+          .addField("primitive_int32", Schema.FieldType.INT32)
+          .addField("primitive_float", Schema.FieldType.FLOAT)
+          .addField("primitive_double", Schema.FieldType.DOUBLE)
+          .build();
+  private static final Proto3SchemaMessages.Primitive PROTO3_PRIMITIVE_DEFAULT_MESSAGE =
+      Proto3SchemaMessages.Primitive.newBuilder().build();
+  private static final Row PROTO3_PRIMITIVE_DEFAULT_ROW =
+      Row.withSchema(PROTO3_PRIMITIVE_SCHEMA)
+          .addValue(0.0) // double
+          .addValue(0f) // float
+          .addValue(0) // int32
+          .addValue(0L) // int64
+          .addValue(0) // uint32
+          .addValue(0L) // uint64
+          .addValue(0) // sint32
+          .addValue(0L) // sint64
+          .addValue(0) // fixed32
+          .addValue(0L) // fixed64
+          .addValue(0) // sfixed32
+          .addValue(0L) // sfixed64
+          .addValue(false) // bool
+          .addValue("") // string
+          .addValue(new byte[0]) // bytes
+          .build();
+  private static final Row PROTO3_PRIMITIVE_DEFAULT_ROW_SHUFFLED =
+      Row.withSchema(PROTO3_PRIMITIVE_SCHEMA_SHUFFLED)
+          .addValue(new byte[0]) // bytes
+          .addValue("") // string
+          .addValue(false) // bool
+          .addValue(0L) // sfixed64
+          .addValue(0) // sfixed32
+          .addValue(0L) // fixed64
+          .addValue(0) // fixed32
+          .addValue(0L) // sint64
+          .addValue(0) // sint32
+          .addValue(0L) // uint64
+          .addValue(0) // uint32
+          .addValue(0L) // int64
+          .addValue(0) // int32
+          .addValue(0f) // float
+          .addValue(0.0) // double
+          .build();
+
+  private static final Schema PROTO3_OPTIONAL_PRIMITIVE2_SCHEMA =
+      Schema.builder()
+          .addField("primitive_double", Schema.FieldType.DOUBLE.withNullable(true))
+          .addField("primitive_float", Schema.FieldType.FLOAT.withNullable(true))
+          .addField("primitive_int32", Schema.FieldType.INT32.withNullable(true))
+          .addField("primitive_int64", Schema.FieldType.INT64.withNullable(true))
+          .addField(
+              "primitive_uint32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.UInt32()).withNullable(true))
+          .addField(
+              "primitive_uint64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.UInt64()).withNullable(true))
+          .addField(
+              "primitive_sint32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SInt32()).withNullable(true))
+          .addField(
+              "primitive_sint64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SInt64()).withNullable(true))
+          .addField(
+              "primitive_fixed32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.Fixed32())
+                  .withNullable(true))
+          .addField(
+              "primitive_fixed64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.Fixed64())
+                  .withNullable(true))
+          .addField(
+              "primitive_sfixed32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SFixed32())
+                  .withNullable(true))
+          .addField(
+              "primitive_sfixed64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.SFixed64())
+                  .withNullable(true))
+          .addField("primitive_bool", Schema.FieldType.BOOLEAN.withNullable(true))
+          .addField("primitive_string", Schema.FieldType.STRING.withNullable(true))
+          .addField("primitive_bytes", Schema.FieldType.BYTES.withNullable(true))
+          .build();
+  private static final Message PROTO3_OPTIONAL_PRIMITIVE2_EMPTY_MESSAGE =
+      Proto3SchemaMessages.OptionalPrimitive2.newBuilder().build();
+  private static final Message PROTO3_OPTIONAL_PRIMITIVE2_DEFAULT_MESSAGE =
+      Proto3SchemaMessages.OptionalPrimitive2.newBuilder()
+          .setPrimitiveDouble(0.0)
+          .setPrimitiveFloat(0f)
+          .setPrimitiveInt32(0)
+          .setPrimitiveInt64(0L)
+          .setPrimitiveUint32(0)
+          .setPrimitiveUint64(0L)
+          .setPrimitiveSint32(0)
+          .setPrimitiveSint64(0L)
+          .setPrimitiveFixed32(0)
+          .setPrimitiveFixed64(0L)
+          .setPrimitiveSfixed32(0)
+          .setPrimitiveSfixed64(0L)
+          .setPrimitiveBool(false)
+          .setPrimitiveString("")
+          .setPrimitiveBytes(ByteString.EMPTY)
+          .build();
+  private static final Row PROTO3_OPTIONAL_PRIMITIVE2_EMPTY_ROW =
+      Row.nullRow(PROTO3_OPTIONAL_PRIMITIVE2_SCHEMA);
+  private static final Row PROTO3_OPTIONAL_PRIMITIVE2_DEFAULT_ROW =
+      Row.withSchema(PROTO3_OPTIONAL_PRIMITIVE2_SCHEMA)
+          .addValue(0.0) // double
+          .addValue(0f) // float
+          .addValue(0) // int32
+          .addValue(0L) // int64
+          .addValue(0) // uint32
+          .addValue(0L) // uint64
+          .addValue(0) // sint32
+          .addValue(0L) // sint64
+          .addValue(0) // fixed32
+          .addValue(0L) // fixed64
+          .addValue(0) // sfixed32
+          .addValue(0L) // sfixed64
+          .addValue(false) // bool
+          .addValue("") // string
+          .addValue(new byte[0]) // bytes
+          .build();
+
+  private static final Message PROTO3_SIMPLE_ONEOF_EMPTY_MESSAGE =
+      Proto3SchemaMessages.SimpleOneof.getDefaultInstance();
+  private static final Message PROTO3_SIMPLE_ONEOF_INT32_MESSAGE =
+      Proto3SchemaMessages.SimpleOneof.newBuilder().setInt32(13).build();
+  private static final OneOfType PROTO3_SIMPLE_ONEOF_SCHEMA_GROUP =
+      OneOfType.create(
+          Schema.Field.of("int32", Schema.FieldType.INT32),
+          Schema.Field.of("string", Schema.FieldType.STRING));
+  private static final OneOfType PROTO3_SIMPLE_ONEOF_SCHEMA_GROUP_SHUFFLED =
+      OneOfType.create(
+          Schema.Field.of("string", Schema.FieldType.STRING),
+          Schema.Field.of("int32", Schema.FieldType.INT32));
+  private static final Schema PROTO3_SIMPLE_ONEOF_SCHEMA =
+      Schema.builder()
+          .addField(
+              "group",
+              Schema.FieldType.logicalType(PROTO3_SIMPLE_ONEOF_SCHEMA_GROUP).withNullable(true))
+          .build();
+  private static final Schema PROTO3_SIMPLE_ONEOF_SCHEMA_SHUFFLED =
+      Schema.builder()
+          .addField(
+              "group",
+              Schema.FieldType.logicalType(PROTO3_SIMPLE_ONEOF_SCHEMA_GROUP_SHUFFLED)
+                  .withNullable(true))
+          .build();
+  private static final Row PROTO3_SIMPLE_ONEOF_EMPTY_ROW = Row.nullRow(PROTO3_SIMPLE_ONEOF_SCHEMA);
+  private static final Row PROTO3_SIMPLE_ONEOF_INT32_ROW =
+      Row.withSchema(PROTO3_SIMPLE_ONEOF_SCHEMA)
+          .addValue(PROTO3_SIMPLE_ONEOF_SCHEMA_GROUP.createValue("int32", 13))
+          .build();
+  private static final Row PROTO3_SIMPLE_ONEOF_INT32_ROW_SHUFFLED =
+      Row.withSchema(PROTO3_SIMPLE_ONEOF_SCHEMA_SHUFFLED)
+          .addValue(PROTO3_SIMPLE_ONEOF_SCHEMA_GROUP_SHUFFLED.createValue("int32", 13))
+          .build();
+
+  private static final Schema PROTO3_WRAP_PRIMITIVE_SCHEMA =
+      Schema.builder()
+          .addField("double", Schema.FieldType.DOUBLE.withNullable(true))
+          .addField("float", Schema.FieldType.FLOAT.withNullable(true))
+          .addField("int32", Schema.FieldType.INT32.withNullable(true))
+          .addField("int64", Schema.FieldType.INT64.withNullable(true))
+          .addField(
+              "uint32",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.UInt32()).withNullable(true))
+          .addField(
+              "uint64",
+              Schema.FieldType.logicalType(new ProtoSchemaLogicalTypes.UInt64()).withNullable(true))
+          .addField("bool", Schema.FieldType.BOOLEAN.withNullable(true))
+          .addField("string", Schema.FieldType.STRING.withNullable(true))
+          .addField("bytes", Schema.FieldType.BYTES.withNullable(true))
+          .build();
+  private static final Message PROTO3_WRAP_PRIMITIVE_EMPTY_MESSAGE =
+      Proto3SchemaMessages.WrapPrimitive.getDefaultInstance();
+  private static final Message PROTO3_WRAP_PRIMITIVE_DEFAULT_MESSAGE =
+      Proto3SchemaMessages.WrapPrimitive.newBuilder()
+          .setDouble(DoubleValue.getDefaultInstance())
+          .setFloat(FloatValue.getDefaultInstance())
+          .setInt32(Int32Value.getDefaultInstance())
+          .setInt64(Int64Value.getDefaultInstance())
+          .setUint32(UInt32Value.getDefaultInstance())
+          .setUint64(UInt64Value.getDefaultInstance())
+          .setBool(BoolValue.getDefaultInstance())
+          .setString(StringValue.getDefaultInstance())
+          .setBytes(BytesValue.getDefaultInstance())
+          .build();
+  private static final Row PROTO3_WRAP_PRIMITIVE_EMPTY_ROW =
+      Row.nullRow(PROTO3_WRAP_PRIMITIVE_SCHEMA);
+  private static final Row PROTO3_WRAP_PRIMITIVE_DEFAULT_ROW =
+      Row.withSchema(PROTO3_WRAP_PRIMITIVE_SCHEMA)
+          .addValue(0.0)
+          .addValue(0f)
+          .addValue(0)
+          .addValue(0L)
+          .addValue(0)
+          .addValue(0L)
+          .addValue(false)
+          .addValue("")
+          .addValue(new byte[0])
+          .build();
+
+  private static final Message PROTO3_NOWRAP_PRIMITIVE_EMPTY_MESSAGE =
+      Proto3SchemaMessages.NoWrapPrimitive.getDefaultInstance();
+  private static final Message PROTO3_NOWRAP_PRIMITIVE_DEFAULT_MESSAGE =
+      Proto3SchemaMessages.NoWrapPrimitive.newBuilder()
+          .setDouble(0.0)
+          .setFloat(0f)
+          .setInt32(0)
+          .setInt64(0L)
+          .setUint32(0)
+          .setUint64(0L)
+          .setBool(false)
+          .setString("")
+          .setBytes(ByteString.EMPTY)
+          .build();
+  private static final Row PROTO3_NOWRAP_PRIMITIVE_EMPTY_ROW = PROTO3_WRAP_PRIMITIVE_EMPTY_ROW;
+  private static final Row PROTO3_NOWRAP_PRIMITIVE_DEFAULT_ROW = PROTO3_WRAP_PRIMITIVE_DEFAULT_ROW;
+  private static final Schema PROTO3_NOWRAP_PRIMITIVE_SCHEMA = PROTO3_WRAP_PRIMITIVE_SCHEMA;
+
+  private static final Message PROTO3_ENUM_DEFAULT_MESSAGE =
+      Proto3SchemaMessages.EnumMessage.getDefaultInstance();
+  private static final Message PROTO3_ENUM_TWO_MESSAGE =
+      Proto3SchemaMessages.EnumMessage.newBuilder()
+          .setEnum(Proto3SchemaMessages.EnumMessage.Enum.TWO)
+          .build();
+  private static final EnumerationType PROTO3_ENUM_SCHEMA_ENUM =
+      EnumerationType.create(ImmutableMap.of("ZERO", 0, "TWO", 2, "THREE", 3));
+  private static final EnumerationType PROTO3_ENUM_SCHEMA_HACKED_ENUM =
+      EnumerationType.create(ImmutableMap.of("TEN", 10, "ELEVEN", 11));
+  private static final Schema PROTO3_ENUM_SCHEMA =
+      Schema.builder()
+          .addField("enum", Schema.FieldType.logicalType(PROTO3_ENUM_SCHEMA_ENUM))
+          .build();
+  private static final Schema PROTO3_ENUM_SCHEMA_HACKED =
+      Schema.builder()
+          .addField("enum", Schema.FieldType.logicalType(PROTO3_ENUM_SCHEMA_HACKED_ENUM))
+          .build();
+  private static final Row PROTO3_ENUM_DEFAULT_ROW =
+      Row.withSchema(PROTO3_ENUM_SCHEMA).addValue(PROTO3_ENUM_SCHEMA_ENUM.valueOf(0)).build();
+  private static final Row PROTO3_ENUM_TWO_ROW =
+      Row.withSchema(PROTO3_ENUM_SCHEMA).addValue(PROTO3_ENUM_SCHEMA_ENUM.valueOf("TWO")).build();
+  private static final Row PROTO3_ENUM_HACKED_ROW =
+      Row.withSchema(PROTO3_ENUM_SCHEMA_HACKED).addValue(new EnumerationType.Value(0)).build();
+
+  @Test
+  public void testToProto_Proto3EnumDescriptor_Proto3EnumDefaultRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.EnumMessage.getDescriptor())
+            .apply(PROTO3_ENUM_DEFAULT_ROW);
+    assertEquals(PROTO3_ENUM_DEFAULT_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3EnumDescriptor_Proto3EnumHackedRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.EnumMessage.getDescriptor())
+            .apply(PROTO3_ENUM_HACKED_ROW);
+    assertEquals(PROTO3_ENUM_DEFAULT_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3EnumDescriptor_Proto3EnumTwoRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.EnumMessage.getDescriptor())
+            .apply(PROTO3_ENUM_TWO_ROW);
+    assertEquals(PROTO3_ENUM_TWO_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3NoWrapPrimitiveDescriptor_Proto3NoWrapPrimitiveDefaultRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.NoWrapPrimitive.getDescriptor())
+            .apply(PROTO3_NOWRAP_PRIMITIVE_DEFAULT_ROW);
+    assertEquals(PROTO3_NOWRAP_PRIMITIVE_DEFAULT_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3NoWrapPrimitiveDescriptor_Proto3NoWrapPrimitiveEmptyRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.NoWrapPrimitive.getDescriptor())
+            .apply(PROTO3_NOWRAP_PRIMITIVE_EMPTY_ROW);
+    assertEquals(PROTO3_NOWRAP_PRIMITIVE_EMPTY_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3OptionalPrimitive2Descriptor_OptionalPrimitive2DefaultRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.OptionalPrimitive2.getDescriptor())
+            .apply(PROTO3_OPTIONAL_PRIMITIVE2_DEFAULT_ROW);
+    assertEquals(PROTO3_OPTIONAL_PRIMITIVE2_DEFAULT_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3OptionalPrimitive2Descriptor_OptionalPrimitive2EmptyRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.OptionalPrimitive2.getDescriptor())
+            .apply(PROTO3_OPTIONAL_PRIMITIVE2_EMPTY_ROW);
+    assertEquals(PROTO3_OPTIONAL_PRIMITIVE2_EMPTY_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3OptionalPrimitive2Descriptor_Proto3PrimitiveDefaultRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.OptionalPrimitive2.getDescriptor())
+            .apply(PROTO3_PRIMITIVE_DEFAULT_ROW);
+    assertEquals(PROTO3_OPTIONAL_PRIMITIVE2_DEFAULT_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3PrimitiveDescriptor_PrimitiveDefaultRowShuffled() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.Primitive.getDescriptor())
+            .apply(PROTO3_PRIMITIVE_DEFAULT_ROW_SHUFFLED);
+    assertEquals(PROTO3_PRIMITIVE_DEFAULT_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3PrimitiveDescriptor_Proto3OptionalPrimitive2DefaultRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.Primitive.getDescriptor())
+            .apply(PROTO3_OPTIONAL_PRIMITIVE2_EMPTY_ROW);
+    assertEquals(PROTO3_PRIMITIVE_DEFAULT_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3PrimitiveDescriptor_Proto3OptionalPrimitive2EmptyRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.Primitive.getDescriptor())
+            .apply(PROTO3_OPTIONAL_PRIMITIVE2_EMPTY_ROW);
+    assertEquals(PROTO3_PRIMITIVE_DEFAULT_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3PrimitiveDescriptor_Proto3PrimitiveDefaultRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.Primitive.getDescriptor())
+            .apply(PROTO3_PRIMITIVE_DEFAULT_ROW);
+    assertEquals(PROTO3_PRIMITIVE_DEFAULT_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3SimpleOneofDescriptor_Proto3SimpleOneofInt32RowShuffled() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.SimpleOneof.getDescriptor())
+            .apply(PROTO3_SIMPLE_ONEOF_INT32_ROW_SHUFFLED);
+    assertEquals(PROTO3_SIMPLE_ONEOF_INT32_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3SimpleOneofDiscriptor_Proto3SimpleOneofEmptyRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.SimpleOneof.getDescriptor())
+            .apply(PROTO3_SIMPLE_ONEOF_EMPTY_ROW);
+
+    assertEquals(PROTO3_SIMPLE_ONEOF_EMPTY_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3SimpleOneofDiscriptor_Proto3SimpleOneofInt32Row() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.SimpleOneof.getDescriptor())
+            .apply(PROTO3_SIMPLE_ONEOF_INT32_ROW);
+
+    assertEquals(PROTO3_SIMPLE_ONEOF_INT32_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3WrapPrimitiveDescriptor_Proto3WrapPrimitiveDefaultRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.WrapPrimitive.getDescriptor())
+            .apply(PROTO3_WRAP_PRIMITIVE_DEFAULT_ROW);
+    assertEquals(PROTO3_WRAP_PRIMITIVE_DEFAULT_MESSAGE, message);
+  }
+
+  @Test
+  public void testToProto_Proto3WrapPrimitiveDescriptor_Proto3WrapPrimitiveEmptyRow() {
+    Message message =
+        ProtoBeamConverter.toProto(Proto3SchemaMessages.WrapPrimitive.getDescriptor())
+            .apply(PROTO3_WRAP_PRIMITIVE_EMPTY_ROW);
+    assertEquals(PROTO3_WRAP_PRIMITIVE_EMPTY_MESSAGE, message);
+  }
+
+  @Test
+  public void testToRow_Prot3EnumSchemaHacked_Prot3EnumDefaultMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_ENUM_SCHEMA_HACKED).apply(PROTO3_ENUM_DEFAULT_MESSAGE);
+    assertEquals(PROTO3_ENUM_HACKED_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3EnumSchema_Proto3EnumDefaultMessage() {
+    Row row = ProtoBeamConverter.toRow(PROTO3_ENUM_SCHEMA).apply(PROTO3_ENUM_DEFAULT_MESSAGE);
+    assertEquals(PROTO3_ENUM_DEFAULT_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3EnumSchema_Proto3EnumTwoMessage() {
+    Row row = ProtoBeamConverter.toRow(PROTO3_ENUM_SCHEMA).apply(PROTO3_ENUM_TWO_MESSAGE);
+    assertEquals(PROTO3_ENUM_TWO_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3NoWrapPrimitiveSchema_Proto3NoWrapPrimitiveDefaultMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_NOWRAP_PRIMITIVE_SCHEMA)
+            .apply(PROTO3_NOWRAP_PRIMITIVE_DEFAULT_MESSAGE);
+    assertEquals(PROTO3_NOWRAP_PRIMITIVE_DEFAULT_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3NoWrapPrimitiveSchema_Proto3NoWrapPrimitiveEmptyMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_NOWRAP_PRIMITIVE_SCHEMA)
+            .apply(PROTO3_NOWRAP_PRIMITIVE_EMPTY_MESSAGE);
+    assertEquals(PROTO3_NOWRAP_PRIMITIVE_EMPTY_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3OptionalPrimitive2Schema_OptionalPrimitive2DefaultMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_OPTIONAL_PRIMITIVE2_SCHEMA)
+            .apply(PROTO3_OPTIONAL_PRIMITIVE2_DEFAULT_MESSAGE);
+    assertEquals(PROTO3_OPTIONAL_PRIMITIVE2_DEFAULT_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3OptionalPrimitive2Schema_OptionalPrimitive2EmptyMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_OPTIONAL_PRIMITIVE2_SCHEMA)
+            .apply(PROTO3_OPTIONAL_PRIMITIVE2_EMPTY_MESSAGE);
+    assertEquals(PROTO3_OPTIONAL_PRIMITIVE2_EMPTY_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3OptionalPrimitive2Schema_Proto3PrimitiveDefaultMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_OPTIONAL_PRIMITIVE2_SCHEMA)
+            .apply(PROTO3_PRIMITIVE_DEFAULT_MESSAGE);
+    assertEquals(PROTO3_OPTIONAL_PRIMITIVE2_DEFAULT_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3PrimitiveSchemaShuffle_PrimitiveDefaultMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_PRIMITIVE_SCHEMA_SHUFFLED)
+            .apply(PROTO3_PRIMITIVE_DEFAULT_MESSAGE);
+    assertEquals(PROTO3_PRIMITIVE_DEFAULT_ROW_SHUFFLED, row);
+  }
+
+  @Test
+  public void testToRow_Proto3PrimitiveSchema_Proto3OptionalPrimitive2DefaultMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_PRIMITIVE_SCHEMA)
+            .apply(PROTO3_OPTIONAL_PRIMITIVE2_DEFAULT_MESSAGE);
+    assertEquals(PROTO3_PRIMITIVE_DEFAULT_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3PrimitiveSchema_Proto3OptionalPrimitive2EmtpyMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_PRIMITIVE_SCHEMA)
+            .apply(PROTO3_OPTIONAL_PRIMITIVE2_EMPTY_MESSAGE);
+    assertEquals(PROTO3_PRIMITIVE_DEFAULT_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3PrimitiveSchema_Proto3PrimitiveDefaultMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_PRIMITIVE_SCHEMA).apply(PROTO3_PRIMITIVE_DEFAULT_MESSAGE);
+    assertEquals(PROTO3_PRIMITIVE_DEFAULT_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3SimpleOneofSchemaShuffled_Proto3SimpleOneofInt32Messsage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_SIMPLE_ONEOF_SCHEMA_SHUFFLED)
+            .apply(PROTO3_SIMPLE_ONEOF_INT32_MESSAGE);
+    assertEquals(PROTO3_SIMPLE_ONEOF_INT32_ROW_SHUFFLED, row);
+  }
+
+  @Test
+  public void testToRow_Proto3SimpleOneofSchema_Proto3SimpleOneofEmptyMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_SIMPLE_ONEOF_SCHEMA)
+            .apply(PROTO3_SIMPLE_ONEOF_EMPTY_MESSAGE);
+    assertEquals(PROTO3_SIMPLE_ONEOF_EMPTY_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3SimpleOneofSchema_Proto3SimpleOneofInt32Message() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_SIMPLE_ONEOF_SCHEMA)
+            .apply(PROTO3_SIMPLE_ONEOF_INT32_MESSAGE);
+    assertEquals(PROTO3_SIMPLE_ONEOF_INT32_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3WrapPrimitiveSchema_Proto3WrapPrimitiveDefaultMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_WRAP_PRIMITIVE_SCHEMA)
+            .apply(PROTO3_WRAP_PRIMITIVE_DEFAULT_MESSAGE);
+    assertEquals(PROTO3_WRAP_PRIMITIVE_DEFAULT_ROW, row);
+  }
+
+  @Test
+  public void testToRow_Proto3WrapPrimitiveSchema_Proto3WrapPrimitiveEmptyMessage() {
+    Row row =
+        ProtoBeamConverter.toRow(PROTO3_WRAP_PRIMITIVE_SCHEMA)
+            .apply(PROTO3_WRAP_PRIMITIVE_EMPTY_MESSAGE);
+    assertEquals(PROTO3_WRAP_PRIMITIVE_EMPTY_ROW, row);
+  }
+}
diff --git a/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteUtilsTest.java b/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteUtilsTest.java
index 6105208d8366..1ae1be485dcb 100644
--- a/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteUtilsTest.java
+++ b/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/ProtoByteUtilsTest.java
@@ -85,8 +85,8 @@ public class ProtoByteUtilsTest {
               "address",
               Schema.FieldType.row(
                   Schema.builder()
-                      .addField("city", Schema.FieldType.STRING)
                       .addField("street", Schema.FieldType.STRING)
+                      .addField("city", Schema.FieldType.STRING)
                       .addField("state", Schema.FieldType.STRING)
                       .addField("zip_code", Schema.FieldType.STRING)
                       .build()))
@@ -202,11 +202,45 @@ public void testRowToProtoSchemaWithPackageFunction() {
             .withFieldValue("address.state", "wa")
             .build();
 
+    // spotless:off
     byte[] byteArray = {
-      8, -46, 9, 18, 3, 68, 111, 101, 34, 35, 10, 7, 115, 101, 97, 116, 116, 108, 101, 18, 11, 102,
-      97, 107, 101, 32, 115, 116, 114, 101, 101, 116, 26, 2, 119, 97, 34, 7, 84, 79, 45, 49, 50, 51,
-      52
+      // id = 1: 1234
+      //   Tag: 1, Wire VARINT => 1 * 8 + 0 => [8]
+      //   1234 => 1001 1010010 => 00001001 11010010 => 11010010 00001001 => 210 9 => [-46 9]
+      8, -46, 9,
+      // name = 2: Doe
+      //   Tag: 2, Wire LEN => 2 * 8 + 2 => [18]
+      //   Length => [3]
+      //   Doe => [68, 111, 101]
+      18, 3, 68, 111, 101,
+      // active = 3: false
+      //   No serialization due to default value
+      // Address address = 4:
+      //   Tag 4, Wire LEN => 4 * 8 + 2 => [34]
+      //   Length: (1 + 1 + 11) + (1 + 1 + 7) + (1 + 1 + 2) + (1 + 1 + 7) = 35
+      34, 35,
+      //   street = 1: fake street
+      //     Tag 1, Wire LEN => 1 * 8 + 2 => [10]
+      //     Length => [11]
+      //     fake street => [102, 97, 107, 101, 32, 115, 116, 114, 101, 101, 116]
+      10, 11, 102, 97, 107, 101, 32, 115, 116, 114, 101, 101, 116,
+      //   city = 2: seattle
+      //     Tag 2, Wire LEN => 2 * 8 + 2 => [18]
+      //     Length => [7]
+      //     seattle => [115, 101, 97, 116, 116, 108, 101]
+      18, 7, 115, 101, 97, 116, 116, 108, 101,
+      //   state = 3: wa
+      //     Tag 3, Wire LEN => 3 * 8 + 2 => [26]
+      //     Length => [2]
+      //     wa => [119, 97]
+      26, 2, 119, 97,
+      //   zip_code = 4: TO-1234
+      //     Tag 4, Wire LEN => 4 * 8 + 2 => [34]
+      //     Length => [7]
+      //     TO-1234 =>  [84, 79, 45, 49, 50, 51, 52]
+      34, 7, 84, 79, 45, 49, 50, 51, 52
     };
+    // spotless:on
 
     byte[] resultBytes =
         ProtoByteUtils.getRowToProtoBytesFromSchema(
diff --git a/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/ProtoMessageSchemaTest.java b/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/ProtoMessageSchemaTest.java
index 3b4568f1fac7..6e2215034915 100644
--- a/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/ProtoMessageSchemaTest.java
+++ b/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/ProtoMessageSchemaTest.java
@@ -69,6 +69,7 @@
 import static org.apache.beam.sdk.extensions.protobuf.TestProtoSchemas.withTypeName;
 import static org.junit.Assert.assertEquals;
 
+import com.google.protobuf.ByteString;
 import org.apache.beam.sdk.extensions.protobuf.Proto2SchemaMessages.OptionalPrimitive;
 import org.apache.beam.sdk.extensions.protobuf.Proto2SchemaMessages.RequiredPrimitive;
 import org.apache.beam.sdk.extensions.protobuf.Proto3SchemaMessages.EnumMessage;
@@ -387,6 +388,82 @@ public void testRowToBytesAndBytesToRowFnWithShuffledFields() {
     assertEquals(WKT_MESSAGE_ROW, convertRow(WKT_MESSAGE_SHUFFLED_ROW));
   }
 
+  @Test
+  public void testOptionalPrimitive_RowToProto_Empty() {
+    SerializableFunction<Row, OptionalPrimitive> fromRow =
+        new ProtoMessageSchema().fromRowFunction(TypeDescriptor.of(OptionalPrimitive.class));
+
+    Schema schema = new ProtoMessageSchema().schemaFor(TypeDescriptor.of(OptionalPrimitive.class));
+    Row row = Row.nullRow(schema);
+
+    OptionalPrimitive message = OptionalPrimitive.getDefaultInstance();
+
+    assertEquals(message, fromRow.apply(row));
+  }
+
+  @Test
+  public void testOptionalPrimitive_ProtoToRow_Empty() {
+    SerializableFunction<OptionalPrimitive, Row> toRow =
+        new ProtoMessageSchema().toRowFunction(TypeDescriptor.of(OptionalPrimitive.class));
+
+    Schema schema = new ProtoMessageSchema().schemaFor(TypeDescriptor.of(OptionalPrimitive.class));
+    Row row = Row.nullRow(schema);
+
+    OptionalPrimitive message = OptionalPrimitive.getDefaultInstance();
+
+    assertEquals(row, toRow.apply(message));
+  }
+
+  @Test
+  public void testOptionalPrimitive_RowToProto_DefaultValues() {
+    SerializableFunction<Row, OptionalPrimitive> fromRow =
+        new ProtoMessageSchema().fromRowFunction(TypeDescriptor.of(OptionalPrimitive.class));
+
+    Schema schema = new ProtoMessageSchema().schemaFor(TypeDescriptor.of(OptionalPrimitive.class));
+    Row row =
+        Row.withSchema(schema)
+            .addValue(0)
+            .addValue(false)
+            .addValue("")
+            .addValue(new byte[0])
+            .build();
+
+    OptionalPrimitive message =
+        OptionalPrimitive.newBuilder()
+            .setPrimitiveInt32(0)
+            .setPrimitiveBool(false)
+            .setPrimitiveString("")
+            .setPrimitiveBytes(ByteString.EMPTY)
+            .build();
+
+    assertEquals(message, fromRow.apply(row));
+  }
+
+  @Test
+  public void testOptionalPrimitive_ProtoToRow_DefaultValues() {
+    SerializableFunction<OptionalPrimitive, Row> toRow =
+        new ProtoMessageSchema().toRowFunction(TypeDescriptor.of(OptionalPrimitive.class));
+
+    Schema schema = new ProtoMessageSchema().schemaFor(TypeDescriptor.of(OptionalPrimitive.class));
+    Row row =
+        Row.withSchema(schema)
+            .addValue(0)
+            .addValue(false)
+            .addValue("")
+            .addValue(new byte[0])
+            .build();
+
+    OptionalPrimitive message =
+        OptionalPrimitive.newBuilder()
+            .setPrimitiveInt32(0)
+            .setPrimitiveBool(false)
+            .setPrimitiveString("")
+            .setPrimitiveBytes(ByteString.EMPTY)
+            .build();
+
+    assertEquals(row, toRow.apply(message));
+  }
+
   private Row convertRow(Row row) {
     SimpleFunction<Row, byte[]> rowToBytes =
         ProtoMessageSchema.getRowToProtoBytesFn(WktMessage.class);
diff --git a/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/TestProtoSchemas.java b/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/TestProtoSchemas.java
index 234ae8cd6852..9b22f38c4e15 100644
--- a/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/TestProtoSchemas.java
+++ b/sdks/java/extensions/protobuf/src/test/java/org/apache/beam/sdk/extensions/protobuf/TestProtoSchemas.java
@@ -113,10 +113,10 @@ static Schema.Options withTypeName(String typeName) {
 
   static final Schema OPTIONAL_PRIMITIVE_SCHEMA =
       Schema.builder()
-          .addField(withFieldNumber("primitive_int32", FieldType.INT32, 1))
-          .addField(withFieldNumber("primitive_bool", FieldType.BOOLEAN, 2))
-          .addField(withFieldNumber("primitive_string", FieldType.STRING, 3))
-          .addField(withFieldNumber("primitive_bytes", FieldType.BYTES, 4))
+          .addField(withFieldNumber("primitive_int32", FieldType.INT32.withNullable(true), 1))
+          .addField(withFieldNumber("primitive_bool", FieldType.BOOLEAN.withNullable(true), 2))
+          .addField(withFieldNumber("primitive_string", FieldType.STRING.withNullable(true), 3))
+          .addField(withFieldNumber("primitive_bytes", FieldType.BYTES.withNullable(true), 4))
           .setOptions(
               Schema.Options.builder()
                   .setOption(
@@ -127,10 +127,10 @@ static Schema.Options withTypeName(String typeName) {
 
   static final Schema PROTO3_OPTIONAL_PRIMITIVE_SCHEMA =
       Schema.builder()
-          .addField(withFieldNumber("primitive_int32", FieldType.INT32, 1))
-          .addField(withFieldNumber("primitive_bool", FieldType.BOOLEAN, 2))
-          .addField(withFieldNumber("primitive_string", FieldType.STRING, 3))
-          .addField(withFieldNumber("primitive_bytes", FieldType.BYTES, 4))
+          .addField(withFieldNumber("primitive_int32", FieldType.INT32.withNullable(true), 1))
+          .addField(withFieldNumber("primitive_bool", FieldType.BOOLEAN.withNullable(true), 2))
+          .addField(withFieldNumber("primitive_string", FieldType.STRING.withNullable(true), 3))
+          .addField(withFieldNumber("primitive_bytes", FieldType.BYTES.withNullable(true), 4))
           .setOptions(
               Schema.Options.builder()
                   .setOption(
@@ -401,7 +401,7 @@ static Schema.Options withTypeName(String typeName) {
   static final Schema ONEOF_SCHEMA =
       Schema.builder()
           .addField(withFieldNumber("place1", FieldType.STRING, 1))
-          .addField("special_oneof", FieldType.logicalType(ONE_OF_TYPE))
+          .addField("special_oneof", FieldType.logicalType(ONE_OF_TYPE).withNullable(true))
           .addField(withFieldNumber("place2", FieldType.INT32, 6))
           .setOptions(withTypeName("proto3_schema_messages.OneOf"))
           .build();
@@ -445,7 +445,7 @@ static Schema.Options withTypeName(String typeName) {
       OneOfType.create(OUTER_ONEOF_FIELDS, OUTER_ONE_OF_ENUM_MAP);
   static final Schema OUTER_ONEOF_SCHEMA =
       Schema.builder()
-          .addField("outer_oneof", FieldType.logicalType(OUTER_ONEOF_TYPE))
+          .addField("outer_oneof", FieldType.logicalType(OUTER_ONEOF_TYPE).withNullable(true))
           .setOptions(withTypeName("proto3_schema_messages.OuterOneOf"))
           .build();
 
@@ -476,7 +476,8 @@ static Schema.Options withTypeName(String typeName) {
   static final Schema REVERSED_ONEOF_SCHEMA =
       Schema.builder()
           .addField(withFieldNumber("place1", FieldType.STRING, 6))
-          .addField("oneof_reversed", FieldType.logicalType(REVERSED_ONE_OF_TYPE))
+          .addField(
+              "oneof_reversed", FieldType.logicalType(REVERSED_ONE_OF_TYPE).withNullable(true))
           .addField(withFieldNumber("place2", FieldType.INT32, 1))
           .setOptions(withTypeName("proto3_schema_messages.ReversedOneOf"))
           .build();
@@ -545,10 +546,12 @@ static Schema.Options withTypeName(String typeName) {
       Schema.builder()
           .addField(withFieldNumber("place1", FieldType.STRING, 76))
           .addField(
-              "oneof_non_contiguous_one", FieldType.logicalType(NONCONTIGUOUS_ONE_ONE_OF_TYPE))
+              "oneof_non_contiguous_one",
+              FieldType.logicalType(NONCONTIGUOUS_ONE_ONE_OF_TYPE).withNullable(true))
           .addField(withFieldNumber("place2", FieldType.INT32, 33))
           .addField(
-              "oneof_non_contiguous_two", FieldType.logicalType(NONCONTIGUOUS_TWO_ONE_OF_TYPE))
+              "oneof_non_contiguous_two",
+              FieldType.logicalType(NONCONTIGUOUS_TWO_ONE_OF_TYPE).withNullable(true))
           .addField(withFieldNumber("place3", FieldType.INT32, 63))
           .setOptions(withTypeName("proto3_schema_messages.NonContiguousOneOf"))
           .build();
diff --git a/sdks/java/extensions/protobuf/src/test/proto/proto3_schema_messages.proto b/sdks/java/extensions/protobuf/src/test/proto/proto3_schema_messages.proto
index 6c8627c130f6..407a803644ef 100644
--- a/sdks/java/extensions/protobuf/src/test/proto/proto3_schema_messages.proto
+++ b/sdks/java/extensions/protobuf/src/test/proto/proto3_schema_messages.proto
@@ -222,3 +222,69 @@ message OptionalPrimitive {
 message OptionalNested {
     optional OptionalPrimitive nested = 1;
 }
+
+// MapPrimitive and MapWrapped have the same Beam Schema.
+message MapWrapped {
+  map<string, google.protobuf.StringValue> string_string_map = 1;
+  map<string, google.protobuf.Int32Value> string_int_map = 2;
+  map<int32, google.protobuf.StringValue> int_string_map = 3;
+  map<string, google.protobuf.BytesValue> string_bytes_map = 4;
+}
+
+message OptionalEnumMessage {
+  enum Enum {
+    ZERO = 0;
+    TWO  = 2;
+    THREE = 3;
+  }
+  optional Enum enum = 1;
+}
+
+message OptionalPrimitive2 {
+  optional double primitive_double = 1;
+  optional float primitive_float = 2;
+  optional int32 primitive_int32 = 3;
+  optional int64 primitive_int64 = 4;
+  optional uint32 primitive_uint32 = 5;
+  optional uint64 primitive_uint64 = 6;
+  optional sint32 primitive_sint32 = 7;
+  optional sint64 primitive_sint64 = 8;
+  optional fixed32 primitive_fixed32 = 9;
+  optional fixed64 primitive_fixed64 = 10;
+  optional sfixed32 primitive_sfixed32 = 11;
+  optional sfixed64 primitive_sfixed64 = 12;
+  optional bool primitive_bool = 13;
+  optional string primitive_string = 14;
+  optional bytes primitive_bytes = 15;
+}
+
+message SimpleOneof {
+  oneof group {
+    int32 int32 = 3;
+    string string = 4;
+  }
+}
+
+message WrapPrimitive {
+  google.protobuf.DoubleValue double = 1;
+  google.protobuf.FloatValue float = 2;
+  google.protobuf.Int32Value int32 = 3;
+  google.protobuf.Int64Value int64 = 4;
+  google.protobuf.UInt32Value uint32 = 5;
+  google.protobuf.UInt64Value uint64 = 6;
+  google.protobuf.BoolValue bool = 13;
+  google.protobuf.StringValue string = 14;
+  google.protobuf.BytesValue bytes = 15;
+}
+
+message NoWrapPrimitive {
+  optional double double = 1;
+  optional float float = 2 ;
+  optional int32 int32 = 3;
+  optional int64 int64 = 4;
+  optional uint32  uint32 = 5;
+  optional uint64 uint64 = 6;
+  optional bool bool = 13;
+  optional string string = 14;
+  optional bytes bytes = 15;
+}
\ No newline at end of file
diff --git a/sdks/java/extensions/sketching/src/test/java/org/apache/beam/sdk/extensions/sketching/ApproximateDistinctTest.java b/sdks/java/extensions/sketching/src/test/java/org/apache/beam/sdk/extensions/sketching/ApproximateDistinctTest.java
index 0cb3e0e5116d..312aae7afdb1 100644
--- a/sdks/java/extensions/sketching/src/test/java/org/apache/beam/sdk/extensions/sketching/ApproximateDistinctTest.java
+++ b/sdks/java/extensions/sketching/src/test/java/org/apache/beam/sdk/extensions/sketching/ApproximateDistinctTest.java
@@ -188,7 +188,7 @@ private static class VerifyAccuracy implements SerializableFunction<Iterable<Lon
     @Override
     public Void apply(Iterable<Long> input) {
       for (Long estimate : input) {
-        boolean isAccurate = Math.abs(estimate - expectedCard) / expectedCard < expectedError;
+        boolean isAccurate = Math.abs(0.0 + estimate - expectedCard) / expectedCard < expectedError;
         Assert.assertTrue(
             "not accurate enough : \nExpected Cardinality : "
                 + expectedCard
diff --git a/sdks/java/extensions/sketching/src/test/java/org/apache/beam/sdk/extensions/sketching/TDigestQuantilesTest.java b/sdks/java/extensions/sketching/src/test/java/org/apache/beam/sdk/extensions/sketching/TDigestQuantilesTest.java
index 9ee901317038..943cd7a52f6e 100644
--- a/sdks/java/extensions/sketching/src/test/java/org/apache/beam/sdk/extensions/sketching/TDigestQuantilesTest.java
+++ b/sdks/java/extensions/sketching/src/test/java/org/apache/beam/sdk/extensions/sketching/TDigestQuantilesTest.java
@@ -122,7 +122,7 @@ public void testMergeAccum() {
     Assert.assertEquals(3000, res.size());
   }
 
-  private <T> boolean encodeDecodeEquals(MergingDigest tDigest) throws IOException {
+  private boolean encodeDecodeEquals(MergingDigest tDigest) throws IOException {
     MergingDigest decoded = CoderUtils.clone(new MergingDigestCoder(), tDigest);
 
     boolean equal = true;
diff --git a/sdks/java/extensions/sql/build.gradle b/sdks/java/extensions/sql/build.gradle
index 259402aa16fb..02cdaab320d0 100644
--- a/sdks/java/extensions/sql/build.gradle
+++ b/sdks/java/extensions/sql/build.gradle
@@ -41,6 +41,8 @@ applyJavaNature(
   ],
   // javacc generated code produces lint warnings
   disableLintWarnings: ['dep-ann', 'rawtypes'],
+  // Disable SpotBugs due to ASM bytecode analysis issue with BeamCalcRel class
+  enableSpotbugs: false,
 )
 
 description = "Apache Beam :: SDKs :: Java :: Extensions :: SQL"
@@ -72,12 +74,8 @@ dependencies {
   javacc "net.java.dev.javacc:javacc:4.0"
   fmppTask "com.googlecode.fmpp-maven-plugin:fmpp-maven-plugin:1.0"
   fmppTask "org.freemarker:freemarker:2.3.31"
-  fmppTemplates library.java.vendored_calcite_1_28_0
+  fmppTemplates library.java.vendored_calcite_1_40_0
   implementation project(path: ":sdks:java:core", configuration: "shadow")
-  implementation project(":sdks:java:managed")
-  implementation project(":sdks:java:io:iceberg")
-  runtimeOnly project(":sdks:java:io:iceberg:bqms")
-  runtimeOnly project(":sdks:java:io:iceberg:hive")
   implementation project(":sdks:java:extensions:avro")
   implementation project(":sdks:java:extensions:join-library")
   permitUnusedDeclared project(":sdks:java:extensions:join-library") // BEAM-11761
@@ -87,11 +85,14 @@ dependencies {
   implementation library.java.commons_csv
   implementation library.java.jackson_databind
   implementation library.java.joda_time
-  implementation library.java.vendored_calcite_1_28_0
+  implementation library.java.vendored_calcite_1_40_0
   implementation "org.codehaus.janino:janino:3.0.11"
   implementation "org.codehaus.janino:commons-compiler:3.0.11"
   implementation library.java.jackson_core
   implementation library.java.mongo_java_driver
+  permitUnusedDeclared library.java.mongo_java_driver
+  implementation library.java.mongo_bson
+  implementation library.java.mongodb_driver_core
   implementation library.java.slf4j_api
   implementation library.java.joda_time
   implementation library.java.vendored_guava_32_1_2_jre
@@ -108,16 +109,15 @@ dependencies {
   implementation library.java.avro
   implementation library.java.protobuf_java
   implementation library.java.protobuf_java_util
-  provided project(":sdks:java:io:parquet")
+  implementation project(":sdks:java:io:parquet")
+  implementation "org.apache.parquet:parquet-hadoop:1.15.2"
   provided library.java.jackson_dataformat_xml
   permitUnusedDeclared library.java.jackson_dataformat_xml
   provided library.java.hadoop_client
   permitUnusedDeclared library.java.hadoop_client
   provided library.java.kafka_clients
 
-  testImplementation "org.apache.iceberg:iceberg-api:1.6.1"
-  testImplementation "org.apache.iceberg:iceberg-core:1.6.1"
-  testImplementation library.java.vendored_calcite_1_28_0
+  testImplementation library.java.vendored_calcite_1_40_0
   testImplementation library.java.vendored_guava_32_1_2_jre
   testImplementation library.java.junit
   testImplementation library.java.quickcheck_core
@@ -126,11 +126,14 @@ dependencies {
   testImplementation library.java.google_cloud_bigtable_client_core_config
   testImplementation library.java.google_cloud_bigtable_emulator
   testImplementation library.java.proto_google_cloud_bigtable_admin_v2
+  implementation library.java.proto_google_cloud_datastore_v1
+  implementation library.java.google_cloud_datastore_v1_proto_client
   testImplementation library.java.proto_google_cloud_datastore_v1
   testImplementation library.java.google_cloud_datastore_v1_proto_client
   testImplementation library.java.kafka_clients
   testImplementation project(":sdks:java:io:kafka")
   testImplementation project(path: ":sdks:java:io:mongodb", configuration: "testRuntimeMigration")
+  testImplementation library.java.mongo_java_driver
   testImplementation project(path: ":sdks:java:io:thrift", configuration: "testRuntimeMigration")
   testImplementation project(path: ":sdks:java:extensions:protobuf", configuration: "testRuntimeMigration")
   testCompileOnly project(":sdks:java:extensions:sql:udf-test-provider")
@@ -164,11 +167,15 @@ task copyFmppTemplatesFromCalciteCore(type: Copy) {
   into "${project.buildDir}/templates-fmpp"
   filter{
     line ->
-      line.replace('import org.apache.calcite.', 'import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.')
+      line.replace('import org.apache.calcite.', 'import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.')
   }
   filter{
     line ->
-      line.replace('import static org.apache.calcite.', 'import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.')
+      line.replace('import static org.apache.calcite.', 'import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.')
+  }
+  filter{
+    line ->
+      line.replace('import com.google.common.', 'import org.apache.beam.vendor.calcite.v1_40_0.com.google.common.')
   }
 }
 
diff --git a/sdks/java/extensions/sql/datacatalog/build.gradle b/sdks/java/extensions/sql/datacatalog/build.gradle
index cb557cc80776..db4a92001501 100644
--- a/sdks/java/extensions/sql/datacatalog/build.gradle
+++ b/sdks/java/extensions/sql/datacatalog/build.gradle
@@ -46,7 +46,6 @@ dependencies {
   implementation library.java.slf4j_api
 
   testImplementation project(":sdks:java:extensions:sql")
-  testImplementation project(":sdks:java:extensions:sql:zetasql")
   testImplementation project(":runners:direct-java")
   testImplementation project(":sdks:java:io:google-cloud-platform")
   testImplementation library.java.google_api_services_bigquery
diff --git a/sdks/java/extensions/sql/datacatalog/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/datacatalog/DataCatalogBigQueryIT.java b/sdks/java/extensions/sql/datacatalog/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/datacatalog/DataCatalogBigQueryIT.java
index b86414db3209..cdfc8ce16316 100644
--- a/sdks/java/extensions/sql/datacatalog/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/datacatalog/DataCatalogBigQueryIT.java
+++ b/sdks/java/extensions/sql/datacatalog/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/datacatalog/DataCatalogBigQueryIT.java
@@ -26,7 +26,6 @@
 import org.apache.beam.sdk.extensions.sql.impl.BeamSqlPipelineOptions;
 import org.apache.beam.sdk.extensions.sql.impl.CalciteQueryPlanner;
 import org.apache.beam.sdk.extensions.sql.impl.QueryPlanner;
-import org.apache.beam.sdk.extensions.sql.zetasql.ZetaSQLQueryPlanner;
 import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
 import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.Method;
 import org.apache.beam.sdk.io.gcp.bigquery.BigQueryUtils;
@@ -59,11 +58,7 @@ public static class DialectSensitiveTests {
     /** Parameterized by which SQL dialect, since the syntax here is the same. */
     @Parameterized.Parameters(name = "{0}")
     public static Iterable<Object[]> dialects() {
-      return Arrays.asList(
-          new Object[][] {
-            {"ZetaSQL", ZetaSQLQueryPlanner.class},
-            {"CalciteSQL", CalciteQueryPlanner.class}
-          });
+      return Arrays.asList(new Object[][] {{"CalciteSQL", CalciteQueryPlanner.class}});
     }
 
     @SuppressWarnings("initialization.fields.uninitialized")
diff --git a/sdks/java/extensions/sql/expansion-service/build.gradle b/sdks/java/extensions/sql/expansion-service/build.gradle
index efa8b8650dcd..8b5bd8c69240 100644
--- a/sdks/java/extensions/sql/expansion-service/build.gradle
+++ b/sdks/java/extensions/sql/expansion-service/build.gradle
@@ -42,7 +42,6 @@ dependencies {
   implementation project(path: ":sdks:java:expansion-service")
   permitUnusedDeclared project(path: ":sdks:java:expansion-service") // BEAM-11761
   implementation project(path: ":sdks:java:extensions:sql")
-  implementation project(path: ":sdks:java:extensions:sql:zetasql")
   implementation library.java.vendored_guava_32_1_2_jre
 
 }
diff --git a/sdks/java/extensions/sql/expansion-service/src/main/java/org/apache/beam/sdk/extensions/sql/expansion/ExternalSqlTransformRegistrar.java b/sdks/java/extensions/sql/expansion-service/src/main/java/org/apache/beam/sdk/extensions/sql/expansion/ExternalSqlTransformRegistrar.java
index d7a9bb288969..19c34668b897 100644
--- a/sdks/java/extensions/sql/expansion-service/src/main/java/org/apache/beam/sdk/extensions/sql/expansion/ExternalSqlTransformRegistrar.java
+++ b/sdks/java/extensions/sql/expansion-service/src/main/java/org/apache/beam/sdk/extensions/sql/expansion/ExternalSqlTransformRegistrar.java
@@ -23,7 +23,6 @@
 import org.apache.beam.sdk.extensions.sql.SqlTransform;
 import org.apache.beam.sdk.extensions.sql.impl.CalciteQueryPlanner;
 import org.apache.beam.sdk.extensions.sql.impl.QueryPlanner;
-import org.apache.beam.sdk.extensions.sql.zetasql.ZetaSQLQueryPlanner;
 import org.apache.beam.sdk.transforms.ExternalTransformBuilder;
 import org.apache.beam.sdk.transforms.PTransform;
 import org.apache.beam.sdk.values.PCollection;
@@ -37,7 +36,6 @@ public class ExternalSqlTransformRegistrar implements ExternalTransformRegistrar
   private static final String URN = "beam:external:java:sql:v1";
   private static final ImmutableMap<String, Class<? extends QueryPlanner>> DIALECTS =
       ImmutableMap.<String, Class<? extends QueryPlanner>>builder()
-          .put("zetasql", ZetaSQLQueryPlanner.class)
           .put("calcite", CalciteQueryPlanner.class)
           .build();
 
diff --git a/sdks/java/extensions/sql/expansion-service/src/main/java/org/apache/beam/sdk/extensions/sql/expansion/SqlTransformSchemaTransformProvider.java b/sdks/java/extensions/sql/expansion-service/src/main/java/org/apache/beam/sdk/extensions/sql/expansion/SqlTransformSchemaTransformProvider.java
index f032da0799d8..3ac890a7370d 100644
--- a/sdks/java/extensions/sql/expansion-service/src/main/java/org/apache/beam/sdk/extensions/sql/expansion/SqlTransformSchemaTransformProvider.java
+++ b/sdks/java/extensions/sql/expansion-service/src/main/java/org/apache/beam/sdk/extensions/sql/expansion/SqlTransformSchemaTransformProvider.java
@@ -50,9 +50,7 @@
 public class SqlTransformSchemaTransformProvider implements SchemaTransformProvider {
 
   private static final Map<String, Class<? extends QueryPlanner>> QUERY_PLANNERS =
-      ImmutableMap.of(
-          "zetasql", org.apache.beam.sdk.extensions.sql.zetasql.ZetaSQLQueryPlanner.class,
-          "calcite", org.apache.beam.sdk.extensions.sql.impl.CalciteQueryPlanner.class);
+      ImmutableMap.of("calcite", org.apache.beam.sdk.extensions.sql.impl.CalciteQueryPlanner.class);
   private static final EnumerationType QUERY_ENUMERATION =
       EnumerationType.create(QUERY_PLANNERS.keySet().stream().collect(Collectors.toList()));
 
diff --git a/sdks/java/extensions/sql/hcatalog/build.gradle b/sdks/java/extensions/sql/hcatalog/build.gradle
index e8abf21b7c3e..0a267a6f424e 100644
--- a/sdks/java/extensions/sql/hcatalog/build.gradle
+++ b/sdks/java/extensions/sql/hcatalog/build.gradle
@@ -26,7 +26,7 @@ applyJavaNature(
 )
 
 def hive_version = "3.1.3"
-def netty_version = "4.1.51.Final"
+def netty_version = "4.1.110.Final"
 
 /*
  * We need to rely on manually specifying these evaluationDependsOn to ensure that
diff --git a/sdks/java/extensions/sql/iceberg/build.gradle b/sdks/java/extensions/sql/iceberg/build.gradle
new file mode 100644
index 000000000000..d5f9e74c53bd
--- /dev/null
+++ b/sdks/java/extensions/sql/iceberg/build.gradle
@@ -0,0 +1,81 @@
+import groovy.json.JsonOutput
+
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+plugins { id 'org.apache.beam.module' }
+
+applyJavaNature(
+  automaticModuleName: 'org.apache.beam.sdk.extensions.sql.meta.provider.hcatalog',
+  // iceberg requires Java11+
+  requireJavaVersion: JavaVersion.VERSION_11,
+)
+
+dependencies {
+  implementation project(":sdks:java:extensions:sql")
+  implementation project(":sdks:java:core")
+  implementation project(":sdks:java:managed")
+  implementation project(":sdks:java:io:iceberg")
+  runtimeOnly project(":sdks:java:io:iceberg:bqms")
+  runtimeOnly project(":sdks:java:io:iceberg:hive")
+  // TODO(https://github.com/apache/beam/issues/21156): Determine how to build without this dependency
+  provided "org.immutables:value:2.8.8"
+  permitUnusedDeclared "org.immutables:value:2.8.8"
+  implementation library.java.slf4j_api
+  implementation library.java.vendored_guava_32_1_2_jre
+  implementation library.java.vendored_calcite_1_40_0
+  implementation library.java.jackson_databind
+
+  testImplementation library.java.joda_time
+  testImplementation library.java.junit
+  testImplementation library.java.google_api_services_bigquery
+  testImplementation "org.apache.iceberg:iceberg-api:1.9.2"
+  testImplementation "org.apache.iceberg:iceberg-core:1.9.2"
+  testImplementation project(":sdks:java:io:google-cloud-platform")
+  testImplementation project(":sdks:java:extensions:google-cloud-platform-core")
+}
+
+task integrationTest(type: Test) {
+  def gcpProject = project.findProperty('gcpProject') ?: 'apache-beam-testing'
+  def gcsTempRoot = project.findProperty('gcsTempRoot') ?: 'gs://temp-storage-for-end-to-end-tests/'
+
+  // Disable Gradle cache (it should not be used because the IT's won't run).
+  outputs.upToDateWhen { false }
+
+  def pipelineOptions = [
+    "--project=${gcpProject}",
+    "--tempLocation=${gcsTempRoot}",
+    "--blockOnRun=false"]
+
+  systemProperty "beamTestPipelineOptions", JsonOutput.toJson(pipelineOptions)
+
+  include '**/*IT.class'
+
+  maxParallelForks 4
+  classpath = project(":sdks:java:extensions:sql:iceberg")
+    .sourceSets
+    .test
+    .runtimeClasspath
+  testClassesDirs = files(project(":sdks:java:extensions:sql:iceberg").sourceSets.test.output.classesDirs)
+  useJUnit { }
+}
+
+configurations.all {
+  // iceberg-core needs avro:1.12.0
+  resolutionStrategy.force 'org.apache.avro:avro:1.12.0'
+}
diff --git a/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergCatalog.java b/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergCatalog.java
new file mode 100644
index 000000000000..1209d2b4663d
--- /dev/null
+++ b/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergCatalog.java
@@ -0,0 +1,86 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.meta.provider.iceberg;
+
+import java.util.Map;
+import java.util.Set;
+import org.apache.beam.sdk.extensions.sql.meta.catalog.InMemoryCatalog;
+import org.apache.beam.sdk.extensions.sql.meta.store.InMemoryMetaStore;
+import org.apache.beam.sdk.io.iceberg.IcebergCatalogConfig;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+
+public class IcebergCatalog extends InMemoryCatalog {
+  // TODO(ahmedabu98): extend this to the IO implementation so
+  //  other SDKs can make use of it too
+  private static final String BEAM_HADOOP_PREFIX = "beam.catalog.hadoop";
+  private final InMemoryMetaStore metaStore = new InMemoryMetaStore();
+  @VisibleForTesting final IcebergCatalogConfig catalogConfig;
+
+  public IcebergCatalog(String name, Map<String, String> properties) {
+    super(name, properties);
+
+    ImmutableMap.Builder<String, String> catalogProps = ImmutableMap.builder();
+    ImmutableMap.Builder<String, String> hadoopProps = ImmutableMap.builder();
+
+    for (Map.Entry<String, String> entry : properties.entrySet()) {
+      if (entry.getKey().startsWith(BEAM_HADOOP_PREFIX)) {
+        hadoopProps.put(entry.getKey(), entry.getValue());
+      } else {
+        catalogProps.put(entry.getKey(), entry.getValue());
+      }
+    }
+
+    catalogConfig =
+        IcebergCatalogConfig.builder()
+            .setCatalogName(name)
+            .setCatalogProperties(catalogProps.build())
+            .setConfigProperties(hadoopProps.build())
+            .build();
+    metaStore.registerProvider(new IcebergTableProvider(catalogConfig));
+  }
+
+  @Override
+  public InMemoryMetaStore metaStore() {
+    return metaStore;
+  }
+
+  @Override
+  public String type() {
+    return "iceberg";
+  }
+
+  @Override
+  public boolean createDatabase(String database) {
+    return catalogConfig.createNamespace(database);
+  }
+
+  @Override
+  public boolean dropDatabase(String database, boolean cascade) {
+    boolean removed = catalogConfig.dropNamespace(database, cascade);
+    if (database.equals(currentDatabase)) {
+      currentDatabase = null;
+    }
+    return removed;
+  }
+
+  @Override
+  public Set<String> listDatabases() {
+    return catalogConfig.listNamespaces();
+  }
+}
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergCatalog.java b/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergCatalogRegistrar.java
similarity index 59%
rename from sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergCatalog.java
rename to sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergCatalogRegistrar.java
index 58c686da12f4..03c524f7b0fc 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergCatalog.java
+++ b/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergCatalogRegistrar.java
@@ -17,25 +17,15 @@
  */
 package org.apache.beam.sdk.extensions.sql.meta.provider.iceberg;
 
-import java.util.Map;
-import org.apache.beam.sdk.extensions.sql.meta.catalog.InMemoryCatalog;
-import org.apache.beam.sdk.extensions.sql.meta.store.InMemoryMetaStore;
-
-public class IcebergCatalog extends InMemoryCatalog {
-  private final InMemoryMetaStore metaStore = new InMemoryMetaStore();
-
-  public IcebergCatalog(String name, Map<String, String> properties) {
-    super(name, properties);
-    metaStore.registerProvider(new IcebergTableProvider(name, properties));
-  }
-
-  @Override
-  public InMemoryMetaStore metaStore() {
-    return metaStore;
-  }
+import com.google.auto.service.AutoService;
+import org.apache.beam.sdk.extensions.sql.meta.catalog.Catalog;
+import org.apache.beam.sdk.extensions.sql.meta.catalog.CatalogRegistrar;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 
+@AutoService(CatalogRegistrar.class)
+public class IcebergCatalogRegistrar implements CatalogRegistrar {
   @Override
-  public String type() {
-    return "iceberg";
+  public Iterable<Class<? extends Catalog>> getCatalogs() {
+    return ImmutableList.<Class<? extends Catalog>>builder().add(IcebergCatalog.class).build();
   }
 }
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilter.java b/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilter.java
similarity index 91%
rename from sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilter.java
rename to sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilter.java
index b3854ced46c6..834aed0e9048 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilter.java
+++ b/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilter.java
@@ -19,18 +19,18 @@
 
 import static org.apache.beam.sdk.io.iceberg.FilterUtils.SUPPORTED_OPS;
 import static org.apache.beam.sdk.util.Preconditions.checkStateNotNull;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.AND;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.OR;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.AND;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.OR;
 
 import java.util.List;
 import java.util.stream.Collectors;
 import org.apache.beam.sdk.extensions.sql.meta.BeamSqlTableFilter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.commons.lang3.tuple.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.commons.lang3.tuple.Pair;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.MoreObjects;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableSet;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTable.java b/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTable.java
similarity index 94%
rename from sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTable.java
rename to sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTable.java
index 596a1d6d0457..000ca50e4309 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTable.java
+++ b/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTable.java
@@ -38,13 +38,13 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.POutput;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rel2sql.SqlImplementor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.dialect.BigQuerySqlDialect;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rel2sql.SqlImplementor;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.dialect.BigQuerySqlDialect;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProvider.java b/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProvider.java
similarity index 76%
rename from sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProvider.java
rename to sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProvider.java
index e52900ce81e5..568893716581 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProvider.java
+++ b/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProvider.java
@@ -28,7 +28,6 @@
 import org.apache.beam.sdk.io.iceberg.IcebergCatalogConfig;
 import org.apache.beam.sdk.io.iceberg.TableAlreadyExistsException;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -38,30 +37,11 @@
  */
 public class IcebergTableProvider implements TableProvider {
   private static final Logger LOG = LoggerFactory.getLogger(IcebergTableProvider.class);
-  // TODO(ahmedabu98): extend this to the IO implementation so
-  //  other SDKs can make use of it too
-  private static final String BEAM_HADOOP_PREFIX = "beam.catalog.hadoop";
   @VisibleForTesting final IcebergCatalogConfig catalogConfig;
   private final Map<String, Table> tables = new HashMap<>();
 
-  public IcebergTableProvider(String name, Map<String, String> properties) {
-    ImmutableMap.Builder<String, String> catalogProps = ImmutableMap.builder();
-    ImmutableMap.Builder<String, String> hadoopProps = ImmutableMap.builder();
-
-    for (Map.Entry<String, String> entry : properties.entrySet()) {
-      if (entry.getKey().startsWith(BEAM_HADOOP_PREFIX)) {
-        hadoopProps.put(entry.getKey(), entry.getValue());
-      } else {
-        catalogProps.put(entry.getKey(), entry.getValue());
-      }
-    }
-
-    catalogConfig =
-        IcebergCatalogConfig.builder()
-            .setCatalogName(name)
-            .setCatalogProperties(catalogProps.build())
-            .setConfigProperties(hadoopProps.build())
-            .build();
+  public IcebergTableProvider(IcebergCatalogConfig catalogConfig) {
+    this.catalogConfig = catalogConfig;
   }
 
   @Override
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/package-info.java b/sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/package-info.java
similarity index 100%
rename from sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/package-info.java
rename to sdks/java/extensions/sql/iceberg/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/package-info.java
diff --git a/sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/BeamSqlCliIcebergTest.java b/sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/BeamSqlCliIcebergTest.java
new file mode 100644
index 000000000000..0c51b31f1927
--- /dev/null
+++ b/sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/BeamSqlCliIcebergTest.java
@@ -0,0 +1,140 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.meta.provider.iceberg;
+
+import static java.lang.String.format;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertNull;
+import static org.junit.Assert.assertTrue;
+
+import java.io.File;
+import java.io.IOException;
+import java.util.UUID;
+import org.apache.beam.sdk.extensions.sql.BeamSqlCli;
+import org.apache.beam.sdk.extensions.sql.meta.catalog.InMemoryCatalogManager;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.runtime.CalciteContextException;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.ClassRule;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+import org.junit.rules.TemporaryFolder;
+
+/** UnitTest for {@link BeamSqlCli} using Iceberg catalog. */
+public class BeamSqlCliIcebergTest {
+  @Rule public transient ExpectedException thrown = ExpectedException.none();
+  private InMemoryCatalogManager catalogManager;
+  private BeamSqlCli cli;
+  private String warehouse;
+  @ClassRule public static final TemporaryFolder TEMPORARY_FOLDER = new TemporaryFolder();
+
+  @Before
+  public void setup() throws IOException {
+    catalogManager = new InMemoryCatalogManager();
+    cli = new BeamSqlCli().catalogManager(catalogManager);
+    File warehouseFile = TEMPORARY_FOLDER.newFolder();
+    Assert.assertTrue(warehouseFile.delete());
+    warehouse = "file:" + warehouseFile + "/" + UUID.randomUUID();
+  }
+
+  private String createCatalog(String name) {
+    return format("CREATE CATALOG %s \n", name)
+        + "TYPE iceberg \n"
+        + "PROPERTIES (\n"
+        + "  'type' = 'hadoop', \n"
+        + format("  'warehouse' = '%s')", warehouse);
+  }
+
+  @Test
+  public void testCreateCatalog() {
+    assertEquals("default", catalogManager.currentCatalog().name());
+
+    cli.execute(createCatalog("my_catalog"));
+    assertNotNull(catalogManager.getCatalog("my_catalog"));
+    assertEquals("default", catalogManager.currentCatalog().name());
+
+    cli.execute("USE CATALOG my_catalog");
+    assertEquals("my_catalog", catalogManager.currentCatalog().name());
+    assertEquals("iceberg", catalogManager.currentCatalog().type());
+  }
+
+  @Test
+  public void testCreateNamespace() {
+    testCreateCatalog();
+
+    IcebergCatalog catalog = (IcebergCatalog) catalogManager.currentCatalog();
+    assertEquals("default", catalog.currentDatabase());
+    cli.execute("CREATE DATABASE new_namespace");
+    assertEquals("new_namespace", Iterables.getOnlyElement(catalog.listDatabases()));
+
+    // Specifies IF NOT EXISTS, so should be a no-op
+    cli.execute("CREATE DATABASE IF NOT EXISTS new_namespace");
+    assertEquals("new_namespace", Iterables.getOnlyElement(catalog.listDatabases()));
+
+    // This one doesn't, so it should throw an error.
+    thrown.expect(CalciteContextException.class);
+    thrown.expectMessage("Database 'new_namespace' already exists.");
+    cli.execute("CREATE DATABASE new_namespace");
+
+    // assert there was a database, and cleanup
+    assertTrue(catalog.dropDatabase("new_namespace", true));
+  }
+
+  @Test
+  public void testUseNamespace() {
+    testCreateCatalog();
+
+    IcebergCatalog catalog = (IcebergCatalog) catalogManager.currentCatalog();
+    cli.execute("CREATE DATABASE new_namespace");
+    assertEquals("default", catalog.currentDatabase());
+    cli.execute("USE DATABASE new_namespace");
+    assertEquals("new_namespace", catalog.currentDatabase());
+
+    // Cannot use a non-existent namespace
+    thrown.expect(CalciteContextException.class);
+    thrown.expectMessage("Cannot use database: 'non_existent' not found.");
+    cli.execute("USE DATABASE non_existent");
+
+    // assert there was a database, and cleanup
+    assertTrue(catalog.dropDatabase("new_namespace", true));
+  }
+
+  @Test
+  public void testDropNamespace() {
+    testCreateCatalog();
+
+    IcebergCatalog catalog = (IcebergCatalog) catalogManager.currentCatalog();
+    cli.execute("CREATE DATABASE new_namespace");
+    cli.execute("USE DATABASE new_namespace");
+    assertEquals("new_namespace", catalog.currentDatabase());
+    cli.execute("DROP DATABASE new_namespace");
+    assertTrue(catalog.listDatabases().isEmpty());
+    assertNull(catalog.currentDatabase());
+
+    // Drop non-existent namespace with IF EXISTS
+    cli.execute("DROP DATABASE IF EXISTS new_namespace");
+
+    // Throw an error when IF EXISTS is not specified
+    thrown.expect(CalciteContextException.class);
+    thrown.expectMessage("Database 'new_namespace' does not exist.");
+    cli.execute("DROP DATABASE new_namespace");
+  }
+}
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilterTest.java b/sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilterTest.java
similarity index 98%
rename from sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilterTest.java
rename to sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilterTest.java
index f14344b4f1fe..c78965d214b2 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilterTest.java
+++ b/sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergFilterTest.java
@@ -32,7 +32,7 @@
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.commons.lang3.tuple.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.commons.lang3.tuple.Pair;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.junit.Before;
 import org.junit.Rule;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergReadWriteIT.java b/sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergReadWriteIT.java
similarity index 96%
rename from sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergReadWriteIT.java
rename to sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergReadWriteIT.java
index 15fe4769c61b..a7b128b2bca3 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergReadWriteIT.java
+++ b/sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergReadWriteIT.java
@@ -19,7 +19,6 @@
 
 import static java.lang.String.format;
 import static java.util.Arrays.asList;
-import static org.apache.beam.sdk.extensions.sql.utils.DateTimeUtils.parseTimestampWithUTCTimeZone;
 import static org.apache.beam.sdk.schemas.Schema.FieldType.BOOLEAN;
 import static org.apache.beam.sdk.schemas.Schema.FieldType.DOUBLE;
 import static org.apache.beam.sdk.schemas.Schema.FieldType.FLOAT;
@@ -64,6 +63,7 @@
 import org.apache.iceberg.catalog.Catalog;
 import org.apache.iceberg.catalog.TableIdentifier;
 import org.joda.time.Duration;
+import org.joda.time.format.DateTimeFormat;
 import org.junit.AfterClass;
 import org.junit.BeforeClass;
 import org.junit.Rule;
@@ -218,9 +218,9 @@ public void runSqlWriteAndRead(boolean withPartitionFields)
             + "'char', "
             + "ARRAY['123', '456'], "
             + "ARRAY["
-            + "ROW(ARRAY['abc', 'xyz'], 123), "
-            + "ROW(ARRAY['foo', 'bar'], 456), "
-            + "ROW(ARRAY['cat', 'dog'], 789)]"
+            + "CAST(ROW(ARRAY['abc', 'xyz'], 123) AS ROW(c_arr_struct_arr VARCHAR ARRAY, c_arr_struct_integer INTEGER)), "
+            + "CAST(ROW(ARRAY['foo', 'bar'], 456) AS ROW(c_arr_struct_arr VARCHAR ARRAY, c_arr_struct_integer INTEGER)), "
+            + "CAST(ROW(ARRAY['cat', 'dog'], 789) AS ROW(c_arr_struct_arr VARCHAR ARRAY, c_arr_struct_integer INTEGER))]"
             + ")";
     BeamSqlRelUtils.toPCollection(writePipeline, sqlEnv.parseQuery(insertStatement));
     writePipeline.run().waitUntilFinish();
@@ -238,7 +238,9 @@ public void runSqlWriteAndRead(boolean withPartitionFields)
                 (float) 1.0,
                 1.0,
                 true,
-                parseTimestampWithUTCTimeZone("2018-05-28 20:17:40.123"),
+                DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss.SSS")
+                    .withZoneUTC()
+                    .parseDateTime("2018-05-28 20:17:40.123"),
                 "varchar",
                 "char",
                 asList("123", "456"),
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProviderTest.java b/sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProviderTest.java
similarity index 90%
rename from sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProviderTest.java
rename to sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProviderTest.java
index d829ee3bed08..cf066b1abed8 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProviderTest.java
+++ b/sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/IcebergTableProviderTest.java
@@ -28,14 +28,14 @@
 import org.apache.beam.sdk.extensions.sql.meta.BeamSqlTable;
 import org.apache.beam.sdk.extensions.sql.meta.Table;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.com.fasterxml.jackson.databind.ObjectMapper;
+import org.apache.beam.vendor.calcite.v1_40_0.com.fasterxml.jackson.databind.ObjectMapper;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 import org.junit.Test;
 
 /** UnitTest for {@link IcebergTableProvider}. */
 public class IcebergTableProviderTest {
-  private final IcebergTableProvider provider =
-      new IcebergTableProvider(
+  private final IcebergCatalog catalog =
+      new IcebergCatalog(
           "test_catalog",
           ImmutableMap.of(
               "catalog-impl", "org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog",
@@ -46,7 +46,7 @@ public class IcebergTableProviderTest {
 
   @Test
   public void testGetTableType() {
-    assertEquals("iceberg", provider.getTableType());
+    assertNotNull(catalog.metaStore().getProvider("iceberg"));
   }
 
   @Test
@@ -59,14 +59,14 @@ public void testBuildBeamSqlTable() throws Exception {
         fakeTableBuilder("my_table")
             .properties(TableUtils.parseProperties(propertiesString))
             .build();
-    BeamSqlTable sqlTable = provider.buildBeamSqlTable(table);
+    BeamSqlTable sqlTable = catalog.metaStore().buildBeamSqlTable(table);
 
     assertNotNull(sqlTable);
     assertTrue(sqlTable instanceof IcebergTable);
 
     IcebergTable icebergTable = (IcebergTable) sqlTable;
     assertEquals("namespace.my_table", icebergTable.tableIdentifier);
-    assertEquals(provider.catalogConfig, icebergTable.catalogConfig);
+    assertEquals(catalog.catalogConfig, icebergTable.catalogConfig);
   }
 
   private static Table.Builder fakeTableBuilder(String name) {
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/PubsubToIcebergIT.java b/sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/PubsubToIcebergIT.java
similarity index 98%
rename from sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/PubsubToIcebergIT.java
rename to sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/PubsubToIcebergIT.java
index 7343c9b9a52f..bdd710c861e0 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/PubsubToIcebergIT.java
+++ b/sdks/java/extensions/sql/iceberg/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/iceberg/PubsubToIcebergIT.java
@@ -15,7 +15,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-package org.apache.beam.sdk.extensions.sql;
+package org.apache.beam.sdk.extensions.sql.meta.provider.iceberg;
 
 import static java.lang.String.format;
 import static java.nio.charset.StandardCharsets.UTF_8;
@@ -33,6 +33,7 @@
 import java.util.UUID;
 import java.util.stream.Collectors;
 import org.apache.beam.sdk.extensions.gcp.options.GcpOptions;
+import org.apache.beam.sdk.extensions.sql.SqlTransform;
 import org.apache.beam.sdk.io.gcp.bigquery.BigQueryUtils;
 import org.apache.beam.sdk.io.gcp.pubsub.PubsubMessage;
 import org.apache.beam.sdk.io.gcp.pubsub.TestPubsub;
diff --git a/sdks/java/extensions/sql/jdbc/build.gradle b/sdks/java/extensions/sql/jdbc/build.gradle
index 1dae8d5c5a2d..91c48b635d50 100644
--- a/sdks/java/extensions/sql/jdbc/build.gradle
+++ b/sdks/java/extensions/sql/jdbc/build.gradle
@@ -38,8 +38,8 @@ dependencies {
   implementation "jline:jline:2.14.6"
   permitUnusedDeclared "jline:jline:2.14.6" // BEAM-11761
   implementation "sqlline:sqlline:1.4.0"
-  implementation library.java.vendored_calcite_1_28_0
-  permitUnusedDeclared library.java.vendored_calcite_1_28_0
+  implementation library.java.vendored_calcite_1_40_0
+  permitUnusedDeclared library.java.vendored_calcite_1_40_0
   testImplementation project(path: ":sdks:java:core", configuration: "shadow")
   testImplementation project(path: ":sdks:java:io:google-cloud-platform")
   testImplementation project(path: ":sdks:java:extensions:google-cloud-platform-core")
diff --git a/sdks/java/extensions/sql/jdbc/src/test/java/org/apache/beam/sdk/extensions/sql/jdbc/BeamSqlLineIT.java b/sdks/java/extensions/sql/jdbc/src/test/java/org/apache/beam/sdk/extensions/sql/jdbc/BeamSqlLineIT.java
index c57c5a530c9e..7df39d2ed625 100644
--- a/sdks/java/extensions/sql/jdbc/src/test/java/org/apache/beam/sdk/extensions/sql/jdbc/BeamSqlLineIT.java
+++ b/sdks/java/extensions/sql/jdbc/src/test/java/org/apache/beam/sdk/extensions/sql/jdbc/BeamSqlLineIT.java
@@ -129,9 +129,9 @@ public void testSelectFromPubsub() throws Exception {
 
     assertThat(
         Arrays.asList(
-            Arrays.asList("2018-07-01 21:25:20", "enroute", "40.702", "-74.001"),
-            Arrays.asList("2018-07-01 21:26:06", "enroute", "40.703", "-74.002"),
-            Arrays.asList("2018-07-02 13:26:06", "enroute", "30.0", "-72.32324")),
+            Arrays.asList("2018-07-01 21:25:20.000000", "enroute", "40.702", "-74.001"),
+            Arrays.asList("2018-07-01 21:26:06.000000", "enroute", "40.703", "-74.002"),
+            Arrays.asList("2018-07-02 13:26:06.000000", "enroute", "30.0", "-72.32324")),
         everyItem(IsIn.isOneOf(expectedResult.get(30, TimeUnit.SECONDS).toArray())));
   }
 
@@ -170,8 +170,8 @@ public void testFilterForSouthManhattan() throws Exception {
 
     assertThat(
         Arrays.asList(
-            Arrays.asList("2018-07-01 21:25:20", "enroute", "40.701", "-74.001"),
-            Arrays.asList("2018-07-01 21:26:06", "enroute", "40.702", "-74.002")),
+            Arrays.asList("2018-07-01 21:25:20.000000", "enroute", "40.701", "-74.001"),
+            Arrays.asList("2018-07-01 21:26:06.000000", "enroute", "40.702", "-74.002")),
         everyItem(IsIn.isOneOf(expectedResult.get(30, TimeUnit.SECONDS).toArray())));
   }
 
diff --git a/sdks/java/extensions/sql/jdbc/src/test/java/org/apache/beam/sdk/extensions/sql/jdbc/BeamSqlLineTest.java b/sdks/java/extensions/sql/jdbc/src/test/java/org/apache/beam/sdk/extensions/sql/jdbc/BeamSqlLineTest.java
index e308fa52d6e4..367336e1e7a0 100644
--- a/sdks/java/extensions/sql/jdbc/src/test/java/org/apache/beam/sdk/extensions/sql/jdbc/BeamSqlLineTest.java
+++ b/sdks/java/extensions/sql/jdbc/src/test/java/org/apache/beam/sdk/extensions/sql/jdbc/BeamSqlLineTest.java
@@ -168,7 +168,8 @@ public void testSqlLine_fixedWindow() throws Exception {
     List<List<String>> lines = toLines(byteArrayOutputStream);
     assertThat(
         Arrays.asList(
-            Arrays.asList("2018-07-01 21:26:06", "1"), Arrays.asList("2018-07-01 21:26:07", "1")),
+            Arrays.asList("2018-07-01 21:26:06.000000", "1"),
+            Arrays.asList("2018-07-01 21:26:07.000000", "1")),
         everyItem(is(oneOf(lines.toArray()))));
   }
 
@@ -190,11 +191,11 @@ public void testSqlLine_slidingWindow() throws Exception {
     List<List<String>> lines = toLines(byteArrayOutputStream);
     assertThat(
         Arrays.asList(
-            Arrays.asList("2018-07-01 21:26:07", "1"),
-            Arrays.asList("2018-07-01 21:26:08", "2"),
-            Arrays.asList("2018-07-01 21:26:09", "2"),
-            Arrays.asList("2018-07-01 21:26:10", "2"),
-            Arrays.asList("2018-07-01 21:26:11", "1")),
+            Arrays.asList("2018-07-01 21:26:07.000000", "1"),
+            Arrays.asList("2018-07-01 21:26:08.000000", "2"),
+            Arrays.asList("2018-07-01 21:26:09.000000", "2"),
+            Arrays.asList("2018-07-01 21:26:10.000000", "2"),
+            Arrays.asList("2018-07-01 21:26:11.000000", "1")),
         everyItem(is(oneOf(lines.toArray()))));
   }
 }
diff --git a/sdks/java/extensions/sql/perf-tests/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryIOPushDownIT.java b/sdks/java/extensions/sql/perf-tests/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryIOPushDownIT.java
index ff3e62f551bd..224dc1ad92c1 100644
--- a/sdks/java/extensions/sql/perf-tests/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryIOPushDownIT.java
+++ b/sdks/java/extensions/sql/perf-tests/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryIOPushDownIT.java
@@ -45,9 +45,9 @@
 import org.apache.beam.sdk.testutils.metrics.TimeMonitor;
 import org.apache.beam.sdk.testutils.publishing.InfluxDBSettings;
 import org.apache.beam.sdk.transforms.ParDo;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSets;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSets;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.junit.Before;
 import org.junit.BeforeClass;
diff --git a/sdks/java/extensions/sql/src/main/codegen/config.fmpp b/sdks/java/extensions/sql/src/main/codegen/config.fmpp
index 623a3e2792f7..77772c5858e3 100644
--- a/sdks/java/extensions/sql/src/main/codegen/config.fmpp
+++ b/sdks/java/extensions/sql/src/main/codegen/config.fmpp
@@ -21,16 +21,20 @@ data: {
 
       # List of import statements.
       imports: [
-        "org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.ColumnStrategy"
-        "org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlCreate"
-        "org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlDrop"
-        "org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName"
+        "org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.ColumnStrategy"
+        "org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlCreate"
+        "org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlDrop"
+        "org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName"
+        "org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.NlsString"
         "org.apache.beam.sdk.extensions.sql.impl.parser.SqlCreateCatalog"
+        "org.apache.beam.sdk.extensions.sql.impl.parser.SqlCreateDatabase"
         "org.apache.beam.sdk.extensions.sql.impl.parser.SqlCreateExternalTable"
         "org.apache.beam.sdk.extensions.sql.impl.parser.SqlCreateFunction"
         "org.apache.beam.sdk.extensions.sql.impl.parser.SqlDropCatalog"
+        "org.apache.beam.sdk.extensions.sql.impl.parser.SqlDropDatabase"
         "org.apache.beam.sdk.extensions.sql.impl.parser.SqlDdlNodes"
         "org.apache.beam.sdk.extensions.sql.impl.parser.SqlUseCatalog"
+        "org.apache.beam.sdk.extensions.sql.impl.parser.SqlUseDatabase"
         "org.apache.beam.sdk.extensions.sql.impl.parser.SqlSetOptionBeam"
         "org.apache.beam.sdk.extensions.sql.impl.utils.CalciteUtils"
         "org.apache.beam.sdk.schemas.Schema"
@@ -61,6 +65,8 @@ data: {
         "AFTER"
         "ALWAYS"
         "APPLY"
+        "ARRAY_AGG"
+        "ARRAY_CONCAT_AGG"
         "ASC"
         "ASSERTION"
         "ASSIGNMENT"
@@ -99,12 +105,19 @@ data: {
         "CONSTRAINTS"
         "CONSTRAINT_SCHEMA"
         "CONSTRUCTOR"
+        "CONTAINS_SUBSTR"
         "CONTINUE"
         "CURSOR_NAME"
         "DATA"
         "DATABASE"
+        "DATE_DIFF"
+        "DATE_TRUNC"
+        "DATETIME_DIFF"
         "DATETIME_INTERVAL_CODE"
         "DATETIME_INTERVAL_PRECISION"
+        "DATETIME_TRUNC"
+        "DAYOFWEEK"
+        "DAYOFYEAR"
         "DAYS"
         "DECADE"
         "DEFAULTS"
@@ -123,6 +136,7 @@ data: {
         "DOMAIN"
         "DOW"
         "DOY"
+        "DOT_FORMAT"
         "DYNAMIC_FUNCTION"
         "DYNAMIC_FUNCTION_CODE"
         "ENCODING"
@@ -145,13 +159,16 @@ data: {
         "GO"
         "GOTO"
         "GRANTED"
+        "GROUP_CONCAT"
         "HIERARCHY"
         "HOP"
         "HOURS"
         "IGNORE"
+        "ILIKE"
         "IMMEDIATE"
         "IMMEDIATELY"
         "IMPLEMENTATION"
+        "INCLUDE"
         "INCLUDING"
         "INCREMENT"
         "INITIALLY"
@@ -219,6 +236,7 @@ data: {
         "PASSTHROUGH"
         "PAST"
         "PATH"
+        "PIVOT"
         "PLACING"
         "PLAN"
         "PLI"
@@ -228,6 +246,7 @@ data: {
         "PRIVILEGES"
         "PUBLIC"
         "QUARTER"
+        "QUARTERS"
         "READ"
         "RELATIVE"
         "REPEATABLE"
@@ -240,6 +259,7 @@ data: {
         "RETURNED_OCTET_LENGTH"
         "RETURNED_SQLSTATE"
         "RETURNING"
+        "RLIKE"
         "ROLE"
         "ROUTINE"
         "ROUTINE_CATALOG"
@@ -257,6 +277,7 @@ data: {
         "SECTION"
         "SECURITY"
         "SELF"
+        "SEPARATOR"
         "SEQUENCE"
         "SERIALIZABLE"
         "SERVER"
@@ -319,6 +340,7 @@ data: {
         "SQL_VARCHAR"
         "STATE"
         "STATEMENT"
+        "STRING_AGG"
         "STRUCTURE"
         "STYLE"
         "SUBCLASS_ORIGIN"
@@ -326,8 +348,12 @@ data: {
         "TABLE_NAME"
         "TEMPORARY"
         "TIES"
+        "TIME_DIFF"
+        "TIME_TRUNC"
         "TIMESTAMPADD"
         "TIMESTAMPDIFF"
+        "TIMESTAMP_DIFF"
+        "TIMESTAMP_TRUNC"
         "TOP_LEVEL_COUNT"
         "TRANSACTION"
         "TRANSACTIONS_ACTIVE"
@@ -344,6 +370,7 @@ data: {
         "UNCOMMITTED"
         "UNCONDITIONAL"
         "UNDER"
+        "UNPIVOT"
         "UNNAMED"
         "USAGE"
         "USER_DEFINED_TYPE_CATALOG"
@@ -356,6 +383,7 @@ data: {
         "VERSION"
         "VIEW"
         "WEEK"
+        "WEEKS"
         "WORK"
         "WRAPPER"
         "WRITE"
@@ -395,6 +423,7 @@ data: {
       # Example: SqlShowDatabases(), SqlShowTables().
       statementParserMethods: [
         "SqlUseCatalog(Span.of(), null)"
+        "SqlUseDatabase(Span.of(), null)"
         "SqlSetOptionBeam(Span.of(), null)"
       ]
 
@@ -427,6 +456,7 @@ data: {
       # Each must accept arguments "(SqlParserPos pos, boolean replace)".
       createStatementParserMethods: [
         "SqlCreateCatalog"
+        "SqlCreateDatabase"
         "SqlCreateExternalTable"
         "SqlCreateFunction"
         "SqlCreateTableNotSupportedMessage"
@@ -436,9 +466,16 @@ data: {
       # Each must accept arguments "(SqlParserPos pos)".
       dropStatementParserMethods: [
         "SqlDropTable"
+        "SqlDropDatabase"
         "SqlDropCatalog"
       ]
 
+      # List of methods for parsing extensions to "TRUNCATE" calls.
+      # Each must accept arguments "(SqlParserPos pos)".
+      # Example: "SqlTruncate".
+      truncateStatementParserMethods: [
+      ]
+
       # Binary operators tokens
       binaryOperatorsTokens: [
       ]
@@ -455,11 +492,12 @@ data: {
         "parserImpls.ftl"
       ]
 
+      setOptionParserMethod: "SqlSetOption"
       includePosixOperators: false
       includeCompoundIdentifier: true
       includeBraces: true
       includeAdditionalDeclarations: false
-
+      includeParsingStringLiteralAsArrayLiteral: false
     }
 }
 
diff --git a/sdks/java/extensions/sql/src/main/codegen/includes/parserImpls.ftl b/sdks/java/extensions/sql/src/main/codegen/includes/parserImpls.ftl
index 450c6eeaff7f..470cbb443895 100644
--- a/sdks/java/extensions/sql/src/main/codegen/includes/parserImpls.ftl
+++ b/sdks/java/extensions/sql/src/main/codegen/includes/parserImpls.ftl
@@ -27,6 +27,15 @@ boolean IfExistsOpt() :
     { return false; }
 }
 
+boolean CascadeOpt() :
+{
+}
+{
+    <CASCADE> { return true; }
+|
+    { return false; }
+}
+
 SqlNodeList Options() :
 {
     final Span s;
@@ -255,6 +264,86 @@ SqlDrop SqlDropCatalog(Span s, boolean replace) :
     }
 }
 
+/**
+ * CREATE DATABASE ( IF NOT EXISTS )? database_name
+ */
+SqlCreate SqlCreateDatabase(Span s, boolean replace) :
+{
+    final boolean ifNotExists;
+    final SqlNode databaseName;
+}
+{
+    <DATABASE> {
+        s.add(this);
+    }
+
+    ifNotExists = IfNotExistsOpt()
+    (
+        databaseName = StringLiteral()
+        |
+        databaseName = SimpleIdentifier()
+    )
+
+    {
+        return new SqlCreateDatabase(
+            s.end(this),
+            replace,
+            ifNotExists,
+            databaseName);
+    }
+}
+
+/**
+ * USE DATABASE database_name
+ */
+SqlCall SqlUseDatabase(Span s, String scope) :
+{
+    final SqlNode databaseName;
+}
+{
+    <USE> {
+        s.add(this);
+    }
+    <DATABASE>
+    (
+        databaseName = StringLiteral()
+        |
+        databaseName = SimpleIdentifier()
+    )
+    {
+        return new SqlUseDatabase(
+            s.end(this),
+            scope,
+            databaseName);
+    }
+}
+
+/**
+ * DROP DATABASE [ IF EXISTS ] database_name [ RESTRICT | CASCADE ]
+ */
+SqlDrop SqlDropDatabase(Span s, boolean replace) :
+{
+    final boolean ifExists;
+    final SqlNode databaseName;
+    final boolean cascade;
+}
+{
+    <DATABASE>
+    ifExists = IfExistsOpt()
+    (
+        databaseName = StringLiteral()
+        |
+        databaseName = SimpleIdentifier()
+    )
+
+    cascade = CascadeOpt()
+
+    {
+        return new SqlDropDatabase(s.end(this), ifExists, databaseName, cascade);
+    }
+}
+
+
 SqlNodeList PartitionFieldList() :
 {
     final List<SqlNode> list = new ArrayList<SqlNode>();
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/TableNameExtractionUtils.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/TableNameExtractionUtils.java
index 0f50499926b6..efd3d8dfc353 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/TableNameExtractionUtils.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/TableNameExtractionUtils.java
@@ -23,13 +23,13 @@
 import java.util.Collections;
 import java.util.List;
 import org.apache.beam.sdk.extensions.sql.impl.TableName;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlAsOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlJoin;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSelect;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSetOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlAsOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlJoin;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSelect;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSetOperator;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteSchema.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteSchema.java
index 0b015c567cda..d684c72b2e69 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteSchema.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteSchema.java
@@ -28,13 +28,13 @@
 import org.apache.beam.sdk.extensions.sql.meta.catalog.Catalog;
 import org.apache.beam.sdk.extensions.sql.meta.catalog.CatalogManager;
 import org.apache.beam.sdk.extensions.sql.meta.provider.TableProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Expression;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelProtoDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaVersion;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Schemas;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.Expression;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelProtoDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Schema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaPlus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaVersion;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Schemas;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /** Adapter from {@link TableProvider} to {@link Schema}. */
@@ -120,7 +120,7 @@ public Set<String> getTypeNames() {
   }
 
   @Override
-  public org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Table getTable(
+  public org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Table getTable(
       String name) {
     Table table = resolveMetastore().getTable(name);
     if (table == null) {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteSchemaFactory.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteSchemaFactory.java
index 7692aadfb158..ce25610422c1 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteSchemaFactory.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteSchemaFactory.java
@@ -30,15 +30,15 @@
 import org.apache.beam.sdk.extensions.sql.meta.provider.TableProvider;
 import org.apache.beam.sdk.extensions.sql.meta.store.InMemoryMetaStore;
 import org.apache.beam.sdk.extensions.sql.meta.store.MetaStore;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteConnection;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Expression;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelProtoDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaVersion;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Table;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteConnection;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.Expression;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelProtoDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Schema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaPlus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaVersion;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Table;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 
 /**
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteTable.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteTable.java
index bd968ad2cb06..eb2c384b1e6f 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteTable.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamCalciteTable.java
@@ -27,20 +27,20 @@
 import org.apache.beam.sdk.extensions.sql.impl.utils.CalciteUtils;
 import org.apache.beam.sdk.extensions.sql.meta.BeamSqlTable;
 import org.apache.beam.sdk.options.PipelineOptions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.java.AbstractQueryableTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.QueryProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.Queryable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.prepare.Prepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableModify;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.ModifiableTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.TranslatableTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.java.AbstractQueryableTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.QueryProvider;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.Queryable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.prepare.Prepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.TableModify;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.ModifiableTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaPlus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.TranslatableTable;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 
 /** Adapter from {@link BeamSqlTable} to a calcite Table. */
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamSqlEnv.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamSqlEnv.java
index 1edb22ac105f..73193f58f131 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamSqlEnv.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamSqlEnv.java
@@ -45,11 +45,11 @@
 import org.apache.beam.sdk.options.PipelineOptionsFactory;
 import org.apache.beam.sdk.transforms.Combine.CombineFn;
 import org.apache.beam.sdk.transforms.SerializableFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalcitePrepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSet;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Strings;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamTableStatistics.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamTableStatistics.java
index 7aaaedd5e3a7..6f2c15223700 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamTableStatistics.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/BeamTableStatistics.java
@@ -21,12 +21,12 @@
 import java.util.Collections;
 import java.util.List;
 import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelDistribution;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelDistributionTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelReferentialConstraint;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Statistic;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.ImmutableBitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelCollation;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelDistribution;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelDistributionTraitDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelReferentialConstraint;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Statistic;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.ImmutableBitSet;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 
 /** This class stores row count statistics. */
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteConnectionWrapper.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteConnectionWrapper.java
index f9161f37143f..333a5f4cdeda 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteConnectionWrapper.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteConnectionWrapper.java
@@ -35,14 +35,15 @@
 import java.util.Map;
 import java.util.Properties;
 import java.util.concurrent.Executor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.java.JavaTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.CalciteConnectionConfig;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteConnection;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalcitePrepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.Enumerator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.Queryable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Expression;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.java.JavaTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.config.CalciteConnectionConfig;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteConnection;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.Enumerator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.Queryable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.Expression;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaPlus;
+import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
  * Abstract wrapper for {@link CalciteConnection} to simplify extension.
@@ -333,8 +334,10 @@ public void setSchema(String schema) throws SQLException {
     connection.setSchema(schema);
   }
 
+  // CalciteConnection.getSchema() marked nullable but Connection.getSchema() does not
+  @SuppressWarnings("override.return")
   @Override
-  public String getSchema() throws SQLException {
+  public @Nullable String getSchema() throws SQLException {
     return connection.getSchema();
   }
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteFactoryWrapper.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteFactoryWrapper.java
index dcce4d1237a0..dd5c0d232bca 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteFactoryWrapper.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteFactoryWrapper.java
@@ -21,18 +21,18 @@
 import java.sql.SQLException;
 import java.util.Properties;
 import java.util.TimeZone;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.java.JavaTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.AvaticaConnection;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.AvaticaFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.AvaticaPreparedStatement;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.AvaticaResultSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.AvaticaSpecificDatabaseMetaData;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.AvaticaStatement;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.Meta;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.QueryState;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.UnregisteredDriver;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.java.JavaTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.AvaticaConnection;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.AvaticaFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.AvaticaPreparedStatement;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.AvaticaResultSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.AvaticaSpecificDatabaseMetaData;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.AvaticaStatement;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.Meta;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.QueryState;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.UnregisteredDriver;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteQueryPlanner.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteQueryPlanner.java
index bd0e7ac00895..606a3c5f71a2 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteQueryPlanner.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/CalciteQueryPlanner.java
@@ -31,41 +31,42 @@
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
 import org.apache.beam.sdk.extensions.sql.impl.udf.BeamBuiltinFunctionProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.com.google.common.collect.Table;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.CalciteConnectionConfig;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Contexts;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.ConventionTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCost;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner.CannotPlanException;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.prepare.CalciteCatalogReader;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelRoot;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.BuiltInMetadata;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.ChainedRelMetadataProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.JaninoRelMetadataProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.MetadataDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.MetadataHandler;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.ReflectiveRelMetadataProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperatorTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParseException;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParser;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserImplFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.util.SqlOperatorTables;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.FrameworkConfig;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Frameworks;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Planner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelConversionException;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.ValidationException;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.BuiltInMethod;
+import org.apache.beam.vendor.calcite.v1_40_0.com.google.common.collect.Table;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.config.CalciteConnectionConfig;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Contexts;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.ConventionTraitDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCost;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner.CannotPlanException;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.prepare.CalciteCatalogReader;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelRoot;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.BuiltInMetadata;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.ChainedRelMetadataProvider;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.JaninoRelMetadataProvider;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.MetadataDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.MetadataHandler;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.ReflectiveRelMetadataProvider;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataProvider;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaPlus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperatorTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParseException;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParser;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserImplFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.util.SqlOperatorTables;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql2rel.SqlToRelConverter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.FrameworkConfig;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.Frameworks;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.Planner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RelConversionException;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.ValidationException;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.BuiltInMethod;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.slf4j.Logger;
@@ -141,6 +142,9 @@ public FrameworkConfig defaultConfig(JdbcConnection connection, Collection<RuleS
     final SqlOperatorTable opTab0 =
         connection.config().fun(SqlOperatorTable.class, SqlStdOperatorTable.instance());
 
+    // Revert the flag flip of CALCITE-3870 which led to missing rules
+    SqlToRelConverter.Config sqlToRelConfig = SqlToRelConverter.config().withExpand(true);
+
     return Frameworks.newConfigBuilder()
         .parserConfig(parserConfig.build())
         .defaultSchema(defaultSchema)
@@ -150,6 +154,7 @@ public FrameworkConfig defaultConfig(JdbcConnection connection, Collection<RuleS
         .costFactory(BeamCostModel.FACTORY)
         .typeSystem(connection.getTypeFactory().getTypeSystem())
         .operatorTable(SqlOperatorTables.chain(opTab0, catalogReader))
+        .sqlToRelConverterConfig(sqlToRelConfig)
         .build();
   }
 
@@ -249,7 +254,7 @@ public RelOptCost getNonCumulativeCost(RelNode rel, RelMetadataQuery mq) {
       // here and based on the design we also need to remove the cached values
 
       // We need to first remove the cached values.
-      List<Table.Cell<RelNode, List, Object>> costKeys =
+      List<Table.Cell<RelNode, ?, Object>> costKeys =
           bmq.map.cellSet().stream()
               .filter(entry -> entry.getValue() instanceof BeamCostModel)
               .filter(entry -> ((BeamCostModel) entry.getValue()).isInfinite())
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JavaUdfLoader.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JavaUdfLoader.java
index ab4b86660a6e..1e584ecdef40 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JavaUdfLoader.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JavaUdfLoader.java
@@ -40,7 +40,7 @@
 import org.apache.beam.sdk.extensions.sql.udf.UdfProvider;
 import org.apache.beam.sdk.io.FileSystems;
 import org.apache.beam.sdk.io.fs.ResourceId;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.commons.codec.digest.DigestUtils;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.commons.codec.digest.DigestUtils;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcConnection.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcConnection.java
index 972674df9f91..f9d7eddbc687 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcConnection.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcConnection.java
@@ -25,9 +25,9 @@
 import org.apache.beam.sdk.extensions.sql.meta.provider.TableProvider;
 import org.apache.beam.sdk.options.PipelineOptions;
 import org.apache.beam.sdk.values.KV;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteConnection;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteConnection;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaPlus;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcDriver.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcDriver.java
index 484708ee4559..b23dee607cc1 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcDriver.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcDriver.java
@@ -17,8 +17,8 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.CalciteConnectionProperty.SCHEMA_FACTORY;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.codehaus.commons.compiler.CompilerFactoryFactory.getDefaultCompilerFactory;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.config.CalciteConnectionProperty.SCHEMA_FACTORY;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.codehaus.commons.compiler.CompilerFactoryFactory.getDefaultCompilerFactory;
 
 import com.google.auto.service.AutoService;
 import java.sql.Connection;
@@ -33,18 +33,18 @@
 import org.apache.beam.sdk.extensions.sql.meta.catalog.CatalogManager;
 import org.apache.beam.sdk.extensions.sql.meta.provider.TableProvider;
 import org.apache.beam.sdk.options.PipelineOptions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.EnumerableRules;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.AvaticaFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteConnection;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.Driver;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollationTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CoreRules;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.runtime.Hook;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.EnumerableRules;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.AvaticaFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteConnection;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.Driver;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelCollationTraitDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.CoreRules;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.runtime.Hook;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSet;
 
 /**
  * Calcite JDBC driver with Beam defaults.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcFactory.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcFactory.java
index 166c6720864b..d6b7273110b0 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcFactory.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/JdbcFactory.java
@@ -18,26 +18,26 @@
 package org.apache.beam.sdk.extensions.sql.impl;
 
 import static org.apache.beam.sdk.extensions.sql.impl.JdbcDriver.TOP_LEVEL_BEAM_SCHEMA;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.BuiltInConnectionProperty.TIME_ZONE;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.CalciteConnectionProperty.LEX;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.CalciteConnectionProperty.PARSER_FACTORY;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.CalciteConnectionProperty.SCHEMA;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.CalciteConnectionProperty.SCHEMA_FACTORY;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.CalciteConnectionProperty.TYPE_SYSTEM;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.BuiltInConnectionProperty.TIME_ZONE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.config.CalciteConnectionProperty.LEX;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.config.CalciteConnectionProperty.PARSER_FACTORY;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.config.CalciteConnectionProperty.SCHEMA;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.config.CalciteConnectionProperty.SCHEMA_FACTORY;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.config.CalciteConnectionProperty.TYPE_SYSTEM;
 
 import java.util.Properties;
 import org.apache.beam.sdk.extensions.sql.impl.parser.BeamSqlParser;
 import org.apache.beam.sdk.extensions.sql.impl.planner.BeamRelDataTypeSystem;
 import org.apache.beam.sdk.extensions.sql.meta.provider.TableProvider;
 import org.apache.beam.sdk.util.ReleaseInfo;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.java.JavaTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.AvaticaConnection;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.AvaticaFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.ConnectionProperty;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.UnregisteredDriver;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.Lex;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.java.JavaTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.AvaticaConnection;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.AvaticaFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.ConnectionProperty;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.UnregisteredDriver;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.config.Lex;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
@@ -46,9 +46,9 @@
  *
  * <p>The purpose of this class is to intercept the connection creation and force a cache-less root
  * schema ({@link
- * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.SimpleCalciteSchema}). Otherwise
+ * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.SimpleCalciteSchema}). Otherwise
  * Calcite uses {@link
- * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CachingCalciteSchema} that eagerly
+ * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CachingCalciteSchema} that eagerly
  * caches table information. This behavior does not work well for dynamic table providers.
  */
 class JdbcFactory extends CalciteFactoryWrapper {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/QueryPlanner.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/QueryPlanner.java
index a6e6405e1c3d..0f0f8970a3ab 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/QueryPlanner.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/QueryPlanner.java
@@ -22,8 +22,8 @@
 import java.util.List;
 import java.util.Map;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSet;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/ScalarFunctionImpl.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/ScalarFunctionImpl.java
index b87635efcdaa..b92e2ce12556 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/ScalarFunctionImpl.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/ScalarFunctionImpl.java
@@ -17,7 +17,7 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Static.RESOURCE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Static.RESOURCE;
 
 import java.lang.reflect.Constructor;
 import java.lang.reflect.Method;
@@ -27,29 +27,29 @@
 import java.util.Arrays;
 import java.util.List;
 import org.apache.beam.sdk.extensions.sql.impl.utils.CalciteUtils;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.CallImplementor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.NullPolicy;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.ReflectiveCallNotNullImplementor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.RexImpTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.RexToLixTranslator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.ByteString;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.function.SemiStrict;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.function.Strict;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Expression;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Expressions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.ImplementableFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.ScalarFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperatorBinding;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.CallImplementor;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.NullPolicy;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.ReflectiveCallNotNullImplementor;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.RexImpTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.RexToLixTranslator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.util.ByteString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.function.SemiStrict;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.function.Strict;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.Expression;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.Expressions;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.ImplementableFunction;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.ScalarFunction;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperatorBinding;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMultimap;
 
 /**
  * Beam-customized version from {@link
- * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.impl.ScalarFunctionImpl} , to
+ * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.impl.ScalarFunctionImpl} , to
  * address BEAM-5921.
  */
 @SuppressWarnings({
@@ -81,7 +81,7 @@ public String getJarPath() {
   }
 
   /**
-   * Creates {@link org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function} for
+   * Creates {@link org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function} for
    * each method in a given class.
    */
   public static ImmutableMultimap<String, Function> createAll(Class<?> clazz) {
@@ -100,7 +100,7 @@ public static ImmutableMultimap<String, Function> createAll(Class<?> clazz) {
   }
 
   /**
-   * Creates {@link org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function} from
+   * Creates {@link org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function} from
    * given method. When {@code eval} method does not suit, {@code null} is returned.
    *
    * @param method method that is used to implement the function
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/TableResolutionUtils.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/TableResolutionUtils.java
index b8081d0c3312..3196667cb8cb 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/TableResolutionUtils.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/TableResolutionUtils.java
@@ -28,8 +28,8 @@
 import org.apache.beam.sdk.extensions.sql.TableNameExtractionUtils;
 import org.apache.beam.sdk.extensions.sql.meta.CustomTableResolver;
 import org.apache.beam.sdk.extensions.sql.meta.provider.TableProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 
 /** Utils to wire up the custom table resolution into Calcite's planner. */
@@ -168,7 +168,7 @@ private static List<TableName> tablesForSchema(
    */
   private static class SchemaWithName {
     String name;
-    org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Schema schema;
+    org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Schema schema;
 
     static SchemaWithName create(JdbcConnection connection, String name) {
       SchemaWithName schemaWithName = new SchemaWithName();
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdafImpl.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdafImpl.java
index bb19f0eca6fc..3b387a8d1a30 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdafImpl.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdafImpl.java
@@ -27,12 +27,12 @@
 import org.apache.beam.sdk.annotations.Internal;
 import org.apache.beam.sdk.extensions.sql.impl.utils.CalciteUtils;
 import org.apache.beam.sdk.transforms.Combine.CombineFn;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.AggImplementor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.AggregateFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.FunctionParameter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.ImplementableAggFunction;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.AggImplementor;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.AggregateFunction;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.FunctionParameter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.ImplementableAggFunction;
 
 /** Implement {@link AggregateFunction} to take a {@link CombineFn} as UDAF. */
 @Internal
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdfImpl.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdfImpl.java
index cdb70fb0d03b..95fddf680279 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdfImpl.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdfImpl.java
@@ -18,9 +18,9 @@
 package org.apache.beam.sdk.extensions.sql.impl;
 
 import java.lang.reflect.Method;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.TranslatableTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.impl.TableMacroImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.TranslatableTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.impl.TableMacroImpl;
 
 /** Beam-customized facade behind {@link Function} to address BEAM-5921. */
 @SuppressWarnings({
@@ -31,7 +31,7 @@ class UdfImpl {
   private UdfImpl() {}
 
   /**
-   * Creates {@link org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function} from
+   * Creates {@link org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function} from
    * given class.
    *
    * <p>If a method of the given name is not found or it does not suit, returns {@code null}.
@@ -49,7 +49,7 @@ public static Function create(Class<?> clazz, String methodName) {
   }
 
   /**
-   * Creates {@link org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function} from
+   * Creates {@link org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function} from
    * given method.
    *
    * @param method method that is used to implement the function
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdfImplReflectiveFunctionBase.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdfImplReflectiveFunctionBase.java
index a37a5b69eaf9..fa6293bbb175 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdfImplReflectiveFunctionBase.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/UdfImplReflectiveFunctionBase.java
@@ -24,12 +24,12 @@
 import java.util.ArrayList;
 import java.util.List;
 import org.apache.beam.sdk.extensions.sql.impl.utils.CalciteUtils;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.FunctionParameter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.impl.ReflectiveFunctionBase;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.ReflectUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.FunctionParameter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.impl.ReflectiveFunctionBase;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.ReflectUtil;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 
 /** Beam-customized version from {@link ReflectiveFunctionBase}, to address BEAM-5921. */
@@ -101,7 +101,7 @@ public static ParameterListBuilder builder() {
 
   /**
    * Helps build lists of {@link
-   * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.FunctionParameter}.
+   * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.FunctionParameter}.
    */
   public static class ParameterListBuilder {
     final List<FunctionParameter> builder = new ArrayList<>();
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/ZetaSqlUserDefinedSQLNativeTableValuedFunction.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/ZetaSqlUserDefinedSQLNativeTableValuedFunction.java
index 7beb33a16f76..6295027c9939 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/ZetaSqlUserDefinedSQLNativeTableValuedFunction.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/ZetaSqlUserDefinedSQLNativeTableValuedFunction.java
@@ -19,13 +19,13 @@
 
 import java.util.List;
 import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlOperandTypeChecker;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlOperandTypeInference;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlReturnTypeInference;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlUserDefinedFunction;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlOperandTypeChecker;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlOperandTypeInference;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlReturnTypeInference;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.validate.SqlUserDefinedFunction;
 
 /** This is a class to indicate that a TVF is a ZetaSQL SQL native UDTVF. */
 @Internal
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPCall.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPCall.java
index 64689d219786..228c2e892a97 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPCall.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPCall.java
@@ -19,11 +19,11 @@
 
 import java.util.ArrayList;
 import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexPatternFieldRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexPatternFieldRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
 
 /**
  * A {@code CEPCall} instance represents an operation (node) that contains an operator and a list of
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPFieldRef.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPFieldRef.java
index 26274e23318a..78d15b8f9fbf 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPFieldRef.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPFieldRef.java
@@ -17,7 +17,7 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl.cep;
 
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexPatternFieldRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexPatternFieldRef;
 
 /**
  * A {@code CEPFieldRef} instance represents a node that points to a specified field in a {@code
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPLiteral.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPLiteral.java
index 911a3fa3e23f..7f33afdcf511 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPLiteral.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPLiteral.java
@@ -20,7 +20,7 @@
 import java.math.BigDecimal;
 import org.apache.beam.sdk.extensions.sql.impl.SqlConversionException;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
 import org.joda.time.ReadableDateTime;
 
 /**
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPOperation.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPOperation.java
index 3ac8d0c4fe22..ba07cac2359e 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPOperation.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPOperation.java
@@ -19,10 +19,10 @@
 
 import java.io.Serializable;
 import org.apache.beam.sdk.extensions.sql.impl.SqlConversionException;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexPatternFieldRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexPatternFieldRef;
 
 /**
  * {@code CEPOperation} is the base class for the evaluation operations defined in the {@code
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPOperator.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPOperator.java
index 14cff75875d9..c13a7261ae4d 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPOperator.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPOperator.java
@@ -19,8 +19,8 @@
 
 import java.io.Serializable;
 import java.util.Map;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 
 /**
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPPattern.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPPattern.java
index 4f802c460346..2e2150f25904 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPPattern.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPPattern.java
@@ -20,7 +20,7 @@
 import java.io.Serializable;
 import javax.annotation.Nullable;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
 
 /** Core pattern class that stores the definition of a single pattern. */
 @SuppressWarnings({
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPUtils.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPUtils.java
index 3948f2191787..c921974fb8d2 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPUtils.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/CEPUtils.java
@@ -21,14 +21,14 @@
 import java.util.List;
 import java.util.Map;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelFieldCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.ImmutableBitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelCollation;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelFieldCollation;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.ImmutableBitSet;
 
 /**
  * Some utility methods for transforming Calcite's constructs into our own Beam constructs (for
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/OrderKey.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/OrderKey.java
index d60afd2462f3..2e34074e7b9f 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/OrderKey.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/cep/OrderKey.java
@@ -18,7 +18,7 @@
 package org.apache.beam.sdk.extensions.sql.impl.cep;
 
 import java.io.Serializable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelFieldCollation;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelFieldCollation;
 
 /**
  * The {@code OrderKey} class stores the information to sort a column.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamSqlParser.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamSqlParser.java
index 5ec9bb2ae4b4..25c0639a4a44 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamSqlParser.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamSqlParser.java
@@ -19,10 +19,10 @@
 
 import java.io.Reader;
 import org.apache.beam.sdk.extensions.sql.impl.parser.impl.BeamSqlParserImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalcitePrepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.server.DdlExecutor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlAbstractParserImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserImplFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.server.DdlExecutor;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlAbstractParserImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserImplFactory;
 
 public class BeamSqlParser {
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCheckConstraint.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCheckConstraint.java
index 176085ed29f0..05ef4c8e1bf6 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCheckConstraint.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCheckConstraint.java
@@ -18,15 +18,15 @@
 package org.apache.beam.sdk.extensions.sql.impl.parser;
 
 import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSpecialOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.ImmutableNullableList;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSpecialOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.ImmutableNullableList;
 
 /**
  * Parse tree for {@code UNIQUE}, {@code PRIMARY KEY} constraints.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlColumnDeclaration.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlColumnDeclaration.java
index bf8907b9d099..d6561e3b2841 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlColumnDeclaration.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlColumnDeclaration.java
@@ -18,15 +18,15 @@
 package org.apache.beam.sdk.extensions.sql.impl.parser;
 
 import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlDataTypeSpec;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSpecialOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlDataTypeSpec;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSpecialOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 
 /** Parse tree for column. */
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateCatalog.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateCatalog.java
index c1d96eea7bae..dd8dc1679298 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateCatalog.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateCatalog.java
@@ -19,7 +19,7 @@
 
 import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
 import static org.apache.beam.sdk.util.Preconditions.checkStateNotNull;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Static.RESOURCE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Static.RESOURCE;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkState;
 
 import java.util.Collections;
@@ -28,20 +28,20 @@
 import java.util.Map;
 import org.apache.beam.sdk.extensions.sql.impl.BeamCalciteSchema;
 import org.apache.beam.sdk.extensions.sql.meta.catalog.CatalogManager;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalcitePrepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlCreate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNodeList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSpecialOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Schema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlCreate;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNodeList;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSpecialOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.slf4j.Logger;
@@ -169,7 +169,7 @@ private Map<String, String> parseProperties() {
           String.format(
               "Unexpected properties entry '%s' of class '%s'", property, property.getClass()));
       SqlNodeList kv = ((SqlNodeList) property);
-      checkState(kv.size() == 2, "Expected 2 items in properties entry, but got " + kv.size());
+      checkState(kv.size() == 2, "Expected 2 items in properties entry, but got %s", kv.size());
       String key = checkStateNotNull(SqlDdlNodes.getString(kv.get(0)));
       String value = checkStateNotNull(SqlDdlNodes.getString(kv.get(1)));
       props.put(key, value);
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateDatabase.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateDatabase.java
new file mode 100644
index 000000000000..c2524e3c9867
--- /dev/null
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateDatabase.java
@@ -0,0 +1,121 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.impl.parser;
+
+import static java.lang.String.format;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Static.RESOURCE;
+
+import java.util.List;
+import org.apache.beam.sdk.extensions.sql.impl.BeamCalciteSchema;
+import org.apache.beam.sdk.extensions.sql.meta.catalog.Catalog;
+import org.apache.beam.sdk.extensions.sql.meta.catalog.CatalogManager;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Schema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlCreate;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSpecialOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+public class SqlCreateDatabase extends SqlCreate implements BeamSqlParser.ExecutableStatement {
+  private static final Logger LOG = LoggerFactory.getLogger(SqlCreateDatabase.class);
+  private final SqlIdentifier databaseName;
+  private static final SqlOperator OPERATOR =
+      new SqlSpecialOperator("CREATE DATABASE", SqlKind.OTHER_DDL);
+
+  public SqlCreateDatabase(
+      SqlParserPos pos, boolean replace, boolean ifNotExists, SqlNode databaseName) {
+    super(OPERATOR, pos, replace, ifNotExists);
+    this.databaseName = SqlDdlNodes.getIdentifier(databaseName, pos);
+  }
+
+  @Override
+  public List<SqlNode> getOperandList() {
+    ImmutableList.Builder<SqlNode> operands = ImmutableList.builder();
+    operands.add(databaseName);
+    return operands.build();
+  }
+
+  @Override
+  public void unparse(SqlWriter writer, int leftPrec, int rightPrec) {
+    writer.keyword("CREATE");
+    if (getReplace()) {
+      writer.keyword("OR REPLACE");
+    }
+    writer.keyword("DATABASE");
+    if (ifNotExists) {
+      writer.keyword("IF NOT EXISTS");
+    }
+    databaseName.unparse(writer, leftPrec, rightPrec);
+  }
+
+  @Override
+  public void execute(CalcitePrepare.Context context) {
+    final Pair<CalciteSchema, String> pair = SqlDdlNodes.schema(context, true, databaseName);
+    Schema schema = pair.left.schema;
+    String name = pair.right;
+
+    if (!(schema instanceof BeamCalciteSchema)) {
+      throw SqlUtil.newContextException(
+          databaseName.getParserPosition(),
+          RESOURCE.internal("Schema is not of instance BeamCalciteSchema"));
+    }
+
+    @Nullable CatalogManager catalogManager = ((BeamCalciteSchema) schema).getCatalogManager();
+    if (catalogManager == null) {
+      throw SqlUtil.newContextException(
+          databaseName.getParserPosition(),
+          RESOURCE.internal(
+              format(
+                  "Unexpected 'CREATE DATABASE' call using Schema '%s' that is not a Catalog.",
+                  name)));
+    }
+
+    // Attempt to create the database.
+    Catalog catalog = catalogManager.currentCatalog();
+    try {
+      LOG.info("Creating database '{}'", name);
+      boolean created = catalog.createDatabase(name);
+
+      if (created) {
+        LOG.info("Successfully created database '{}'", name);
+      } else if (ifNotExists) {
+        LOG.info("Database '{}' already exists.", name);
+      } else {
+        throw SqlUtil.newContextException(
+            databaseName.getParserPosition(),
+            RESOURCE.internal(format("Database '%s' already exists.", name)));
+      }
+    } catch (Exception e) {
+      throw SqlUtil.newContextException(
+          databaseName.getParserPosition(),
+          RESOURCE.internal(
+              format("Encountered an error when creating database '%s': %s", name, e)));
+    }
+  }
+}
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateExternalTable.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateExternalTable.java
index 2d98c03574ff..96b534e36d25 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateExternalTable.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateExternalTable.java
@@ -18,7 +18,7 @@
 package org.apache.beam.sdk.extensions.sql.impl.parser;
 
 import static org.apache.beam.sdk.schemas.Schema.toSchema;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Static.RESOURCE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Static.RESOURCE;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkNotNull;
 
@@ -29,19 +29,19 @@
 import org.apache.beam.sdk.extensions.sql.impl.utils.CalciteUtils;
 import org.apache.beam.sdk.extensions.sql.meta.Table;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalcitePrepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlCreate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNodeList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSpecialOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlCreate;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNodeList;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSpecialOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /** Parse tree for {@code CREATE EXTERNAL TABLE} statement. */
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateFunction.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateFunction.java
index 0378be8b9a74..978cb88a709d 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateFunction.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlCreateFunction.java
@@ -17,7 +17,7 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl.parser;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Static.RESOURCE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Static.RESOURCE;
 
 import java.lang.reflect.Method;
 import java.util.Arrays;
@@ -28,21 +28,21 @@
 import org.apache.beam.sdk.extensions.sql.impl.ScalarFnReflector;
 import org.apache.beam.sdk.extensions.sql.impl.ScalarFunctionImpl;
 import org.apache.beam.sdk.extensions.sql.udf.ScalarFn;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalcitePrepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlCharStringLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlCreate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSpecialOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaPlus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlCharStringLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlCreate;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSpecialOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 
 /** Parse tree for {@code CREATE FUNCTION} statement. */
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDdlNodes.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDdlNodes.java
index e0378d859e2a..4c99b3aa3518 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDdlNodes.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDdlNodes.java
@@ -20,16 +20,16 @@
 import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
 
 import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalcitePrepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlDataTypeSpec;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.NlsString;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Util;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlDataTypeSpec;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.NlsString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Util;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /** Utilities concerning {@link SqlNode} for DDL. */
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropCatalog.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropCatalog.java
index ac1dfe5c2a83..8985484128cf 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropCatalog.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropCatalog.java
@@ -17,24 +17,24 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl.parser;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Static.RESOURCE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Static.RESOURCE;
 
 import java.util.List;
 import org.apache.beam.sdk.extensions.sql.impl.BeamCalciteSchema;
 import org.apache.beam.sdk.extensions.sql.meta.catalog.CatalogManager;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalcitePrepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlDrop;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSpecialOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Schema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlDrop;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSpecialOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.slf4j.Logger;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropDatabase.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropDatabase.java
new file mode 100644
index 000000000000..f4938b5fff45
--- /dev/null
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropDatabase.java
@@ -0,0 +1,122 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.impl.parser;
+
+import static java.lang.String.format;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Static.RESOURCE;
+
+import java.util.List;
+import org.apache.beam.sdk.extensions.sql.impl.BeamCalciteSchema;
+import org.apache.beam.sdk.extensions.sql.meta.catalog.Catalog;
+import org.apache.beam.sdk.extensions.sql.meta.catalog.CatalogManager;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Schema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlDrop;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSpecialOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+public class SqlDropDatabase extends SqlDrop implements BeamSqlParser.ExecutableStatement {
+  private static final Logger LOG = LoggerFactory.getLogger(SqlDropDatabase.class);
+  private static final SqlOperator OPERATOR =
+      new SqlSpecialOperator("DROP DATABASE", SqlKind.OTHER_DDL);
+  private final SqlIdentifier databaseName;
+  private final boolean cascade;
+
+  public SqlDropDatabase(
+      SqlParserPos pos, boolean ifExists, SqlNode databaseName, boolean cascade) {
+    super(OPERATOR, pos, ifExists);
+    this.databaseName = SqlDdlNodes.getIdentifier(databaseName, pos);
+    this.cascade = cascade;
+  }
+
+  @Override
+  public void unparse(SqlWriter writer, int leftPrec, int rightPrec) {
+    writer.keyword(getOperator().getName());
+    if (ifExists) {
+      writer.keyword("IF EXISTS");
+    }
+    databaseName.unparse(writer, leftPrec, rightPrec);
+    if (cascade) {
+      writer.keyword("CASCADE");
+    } else {
+      writer.keyword("RESTRICT");
+    }
+  }
+
+  @Override
+  public void execute(CalcitePrepare.Context context) {
+    final Pair<CalciteSchema, String> pair = SqlDdlNodes.schema(context, true, databaseName);
+    Schema schema = pair.left.schema;
+    String name = pair.right;
+
+    if (!(schema instanceof BeamCalciteSchema)) {
+      throw SqlUtil.newContextException(
+          databaseName.getParserPosition(),
+          RESOURCE.internal("Schema is not of instance BeamCalciteSchema"));
+    }
+
+    BeamCalciteSchema beamCalciteSchema = (BeamCalciteSchema) schema;
+    @Nullable CatalogManager catalogManager = beamCalciteSchema.getCatalogManager();
+    if (catalogManager == null) {
+      throw SqlUtil.newContextException(
+          databaseName.getParserPosition(),
+          RESOURCE.internal(
+              String.format(
+                  "Unexpected 'DROP DATABASE' call using Schema '%s' that is not a Catalog.",
+                  name)));
+    }
+
+    Catalog catalog = catalogManager.currentCatalog();
+    try {
+      LOG.info("Dropping database '{}'", name);
+      boolean dropped = catalog.dropDatabase(name, cascade);
+
+      if (dropped) {
+        LOG.info("Successfully dropped database '{}'", name);
+      } else if (ifExists) {
+        LOG.info("Database '{}' does not exist.", name);
+      } else {
+        throw SqlUtil.newContextException(
+            databaseName.getParserPosition(),
+            RESOURCE.internal(String.format("Database '%s' does not exist.", name)));
+      }
+    } catch (Exception e) {
+      throw SqlUtil.newContextException(
+          databaseName.getParserPosition(),
+          RESOURCE.internal(
+              format("Encountered an error when dropping database '%s': %s", name, e)));
+    }
+  }
+
+  @Override
+  public List<SqlNode> getOperandList() {
+    return ImmutableList.of(databaseName);
+  }
+}
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropObject.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropObject.java
index 16e2e536eaa5..1efcb373f1f8 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropObject.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropObject.java
@@ -17,19 +17,19 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl.parser;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Static.RESOURCE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Static.RESOURCE;
 
 import java.util.List;
 import org.apache.beam.sdk.extensions.sql.impl.BeamCalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalcitePrepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlDrop;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlDrop;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 
 /**
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropTable.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropTable.java
index 5a62b0ee931e..18d06ef8aebc 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropTable.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlDropTable.java
@@ -17,11 +17,11 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl.parser;
 
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSpecialOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSpecialOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
 
 /** Parse tree for {@code DROP TABLE} statement. */
 public class SqlDropTable extends SqlDropObject {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlSetOptionBeam.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlSetOptionBeam.java
index 239d117b5f72..f949a1fc9ae7 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlSetOptionBeam.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlSetOptionBeam.java
@@ -17,17 +17,17 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl.parser;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Static.RESOURCE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Static.RESOURCE;
 
 import org.apache.beam.sdk.extensions.sql.impl.BeamCalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalcitePrepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSetOption;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSetOption;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 
 /** SQL parse tree node to represent {@code SET} and {@code RESET} statements. */
 @SuppressWarnings({
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlUseCatalog.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlUseCatalog.java
index 7088c7183027..1e96e3799ad1 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlUseCatalog.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlUseCatalog.java
@@ -17,24 +17,24 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl.parser;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Static.RESOURCE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Static.RESOURCE;
 
 import java.util.Collections;
 import java.util.List;
 import org.apache.beam.sdk.extensions.sql.impl.BeamCalciteSchema;
 import org.apache.beam.sdk.extensions.sql.meta.catalog.CatalogManager;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalcitePrepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSetOption;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSpecialOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Schema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSetOption;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSpecialOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlUseDatabase.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlUseDatabase.java
new file mode 100644
index 000000000000..b3bf122cadbf
--- /dev/null
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/parser/SqlUseDatabase.java
@@ -0,0 +1,103 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.impl.parser;
+
+import static org.apache.beam.sdk.util.Preconditions.checkStateNotNull;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Static.RESOURCE;
+
+import java.util.Collections;
+import java.util.List;
+import org.apache.beam.sdk.extensions.sql.impl.BeamCalciteSchema;
+import org.apache.beam.sdk.extensions.sql.meta.catalog.Catalog;
+import org.apache.beam.sdk.extensions.sql.meta.catalog.CatalogManager;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalcitePrepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Schema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSetOption;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlSpecialOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+public class SqlUseDatabase extends SqlSetOption implements BeamSqlParser.ExecutableStatement {
+  private static final Logger LOG = LoggerFactory.getLogger(SqlUseDatabase.class);
+  private final SqlIdentifier databaseName;
+
+  private static final SqlOperator OPERATOR = new SqlSpecialOperator("USE DATABASE", SqlKind.OTHER);
+
+  public SqlUseDatabase(SqlParserPos pos, String scope, SqlNode databaseName) {
+    super(pos, scope, SqlDdlNodes.getIdentifier(databaseName, pos), null);
+    this.databaseName = SqlDdlNodes.getIdentifier(databaseName, pos);
+  }
+
+  @Override
+  public SqlOperator getOperator() {
+    return OPERATOR;
+  }
+
+  @Override
+  public List<SqlNode> getOperandList() {
+    return Collections.singletonList(databaseName);
+  }
+
+  @Override
+  public void execute(CalcitePrepare.Context context) {
+    final Pair<CalciteSchema, String> pair = SqlDdlNodes.schema(context, true, databaseName);
+    Schema schema = pair.left.schema;
+    String name = checkStateNotNull(pair.right);
+
+    if (!(schema instanceof BeamCalciteSchema)) {
+      throw SqlUtil.newContextException(
+          databaseName.getParserPosition(),
+          RESOURCE.internal("Schema is not of instance BeamCalciteSchema"));
+    }
+
+    BeamCalciteSchema beamCalciteSchema = (BeamCalciteSchema) schema;
+    @Nullable CatalogManager catalogManager = beamCalciteSchema.getCatalogManager();
+    if (catalogManager == null) {
+      throw SqlUtil.newContextException(
+          databaseName.getParserPosition(),
+          RESOURCE.internal(
+              String.format(
+                  "Unexpected 'USE DATABASE' call using Schema '%s' that is not a Catalog.",
+                  name)));
+    }
+
+    Catalog catalog = catalogManager.currentCatalog();
+    if (!catalog.listDatabases().contains(name)) {
+      throw SqlUtil.newContextException(
+          databaseName.getParserPosition(),
+          RESOURCE.internal(String.format("Cannot use database: '%s' not found.", name)));
+    }
+
+    if (name.equals(catalog.currentDatabase())) {
+      LOG.info("Database '{}' is already in use.", name);
+      return;
+    }
+
+    catalog.useDatabase(name);
+    LOG.info("Switched to database '{}'.", name);
+  }
+}
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamCostModel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamCostModel.java
index bceaf22805fe..4558acd40648 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamCostModel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamCostModel.java
@@ -18,9 +18,9 @@
 package org.apache.beam.sdk.extensions.sql.impl.planner;
 
 import java.util.Objects;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCost;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCostFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCost;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCostFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptUtil;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
@@ -218,7 +218,7 @@ public static BeamCostModel convertRelOptCost(RelOptCost ic) {
 
   /**
    * Implementation of {@link
-   * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCostFactory} that creates
+   * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCostFactory} that creates
    * {@link BeamCostModel}s.
    */
   public static class Factory implements RelOptCostFactory {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamJavaTypeFactory.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamJavaTypeFactory.java
index 0892bf6fe09c..b331f4d8e8b9 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamJavaTypeFactory.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamJavaTypeFactory.java
@@ -18,12 +18,12 @@
 package org.apache.beam.sdk.extensions.sql.impl.planner;
 
 import java.lang.reflect.Type;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.java.JavaTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.JavaTypeFactoryImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.BasicSqlType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.IntervalSqlType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.java.JavaTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.JavaTypeFactoryImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.BasicSqlType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.IntervalSqlType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
 
 /** customized data type in Beam. */
 public class BeamJavaTypeFactory extends JavaTypeFactoryImpl {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRelDataTypeSystem.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRelDataTypeSystem.java
index 37fc4b7078c4..fb7f738fd2cc 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRelDataTypeSystem.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRelDataTypeSystem.java
@@ -17,9 +17,9 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl.planner;
 
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeSystem;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeSystemImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeSystem;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeSystemImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
 
 /** customized data type in Beam. */
 public class BeamRelDataTypeSystem extends RelDataTypeSystemImpl {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRelMetadataQuery.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRelMetadataQuery.java
index 91257f4abd4c..3ee9242fb219 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRelMetadataQuery.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRelMetadataQuery.java
@@ -17,9 +17,9 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl.planner;
 
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.JaninoRelMetadataProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.JaninoRelMetadataProvider;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
 
 public class BeamRelMetadataQuery extends RelMetadataQuery {
   private NodeStatsMetadata.Handler nodeStatsMetadataHandler;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRuleSets.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRuleSets.java
index 8d5b4d4fa08d..8382fcb6e382 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRuleSets.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/BeamRuleSets.java
@@ -42,12 +42,12 @@
 import org.apache.beam.sdk.extensions.sql.impl.rule.BeamUnnestRule;
 import org.apache.beam.sdk.extensions.sql.impl.rule.BeamValuesRule;
 import org.apache.beam.sdk.extensions.sql.impl.rule.BeamWindowRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CoreRules;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.PruneEmptyRules;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSets;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.CoreRules;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.PruneEmptyRules;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSets;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 
 /**
@@ -72,6 +72,7 @@ public class BeamRuleSets {
 
           // push a filter into a join
           CoreRules.FILTER_INTO_JOIN,
+          CoreRules.FILTER_SUB_QUERY_TO_CORRELATE,
           // push filter into the children of a join
           CoreRules.JOIN_CONDITION_PUSH,
           // push filter through an aggregation
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/NodeStatsMetadata.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/NodeStatsMetadata.java
index c8d4d0e0a4c3..f42cb01174de 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/NodeStatsMetadata.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/NodeStatsMetadata.java
@@ -18,12 +18,12 @@
 package org.apache.beam.sdk.extensions.sql.impl.planner;
 
 import java.lang.reflect.Method;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Types;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.Metadata;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.MetadataDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.MetadataHandler;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.Types;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.Metadata;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.MetadataDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.MetadataHandler;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
 
 /**
  * This is a metadata used for row count and rate estimation. It extends Calcite's Metadata
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/RelMdNodeStats.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/RelMdNodeStats.java
index 63787b514259..40db8b074efa 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/RelMdNodeStats.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/planner/RelMdNodeStats.java
@@ -22,13 +22,13 @@
 import java.util.List;
 import java.util.stream.Collectors;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.com.google.common.collect.Table;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.MetadataDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.MetadataHandler;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.ReflectiveRelMetadataProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
+import org.apache.beam.vendor.calcite.v1_40_0.com.google.common.collect.Table;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.MetadataDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.MetadataHandler;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.ReflectiveRelMetadataProvider;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataProvider;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
 
 /**
  * This is the implementation of NodeStatsMetadata. Methods to estimate rate and row count for
@@ -75,7 +75,7 @@ private NodeStats getBeamNodeStats(BeamRelNode rel, BeamRelMetadataQuery mq) {
     // wraps the metadata provider with CachingRelMetadataProvider. However,
     // CachingRelMetadataProvider checks timestamp before returning previous results. Therefore,
     // there wouldn't be a problem in that case.
-    List<Table.Cell<RelNode, List, Object>> keys =
+    List<Table.Cell<RelNode, ?, Object>> keys =
         mq.map.cellSet().stream()
             .filter(entry -> entry != null)
             .filter(entry -> entry.getValue() != null)
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/AbstractBeamCalcRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/AbstractBeamCalcRel.java
index 626e680ba66e..cb2c9598f34f 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/AbstractBeamCalcRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/AbstractBeamCalcRel.java
@@ -21,15 +21,15 @@
 import org.apache.beam.sdk.extensions.sql.impl.planner.BeamCostModel;
 import org.apache.beam.sdk.extensions.sql.impl.planner.BeamRelMetadataQuery;
 import org.apache.beam.sdk.extensions.sql.impl.planner.NodeStats;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLocalRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexProgram;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Calc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLocalRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexProgram;
 
 /** BeamRelNode to replace {@code Project} and {@code Filter} node. */
 @Internal
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamAggregationRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamAggregationRel.java
index a1880f6cb8c8..3f635cd081a7 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamAggregationRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamAggregationRel.java
@@ -52,14 +52,14 @@
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.sdk.values.WindowingStrategy;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Aggregate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.AggregateCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.ImmutableBitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Aggregate;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.AggregateCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.ImmutableBitSet;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.joda.time.Duration;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCalcRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCalcRel.java
index 4895c1478766..5c6534f2dc2b 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCalcRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCalcRel.java
@@ -70,39 +70,39 @@
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.sdk.values.TupleTag;
 import org.apache.beam.sdk.values.TupleTagList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.DataContext;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.JavaRowFormat;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.PhysType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.PhysTypeImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.RexToLixTranslator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.java.JavaTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.ByteString;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.QueryProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.BlockBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Expression;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Expressions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.MemberDeclaration;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.ParameterExpression;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Types;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPredicateList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexProgram;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexSimplify;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.runtime.SqlFunctions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlConformance;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlConformanceEnum;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlUserDefinedFunction;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.DataContext;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.JavaRowFormat;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.PhysType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.PhysTypeImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.RexToLixTranslator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.java.JavaTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.util.ByteString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.QueryProvider;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.BlockBuilder;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.Expression;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.Expressions;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.MemberDeclaration;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.ParameterExpression;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.Types;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPredicateList;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Calc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexBuilder;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexProgram;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexSimplify;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.runtime.SqlFunctions;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Function;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaPlus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.validate.SqlConformance;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.validate.SqlConformanceEnum;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.validate.SqlUserDefinedFunction;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Maps;
 import org.checkerframework.checker.nullness.qual.Nullable;
@@ -110,6 +110,7 @@
 import org.codehaus.janino.ScriptEvaluator;
 import org.joda.time.DateTime;
 import org.joda.time.Instant;
+import org.joda.time.base.AbstractInstant;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -632,8 +633,10 @@ private static Expression toCalciteValue(Expression value, FieldType fieldType)
         case BOOLEAN:
           return Expressions.convert_(value, Boolean.class);
         case DATETIME:
+          // AbstractInstant handles both joda Instant and DateTime
           return nullOr(
-              value, Expressions.call(Expressions.convert_(value, DateTime.class), "getMillis"));
+              value,
+              Expressions.call(Expressions.convert_(value, AbstractInstant.class), "getMillis"));
         case BYTES:
           return nullOr(
               value, Expressions.new_(ByteString.class, Expressions.convert_(value, byte[].class)));
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRel.java
index d8ef988fbf81..b16755d22180 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRel.java
@@ -40,14 +40,14 @@
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.sdk.values.WindowingStrategy;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.CorrelationId;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Join;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.JoinRelType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.CorrelationId;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Join;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.JoinRelType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 
 /**
  * A {@code BeamJoinRel} which does CoGBK Join
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamEnumerableConverter.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamEnumerableConverter.java
index 7866d86971f9..c54ab14ba8d8 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamEnumerableConverter.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamEnumerableConverter.java
@@ -58,24 +58,24 @@
 import org.apache.beam.sdk.values.PCollection.IsBounded;
 import org.apache.beam.sdk.values.PValue;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.EnumerableRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.EnumerableRelImplementor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.PhysType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.PhysTypeImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.Enumerable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.Linq4j;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.BlockBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Expression;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Expressions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.ConventionTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCost;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.EnumerableRel;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.EnumerableRelImplementor;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.PhysType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.PhysTypeImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.Enumerable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.Linq4j;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.BlockBuilder;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.Expression;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.tree.Expressions;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.ConventionTraitDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCost;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.joda.time.Duration;
 import org.joda.time.ReadableInstant;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSinkRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSinkRel.java
index 7c292e7ec4ea..cc81d0c1d6d2 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSinkRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSinkRel.java
@@ -33,15 +33,15 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.prepare.Prepare;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableModify;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql2rel.RelStructuredTypeFlattener;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.prepare.Prepare;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.TableModify;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql2rel.RelStructuredTypeFlattener;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /** BeamRelNode to replace a {@code TableModify} node. */
@@ -63,7 +63,7 @@ public BeamIOSinkRel(
       boolean flattened,
       BeamSqlTable sqlTable,
       Map<String, String> pipelineOptions) {
-    super(
+    this(
         cluster,
         cluster.traitSetOf(BeamLogicalConvention.INSTANCE),
         table,
@@ -72,6 +72,33 @@ public BeamIOSinkRel(
         operation,
         updateColumnList,
         sourceExpressionList,
+        flattened,
+        sqlTable,
+        pipelineOptions);
+  }
+
+  /** For copy. */
+  private BeamIOSinkRel(
+      RelOptCluster cluster,
+      RelTraitSet traitSet,
+      RelOptTable table,
+      Prepare.CatalogReader catalogReader,
+      RelNode child,
+      Operation operation,
+      @Nullable List<String> updateColumnList,
+      @Nullable List<RexNode> sourceExpressionList,
+      boolean flattened,
+      BeamSqlTable sqlTable,
+      Map<String, String> pipelineOptions) {
+    super(
+        cluster,
+        traitSet,
+        table,
+        catalogReader,
+        child,
+        operation,
+        updateColumnList,
+        sourceExpressionList,
         flattened);
     this.sqlTable = sqlTable;
     this.pipelineOptions = pipelineOptions;
@@ -91,20 +118,18 @@ public BeamCostModel beamComputeSelfCost(RelOptPlanner planner, BeamRelMetadataQ
   @Override
   public RelNode copy(RelTraitSet traitSet, List<RelNode> inputs) {
     boolean flattened = isFlattened() || isFlattening;
-    BeamIOSinkRel newRel =
-        new BeamIOSinkRel(
-            getCluster(),
-            getTable(),
-            getCatalogReader(),
-            sole(inputs),
-            getOperation(),
-            getUpdateColumnList(),
-            getSourceExpressionList(),
-            flattened,
-            sqlTable,
-            pipelineOptions);
-    newRel.traitSet = traitSet;
-    return newRel;
+    return new BeamIOSinkRel(
+        getCluster(),
+        traitSet,
+        getTable(),
+        getCatalogReader(),
+        sole(inputs),
+        getOperation(),
+        getUpdateColumnList(),
+        getSourceExpressionList(),
+        flattened,
+        sqlTable,
+        pipelineOptions);
   }
 
   @Override
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSourceRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSourceRel.java
index 989172769992..5a46414dbb7f 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSourceRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSourceRel.java
@@ -32,15 +32,15 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCost;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.prepare.RelOptTableImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableScan;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCost;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.prepare.RelOptTableImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.TableScan;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
 
 /** BeamRelNode to replace a {@code TableScan} node. */
 public class BeamIOSourceRel extends TableScan implements BeamRelNode {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIntersectRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIntersectRel.java
index 38001368e0e6..b02ea788a620 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIntersectRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIntersectRel.java
@@ -25,12 +25,12 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Intersect;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.SetOp;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Intersect;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.SetOp;
 
 /**
  * {@code BeamRelNode} to replace a {@code Intersect} node.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamJoinRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamJoinRel.java
index d6e2e4fe27b1..7b5a8941f25d 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamJoinRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamJoinRel.java
@@ -27,20 +27,20 @@
 import org.apache.beam.sdk.extensions.sql.meta.BeamSqlTable;
 import org.apache.beam.sdk.util.Preconditions;
 import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.volcano.RelSubset;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.CorrelationId;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Join;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.JoinRelType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexFieldAccess;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.volcano.RelSubset;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.CorrelationId;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Join;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.JoinRelType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexFieldAccess;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Optional;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamLogicalConvention.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamLogicalConvention.java
index 8801d3dcd180..d51115d1f2a0 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamLogicalConvention.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamLogicalConvention.java
@@ -17,12 +17,12 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl.rel;
 
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.ConventionTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTrait;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.ConventionTraitDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTrait;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
 
 /** Convention for Beam SQL. */
 @SuppressWarnings({
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMatchRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMatchRel.java
index d1b118b3f295..d2a738beacf3 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMatchRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMatchRel.java
@@ -49,17 +49,17 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Match;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.ImmutableBitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelCollation;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Match;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.ImmutableBitSet;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMinusRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMinusRel.java
index 3966d5caddb6..1e8fd577a138 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMinusRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMinusRel.java
@@ -25,12 +25,12 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Minus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.SetOp;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Minus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.SetOp;
 
 /**
  * {@code BeamRelNode} to replace a {@code Minus} node.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamPushDownIOSourceRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamPushDownIOSourceRel.java
index 06493333b12b..cc580ebd354d 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamPushDownIOSourceRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamPushDownIOSourceRel.java
@@ -34,11 +34,11 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelWriter;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
 
 public class BeamPushDownIOSourceRel extends BeamIOSourceRel {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamRelNode.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamRelNode.java
index 0a9f0f62781d..a1a9ae861b60 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamRelNode.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamRelNode.java
@@ -27,8 +27,8 @@
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.POutput;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /** A {@link RelNode} that can also give a {@link PTransform} that implements the expression. */
@@ -85,8 +85,8 @@ default Map<String, String> getPipelineOptions() {
    * estimate its NodeStats, it may need NodeStat of its inputs. However, it should not call this
    * directly (because maybe its inputs are not physical yet). It should call {@link
    * org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils#getNodeStats(
-   * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode,
-   * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery)}
+   * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode,
+   * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataQuery)}
    * instead.
    */
   NodeStats estimateNodeStats(BeamRelMetadataQuery mq);
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputJoinRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputJoinRel.java
index ca9eceb78fcf..1c14ee2310b5 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputJoinRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputJoinRel.java
@@ -32,14 +32,14 @@
 import org.apache.beam.sdk.values.PCollection.IsBounded;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.CorrelationId;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Join;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.JoinRelType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.CorrelationId;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Join;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.JoinRelType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
 
 /**
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputLookupJoinRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputLookupJoinRel.java
index 95863d66a2c4..5b25139525ab 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputLookupJoinRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputLookupJoinRel.java
@@ -26,13 +26,13 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.CorrelationId;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Join;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.JoinRelType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.CorrelationId;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Join;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.JoinRelType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 
 /**
  * A {@code BeamJoinRel} which does Lookup Join
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSortRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSortRel.java
index d94f228c1f99..aaa4d66011a6 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSortRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSortRel.java
@@ -51,18 +51,18 @@
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.sdk.values.WindowingStrategy;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollationImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelFieldCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Sort;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelCollation;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelCollationImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelFieldCollation;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Sort;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSqlRelUtils.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSqlRelUtils.java
index 8bcb8389e563..43e9b7ff333b 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSqlRelUtils.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSqlRelUtils.java
@@ -31,9 +31,9 @@
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.POutput;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.volcano.RelSubset;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.volcano.RelSubset;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /** Utilities for {@code BeamRelNode}. */
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamTableFunctionScanRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamTableFunctionScanRel.java
index a975f7fdabdc..20fcf25cb07b 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamTableFunctionScanRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamTableFunctionScanRel.java
@@ -52,17 +52,17 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableFunctionScan;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelColumnMapping;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.TableFunctionScan;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelColumnMapping;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 import org.joda.time.Duration;
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUncollectRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUncollectRel.java
index 55dc9afe4e17..ce6a11544da6 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUncollectRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUncollectRel.java
@@ -31,11 +31,11 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Uncollect;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Uncollect;
 
 /** {@link BeamRelNode} to implement an uncorrelated {@link Uncollect}, aka UNNEST. */
 @SuppressWarnings({
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnionRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnionRel.java
index 4cb8e81c40b0..a0b710213a51 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnionRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnionRel.java
@@ -26,12 +26,12 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.SetOp;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Union;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.SetOp;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Union;
 
 /**
  * {@link BeamRelNode} to replace a {@link Union}.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnnestRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnnestRel.java
index 157e5b9d1017..a37ade47f925 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnnestRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnnestRel.java
@@ -35,16 +35,16 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Correlate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.JoinRelType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Uncollect;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlValidatorUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Correlate;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.JoinRelType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Uncollect;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.validate.SqlValidatorUtil;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamValuesRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamValuesRel.java
index 6cd1716d5c5d..6163dbe770ae 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamValuesRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamValuesRel.java
@@ -35,13 +35,13 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Values;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.com.google.common.collect.ImmutableList;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Values;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 
 /**
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamWindowRel.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamWindowRel.java
index de7ef2a29f69..b47ff66329df 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamWindowRel.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamWindowRel.java
@@ -42,17 +42,17 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionList;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelFieldCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.AggregateCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Window;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelFieldCollation;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.AggregateCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Window;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
 
 /**
@@ -431,6 +431,11 @@ public RelNode copy(RelTraitSet traitSet, List<RelNode> inputs) {
     return this.copy(traitSet, sole(inputs), this.constants, this.rowType, this.groups);
   }
 
+  @Override
+  public Window copy(List<RexLiteral> constants) {
+    return this.copy(traitSet, input, constants, this.rowType, this.groups);
+  }
+
   public BeamWindowRel copy(
       RelTraitSet traitSet,
       RelNode input,
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/CalcRelSplitter.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/CalcRelSplitter.java
index 4cad8f671690..a6bddd142cfc 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/CalcRelSplitter.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/CalcRelSplitter.java
@@ -24,32 +24,32 @@
 import java.util.Collections;
 import java.util.List;
 import java.util.Set;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalCalc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexDynamicParam;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexFieldAccess;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLocalRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexProgram;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexShuttle;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexVisitorImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Litmus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Util;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.graph.DefaultDirectedGraph;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.graph.DefaultEdge;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.graph.DirectedGraph;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.graph.TopologicalOrderIterator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Calc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalCalc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexDynamicParam;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexFieldAccess;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLocalRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexProgram;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexShuttle;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexVisitorImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RelBuilder;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Litmus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Util;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.graph.DefaultDirectedGraph;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.graph.DefaultEdge;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.graph.DirectedGraph;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.graph.TopologicalOrderIterator;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.primitives.Ints;
 import org.checkerframework.checker.nullness.qual.Nullable;
@@ -60,7 +60,7 @@
  * cannot all be implemented by a single concrete {@link RelNode}.
  *
  * <p>This is a copy of {@link
- * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CalcRelSplitter} modified to
+ * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.CalcRelSplitter} modified to
  * work with Beam. TODO(CALCITE-4538) consider contributing these changes back upstream.
  *
  * <p>For example, the Java and Fennel calculator do not implement an identical set of operators.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/package-info.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/package-info.java
index 6c86d780d021..7a973d9c8b33 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/package-info.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/package-info.java
@@ -18,7 +18,7 @@
 
 /**
  * BeamSQL specified nodes, to replace {@link
- * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode}.
+ * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode}.
  */
 @DefaultAnnotation(NonNull.class)
 package org.apache.beam.sdk.extensions.sql.impl.rel;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamAggregateProjectMergeRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamAggregateProjectMergeRule.java
index 4ec26845549c..7b5d4c1c05d1 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamAggregateProjectMergeRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamAggregateProjectMergeRule.java
@@ -21,16 +21,16 @@
 import java.util.List;
 import java.util.Set;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamIOSourceRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.volcano.RelSubset;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.SingleRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Aggregate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Filter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Project;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.RelFactories;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.AggregateProjectMergeRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelBuilderFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.volcano.RelSubset;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.SingleRel;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Aggregate;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Filter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Project;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.RelFactories;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.AggregateProjectMergeRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RelBuilderFactory;
 
 /**
  * This rule is essentially a wrapper around Calcite's {@code AggregateProjectMergeRule}. In the
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamAggregationRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamAggregationRule.java
index 2e434f212b7c..b737ecdbd800 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamAggregationRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamAggregationRule.java
@@ -25,18 +25,18 @@
 import org.apache.beam.sdk.transforms.windowing.Sessions;
 import org.apache.beam.sdk.transforms.windowing.SlidingWindows;
 import org.apache.beam.sdk.transforms.windowing.WindowFn;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Aggregate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Project;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.RelFactories;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelBuilderFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.ImmutableBitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Aggregate;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Project;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.RelFactories;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RelBuilderFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.ImmutableBitSet;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.joda.time.Duration;
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamBasicAggregationRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamBasicAggregationRule.java
index 57fa2ca94c1c..e2df3c3a685f 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamBasicAggregationRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamBasicAggregationRule.java
@@ -22,19 +22,19 @@
 import java.util.stream.Collectors;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamAggregationRel;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.volcano.RelSubset;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Aggregate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Filter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Project;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.RelFactories;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelBuilderFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.volcano.RelSubset;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Aggregate;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Calc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Filter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Project;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.RelFactories;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RelBuilderFactory;
 
 /**
  * Aggregation rule that doesn't include projection.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcMergeRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcMergeRule.java
index 6f24cf658e1a..dffdc7bd993f 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcMergeRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcMergeRule.java
@@ -18,11 +18,11 @@
 package org.apache.beam.sdk.extensions.sql.impl.rule;
 
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamCalcRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleOperand;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CalcMergeRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CoreRules;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleOperand;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.CalcMergeRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.CoreRules;
 
 /**
  * Planner rule to merge a {@link BeamCalcRel} with a {@link BeamCalcRel}. Subset of {@link
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcRule.java
index 00871b74530b..1319e20fdb4c 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcRule.java
@@ -20,15 +20,15 @@
 import java.util.List;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamCalcRel;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalCalc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexOver;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Calc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalCalc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexOver;
 
 /** A {@code ConverterRule} to replace {@link Calc} with {@link BeamCalcRel}. */
 public class BeamCalcRule extends ConverterRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcSplittingRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcSplittingRule.java
index cc4ba1f002b0..3f0f02c76657 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcSplittingRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCalcSplittingRule.java
@@ -18,13 +18,13 @@
 package org.apache.beam.sdk.extensions.sql.impl.rule;
 
 import org.apache.beam.sdk.extensions.sql.impl.rel.CalcRelSplitter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.RelFactories;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalCalc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Calc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.RelFactories;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalCalc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCoGBKJoinRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCoGBKJoinRule.java
index 195a2675f957..f44839de062d 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCoGBKJoinRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamCoGBKJoinRule.java
@@ -21,12 +21,12 @@
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamJoinRel;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Join;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.RelFactories;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalJoin;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Join;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.RelFactories;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalJoin;
 
 /**
  * Rule to convert {@code LogicalJoin} node to {@code BeamCoGBKJoinRel} node.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamEnumerableConverterRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamEnumerableConverterRule.java
index d6b7f4f65eaa..cc508c50d9c9 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamEnumerableConverterRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamEnumerableConverterRule.java
@@ -20,10 +20,10 @@
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamEnumerableConverter;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.EnumerableConvention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.EnumerableConvention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
 
 /** A {@code ConverterRule} to Convert {@link BeamRelNode} to {@link EnumerableConvention}. */
 public class BeamEnumerableConverterRule extends ConverterRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIOPushDownRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIOPushDownRule.java
index 5abce12fa200..4c37d1be334b 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIOPushDownRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIOPushDownRule.java
@@ -38,25 +38,25 @@
 import org.apache.beam.sdk.schemas.FieldAccessDescriptor.FieldDescriptor;
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.schemas.utils.SelectHelpers;
-import org.apache.beam.vendor.calcite.v1_28_0.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.RelFactories;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeField;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelRecordType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLocalRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexProgram;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelBuilderFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.com.google.common.collect.ImmutableList;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Calc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.RelFactories;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeField;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelRecordType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLocalRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexProgram;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexUtil;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RelBuilder;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RelBuilderFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
 
 @SuppressWarnings({
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIOSinkRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIOSinkRule.java
index 0f7698687a08..9c2f9ed13382 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIOSinkRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIOSinkRule.java
@@ -20,9 +20,9 @@
 import java.util.Arrays;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamIOSinkRel;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableModify;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.TableModify;
 
 /** A {@code ConverterRule} to replace {@link TableModify} with {@link BeamIOSinkRel}. */
 public class BeamIOSinkRule extends ConverterRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIntersectRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIntersectRule.java
index cb0bcb36e75d..dd0c570dd7f0 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIntersectRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamIntersectRule.java
@@ -20,11 +20,11 @@
 import java.util.List;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamIntersectRel;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Intersect;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalIntersect;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Intersect;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalIntersect;
 
 /** {@code ConverterRule} to replace {@code Intersect} with {@code BeamIntersectRel}. */
 public class BeamIntersectRule extends ConverterRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamJoinAssociateRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamJoinAssociateRule.java
index ccf7a1e94bd9..868dfff756b6 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamJoinAssociateRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamJoinAssociateRule.java
@@ -18,15 +18,15 @@
 package org.apache.beam.sdk.extensions.sql.impl.rule;
 
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamJoinRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Join;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.RelFactories;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.JoinAssociateRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelBuilderFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Join;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.RelFactories;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.JoinAssociateRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RelBuilderFactory;
 
 /**
  * This is very similar to {@link
- * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.JoinAssociateRule}. It only
+ * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.JoinAssociateRule}. It only
  * checks if the resulting condition is supported before transforming.
  */
 public class BeamJoinAssociateRule extends JoinAssociateRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamJoinPushThroughJoinRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamJoinPushThroughJoinRule.java
index 7ecdc8e2e2ab..649cce2f39f8 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamJoinPushThroughJoinRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamJoinPushThroughJoinRule.java
@@ -18,17 +18,17 @@
 package org.apache.beam.sdk.extensions.sql.impl.rule;
 
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamJoinRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Join;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.RelFactories;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalJoin;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.JoinPushThroughJoinRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Join;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.RelFactories;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalJoin;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.JoinPushThroughJoinRule;
 
 /**
  * This is exactly similar to {@link
- * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.JoinPushThroughJoinRule}. It
+ * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.JoinPushThroughJoinRule}. It
  * only checks if the condition of the new bottom join is supported.
  */
 public class BeamJoinPushThroughJoinRule extends RelOptRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamMatchRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamMatchRule.java
index c75d06e30d1a..1efd08769b00 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamMatchRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamMatchRule.java
@@ -19,11 +19,11 @@
 
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamMatchRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Match;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalMatch;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Match;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalMatch;
 
 /** {@code ConverterRule} to replace {@code Match} with {@code BeamMatchRel}. */
 public class BeamMatchRule extends ConverterRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamMinusRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamMinusRule.java
index 18d37584b8a8..2066546ba4a8 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamMinusRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamMinusRule.java
@@ -20,11 +20,11 @@
 import java.util.List;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamMinusRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Minus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalMinus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Minus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalMinus;
 
 /** {@code ConverterRule} to replace {@code Minus} with {@code BeamMinusRel}. */
 public class BeamMinusRule extends ConverterRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSideInputJoinRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSideInputJoinRule.java
index f8ff4adc762b..7cac7c0867e3 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSideInputJoinRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSideInputJoinRule.java
@@ -21,12 +21,12 @@
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSideInputJoinRel;
 import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Join;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.RelFactories;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalJoin;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Join;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.RelFactories;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalJoin;
 
 /**
  * Rule to convert {@code LogicalJoin} node to {@code BeamSideInputJoinRel} node.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSideInputLookupJoinRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSideInputLookupJoinRule.java
index b164ed40c59e..b95ec6df0d7f 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSideInputLookupJoinRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSideInputLookupJoinRule.java
@@ -20,12 +20,12 @@
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamJoinRel;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSideInputLookupJoinRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Join;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalJoin;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Join;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalJoin;
 
 /**
  * Rule to convert {@code LogicalJoin} node to {@code BeamSideInputLookupJoinRel} node.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSortRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSortRule.java
index 446b75e4fd23..8350498185c3 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSortRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamSortRule.java
@@ -19,11 +19,11 @@
 
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSortRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Sort;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalSort;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Sort;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalSort;
 
 /** {@code ConverterRule} to replace {@code Sort} with {@code BeamSortRel}. */
 public class BeamSortRule extends ConverterRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamTableFunctionScanRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamTableFunctionScanRule.java
index 9d73f8abc4ff..e23bfebf3313 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamTableFunctionScanRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamTableFunctionScanRule.java
@@ -24,11 +24,11 @@
 import java.util.List;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamTableFunctionScanRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableFunctionScan;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalTableFunctionScan;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.TableFunctionScan;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalTableFunctionScan;
 
 /**
  * This is the conveter rule that converts a Calcite {@code TableFunctionScan} to Beam {@code
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUncollectRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUncollectRule.java
index a951cf291183..daf6ebcc6f87 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUncollectRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUncollectRule.java
@@ -19,10 +19,10 @@
 
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamUncollectRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Uncollect;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Uncollect;
 
 /** A {@code ConverterRule} to replace {@link Uncollect} with {@link BeamUncollectRule}. */
 public class BeamUncollectRule extends ConverterRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUnionRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUnionRule.java
index 09ab2f0dd301..e41e2b5a6235 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUnionRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUnionRule.java
@@ -19,15 +19,15 @@
 
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamUnionRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Union;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalUnion;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Union;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalUnion;
 
 /**
  * A {@code ConverterRule} to replace {@link
- * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Union} with {@link
+ * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Union} with {@link
  * BeamUnionRule}.
  */
 public class BeamUnionRule extends ConverterRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUnnestRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUnnestRule.java
index e2fa2bc6f586..56feed20a634 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUnnestRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamUnnestRule.java
@@ -19,18 +19,18 @@
 
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamUnnestRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.volcano.RelSubset;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.SingleRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Correlate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.JoinRelType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Uncollect;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalCorrelate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalProject;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexFieldAccess;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.volcano.RelSubset;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.SingleRel;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Correlate;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.JoinRelType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Uncollect;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalCorrelate;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalProject;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexFieldAccess;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 
 /**
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamValuesRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamValuesRule.java
index 3b01e80f902d..bfa4c477dc85 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamValuesRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamValuesRule.java
@@ -19,11 +19,11 @@
 
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamValuesRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Values;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalValues;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Values;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalValues;
 
 /** {@code ConverterRule} to replace {@code Values} with {@code BeamValuesRel}. */
 public class BeamValuesRule extends ConverterRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamWindowRule.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamWindowRule.java
index 3f554827ce53..c442ae3d1947 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamWindowRule.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/BeamWindowRule.java
@@ -19,11 +19,11 @@
 
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamWindowRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Window;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalWindow;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.Convention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.convert.ConverterRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Window;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.logical.LogicalWindow;
 
 /** A {@code ConverterRule} to replace {@link Window} with {@link BeamWindowRel}. */
 public class BeamWindowRule extends ConverterRule {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/JoinRelOptRuleCall.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/JoinRelOptRuleCall.java
index f64c32cef253..39e1cf996843 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/JoinRelOptRuleCall.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/JoinRelOptRuleCall.java
@@ -19,14 +19,14 @@
 
 import java.util.List;
 import java.util.Map;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelHintsPropagator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleOperand;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelBuilder;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelHintsPropagator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRuleOperand;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RelBuilder;
 
 /**
  * This is a class to catch the built join and check if it is a legal join before passing it to the
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/package-info.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/package-info.java
index b10dce86b42f..8a7179731138 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/package-info.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rule/package-info.java
@@ -17,7 +17,7 @@
  */
 
 /**
- * {@link org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule} to generate
+ * {@link org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule} to generate
  * {@link org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode}.
  */
 @DefaultAnnotation(NonNull.class)
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/schema/BeamTableUtils.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/schema/BeamTableUtils.java
index 3dbd14aedf06..d9dbf881850e 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/schema/BeamTableUtils.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/schema/BeamTableUtils.java
@@ -35,8 +35,8 @@
 import org.apache.beam.sdk.schemas.Schema.FieldType;
 import org.apache.beam.sdk.schemas.Schema.TypeName;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.ByteString;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.NlsString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.util.ByteString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.NlsString;
 import org.apache.commons.csv.CSVFormat;
 import org.apache.commons.csv.CSVParser;
 import org.apache.commons.csv.CSVPrinter;
@@ -108,6 +108,7 @@ public static String beamRow2CsvLine(Row row, CSVFormat csvFormat) {
    * @return The casted object in Schema.Field.Type.
    */
   public static Object autoCastField(Schema.Field field, @Nullable Object rawObj) {
+    // handle null
     if (rawObj == null) {
       if (!field.getType().getNullable()) {
         throw new IllegalArgumentException(String.format("Field %s not nullable", field.getName()));
@@ -116,12 +117,14 @@ public static Object autoCastField(Schema.Field field, @Nullable Object rawObj)
     }
 
     FieldType type = field.getType();
+    // handle NlsString
     if (CalciteUtils.isStringType(type)) {
       if (rawObj instanceof NlsString) {
         return ((NlsString) rawObj).getValue();
       } else {
         return rawObj;
       }
+      // handle date/time
     } else if (CalciteUtils.DATE.typesEqual(type) || CalciteUtils.NULLABLE_DATE.typesEqual(type)) {
       if (rawObj instanceof GregorianCalendar) { // used by the SQL CLI
         GregorianCalendar calendar = (GregorianCalendar) rawObj;
@@ -143,6 +146,7 @@ public static Object autoCastField(Schema.Field field, @Nullable Object rawObj)
     } else if (CalciteUtils.isDateTimeType(type)) {
       // Internal representation of Date in Calcite is convertible to Joda's Datetime.
       return new DateTime(rawObj);
+      // handle decimal
     } else if (type.getTypeName().isNumericType()
         && ((rawObj instanceof String)
             || (rawObj instanceof BigDecimal && type.getTypeName() != TypeName.DECIMAL))) {
@@ -168,8 +172,14 @@ public static Object autoCastField(Schema.Field field, @Nullable Object rawObj)
               String.format("Column type %s is not supported yet!", type));
       }
     } else if (type.getTypeName().isPrimitiveType()) {
+      // handle bytes represented by ByteString
       if (TypeName.BYTES.equals(type.getTypeName()) && rawObj instanceof ByteString) {
         return ((ByteString) rawObj).getBytes();
+        // handle Float <-> Double mixed use
+      } else if (TypeName.FLOAT.equals(type.getTypeName()) && rawObj instanceof Double) {
+        return ((Double) rawObj).floatValue();
+      } else if (TypeName.DOUBLE.equals(type.getTypeName()) && rawObj instanceof Float) {
+        return ((Float) rawObj).doubleValue();
       }
     }
     return rawObj;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/BeamJoinTransforms.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/BeamJoinTransforms.java
index d25f98729bd4..3f2d98f94b2d 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/BeamJoinTransforms.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/BeamJoinTransforms.java
@@ -34,10 +34,10 @@
 import org.apache.beam.sdk.transforms.ParDo;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 
 /** Collections of {@code PTransform} and {@code DoFn} used to perform JOIN operation. */
 @SuppressWarnings({
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/AggregationCombineFnAdapter.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/AggregationCombineFnAdapter.java
index 294364cc8a78..ebd42a9ee6b2 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/AggregationCombineFnAdapter.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/AggregationCombineFnAdapter.java
@@ -27,8 +27,8 @@
 import org.apache.beam.sdk.schemas.SchemaCoder;
 import org.apache.beam.sdk.transforms.Combine.CombineFn;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.AggregateCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlUserDefinedAggFunction;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.AggregateCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.validate.SqlUserDefinedAggFunction;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
 /** Wrapper {@link CombineFn}s for aggregation function calls. */
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/CovarianceFn.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/CovarianceFn.java
index 4727023c8320..cd90511e6e74 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/CovarianceFn.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/CovarianceFn.java
@@ -32,7 +32,7 @@
 import org.apache.beam.sdk.transforms.Combine;
 import org.apache.beam.sdk.transforms.SerializableFunction;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.runtime.SqlFunctions;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.runtime.SqlFunctions;
 
 /**
  * {@link Combine.CombineFn} for <em>Covariance</em> on {@link Number} types.
@@ -58,7 +58,7 @@ public class CovarianceFn<T extends Number>
   private boolean isSample; // flag to determine return value should be Covariance Pop or Sample
   private SerializableFunction<BigDecimal, T> decimalConverter;
 
-  public static <V extends Number> CovarianceFn newPopulation(Schema.TypeName typeName) {
+  public static CovarianceFn newPopulation(Schema.TypeName typeName) {
     return newPopulation(BigDecimalConverter.forSqlType(typeName));
   }
 
@@ -68,7 +68,7 @@ public static <V extends Number> CovarianceFn newPopulation(
     return new CovarianceFn<>(POP, decimalConverter);
   }
 
-  public static <V extends Number> CovarianceFn newSample(Schema.TypeName typeName) {
+  public static CovarianceFn newSample(Schema.TypeName typeName) {
     return newSample(BigDecimalConverter.forSqlType(typeName));
   }
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/VarianceFn.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/VarianceFn.java
index 65e2fa92ee95..b7c0459fd853 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/VarianceFn.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/transform/agg/VarianceFn.java
@@ -29,7 +29,7 @@
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.transforms.Combine;
 import org.apache.beam.sdk.transforms.SerializableFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.runtime.SqlFunctions;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.runtime.SqlFunctions;
 
 /**
  * {@link Combine.CombineFn} for <em>Variance</em> on {@link Number} types.
@@ -78,7 +78,7 @@ public class VarianceFn<T extends Number> extends Combine.CombineFn<T, VarianceA
   private boolean isSample; // flag to determine return value should be Variance Pop or Sample
   private SerializableFunction<BigDecimal, T> decimalConverter;
 
-  public static <V extends Number> VarianceFn newPopulation(Schema.TypeName typeName) {
+  public static VarianceFn newPopulation(Schema.TypeName typeName) {
     return newPopulation(BigDecimalConverter.forSqlType(typeName));
   }
 
@@ -88,7 +88,7 @@ public static <V extends Number> VarianceFn newPopulation(
     return new VarianceFn<>(POP, decimalConverter);
   }
 
-  public static <V extends Number> VarianceFn newSample(Schema.TypeName typeName) {
+  public static VarianceFn newSample(Schema.TypeName typeName) {
     return newSample(BigDecimalConverter.forSqlType(typeName));
   }
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/udf/BuiltinHashFunctions.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/udf/BuiltinHashFunctions.java
index 6b4e3475f73c..9dd36c133bde 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/udf/BuiltinHashFunctions.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/udf/BuiltinHashFunctions.java
@@ -19,7 +19,7 @@
 
 import com.google.auto.service.AutoService;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.function.Strict;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.function.Strict;
 
 /** Hash Functions. */
 @AutoService(BeamBuiltinFunctionProvider.class)
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/udf/BuiltinStringFunctions.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/udf/BuiltinStringFunctions.java
index 1ed7cf186cc8..48463e755489 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/udf/BuiltinStringFunctions.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/udf/BuiltinStringFunctions.java
@@ -24,7 +24,7 @@
 import org.apache.beam.repackaged.core.org.apache.commons.lang3.ArrayUtils;
 import org.apache.beam.repackaged.core.org.apache.commons.lang3.StringUtils;
 import org.apache.beam.sdk.schemas.Schema.TypeName;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.function.Strict;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.function.Strict;
 import org.apache.commons.codec.DecoderException;
 import org.apache.commons.codec.binary.Hex;
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/CalciteUtils.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/CalciteUtils.java
index 610dd4f6888a..5bcac6ad256f 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/CalciteUtils.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/CalciteUtils.java
@@ -29,12 +29,12 @@
 import org.apache.beam.sdk.schemas.logicaltypes.PassThroughLogicalType;
 import org.apache.beam.sdk.schemas.logicaltypes.SqlTypes;
 import org.apache.beam.sdk.util.Preconditions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.ByteString;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeField;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlTypeNameSpec;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.util.ByteString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeField;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlTypeNameSpec;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.BiMap;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableBiMap;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexFieldAccess.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexFieldAccess.java
index 50a8062e1e25..d2f450ac9fc3 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexFieldAccess.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexFieldAccess.java
@@ -20,8 +20,8 @@
 import java.util.ArrayList;
 import java.util.Collections;
 import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexFieldAccess;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexFieldAccess;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
 
 /** SerializableRexFieldAccess. */
 public class SerializableRexFieldAccess extends SerializableRexNode {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexInputRef.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexInputRef.java
index 2630dd7e1d3b..9520282c1aa7 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexInputRef.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexInputRef.java
@@ -17,7 +17,7 @@
  */
 package org.apache.beam.sdk.extensions.sql.impl.utils;
 
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
 
 /** SerializableRexInputRef. */
 public class SerializableRexInputRef extends SerializableRexNode {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexNode.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexNode.java
index 0bbcd7f17956..cb09c43bac47 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexNode.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/utils/SerializableRexNode.java
@@ -18,9 +18,9 @@
 package org.apache.beam.sdk.extensions.sql.impl.utils;
 
 import java.io.Serializable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexFieldAccess;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexFieldAccess;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 
 /** SerializableRexNode. */
 public abstract class SerializableRexNode implements Serializable {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BaseBeamTable.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BaseBeamTable.java
index 31944ebee134..019ada39a425 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BaseBeamTable.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BaseBeamTable.java
@@ -23,7 +23,7 @@
 import org.apache.beam.sdk.values.PBegin;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 
 /** Basic implementation of {@link BeamSqlTable}. */
 public abstract class BaseBeamTable implements BeamSqlTable {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BeamSqlTable.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BeamSqlTable.java
index f3bd34d8b1b9..e658db8c3d82 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BeamSqlTable.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BeamSqlTable.java
@@ -25,7 +25,7 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.POutput;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 
 /** This interface defines a Beam Sql Table. */
 public interface BeamSqlTable {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BeamSqlTableFilter.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BeamSqlTableFilter.java
index 8cdd430ad01b..7dd0088ea381 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BeamSqlTableFilter.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/BeamSqlTableFilter.java
@@ -18,8 +18,8 @@
 package org.apache.beam.sdk.extensions.sql.meta;
 
 import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 
 /** This interface defines Beam SQL Table Filter. */
 public interface BeamSqlTableFilter {
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/DefaultTableFilter.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/DefaultTableFilter.java
index 5a63f51ba399..e23e202511e6 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/DefaultTableFilter.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/DefaultTableFilter.java
@@ -18,7 +18,7 @@
 package org.apache.beam.sdk.extensions.sql.meta;
 
 import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 
 /**
  * This default implementation of {@link BeamSqlTableFilter} interface. Assumes that predicate
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/Catalog.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/Catalog.java
index 2a99209e06f5..e347584654cd 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/Catalog.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/Catalog.java
@@ -18,8 +18,10 @@
 package org.apache.beam.sdk.extensions.sql.meta.catalog;
 
 import java.util.Map;
+import java.util.Set;
 import org.apache.beam.sdk.annotations.Internal;
 import org.apache.beam.sdk.extensions.sql.meta.store.MetaStore;
+import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
  * Represents a named and configurable container for managing tables. Is defined with a type and
@@ -28,12 +30,55 @@
  */
 @Internal
 public interface Catalog {
+  // Default database name
+  String DEFAULT = "default";
+
   /** A type that defines this catalog. */
   String type();
 
   /** The underlying {@link MetaStore} that actually manages tables. */
   MetaStore metaStore();
 
+  /**
+   * Produces the currently active database. Can be null if no database is active.
+   *
+   * @return the current active database
+   */
+  @Nullable
+  String currentDatabase();
+
+  /**
+   * Creates a database with this name.
+   *
+   * @param databaseName
+   * @return true if the database was created, false otherwise.
+   */
+  boolean createDatabase(String databaseName);
+
+  /**
+   * Returns a set of existing databases accessible to this catalog.
+   *
+   * @return a set of existing database names
+   */
+  Set<String> listDatabases();
+
+  /**
+   * Switches to use the specified database.
+   *
+   * @param databaseName
+   */
+  void useDatabase(String databaseName);
+
+  /**
+   * Drops the database with this name. If cascade is true, the catalog should first drop all tables
+   * contained in this database.
+   *
+   * @param databaseName
+   * @param cascade
+   * @return true if the database was dropped, false otherwise.
+   */
+  boolean dropDatabase(String databaseName, boolean cascade);
+
   /** The name of this catalog, specified by the user. */
   String name();
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/InMemoryCatalog.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/InMemoryCatalog.java
index 68ab13ef6187..1279eaaaf217 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/InMemoryCatalog.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/InMemoryCatalog.java
@@ -17,15 +17,24 @@
  */
 package org.apache.beam.sdk.extensions.sql.meta.catalog;
 
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkState;
+
+import java.util.Collections;
+import java.util.HashSet;
 import java.util.Map;
+import java.util.Set;
 import org.apache.beam.sdk.extensions.sql.meta.store.InMemoryMetaStore;
 import org.apache.beam.sdk.extensions.sql.meta.store.MetaStore;
 import org.apache.beam.sdk.util.Preconditions;
+import org.checkerframework.checker.nullness.qual.Nullable;
 
 public class InMemoryCatalog implements Catalog {
   private final String name;
   private final Map<String, String> properties;
   private final InMemoryMetaStore metaStore = new InMemoryMetaStore();
+  private final HashSet<String> databases = new HashSet<>(Collections.singleton(DEFAULT));
+  protected @Nullable String currentDatabase = DEFAULT;
 
   public InMemoryCatalog(String name, Map<String, String> properties) {
     this.name = name;
@@ -52,4 +61,36 @@ public MetaStore metaStore() {
   public Map<String, String> properties() {
     return Preconditions.checkStateNotNull(properties, "InMemoryCatalog has not been initialized");
   }
+
+  @Override
+  public boolean createDatabase(String database) {
+    return databases.add(database);
+  }
+
+  @Override
+  public void useDatabase(String database) {
+    checkArgument(listDatabases().contains(database), "Database '%s' does not exist.");
+    currentDatabase = database;
+  }
+
+  @Override
+  public @Nullable String currentDatabase() {
+    return currentDatabase;
+  }
+
+  @Override
+  public boolean dropDatabase(String database, boolean cascade) {
+    checkState(!cascade, "%s does not support CASCADE.", getClass().getSimpleName());
+
+    boolean removed = databases.remove(database);
+    if (database.equals(currentDatabase)) {
+      currentDatabase = null;
+    }
+    return removed;
+  }
+
+  @Override
+  public Set<String> listDatabases() {
+    return databases;
+  }
 }
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/InMemoryCatalogRegistrar.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/InMemoryCatalogRegistrar.java
index 2d94e19c1689..afffa24e6cd7 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/InMemoryCatalogRegistrar.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/catalog/InMemoryCatalogRegistrar.java
@@ -18,16 +18,12 @@
 package org.apache.beam.sdk.extensions.sql.meta.catalog;
 
 import com.google.auto.service.AutoService;
-import org.apache.beam.sdk.extensions.sql.meta.provider.iceberg.IcebergCatalog;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 
 @AutoService(CatalogRegistrar.class)
 public class InMemoryCatalogRegistrar implements CatalogRegistrar {
   @Override
   public Iterable<Class<? extends Catalog>> getCatalogs() {
-    return ImmutableList.<Class<? extends Catalog>>builder()
-        .add(InMemoryCatalog.class)
-        .add(IcebergCatalog.class)
-        .build();
+    return ImmutableList.<Class<? extends Catalog>>builder().add(InMemoryCatalog.class).build();
   }
 }
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BeamBigQuerySqlDialect.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BeamBigQuerySqlDialect.java
index 91ba2c708d28..0b03cd73c51c 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BeamBigQuerySqlDialect.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BeamBigQuerySqlDialect.java
@@ -19,17 +19,17 @@
 
 import java.util.List;
 import java.util.Map;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.Casing;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.NullCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeSystem;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlAbstractDateTimeLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlDialect;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIntervalLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlTimestampLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.dialect.BigQuerySqlDialect;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.util.Casing;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.config.NullCollation;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeSystem;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlAbstractDateTimeLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlDialect;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIntervalLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlTimestampLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.dialect.BigQuerySqlDialect;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BeamSqlUnparseContext.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BeamSqlUnparseContext.java
index 5ab6bbcacec3..143bab558866 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BeamSqlUnparseContext.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BeamSqlUnparseContext.java
@@ -17,7 +17,7 @@
  */
 package org.apache.beam.sdk.extensions.sql.meta.provider.bigquery;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rel2sql.SqlImplementor.POS;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rel2sql.SqlImplementor.POS;
 
 import java.util.HashMap;
 import java.util.Map;
@@ -27,27 +27,27 @@
 import org.apache.beam.repackaged.core.org.apache.commons.lang3.text.translate.EntityArrays;
 import org.apache.beam.repackaged.core.org.apache.commons.lang3.text.translate.JavaUnicodeEscaper;
 import org.apache.beam.repackaged.core.org.apache.commons.lang3.text.translate.LookupTranslator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.ByteString;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.TimeUnitRange;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rel2sql.RelToSqlConverter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rel2sql.SqlImplementor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexDynamicParam;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLocalRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexProgram;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlDynamicParam;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeFamily;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.BitString;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.TimestampString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.util.ByteString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.util.TimeUnitRange;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rel2sql.RelToSqlConverter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rel2sql.SqlImplementor;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexDynamicParam;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLocalRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexProgram;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlDynamicParam;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeFamily;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.BitString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.TimestampString;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.checkerframework.checker.nullness.qual.Nullable;
 
@@ -128,9 +128,11 @@ public SqlNode toSql(RexProgram program, RexNode rex) {
     } else if (SqlKind.SEARCH.equals(rex.getKind())) {
       // Workaround CALCITE-4716
       RexCall search = (RexCall) rex;
-      RexLocalRef ref = (RexLocalRef) search.operands.get(1);
-      RexLiteral literal = (RexLiteral) program.getExprList().get(ref.getIndex());
-      rex = search.clone(search.getType(), ImmutableList.of(search.operands.get(0), literal));
+      if (search.operands.get(1) instanceof RexLocalRef) {
+        RexLocalRef ref = (RexLocalRef) search.operands.get(1);
+        RexLiteral literal = (RexLiteral) program.getExprList().get(ref.getIndex());
+        rex = search.clone(search.getType(), ImmutableList.of(search.operands.get(0), literal));
+      }
     }
 
     return super.toSql(program, rex);
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryFilter.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryFilter.java
index 36450e3914cc..7ae198fd1cd7 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryFilter.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryFilter.java
@@ -17,29 +17,29 @@
  */
 package org.apache.beam.sdk.extensions.sql.meta.provider.bigquery;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.AND;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.BETWEEN;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.CAST;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.COMPARISON;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.DIVIDE;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.LIKE;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.MINUS;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.MOD;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.OR;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.PLUS;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.TIMES;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.AND;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.BETWEEN;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.CAST;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.COMPARISON;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.DIVIDE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.LIKE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.MINUS;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.MOD;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.OR;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.PLUS;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.TIMES;
 
 import java.util.ArrayList;
 import java.util.List;
 import java.util.stream.Collectors;
 import org.apache.beam.repackaged.core.org.apache.commons.lang3.tuple.Pair;
 import org.apache.beam.sdk.extensions.sql.meta.BeamSqlTableFilter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableSet;
 
 @SuppressWarnings({
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryTable.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryTable.java
index 1898c28f670c..bc10d6b99a95 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryTable.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigquery/BigQueryTable.java
@@ -47,12 +47,12 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.POutput;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rel2sql.SqlImplementor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rel2sql.SqlImplementor;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.slf4j.Logger;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigtable/BigtableFilter.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigtable/BigtableFilter.java
index f419896f2ed5..d42fff695b2f 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigtable/BigtableFilter.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigtable/BigtableFilter.java
@@ -20,7 +20,7 @@
 import static java.util.stream.Collectors.toList;
 import static org.apache.beam.sdk.io.gcp.bigtable.RowUtils.KEY;
 import static org.apache.beam.sdk.io.gcp.bigtable.RowUtils.byteStringUtf8;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.LIKE;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.LIKE;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
 
 import com.google.bigtable.v2.RowFilter;
@@ -28,10 +28,10 @@
 import java.util.stream.Collectors;
 import org.apache.beam.sdk.extensions.sql.meta.BeamSqlTableFilter;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 
 /**
  * BigtableFilter for queries with WHERE clause.
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigtable/BigtableTable.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigtable/BigtableTable.java
index 60c722d32d2d..e24b9a437e36 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigtable/BigtableTable.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/bigtable/BigtableTable.java
@@ -46,7 +46,7 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.POutput;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Splitter;
 
 public class BigtableTable extends SchemaBaseBeamTable implements Serializable {
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SingleRowScanConverter.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/AdvancingTimestampFn.java
similarity index 53%
rename from sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SingleRowScanConverter.java
rename to sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/AdvancingTimestampFn.java
index e339b0ce6552..a09f36c748e4 100644
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SingleRowScanConverter.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/AdvancingTimestampFn.java
@@ -15,26 +15,24 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
+package org.apache.beam.sdk.extensions.sql.meta.provider.datagen;
 
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedSingleRowScan;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import java.util.concurrent.ThreadLocalRandom;
+import org.apache.beam.sdk.transforms.SerializableFunction;
+import org.joda.time.Duration;
+import org.joda.time.Instant;
 
-/** Converts a single row value. */
-class SingleRowScanConverter extends RelConverter<ResolvedSingleRowScan> {
+class AdvancingTimestampFn implements SerializableFunction<Long, Instant> {
+  private final long maxOutOfOrdernessMs;
+  private final Instant baseTime = Instant.now();
 
-  SingleRowScanConverter(ConversionContext context) {
-    super(context);
+  AdvancingTimestampFn(long maxOutOfOrdernessMs) {
+    this.maxOutOfOrdernessMs = maxOutOfOrdernessMs;
   }
 
   @Override
-  public boolean canConvert(ResolvedSingleRowScan zetaNode) {
-    return zetaNode.getColumnList() == null || zetaNode.getColumnList().isEmpty();
-  }
-
-  @Override
-  public RelNode convert(ResolvedSingleRowScan zetaNode, List<RelNode> inputs) {
-    return createOneRow(getCluster());
+  public Instant apply(Long index) {
+    long delay = (long) (ThreadLocalRandom.current().nextDouble() * maxOutOfOrdernessMs);
+    return baseTime.plus(Duration.millis(index * 1000)).minus(Duration.millis(delay));
   }
 }
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorPTransform.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorPTransform.java
new file mode 100644
index 000000000000..1149b773d06c
--- /dev/null
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorPTransform.java
@@ -0,0 +1,97 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.meta.provider.datagen;
+
+import com.fasterxml.jackson.databind.JsonNode;
+import com.fasterxml.jackson.databind.node.ObjectNode;
+import javax.annotation.Nullable;
+import org.apache.beam.sdk.io.GenerateSequence;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.values.PBegin;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.Row;
+import org.joda.time.Duration;
+
+/** The main PTransform that encapsulates the data generation logic. */
+public class DataGeneratorPTransform extends PTransform<PBegin, PCollection<Row>> {
+  private final Schema schema;
+  private final ObjectNode properties;
+
+  public DataGeneratorPTransform(Schema schema, ObjectNode properties) {
+    this.schema = schema;
+    this.properties = properties;
+  }
+
+  @Override
+  public PCollection<Row> expand(PBegin input) {
+    GenerateSequence generator;
+    JsonNode rpsNode = properties.path("rows-per-second");
+    JsonNode numRowsNode = properties.path("number-of-rows");
+
+    if (!rpsNode.isMissingNode()) {
+      generator = GenerateSequence.from(0).withRate(rpsNode.asLong(), Duration.standardSeconds(1));
+    } else if (!numRowsNode.isMissingNode()) {
+      generator = GenerateSequence.from(0).to(numRowsNode.asLong());
+    } else {
+      throw new IllegalArgumentException(
+          "A 'datagen' table requires either 'rows-per-second' (for unbounded) or "
+              + "'number-of-rows' (for bounded) in TBLPROPERTIES.");
+    }
+
+    String behavior = properties.path("timestamp.behavior").asText("processing-time");
+    @Nullable String eventTimeColumn = null;
+
+    if ("event-time".equalsIgnoreCase(behavior)) {
+      JsonNode columnNode = properties.path("event-time.timestamp-column");
+
+      if (columnNode.isMissingNode() || columnNode.isNull()) {
+        throw new IllegalArgumentException(
+            "For 'event-time' behavior, 'event-time.timestamp-column' must be specified.");
+      }
+      eventTimeColumn = columnNode.asText();
+
+      // Validate that the specified column exists and is of type TIMESTAMP.
+      if (!schema.hasField(eventTimeColumn)) {
+        throw new IllegalArgumentException(
+            String.format(
+                "The specified 'event-time.timestamp-column' ('%s') does not exist in the table schema.",
+                eventTimeColumn));
+      }
+
+      Schema.Field eventTimeField = schema.getField(eventTimeColumn);
+      if (!Schema.TypeName.DATETIME.equals(eventTimeField.getType().getTypeName())) {
+        throw new IllegalArgumentException(
+            String.format(
+                "The specified 'event-time.timestamp-column' ('%s') must be of type TIMESTAMP, but was '%s'.",
+                eventTimeColumn, eventTimeField.getType()));
+      }
+
+      long maxOutOfOrdernessMs = properties.path("event_time.max-out-of-orderness").asLong(0L);
+      generator = generator.withTimestampFn(new AdvancingTimestampFn(maxOutOfOrdernessMs));
+    }
+
+    return input
+        .getPipeline()
+        .apply("GenerateSequence", generator)
+        .apply(
+            "GenerateRows", ParDo.of(new DataGeneratorRowFn(schema, properties, eventTimeColumn)))
+        .setRowSchema(schema);
+  }
+}
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorRowFn.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorRowFn.java
new file mode 100644
index 000000000000..944d8ec9b002
--- /dev/null
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorRowFn.java
@@ -0,0 +1,200 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.meta.provider.datagen;
+
+import com.fasterxml.jackson.databind.JsonNode;
+import com.fasterxml.jackson.databind.node.ObjectNode;
+import java.io.Serializable;
+import java.math.BigDecimal;
+import java.util.HashMap;
+import java.util.Map;
+import java.util.concurrent.ThreadLocalRandom;
+import javax.annotation.Nullable;
+import org.apache.beam.sdk.extensions.sql.impl.utils.CalciteUtils;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.commons.lang3.RandomStringUtils;
+import org.joda.time.Duration;
+import org.joda.time.Instant;
+
+/** A stateful DoFn that converts a sequence of Longs into structured Rows. */
+public class DataGeneratorRowFn extends DoFn<Long, Row> {
+  private final Schema schema;
+  private final ObjectNode properties;
+  private final @Nullable String primaryTimestampField;
+
+  private transient Map<String, FieldGenerator> fieldGenerators;
+
+  @SuppressWarnings("initialization")
+  public DataGeneratorRowFn(
+      Schema schema, ObjectNode properties, @Nullable String primaryTimestampField) {
+    this.schema = schema;
+    this.properties = properties;
+    this.primaryTimestampField = primaryTimestampField;
+  }
+
+  @Setup
+  public void setup() {
+    this.fieldGenerators = new HashMap<>();
+
+    for (Schema.Field field : schema.getFields()) {
+      fieldGenerators.put(field.getName(), createGeneratorForField(field));
+    }
+  }
+
+  @ProcessElement
+  public void processElement(
+      @Element Long index, @Timestamp Instant timestamp, OutputReceiver<Row> out) {
+    Row.Builder rowBuilder = Row.withSchema(schema);
+    for (Schema.Field field : schema.getFields()) {
+      Object value;
+      if (field.getName().equals(this.primaryTimestampField)) {
+        value = timestamp.toDateTime();
+      } else {
+        FieldGenerator generator = fieldGenerators.get(field.getName());
+        if (generator == null) {
+          throw new IllegalStateException("No generator found for field: " + field.getName());
+        }
+        value = generator.generate(index);
+      }
+      rowBuilder.addValue(value);
+    }
+    out.output(rowBuilder.build());
+  }
+
+  @FunctionalInterface
+  private interface FieldGenerator extends Serializable {
+    @Nullable
+    Object generate(long index);
+  }
+
+  private FieldGenerator createGeneratorForField(Schema.Field field) {
+    String fieldName = field.getName();
+    FieldGenerator valueGenerator = createValueGeneratorForField(field);
+    double nullRate = properties.path("fields." + fieldName + ".null-rate").asDouble(0.0);
+
+    if (nullRate > 0) {
+      return (index) ->
+          ThreadLocalRandom.current().nextDouble() < nullRate
+              ? null
+              : valueGenerator.generate(index);
+    }
+    return valueGenerator;
+  }
+
+  private FieldGenerator createValueGeneratorForField(Schema.Field field) {
+    String fieldName = field.getName();
+    String kind = properties.path("fields." + fieldName + ".kind").asText("random");
+
+    final SqlTypeName sqlTypeName = CalciteUtils.toSqlTypeName(field.getType());
+    if (sqlTypeName == null) {
+      throw new UnsupportedOperationException(
+          "Data generator requires a defined SQL type. Beam type '"
+              + field.getType().getTypeName()
+              + "' on field '"
+              + field.getName()
+              + "' is not supported.");
+    }
+
+    if ("sequence".equalsIgnoreCase(kind)) {
+      if (!SqlTypeName.INT_TYPES.contains(sqlTypeName)) {
+        throw new IllegalArgumentException(
+            String.format(
+                "The 'sequence' generator for integers only supports integer types, but field '%s' is of type '%s'.",
+                field.getName(), sqlTypeName));
+      }
+
+      JsonNode startNode = properties.path("fields." + fieldName + ".start");
+      JsonNode endNode = properties.path("fields." + fieldName + ".end");
+
+      if (startNode.isMissingNode() && endNode.isMissingNode()) {
+        return (index) -> index;
+      }
+
+      if (startNode.isMissingNode() || endNode.isMissingNode()) {
+        throw new IllegalArgumentException(
+            "For a cycling sequence generator, both 'start' and 'end' must be specified.");
+      }
+
+      long start = startNode.asLong(0L);
+      long end = endNode.asLong(Long.MAX_VALUE);
+
+      if (start > end) {
+        throw new IllegalArgumentException(
+            String.format(
+                "For sequence generator, 'start' (%d) cannot be greater than 'end' (%d).",
+                start, end));
+      }
+      long cycleLength = end - start + 1;
+      switch (sqlTypeName) {
+        case INTEGER:
+          return (index) -> (int) (start + (index % cycleLength));
+        case SMALLINT:
+          return (index) -> (short) (start + (index % cycleLength));
+        case TINYINT:
+          return (index) -> (byte) (start + (index % cycleLength));
+        default: // BIGINT
+          return (index) -> start + (index % cycleLength);
+      }
+    }
+
+    switch (sqlTypeName) {
+      case CHAR:
+      case VARCHAR:
+        int length = properties.path("fields." + fieldName + ".length").asInt(10);
+        return (index) -> RandomStringUtils.randomAlphanumeric(length);
+      case BOOLEAN:
+        return (index) -> ThreadLocalRandom.current().nextBoolean();
+      case FLOAT:
+      case DOUBLE:
+        double minD = properties.path("fields." + fieldName + ".min").asDouble(0.0);
+        double maxD = properties.path("fields." + fieldName + ".max").asDouble(1.0);
+        return (index) -> minD + (maxD - minD) * ThreadLocalRandom.current().nextDouble();
+      case TINYINT:
+      case SMALLINT:
+      case INTEGER:
+      case BIGINT:
+        long minL = properties.path("fields." + fieldName + ".min").asLong(0L);
+        long maxL = properties.path("fields." + fieldName + ".max").asLong(Long.MAX_VALUE);
+        return (index) -> minL + (long) (ThreadLocalRandom.current().nextDouble() * (maxL - minL));
+      case DECIMAL:
+        double minBd = properties.path("fields." + fieldName + ".min").asDouble(0.0);
+        double maxBd = properties.path("fields." + fieldName + ".max").asDouble(1000.0);
+        return (index) ->
+            BigDecimal.valueOf(minBd + (maxBd - minBd) * ThreadLocalRandom.current().nextDouble());
+      case TIMESTAMP:
+        JsonNode maxPastNode = properties.path("fields." + fieldName + ".max-past");
+        if (!maxPastNode.isMissingNode()) {
+          long maxPastMs = maxPastNode.asLong();
+          if (maxPastMs <= 0) {
+            throw new IllegalArgumentException("'max-past' must be a positive long value.");
+          }
+          return (index) ->
+              Instant.now()
+                  .minus(
+                      Duration.millis(
+                          (long) (ThreadLocalRandom.current().nextDouble() * maxPastMs)));
+        }
+        return (index) -> Instant.now();
+      default:
+        throw new UnsupportedOperationException("Unsupported SQL type for datagen: " + sqlTypeName);
+    }
+  }
+}
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorTable.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorTable.java
new file mode 100644
index 000000000000..5f02ac9035b8
--- /dev/null
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorTable.java
@@ -0,0 +1,89 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.meta.provider.datagen;
+
+import com.fasterxml.jackson.databind.node.ObjectNode;
+import java.util.List;
+import org.apache.beam.sdk.extensions.sql.impl.BeamTableStatistics;
+import org.apache.beam.sdk.extensions.sql.meta.BeamSqlTableFilter;
+import org.apache.beam.sdk.extensions.sql.meta.SchemaBaseBeamTable;
+import org.apache.beam.sdk.options.PipelineOptions;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.values.PBegin;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollection.IsBounded;
+import org.apache.beam.sdk.values.POutput;
+import org.apache.beam.sdk.values.Row;
+
+/**
+ * Represents a 'datagen' table within a Beam SQL pipeline. This class extends {@link
+ * SchemaBaseBeamTable} to correctly implement the full {@link
+ * org.apache.beam.sdk.extensions.sql.meta.BeamSqlTable} interface.
+ */
+public class DataGeneratorTable extends SchemaBaseBeamTable {
+  private final ObjectNode properties;
+
+  public DataGeneratorTable(Schema schema, ObjectNode properties) {
+    super(schema);
+    this.properties = properties;
+  }
+
+  @Override
+  public IsBounded isBounded() {
+    // The table is bounded if 'number-of-rows' is specified, otherwise it is unbounded.
+    return properties.has("number-of-rows") ? IsBounded.BOUNDED : IsBounded.UNBOUNDED;
+  }
+
+  @Override
+  public PCollection<Row> buildIOReader(PBegin begin) {
+    return begin.apply(
+        "ReadFromDataGenerator", new DataGeneratorPTransform(getSchema(), properties));
+  }
+
+  @Override
+  public PCollection<Row> buildIOReader(
+      PBegin begin, BeamSqlTableFilter filters, List<String> fieldNames) {
+    // DataGenerator does not support filter or project push-down.
+    // This is a common pattern for IOs that do not support these optimizations.
+    return buildIOReader(begin);
+  }
+
+  @Override
+  public POutput buildIOWriter(PCollection<Row> input) {
+    throw new UnsupportedOperationException("The 'datagen' table type is read-only.");
+  }
+
+  @Override
+  public BeamTableStatistics getTableStatistics(PipelineOptions options) {
+    // Safely provide statistics to the query planner.
+    // Check for the bounded property first.
+    if (properties.has("number-of-rows")) {
+      double rowCount = properties.get("number-of-rows").asDouble();
+      return BeamTableStatistics.createBoundedTableStatistics(rowCount);
+    }
+    // Then check for the unbounded property.
+    else if (properties.has("rows-per-second")) {
+      double rate = properties.get("rows-per-second").asDouble();
+      return BeamTableStatistics.createUnboundedTableStatistics(rate);
+    }
+
+    // If neither property is present, the table is misconfigured. Return UNKNOWN
+    // and let the PTransform's expand() method throw the detailed error.
+    return BeamTableStatistics.BOUNDED_UNKNOWN;
+  }
+}
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorTableProvider.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorTableProvider.java
new file mode 100644
index 000000000000..302d3d129dad
--- /dev/null
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorTableProvider.java
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.meta.provider.datagen;
+
+import com.google.auto.service.AutoService;
+import org.apache.beam.sdk.extensions.sql.meta.BeamSqlTable;
+import org.apache.beam.sdk.extensions.sql.meta.Table;
+import org.apache.beam.sdk.extensions.sql.meta.provider.InMemoryMetaTableProvider;
+import org.apache.beam.sdk.extensions.sql.meta.provider.TableProvider;
+
+/**
+ * The service entry point for the 'datagen' table type.
+ *
+ * <p>This provider allows for the creation of SQL-configurable test data sources. Tables of this
+ * type are defined using the {@code CREATE EXTERNAL TABLE} statement.
+ *
+ * <p>The provider supports generating both bounded data (for batch pipelines) using the {@code
+ * "number-of-rows"} property, and unbounded data (for streaming pipelines) using the {@code
+ * "rows-per-second"} property.
+ *
+ * <pre>{@code
+ * CREATE EXTERNAL TABLE user_clicks (
+ * event_id BIGINT,
+ * user_id VARCHAR,
+ * click_timestamp TIMESTAMP,
+ * score DOUBLE
+ * )
+ * TYPE 'datagen'
+ * TBLPROPERTIES '{
+ * "rows-per-second": "100",
+ *
+ * "fields.event_id.kind": "sequence",
+ * "fields.event_id.start": "1",
+ * "fields.event_id.end": "1000000",
+ *
+ * "fields.user_id.kind": "random",
+ * "fields.user_id.length": "12",
+ *
+ * "fields.click_timestamp.kind": "random",
+ * "fields.click_timestamp.max-past": "60000",
+ *
+ * "fields.score.kind": "random",
+ * "fields.score.min": "0.0",
+ * "fields.score.max": "1.0",
+ * "fields.score.null-rate": "0.1"
+ * }'
+ * }</pre>
+ */
+@AutoService(TableProvider.class)
+public class DataGeneratorTableProvider extends InMemoryMetaTableProvider {
+
+  @Override
+  public String getTableType() {
+    return "datagen";
+  }
+
+  /**
+   * Instantiates the {@link DataGeneratorTable} when a {@code CREATE EXTERNAL TABLE} statement with
+   * {@code TYPE 'datagen'} is executed.
+   */
+  @Override
+  public BeamSqlTable buildBeamSqlTable(Table table) {
+    return new DataGeneratorTable(table.getSchema(), table.getProperties());
+  }
+}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/package-info.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/package-info.java
similarity index 84%
rename from sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/package-info.java
rename to sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/package-info.java
index d01c6565c7e5..31672fd740a6 100644
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/package-info.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/package-info.java
@@ -16,5 +16,5 @@
  * limitations under the License.
  */
 
-/** Conversion logic between ZetaSQL resolved query nodes and Calcite rel nodes. */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
+/** Table schema for Datagen. */
+package org.apache.beam.sdk.extensions.sql.meta.provider.datagen;
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/mongodb/MongoDbTable.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/mongodb/MongoDbTable.java
index 576b623b28ab..35ea74996f31 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/mongodb/MongoDbTable.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/mongodb/MongoDbTable.java
@@ -17,10 +17,12 @@
  */
 package org.apache.beam.sdk.extensions.sql.meta.provider.mongodb;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.AND;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.COMPARISON;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.OR;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.AND;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.COMPARISON;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.OR;
 
+import com.mongodb.BasicDBObject;
+import com.mongodb.MongoClientSettings;
 import com.mongodb.client.model.Filters;
 import java.io.Serializable;
 import java.util.ArrayList;
@@ -56,14 +58,15 @@
 import org.apache.beam.sdk.values.PCollection.IsBounded;
 import org.apache.beam.sdk.values.POutput;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.bson.BsonDocument;
 import org.bson.Document;
 import org.bson.conversions.Bson;
 import org.bson.json.JsonMode;
@@ -178,7 +181,21 @@ private Bson constructPredicate(List<RexNode> supported) {
     if (cnf.size() == 1) {
       return cnf.get(0);
     }
-    return Filters.and(cnf);
+    // Convert all filters to BsonDocument and merge them into a single Document
+    // This avoids wrapping in $and which changed behavior in MongoDB driver 5.x
+    Document compositeFilter = new Document();
+    for (Bson filter : cnf) {
+      // Convert any Bson filter to BsonDocument first
+      BsonDocument bsonDoc =
+          filter.toBsonDocument(BasicDBObject.class, MongoClientSettings.getDefaultCodecRegistry());
+      // Convert BsonDocument to Document for easier manipulation
+      Document doc = Document.parse(bsonDoc.toJson());
+      // Merge all top-level conditions into the composite filter
+      for (String key : doc.keySet()) {
+        compositeFilter.append(key, doc.get(key));
+      }
+    }
+    return compositeFilter;
   }
 
   /**
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetFilter.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetFilter.java
new file mode 100644
index 000000000000..c42be62454d7
--- /dev/null
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetFilter.java
@@ -0,0 +1,212 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.meta.provider.parquet;
+
+import static org.apache.beam.sdk.util.Preconditions.checkStateNotNull;
+
+import java.util.HashSet;
+import java.util.List;
+import java.util.Set;
+import org.apache.beam.sdk.extensions.sql.meta.BeamSqlTableFilter;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.commons.lang3.tuple.Pair;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableSet;
+
+/**
+ * A {@link BeamSqlTableFilter} for ParquetIO that classifies filters as supported or unsupported.
+ *
+ * <p>This filter implementation analyzes SQL filter expressions and determines which ones can be
+ * pushed down to the Parquet storage layer for efficient filtering. It supports a comprehensive set
+ * of SQL operations including comparisons, logical operators, set operations, and null checks.
+ *
+ * <p>The filter works by:
+ *
+ * <ol>
+ *   <li>Analyzing each RexNode expression in the filter
+ *   <li>Classifying expressions as supported or unsupported based on operation type and structure
+ *   <li>Providing methods to access both supported and unsupported expressions
+ *   <li>Extracting field references for projection optimization
+ * </ol>
+ *
+ * <p>Supported operations include:
+ *
+ * <ul>
+ *   <li>Comparison operators: =, !=, &lt;, &lt;=, &gt;, &gt;=
+ *   <li>Logical operators: AND, OR, NOT
+ *   <li>Set operations: IN, NOT IN
+ *   <li>Null checks: IS NULL, IS NOT NULL
+ * </ul>
+ *
+ * <p>Limitations:
+ *
+ * <ul>
+ *   <li>Only supports single-column comparisons (column = literal)
+ *   <li>Does not support complex expressions or function calls
+ *   <li>Does not support cross-column comparisons
+ * </ul>
+ *
+ * <p>Example usage:
+ *
+ * <pre>{@code
+ * // Create filter from SQL WHERE clause expressions
+ * List<RexNode> filterExpressions = ...; // from SQL parser
+ * ParquetFilter filter = new ParquetFilter(filterExpressions);
+ *
+ * // Get supported expressions for pushdown
+ * List<RexNode> supported = filter.getSupported();
+ *
+ * // Get unsupported expressions (will be applied in Beam)
+ * List<RexNode> unsupported = filter.getNotSupported();
+ *
+ * // Get field names referenced in supported filters
+ * Set<String> referencedFields = filter.getReferencedFields(schema);
+ * }</pre>
+ */
+public class ParquetFilter implements BeamSqlTableFilter {
+  // The set of operators that can be pushed down.
+  private static final ImmutableSet<SqlKind> SUPPORTED_OPS =
+      ImmutableSet.of(
+          SqlKind.AND,
+          SqlKind.OR,
+          SqlKind.NOT,
+          SqlKind.EQUALS,
+          SqlKind.NOT_EQUALS,
+          SqlKind.GREATER_THAN,
+          SqlKind.GREATER_THAN_OR_EQUAL,
+          SqlKind.LESS_THAN,
+          SqlKind.LESS_THAN_OR_EQUAL,
+          SqlKind.IN,
+          SqlKind.IS_NULL,
+          SqlKind.IS_NOT_NULL);
+
+  private final List<RexNode> supported;
+  private final List<RexNode> unsupported;
+
+  /**
+   * Creates a new ParquetFilter by analyzing the given filter expressions.
+   *
+   * @param predicateCNF List of RexNode expressions in Conjunctive Normal Form (CNF) representing
+   *     the SQL WHERE clause conditions.
+   */
+  public ParquetFilter(List<RexNode> predicateCNF) {
+    Pair<List<RexNode>, List<RexNode>> classifiedFilters = classify(predicateCNF);
+    this.supported = classifiedFilters.getLeft();
+    this.unsupported = classifiedFilters.getRight();
+  }
+
+  /**
+   * Returns the set of field names referenced in the supported filter expressions.
+   *
+   * <p>This method is useful for determining which columns need to be read from the Parquet files
+   * to evaluate the filter conditions. It helps optimize projection by ensuring all required fields
+   * are included in the read schema.
+   *
+   * @param beamSchema The Beam schema for the table
+   * @return Set of field names referenced in supported filter expressions
+   */
+  public Set<String> getReferencedFields(Schema beamSchema) {
+    Set<String> fields = new HashSet<>();
+    for (RexNode node : supported) {
+      collectReferencedFields(node, beamSchema, fields);
+    }
+    return fields;
+  }
+
+  private static void collectReferencedFields(RexNode node, Schema beamSchema, Set<String> fields) {
+    if (node instanceof RexInputRef) {
+      fields.add(beamSchema.getField(((RexInputRef) node).getIndex()).getName());
+    } else if (node instanceof RexCall) {
+      for (RexNode operand : ((RexCall) node).getOperands()) {
+        collectReferencedFields(operand, beamSchema, fields);
+      }
+    }
+  }
+
+  /** Static helper method to classify filters. */
+  private static Pair<List<RexNode>, List<RexNode>> classify(List<RexNode> predicates) {
+    ImmutableList.Builder<RexNode> supportedBuilder = ImmutableList.builder();
+    ImmutableList.Builder<RexNode> unsupportedBuilder = ImmutableList.builder();
+
+    for (RexNode node : predicates) {
+      if (isSupported(node).getLeft()) {
+        supportedBuilder.add(node);
+      } else {
+        unsupportedBuilder.add(node);
+      }
+    }
+    return Pair.of(supportedBuilder.build(), unsupportedBuilder.build());
+  }
+
+  @Override
+  public List<RexNode> getNotSupported() {
+    return unsupported;
+  }
+
+  @Override
+  public int numSupported() {
+    return BeamSqlTableFilter.expressionsInFilter(checkStateNotNull(supported));
+  }
+
+  /**
+   * Returns the list of supported filter expressions that can be pushed down to Parquet.
+   *
+   * <p>These expressions will be converted to Parquet filter predicates and applied during file
+   * reading for optimal performance.
+   *
+   * @return List of supported RexNode expressions
+   */
+  public List<RexNode> getSupported() {
+    return supported;
+  }
+
+  private static Pair<Boolean, Integer> isSupported(RexNode node) {
+    if (!(node instanceof RexCall)) {
+      return Pair.of(node instanceof RexLiteral || node instanceof RexInputRef, 0);
+    }
+
+    RexCall call = (RexCall) node;
+    if (!SUPPORTED_OPS.contains(call.getKind())) {
+      return Pair.of(false, 0);
+    }
+
+    boolean allOperandsSupported = true;
+    int inputRefCount = 0;
+    for (RexNode operand : call.getOperands()) {
+      if (operand instanceof RexInputRef) {
+        inputRefCount++;
+      } else if (operand instanceof RexCall) {
+        Pair<Boolean, Integer> childSupport = isSupported(operand);
+        if (!childSupport.getLeft()) {
+          allOperandsSupported = false;
+          break;
+        }
+        inputRefCount += childSupport.getRight();
+      }
+    }
+
+    boolean isStructureSupported = inputRefCount <= 1;
+
+    return Pair.of(allOperandsSupported && isStructureSupported, inputRefCount);
+  }
+}
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetTable.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetTable.java
index bdbb48bf1b71..88122ab11add 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetTable.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetTable.java
@@ -19,8 +19,11 @@
 
 import java.io.Serializable;
 import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.LinkedHashSet;
 import java.util.List;
 import java.util.Map;
+import java.util.Set;
 import org.apache.avro.Schema;
 import org.apache.avro.Schema.Field;
 import org.apache.avro.generic.GenericRecord;
@@ -31,14 +34,17 @@
 import org.apache.beam.sdk.extensions.sql.meta.SchemaBaseBeamTable;
 import org.apache.beam.sdk.extensions.sql.meta.Table;
 import org.apache.beam.sdk.io.FileIO;
+import org.apache.beam.sdk.io.parquet.ParquetFilterFactory;
 import org.apache.beam.sdk.io.parquet.ParquetIO;
 import org.apache.beam.sdk.io.parquet.ParquetIO.Read;
 import org.apache.beam.sdk.schemas.transforms.Convert;
+import org.apache.beam.sdk.schemas.transforms.Select;
 import org.apache.beam.sdk.values.PBegin;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollection.IsBounded;
 import org.apache.beam.sdk.values.POutput;
 import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -64,23 +70,74 @@ public PCollection<Row> buildIOReader(PBegin begin) {
 
   @Override
   public PCollection<Row> buildIOReader(
-      PBegin begin, BeamSqlTableFilter filters, List<String> fieldNames) {
+      PBegin begin, BeamSqlTableFilter filter, List<String> projectedFieldNames) {
+
+    // Determine ALL fields required for the read (projection + filter fields).
+    Set<String> requiredFieldsForRead = new HashSet<>(projectedFieldNames);
+    if (filter instanceof ParquetFilter) {
+      ParquetFilter parquetFilter = (ParquetFilter) filter;
+      requiredFieldsForRead.addAll(parquetFilter.getReferencedFields(getSchema()));
+    }
+
+    // If no fields are projected or filtered, read the full schema.
     final Schema schema = AvroUtils.toAvroSchema(table.getSchema());
+    Schema readSchema =
+        requiredFieldsForRead.isEmpty()
+            ? schema
+            : projectSchema(schema, new ArrayList<>(requiredFieldsForRead));
+
+    LOG.info("Projecting fields schema: {}", readSchema);
+
     String filePattern = resolveFilePattern(table.getLocation());
     Read read = ParquetIO.read(schema).withBeamSchemas(true).from(filePattern);
-    if (!fieldNames.isEmpty()) {
-      Schema projectionSchema = projectSchema(schema, fieldNames);
-      LOG.info("Projecting fields schema: {}", projectionSchema);
-      read = read.withProjection(projectionSchema, projectionSchema);
+
+    // Create encoder schema with unwanted columns made nullable
+    Schema encoderSchema = createEncoderSchema(schema, requiredFieldsForRead);
+    read = read.withProjection(readSchema, encoderSchema);
+
+    if (filter instanceof ParquetFilter) {
+      ParquetFilter parquetFilter = (ParquetFilter) filter;
+      List<RexNode> supported = parquetFilter.getSupported();
+      if (!supported.isEmpty()) {
+        org.apache.beam.sdk.io.parquet.ParquetFilter predicate =
+            ParquetFilterFactory.create(supported, getSchema());
+        read = read.withFilter(predicate);
+      }
+    }
+
+    PCollection<Row> rowsWithRequiredFields =
+        begin.apply("ParquetIORead With Filtering", read).apply("ToRows", Convert.toRows());
+
+    // If we read extra fields for filtering, project them away to match the final SELECT list.
+    if (!projectedFieldNames.isEmpty()
+        && !new HashSet<>(projectedFieldNames).equals(requiredFieldsForRead)) {
+      return rowsWithRequiredFields.apply(
+          Select.fieldNames(projectedFieldNames.toArray(new String[0])));
+    } else {
+      return rowsWithRequiredFields;
     }
-    return begin.apply("ParquetIORead", read).apply("ToRows", Convert.toRows());
+  }
+
+  @Override
+  public BeamSqlTableFilter constructFilter(List<RexNode> filter) {
+    return new ParquetFilter(filter);
   }
 
   /** Returns a copy of the {@link Schema} with only the fieldNames fields. */
   private static Schema projectSchema(Schema schema, List<String> fieldNames) {
-    List<Field> selectedFields = new ArrayList<>();
-    for (String fieldName : fieldNames) {
-      selectedFields.add(deepCopyField(schema.getField(fieldName)));
+    if (fieldNames.isEmpty()) {
+      return schema;
+    }
+
+    // Use LinkedHashSet to maintain order and provide O(1) lookup
+    Set<String> fieldNameSet = new LinkedHashSet<>(fieldNames);
+    List<Field> selectedFields = new ArrayList<>(fieldNames.size());
+
+    // Iterate through the original schema's fields to maintain their order.
+    for (Schema.Field field : schema.getFields()) {
+      if (fieldNameSet.contains(field.name())) {
+        selectedFields.add(deepCopyField(field));
+      }
     }
     return Schema.createRecord(
         schema.getName() + "_projected",
@@ -90,6 +147,38 @@ private static Schema projectSchema(Schema schema, List<String> fieldNames) {
         selectedFields);
   }
 
+  /**
+   * Creates an encoder schema where unwanted columns are made nullable. This follows the ParquetIO
+   * javadoc requirement for projection.
+   */
+  private static Schema createEncoderSchema(Schema originalSchema, Set<String> requiredFields) {
+    if (requiredFields.isEmpty()) {
+      return originalSchema;
+    }
+
+    // Pre-allocate list with expected size for better performance
+    List<Field> encoderFields = new ArrayList<>(originalSchema.getFields().size());
+    for (Schema.Field field : originalSchema.getFields()) {
+      if (requiredFields.contains(field.name())) {
+        // Keep the field as-is for required fields
+        encoderFields.add(deepCopyField(field));
+      } else {
+        // Make unwanted columns nullable
+        Schema nullableSchema = Schema.createUnion(Schema.create(Schema.Type.NULL), field.schema());
+        Field nullableField =
+            new Schema.Field(field.name(), nullableSchema, field.doc(), null, field.order());
+        encoderFields.add(nullableField);
+      }
+    }
+
+    return Schema.createRecord(
+        originalSchema.getName() + "_encoder",
+        originalSchema.getDoc(),
+        originalSchema.getNamespace(),
+        originalSchema.isError(),
+        encoderFields);
+  }
+
   private static Field deepCopyField(Field field) {
     Schema.Field newField =
         new Schema.Field(
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableFilter.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableFilter.java
index 9646b91a0386..771ebad1a996 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableFilter.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableFilter.java
@@ -17,18 +17,18 @@
  */
 package org.apache.beam.sdk.extensions.sql.meta.provider.test;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.COMPARISON;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind.IN;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.COMPARISON;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind.IN;
 
 import java.util.ArrayList;
 import java.util.List;
 import java.util.stream.Collectors;
 import org.apache.beam.sdk.extensions.sql.meta.BeamSqlTableFilter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
 
 @SuppressWarnings({
   "nullness" // TODO(https://github.com/apache/beam/issues/20497)
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProvider.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProvider.java
index 5e0851a3685e..375cb42c4900 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProvider.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProvider.java
@@ -55,11 +55,11 @@
 import org.apache.beam.sdk.values.PDone;
 import org.apache.beam.sdk.values.POutput;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
 
 /**
  * Test in-memory table provider for use in tests.
@@ -113,7 +113,7 @@ public synchronized BeamSqlTable buildBeamSqlTable(Table table) {
   }
 
   public void addRows(String tableName, Row... rows) {
-    checkArgument(tables().containsKey(tableName), "Table not found: " + tableName);
+    checkArgument(tables().containsKey(tableName), "Table not found: %s", tableName);
     tables().get(tableName).rows.addAll(Arrays.asList(rows));
   }
 
diff --git a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestUnboundedTable.java b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestUnboundedTable.java
index 5433f9be380b..c18df6b0d3f8 100644
--- a/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestUnboundedTable.java
+++ b/sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestUnboundedTable.java
@@ -28,7 +28,7 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.sdk.values.TimestampedValue;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 import org.joda.time.Duration;
 import org.joda.time.Instant;
 
diff --git a/sdks/java/extensions/sql/src/main/resources/org.apache.beam.vendor.calcite.v1_28_0.org.codehaus.commons.compiler.properties b/sdks/java/extensions/sql/src/main/resources/org.apache.beam.vendor.calcite.v1_40_0.org.codehaus.commons.compiler.properties
similarity index 93%
rename from sdks/java/extensions/sql/src/main/resources/org.apache.beam.vendor.calcite.v1_28_0.org.codehaus.commons.compiler.properties
rename to sdks/java/extensions/sql/src/main/resources/org.apache.beam.vendor.calcite.v1_40_0.org.codehaus.commons.compiler.properties
index bc2e006e9bb6..980241fe8488 100644
--- a/sdks/java/extensions/sql/src/main/resources/org.apache.beam.vendor.calcite.v1_28_0.org.codehaus.commons.compiler.properties
+++ b/sdks/java/extensions/sql/src/main/resources/org.apache.beam.vendor.calcite.v1_40_0.org.codehaus.commons.compiler.properties
@@ -15,4 +15,4 @@
 #  See the License for the specific language governing permissions and
 # limitations under the License.
 ################################################################################
-compilerFactory=org.apache.beam.vendor.calcite.v1_28_0.org.codehaus.janino.CompilerFactory
+compilerFactory=org.apache.beam.vendor.calcite.v1_40_0.org.codehaus.janino.CompilerFactory
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamComplexTypeTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamComplexTypeTest.java
index a98732cba282..a5f78f715293 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamComplexTypeTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamComplexTypeTest.java
@@ -23,6 +23,7 @@
 import java.time.LocalTime;
 import java.util.Arrays;
 import java.util.HashMap;
+import java.util.List;
 import java.util.Map;
 import org.apache.beam.sdk.extensions.sql.impl.BeamSqlEnv;
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
@@ -43,6 +44,7 @@
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
 import org.joda.time.Duration;
 import org.joda.time.Instant;
 import org.junit.Ignore;
@@ -349,6 +351,44 @@ public void testNestedArrayOfBytes() {
     pipeline.run();
   }
 
+  @Test
+  public void testNestedDatetime() {
+    List<Instant> dateTimes =
+        ImmutableList.of(Instant.EPOCH, Instant.ofEpochSecond(10000), Instant.now());
+    List<Instant> nullDateTimes = Lists.newArrayList(Instant.EPOCH, null, Instant.now());
+
+    Schema nestedInputSchema =
+        Schema.of(
+            Schema.Field.of("c_dts", Schema.FieldType.array(Schema.FieldType.DATETIME)),
+            Schema.Field.of(
+                "c_null_dts",
+                Schema.FieldType.array(Schema.FieldType.DATETIME.withNullable(true))));
+    Schema inputSchema =
+        Schema.of(Schema.Field.of("nested", Schema.FieldType.row(nestedInputSchema)));
+
+    Schema outputSchema =
+        Schema.of(
+            Schema.Field.of("f0", Schema.FieldType.DATETIME),
+            Schema.Field.of("f1", Schema.FieldType.DATETIME.withNullable(true)));
+
+    Row nestedRow =
+        Row.withSchema(nestedInputSchema).addValue(dateTimes).addValue(nullDateTimes).build();
+    Row row = Row.withSchema(inputSchema).addValue(nestedRow).build();
+    Row expected =
+        Row.withSchema(outputSchema).addValues(dateTimes.get(1), nullDateTimes.get(1)).build();
+
+    PCollection<Row> result =
+        pipeline
+            .apply(Create.of(row).withRowSchema(inputSchema))
+            .apply(
+                SqlTransform.query(
+                    "SELECT t.nested.c_dts[2], t.nested.c_null_dts[2] AS f0 FROM PCOLLECTION t"));
+
+    PAssert.that(result).containsInAnyOrder(expected);
+
+    pipeline.run();
+  }
+
   @Test
   public void testRowConstructor() {
     BeamSqlEnv sqlEnv = BeamSqlEnv.inMemory(readOnlyTableProvider);
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlCliDatabaseTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlCliDatabaseTest.java
new file mode 100644
index 000000000000..0d93792bcad2
--- /dev/null
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlCliDatabaseTest.java
@@ -0,0 +1,101 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql;
+
+import static org.junit.Assert.assertEquals;
+
+import org.apache.beam.sdk.extensions.sql.meta.catalog.InMemoryCatalogManager;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.runtime.CalciteContextException;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableSet;
+import org.junit.Before;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+
+/** UnitTest for {@link BeamSqlCli} using databases. */
+public class BeamSqlCliDatabaseTest {
+  @Rule public transient ExpectedException thrown = ExpectedException.none();
+  private InMemoryCatalogManager catalogManager;
+  private BeamSqlCli cli;
+
+  @Before
+  public void setupCli() {
+    catalogManager = new InMemoryCatalogManager();
+    cli = new BeamSqlCli().catalogManager(catalogManager);
+  }
+
+  @Test
+  public void testCreateDatabase() {
+    cli.execute("CREATE DATABASE my_database");
+    assertEquals(
+        ImmutableSet.of("default", "my_database"), catalogManager.currentCatalog().listDatabases());
+  }
+
+  @Test
+  public void testCreateDuplicateDatabase_error() {
+    cli.execute("CREATE DATABASE my_database");
+    thrown.expect(CalciteContextException.class);
+    thrown.expectMessage("Database 'my_database' already exists.");
+    cli.execute("CREATE DATABASE my_database");
+  }
+
+  @Test
+  public void testCreateDuplicateDatabase_ifNotExists() {
+    cli.execute("CREATE DATABASE my_database");
+    cli.execute("CREATE DATABASE IF NOT EXISTS my_database");
+    assertEquals(
+        ImmutableSet.of("default", "my_database"), catalogManager.currentCatalog().listDatabases());
+  }
+
+  @Test
+  public void testUseDatabase() {
+    assertEquals("default", catalogManager.currentCatalog().currentDatabase());
+    cli.execute("CREATE DATABASE my_database");
+    cli.execute("CREATE DATABASE my_database2");
+    assertEquals("default", catalogManager.currentCatalog().currentDatabase());
+    cli.execute("USE DATABASE my_database");
+    assertEquals("my_database", catalogManager.currentCatalog().currentDatabase());
+    cli.execute("USE DATABASE my_database2");
+    assertEquals("my_database2", catalogManager.currentCatalog().currentDatabase());
+  }
+
+  @Test
+  public void testUseDatabase_doesNotExist() {
+    assertEquals("default", catalogManager.currentCatalog().currentDatabase());
+    thrown.expect(CalciteContextException.class);
+    thrown.expectMessage("Cannot use database: 'non_existent' not found.");
+    cli.execute("USE DATABASE non_existent");
+  }
+
+  @Test
+  public void testDropDatabase() {
+    cli.execute("CREATE DATABASE my_database");
+    assertEquals(
+        ImmutableSet.of("default", "my_database"), catalogManager.currentCatalog().listDatabases());
+    cli.execute("DROP DATABASE my_database");
+    assertEquals(ImmutableSet.of("default"), catalogManager.currentCatalog().listDatabases());
+  }
+
+  @Test
+  public void testDropDatabase_nonexistent() {
+    assertEquals(ImmutableSet.of("default"), catalogManager.currentCatalog().listDatabases());
+    thrown.expect(CalciteContextException.class);
+    thrown.expectMessage("Database 'my_database' does not exist.");
+    cli.execute("DROP DATABASE my_database");
+  }
+}
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlCliTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlCliTest.java
index b8aa030649ab..b8e6e90d680c 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlCliTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlCliTest.java
@@ -43,7 +43,7 @@
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.schemas.Schema.Field;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.runtime.CalciteContextException;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.runtime.CalciteContextException;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 import org.junit.Rule;
 import org.junit.Test;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlDslSqlStdOperatorsTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlDslSqlStdOperatorsTest.java
index cfa2df719679..90c8008c6d0a 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlDslSqlStdOperatorsTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlDslSqlStdOperatorsTest.java
@@ -44,10 +44,10 @@
 import org.apache.beam.sdk.extensions.sql.integrationtest.BeamSqlBuiltinFunctionsIntegrationTestBase;
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.schemas.Schema.FieldType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.runtime.SqlFunctions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.runtime.SqlFunctions;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Joiner;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
@@ -59,13 +59,14 @@
 
 /**
  * DSL compliance tests for the row-level operators of {@link
- * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable}.
+ * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.fun.SqlStdOperatorTable}.
  */
 public class BeamSqlDslSqlStdOperatorsTest extends BeamSqlBuiltinFunctionsIntegrationTestBase {
   private static final BigDecimal ZERO_0 = BigDecimal.valueOf(0).setScale(0, UNNECESSARY);
   private static final BigDecimal ZERO_1 = BigDecimal.valueOf(0).setScale(1, UNNECESSARY);
   private static final BigDecimal ONE_0 = BigDecimal.valueOf(1).setScale(0, UNNECESSARY);
   private static final BigDecimal ONE_1 = BigDecimal.valueOf(1).setScale(1, UNNECESSARY);
+  private static final BigDecimal ONE_2 = BigDecimal.valueOf(1).setScale(2, UNNECESSARY);
   private static final BigDecimal TWO_0 = BigDecimal.valueOf(2).setScale(0, UNNECESSARY);
   private static final BigDecimal TWO_1 = BigDecimal.valueOf(2).setScale(1, UNNECESSARY);
 
@@ -311,7 +312,7 @@ public void testArithmeticOperator() {
             .addExpr("c_double + c_bigint", 2.0)
             .addExpr("1 - 1", 0)
             .addExpr("1.0 - 1", ZERO_1)
-            .addExpr("1 - 0.0", ONE_0)
+            .addExpr("1 - 0.0", ONE_1)
             .addExpr("1.0 - 1.0", ZERO_1)
             .addExpr("c_tinyint - c_tinyint", (byte) 0)
             .addExpr("c_smallint - c_smallint", (short) 0)
@@ -326,9 +327,9 @@ public void testArithmeticOperator() {
             .addExpr("c_float - c_bigint", 0.0f)
             .addExpr("c_double - c_bigint", 0.0)
             .addExpr("1 * 1", 1)
-            .addExpr("1.0 * 1", ONE_0)
+            .addExpr("1.0 * 1", ONE_1)
             .addExpr("1 * 1.0", ONE_1)
-            .addExpr("1.0 * 1.0", ONE_1)
+            .addExpr("1.0 * 1.0", ONE_2)
             .addExpr("c_tinyint * c_tinyint", (byte) 1)
             .addExpr("c_smallint * c_smallint", (short) 1)
             .addExpr("c_bigint * c_bigint", 1L)
@@ -366,11 +367,11 @@ public void testArithmeticOperator() {
             .addExpr("mod(c_bigint, c_bigint)", 0L)
             .addExpr("mod(c_decimal, c_decimal)", ZERO_0)
             .addExpr("mod(c_tinyint, c_decimal)", ZERO_0)
-            // Test overflow
-            .addExpr("c_tinyint_max + c_tinyint_max", (byte) -2)
-            .addExpr("c_smallint_max + c_smallint_max", (short) -2)
-            .addExpr("c_integer_max + c_integer_max", -2)
-            .addExpr("c_bigint_max + c_bigint_max", -2L);
+            // conversions
+            .addExpr("c_tinyint_max + c_smallint", (short) 128)
+            .addExpr("c_integer + c_smallint_max", 32768)
+            .addExpr("c_integer_max + c_bigint", 2147483648L)
+            .addExpr("c_smallint - c_integer_max", -2147483646);
 
     checker.buildRunAndCheck();
   }
@@ -708,8 +709,8 @@ public void testAggrationFunctions() {
 
   @Test
   @SqlOperatorTests({
-    @SqlOperatorTest(name = "CHARACTER_LENGTH", kind = "OTHER_FUNCTION"),
-    @SqlOperatorTest(name = "CHAR_LENGTH", kind = "OTHER_FUNCTION"),
+    @SqlOperatorTest(name = "CHARACTER_LENGTH", kind = "CHAR_LENGTH"),
+    @SqlOperatorTest(name = "CHAR_LENGTH", kind = "CHAR_LENGTH"),
     @SqlOperatorTest(name = "INITCAP", kind = "OTHER_FUNCTION"),
     @SqlOperatorTest(name = "LOWER", kind = "OTHER_FUNCTION"),
     @SqlOperatorTest(name = "POSITION", kind = "POSITION"),
@@ -1129,8 +1130,7 @@ public void testBasicDateTimeFunctions() {
   }
 
   @Test
-  // https://github.com/apache/beam/issues/19001
-  // @SqlOperatorTest(name = "FLOOR", kind = "FLOOR")
+  @SqlOperatorTest(name = "FLOOR", kind = "FLOOR")
   public void testFloor() {
     ExpressionChecker checker =
         new ExpressionChecker()
@@ -1140,14 +1140,14 @@ public void testFloor() {
             .addExpr("FLOOR(ts TO DAY)", parseTimestampWithUTCTimeZone("1986-02-15 00:00:00"))
             .addExpr("FLOOR(ts TO MONTH)", parseTimestampWithUTCTimeZone("1986-02-01 00:00:00"))
             .addExpr("FLOOR(ts TO YEAR)", parseTimestampWithUTCTimeZone("1986-01-01 00:00:00"))
-            .addExpr("FLOOR(c_double)", 1.0);
+            .addExpr("FLOOR(c_double)", 1.0)
+            .addExpr("FLOOR(-c_double)", -2.0);
 
     checker.buildRunAndCheck(getFloorCeilingTestPCollection());
   }
 
   @Test
-  // https://github.com/apache/beam/issues/19001
-  // @SqlOperatorTest(name = "CEIL", kind = "CEIL")
+  @SqlOperatorTest(name = "CEIL", kind = "CEIL")
   public void testCeil() {
     ExpressionChecker checker =
         new ExpressionChecker()
@@ -1157,7 +1157,8 @@ public void testCeil() {
             .addExpr("CEIL(ts TO DAY)", parseTimestampWithUTCTimeZone("1986-02-16 00:00:00"))
             .addExpr("CEIL(ts TO MONTH)", parseTimestampWithUTCTimeZone("1986-03-01 00:00:00"))
             .addExpr("CEIL(ts TO YEAR)", parseTimestampWithUTCTimeZone("1987-01-01 00:00:00"))
-            .addExpr("CEIL(c_double)", 2.0);
+            .addExpr("CEIL(c_double)", 2.0)
+            .addExpr("CEIL(-c_double)", -1.0);
 
     checker.buildRunAndCheck(getFloorCeilingTestPCollection());
   }
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlDslUdfUdafTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlDslUdfUdafTest.java
index 991d4d260fd9..41288dd21e36 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlDslUdfUdafTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlDslUdfUdafTest.java
@@ -48,8 +48,8 @@
 import org.apache.beam.sdk.values.PCollectionTuple;
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.sdk.values.TupleTag;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.function.Parameter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.TranslatableTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.function.Parameter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.TranslatableTable;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 import org.joda.time.Instant;
 import org.junit.Test;
@@ -302,7 +302,7 @@ public void testBeamSqlUdfWithDefaultParameters() throws Exception {
   }
 
   /**
-   * test {@link org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.TableMacro} UDF.
+   * test {@link org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.TableMacro} UDF.
    */
   @Test
   public void testTableMacroUdf() throws Exception {
@@ -503,7 +503,7 @@ public static Integer eval(java.util.List<Long> i) {
 
   /**
    * UDF to test support for {@link
-   * org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.TableMacro}.
+   * org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.TableMacro}.
    */
   public static final class RangeUdf implements BeamSqlUdf {
     public static TranslatableTable eval(int startInclusive, int endExclusive) {
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlExplainTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlExplainTest.java
index 06e30936b4c3..fa891914db1b 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlExplainTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/BeamSqlExplainTest.java
@@ -21,9 +21,9 @@
 
 import org.apache.beam.sdk.extensions.sql.meta.provider.text.TextTableProvider;
 import org.apache.beam.sdk.extensions.sql.meta.store.InMemoryMetaStore;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParseException;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelConversionException;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.ValidationException;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParseException;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RelConversionException;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.ValidationException;
 import org.junit.Before;
 import org.junit.Ignore;
 
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/TypedCombineFnDelegateTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/TypedCombineFnDelegateTest.java
index 70938ff9d692..5dc194d48167 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/TypedCombineFnDelegateTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/TypedCombineFnDelegateTest.java
@@ -25,11 +25,11 @@
 import org.apache.beam.sdk.extensions.sql.impl.UdafImpl;
 import org.apache.beam.sdk.transforms.Combine;
 import org.apache.beam.sdk.transforms.Max;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.JavaTypeFactoryImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeSystem;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.FunctionParameter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.JavaTypeFactoryImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeSystem;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.FunctionParameter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
 import org.junit.Rule;
 import org.junit.Test;
 import org.junit.rules.ExpectedException;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/JdbcDriverTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/JdbcDriverTest.java
index e83ee61af4ab..b9aa4ae2ecc7 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/JdbcDriverTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/JdbcDriverTest.java
@@ -50,9 +50,9 @@
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.util.ReleaseInfo;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteConnection;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteConnection;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaPlus;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 import org.joda.time.DateTime;
 import org.joda.time.Duration;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/LazyAggregateCombineFnTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/LazyAggregateCombineFnTest.java
index fda230756c24..17636c628eb8 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/LazyAggregateCombineFnTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/LazyAggregateCombineFnTest.java
@@ -28,12 +28,12 @@
 import org.apache.beam.sdk.coders.CoderRegistry;
 import org.apache.beam.sdk.coders.VarLongCoder;
 import org.apache.beam.sdk.extensions.sql.udf.AggregateFn;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.JavaTypeFactoryImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeSystem;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.AggregateFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.FunctionParameter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.JavaTypeFactoryImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeSystem;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.AggregateFunction;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.FunctionParameter;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.junit.Rule;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamDDLNestedTypesTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamDDLNestedTypesTest.java
index 6876caff3274..e9daf57816bf 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamDDLNestedTypesTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamDDLNestedTypesTest.java
@@ -32,7 +32,7 @@
 import org.apache.beam.sdk.extensions.sql.utils.QuickCheckGenerators.PrimitiveTypes;
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.schemas.Schema.FieldType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParseException;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParseException;
 import org.junit.runner.RunWith;
 
 /**
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamDDLTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamDDLTest.java
index 704a9d4586e1..518a830041e2 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamDDLTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/parser/BeamDDLTest.java
@@ -35,12 +35,12 @@
 import org.apache.beam.sdk.extensions.sql.meta.provider.test.TestTableProvider;
 import org.apache.beam.sdk.options.PipelineOptionsFactory;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.pretty.SqlPrettyWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlWriter;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParserPos;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.pretty.SqlPrettyWriter;
 import org.junit.Test;
 
 /** UnitTest for {@link BeamSqlParserImpl}. */
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/planner/NodeStatsTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/planner/NodeStatsTest.java
index 17611fa12f19..95bf8e1ea9d6 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/planner/NodeStatsTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/planner/NodeStatsTest.java
@@ -21,11 +21,11 @@
 import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
 import org.apache.beam.sdk.extensions.sql.meta.provider.test.TestBoundedTable;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.volcano.RelSubset;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.SingleRel;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.volcano.RelSubset;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.SingleRel;
 import org.junit.Assert;
 import org.junit.BeforeClass;
 import org.junit.Test;
@@ -62,7 +62,7 @@ public static void prepare() {
   public void testUnknownRel() {
     String sql = " select * from ORDER_DETAILS1 ";
     RelNode root = env.parseQuery(sql);
-    RelNode unknown = new UnknownRel(root.getCluster(), null, null);
+    RelNode unknown = new UnknownRel(root.getCluster(), RelTraitSet.createEmpty(), null);
     NodeStats nodeStats =
         unknown
             .metadata(NodeStatsMetadata.class, unknown.getCluster().getMetadataQuery())
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamAggregationRelTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamAggregationRelTest.java
index d227113cd08b..3692c2514c62 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamAggregationRelTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamAggregationRelTest.java
@@ -24,7 +24,7 @@
 import org.apache.beam.sdk.extensions.sql.meta.provider.test.TestBoundedTable;
 import org.apache.beam.sdk.extensions.sql.meta.provider.test.TestUnboundedTable;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.joda.time.DateTime;
 import org.joda.time.Duration;
 import org.junit.Assert;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCalcRelTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCalcRelTest.java
index 5d53ca364075..8aeb77fc0490 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCalcRelTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCalcRelTest.java
@@ -33,7 +33,7 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PValue;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
 import org.joda.time.DateTime;
 import org.joda.time.Duration;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRelBoundedVsBoundedTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRelBoundedVsBoundedTest.java
index cbaf56b09d41..9a0977e9018c 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRelBoundedVsBoundedTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRelBoundedVsBoundedTest.java
@@ -28,7 +28,7 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.hamcrest.core.StringContains;
 import org.junit.Assert;
 import org.junit.BeforeClass;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRelUnboundedVsUnboundedTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRelUnboundedVsUnboundedTest.java
index efe8516240ad..0caa08d0a634 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRelUnboundedVsUnboundedTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamCoGBKJoinRelUnboundedVsUnboundedTest.java
@@ -29,7 +29,7 @@
 import org.apache.beam.sdk.transforms.ParDo;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.joda.time.DateTime;
 import org.joda.time.Duration;
 import org.junit.Assert;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamEnumerableConverterTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamEnumerableConverterTest.java
index ffb9b6064c5d..99915b33bf8b 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamEnumerableConverterTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamEnumerableConverterTest.java
@@ -38,19 +38,20 @@
 import org.apache.beam.sdk.values.PDone;
 import org.apache.beam.sdk.values.POutput;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.java.JavaTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.JavaTypeFactoryImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.Enumerable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.Enumerator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.volcano.VolcanoPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.prepare.RelOptTableImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableModify.Operation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeSystem;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.com.google.common.collect.ImmutableList;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.java.JavaTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.JavaTypeFactoryImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.Enumerable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.Enumerator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptCluster;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.volcano.VolcanoPlanner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.prepare.RelOptTableImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.TableModify.Operation;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeSystem;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexBuilder;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
 import org.junit.Test;
 import org.junit.experimental.categories.Category;
 import org.junit.runner.RunWith;
@@ -74,7 +75,7 @@ public void testToEnumerable_collectSingle() {
       RelDataType type = CalciteUtils.toCalciteRowType(schema, TYPE_FACTORY);
       ImmutableList<ImmutableList<RexLiteral>> tuples =
           ImmutableList.of(ImmutableList.of(rexBuilder.makeBigintLiteral(BigDecimal.ZERO)));
-      BeamRelNode node = new BeamValuesRel(cluster, type, tuples, null);
+      BeamRelNode node = new BeamValuesRel(cluster, type, tuples, RelTraitSet.createEmpty());
 
       Enumerable<Object> enumerable = BeamEnumerableConverter.toEnumerable(options, node);
       Enumerator<Object> enumerator = enumerable.enumerator();
@@ -94,7 +95,7 @@ public void testToEnumerable_collectMultiple() {
               ImmutableList.of(
                   rexBuilder.makeBigintLiteral(BigDecimal.ZERO),
                   rexBuilder.makeBigintLiteral(BigDecimal.ONE)));
-      BeamRelNode node = new BeamValuesRel(cluster, type, tuples, null);
+      BeamRelNode node = new BeamValuesRel(cluster, type, tuples, RelTraitSet.createEmpty());
 
       Enumerable<Object> enumerable = BeamEnumerableConverter.toEnumerable(options, node);
       Enumerator<Object> enumerator = enumerable.enumerator();
@@ -117,7 +118,7 @@ public void testToListRow_collectMultiple() {
               ImmutableList.of(
                   rexBuilder.makeBigintLiteral(BigDecimal.ZERO),
                   rexBuilder.makeBigintLiteral(BigDecimal.ONE)));
-      BeamRelNode node = new BeamValuesRel(cluster, type, tuples, null);
+      BeamRelNode node = new BeamValuesRel(cluster, type, tuples, RelTraitSet.createEmpty());
 
       List<Row> rowList = BeamEnumerableConverter.toRowList(options, node);
       assertTrue(rowList.size() == 1);
@@ -164,7 +165,7 @@ public void testToEnumerable_count() {
               cluster,
               RelOptTableImpl.create(null, type, ImmutableList.of(), null),
               null,
-              new BeamValuesRel(cluster, type, tuples, null),
+              new BeamValuesRel(cluster, type, tuples, RelTraitSet.createEmpty()),
               Operation.INSERT,
               null,
               null,
@@ -222,7 +223,7 @@ public void testToEnumerable_collectNullValue() {
           ImmutableList.of(
               ImmutableList.of(
                   rexBuilder.makeNullLiteral(CalciteUtils.toRelDataType(TYPE_FACTORY, fieldType))));
-      BeamRelNode node = new BeamValuesRel(cluster, type, tuples, null);
+      BeamRelNode node = new BeamValuesRel(cluster, type, tuples, RelTraitSet.createEmpty());
 
       Enumerable<Object> enumerable = BeamEnumerableConverter.toEnumerable(options, node);
       Enumerator<Object> enumerator = enumerable.enumerator();
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSourceRelTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSourceRelTest.java
index 174c5fbe1d41..beb6db955229 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSourceRelTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIOSourceRelTest.java
@@ -25,8 +25,8 @@
 import org.apache.beam.sdk.extensions.sql.meta.provider.test.TestUnboundedTable;
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
 import org.joda.time.DateTime;
 import org.joda.time.Duration;
 import org.junit.Assert;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIntersectRelTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIntersectRelTest.java
index 3baf1e471480..0dc39ccfa173 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIntersectRelTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamIntersectRelTest.java
@@ -27,7 +27,7 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.junit.Assert;
 import org.junit.BeforeClass;
 import org.junit.Rule;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMinusRelTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMinusRelTest.java
index 21d01df18827..3c1c40ad4532 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMinusRelTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamMinusRelTest.java
@@ -29,7 +29,7 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.joda.time.DateTime;
 import org.joda.time.Duration;
 import org.junit.Assert;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputJoinRelTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputJoinRelTest.java
index c5f8f28bfa6d..581cf6c005c2 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputJoinRelTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSideInputJoinRelTest.java
@@ -29,7 +29,7 @@
 import org.apache.beam.sdk.transforms.ParDo;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.joda.time.DateTime;
 import org.joda.time.Duration;
 import org.junit.Assert;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSortRelTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSortRelTest.java
index e2fa12350452..dbe8be441ac6 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSortRelTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamSortRelTest.java
@@ -26,7 +26,7 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.joda.time.DateTime;
 import org.junit.Assert;
 import org.junit.Before;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUncollectRelTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUncollectRelTest.java
index 98791ead1228..2feaad7bf06a 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUncollectRelTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUncollectRelTest.java
@@ -28,7 +28,7 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.junit.Assert;
 import org.junit.Rule;
 import org.junit.Test;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnionRelTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnionRelTest.java
index af81a5b97de5..ed2b5df68358 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnionRelTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamUnionRelTest.java
@@ -27,7 +27,7 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.junit.Assert;
 import org.junit.BeforeClass;
 import org.junit.Rule;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamValuesRelTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamValuesRelTest.java
index 1ae169f5d062..bc21cd443cde 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamValuesRelTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamValuesRelTest.java
@@ -26,7 +26,7 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
 import org.junit.Assert;
 import org.junit.BeforeClass;
 import org.junit.Rule;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rule/IOPushDownRuleTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rule/IOPushDownRuleTest.java
index 32f59ddad79b..e9b0a995297e 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rule/IOPushDownRuleTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rule/IOPushDownRuleTest.java
@@ -36,13 +36,13 @@
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CoreRules;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSets;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.com.google.common.collect.ImmutableList;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Calc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.CoreRules;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSets;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.Pair;
 import org.junit.Before;
 import org.junit.Rule;
 import org.junit.Test;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rule/JoinReorderingTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rule/JoinReorderingTest.java
index 77de4cdec0f9..92b77ec9efbd 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rule/JoinReorderingTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/rule/JoinReorderingTest.java
@@ -30,41 +30,41 @@
 import org.apache.beam.sdk.extensions.sql.meta.provider.test.TestTableProvider;
 import org.apache.beam.sdk.options.PipelineOptionsFactory;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.DataContext;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.EnumerableConvention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.EnumerableRules;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.Enumerable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.Linq4j;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.ConventionTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollationTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollations;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelFieldCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelRoot;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Join;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.TableScan;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CoreRules;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.JoinCommuteRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.ScannableTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Statistic;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Statistics;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Table;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.impl.AbstractSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.impl.AbstractTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParser;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.FrameworkConfig;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Frameworks;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Planner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Programs;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSets;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.ImmutableBitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.DataContext;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.EnumerableConvention;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.adapter.enumerable.EnumerableRules;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.Enumerable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.linq4j.Linq4j;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.ConventionTraitDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelTraitSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelCollationTraitDef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelCollations;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelFieldCollation;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.RelRoot;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Join;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.TableScan;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.CoreRules;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.JoinCommuteRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.ScannableTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.SchemaPlus;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Statistic;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Statistics;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Table;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.impl.AbstractSchema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.impl.AbstractTable;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParser;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.FrameworkConfig;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.Frameworks;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.Planner;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.Programs;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSet;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSets;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.ImmutableBitSet;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 import org.junit.Assert;
@@ -415,7 +415,7 @@ public ThreeTablesSchema() {
   }
 
   @Override
-  protected Map<String, org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Table>
+  protected Map<String, org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.schema.Table>
       getTableMap() {
     return tables;
   }
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/schema/BeamSqlRowCoderTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/schema/BeamSqlRowCoderTest.java
index cc4e8c6b0b5e..41cd9ed2d7ef 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/schema/BeamSqlRowCoderTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/schema/BeamSqlRowCoderTest.java
@@ -27,10 +27,10 @@
 import org.apache.beam.sdk.schemas.SchemaCoder;
 import org.apache.beam.sdk.testing.CoderProperties;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.JavaTypeFactoryImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeSystem;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.JavaTypeFactoryImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeSystem;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
 import org.joda.time.DateTime;
 import org.junit.Test;
 
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/utils/CalciteUtilsTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/utils/CalciteUtilsTest.java
index e51a1b2dc6c8..6c85c3582e95 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/utils/CalciteUtilsTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/impl/utils/CalciteUtilsTest.java
@@ -24,11 +24,11 @@
 import java.util.Map;
 import java.util.stream.Collectors;
 import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeSystem;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeFactoryImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataType;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeFactory;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.type.RelDataTypeSystem;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeFactoryImpl;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
 import org.junit.Before;
 import org.junit.Rule;
 import org.junit.Test;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/integrationtest/BeamSqlDateFunctionsIntegrationTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/integrationtest/BeamSqlDateFunctionsIntegrationTest.java
index d612c9d19145..559aeb04cfb6 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/integrationtest/BeamSqlDateFunctionsIntegrationTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/integrationtest/BeamSqlDateFunctionsIntegrationTest.java
@@ -17,8 +17,8 @@
  */
 package org.apache.beam.sdk.extensions.sql.integrationtest;
 
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.DateTimeUtils.MILLIS_PER_DAY;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.DateTimeUtils.MILLIS_PER_SECOND;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.util.DateTimeUtils.MILLIS_PER_DAY;
+import static org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.util.DateTimeUtils.MILLIS_PER_SECOND;
 import static org.junit.Assert.assertFalse;
 import static org.junit.Assert.assertTrue;
 
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/CustomTableResolverTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/CustomTableResolverTest.java
index 7f9d52f02906..b19026af72c6 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/CustomTableResolverTest.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/CustomTableResolverTest.java
@@ -32,7 +32,7 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.joda.time.Duration;
 import org.junit.Rule;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorTableProviderTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorTableProviderTest.java
new file mode 100644
index 000000000000..fdd5e4fd883c
--- /dev/null
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/datagen/DataGeneratorTableProviderTest.java
@@ -0,0 +1,379 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.meta.provider.datagen;
+
+import java.math.BigDecimal;
+import org.apache.beam.sdk.extensions.sql.SqlTransform;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.testing.PAssert;
+import org.apache.beam.sdk.testing.TestPipeline;
+import org.apache.beam.sdk.transforms.Count;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollection.IsBounded;
+import org.apache.beam.sdk.values.Row;
+import org.joda.time.Instant;
+import org.junit.Assert;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+
+/** Unit tests for the {@link DataGeneratorTableProvider}. */
+public class DataGeneratorTableProviderTest {
+
+  @Rule public TestPipeline pipeline = TestPipeline.create();
+  @Rule public ExpectedException thrown = ExpectedException.none();
+
+  @Test
+  public void testBoundedGeneration() {
+    String createDdl =
+        "CREATE EXTERNAL TABLE bounded_table (\n"
+            + "  id BIGINT,\n"
+            + "  name VARCHAR\n"
+            + ") TYPE 'datagen' TBLPROPERTIES '{\n"
+            + "  \"number-of-rows\": \"100\"\n"
+            + "}'";
+
+    PCollection<Row> result =
+        pipeline.apply(
+            "testBoundedGeneration",
+            SqlTransform.query("SELECT * FROM bounded_table").withDdlString(createDdl));
+
+    Assert.assertEquals(IsBounded.BOUNDED, result.isBounded());
+    PAssert.that(result.apply(Count.globally())).containsInAnyOrder(100L);
+
+    pipeline.run().waitUntilFinish();
+  }
+
+  private static class ValidateFieldsFn extends DoFn<Row, Void> {
+    private final long startId;
+    private final long endId;
+    private final int nameLength;
+    private final double minScore;
+    private final double maxScore;
+
+    ValidateFieldsFn(long startId, long endId, int nameLength, double minScore, double maxScore) {
+      this.startId = startId;
+      this.endId = endId;
+      this.nameLength = nameLength;
+      this.minScore = minScore;
+      this.maxScore = maxScore;
+    }
+
+    @ProcessElement
+    public void processElement(@Element Row row) {
+      Long eventId = row.getInt64("event_id");
+      Assert.assertTrue("Event ID should be within range", eventId >= startId && eventId <= endId);
+
+      String eventName = row.getString("event_name");
+      Assert.assertEquals("Event name should have correct length", nameLength, eventName.length());
+
+      Double score = row.getDouble("score");
+      Assert.assertTrue("Score should be within range", score >= minScore && score <= maxScore);
+    }
+  }
+
+  @Test
+  public void testFieldGenerators() {
+    String createDdl =
+        "CREATE EXTERNAL TABLE complex_table (\n"
+            + "  event_id BIGINT,\n"
+            + "  event_name VARCHAR,\n"
+            + "  score DOUBLE\n"
+            + ") TYPE 'datagen' TBLPROPERTIES '{\n"
+            + "  \"number-of-rows\": \"10\",\n"
+            + "  \"fields.event_id.kind\": \"sequence\",\n"
+            + "  \"fields.event_id.start\": \"100\",\n"
+            + "  \"fields.event_id.end\": \"109\",\n"
+            + "  \"fields.event_name.kind\": \"random\",\n"
+            + "  \"fields.event_name.length\": \"15\",\n"
+            + "  \"fields.score.kind\": \"random\",\n"
+            + "  \"fields.score.min\": \"50.0\",\n"
+            + "  \"fields.score.max\": \"100.0\"\n"
+            + "}'";
+
+    PCollection<Row> result =
+        pipeline.apply(
+            "testFieldGenerators",
+            SqlTransform.query("SELECT * FROM complex_table").withDdlString(createDdl));
+
+    result.apply("ValidateFields", ParDo.of(new ValidateFieldsFn(100, 109, 15, 50.0, 100.0)));
+    pipeline.run().waitUntilFinish();
+  }
+
+  private static class ValidateNullsFn extends DoFn<Row, Void> {
+    @ProcessElement
+    public void processElement(@Element Row row) {
+      Assert.assertNull("Field should be null", row.getValue("nullable_field"));
+      Assert.assertNotNull("Field should not be null", row.getInt64("non_nullable_field"));
+    }
+  }
+
+  @Test
+  public void testNullRate() {
+    String createDdl =
+        "CREATE EXTERNAL TABLE null_rate_table (\n"
+            + "  nullable_field VARCHAR,\n"
+            + "  non_nullable_field BIGINT\n"
+            + ") TYPE 'datagen' TBLPROPERTIES '{\n"
+            + "  \"number-of-rows\": \"50\",\n"
+            + "  \"fields.nullable_field.null-rate\": \"1.0\",\n"
+            + "  \"fields.non_nullable_field.kind\": \"sequence\"\n"
+            + "}'";
+
+    PCollection<Row> result =
+        pipeline.apply(
+            "testNullRate",
+            SqlTransform.query("SELECT * FROM null_rate_table").withDdlString(createDdl));
+
+    result.apply("ValidateNulls", ParDo.of(new ValidateNullsFn()));
+
+    pipeline.run().waitUntilFinish();
+  }
+
+  private static class ValidateAllTypesFn extends DoFn<Row, Void> {
+    @ProcessElement
+    public void processElement(@Element Row row) {
+      Assert.assertNotNull(row.getBoolean("is_active"));
+
+      BigDecimal cost = row.getDecimal("cost");
+      Assert.assertTrue("Cost should be >= 10.50", cost.compareTo(BigDecimal.valueOf(10.50)) >= 0);
+      Assert.assertTrue("Cost should be <= 99.99", cost.compareTo(BigDecimal.valueOf(99.99)) <= 0);
+
+      Instant pastTimestamp = row.getDateTime("past_timestamp").toInstant();
+      Instant nowTimestamp = row.getDateTime("now_timestamp").toInstant();
+
+      Assert.assertTrue(
+          "'now_timestamp' should be generated after 'past_timestamp'",
+          nowTimestamp.isAfter(pastTimestamp));
+    }
+  }
+
+  @Test
+  public void testAllDataTypes() {
+    String createDdl =
+        "CREATE EXTERNAL TABLE all_types_table (\n"
+            + "  is_active BOOLEAN,\n"
+            + "  cost DECIMAL,\n"
+            + "  past_timestamp TIMESTAMP,\n"
+            + "  now_timestamp TIMESTAMP\n"
+            + ") TYPE 'datagen' TBLPROPERTIES '{\n"
+            + "  \"number-of-rows\": \"10\",\n"
+            + "  \"fields.cost.min\": \"10.50\",\n"
+            + "  \"fields.cost.max\": \"99.99\",\n"
+            + "  \"fields.past_timestamp.max-past\": \"3600000\"\n"
+            + "}'";
+
+    pipeline
+        .apply(
+            "testAllDataTypes",
+            SqlTransform.query("SELECT * FROM all_types_table").withDdlString(createDdl))
+        .apply("ValidateAllTypes", ParDo.of(new ValidateAllTypesFn()));
+
+    pipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testMissingRequiredPropertyThrowsException() {
+    String createDdl = "CREATE EXTERNAL TABLE bad_table (id INT) TYPE 'datagen' TBLPROPERTIES '{}'";
+
+    thrown.expect(IllegalArgumentException.class);
+    thrown.expectMessage(
+        "A 'datagen' table requires either 'rows-per-second' (for unbounded) or 'number-of-rows' (for bounded) in TBLPROPERTIES.");
+
+    pipeline.apply(
+        "testMissingRequiredProperty",
+        SqlTransform.query("SELECT * FROM bad_table").withDdlString(createDdl));
+    pipeline.run().waitUntilFinish();
+  }
+
+  /**
+   * Tests the default processing-time behavior to ensure it still works correctly and that
+   * event-time configuration is not required.
+   */
+  @Test
+  public void testProcessingTimeBehavior() {
+    String createDdl =
+        "CREATE EXTERNAL TABLE processing_time_table (\n"
+            + "  id BIGINT\n"
+            + ") TYPE 'datagen' TBLPROPERTIES '{\n"
+            + "  \"number-of-rows\": \"20\"\n"
+            + "}'";
+
+    PCollection<Row> result =
+        pipeline.apply(
+            "testProcessingTimeBehavior",
+            SqlTransform.query("SELECT * FROM processing_time_table").withDdlString(createDdl));
+
+    PAssert.that(result.apply("CountRows", Count.globally())).containsInAnyOrder(20L);
+
+    pipeline.run().waitUntilFinish();
+  }
+
+  private static class ValidateMultiTimestampFn extends DoFn<Row, Void> {
+    @ProcessElement
+    public void processElement(@Element Row row, @Timestamp Instant rowTs) {
+      Instant mainEventTime = row.getDateTime("main_event_time").toInstant();
+      Instant secondaryTime = row.getDateTime("secondary_time").toInstant();
+      Assert.assertEquals(mainEventTime, rowTs);
+
+      Assert.assertNotEquals(mainEventTime, secondaryTime);
+    }
+  }
+
+  /**
+   * Verifies that a table with multiple timestamp columns works correctly, with one column driving
+   * the watermark and the other being populated independently.
+   */
+  @Test
+  public void testMultipleTimestampColumns() {
+    String createDdl =
+        "CREATE EXTERNAL TABLE multi_ts_table (\n"
+            + "  main_event_time TIMESTAMP,\n"
+            + "  secondary_time TIMESTAMP\n"
+            + ") TYPE 'datagen' TBLPROPERTIES '{\n"
+            + "  \"number-of-rows\": \"10\",\n"
+            + "  \"timestamp.behavior\": \"event-time\",\n"
+            + "  \"event-time.timestamp-column\": \"main_event_time\",\n"
+            + "  \"fields.secondary_time.kind\": \"datetime\",\n"
+            + "  \"fields.secondary_time.now\": \"true\"\n"
+            + "}'";
+
+    PCollection<Row> result =
+        pipeline.apply(
+            "testMultiTimestamp",
+            SqlTransform.query("SELECT * FROM multi_ts_table").withDdlString(createDdl));
+
+    result.apply("ValidateTimestamps", ParDo.of(new ValidateMultiTimestampFn()));
+
+    pipeline.run().waitUntilFinish();
+  }
+
+  /**
+   * Ensures that a misconfiguration (specifying event-time behavior without the required column)
+   * throws a descriptive error.
+   */
+  @Test
+  public void testEventTimeMissingColumnThrowsException() {
+    String createDdl =
+        "CREATE EXTERNAL TABLE bad_event_time_table (\n"
+            + "  ts TIMESTAMP\n"
+            + ") TYPE 'datagen' TBLPROPERTIES '{\n"
+            + "  \"number-of-rows\": \"10\",\n"
+            + "  \"timestamp.behavior\": \"event-time\"\n"
+            + "}'";
+
+    thrown.expect(IllegalArgumentException.class);
+    thrown.expectMessage(
+        "For 'event-time' behavior, 'event-time.timestamp-column' must be specified.");
+
+    pipeline.apply(
+        "testMissingEventTimeColumn",
+        SqlTransform.query("SELECT * FROM bad_event_time_table").withDdlString(createDdl));
+    pipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testEventTimeColumnNotFoundThrowsException() {
+    String createDdl =
+        "CREATE EXTERNAL TABLE bad_ts_table (id BIGINT) "
+            + "TYPE 'datagen' TBLPROPERTIES '{"
+            + "  \"rows-per-second\": \"10\","
+            + "  \"timestamp.behavior\": \"event-time\","
+            + "  \"event-time.timestamp-column\": \"ts\"" // "ts" does not exist
+            + "}'";
+
+    thrown.expect(IllegalArgumentException.class);
+    thrown.expectMessage("does not exist in the table schema");
+
+    pipeline.apply(
+        "testEventTimeColumnNotFound",
+        SqlTransform.query("SELECT * FROM bad_ts_table").withDdlString(createDdl));
+    pipeline.run();
+  }
+
+  @Test
+  public void testEventTimeColumnWrongTypeThrowsException() {
+    String createDdl =
+        "CREATE EXTERNAL TABLE bad_ts_table (ts VARCHAR) "
+            + "TYPE 'datagen' TBLPROPERTIES '{"
+            + "  \"rows-per-second\": \"10\","
+            + "  \"timestamp.behavior\": \"event-time\","
+            + "  \"event-time.timestamp-column\": \"ts\""
+            + "}'";
+
+    thrown.expect(IllegalArgumentException.class);
+    thrown.expectMessage("must be of type TIMESTAMP, but was");
+
+    pipeline.apply(
+        "testEventTimeColumnWrongType",
+        SqlTransform.query("SELECT * FROM bad_ts_table").withDdlString(createDdl));
+    pipeline.run();
+  }
+
+  @Test
+  public void testSequenceOnWrongTypeThrowsException() {
+    String createDdl =
+        "CREATE EXTERNAL TABLE bad_seq_table (name VARCHAR) "
+            + "TYPE 'datagen' TBLPROPERTIES '{"
+            + "  \"number-of-rows\": \"10\","
+            + "  \"fields.name.kind\": \"sequence\""
+            + "}'";
+
+    thrown.expectMessage("generator for integers only supports integer types");
+
+    pipeline.apply(
+        "testSequenceOnWrongType",
+        SqlTransform.query("SELECT * FROM bad_seq_table").withDdlString(createDdl));
+    pipeline.run();
+  }
+
+  @Test
+  public void testEventTimeWatermarkAdvances() {
+    String createDdl =
+        "CREATE EXTERNAL TABLE unbounded_table (\n"
+            + "  ts TIMESTAMP,\n"
+            + "  id BIGINT\n"
+            + ") TYPE 'datagen' TBLPROPERTIES '{\n"
+            + "  \"number-of-rows\": \"5\",\n"
+            + "  \"timestamp.behavior\": \"event-time\",\n"
+            + "  \"event-time.timestamp-column\": \"ts\",\n"
+            + "  \"fields.id.kind\": \"sequence\"\n"
+            + "}'";
+
+    String sql = "SELECT COUNT(id) FROM unbounded_table GROUP BY TUMBLE(ts, INTERVAL '2' SECOND)";
+
+    PCollection<Row> results =
+        pipeline.apply("testWatermarkAdvances", SqlTransform.query(sql).withDdlString(createDdl));
+
+    PAssert.that(results)
+        .containsInAnyOrder(
+            Row.withSchema(Schema.of(Schema.Field.of("c0", Schema.FieldType.INT64)))
+                .addValue(2L) // for window [0s, 2s)
+                .build(),
+            Row.withSchema(Schema.of(Schema.Field.of("c0", Schema.FieldType.INT64)))
+                .addValue(2L) // for window [2s, 4s)
+                .build(),
+            Row.withSchema(Schema.of(Schema.Field.of("c0", Schema.FieldType.INT64)))
+                .addValue(1L) // for window [4s, 6s)
+                .build());
+
+    pipeline.run().waitUntilFinish();
+  }
+}
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/datastore/DataStoreReadWriteIT.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/datastore/DataStoreReadWriteIT.java
index 1937c6ea9efd..ea9cf4a02e54 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/datastore/DataStoreReadWriteIT.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/datastore/DataStoreReadWriteIT.java
@@ -50,7 +50,7 @@
 import org.apache.beam.sdk.transforms.Create;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.ByteString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.avatica.util.ByteString;
 import org.joda.time.Duration;
 import org.junit.Rule;
 import org.junit.Test;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/mongodb/MongoDbReadWriteIT.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/mongodb/MongoDbReadWriteIT.java
index 76be08fe9a6e..804639cacfc3 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/mongodb/MongoDbReadWriteIT.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/mongodb/MongoDbReadWriteIT.java
@@ -31,7 +31,8 @@
 import static org.hamcrest.core.IsInstanceOf.instanceOf;
 
 import com.mongodb.BasicDBObject;
-import com.mongodb.MongoClient;
+import com.mongodb.client.MongoClient;
+import com.mongodb.client.MongoClients;
 import com.mongodb.client.MongoCollection;
 import com.mongodb.client.MongoDatabase;
 import com.mongodb.client.model.Filters;
@@ -128,14 +129,14 @@ public static void setUp() throws Exception {
             .build();
     mongodExecutable = mongodStarter.prepare(mongodConfig);
     mongodProcess = mongodExecutable.start();
-    client = new MongoClient(hostname, port);
+    client = MongoClients.create("mongodb://" + hostname + ":" + port);
 
     mongoSqlUrl = String.format("mongodb://%s:%d/%s/%s", hostname, port, database, collection);
   }
 
   @AfterClass
   public static void tearDown() throws Exception {
-    client.dropDatabase(database);
+    client.getDatabase(database).drop();
     client.close();
     mongodProcess.stop();
     mongodExecutable.stop();
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetTableIntegrationTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetTableIntegrationTest.java
new file mode 100644
index 000000000000..ab3d8bf92f2e
--- /dev/null
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetTableIntegrationTest.java
@@ -0,0 +1,276 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.meta.provider.parquet;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertThat;
+
+import java.io.File;
+import java.io.Serializable;
+import java.util.Arrays;
+import java.util.List;
+import org.apache.beam.sdk.PipelineResult;
+import org.apache.beam.sdk.extensions.sql.impl.BeamSqlEnv;
+import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
+import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.testing.NeedsRunner;
+import org.apache.beam.sdk.testing.PAssert;
+import org.apache.beam.sdk.testing.TestPipeline;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.Row;
+import org.hamcrest.Matchers;
+import org.junit.BeforeClass;
+import org.junit.ClassRule;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.experimental.categories.Category;
+import org.junit.rules.TemporaryFolder;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+
+/** Integration tests for ParquetTable with filter and projection pushdown. */
+@RunWith(JUnit4.class)
+@Category(NeedsRunner.class)
+public class ParquetTableIntegrationTest implements Serializable {
+
+  @ClassRule public static final TemporaryFolder TEMP_FOLDER = new TemporaryFolder();
+  @ClassRule public static final TestPipeline WRITE_PIPELINE = TestPipeline.create();
+  @Rule public final transient TestPipeline readPipeline = TestPipeline.create();
+
+  private static BeamSqlEnv env;
+  private static final Schema TEST_SCHEMA =
+      Schema.builder()
+          .addInt32Field("id")
+          .addStringField("name")
+          .addBooleanField("active")
+          .addDoubleField("score")
+          .addInt64Field("timestamp")
+          .build();
+
+  @BeforeClass
+  public static void setupAll() throws Exception {
+    File testDataDir = new File(TEMP_FOLDER.getRoot(), "test-data");
+    env = BeamSqlEnv.inMemory(new ParquetTableProvider());
+    env.executeDdl(
+        String.format(
+            "CREATE EXTERNAL TABLE TestTable %s TYPE parquet LOCATION '%s'",
+            "(id INT, name VARCHAR, active BOOLEAN, score DOUBLE, timestamp BIGINT)",
+            testDataDir.getAbsolutePath() + File.separator));
+
+    // Insert test data
+    BeamSqlRelUtils.toPCollection(
+        WRITE_PIPELINE,
+        env.parseQuery(
+            "INSERT INTO TestTable VALUES "
+                + "(1, 'Alice', TRUE, 95.5, 1000), "
+                + "(2, 'Bob', FALSE, 87.2, 2000), "
+                + "(3, 'Charlie', TRUE, 92.8, 3000), "
+                + "(4, 'David', TRUE, 78.9, 4000), "
+                + "(5, 'Eve', FALSE, 88.1, 5000), "
+                + "(6, 'Frank', TRUE, 91.3, 6000), "
+                + "(7, 'Grace', FALSE, 85.7, 7000), "
+                + "(8, 'Henry', TRUE, 94.2, 8000)"));
+    WRITE_PIPELINE.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testSimpleFilter() {
+    String query = "SELECT * FROM TestTable WHERE active = TRUE";
+    BeamRelNode relNode = env.parseQuery(query);
+    PCollection<Row> result = BeamSqlRelUtils.toPCollection(readPipeline, relNode);
+
+    List<Row> expectedRows =
+        Arrays.asList(
+            Row.withSchema(TEST_SCHEMA).addValues(1, "Alice", true, 95.5, 1000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(3, "Charlie", true, 92.8, 3000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(4, "David", true, 78.9, 4000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(6, "Frank", true, 91.3, 6000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(8, "Henry", true, 94.2, 8000L).build());
+
+    PAssert.that(result).containsInAnyOrder(expectedRows);
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testRangeFilter() {
+    String query = "SELECT * FROM TestTable WHERE score >= 90.0 AND score <= 95.0";
+    BeamRelNode relNode = env.parseQuery(query);
+    PCollection<Row> result = BeamSqlRelUtils.toPCollection(readPipeline, relNode);
+
+    List<Row> expectedRows =
+        Arrays.asList(
+            Row.withSchema(TEST_SCHEMA).addValues(1, "Alice", true, 95.5, 1000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(3, "Charlie", true, 92.8, 3000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(6, "Frank", true, 91.3, 6000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(8, "Henry", true, 94.2, 8000L).build());
+
+    PAssert.that(result).containsInAnyOrder(expectedRows);
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testInFilter() {
+    String query = "SELECT * FROM TestTable WHERE id IN (1, 3, 5, 7)";
+    BeamRelNode relNode = env.parseQuery(query);
+    PCollection<Row> result = BeamSqlRelUtils.toPCollection(readPipeline, relNode);
+
+    List<Row> expectedRows =
+        Arrays.asList(
+            Row.withSchema(TEST_SCHEMA).addValues(1, "Alice", true, 95.5, 1000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(3, "Charlie", true, 92.8, 3000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(5, "Eve", false, 88.1, 5000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(7, "Grace", false, 85.7, 7000L).build());
+
+    PAssert.that(result).containsInAnyOrder(expectedRows);
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testIsNullFilter() {
+    // First insert a row with null name
+    BeamSqlRelUtils.toPCollection(
+        readPipeline, env.parseQuery("INSERT INTO TestTable VALUES (9, NULL, TRUE, 90.0, 9000)"));
+    readPipeline.run().waitUntilFinish();
+
+    String query = "SELECT * FROM TestTable WHERE name IS NULL";
+    BeamRelNode relNode = env.parseQuery(query);
+    PCollection<Row> result = BeamSqlRelUtils.toPCollection(readPipeline, relNode);
+
+    List<Row> expectedRows =
+        Arrays.asList(Row.withSchema(TEST_SCHEMA).addValues(9, null, true, 90.0, 9000L).build());
+
+    PAssert.that(result).containsInAnyOrder(expectedRows);
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testProjectionOnly() {
+    String query = "SELECT id, name FROM TestTable";
+    BeamRelNode relNode = env.parseQuery(query);
+    PCollection<Row> result = BeamSqlRelUtils.toPCollection(readPipeline, relNode);
+
+    Schema projectedSchema = Schema.builder().addInt32Field("id").addStringField("name").build();
+
+    List<Row> expectedRows =
+        Arrays.asList(
+            Row.withSchema(projectedSchema).addValues(1, "Alice").build(),
+            Row.withSchema(projectedSchema).addValues(2, "Bob").build(),
+            Row.withSchema(projectedSchema).addValues(3, "Charlie").build(),
+            Row.withSchema(projectedSchema).addValues(4, "David").build(),
+            Row.withSchema(projectedSchema).addValues(5, "Eve").build(),
+            Row.withSchema(projectedSchema).addValues(6, "Frank").build(),
+            Row.withSchema(projectedSchema).addValues(7, "Grace").build(),
+            Row.withSchema(projectedSchema).addValues(8, "Henry").build());
+
+    PAssert.that(result).containsInAnyOrder(expectedRows);
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testProjectionWithFilter() {
+    String query = "SELECT name, score FROM TestTable WHERE active = TRUE AND score > 90.0";
+    BeamRelNode relNode = env.parseQuery(query);
+    PCollection<Row> result = BeamSqlRelUtils.toPCollection(readPipeline, relNode);
+
+    Schema projectedSchema =
+        Schema.builder().addStringField("name").addDoubleField("score").build();
+
+    List<Row> expectedRows =
+        Arrays.asList(
+            Row.withSchema(projectedSchema).addValues("Alice", 95.5).build(),
+            Row.withSchema(projectedSchema).addValues("Charlie", 92.8).build(),
+            Row.withSchema(projectedSchema).addValues("Frank", 91.3).build(),
+            Row.withSchema(projectedSchema).addValues("Henry", 94.2).build());
+
+    PAssert.that(result).containsInAnyOrder(expectedRows);
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testComplexFilter() {
+    String query =
+        "SELECT * FROM TestTable WHERE (active = TRUE AND score > 90.0) OR (active = FALSE AND score < 90.0)";
+    BeamRelNode relNode = env.parseQuery(query);
+    PCollection<Row> result = BeamSqlRelUtils.toPCollection(readPipeline, relNode);
+
+    List<Row> expectedRows =
+        Arrays.asList(
+            Row.withSchema(TEST_SCHEMA).addValues(1, "Alice", true, 95.5, 1000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(2, "Bob", false, 87.2, 2000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(3, "Charlie", true, 92.8, 3000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(5, "Eve", false, 88.1, 5000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(6, "Frank", true, 91.3, 6000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(7, "Grace", false, 85.7, 7000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(8, "Henry", true, 94.2, 8000L).build());
+
+    PAssert.that(result).containsInAnyOrder(expectedRows);
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testNotInFilter() {
+    String query = "SELECT * FROM TestTable WHERE id NOT IN (1, 3, 5, 7)";
+    BeamRelNode relNode = env.parseQuery(query);
+    PCollection<Row> result = BeamSqlRelUtils.toPCollection(readPipeline, relNode);
+
+    List<Row> expectedRows =
+        Arrays.asList(
+            Row.withSchema(TEST_SCHEMA).addValues(2, "Bob", false, 87.2, 2000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(4, "David", true, 78.9, 4000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(6, "Frank", true, 91.3, 6000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(8, "Henry", true, 94.2, 8000L).build());
+
+    PAssert.that(result).containsInAnyOrder(expectedRows);
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testTimestampFilter() {
+    String query = "SELECT * FROM TestTable WHERE timestamp > 3000";
+    BeamRelNode relNode = env.parseQuery(query);
+    PCollection<Row> result = BeamSqlRelUtils.toPCollection(readPipeline, relNode);
+
+    List<Row> expectedRows =
+        Arrays.asList(
+            Row.withSchema(TEST_SCHEMA).addValues(4, "David", true, 78.9, 4000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(5, "Eve", false, 88.1, 5000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(6, "Frank", true, 91.3, 6000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(7, "Grace", false, 85.7, 7000L).build(),
+            Row.withSchema(TEST_SCHEMA).addValues(8, "Henry", true, 94.2, 8000L).build());
+
+    PAssert.that(result).containsInAnyOrder(expectedRows);
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testNoFilterNoProjection() {
+    String query = "SELECT * FROM TestTable";
+    BeamRelNode relNode = env.parseQuery(query);
+    PCollection<Row> result = BeamSqlRelUtils.toPCollection(readPipeline, relNode);
+
+    // Should return all rows with full schema
+    assertThat(result.getSchema(), Matchers.equalTo(TEST_SCHEMA));
+
+    PipelineResult pipelineResult = readPipeline.run();
+    pipelineResult.waitUntilFinish();
+
+    // Verify that the pipeline completed successfully
+    assertEquals(PipelineResult.State.DONE, pipelineResult.getState());
+  }
+}
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetTableProviderFilterPushDownTest.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetTableProviderFilterPushDownTest.java
new file mode 100644
index 000000000000..50de699bbc6d
--- /dev/null
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/parquet/ParquetTableProviderFilterPushDownTest.java
@@ -0,0 +1,223 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.extensions.sql.meta.provider.parquet;
+
+import java.io.File;
+import java.io.Serializable;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.Collections;
+import java.util.List;
+import org.apache.beam.sdk.PipelineResult;
+import org.apache.beam.sdk.extensions.sql.impl.BeamSqlEnv;
+import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
+import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
+import org.apache.beam.sdk.metrics.Counter;
+import org.apache.beam.sdk.metrics.Metrics;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.testing.NeedsRunner;
+import org.apache.beam.sdk.testing.PAssert;
+import org.apache.beam.sdk.testing.TestPipeline;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.Row;
+import org.junit.BeforeClass;
+import org.junit.ClassRule;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.experimental.categories.Category;
+import org.junit.rules.TemporaryFolder;
+import org.junit.runner.RunWith;
+import org.junit.runners.Parameterized;
+import org.junit.runners.Parameterized.Parameter;
+import org.junit.runners.Parameterized.Parameters;
+
+/** Parameterized test for ParquetTable's filter and projection pushdown capabilities. */
+@RunWith(Parameterized.class)
+@Category(NeedsRunner.class)
+public class ParquetTableProviderFilterPushDownTest implements Serializable {
+
+  @ClassRule public static final TemporaryFolder TEMP_FOLDER = new TemporaryFolder();
+  @ClassRule public static final TestPipeline WRITE_PIPELINE = TestPipeline.create();
+  @Rule public final transient TestPipeline readPipeline = TestPipeline.create();
+
+  private static BeamSqlEnv env;
+  private static final Schema FULL_SCHEMA =
+      Schema.builder()
+          .addInt32Field("id")
+          .addNullableField("product_name", Schema.FieldType.STRING)
+          .addBooleanField("is_stocked")
+          .addDoubleField("price")
+          .addInt32Field("category_id")
+          .build();
+
+  private static final Schema PROJECTED_ID_PRICE_SCHEMA =
+      Schema.builder().addInt32Field("id").addDoubleField("price").build();
+
+  private static final Schema PROJECTED_NAME_CAT_SCHEMA =
+      Schema.builder()
+          .addNullableField("product_name", Schema.FieldType.STRING)
+          .addInt32Field("category_id")
+          .build();
+
+  @Parameter(0)
+  public String testCaseName;
+
+  @Parameter(1)
+  public String query;
+
+  @Parameter(2)
+  public List<Object> params;
+
+  @Parameter(3)
+  public List<Row> expectedRows;
+
+  @Parameters(name = "{0}")
+  public static Collection<Object[]> data() {
+    return Arrays.asList(
+        new Object[][] {
+          {
+            "Filter: PriceGreaterThan",
+            "SELECT * FROM ProductInfo WHERE price > 200.0",
+            Collections.emptyList(),
+            Arrays.asList(
+                Row.withSchema(FULL_SCHEMA).addValues(1, "Laptop", true, 1200.50, 101).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(4, "Monitor", true, 300.0, 102).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(6, "Dock", true, 250.0, 103).build())
+          },
+          {
+            "Filter: StockedAndCategory",
+            "SELECT * FROM ProductInfo WHERE is_stocked = TRUE AND category_id = 101",
+            Collections.emptyList(),
+            Arrays.asList(
+                Row.withSchema(FULL_SCHEMA).addValues(1, "Laptop", true, 1200.50, 101).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(2, "Mouse", true, 25.0, 101).build())
+          },
+          {
+            "Filter: IsNotNull",
+            "SELECT * FROM ProductInfo WHERE product_name IS NOT NULL",
+            Collections.emptyList(),
+            Arrays.asList(
+                Row.withSchema(FULL_SCHEMA).addValues(1, "Laptop", true, 1200.50, 101).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(2, "Mouse", true, 25.0, 101).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(3, "Keyboard", false, 75.25, 101).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(4, "Monitor", true, 300.0, 102).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(5, "Webcam", false, 150.0, 102).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(6, "Dock", true, 250.0, 103).build())
+          },
+          {
+            "Filter: Parameterized (No Pushdown)",
+            "SELECT * FROM ProductInfo WHERE price > 100.0 AND is_stocked = true",
+            Collections.emptyList(),
+            Arrays.asList(
+                Row.withSchema(FULL_SCHEMA).addValues(1, "Laptop", true, 1200.50, 101).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(2, "Mouse", true, 25.0, 101).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(4, "Monitor", true, 300.0, 102).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(6, "Dock", true, 250.0, 103).build())
+          },
+          {
+            "Projection: Simple",
+            "SELECT id, price FROM ProductInfo",
+            Collections.emptyList(),
+            Arrays.asList(
+                Row.withSchema(PROJECTED_ID_PRICE_SCHEMA).addValues(1, 1200.50).build(),
+                Row.withSchema(PROJECTED_ID_PRICE_SCHEMA).addValues(2, 25.0).build(),
+                Row.withSchema(PROJECTED_ID_PRICE_SCHEMA).addValues(3, 75.25).build(),
+                Row.withSchema(PROJECTED_ID_PRICE_SCHEMA).addValues(4, 300.0).build(),
+                Row.withSchema(PROJECTED_ID_PRICE_SCHEMA).addValues(5, 150.0).build(),
+                Row.withSchema(PROJECTED_ID_PRICE_SCHEMA).addValues(6, 250.0).build(),
+                Row.withSchema(PROJECTED_ID_PRICE_SCHEMA).addValues(7, 45.0).build())
+          },
+          {
+            "Projection with Filter",
+            "SELECT product_name, category_id FROM ProductInfo WHERE price < 100.0",
+            Collections.emptyList(),
+            Arrays.asList(
+                Row.withSchema(PROJECTED_NAME_CAT_SCHEMA).addValues("Mouse", 101).build(),
+                Row.withSchema(PROJECTED_NAME_CAT_SCHEMA).addValues("Keyboard", 101).build(),
+                Row.withSchema(PROJECTED_NAME_CAT_SCHEMA).addValues(null, 103).build())
+          },
+          {
+            "No Filter: No Pushdown",
+            "SELECT * FROM ProductInfo",
+            Collections.emptyList(),
+            Arrays.asList(
+                Row.withSchema(FULL_SCHEMA).addValues(1, "Laptop", true, 1200.50, 101).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(2, "Mouse", true, 25.0, 101).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(3, "Keyboard", false, 75.25, 101).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(4, "Monitor", true, 300.0, 102).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(5, "Webcam", false, 150.0, 102).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(6, "Dock", true, 250.0, 103).build(),
+                Row.withSchema(FULL_SCHEMA).addValues(7, null, false, 45.0, 103).build())
+          },
+        });
+  }
+
+  @BeforeClass
+  public static void setupAll() {
+    File complexDestFile = new File(TEMP_FOLDER.getRoot(), "product-info");
+    env = BeamSqlEnv.inMemory(new ParquetTableProvider());
+    env.executeDdl(
+        String.format(
+            "CREATE EXTERNAL TABLE ProductInfo %s TYPE parquet LOCATION '%s'",
+            "(id INT, product_name VARCHAR NULL, is_stocked BOOLEAN, price DOUBLE, category_id INT)",
+            complexDestFile.getAbsolutePath() + File.separator));
+
+    BeamSqlRelUtils.toPCollection(
+        WRITE_PIPELINE,
+        env.parseQuery(
+            "INSERT INTO ProductInfo VALUES "
+                + "(1, 'Laptop', TRUE, 1200.50, 101), "
+                + "(2, 'Mouse', TRUE, 25.0, 101), "
+                + "(3, 'Keyboard', FALSE, 75.25, 101), "
+                + "(4, 'Monitor', TRUE, 300.0, 102), "
+                + "(5, 'Webcam', FALSE, 150.0, 102), "
+                + "(6, 'Dock', TRUE, 250.0, 103), "
+                + "(7, NULL, FALSE, 45.0, 103)"));
+    WRITE_PIPELINE.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testPushdown() {
+    BeamRelNode relNode = env.parseQuery(query);
+
+    PCollection<Row> rows = BeamSqlRelUtils.toPCollection(readPipeline, relNode);
+
+    // Get the expected schema from the first expected row
+    Schema expectedSchema = expectedRows.isEmpty() ? FULL_SCHEMA : expectedRows.get(0).getSchema();
+    PCollection<Row> countedRows =
+        rows.apply("CountRecords", ParDo.of(new CounterFn())).setRowSchema(expectedSchema);
+
+    PAssert.that(countedRows).containsInAnyOrder(expectedRows);
+
+    PipelineResult result = readPipeline.run();
+    result.waitUntilFinish();
+  }
+
+  private static class CounterFn extends DoFn<Row, Row> {
+    private final Counter elementsProcessed =
+        Metrics.counter(CounterFn.class, "elements_processed");
+
+    @ProcessElement
+    public void processElement(ProcessContext c) {
+      elementsProcessed.inc();
+      c.output(c.element());
+    }
+  }
+}
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/pubsub/PubsubTableProviderIT.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/pubsub/PubsubTableProviderIT.java
index 6153e6e33c2c..b3684d88d220 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/pubsub/PubsubTableProviderIT.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/pubsub/PubsubTableProviderIT.java
@@ -71,7 +71,7 @@
 import org.apache.beam.sdk.util.common.ReflectHelpers;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteConnection;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.jdbc.CalciteConnection;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableSet;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithFilterAndProjectPushDown.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithFilterAndProjectPushDown.java
index ce9f52ffea94..c3a07fafa584 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithFilterAndProjectPushDown.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithFilterAndProjectPushDown.java
@@ -41,9 +41,9 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CoreRules;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSets;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.CoreRules;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSets;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.joda.time.Duration;
 import org.junit.Before;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithFilterPushDown.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithFilterPushDown.java
index eae1dfb49ee8..823aa8998e98 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithFilterPushDown.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithFilterPushDown.java
@@ -42,10 +42,10 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CoreRules;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSets;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.core.Calc;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.CoreRules;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSets;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.hamcrest.collection.IsIterableContainingInAnyOrder;
 import org.joda.time.Duration;
diff --git a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithProjectPushDown.java b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithProjectPushDown.java
index 4fa45cbab9ec..bd65e8353b06 100644
--- a/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithProjectPushDown.java
+++ b/sdks/java/extensions/sql/src/test/java/org/apache/beam/sdk/extensions/sql/meta/provider/test/TestTableProviderWithProjectPushDown.java
@@ -41,9 +41,9 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CoreRules;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSets;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.plan.RelOptRule;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rel.rules.CoreRules;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.tools.RuleSets;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.joda.time.Duration;
 import org.junit.Before;
diff --git a/sdks/java/extensions/sql/zetasql/build.gradle b/sdks/java/extensions/sql/zetasql/build.gradle
deleted file mode 100644
index 29a3f95402b0..000000000000
--- a/sdks/java/extensions/sql/zetasql/build.gradle
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * License); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-plugins {
-  id 'org.apache.beam.module'
-}
-
-applyJavaNature(
-  automaticModuleName: 'org.apache.beam.sdk.extensions.sql.zetasql',
-)
-
-description = "Apache Beam :: SDKs :: Java :: Extensions :: SQL :: ZetaSQL"
-ext.summary = "ZetaSQL to Calcite translator"
-
-def zetasql_version = "2024.11.1"
-
-dependencies {
-  // TODO(https://github.com/apache/beam/issues/21156): Determine how to build without this dependency
-  provided "org.immutables:value:2.8.8"
-  permitUnusedDeclared "org.immutables:value:2.8.8"
-  implementation enforcedPlatform(library.java.google_cloud_platform_libraries_bom)
-  permitUnusedDeclared enforcedPlatform(library.java.google_cloud_platform_libraries_bom)
-  implementation project(path: ":sdks:java:core", configuration: "shadow")
-  implementation project(":sdks:java:extensions:sql")
-  implementation project(":sdks:java:extensions:sql:udf")
-  implementation library.java.vendored_calcite_1_28_0
-  implementation library.java.guava
-  implementation library.java.grpc_api
-  implementation library.java.joda_time
-  implementation library.java.protobuf_java
-  implementation library.java.protobuf_java_util
-  permitUnusedDeclared library.java.protobuf_java_util // BEAM-11761
-  implementation library.java.slf4j_api
-  implementation library.java.vendored_guava_32_1_2_jre
-  implementation library.java.proto_google_common_protos // Interfaces with ZetaSQL use this
-  permitUnusedDeclared library.java.proto_google_common_protos // BEAM-11761
-  implementation library.java.grpc_google_common_protos // Interfaces with ZetaSQL use this
-  permitUnusedDeclared library.java.grpc_google_common_protos // BEAM-11761
-  implementation "com.google.zetasql:zetasql-client:$zetasql_version"
-  implementation "com.google.zetasql:zetasql-types:$zetasql_version"
-  implementation "com.google.zetasql:zetasql-jni-channel:$zetasql_version"
-  permitUnusedDeclared "com.google.zetasql:zetasql-jni-channel:$zetasql_version" // BEAM-11761
-  testImplementation library.java.vendored_calcite_1_28_0
-  testImplementation library.java.vendored_guava_32_1_2_jre
-  testImplementation library.java.junit
-  testImplementation library.java.hamcrest
-  testImplementation library.java.mockito_core
-  testImplementation library.java.quickcheck_core
-  testImplementation library.java.jackson_databind
-  testImplementation "org.codehaus.janino:janino:3.0.11"
-  testCompileOnly project(":sdks:java:extensions:sql:udf-test-provider")
-  testRuntimeOnly library.java.slf4j_jdk14
-}
-
-test {
-  dependsOn ":sdks:java:extensions:sql:emptyJar"
-  // Pass jars used by Java UDF tests via system properties.
-  systemProperty "beam.sql.udf.test.jar_path", project(":sdks:java:extensions:sql:udf-test-provider").jarPath
-  systemProperty "beam.sql.udf.test.empty_jar_path", project(":sdks:java:extensions:sql").emptyJar.archivePath
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamCalcRelType.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamCalcRelType.java
deleted file mode 100644
index 70e583d677a2..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamCalcRelType.java
+++ /dev/null
@@ -1,159 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamCalcRel;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
-import org.apache.beam.sdk.extensions.sql.impl.rel.CalcRelSplitter;
-import org.apache.beam.sdk.extensions.sql.zetasql.translation.ZetaSqlScalarFunctionImpl;
-import org.apache.beam.sdk.util.Preconditions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.RexImpTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexDynamicParam;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexFieldAccess;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLocalRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexProgram;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlUserDefinedFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelBuilder;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-/** {@link CalcRelSplitter.RelType} for {@link BeamCalcRel}. */
-class BeamCalcRelType extends CalcRelSplitter.RelType {
-  private static final Logger LOG = LoggerFactory.getLogger(BeamCalcRelType.class);
-
-  BeamCalcRelType(String name) {
-    super(name);
-  }
-
-  @Override
-  protected boolean canImplement(RexFieldAccess field) {
-    return supportsType(field.getType());
-  }
-
-  @Override
-  protected boolean canImplement(RexLiteral literal) {
-    return supportsType(literal.getType());
-  }
-
-  @Override
-  protected boolean canImplement(RexDynamicParam param) {
-    return supportsType(param.getType());
-  }
-
-  @Override
-  protected boolean canImplement(RexCall call) {
-    final SqlOperator operator = call.getOperator();
-
-    RexImpTable.RexCallImplementor implementor = RexImpTable.INSTANCE.get(operator);
-    if (implementor == null) {
-      // Reject methods with no implementation
-      return false;
-    }
-
-    if (operator instanceof SqlUserDefinedFunction) {
-      SqlUserDefinedFunction udf = (SqlUserDefinedFunction) call.op;
-      if (udf.function instanceof ZetaSqlScalarFunctionImpl) {
-        ZetaSqlScalarFunctionImpl scalarFunction = (ZetaSqlScalarFunctionImpl) udf.function;
-        if (!scalarFunction.functionGroup.equals(
-            BeamZetaSqlCatalog.USER_DEFINED_JAVA_SCALAR_FUNCTIONS)) {
-          // Reject ZetaSQL Builtin Scalar Functions
-          return false;
-        }
-        for (RexNode operand : call.getOperands()) {
-          if (operand instanceof RexLocalRef) {
-            if (!supportsType(operand.getType())) {
-              LOG.error(
-                  "User-defined function {} received unsupported operand type {}.",
-                  call.op.getName(),
-                  ((RexLocalRef) operand).getType());
-              return false;
-            }
-          } else {
-            LOG.error(
-                "User-defined function {} received unrecognized operand kind {}.",
-                call.op.getName(),
-                operand.getKind());
-            return false;
-          }
-        }
-      } else {
-        // Reject other UDFs
-        return false;
-      }
-    } else {
-      // Reject Calcite implementations
-      return false;
-    }
-    return true;
-  }
-
-  @Override
-  protected RelNode makeRel(
-      RelOptCluster cluster,
-      RelTraitSet traitSet,
-      RelBuilder relBuilder,
-      RelNode input,
-      RexProgram program) {
-    RexProgram normalizedProgram = program.normalize(cluster.getRexBuilder(), false);
-    return new BeamCalcRel(
-        cluster,
-        traitSet.replace(BeamLogicalConvention.INSTANCE),
-        RelOptRule.convert(input, input.getTraitSet().replace(BeamLogicalConvention.INSTANCE)),
-        normalizedProgram);
-  }
-
-  /**
-   * Returns true only if the data type can be correctly implemented by {@link
-   * org.apache.beam.sdk.extensions.sql.impl.rel.BeamCalcRel} in ZetaSQL.
-   */
-  private boolean supportsType(RelDataType type) {
-    switch (type.getSqlTypeName()) {
-      case BIGINT:
-      case BINARY:
-      case BOOLEAN:
-      case CHAR:
-      case DATE:
-      case DECIMAL:
-      case DOUBLE:
-      case NULL:
-      case TIMESTAMP:
-      case VARBINARY:
-      case VARCHAR:
-        return true;
-      case ARRAY:
-        return supportsType(
-            Preconditions.checkArgumentNotNull(
-                type.getComponentType(), "Encountered ARRAY type with no component type."));
-      case ROW:
-        return type.getFieldList().stream().allMatch((field) -> supportsType(field.getType()));
-      case TIME: // BEAM-12086
-      case TIMESTAMP_WITH_LOCAL_TIME_ZONE: // BEAM-12087
-      default:
-        return false;
-    }
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamJavaUdfCalcRule.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamJavaUdfCalcRule.java
deleted file mode 100644
index 84ee347c3d00..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamJavaUdfCalcRule.java
+++ /dev/null
@@ -1,48 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamCalcRel;
-import org.apache.beam.sdk.extensions.sql.impl.rel.CalcRelSplitter;
-import org.apache.beam.sdk.extensions.sql.impl.rule.BeamCalcRule;
-import org.apache.beam.sdk.extensions.sql.impl.rule.BeamCalcSplittingRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-
-/**
- * A {@link BeamCalcSplittingRule} to replace {@link Calc} with {@link BeamCalcRel}.
- *
- * <p>Equivalent to {@link BeamCalcRule} but with limits to supported types and operators.
- *
- * <p>This class is intended only for testing purposes. See {@link BeamZetaSqlCalcSplittingRule}.
- */
-public class BeamJavaUdfCalcRule extends BeamCalcSplittingRule {
-  public static final BeamJavaUdfCalcRule INSTANCE = new BeamJavaUdfCalcRule();
-
-  private BeamJavaUdfCalcRule() {
-    super("BeamJavaUdfCalcRule");
-  }
-
-  @Override
-  protected CalcRelSplitter.RelType[] getRelTypes() {
-    // "Split" the Calc between two identical RelTypes. The second one is just a placeholder; if the
-    // first isn't usable, the second one won't be usable either, and the planner will fail.
-    return new CalcRelSplitter.RelType[] {
-      new BeamCalcRelType("BeamCalcRelType"), new BeamCalcRelType("BeamCalcRelType2")
-    };
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcMergeRule.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcMergeRule.java
deleted file mode 100644
index 018cd541d9b8..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcMergeRule.java
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleOperand;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CalcMergeRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.CoreRules;
-
-/**
- * Planner rule to merge a {@link BeamZetaSqlCalcRel} with a {@link BeamZetaSqlCalcRel}. Subset of
- * {@link CalcMergeRule}.
- */
-public class BeamZetaSqlCalcMergeRule extends RelOptRule {
-  public static final BeamZetaSqlCalcMergeRule INSTANCE = new BeamZetaSqlCalcMergeRule();
-
-  public BeamZetaSqlCalcMergeRule() {
-    super(
-        operand(
-            BeamZetaSqlCalcRel.class,
-            operand(BeamZetaSqlCalcRel.class, any()),
-            new RelOptRuleOperand[0]));
-  }
-
-  @Override
-  public void onMatch(RelOptRuleCall call) {
-    CoreRules.CALC_MERGE.onMatch(call);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcRel.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcRel.java
deleted file mode 100644
index d60ebe46b370..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcRel.java
+++ /dev/null
@@ -1,404 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.apache.beam.sdk.schemas.Schema.Field;
-import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
-
-import com.google.auto.value.AutoValue;
-import com.google.zetasql.AnalyzerOptions;
-import com.google.zetasql.PreparedExpression;
-import com.google.zetasql.Value;
-import edu.umd.cs.findbugs.annotations.SuppressFBWarnings;
-import java.util.ArrayDeque;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.Queue;
-import java.util.concurrent.ExecutionException;
-import java.util.concurrent.Future;
-import java.util.function.IntFunction;
-import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.sdk.extensions.sql.impl.BeamSqlPipelineOptions;
-import org.apache.beam.sdk.extensions.sql.impl.QueryPlanner.QueryParameters;
-import org.apache.beam.sdk.extensions.sql.impl.rel.AbstractBeamCalcRel;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.extensions.sql.impl.utils.CalciteUtils;
-import org.apache.beam.sdk.extensions.sql.meta.provider.bigquery.BeamBigQuerySqlDialect;
-import org.apache.beam.sdk.extensions.sql.meta.provider.bigquery.BeamSqlUnparseContext;
-import org.apache.beam.sdk.schemas.FieldAccessDescriptor;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.schemas.utils.SelectHelpers;
-import org.apache.beam.sdk.transforms.DoFn;
-import org.apache.beam.sdk.transforms.PTransform;
-import org.apache.beam.sdk.transforms.ParDo;
-import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.PCollectionList;
-import org.apache.beam.sdk.values.PCollectionTuple;
-import org.apache.beam.sdk.values.POutput;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.sdk.values.TupleTag;
-import org.apache.beam.sdk.values.TupleTagList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexProgram;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlDialect;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.checkerframework.checker.nullness.qual.Nullable;
-import org.joda.time.Duration;
-import org.joda.time.Instant;
-
-/**
- * BeamRelNode to replace {@code Project} and {@code Filter} node based on the {@code ZetaSQL}
- * expression evaluator.
- */
-@SuppressWarnings(
-    "unused") // TODO(https://github.com/apache/beam/issues/21230): Remove when new version of
-// errorprone is released (2.11.0)
-@Internal
-public class BeamZetaSqlCalcRel extends AbstractBeamCalcRel {
-
-  private static final SqlDialect DIALECT = BeamBigQuerySqlDialect.DEFAULT;
-  private static final int MAX_PENDING_WINDOW = 32;
-  private final BeamSqlUnparseContext context;
-
-  private static final TupleTag<Row> rows = new TupleTag<Row>("output") {};
-  private static final TupleTag<Row> errors = new TupleTag<Row>("errors") {};
-
-  private static String columnName(int i) {
-    return "_" + i;
-  }
-
-  public BeamZetaSqlCalcRel(
-      RelOptCluster cluster, RelTraitSet traits, RelNode input, RexProgram program) {
-    super(cluster, traits, input, program);
-    final IntFunction<SqlNode> fn = i -> new SqlIdentifier(columnName(i), SqlParserPos.ZERO);
-    context = new BeamSqlUnparseContext(fn);
-  }
-
-  @Override
-  public Calc copy(RelTraitSet traitSet, RelNode input, RexProgram program) {
-    return new BeamZetaSqlCalcRel(getCluster(), traitSet, input, program);
-  }
-
-  @Override
-  public PTransform<PCollectionList<Row>, PCollection<Row>> buildPTransform() {
-    return buildPTransform(null);
-  }
-
-  @Override
-  public PTransform<PCollectionList<Row>, PCollection<Row>> buildPTransform(
-      @Nullable PTransform<PCollection<Row>, ? extends POutput> errorsTransformer) {
-    return new Transform(errorsTransformer);
-  }
-
-  @AutoValue
-  abstract static class TimestampedFuture {
-    private static TimestampedFuture create(Instant t, Future<Value> f, Row r) {
-      return new AutoValue_BeamZetaSqlCalcRel_TimestampedFuture(t, f, r);
-    }
-
-    abstract Instant timestamp();
-
-    abstract Future<Value> future();
-
-    abstract Row row();
-  }
-
-  private class Transform extends PTransform<PCollectionList<Row>, PCollection<Row>> {
-
-    private final @Nullable PTransform<PCollection<Row>, ? extends POutput> errorsTransformer;
-
-    Transform(@Nullable PTransform<PCollection<Row>, ? extends POutput> errorsTransformer) {
-      this.errorsTransformer = errorsTransformer;
-    }
-
-    @Override
-    public PCollection<Row> expand(PCollectionList<Row> pinput) {
-      Preconditions.checkArgument(
-          pinput.size() == 1,
-          "%s expected a single input PCollection, but received %d.",
-          BeamZetaSqlCalcRel.class.getSimpleName(),
-          pinput.size());
-      PCollection<Row> upstream = pinput.get(0);
-
-      final RexBuilder rexBuilder = getCluster().getRexBuilder();
-      RexNode rex = rexBuilder.makeCall(SqlStdOperatorTable.ROW, getProgram().getProjectList());
-
-      final RexNode condition = getProgram().getCondition();
-      if (condition != null) {
-        rex =
-            rexBuilder.makeCall(
-                SqlStdOperatorTable.CASE, condition, rex, rexBuilder.makeNullLiteral(getRowType()));
-      }
-
-      final Schema outputSchema = CalciteUtils.toSchema(getRowType());
-
-      BeamSqlPipelineOptions options =
-          pinput.getPipeline().getOptions().as(BeamSqlPipelineOptions.class);
-      CalcFn calcFn =
-          new CalcFn(
-              context.toSql(getProgram(), rex).toSqlString(DIALECT).getSql(),
-              createNullParams(context.getNullParams()),
-              upstream.getSchema(),
-              outputSchema,
-              options.getZetaSqlDefaultTimezone(),
-              options.getVerifyRowValues(),
-              errorsTransformer != null);
-
-      PCollectionTuple tuple =
-          upstream.apply(ParDo.of(calcFn).withOutputTags(rows, TupleTagList.of(errors)));
-      tuple.get(errors).setRowSchema(calcFn.errorsSchema);
-
-      if (errorsTransformer != null) {
-        tuple.get(errors).apply(errorsTransformer);
-      }
-
-      return tuple.get(rows).setRowSchema(outputSchema);
-    }
-  }
-
-  private static Map<String, Value> createNullParams(Map<String, RelDataType> input) {
-    Map<String, Value> result = new HashMap<>();
-    for (Map.Entry<String, RelDataType> entry : input.entrySet()) {
-      result.put(
-          entry.getKey(),
-          Value.createNullValue(ZetaSqlCalciteTranslationUtils.toZetaSqlType(entry.getValue())));
-    }
-    return result;
-  }
-
-  /**
-   * {@code CalcFn} is the executor for a {@link BeamZetaSqlCalcRel} step. The implementation is
-   * based on the {@code ZetaSQL} expression evaluator.
-   */
-  @SuppressFBWarnings("SE_TRANSIENT_FIELD_NOT_RESTORED")
-  private static class CalcFn extends DoFn<Row, Row> {
-    private final String sql;
-    private final Map<String, Value> nullParams;
-    private final Schema inputSchema;
-    private final Schema outputSchema;
-    private final String defaultTimezone;
-    private final boolean verifyRowValues;
-    private final boolean dlqTransformDownstream;
-
-    final Schema errorsSchema;
-    private final List<Integer> referencedColumns;
-
-    @FieldAccess("row")
-    private final FieldAccessDescriptor fieldAccess;
-
-    private transient Map<BoundedWindow, Queue<TimestampedFuture>> pending = new HashMap<>();
-    private transient PreparedExpression exp;
-    private transient PreparedExpression.@Nullable Stream stream;
-
-    CalcFn(
-        String sql,
-        Map<String, Value> nullParams,
-        Schema inputSchema,
-        Schema outputSchema,
-        String defaultTimezone,
-        boolean verifyRowValues,
-        boolean dlqTransformDownstream) {
-      this.sql = sql;
-      this.exp = new PreparedExpression(sql);
-      this.nullParams = nullParams;
-      this.inputSchema = inputSchema;
-      this.outputSchema = outputSchema;
-      this.defaultTimezone = defaultTimezone;
-      this.verifyRowValues = verifyRowValues;
-      this.dlqTransformDownstream = dlqTransformDownstream;
-
-      try (PreparedExpression exp =
-          prepareExpression(sql, nullParams, inputSchema, defaultTimezone)) {
-        ImmutableList.Builder<Integer> columns = new ImmutableList.Builder<>();
-        for (String c : exp.getReferencedColumns()) {
-          columns.add(Integer.parseInt(c.substring(1)));
-        }
-        this.referencedColumns = columns.build();
-        this.fieldAccess = FieldAccessDescriptor.withFieldIds(this.referencedColumns);
-        Schema inputRowSchema = SelectHelpers.getOutputSchema(inputSchema, fieldAccess);
-        this.errorsSchema = BeamSqlRelUtils.getErrorRowSchema(inputRowSchema);
-      }
-    }
-
-    /** exp cannot be reused and is transient so needs to be reinitialized. */
-    private static PreparedExpression prepareExpression(
-        String sql, Map<String, Value> nullParams, Schema inputSchema, String defaultTimezone) {
-      AnalyzerOptions options =
-          SqlAnalyzer.getAnalyzerOptions(QueryParameters.ofNamed(nullParams), defaultTimezone);
-      for (int i = 0; i < inputSchema.getFieldCount(); i++) {
-        options.addExpressionColumn(
-            columnName(i),
-            ZetaSqlBeamTranslationUtils.toZetaSqlType(inputSchema.getField(i).getType()));
-      }
-
-      PreparedExpression exp = new PreparedExpression(sql);
-      exp.prepare(options);
-      return exp;
-    }
-
-    @Setup
-    public void setup() {
-      this.exp = prepareExpression(sql, nullParams, inputSchema, defaultTimezone);
-      this.stream = exp.stream();
-    }
-
-    @StartBundle
-    public void startBundle() {
-      pending = new HashMap<>();
-    }
-
-    @Override
-    public Duration getAllowedTimestampSkew() {
-      return Duration.millis(Long.MAX_VALUE);
-    }
-
-    @ProcessElement
-    public void processElement(
-        @FieldAccess("row") Row row,
-        @Timestamp Instant t,
-        BoundedWindow w,
-        OutputReceiver<Row> r,
-        MultiOutputReceiver multiOutputReceiver)
-        throws InterruptedException {
-
-      @Nullable Queue<TimestampedFuture> pendingWindow = pending.get(w);
-      if (pendingWindow == null) {
-        pendingWindow = new ArrayDeque<>();
-        pending.put(w, pendingWindow);
-      }
-      try {
-        Map<String, Value> columns = new HashMap<>();
-        for (int i : referencedColumns) {
-          final Field field = inputSchema.getField(i);
-          columns.put(
-              columnName(i),
-              ZetaSqlBeamTranslationUtils.toZetaSqlValue(
-                  row.getBaseValue(field.getName(), Object.class), field.getType()));
-        }
-        Future<Value> valueFuture = checkArgumentNotNull(stream).execute(columns, nullParams);
-        pendingWindow.add(TimestampedFuture.create(t, valueFuture, row));
-
-      } catch (UnsupportedOperationException | ArithmeticException | IllegalArgumentException e) {
-        if (!dlqTransformDownstream) {
-          throw e;
-        }
-        multiOutputReceiver
-            .get(errors)
-            .output(Row.withSchema(errorsSchema).addValues(row, e.toString()).build());
-      }
-
-      while ((!pendingWindow.isEmpty() && pendingWindow.element().future().isDone())
-          || pendingWindow.size() > MAX_PENDING_WINDOW) {
-        outputRow(pendingWindow.remove(), r, multiOutputReceiver.get(errors));
-      }
-    }
-
-    @FinishBundle
-    public void finishBundle(FinishBundleContext c) throws InterruptedException {
-      checkArgumentNotNull(stream).flush();
-      for (Map.Entry<BoundedWindow, Queue<TimestampedFuture>> pendingWindow : pending.entrySet()) {
-        OutputReceiver<Row> rowOutputReciever =
-            new OutputReceiverForFinishBundle(c, pendingWindow.getKey(), rows);
-        OutputReceiver<Row> errorOutputReciever =
-            new OutputReceiverForFinishBundle(c, pendingWindow.getKey(), errors);
-
-        for (TimestampedFuture timestampedFuture : pendingWindow.getValue()) {
-          outputRow(timestampedFuture, rowOutputReciever, errorOutputReciever);
-        }
-      }
-    }
-
-    // TODO(https://github.com/apache/beam/issues/18203): Remove this when FinishBundle has added
-    // support for an {@link OutputReceiver}
-    private static class OutputReceiverForFinishBundle implements OutputReceiver<Row> {
-
-      private final FinishBundleContext c;
-      private final BoundedWindow w;
-
-      private final TupleTag<Row> tag;
-
-      private OutputReceiverForFinishBundle(
-          FinishBundleContext c, BoundedWindow w, TupleTag<Row> tag) {
-        this.c = c;
-        this.w = w;
-        this.tag = tag;
-      }
-
-      @Override
-      public void output(Row output) {
-        throw new RuntimeException("Unsupported");
-      }
-
-      @Override
-      public void outputWithTimestamp(Row output, Instant timestamp) {
-        c.output(tag, output, timestamp, w);
-      }
-    }
-
-    private static RuntimeException extractException(Throwable e) {
-      try {
-        throw checkArgumentNotNull(e.getCause());
-      } catch (RuntimeException r) {
-        return r;
-      } catch (Throwable t) {
-        return new RuntimeException(t);
-      }
-    }
-
-    private void outputRow(
-        TimestampedFuture c, OutputReceiver<Row> r, OutputReceiver<Row> errorOutputReceiver)
-        throws InterruptedException {
-      final Value v;
-      try {
-        v = c.future().get();
-      } catch (ExecutionException e) {
-        if (!dlqTransformDownstream) {
-          throw extractException(e);
-        }
-        errorOutputReceiver.outputWithTimestamp(
-            Row.withSchema(errorsSchema).addValues(c.row(), e.toString()).build(), c.timestamp());
-        return;
-      } catch (Throwable thr) {
-        throw extractException(thr);
-      }
-      if (!v.isNull()) {
-        Row row = ZetaSqlBeamTranslationUtils.toBeamRow(v, outputSchema, verifyRowValues);
-        r.outputWithTimestamp(row, c.timestamp());
-      }
-    }
-
-    @Teardown
-    public void teardown() {
-      checkArgumentNotNull(stream).close();
-      exp.close();
-    }
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcRule.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcRule.java
deleted file mode 100644
index a5957a9107b1..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcRule.java
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import org.apache.beam.sdk.extensions.sql.impl.rel.CalcRelSplitter;
-import org.apache.beam.sdk.extensions.sql.impl.rule.BeamCalcSplittingRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Calc;
-
-/** A {@link BeamCalcSplittingRule} to replace {@link Calc} with {@link BeamZetaSqlCalcRel}. */
-public class BeamZetaSqlCalcRule extends BeamCalcSplittingRule {
-  public static final BeamZetaSqlCalcRule INSTANCE = new BeamZetaSqlCalcRule();
-
-  private BeamZetaSqlCalcRule() {
-    super("BeamZetaSqlCalcRule");
-  }
-
-  @Override
-  protected CalcRelSplitter.RelType[] getRelTypes() {
-    // "Split" the Calc between two identical RelTypes. The second one is just a placeholder; if the
-    // first isn't usable, the second one won't be usable either, and the planner will fail.
-    return new CalcRelSplitter.RelType[] {
-      new BeamZetaSqlRelType("BeamZetaSqlRelType"), new BeamZetaSqlRelType("BeamZetaSqlRelType2")
-    };
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcSplittingRule.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcSplittingRule.java
deleted file mode 100644
index 3b9bb385fbe7..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcSplittingRule.java
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamCalcRel;
-import org.apache.beam.sdk.extensions.sql.impl.rel.CalcRelSplitter;
-import org.apache.beam.sdk.extensions.sql.impl.rule.BeamCalcSplittingRule;
-
-/**
- * A {@link BeamCalcSplittingRule} that converts a {@link LogicalCalc} to a chain of {@link
- * BeamZetaSqlCalcRel} and/or {@link BeamCalcRel} via {@link CalcRelSplitter}.
- *
- * <p>Only Java UDFs are implemented using {@link BeamCalcRel}. All other expressions are
- * implemented using {@link BeamZetaSqlCalcRel}.
- */
-public class BeamZetaSqlCalcSplittingRule extends BeamCalcSplittingRule {
-  public static final BeamZetaSqlCalcSplittingRule INSTANCE = new BeamZetaSqlCalcSplittingRule();
-
-  private BeamZetaSqlCalcSplittingRule() {
-    super("BeamZetaSqlCalcRule");
-  }
-
-  @Override
-  protected CalcRelSplitter.RelType[] getRelTypes() {
-    return new CalcRelSplitter.RelType[] {
-      new BeamZetaSqlRelType("BeamZetaSqlRelType"), new BeamCalcRelType("BeamCalcRelType")
-    };
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCatalog.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCatalog.java
deleted file mode 100644
index 719abe4041bc..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCatalog.java
+++ /dev/null
@@ -1,593 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import com.google.common.collect.ImmutableList;
-import com.google.zetasql.Analyzer;
-import com.google.zetasql.AnalyzerOptions;
-import com.google.zetasql.Function;
-import com.google.zetasql.FunctionArgumentType;
-import com.google.zetasql.FunctionSignature;
-import com.google.zetasql.SimpleCatalog;
-import com.google.zetasql.TVFRelation;
-import com.google.zetasql.TableValuedFunction;
-import com.google.zetasql.Type;
-import com.google.zetasql.TypeFactory;
-import com.google.zetasql.ZetaSQLBuiltinFunctionOptions;
-import com.google.zetasql.ZetaSQLFunctions;
-import com.google.zetasql.ZetaSQLType;
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes;
-import java.lang.reflect.Method;
-import java.util.Arrays;
-import java.util.Collection;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.Optional;
-import java.util.stream.Collectors;
-import org.apache.beam.sdk.extensions.sql.impl.JavaUdfLoader;
-import org.apache.beam.sdk.extensions.sql.impl.LazyAggregateCombineFn;
-import org.apache.beam.sdk.extensions.sql.impl.ScalarFnReflector;
-import org.apache.beam.sdk.extensions.sql.impl.ScalarFunctionImpl;
-import org.apache.beam.sdk.extensions.sql.impl.UdafImpl;
-import org.apache.beam.sdk.extensions.sql.impl.utils.TVFStreamingUtils;
-import org.apache.beam.sdk.extensions.sql.udf.ScalarFn;
-import org.apache.beam.sdk.extensions.sql.zetasql.translation.UserFunctionDefinitions;
-import org.apache.beam.sdk.transforms.Combine;
-import org.apache.beam.sdk.util.Preconditions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.java.JavaTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeField;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.FunctionParameter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-
-/**
- * Catalog for registering tables and functions. Populates a {@link SimpleCatalog} based on a {@link
- * SchemaPlus}.
- */
-public class BeamZetaSqlCatalog {
-  // ZetaSQL function group identifiers. Different function groups may have divergent translation
-  // paths.
-  public static final String PRE_DEFINED_WINDOW_FUNCTIONS = "pre_defined_window_functions";
-  public static final String USER_DEFINED_SQL_FUNCTIONS = "user_defined_functions";
-  public static final String USER_DEFINED_JAVA_SCALAR_FUNCTIONS =
-      "user_defined_java_scalar_functions";
-  public static final String USER_DEFINED_JAVA_AGGREGATE_FUNCTIONS =
-      "user_defined_java_aggregate_functions";
-  /**
-   * Same as {@link Function}.ZETASQL_FUNCTION_GROUP_NAME. Identifies built-in ZetaSQL functions.
-   */
-  public static final String ZETASQL_FUNCTION_GROUP_NAME = "ZetaSQL";
-
-  private static final ImmutableList<String> PRE_DEFINED_WINDOW_FUNCTION_DECLARATIONS =
-      ImmutableList.of(
-          // TODO: support optional function argument (for window_offset).
-          "CREATE FUNCTION TUMBLE(ts TIMESTAMP, window_size STRING) AS (1);",
-          "CREATE FUNCTION TUMBLE_START(window_size STRING) RETURNS TIMESTAMP AS (null);",
-          "CREATE FUNCTION TUMBLE_END(window_size STRING) RETURNS TIMESTAMP AS (null);",
-          "CREATE FUNCTION HOP(ts TIMESTAMP, emit_frequency STRING, window_size STRING) AS (1);",
-          "CREATE FUNCTION HOP_START(emit_frequency STRING, window_size STRING) "
-              + "RETURNS TIMESTAMP AS (null);",
-          "CREATE FUNCTION HOP_END(emit_frequency STRING, window_size STRING) "
-              + "RETURNS TIMESTAMP AS (null);",
-          "CREATE FUNCTION SESSION(ts TIMESTAMP, session_gap STRING) AS (1);",
-          "CREATE FUNCTION SESSION_START(session_gap STRING) RETURNS TIMESTAMP AS (null);",
-          "CREATE FUNCTION SESSION_END(session_gap STRING) RETURNS TIMESTAMP AS (null);");
-
-  /** The top-level Calcite schema, which may contain sub-schemas. */
-  private final SchemaPlus calciteSchema;
-  /**
-   * The top-level ZetaSQL catalog, which may contain nested catalogs for qualified table and
-   * function references.
-   */
-  private final SimpleCatalog zetaSqlCatalog;
-
-  private final JavaTypeFactory typeFactory;
-
-  private final JavaUdfLoader javaUdfLoader = new JavaUdfLoader();
-  private final Map<List<String>, ResolvedNodes.ResolvedCreateFunctionStmt> sqlScalarUdfs =
-      new HashMap<>();
-  /** User-defined table valued functions. */
-  private final Map<List<String>, ResolvedNode> sqlUdtvfs = new HashMap<>();
-
-  private final Map<List<String>, UserFunctionDefinitions.JavaScalarFunction> javaScalarUdfs =
-      new HashMap<>();
-  private final Map<List<String>, Combine.CombineFn<?, ?, ?>> javaUdafs = new HashMap<>();
-
-  private BeamZetaSqlCatalog(
-      SchemaPlus calciteSchema, SimpleCatalog zetaSqlCatalog, JavaTypeFactory typeFactory) {
-    this.calciteSchema = calciteSchema;
-    this.zetaSqlCatalog = zetaSqlCatalog;
-    this.typeFactory = typeFactory;
-  }
-
-  /** Return catalog pre-populated with builtin functions. */
-  static BeamZetaSqlCatalog create(
-      SchemaPlus calciteSchema, JavaTypeFactory typeFactory, AnalyzerOptions options) {
-    BeamZetaSqlCatalog catalog =
-        new BeamZetaSqlCatalog(
-            calciteSchema, new SimpleCatalog(calciteSchema.getName()), typeFactory);
-    catalog.addFunctionsToCatalog(options);
-    return catalog;
-  }
-
-  SimpleCatalog getZetaSqlCatalog() {
-    return zetaSqlCatalog;
-  }
-
-  void addTables(List<List<String>> tables, QueryTrait queryTrait) {
-    tables.forEach(table -> addTableToLeafCatalog(table, queryTrait));
-  }
-
-  void addFunction(ResolvedNodes.ResolvedCreateFunctionStmt createFunctionStmt) {
-    String functionGroup = getFunctionGroup(createFunctionStmt);
-    switch (functionGroup) {
-      case USER_DEFINED_SQL_FUNCTIONS:
-        sqlScalarUdfs.put(createFunctionStmt.getNamePath(), createFunctionStmt);
-        break;
-      case USER_DEFINED_JAVA_SCALAR_FUNCTIONS:
-        String functionName = String.join(".", createFunctionStmt.getNamePath());
-        for (FunctionArgumentType argumentType :
-            createFunctionStmt.getSignature().getFunctionArgumentList()) {
-          Type type = argumentType.getType();
-          if (type == null) {
-            throw new UnsupportedOperationException(
-                "UDF templated argument types are not supported.");
-          }
-          validateJavaUdfZetaSqlType(type, functionName);
-        }
-        if (createFunctionStmt.getReturnType() == null) {
-          throw new IllegalArgumentException("UDF return type must not be null.");
-        }
-        validateJavaUdfZetaSqlType(createFunctionStmt.getReturnType(), functionName);
-        String jarPath = getJarPath(createFunctionStmt);
-        ScalarFn scalarFn =
-            javaUdfLoader.loadScalarFunction(createFunctionStmt.getNamePath(), jarPath);
-        Method method = ScalarFnReflector.getApplyMethod(scalarFn);
-        javaScalarUdfs.put(
-            createFunctionStmt.getNamePath(),
-            UserFunctionDefinitions.JavaScalarFunction.create(method, jarPath));
-        break;
-      case USER_DEFINED_JAVA_AGGREGATE_FUNCTIONS:
-        jarPath = getJarPath(createFunctionStmt);
-        // Try loading the aggregate function just to make sure it exists. LazyAggregateCombineFn
-        // will need to fetch it again at runtime.
-        javaUdfLoader.loadAggregateFunction(createFunctionStmt.getNamePath(), jarPath);
-        Combine.CombineFn<?, ?, ?> combineFn =
-            new LazyAggregateCombineFn<>(createFunctionStmt.getNamePath(), jarPath);
-        javaUdafs.put(createFunctionStmt.getNamePath(), combineFn);
-        break;
-      default:
-        throw new IllegalArgumentException(
-            String.format("Encountered unrecognized function group %s.", functionGroup));
-    }
-    zetaSqlCatalog.addFunction(
-        new Function(
-            createFunctionStmt.getNamePath(),
-            functionGroup,
-            createFunctionStmt.getIsAggregate()
-                ? ZetaSQLFunctions.FunctionEnums.Mode.AGGREGATE
-                : ZetaSQLFunctions.FunctionEnums.Mode.SCALAR,
-            ImmutableList.of(createFunctionStmt.getSignature())));
-  }
-
-  /**
-   * Throws {@link UnsupportedOperationException} if ZetaSQL type is not supported in Java UDF.
-   * Supported types are a subset of the types supported by {@link BeamJavaUdfCalcRule}.
-   *
-   * <p>Supported types should be kept in sync with {@link #validateJavaUdfCalciteType(RelDataType,
-   * String)}.
-   */
-  void validateJavaUdfZetaSqlType(Type type, String functionName) {
-    switch (type.getKind()) {
-      case TYPE_BOOL:
-      case TYPE_BYTES:
-      case TYPE_DATE:
-      case TYPE_DOUBLE:
-      case TYPE_INT64:
-      case TYPE_NUMERIC:
-      case TYPE_STRING:
-      case TYPE_TIMESTAMP:
-        // These types are supported.
-        break;
-      case TYPE_ARRAY:
-        validateJavaUdfZetaSqlType(type.asArray().getElementType(), functionName);
-        break;
-      case TYPE_TIME:
-      case TYPE_DATETIME:
-      case TYPE_STRUCT:
-      default:
-        throw new UnsupportedOperationException(
-            String.format(
-                "ZetaSQL type %s not allowed in function %s", type.getKind().name(), functionName));
-    }
-  }
-
-  void addTableValuedFunction(
-      ResolvedNodes.ResolvedCreateTableFunctionStmt createTableFunctionStmt) {
-    zetaSqlCatalog.addTableValuedFunction(
-        new TableValuedFunction.FixedOutputSchemaTVF(
-            createTableFunctionStmt.getNamePath(),
-            createTableFunctionStmt.getSignature(),
-            TVFRelation.createColumnBased(
-                createTableFunctionStmt.getQuery().getColumnList().stream()
-                    .map(c -> TVFRelation.Column.create(c.getName(), c.getType()))
-                    .collect(Collectors.toList()))));
-    sqlUdtvfs.put(createTableFunctionStmt.getNamePath(), createTableFunctionStmt.getQuery());
-  }
-
-  UserFunctionDefinitions getUserFunctionDefinitions() {
-    return UserFunctionDefinitions.newBuilder()
-        .setSqlScalarFunctions(ImmutableMap.copyOf(sqlScalarUdfs))
-        .setSqlTableValuedFunctions(ImmutableMap.copyOf(sqlUdtvfs))
-        .setJavaScalarFunctions(ImmutableMap.copyOf(javaScalarUdfs))
-        .setJavaAggregateFunctions(ImmutableMap.copyOf(javaUdafs))
-        .build();
-  }
-
-  private void addFunctionsToCatalog(AnalyzerOptions options) {
-    // Enable ZetaSQL builtin functions.
-    ZetaSQLBuiltinFunctionOptions zetasqlBuiltinFunctionOptions =
-        new ZetaSQLBuiltinFunctionOptions(options.getLanguageOptions());
-    SupportedZetaSqlBuiltinFunctions.ALLOWLIST.forEach(
-        zetasqlBuiltinFunctionOptions::includeFunctionSignatureId);
-    zetaSqlCatalog.addZetaSQLFunctions(zetasqlBuiltinFunctionOptions);
-
-    // Enable Beam SQL's builtin windowing functions.
-    addWindowScalarFunctions(options);
-    addWindowTvfs();
-
-    // Add user-defined functions already defined in the schema, if any.
-    addUdfsFromSchema();
-  }
-
-  private void addWindowScalarFunctions(AnalyzerOptions options) {
-    PRE_DEFINED_WINDOW_FUNCTION_DECLARATIONS.stream()
-        .map(
-            func ->
-                (ResolvedNodes.ResolvedCreateFunctionStmt)
-                    Analyzer.analyzeStatement(func, options, zetaSqlCatalog))
-        .map(
-            resolvedFunc ->
-                new Function(
-                    String.join(".", resolvedFunc.getNamePath()),
-                    PRE_DEFINED_WINDOW_FUNCTIONS,
-                    ZetaSQLFunctions.FunctionEnums.Mode.SCALAR,
-                    ImmutableList.of(resolvedFunc.getSignature())))
-        .forEach(zetaSqlCatalog::addFunction);
-  }
-
-  @SuppressWarnings({
-    "nullness" // customContext and volatility are in fact nullable, but they are missing the
-    // annotation upstream. TODO Unsuppress when this is fixed in ZetaSQL.
-  })
-  private void addWindowTvfs() {
-    FunctionArgumentType retType =
-        new FunctionArgumentType(ZetaSQLFunctions.SignatureArgumentKind.ARG_TYPE_RELATION);
-
-    FunctionArgumentType inputTableType =
-        new FunctionArgumentType(ZetaSQLFunctions.SignatureArgumentKind.ARG_TYPE_RELATION);
-
-    FunctionArgumentType descriptorType =
-        new FunctionArgumentType(
-            ZetaSQLFunctions.SignatureArgumentKind.ARG_TYPE_DESCRIPTOR,
-            FunctionArgumentType.FunctionArgumentTypeOptions.builder()
-                .setDescriptorResolutionTableOffset(0)
-                .build(),
-            1);
-
-    FunctionArgumentType stringType =
-        new FunctionArgumentType(TypeFactory.createSimpleType(ZetaSQLType.TypeKind.TYPE_STRING));
-
-    // TUMBLE
-    zetaSqlCatalog.addTableValuedFunction(
-        new TableValuedFunction.ForwardInputSchemaToOutputSchemaWithAppendedColumnTVF(
-            ImmutableList.of(TVFStreamingUtils.FIXED_WINDOW_TVF),
-            new FunctionSignature(
-                retType, ImmutableList.of(inputTableType, descriptorType, stringType), -1),
-            ImmutableList.of(
-                TVFRelation.Column.create(
-                    TVFStreamingUtils.WINDOW_START,
-                    TypeFactory.createSimpleType(ZetaSQLType.TypeKind.TYPE_TIMESTAMP)),
-                TVFRelation.Column.create(
-                    TVFStreamingUtils.WINDOW_END,
-                    TypeFactory.createSimpleType(ZetaSQLType.TypeKind.TYPE_TIMESTAMP))),
-            null,
-            null));
-
-    // HOP
-    zetaSqlCatalog.addTableValuedFunction(
-        new TableValuedFunction.ForwardInputSchemaToOutputSchemaWithAppendedColumnTVF(
-            ImmutableList.of(TVFStreamingUtils.SLIDING_WINDOW_TVF),
-            new FunctionSignature(
-                retType,
-                ImmutableList.of(inputTableType, descriptorType, stringType, stringType),
-                -1),
-            ImmutableList.of(
-                TVFRelation.Column.create(
-                    TVFStreamingUtils.WINDOW_START,
-                    TypeFactory.createSimpleType(ZetaSQLType.TypeKind.TYPE_TIMESTAMP)),
-                TVFRelation.Column.create(
-                    TVFStreamingUtils.WINDOW_END,
-                    TypeFactory.createSimpleType(ZetaSQLType.TypeKind.TYPE_TIMESTAMP))),
-            null,
-            null));
-
-    // SESSION
-    zetaSqlCatalog.addTableValuedFunction(
-        new TableValuedFunction.ForwardInputSchemaToOutputSchemaWithAppendedColumnTVF(
-            ImmutableList.of(TVFStreamingUtils.SESSION_WINDOW_TVF),
-            new FunctionSignature(
-                retType,
-                ImmutableList.of(inputTableType, descriptorType, descriptorType, stringType),
-                -1),
-            ImmutableList.of(
-                TVFRelation.Column.create(
-                    TVFStreamingUtils.WINDOW_START,
-                    TypeFactory.createSimpleType(ZetaSQLType.TypeKind.TYPE_TIMESTAMP)),
-                TVFRelation.Column.create(
-                    TVFStreamingUtils.WINDOW_END,
-                    TypeFactory.createSimpleType(ZetaSQLType.TypeKind.TYPE_TIMESTAMP))),
-            null,
-            null));
-  }
-
-  private void addUdfsFromSchema() {
-    for (String functionName : calciteSchema.getFunctionNames()) {
-      Collection<org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function>
-          functions = calciteSchema.getFunctions(functionName);
-      if (functions.size() != 1) {
-        throw new IllegalArgumentException(
-            String.format(
-                "Expected exactly 1 definition for function '%s', but found %d."
-                    + " Beam ZetaSQL supports only a single function definition per function name (https://github.com/apache/beam/issues/20828).",
-                functionName, functions.size()));
-      }
-      for (org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function function :
-          functions) {
-        List<String> path = Arrays.asList(functionName.split("\\."));
-        if (function instanceof ScalarFunctionImpl) {
-          ScalarFunctionImpl scalarFunction = (ScalarFunctionImpl) function;
-          // Validate types before converting from Calcite to ZetaSQL, since the conversion may fail
-          // for unsupported types.
-          for (FunctionParameter parameter : scalarFunction.getParameters()) {
-            validateJavaUdfCalciteType(parameter.getType(typeFactory), functionName);
-          }
-          validateJavaUdfCalciteType(scalarFunction.getReturnType(typeFactory), functionName);
-          Method method = scalarFunction.method;
-          javaScalarUdfs.put(path, UserFunctionDefinitions.JavaScalarFunction.create(method, ""));
-          FunctionArgumentType resultType =
-              new FunctionArgumentType(
-                  ZetaSqlCalciteTranslationUtils.toZetaSqlType(
-                      scalarFunction.getReturnType(typeFactory)));
-          FunctionSignature functionSignature =
-              new FunctionSignature(resultType, getArgumentTypes(scalarFunction), 0L);
-          zetaSqlCatalog.addFunction(
-              new Function(
-                  path,
-                  USER_DEFINED_JAVA_SCALAR_FUNCTIONS,
-                  ZetaSQLFunctions.FunctionEnums.Mode.SCALAR,
-                  ImmutableList.of(functionSignature)));
-        } else if (function instanceof UdafImpl) {
-          UdafImpl<?, ?, ?> udaf = (UdafImpl) function;
-          javaUdafs.put(path, udaf.getCombineFn());
-          FunctionArgumentType resultType =
-              new FunctionArgumentType(
-                  ZetaSqlCalciteTranslationUtils.toZetaSqlType(udaf.getReturnType(typeFactory)));
-          FunctionSignature functionSignature =
-              new FunctionSignature(resultType, getArgumentTypes(udaf), 0L);
-          zetaSqlCatalog.addFunction(
-              new Function(
-                  path,
-                  USER_DEFINED_JAVA_AGGREGATE_FUNCTIONS,
-                  ZetaSQLFunctions.FunctionEnums.Mode.AGGREGATE,
-                  ImmutableList.of(functionSignature)));
-        } else {
-          throw new IllegalArgumentException(
-              String.format(
-                  "Function %s has unrecognized implementation type %s.",
-                  functionName, function.getClass().getName()));
-        }
-      }
-    }
-  }
-
-  private List<FunctionArgumentType> getArgumentTypes(
-      org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function function) {
-    return function.getParameters().stream()
-        .map(
-            (arg) ->
-                new FunctionArgumentType(
-                    ZetaSqlCalciteTranslationUtils.toZetaSqlType(arg.getType(typeFactory))))
-        .collect(Collectors.toList());
-  }
-
-  /**
-   * Throws {@link UnsupportedOperationException} if Calcite type is not supported in Java UDF.
-   * Supported types are a subset of the corresponding Calcite types supported by {@link
-   * BeamJavaUdfCalcRule}.
-   *
-   * <p>Supported types should be kept in sync with {@link #validateJavaUdfZetaSqlType(Type,
-   * String)}.
-   */
-  private void validateJavaUdfCalciteType(RelDataType type, String functionName) {
-    switch (type.getSqlTypeName()) {
-      case BIGINT:
-      case BOOLEAN:
-      case DATE:
-      case DECIMAL:
-      case DOUBLE:
-      case TIMESTAMP:
-      case VARCHAR:
-      case VARBINARY:
-        // These types are supported.
-        break;
-      case ARRAY:
-        validateJavaUdfCalciteType(
-            Preconditions.checkArgumentNotNull(
-                type.getComponentType(), "Encountered ARRAY type with no component type."),
-            functionName);
-        break;
-      case TIME:
-      case TIMESTAMP_WITH_LOCAL_TIME_ZONE:
-      case ROW:
-      default:
-        throw new UnsupportedOperationException(
-            String.format(
-                "Calcite type %s not allowed in function %s",
-                type.getSqlTypeName().getName(), functionName));
-    }
-  }
-
-  private String getFunctionGroup(ResolvedNodes.ResolvedCreateFunctionStmt createFunctionStmt) {
-    switch (createFunctionStmt.getLanguage().toUpperCase()) {
-      case "JAVA":
-        return createFunctionStmt.getIsAggregate()
-            ? USER_DEFINED_JAVA_AGGREGATE_FUNCTIONS
-            : USER_DEFINED_JAVA_SCALAR_FUNCTIONS;
-      case "SQL":
-        if (createFunctionStmt.getIsAggregate()) {
-          throw new UnsupportedOperationException(
-              "Native SQL aggregate functions are not supported (https://github.com/apache/beam/issues/20193).");
-        }
-        return USER_DEFINED_SQL_FUNCTIONS;
-      case "PY":
-      case "PYTHON":
-      case "JS":
-      case "JAVASCRIPT":
-        throw new UnsupportedOperationException(
-            String.format(
-                "Function %s uses unsupported language %s.",
-                String.join(".", createFunctionStmt.getNamePath()),
-                createFunctionStmt.getLanguage()));
-      default:
-        throw new IllegalArgumentException(
-            String.format(
-                "Function %s uses unrecognized language %s.",
-                String.join(".", createFunctionStmt.getNamePath()),
-                createFunctionStmt.getLanguage()));
-    }
-  }
-
-  /**
-   * Assume last element in tablePath is a table name, and everything before is catalogs. So the
-   * logic is to create nested catalogs until the last level, then add a table at the last level.
-   *
-   * <p>Table schema is extracted from Calcite schema based on the table name resolution strategy,
-   * e.g. either by drilling down the schema.getSubschema() path or joining the table name with dots
-   * to construct a single compound identifier (e.g. Data Catalog use case).
-   */
-  private void addTableToLeafCatalog(List<String> tablePath, QueryTrait queryTrait) {
-
-    SimpleCatalog leafCatalog = createNestedCatalogs(zetaSqlCatalog, tablePath);
-
-    org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Table calciteTable =
-        TableResolution.resolveCalciteTable(calciteSchema, tablePath);
-
-    if (calciteTable == null) {
-      throw new ZetaSqlException(
-          "Wasn't able to resolve the path "
-              + tablePath
-              + " in schema: "
-              + calciteSchema.getName());
-    }
-
-    RelDataType rowType = calciteTable.getRowType(typeFactory);
-
-    TableResolution.SimpleTableWithPath tableWithPath =
-        TableResolution.SimpleTableWithPath.of(tablePath);
-    queryTrait.addResolvedTable(tableWithPath);
-
-    addFieldsToTable(tableWithPath, rowType);
-    leafCatalog.addSimpleTable(tableWithPath.getTable());
-  }
-
-  private static void addFieldsToTable(
-      TableResolution.SimpleTableWithPath tableWithPath, RelDataType rowType) {
-    for (RelDataTypeField field : rowType.getFieldList()) {
-      tableWithPath
-          .getTable()
-          .addSimpleColumn(
-              field.getName(), ZetaSqlCalciteTranslationUtils.toZetaSqlType(field.getType()));
-    }
-  }
-
-  /** For table path like a.b.c we assume c is the table and a.b are the nested catalogs/schemas. */
-  private static SimpleCatalog createNestedCatalogs(SimpleCatalog catalog, List<String> tablePath) {
-    SimpleCatalog currentCatalog = catalog;
-    for (int i = 0; i < tablePath.size() - 1; i++) {
-      String nextCatalogName = tablePath.get(i);
-
-      Optional<SimpleCatalog> existing = tryGetExisting(currentCatalog, nextCatalogName);
-
-      currentCatalog =
-          existing.isPresent() ? existing.get() : addNewCatalog(currentCatalog, nextCatalogName);
-    }
-    return currentCatalog;
-  }
-
-  private static Optional<SimpleCatalog> tryGetExisting(
-      SimpleCatalog currentCatalog, String nextCatalogName) {
-    return currentCatalog.getCatalogList().stream()
-        .filter(c -> nextCatalogName.equals(c.getFullName()))
-        .findFirst();
-  }
-
-  private static SimpleCatalog addNewCatalog(SimpleCatalog currentCatalog, String nextCatalogName) {
-    SimpleCatalog nextCatalog = new SimpleCatalog(nextCatalogName);
-    currentCatalog.addSimpleCatalog(nextCatalog);
-    return nextCatalog;
-  }
-
-  private static String getJarPath(ResolvedNodes.ResolvedCreateFunctionStmt createFunctionStmt) {
-    String jarPath = getOptionStringValue(createFunctionStmt, "path");
-    if (jarPath.isEmpty()) {
-      throw new IllegalArgumentException(
-          String.format(
-              "No jar was provided to define function %s. Add 'OPTIONS (path=<jar location>)' to the CREATE FUNCTION statement.",
-              String.join(".", createFunctionStmt.getNamePath())));
-    }
-    return jarPath;
-  }
-
-  private static String getOptionStringValue(
-      ResolvedNodes.ResolvedCreateFunctionStmt createFunctionStmt, String optionName) {
-    for (ResolvedNodes.ResolvedOption option : createFunctionStmt.getOptionList()) {
-      if (optionName.equals(option.getName())) {
-        if (option.getValue() == null) {
-          throw new IllegalArgumentException(
-              String.format(
-                  "Option '%s' has null value (expected %s).",
-                  optionName, ZetaSQLType.TypeKind.TYPE_STRING));
-        }
-        if (option.getValue().getType().getKind() != ZetaSQLType.TypeKind.TYPE_STRING) {
-          throw new IllegalArgumentException(
-              String.format(
-                  "Option '%s' has type %s (expected %s).",
-                  optionName,
-                  option.getValue().getType().getKind(),
-                  ZetaSQLType.TypeKind.TYPE_STRING));
-        }
-        return ((ResolvedNodes.ResolvedLiteral) option.getValue()).getValue().getStringValue();
-      }
-    }
-    return "";
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlRelType.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlRelType.java
deleted file mode 100644
index c952273ce106..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlRelType.java
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
-import org.apache.beam.sdk.extensions.sql.impl.rel.CalcRelSplitter;
-import org.apache.beam.sdk.extensions.sql.zetasql.translation.ZetaSqlScalarFunctionImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexDynamicParam;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexFieldAccess;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexProgram;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlUserDefinedFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RelBuilder;
-
-/** {@link CalcRelSplitter.RelType} for {@link BeamZetaSqlCalcRel}. */
-class BeamZetaSqlRelType extends CalcRelSplitter.RelType {
-  BeamZetaSqlRelType(String name) {
-    super(name);
-  }
-
-  @Override
-  protected boolean canImplement(RexFieldAccess field) {
-    return true;
-  }
-
-  @Override
-  protected boolean canImplement(RexDynamicParam param) {
-    return true;
-  }
-
-  @Override
-  protected boolean canImplement(RexLiteral literal) {
-    return true;
-  }
-
-  @Override
-  protected boolean canImplement(RexCall call) {
-    if (call.getOperator() instanceof SqlUserDefinedFunction) {
-      SqlUserDefinedFunction udf = (SqlUserDefinedFunction) call.op;
-      if (udf.function instanceof ZetaSqlScalarFunctionImpl) {
-        ZetaSqlScalarFunctionImpl scalarFunction = (ZetaSqlScalarFunctionImpl) udf.function;
-        if (scalarFunction.functionGroup.equals(
-            BeamZetaSqlCatalog.USER_DEFINED_JAVA_SCALAR_FUNCTIONS)) {
-          return false;
-        }
-      }
-    }
-    return true;
-  }
-
-  @Override
-  protected RelNode makeRel(
-      RelOptCluster cluster,
-      RelTraitSet traitSet,
-      RelBuilder relBuilder,
-      RelNode input,
-      RexProgram program) {
-    RexProgram normalizedProgram = program.normalize(cluster.getRexBuilder(), false);
-    return new BeamZetaSqlCalcRel(
-        cluster,
-        traitSet.replace(BeamLogicalConvention.INSTANCE),
-        RelOptRule.convert(input, input.getTraitSet().replace(BeamLogicalConvention.INSTANCE)),
-        normalizedProgram);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/DateTimeUtils.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/DateTimeUtils.java
deleted file mode 100644
index a60cd395b581..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/DateTimeUtils.java
+++ /dev/null
@@ -1,244 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkNotNull;
-
-import com.google.zetasql.Value;
-import io.grpc.Status;
-import java.time.LocalTime;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.TimeUnit;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Splitter;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.math.LongMath;
-import org.checkerframework.checker.nullness.qual.Nullable;
-import org.joda.time.DateTime;
-import org.joda.time.DateTimeZone;
-import org.joda.time.format.DateTimeFormat;
-import org.joda.time.format.DateTimeFormatter;
-
-/** DateTimeUtils. */
-public class DateTimeUtils {
-  public static final Long MILLIS_PER_DAY = 86400000L;
-  private static final Long MICROS_PER_MILLI = 1000L;
-
-  private enum TimestampPatterns {
-    TIMESTAMP_PATTERN,
-    TIMESTAMP_PATTERN_SUBSECOND,
-    TIMESTAMP_PATTERN_T,
-    TIMESTAMP_PATTERN_SUBSECOND_T,
-  }
-
-  private static final ImmutableMap<TimestampPatterns, DateTimeFormatter>
-      TIMESTAMP_PATTERN_WITHOUT_TZ =
-          ImmutableMap.of(
-              TimestampPatterns.TIMESTAMP_PATTERN, DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss"),
-              TimestampPatterns.TIMESTAMP_PATTERN_SUBSECOND,
-                  DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss.SSS"),
-              TimestampPatterns.TIMESTAMP_PATTERN_T,
-                  DateTimeFormat.forPattern("yyyy-MM-dd'T'HH:mm:ss"),
-              TimestampPatterns.TIMESTAMP_PATTERN_SUBSECOND_T,
-                  DateTimeFormat.forPattern("yyyy-MM-dd'T'HH:mm:ss.SSS"));
-
-  private static final ImmutableMap<TimestampPatterns, DateTimeFormatter>
-      TIMESTAMP_PATTERN_WITH_TZ =
-          ImmutableMap.of(
-              TimestampPatterns.TIMESTAMP_PATTERN,
-                  DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ssZZ"),
-              TimestampPatterns.TIMESTAMP_PATTERN_SUBSECOND,
-                  DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss.SSSZZ"),
-              TimestampPatterns.TIMESTAMP_PATTERN_T,
-                  DateTimeFormat.forPattern("yyyy-MM-dd'T'HH:mm:ssZZ"),
-              TimestampPatterns.TIMESTAMP_PATTERN_SUBSECOND_T,
-                  DateTimeFormat.forPattern("yyyy-MM-dd'T'HH:mm:ss.SSSZZ"));
-
-  public static DateTimeFormatter findDateTimePattern(String str) {
-    if (str.indexOf('+') == -1) {
-      return findDateTimePattern(str, TIMESTAMP_PATTERN_WITHOUT_TZ);
-    } else {
-      return findDateTimePattern(str, TIMESTAMP_PATTERN_WITH_TZ);
-    }
-  }
-
-  public static DateTimeFormatter findDateTimePattern(
-      String str, ImmutableMap<TimestampPatterns, DateTimeFormatter> patternMap) {
-    if (str.indexOf('.') == -1) {
-      if (str.indexOf('T') == -1) {
-        return checkNotNull(patternMap.get(TimestampPatterns.TIMESTAMP_PATTERN));
-      } else {
-        return checkNotNull(patternMap.get(TimestampPatterns.TIMESTAMP_PATTERN_T));
-      }
-    } else {
-      if (str.indexOf('T') == -1) {
-        return checkNotNull(patternMap.get(TimestampPatterns.TIMESTAMP_PATTERN_SUBSECOND));
-      } else {
-        return checkNotNull(patternMap.get(TimestampPatterns.TIMESTAMP_PATTERN_SUBSECOND_T));
-      }
-    }
-  }
-
-  // https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#timestamp_differences
-  // 0001-01-01 00:00:00 to 9999-12-31 23:59:59.999999 UTC.
-  // -62135596800000000 to 253402300799999999
-  @SuppressWarnings("GoodTime")
-  public static final Long MIN_UNIX_MILLIS = -62135596800000L;
-
-  @SuppressWarnings("GoodTime")
-  public static final Long MAX_UNIX_MILLIS = 253402300799999L;
-
-  public static DateTime parseTimestampWithUTCTimeZone(String str) {
-    return findDateTimePattern(str).withZoneUTC().parseDateTime(str);
-  }
-
-  @SuppressWarnings("unused")
-  public static DateTime parseTimestampWithLocalTimeZone(String str) {
-    return findDateTimePattern(str).withZone(DateTimeZone.getDefault()).parseDateTime(str);
-  }
-
-  public static DateTime parseTimestampWithTimeZone(String str) {
-    // for example, accept "1990-10-20 13:24:01+0730"
-    if (str.indexOf('.') == -1) {
-      return DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ssZ").parseDateTime(str);
-    } else {
-      return DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss.SSSZ").parseDateTime(str);
-    }
-  }
-
-  public static String formatTimestampWithTimeZone(DateTime dt) {
-    String resultWithoutZone;
-    if (dt.getMillisOfSecond() == 0) {
-      resultWithoutZone = dt.toString(DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss"));
-    } else {
-      resultWithoutZone = dt.toString(DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss.SSS"));
-    }
-
-    // ZetaSQL expects a 2-digit timezone offset (-05) if the minute part is zero, and it expects
-    // a 4-digit timezone with a colon (-07:52) if the minute part is non-zero. None of the
-    // variations on z,Z,ZZ,.. do this for us so we have to do it manually here.
-    String zone = dt.toString(DateTimeFormat.forPattern("ZZ"));
-    List<String> zoneParts = Lists.newArrayList(Splitter.on(':').limit(2).split(zone));
-    if (zoneParts.size() == 2 && zoneParts.get(1).equals("00")) {
-      zone = zoneParts.get(0);
-    }
-
-    return resultWithoutZone + zone;
-  }
-
-  @SuppressWarnings("unused")
-  public static DateTime parseTimestampWithoutTimeZone(String str) {
-    return DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss").parseDateTime(str);
-  }
-
-  public static DateTime parseDate(String str) {
-    return DateTimeFormat.forPattern("yyyy-MM-dd").withZoneUTC().parseDateTime(str);
-  }
-
-  public static DateTime parseTime(String str) {
-    // DateTimeFormat does not parse "08:10:10" for pattern "HH:mm:ss.SSS". In this case, '.' must
-    // appear.
-    if (str.indexOf('.') == -1) {
-      return DateTimeFormat.forPattern("HH:mm:ss").withZoneUTC().parseDateTime(str);
-    } else {
-      return DateTimeFormat.forPattern("HH:mm:ss.SSS").withZoneUTC().parseDateTime(str);
-    }
-  }
-
-  public static Value parseDateToValue(String dateString) {
-    DateTime dateTime = parseDate(dateString);
-    return Value.createDateValue((int) (dateTime.getMillis() / MILLIS_PER_DAY));
-  }
-
-  public static Value parseTimeToValue(String timeString) {
-    LocalTime localTime = LocalTime.parse(timeString);
-    return Value.createTimeValue(localTime);
-  }
-
-  public static Value parseTimestampWithTZToValue(String timestampString) {
-    DateTime dateTime = parseTimestampWithTimeZone(timestampString);
-    // convert from micros.
-    // TODO: how to handle overflow.
-    return Value.createTimestampValueFromUnixMicros(
-        LongMath.checkedMultiply(dateTime.getMillis(), MICROS_PER_MILLI));
-  }
-
-  /**
-   * This function validates that Long representation of timestamp is compatible with ZetaSQL
-   * timestamp values range.
-   *
-   * <p>Invoked via reflection. @see SqlOperators
-   *
-   * @param ts Timestamp to validate.
-   * @return Unchanged timestamp sent for validation.
-   */
-  @SuppressWarnings("GoodTime")
-  public static @Nullable Long validateTimestamp(@Nullable Long ts) {
-    if (ts == null) {
-      return null;
-    }
-
-    if ((ts < MIN_UNIX_MILLIS) || (ts > MAX_UNIX_MILLIS)) {
-      throw Status.OUT_OF_RANGE
-          .withDescription("Timestamp is out of valid range.")
-          .asRuntimeException();
-    }
-
-    return ts;
-  }
-
-  /**
-   * This function validates that interval is compatible with ZetaSQL timestamp values range.
-   *
-   * <p>ZetaSQL validates that if we represent interval in milliseconds, it will fit into Long.
-   *
-   * <p>In case of SECOND or smaller time unit, it converts timestamp to microseconds, so we need to
-   * convert those to microsecond and verify that we do not cause overflow.
-   *
-   * <p>Invoked via reflection. @see SqlOperators
-   *
-   * @param arg Argument for the interval.
-   * @param unit Time unit used in this interval.
-   * @return Argument for the interval.
-   */
-  @SuppressWarnings("GoodTime")
-  public static @Nullable Long validateTimeInterval(@Nullable Long arg, TimeUnit unit) {
-    if (arg == null) {
-      return null;
-    }
-
-    // multiplier to convert to milli or microseconds.
-    long multiplier = unit.multiplier.longValue();
-    switch (unit) {
-      case SECOND:
-      case MILLISECOND:
-        multiplier *= 1000L; // Change multiplier from milliseconds to microseconds.
-        break;
-      default:
-        break;
-    }
-
-    if ((arg > Long.MAX_VALUE / multiplier) || (arg < Long.MIN_VALUE / multiplier)) {
-      throw Status.OUT_OF_RANGE
-          .withDescription("Interval is out of valid range")
-          .asRuntimeException();
-    }
-
-    return arg;
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/QueryTrait.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/QueryTrait.java
deleted file mode 100644
index ac1cd0820387..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/QueryTrait.java
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
-
-import com.google.zetasql.Table;
-import com.google.zetasql.resolvedast.ResolvedColumn;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedOutputColumn;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedWithEntry;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.stream.Collectors;
-import org.apache.beam.sdk.extensions.sql.zetasql.TableResolution.SimpleTableWithPath;
-
-/** QueryTrait. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-public class QueryTrait {
-  public Map<String, ResolvedWithEntry> withEntries = new HashMap<>();
-
-  public Map<ResolvedColumn, String> outputColumnMap = new HashMap<>();
-
-  public Map<Long, SimpleTableWithPath> resolvedTables = new HashMap<>();
-
-  // TODO: move query parameter map to QueryTrait.
-
-  public void addOutputColumnList(List<ResolvedOutputColumn> outputColumnList) {
-    outputColumnList.forEach(
-        column -> {
-          outputColumnMap.put(column.getColumn(), column.getName());
-        });
-  }
-
-  /** Store a table together with its full path for repeated resolutions. */
-  public void addResolvedTable(SimpleTableWithPath tableWithPath) {
-    // table ids are autoincremted in SimpleTable
-    resolvedTables.put(tableWithPath.getTable().getId(), tableWithPath);
-  }
-
-  /** True if the table was resolved using the Calcite schema. */
-  public boolean isTableResolved(Table table) {
-    return resolvedTables.containsKey(table.getId());
-  }
-
-  /** Returns a full table path (exlucding top-level schema) for a given ZetaSQL Table. */
-  public List<String> getTablePath(Table table) {
-    checkArgument(
-        isTableResolved(table),
-        "Attempting to get a path of an unresolved table. Resolve and add the table first: %s",
-        table.getFullName());
-    return resolvedTables.get(table.getId()).getPath();
-  }
-
-  public List<String> retrieveFieldNames(List<ResolvedColumn> resolvedColumnList) {
-    return resolvedColumnList.stream().map(this::resolveAlias).collect(Collectors.toList());
-  }
-
-  public String resolveAlias(ResolvedColumn resolvedColumn) {
-    return this.outputColumnMap.getOrDefault(resolvedColumn, resolvedColumn.getName());
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/SqlAnalyzer.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/SqlAnalyzer.java
deleted file mode 100644
index 0b5d09515b0e..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/SqlAnalyzer.java
+++ /dev/null
@@ -1,180 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_CREATE_FUNCTION_STMT;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_CREATE_TABLE_FUNCTION_STMT;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_QUERY_STMT;
-import static java.nio.charset.StandardCharsets.UTF_8;
-
-import com.google.zetasql.Analyzer;
-import com.google.zetasql.AnalyzerOptions;
-import com.google.zetasql.ParseResumeLocation;
-import com.google.zetasql.Value;
-import com.google.zetasql.ZetaSQLOptions.ErrorMessageMode;
-import com.google.zetasql.ZetaSQLOptions.LanguageFeature;
-import com.google.zetasql.ZetaSQLOptions.ParameterMode;
-import com.google.zetasql.ZetaSQLOptions.ProductMode;
-import com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind;
-import com.google.zetasql.resolvedast.ResolvedNodes;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedCreateFunctionStmt;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedCreateTableFunctionStmt;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedStatement;
-import java.util.Arrays;
-import java.util.HashSet;
-import java.util.List;
-import java.util.Map;
-import org.apache.beam.sdk.extensions.sql.impl.QueryPlanner.QueryParameters;
-import org.apache.beam.sdk.extensions.sql.impl.QueryPlanner.QueryParameters.Kind;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableSet;
-
-/** Adapter for {@link Analyzer} to simplify the API for parsing the query and resolving the AST. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-public class SqlAnalyzer {
-  private static final ImmutableSet<ResolvedNodeKind> SUPPORTED_STATEMENT_KINDS =
-      ImmutableSet.of(
-          RESOLVED_QUERY_STMT, RESOLVED_CREATE_FUNCTION_STMT, RESOLVED_CREATE_TABLE_FUNCTION_STMT);
-
-  SqlAnalyzer() {}
-
-  /** Returns table names from all statements in the SQL string. */
-  List<List<String>> extractTableNames(String sql, AnalyzerOptions options) {
-    ParseResumeLocation parseResumeLocation = new ParseResumeLocation(sql);
-    ImmutableList.Builder<List<String>> tables = ImmutableList.builder();
-    while (!isEndOfInput(parseResumeLocation)) {
-      List<List<String>> statementTables =
-          Analyzer.extractTableNamesFromNextStatement(parseResumeLocation, options);
-      tables.addAll(statementTables);
-    }
-    return tables.build();
-  }
-
-  /**
-   * Analyzes the entire SQL code block (which may consist of multiple statements) and returns the
-   * resolved query.
-   *
-   * <p>Assumes there is exactly one SELECT statement in the input, and it must be the last
-   * statement in the input.
-   */
-  ResolvedNodes.ResolvedQueryStmt analyzeQuery(
-      String sql, AnalyzerOptions options, BeamZetaSqlCatalog catalog) {
-    ParseResumeLocation parseResumeLocation = new ParseResumeLocation(sql);
-    ResolvedStatement statement;
-    do {
-      statement = analyzeNextStatement(parseResumeLocation, options, catalog);
-      if (statement.nodeKind() == RESOLVED_QUERY_STMT) {
-        if (!SqlAnalyzer.isEndOfInput(parseResumeLocation)) {
-          throw new UnsupportedOperationException(
-              "No additional statements are allowed after a SELECT statement.");
-        }
-      }
-    } while (!SqlAnalyzer.isEndOfInput(parseResumeLocation));
-
-    if (!(statement instanceof ResolvedNodes.ResolvedQueryStmt)) {
-      throw new UnsupportedOperationException(
-          "Statement list must end in a SELECT statement, not " + statement.nodeKindString());
-    }
-    return (ResolvedNodes.ResolvedQueryStmt) statement;
-  }
-
-  private static boolean isEndOfInput(ParseResumeLocation parseResumeLocation) {
-    return parseResumeLocation.getBytePosition()
-        >= parseResumeLocation.getInput().getBytes(UTF_8).length;
-  }
-
-  /**
-   * Accepts the ParseResumeLocation for the current position in the SQL string. Advances the
-   * ParseResumeLocation to the start of the next statement. Adds user-defined functions to the
-   * catalog for use in following statements. Returns the resolved AST.
-   */
-  private ResolvedStatement analyzeNextStatement(
-      ParseResumeLocation parseResumeLocation,
-      AnalyzerOptions options,
-      BeamZetaSqlCatalog catalog) {
-    ResolvedStatement resolvedStatement =
-        Analyzer.analyzeNextStatement(parseResumeLocation, options, catalog.getZetaSqlCatalog());
-    if (resolvedStatement.nodeKind() == RESOLVED_CREATE_FUNCTION_STMT) {
-      ResolvedCreateFunctionStmt createFunctionStmt =
-          (ResolvedCreateFunctionStmt) resolvedStatement;
-      try {
-        catalog.addFunction(createFunctionStmt);
-      } catch (IllegalArgumentException e) {
-        throw new RuntimeException(
-            String.format(
-                "Failed to define function '%s'",
-                String.join(".", createFunctionStmt.getNamePath())),
-            e);
-      }
-    } else if (resolvedStatement.nodeKind() == RESOLVED_CREATE_TABLE_FUNCTION_STMT) {
-      ResolvedCreateTableFunctionStmt createTableFunctionStmt =
-          (ResolvedCreateTableFunctionStmt) resolvedStatement;
-      catalog.addTableValuedFunction(createTableFunctionStmt);
-    } else if (!SUPPORTED_STATEMENT_KINDS.contains(resolvedStatement.nodeKind())) {
-      throw new UnsupportedOperationException(
-          "Unrecognized statement type " + resolvedStatement.nodeKindString());
-    }
-    return resolvedStatement;
-  }
-
-  static AnalyzerOptions baseAnalyzerOptions() {
-    AnalyzerOptions options = new AnalyzerOptions();
-    options.setErrorMessageMode(ErrorMessageMode.ERROR_MESSAGE_MULTI_LINE_WITH_CARET);
-
-    options.getLanguageOptions().setProductMode(ProductMode.PRODUCT_EXTERNAL);
-    options
-        .getLanguageOptions()
-        .setEnabledLanguageFeatures(
-            new HashSet<>(
-                Arrays.asList(
-                    LanguageFeature.FEATURE_CREATE_AGGREGATE_FUNCTION,
-                    LanguageFeature.FEATURE_CREATE_TABLE_FUNCTION,
-                    LanguageFeature.FEATURE_DISALLOW_GROUP_BY_FLOAT,
-                    LanguageFeature.FEATURE_NUMERIC_TYPE,
-                    LanguageFeature.FEATURE_TABLE_VALUED_FUNCTIONS,
-                    LanguageFeature.FEATURE_TEMPLATE_FUNCTIONS,
-                    LanguageFeature.FEATURE_V_1_1_SELECT_STAR_EXCEPT_REPLACE,
-                    LanguageFeature.FEATURE_V_1_2_CIVIL_TIME,
-                    LanguageFeature.FEATURE_V_1_3_ADDITIONAL_STRING_FUNCTIONS)));
-    options.getLanguageOptions().setSupportedStatementKinds(SUPPORTED_STATEMENT_KINDS);
-
-    return options;
-  }
-
-  static AnalyzerOptions getAnalyzerOptions(QueryParameters queryParams, String defaultTimezone) {
-    AnalyzerOptions options = baseAnalyzerOptions();
-
-    options.setDefaultTimezone(defaultTimezone);
-
-    if (queryParams.getKind() == Kind.NAMED) {
-      options.setParameterMode(ParameterMode.PARAMETER_NAMED);
-      for (Map.Entry<String, Value> entry : ((Map<String, Value>) queryParams.named()).entrySet()) {
-        options.addQueryParameter(entry.getKey(), entry.getValue().getType());
-      }
-    } else if (queryParams.getKind() == Kind.POSITIONAL) {
-      options.setParameterMode(ParameterMode.PARAMETER_POSITIONAL);
-      for (Value param : (List<Value>) queryParams.positional()) {
-        options.addPositionalQueryParameter(param.getType());
-      }
-    }
-
-    return options;
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/SupportedZetaSqlBuiltinFunctions.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/SupportedZetaSqlBuiltinFunctions.java
deleted file mode 100644
index b3f71cea9e34..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/SupportedZetaSqlBuiltinFunctions.java
+++ /dev/null
@@ -1,663 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import com.google.zetasql.ZetaSQLFunction.FunctionSignatureId;
-import java.util.List;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-
-/**
- * List of ZetaSQL builtin functions supported by Beam ZetaSQL. Keep this list in sync with
- * https://github.com/google/zetasql/blob/master/zetasql/public/builtin_function.proto. Uncomment
- * the corresponding entries to enable parser support to the operators/functions.
- *
- * <p>Last synced ZetaSQL release: 2020.06.01
- */
-class SupportedZetaSqlBuiltinFunctions {
-  static final List<FunctionSignatureId> ALLOWLIST =
-      ImmutableList.of(
-          FunctionSignatureId.FN_ADD_DOUBLE, // $add
-          FunctionSignatureId.FN_ADD_INT64, // $add
-          FunctionSignatureId.FN_ADD_NUMERIC, // $add
-          // FunctionSignatureId.FN_ADD_BIGNUMERIC, // $add
-          // FunctionSignatureId.FN_ADD_DATE_INT64, // $add
-          // FunctionSignatureId.FN_ADD_INT64_DATE, // $add
-          FunctionSignatureId.FN_AND, // $and
-          FunctionSignatureId.FN_CASE_NO_VALUE, // $case_no_value
-          FunctionSignatureId.FN_CASE_WITH_VALUE, // $case_with_value
-          FunctionSignatureId.FN_DIVIDE_DOUBLE, // $divide
-          FunctionSignatureId.FN_DIVIDE_NUMERIC, // $divide
-          // FunctionSignatureId.FN_DIVIDE_BIGNUMERIC, // $divide
-          FunctionSignatureId.FN_GREATER, // $greater
-          FunctionSignatureId.FN_GREATER_OR_EQUAL, // $greater_or_equal
-          FunctionSignatureId.FN_LESS, // $less
-          FunctionSignatureId.FN_LESS_OR_EQUAL, // $less_or_equal
-          FunctionSignatureId.FN_EQUAL, // $equal
-          FunctionSignatureId.FN_STRING_LIKE, // $like
-          FunctionSignatureId.FN_BYTE_LIKE, // $like
-          FunctionSignatureId.FN_IN, // $in
-          FunctionSignatureId.FN_IN_ARRAY, // $in_array
-          // FunctionSignatureId.FN_BETWEEN, // $between
-          FunctionSignatureId.FN_IS_NULL, // $is_null
-          FunctionSignatureId.FN_IS_TRUE, // $is_true
-          FunctionSignatureId.FN_IS_FALSE, // $is_false
-          FunctionSignatureId.FN_MULTIPLY_DOUBLE, // $multiply
-          FunctionSignatureId.FN_MULTIPLY_INT64, // $multiply
-          FunctionSignatureId.FN_MULTIPLY_NUMERIC, // $multiply
-          // FunctionSignatureId.FN_MULTIPLY_BIGNUMERIC, // $multiply
-          FunctionSignatureId.FN_NOT, // $not
-          FunctionSignatureId.FN_NOT_EQUAL, // $not_equal
-          FunctionSignatureId.FN_OR, // $or
-          FunctionSignatureId.FN_SUBTRACT_DOUBLE, // $subtract
-          FunctionSignatureId.FN_SUBTRACT_INT64, // $subtract
-          FunctionSignatureId.FN_SUBTRACT_NUMERIC, // $subtract
-          // FunctionSignatureId.FN_SUBTRACT_BIGNUMERIC, // $subtract
-          // FunctionSignatureId.FN_SUBTRACT_DATE_INT64, // $subtract
-          FunctionSignatureId.FN_UNARY_MINUS_INT64, // $unary_minus
-          FunctionSignatureId.FN_UNARY_MINUS_DOUBLE, // $unary_minus
-          FunctionSignatureId.FN_UNARY_MINUS_NUMERIC, // $unary_minus
-          // FunctionSignatureId.FN_UNARY_MINUS_BIGNUMERIC, // $unary_minus
-
-          // Bitwise unary operators.
-          // FunctionSignatureId.FN_BITWISE_NOT_INT64, // $bitwise_not
-          // FunctionSignatureId.FN_BITWISE_NOT_BYTES, // $bitwise_not
-          // Bitwise binary operators.
-          // FunctionSignatureId.FN_BITWISE_OR_INT64, // $bitwise_or
-          // FunctionSignatureId.FN_BITWISE_OR_BYTES, // $bitwise_or
-          // FunctionSignatureId.FN_BITWISE_XOR_INT64, // $bitwise_xor
-          // FunctionSignatureId.FN_BITWISE_XOR_BYTES, // $bitwise_xor
-          // FunctionSignatureId.FN_BITWISE_AND_INT64, // $bitwise_and
-          // FunctionSignatureId.FN_BITWISE_AND_BYTES, // $bitwise_and
-          // FunctionSignatureId.FN_BITWISE_LEFT_SHIFT_INT64, // $bitwise_left_shift
-          // FunctionSignatureId.FN_BITWISE_LEFT_SHIFT_BYTES, // $bitwise_left_shift
-          // FunctionSignatureId.FN_BITWISE_RIGHT_SHIFT_INT64, // $bitwise_right_shift
-          // FunctionSignatureId.FN_BITWISE_RIGHT_SHIFT_BYTES, // $bitwise_right_shift
-
-          // BIT_COUNT functions.
-          // FunctionSignatureId.FN_BIT_COUNT_INT64, // bit_count(int64) -> int64
-          // FunctionSignatureId.FN_BIT_COUNT_BYTES, // bit_count(bytes) -> int64
-
-          // FunctionSignatureId.FN_ERROR,// error(string) -> {unused result, coercible to any type}
-
-          FunctionSignatureId.FN_COUNT_STAR, // $count_star
-
-          //
-          // The following functions use standard function call syntax.
-          //
-
-          // String functions
-          FunctionSignatureId.FN_CONCAT_STRING, // concat(repeated string) -> string
-          // FunctionSignatureId.FN_CONCAT_BYTES, // concat(repeated bytes) -> bytes
-          // FunctionSignatureId.FN_CONCAT_OP_STRING, // concat(string, string) -> string
-          // FunctionSignatureId.FN_CONCAT_OP_BYTES, // concat(bytes, bytes) -> bytes
-          FunctionSignatureId.FN_STRPOS_STRING, // strpos(string, string) -> int64
-          // FunctionSignatureId.FN_STRPOS_BYTES, // strpos(bytes, bytes) -> int64
-
-          // FunctionSignatureId.FN_INSTR_STRING,// instr(string, string[, int64[, int64]]) -> int64
-          // FunctionSignatureId.FN_INSTR_BYTES, // instr(bytes, bytes[, int64[, int64]]) -> int64
-          FunctionSignatureId.FN_LOWER_STRING, // lower(string) -> string
-          // FunctionSignatureId.FN_LOWER_BYTES, // lower(bytes) -> bytes
-          FunctionSignatureId.FN_UPPER_STRING, // upper(string) -> string
-          // FunctionSignatureId.FN_UPPER_BYTES, // upper(bytes) -> bytes
-          FunctionSignatureId.FN_LENGTH_STRING, // length(string) -> int64
-          // FunctionSignatureId.FN_LENGTH_BYTES, // length(bytes) -> int64
-          FunctionSignatureId.FN_STARTS_WITH_STRING, // starts_with(string, string) -> string
-          // FunctionSignatureId.FN_STARTS_WITH_BYTES, // starts_with(bytes, bytes) -> bytes
-          FunctionSignatureId.FN_ENDS_WITH_STRING, // ends_with(string, string) -> string
-          // FunctionSignatureId.FN_ENDS_WITH_BYTES, // ends_with(bytes, bytes) -> bytes
-          FunctionSignatureId.FN_SUBSTR_STRING, // substr(string, int64[, int64]) -> string
-          // FunctionSignatureId.FN_SUBSTR_BYTES, // substr(bytes, int64[, int64]) -> bytes
-          FunctionSignatureId.FN_TRIM_STRING, // trim(string[, string]) -> string
-          // FunctionSignatureId.FN_TRIM_BYTES, // trim(bytes, bytes) -> bytes
-          FunctionSignatureId.FN_LTRIM_STRING, // ltrim(string[, string]) -> string
-          // FunctionSignatureId.FN_LTRIM_BYTES, // ltrim(bytes, bytes) -> bytes
-          FunctionSignatureId.FN_RTRIM_STRING, // rtrim(string[, string]) -> string
-          // FunctionSignatureId.FN_RTRIM_BYTES, // rtrim(bytes, bytes) -> bytes
-          FunctionSignatureId.FN_REPLACE_STRING, // replace(string, string, string) -> string
-          // FunctionSignatureId.FN_REPLACE_BYTES, // replace(bytes, bytes, bytes) -> bytes
-          // FunctionSignatureId.FN_REGEXP_MATCH_STRING, // regexp_match(string, string) -> bool
-          // FunctionSignatureId.FN_REGEXP_MATCH_BYTES, // regexp_match(bytes, bytes) -> bool
-          // FunctionSignatureId.FN_REGEXP_EXTRACT_STRING,//regexp_extract(string, string) -> string
-          // FunctionSignatureId.FN_REGEXP_EXTRACT_BYTES, // regexp_extract(bytes, bytes) -> bytes
-          // FunctionSignatureId.FN_REGEXP_REPLACE_STRING,
-          // regexp_replace(string, string, string) -> string
-          // FunctionSignatureId.FN_REGEXP_REPLACE_BYTES,
-          // regexp_replace(bytes, bytes, bytes) -> bytes
-          // FunctionSignatureId.FN_REGEXP_EXTRACT_ALL_STRING,
-          // regexp_extract_all(string, string) -> array of string
-          // FunctionSignatureId.FN_REGEXP_EXTRACT_ALL_BYTES,
-          // regexp_extract_all(bytes, bytes) -> array of bytes
-          // FunctionSignatureId.FN_BYTE_LENGTH_STRING, // byte_length(string) -> int64
-          // FunctionSignatureId.FN_BYTE_LENGTH_BYTES, // byte_length(bytes) -> int64
-          // semantically identical to FN_LENGTH_BYTES
-          FunctionSignatureId.FN_CHAR_LENGTH_STRING, // char_length(string) -> int64
-          // semantically identical to FN_LENGTH_STRING
-          // FunctionSignatureId.FN_FORMAT_STRING, // format(string, ...) -> string
-          // FunctionSignatureId.FN_SPLIT_STRING, // split(string, string) -> array of string
-          // FunctionSignatureId.FN_SPLIT_BYTES, // split(bytes, bytes) -> array of bytes
-          // FunctionSignatureId.FN_REGEXP_CONTAINS_STRING,//regexp_contains(string, string) -> bool
-          // FunctionSignatureId.FN_REGEXP_CONTAINS_BYTES, // regexp_contains(bytes, bytes) -> bool
-          // Converts bytes to string by replacing invalid UTF-8 characters with
-          // replacement char U+FFFD.
-          // FunctionSignatureId.FN_SAFE_CONVERT_BYTES_TO_STRING,
-          // Unicode normalization and casefolding functions.
-          // FunctionSignatureId.FN_NORMALIZE_STRING, // normalize(string [, mode]) -> string
-          // normalize_and_casefold(string [, mode]) -> string
-          // FunctionSignatureId.FN_NORMALIZE_AND_CASEFOLD_STRING,
-          // FunctionSignatureId.FN_TO_BASE64, // to_base64(bytes) -> string
-          // FunctionSignatureId.FN_FROM_BASE64, // from_base64(string) -> bytes
-          // FunctionSignatureId.FN_TO_HEX, // to_hex(bytes) -> string
-          // FunctionSignatureId.FN_FROM_HEX, // from_hex(string) -> bytes
-          // FunctionSignatureId.FN_TO_BASE32, // to_base32(bytes) -> string
-          // FunctionSignatureId.FN_FROM_BASE32, // from_base32(string) -> bytes
-          // to_code_points(string) -> array<int64>
-          // FunctionSignatureId.FN_TO_CODE_POINTS_STRING,
-          // to_code_points(bytes) -> array<int64>
-          // FunctionSignatureId.FN_TO_CODE_POINTS_BYTES,
-          // code_points_to_string(array<int64>) -> string
-          // FunctionSignatureId.FN_CODE_POINTS_TO_STRING,
-          // code_points_to_bytes(array<int64>) -> bytes
-          // FunctionSignatureId.FN_CODE_POINTS_TO_BYTES,
-          // FunctionSignatureId.FN_LPAD_BYTES, // lpad(bytes, int64[, bytes]) -> bytes
-          // FunctionSignatureId.FN_LPAD_STRING, // lpad(string, int64[, string]) -> string
-          // FunctionSignatureId.FN_RPAD_BYTES, // rpad(bytes, int64[, bytes]) -> bytes
-          // FunctionSignatureId.FN_RPAD_STRING, // rpad(string, int64[, string]) -> string
-          FunctionSignatureId.FN_LEFT_STRING, // left(string, int64) -> string
-          // FunctionSignatureId.FN_LEFT_BYTES, // left(bytes, int64) -> bytes
-          FunctionSignatureId.FN_RIGHT_STRING, // right(string, int64) -> string
-          // FunctionSignatureId.FN_RIGHT_BYTES, // right(bytes, int64) -> bytes
-          // FunctionSignatureId.FN_REPEAT_BYTES, // repeat(bytes, int64) -> bytes
-          // FunctionSignatureId.FN_REPEAT_STRING, // repeat(string, int64) -> string
-          FunctionSignatureId.FN_REVERSE_STRING, // reverse(string) -> string
-          // FunctionSignatureId.FN_REVERSE_BYTES, // reverse(bytes) -> bytes
-          // FunctionSignatureId.FN_SOUNDEX_STRING, // soundex(string) -> string
-          // FunctionSignatureId.FN_ASCII_STRING, // ASCII(string) -> int64
-          // FunctionSignatureId.FN_ASCII_BYTES, // ASCII(bytes) -> int64
-          // FunctionSignatureId.FN_TRANSLATE_STRING, // translate(string, string, string) -> string
-          // FunctionSignatureId.FN_TRANSLATE_BYTES, // soundex(bytes, bytes, bytes) -> bytes
-          // FunctionSignatureId.FN_INITCAP_STRING, // initcap(string[, string]) -> string
-          // FunctionSignatureId.FN_UNICODE_STRING, // unicode(string) -> int64
-          // FunctionSignatureId.FN_CHR_STRING, // chr(int64) -> string
-
-          // Control flow functions
-          FunctionSignatureId.FN_IF, // if
-          // Coalesce is used to express the output join column in FULL JOIN.
-          FunctionSignatureId.FN_COALESCE, // coalesce
-          FunctionSignatureId.FN_IFNULL, // ifnull
-          FunctionSignatureId.FN_NULLIF, // nullif
-
-          // Time functions
-          FunctionSignatureId.FN_CURRENT_DATE, // current_date
-          FunctionSignatureId.FN_CURRENT_DATETIME, // current_datetime
-          FunctionSignatureId.FN_CURRENT_TIME, // current_time
-          FunctionSignatureId.FN_CURRENT_TIMESTAMP, // current_timestamp
-          FunctionSignatureId.FN_DATE_ADD_DATE, // date_add
-          FunctionSignatureId.FN_DATETIME_ADD, // datetime_add
-          FunctionSignatureId.FN_TIME_ADD, // time_add
-          FunctionSignatureId.FN_TIMESTAMP_ADD, // timestamp_add
-          FunctionSignatureId.FN_DATE_DIFF_DATE, // date_diff
-          FunctionSignatureId.FN_DATETIME_DIFF, // datetime_diff
-          FunctionSignatureId.FN_TIME_DIFF, // time_diff
-          FunctionSignatureId.FN_TIMESTAMP_DIFF, // timestamp_diff
-          FunctionSignatureId.FN_DATE_SUB_DATE, // date_sub
-          FunctionSignatureId.FN_DATETIME_SUB, // datetime_sub
-          FunctionSignatureId.FN_TIME_SUB, // time_sub
-          FunctionSignatureId.FN_TIMESTAMP_SUB, // timestamp_sub
-          FunctionSignatureId.FN_DATE_TRUNC_DATE, // date_trunc
-          FunctionSignatureId.FN_DATETIME_TRUNC, // datetime_trunc
-          FunctionSignatureId.FN_TIME_TRUNC, // time_trunc
-          FunctionSignatureId.FN_TIMESTAMP_TRUNC, // timestamp_trunc
-          FunctionSignatureId.FN_DATE_FROM_UNIX_DATE, // date_from_unix_date
-          FunctionSignatureId.FN_TIMESTAMP_FROM_INT64_SECONDS, // timestamp_seconds
-          FunctionSignatureId.FN_TIMESTAMP_FROM_INT64_MILLIS, // timestamp_millis
-          // FunctionSignatureId.FN_TIMESTAMP_FROM_INT64_MICROS, // timestamp_micros
-          FunctionSignatureId.FN_TIMESTAMP_FROM_UNIX_SECONDS_INT64, // timestamp_from_unix_seconds
-          // timestamp_from_unix_seconds
-          // FunctionSignatureId.FN_TIMESTAMP_FROM_UNIX_SECONDS_TIMESTAMP,
-          FunctionSignatureId.FN_TIMESTAMP_FROM_UNIX_MILLIS_INT64, // timestamp_from_unix_millis
-          // timestamp_from_unix_millis
-          // FunctionSignatureId.FN_TIMESTAMP_FROM_UNIX_MILLIS_TIMESTAMP,
-          // FunctionSignatureId.FN_TIMESTAMP_FROM_UNIX_MICROS_INT64, // timestamp_from_unix_micros
-          // timestamp_from_unix_micros
-          // FunctionSignatureId.FN_TIMESTAMP_FROM_UNIX_MICROS_TIMESTAMP,
-          FunctionSignatureId.FN_UNIX_DATE, // unix_date
-          FunctionSignatureId.FN_UNIX_SECONDS_FROM_TIMESTAMP,
-          FunctionSignatureId.FN_UNIX_MILLIS_FROM_TIMESTAMP,
-          // FunctionSignatureId.FN_UNIX_MICROS_FROM_TIMESTAMP,
-          FunctionSignatureId.FN_DATE_FROM_TIMESTAMP, // date
-          FunctionSignatureId.FN_DATE_FROM_DATETIME, // date
-          // FunctionSignatureId.FN_DATE_FROM_DATE, // date
-          // FunctionSignatureId.FN_DATE_FROM_STRING, // date
-          FunctionSignatureId.FN_DATE_FROM_YEAR_MONTH_DAY, // date
-          FunctionSignatureId.FN_TIMESTAMP_FROM_STRING, // timestamp
-          FunctionSignatureId.FN_TIMESTAMP_FROM_DATE, // timestamp
-          FunctionSignatureId.FN_TIMESTAMP_FROM_DATETIME, // timestamp
-          // FunctionSignatureId.FN_TIMESTAMP_FROM_TIMESTAMP, // timestamp
-          FunctionSignatureId.FN_TIME_FROM_HOUR_MINUTE_SECOND, // time
-          FunctionSignatureId.FN_TIME_FROM_TIMESTAMP, // time
-          FunctionSignatureId.FN_TIME_FROM_DATETIME, // time
-          // FunctionSignatureId.FN_TIME_FROM_TIME, // time
-          // FunctionSignatureId.FN_TIME_FROM_STRING, // time
-          FunctionSignatureId.FN_DATETIME_FROM_DATE_AND_TIME, // datetime
-          FunctionSignatureId.FN_DATETIME_FROM_YEAR_MONTH_DAY_HOUR_MINUTE_SECOND, // datetime
-          FunctionSignatureId.FN_DATETIME_FROM_TIMESTAMP, // datetime
-          FunctionSignatureId.FN_DATETIME_FROM_DATE, // datetime
-          // FunctionSignatureId.FN_DATETIME_FROM_DATETIME, // datetime
-          // FunctionSignatureId.FN_DATETIME_FROM_STRING, // datetime
-          // FunctionSignatureId.FN_STRING_FROM_DATE, // string
-          FunctionSignatureId.FN_STRING_FROM_TIMESTAMP, // string
-          // FunctionSignatureId.FN_STRING_FROM_DATETIME, // string
-          // FunctionSignatureId.FN_STRING_FROM_TIME, // string
-
-          // Signatures for extracting date parts, taking a date/timestamp
-          // and the target date part as arguments.
-          FunctionSignatureId.FN_EXTRACT_FROM_DATE, // $extract
-          FunctionSignatureId.FN_EXTRACT_FROM_DATETIME, // $extract
-          FunctionSignatureId.FN_EXTRACT_FROM_TIME, // $extract
-          FunctionSignatureId.FN_EXTRACT_FROM_TIMESTAMP, // $extract
-
-          // Signatures specific to extracting the DATE date part from a DATETIME or a
-          // TIMESTAMP.
-          FunctionSignatureId.FN_EXTRACT_DATE_FROM_DATETIME, // $extract_date
-          FunctionSignatureId.FN_EXTRACT_DATE_FROM_TIMESTAMP, // $extract_date
-
-          // Signatures specific to extracting the TIME date part from a DATETIME or a
-          // TIMESTAMP.
-          FunctionSignatureId.FN_EXTRACT_TIME_FROM_DATETIME, // $extract_time
-          FunctionSignatureId.FN_EXTRACT_TIME_FROM_TIMESTAMP, // $extract_time
-
-          // Signature specific to extracting the DATETIME date part from a TIMESTAMP.
-          FunctionSignatureId.FN_EXTRACT_DATETIME_FROM_TIMESTAMP, // $extract_datetime
-
-          // Signature for formatting and parsing
-          FunctionSignatureId.FN_FORMAT_DATE, // format_date
-          FunctionSignatureId.FN_FORMAT_DATETIME, // format_datetime
-          FunctionSignatureId.FN_FORMAT_TIME, // format_time
-          FunctionSignatureId.FN_FORMAT_TIMESTAMP, // format_timestamp
-          FunctionSignatureId.FN_PARSE_DATE, // parse_date
-          FunctionSignatureId.FN_PARSE_DATETIME, // parse_datetime
-          FunctionSignatureId.FN_PARSE_TIME, // parse_time
-          FunctionSignatureId.FN_PARSE_TIMESTAMP, // parse_timestamp
-          // FunctionSignatureId.FN_LAST_DAY_DATE, // last_day date
-          // FunctionSignatureId.FN_LAST_DAY_DATETIME, // last_day datetime
-
-          // Math functions
-          FunctionSignatureId.FN_ABS_INT64, // abs
-          FunctionSignatureId.FN_ABS_DOUBLE, // abs
-          FunctionSignatureId.FN_ABS_NUMERIC, // abs
-          // FunctionSignatureId.FN_ABS_BIGNUMERIC, // abs
-          FunctionSignatureId.FN_SIGN_INT64, // sign
-          FunctionSignatureId.FN_SIGN_DOUBLE, // sign
-          FunctionSignatureId.FN_SIGN_NUMERIC, // sign
-          // FunctionSignatureId.FN_SIGN_BIGNUMERIC, // sign
-
-          FunctionSignatureId.FN_ROUND_DOUBLE, // round(double) -> double
-          FunctionSignatureId.FN_ROUND_NUMERIC, // round(numeric) -> numeric
-          // FunctionSignatureId.FN_ROUND_BIGNUMERIC, // round(bignumeric) -> bignumeric
-          FunctionSignatureId.FN_ROUND_WITH_DIGITS_DOUBLE, // round(double, int64) -> double
-          FunctionSignatureId.FN_ROUND_WITH_DIGITS_NUMERIC, // round(numeric, int64) -> numeric
-          // round(bignumeric, int64) -> bignumeric
-          // FunctionSignatureId.FN_ROUND_WITH_DIGITS_BIGNUMERIC,
-          FunctionSignatureId.FN_TRUNC_DOUBLE, // trunc(double) -> double
-          FunctionSignatureId.FN_TRUNC_NUMERIC, // trunc(numeric) -> numeric
-          // FunctionSignatureId.FN_TRUNC_BIGNUMERIC, // trunc(bignumeric) -> bignumeric
-          FunctionSignatureId.FN_TRUNC_WITH_DIGITS_DOUBLE, // trunc(double, int64) -> double
-          FunctionSignatureId.FN_TRUNC_WITH_DIGITS_NUMERIC, // trunc(numeric, int64) -> numeric
-          // trunc(bignumeric, int64) -> bignumeric
-          // FunctionSignatureId.FN_TRUNC_WITH_DIGITS_BIGNUMERIC,
-          FunctionSignatureId.FN_CEIL_DOUBLE, // ceil(double) -> double
-          FunctionSignatureId.FN_CEIL_NUMERIC, // ceil(numeric) -> numeric
-          // FunctionSignatureId.FN_CEIL_BIGNUMERIC, // ceil(bignumeric) -> bignumeric
-          FunctionSignatureId.FN_FLOOR_DOUBLE, // floor(double) -> double
-          FunctionSignatureId.FN_FLOOR_NUMERIC, // floor(numeric) -> numeric
-          // FunctionSignatureId.FN_FLOOR_BIGNUMERIC, // floor(bignumeric) -> bignumeric
-
-          FunctionSignatureId.FN_MOD_INT64, // mod(int64, int64) -> int64
-          FunctionSignatureId.FN_MOD_NUMERIC, // mod(numeric, numeric) -> numeric
-          // FunctionSignatureId.FN_MOD_BIGNUMERIC, // mod(bignumeric, bignumeric) -> bignumeric
-          FunctionSignatureId.FN_DIV_INT64, // div(int64, int64) -> int64
-          FunctionSignatureId.FN_DIV_NUMERIC, // div(numeric, numeric) -> numeric
-          // FunctionSignatureId.FN_DIV_BIGNUMERIC, // div(bignumeric, bignumeric) -> bignumeric
-
-          FunctionSignatureId.FN_IS_INF, // is_inf
-          FunctionSignatureId.FN_IS_NAN, // is_nan
-          FunctionSignatureId.FN_IEEE_DIVIDE_DOUBLE, // ieee_divide
-          FunctionSignatureId.FN_SAFE_DIVIDE_DOUBLE, // safe_divide
-          FunctionSignatureId.FN_SAFE_DIVIDE_NUMERIC, // safe_divide
-          // FunctionSignatureId.FN_SAFE_DIVIDE_BIGNUMERIC, // safe_divide
-          FunctionSignatureId.FN_SAFE_ADD_INT64, // safe_add
-          FunctionSignatureId.FN_SAFE_ADD_DOUBLE, // safe_add
-          FunctionSignatureId.FN_SAFE_ADD_NUMERIC, // safe_add
-          // FunctionSignatureId.FN_SAFE_ADD_BIGNUMERIC, // safe_add
-          FunctionSignatureId.FN_SAFE_SUBTRACT_INT64, // safe_subtract
-          FunctionSignatureId.FN_SAFE_SUBTRACT_DOUBLE, // safe_subtract
-          FunctionSignatureId.FN_SAFE_SUBTRACT_NUMERIC, // safe_subtract
-          // FunctionSignatureId.FN_SAFE_SUBTRACT_BIGNUMERIC, // safe_subtract
-          FunctionSignatureId.FN_SAFE_MULTIPLY_INT64, // safe_multiply
-          FunctionSignatureId.FN_SAFE_MULTIPLY_DOUBLE, // safe_multiply
-          FunctionSignatureId.FN_SAFE_MULTIPLY_NUMERIC, // safe_multiply
-          // FunctionSignatureId.FN_SAFE_MULTIPLY_BIGNUMERIC, // safe_multiply
-          FunctionSignatureId.FN_SAFE_UNARY_MINUS_INT64, // safe_negate
-          FunctionSignatureId.FN_SAFE_UNARY_MINUS_DOUBLE, // safe_negate
-          FunctionSignatureId.FN_SAFE_UNARY_MINUS_NUMERIC, // safe_negate
-          // FunctionSignatureId.FN_SAFE_UNARY_MINUS_BIGNUMERIC, // safe_negate
-
-          // FunctionSignatureId.FN_GREATEST, // greatest
-          // FunctionSignatureId.FN_LEAST, // least
-
-          FunctionSignatureId.FN_SQRT_DOUBLE, // sqrt
-          FunctionSignatureId.FN_SQRT_NUMERIC, // sqrt(numeric) -> numeric
-          // FunctionSignatureId.FN_SQRT_BIGNUMERIC, // sqrt(bignumeric) -> bignumeric
-          FunctionSignatureId.FN_POW_DOUBLE, // pow
-          FunctionSignatureId.FN_POW_NUMERIC, // pow(numeric, numeric) -> numeric
-          // FunctionSignatureId.FN_POW_BIGNUMERIC, // pow(bignumeric, bignumeric) -> bignumeric
-          FunctionSignatureId.FN_EXP_DOUBLE, // exp
-          FunctionSignatureId.FN_EXP_NUMERIC, // exp(numeric) -> numeric
-          // FunctionSignatureId.FN_EXP_BIGNUMERIC, // exp(bignumeric) -> bignumeric
-          FunctionSignatureId.FN_NATURAL_LOGARITHM_DOUBLE, // ln
-          FunctionSignatureId.FN_NATURAL_LOGARITHM_NUMERIC, // ln(numeric) -> numeric
-          // FunctionSignatureId.FN_NATURAL_LOGARITHM_BIGNUMERIC, // ln(bignumeric) -> bignumeric
-          FunctionSignatureId.FN_DECIMAL_LOGARITHM_DOUBLE, // log10
-          FunctionSignatureId.FN_DECIMAL_LOGARITHM_NUMERIC, // log10(numeric) -> numeric
-          // FunctionSignatureId.FN_DECIMAL_LOGARITHM_BIGNUMERIC, // log10(bignumeric) -> bignumeric
-          FunctionSignatureId.FN_LOGARITHM_DOUBLE, // log
-          FunctionSignatureId.FN_LOGARITHM_NUMERIC, // log(numeric, numeric) -> numeric
-          // FunctionSignatureId.FN_LOGARITHM_BIGNUMERIC,//log(bignumeric, bignumeric) -> bignumeric
-
-          FunctionSignatureId.FN_COS_DOUBLE, // cos
-          FunctionSignatureId.FN_COSH_DOUBLE, // cosh
-          FunctionSignatureId.FN_ACOS_DOUBLE, // acos
-          FunctionSignatureId.FN_ACOSH_DOUBLE, // acosh
-          FunctionSignatureId.FN_SIN_DOUBLE, // sin
-          FunctionSignatureId.FN_SINH_DOUBLE, // sinh
-          FunctionSignatureId.FN_ASIN_DOUBLE, // asin
-          FunctionSignatureId.FN_ASINH_DOUBLE, // asinh
-          FunctionSignatureId.FN_TAN_DOUBLE, // tan
-          FunctionSignatureId.FN_TANH_DOUBLE, // tanh
-          FunctionSignatureId.FN_ATAN_DOUBLE, // atan
-          FunctionSignatureId.FN_ATANH_DOUBLE, // atanh
-          FunctionSignatureId.FN_ATAN2_DOUBLE, // atan2
-
-          // Aggregate functions.
-          FunctionSignatureId.FN_ANY_VALUE, // any_value
-          FunctionSignatureId.FN_ARRAY_AGG, // array_agg
-          // FunctionSignatureId.FN_ARRAY_CONCAT_AGG, // array_concat_agg
-          FunctionSignatureId.FN_AVG_INT64, // avg
-          FunctionSignatureId.FN_AVG_DOUBLE, // avg
-          FunctionSignatureId.FN_AVG_NUMERIC, // avg
-          // FunctionSignatureId.FN_AVG_BIGNUMERIC, // avg
-          FunctionSignatureId.FN_COUNT, // count
-          FunctionSignatureId.FN_MAX, // max
-          FunctionSignatureId.FN_MIN, // min
-          FunctionSignatureId.FN_STRING_AGG_STRING, // string_agg(s)
-          FunctionSignatureId.FN_STRING_AGG_DELIM_STRING, // string_agg(s, delim_s)
-          FunctionSignatureId.FN_STRING_AGG_BYTES, // string_agg(b)
-          FunctionSignatureId.FN_STRING_AGG_DELIM_BYTES, // string_agg(b, delim_b)
-          FunctionSignatureId.FN_SUM_INT64, // sum
-          FunctionSignatureId.FN_SUM_DOUBLE, // sum
-          FunctionSignatureId.FN_SUM_NUMERIC, // sum
-          // FunctionSignatureId.FN_SUM_BIGNUMERIC, // sum
-          FunctionSignatureId.FN_BIT_AND_INT64, // bit_and
-          FunctionSignatureId.FN_BIT_OR_INT64, // bit_or
-          FunctionSignatureId.FN_BIT_XOR_INT64, // bit_xor
-          // FunctionSignatureId.FN_LOGICAL_AND, // logical_and
-          // FunctionSignatureId.FN_LOGICAL_OR, // logical_or
-          // Approximate aggregate functions.
-          // FunctionSignatureId.FN_APPROX_COUNT_DISTINCT, // approx_count_distinct
-          // FunctionSignatureId.FN_APPROX_QUANTILES, // approx_quantiles
-          // FunctionSignatureId.FN_APPROX_TOP_COUNT, // approx_top_count
-          // FunctionSignatureId.FN_APPROX_TOP_SUM_INT64, // approx_top_sum
-          // FunctionSignatureId.FN_APPROX_TOP_SUM_DOUBLE, // approx_top_sum
-          // FunctionSignatureId.FN_APPROX_TOP_SUM_NUMERIC, // approx_top_sum
-          // FunctionSignatureId.FN_APPROX_TOP_SUM_BIGNUMERIC, // approx_top_sum
-
-          // Approximate count functions that expose the intermediate sketch.
-          // These are all found in the "hll_count.*" namespace.
-          // FunctionSignatureId.FN_HLL_COUNT_MERGE, // hll_count.merge(bytes)
-          // FunctionSignatureId.FN_HLL_COUNT_EXTRACT, // hll_count.extract(bytes), scalar
-          // FunctionSignatureId.FN_HLL_COUNT_INIT_INT64, // hll_count.init(int64)
-          // FunctionSignatureId.FN_HLL_COUNT_INIT_NUMERIC, // hll_count.init(numeric)
-          // FunctionSignatureId.FN_HLL_COUNT_INIT_BIGNUMERIC, // hll_count.init(bignumeric)
-          // FunctionSignatureId.FN_HLL_COUNT_INIT_STRING, // hll_count.init(string)
-          // FunctionSignatureId.FN_HLL_COUNT_INIT_BYTES, // hll_count.init(bytes)
-          // FunctionSignatureId.FN_HLL_COUNT_MERGE_PARTIAL, // hll_count.merge_partial(bytes)
-
-          // Statistical aggregate functions.
-          // FunctionSignatureId.FN_CORR, // corr
-          // FunctionSignatureId.FN_CORR_NUMERIC, // corr
-          // FunctionSignatureId.FN_CORR_BIGNUMERIC, // corr
-          // FunctionSignatureId.FN_COVAR_POP, // covar_pop
-          // FunctionSignatureId.FN_COVAR_POP_NUMERIC, // covar_pop
-          // FunctionSignatureId.FN_COVAR_POP_BIGNUMERIC, // covar_pop
-          // FunctionSignatureId.FN_COVAR_SAMP, // covar_samp
-          // FunctionSignatureId.FN_COVAR_SAMP_NUMERIC, // covar_samp
-          // FunctionSignatureId.FN_COVAR_SAMP_BIGNUMERIC, // covar_samp
-          // FunctionSignatureId.FN_STDDEV_POP, // stddev_pop
-          // FunctionSignatureId.FN_STDDEV_POP_NUMERIC, // stddev_pop
-          // FunctionSignatureId.FN_STDDEV_POP_BIGNUMERIC, // stddev_pop
-          // FunctionSignatureId.FN_STDDEV_SAMP, // stddev_samp
-          // FunctionSignatureId.FN_STDDEV_SAMP_NUMERIC, // stddev_samp
-          // FunctionSignatureId.FN_STDDEV_SAMP_BIGNUMERIC, // stddev_samp
-          // FunctionSignatureId.FN_VAR_POP, // var_pop
-          // FunctionSignatureId.FN_VAR_POP_NUMERIC, // var_pop
-          // FunctionSignatureId.FN_VAR_POP_BIGNUMERIC, // var_pop
-          // FunctionSignatureId.FN_VAR_SAMP, // var_samp
-          // FunctionSignatureId.FN_VAR_SAMP_NUMERIC, // var_samp
-          // FunctionSignatureId.FN_VAR_SAMP_BIGNUMERIC, // var_samp
-
-          FunctionSignatureId.FN_COUNTIF, // countif
-
-          // Approximate quantiles functions that produce or consume intermediate
-          // sketches. All found in the "kll_quantiles.*" namespace.
-          // FunctionSignatureId.FN_KLL_QUANTILES_INIT_INT64,
-          // FunctionSignatureId.FN_KLL_QUANTILES_INIT_DOUBLE,
-          // FunctionSignatureId.FN_KLL_QUANTILES_MERGE_PARTIAL,
-          // FunctionSignatureId.FN_KLL_QUANTILES_MERGE_INT64,
-          // FunctionSignatureId.FN_KLL_QUANTILES_MERGE_DOUBLE,
-          // FunctionSignatureId.FN_KLL_QUANTILES_EXTRACT_INT64, // scalar
-          // FunctionSignatureId.FN_KLL_QUANTILES_EXTRACT_DOUBLE, // scalar
-          // FunctionSignatureId.FN_KLL_QUANTILES_MERGE_POINT_INT64,
-          // FunctionSignatureId.FN_KLL_QUANTILES_MERGE_POINT_DOUBLE,
-          // FunctionSignatureId.FN_KLL_QUANTILES_EXTRACT_POINT_INT64, // scalar
-          // FunctionSignatureId.FN_KLL_QUANTILES_EXTRACT_POINT_DOUBLE, // scalar
-
-          // Analytic functions.
-          // FunctionSignatureId.FN_DENSE_RANK, // dense_rank
-          // FunctionSignatureId.FN_RANK, // rank
-          // FunctionSignatureId.FN_ROW_NUMBER, // row_number
-          // FunctionSignatureId.FN_PERCENT_RANK, // percent_rank
-          // FunctionSignatureId.FN_CUME_DIST, // cume_dist
-          // FunctionSignatureId.FN_NTILE, // ntile
-          // FunctionSignatureId.FN_LEAD, // lead
-          // FunctionSignatureId.FN_LAG, // lag
-          // FunctionSignatureId.FN_FIRST_VALUE, // first_value
-          // FunctionSignatureId.FN_LAST_VALUE, // last_value
-          // FunctionSignatureId.FN_NTH_VALUE, // nth_value
-          // FunctionSignatureId.FN_PERCENTILE_CONT, // percentile_cont
-          // FunctionSignatureId.FN_PERCENTILE_CONT_NUMERIC, // percentile_cont
-          // FunctionSignatureId.FN_PERCENTILE_CONT_BIGNUMERIC, // percentile_cont
-          // FunctionSignatureId.FN_PERCENTILE_DISC, // percentile_disc
-          // FunctionSignatureId.FN_PERCENTILE_DISC_NUMERIC, // percentile_disc
-          // FunctionSignatureId.FN_PERCENTILE_DISC_BIGNUMERIC, // percentile_disc
-
-          // Misc functions.
-          // FunctionSignatureId.FN_BIT_CAST_INT64_TO_INT64, // bit_cast_to_int64(int64)
-
-          // FunctionSignatureId.FN_SESSION_USER, // session_user
-
-          // FunctionSignatureId.FN_GENERATE_ARRAY_INT64, // generate_array(int64)
-          // FunctionSignatureId.FN_GENERATE_ARRAY_NUMERIC, // generate_array(numeric)
-          // FunctionSignatureId.FN_GENERATE_ARRAY_BIGNUMERIC, // generate_array(bignumeric)
-          // FunctionSignatureId.FN_GENERATE_ARRAY_DOUBLE, // generate_array(double)
-          // FunctionSignatureId.FN_GENERATE_DATE_ARRAY, // generate_date_array(date)
-          // FunctionSignatureId.FN_GENERATE_TIMESTAMP_ARRAY, // generate_timestamp_array(timestamp)
-
-          // FunctionSignatureId.FN_ARRAY_REVERSE, // array_reverse(array) -> array
-
-          // FunctionSignatureId.FN_RANGE_BUCKET, //  range_bucket(T, array<T>) -> int64
-
-          // FunctionSignatureId.FN_RAND // rand() -> double
-          // FunctionSignatureId.FN_GENERATE_UUID, // generate_uuid() -> string
-
-          FunctionSignatureId.FN_JSON_EXTRACT, // json_extract(string, string)
-          // FunctionSignatureId.FN_JSON_EXTRACT_JSON, // json_extract(json, string) -> json
-          FunctionSignatureId.FN_JSON_EXTRACT_SCALAR, // json_extract_scalar(string, string)
-          // json_extract_scalar(json, string) -> string
-          // FunctionSignatureId.FN_JSON_EXTRACT_SCALAR_JSON,
-          // json_extract_array(string[, string]) -> array
-          FunctionSignatureId.FN_JSON_EXTRACT_ARRAY,
-          FunctionSignatureId.FN_TO_JSON_STRING, // to_json_string(any[, bool]) -> string
-          FunctionSignatureId.FN_JSON_QUERY, // json_query(string, string) -> string
-          // FunctionSignatureId.FN_JSON_QUERY_JSON, // json_query(json, string) -> json
-          FunctionSignatureId.FN_JSON_VALUE, // json_value(string, string) -> string
-          // FunctionSignatureId.FN_JSON_VALUE_JSON, // json_value(json, string) -> json
-
-          // Net functions. These are all found in the "net.*" namespace.
-          // FunctionSignatureId.FN_NET_FORMAT_IP,
-          // FunctionSignatureId.FN_NET_PARSE_IP,
-          // FunctionSignatureId.FN_NET_FORMAT_PACKED_IP,
-          // FunctionSignatureId.FN_NET_PARSE_PACKED_IP,
-          // FunctionSignatureId.FN_NET_IP_IN_NET,
-          // FunctionSignatureId.FN_NET_MAKE_NET,
-          // FunctionSignatureId.FN_NET_HOST, // net.host(string)
-          // FunctionSignatureId.FN_NET_REG_DOMAIN, // net.reg_domain(string)
-          // FunctionSignatureId.FN_NET_PUBLIC_SUFFIX, // net.public_suffix(string)
-          // FunctionSignatureId.FN_NET_IP_FROM_STRING, // net.ip_from_string(string)
-          // FunctionSignatureId.FN_NET_SAFE_IP_FROM_STRING, // net.safe_ip_from_string(string)
-          // FunctionSignatureId.FN_NET_IP_TO_STRING, // net.ip_to_string(bytes)
-          // FunctionSignatureId.FN_NET_IP_NET_MASK, // net.ip_net_mask(int64, int64)
-          // FunctionSignatureId.FN_NET_IP_TRUNC, // net.ip_net_mask(bytes, int64)
-          // FunctionSignatureId.FN_NET_IPV4_FROM_INT64, // net.ipv4_from_int64(int64)
-          // FunctionSignatureId.FN_NET_IPV4_TO_INT64, // net.ipv4_to_int64(bytes)
-
-          // Hashing functions.
-          FunctionSignatureId.FN_MD5_BYTES, // md5(bytes)
-          FunctionSignatureId.FN_MD5_STRING, // md5(string)
-          FunctionSignatureId.FN_SHA1_BYTES, // sha1(bytes)
-          FunctionSignatureId.FN_SHA1_STRING, // sha1(string)
-          FunctionSignatureId.FN_SHA256_BYTES, // sha256(bytes)
-          FunctionSignatureId.FN_SHA256_STRING, // sha256(string)
-          FunctionSignatureId.FN_SHA512_BYTES, // sha512(bytes)
-          FunctionSignatureId.FN_SHA512_STRING // sha512(string)
-
-          // Fingerprinting functions
-          // FunctionSignatureId.FN_FARM_FINGERPRINT_BYTES, // farm_fingerprint(bytes) -> int64
-          // FunctionSignatureId.FN_FARM_FINGERPRINT_STRING, // farm_fingerprint(string) -> int64
-
-          // Keyset management, encryption, and decryption functions
-          // Requires that FEATURE_ENCRYPTION is enabled.
-          // FunctionSignatureId.FN_KEYS_NEW_KEYSET, // keys.new_keyset(string)
-          // keys.add_key_from_raw_bytes(bytes, string, bytes)
-          // FunctionSignatureId.FN_KEYS_ADD_KEY_FROM_RAW_BYTES,
-          // FunctionSignatureId.FN_KEYS_ROTATE_KEYSET, // keys.rotate_keyset(bytes, string)
-          // FunctionSignatureId.FN_KEYS_KEYSET_LENGTH, // keys.keyset_length(bytes)
-          // FunctionSignatureId.FN_KEYS_KEYSET_TO_JSON, // keys.keyset_to_json(bytes)
-          // FunctionSignatureId.FN_KEYS_KEYSET_FROM_JSON, // keys.keyset_from_json(string)
-          // FunctionSignatureId.FN_AEAD_ENCRYPT_STRING, // aead.encrypt(bytes, string, string)
-          // FunctionSignatureId.FN_AEAD_ENCRYPT_BYTES, // aead.encrypt(bytes, bytes, bytes)
-          // FunctionSignatureId.FN_AEAD_DECRYPT_STRING,// aead.decrypt_string(bytes, bytes, string)
-          // FunctionSignatureId.FN_AEAD_DECRYPT_BYTES, // aead.decrypt_bytes(bytes, bytes, bytes)
-          // FunctionSignatureId.FN_KMS_ENCRYPT_STRING, // kms.encrypt(string, string)
-          // FunctionSignatureId.FN_KMS_ENCRYPT_BYTES, // kms.encrypt(string, bytes)
-          // FunctionSignatureId.FN_KMS_DECRYPT_STRING, // kms.decrypt_string(string, bytes)
-          // FunctionSignatureId.FN_KMS_DECRYPT_BYTES, // kms.decrypt_bytes(string, bytes)
-
-          // ST_ family of functions (Geography related)
-          // Constructors
-          // FunctionSignatureId.FN_ST_GEOG_POINT,
-          // FunctionSignatureId.FN_ST_MAKE_LINE,
-          // FunctionSignatureId.FN_ST_MAKE_LINE_ARRAY,
-          // FunctionSignatureId.FN_ST_MAKE_POLYGON,
-          // FunctionSignatureId.FN_ST_MAKE_POLYGON_ORIENTED,
-          // Transformations
-          // FunctionSignatureId.FN_ST_INTERSECTION,
-          // FunctionSignatureId.FN_ST_UNION,
-          // FunctionSignatureId.FN_ST_UNION_ARRAY,
-          // FunctionSignatureId.FN_ST_DIFFERENCE,
-          // FunctionSignatureId.FN_ST_UNARY_UNION,
-          // FunctionSignatureId.FN_ST_CENTROID,
-          // FunctionSignatureId.FN_ST_BUFFER,
-          // FunctionSignatureId.FN_ST_BUFFER_WITH_TOLERANCE,
-          // FunctionSignatureId.FN_ST_SIMPLIFY,
-          // FunctionSignatureId.FN_ST_SNAP_TO_GRID,
-          // FunctionSignatureId.FN_ST_CLOSEST_POINT,
-          // FunctionSignatureId.FN_ST_BOUNDARY,
-          // FunctionSignatureId.FN_ST_CONVEXHULL,
-          // Predicates
-          // FunctionSignatureId.FN_ST_EQUALS,
-          // FunctionSignatureId.FN_ST_INTERSECTS,
-          // FunctionSignatureId.FN_ST_CONTAINS,
-          // FunctionSignatureId.FN_ST_COVERS,
-          // FunctionSignatureId.FN_ST_DISJOINT,
-          // FunctionSignatureId.FN_ST_INTERSECTS_BOX,
-          // FunctionSignatureId.FN_ST_DWITHIN,
-          // FunctionSignatureId.FN_ST_WITHIN,
-          // FunctionSignatureId.FN_ST_COVEREDBY,
-          // FunctionSignatureId.FN_ST_TOUCHES,
-          // Accessors
-          // FunctionSignatureId.FN_ST_IS_EMPTY,
-          // FunctionSignatureId.FN_ST_IS_COLLECTION,
-          // FunctionSignatureId.FN_ST_DIMENSION,
-          // FunctionSignatureId.FN_ST_NUM_POINTS,
-          // FunctionSignatureId.FN_ST_DUMP,
-          // Measures
-          // FunctionSignatureId.FN_ST_LENGTH,
-          // FunctionSignatureId.FN_ST_PERIMETER,
-          // FunctionSignatureId.FN_ST_AREA,
-          // FunctionSignatureId.FN_ST_DISTANCE,
-          // FunctionSignatureId.FN_ST_MAX_DISTANCE,
-          // Parsers/formatters
-          // FunctionSignatureId.FN_ST_GEOG_FROM_TEXT,
-          // FunctionSignatureId.FN_ST_GEOG_FROM_KML,
-          // FunctionSignatureId.FN_ST_GEOG_FROM_GEO_JSON,
-          // FunctionSignatureId.FN_ST_GEOG_FROM_WKB,
-          // FunctionSignatureId.FN_ST_AS_TEXT,
-          // FunctionSignatureId.FN_ST_AS_KML,
-          // FunctionSignatureId.FN_ST_AS_GEO_JSON,
-          // FunctionSignatureId.FN_ST_AS_BINARY,
-          // FunctionSignatureId.FN_ST_GEOHASH,
-          // FunctionSignatureId.FN_ST_GEOG_POINT_FROM_GEOHASH,
-          // Aggregate functions
-          // FunctionSignatureId.FN_ST_UNION_AGG,
-          // FunctionSignatureId.FN_ST_ACCUM,
-          // FunctionSignatureId.FN_ST_CENTROID_AGG,
-          // FunctionSignatureId.FN_ST_NEAREST_NEIGHBORS,
-          // Other geography functions
-          // FunctionSignatureId.FN_ST_X,
-          // FunctionSignatureId.FN_ST_Y,
-          // FunctionSignatureId.FN_ST_CLUSTERDBSCAN,
-
-          // Array functions.
-          // FunctionSignatureId.FN_FLATTEN, // flatten(array path) -> array
-          // FunctionSignatureId.FN_ARRAY_AT_OFFSET, // $array_at_offset
-          // FunctionSignatureId.FN_ARRAY_AT_ORDINAL, // $array_at_ordinal
-          // FunctionSignatureId.FN_ARRAY_CONCAT, // array_concat(repeated array) -> array
-          // FunctionSignatureId.FN_ARRAY_CONCAT_OP, // array_concat(array, array) -> array
-          // FunctionSignatureId.FN_ARRAY_LENGTH, // array_length(array) -> int64
-          // array_to_string(array, bytes[, bytes]) -> bytes
-          // FunctionSignatureId.FN_ARRAY_TO_BYTES,
-          // array_to_string(array, string[, string]) -> string
-          // FunctionSignatureId.FN_ARRAY_TO_STRING,
-          // FunctionSignatureId.FN_MAKE_ARRAY, // $make_array
-          // FunctionSignatureId.FN_SAFE_ARRAY_AT_OFFSET, // $safe_array_at_offset
-          // FunctionSignatureId.FN_SAFE_ARRAY_AT_ORDINAL, // $safe_array_at_ordinal
-          // FunctionSignatureId.FN_ARRAY_IS_DISTINCT, // array_is_distinct(array) -> bool
-          // FunctionSignatureId.FN_PROTO_MAP_AT_KEY, // $proto_map_at_key
-          // FunctionSignatureId.FN_SAFE_PROTO_MAP_AT_KEY, // $safe_proto_map_at_key
-          );
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/TableResolution.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/TableResolution.java
deleted file mode 100644
index d077ef40ee64..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/TableResolution.java
+++ /dev/null
@@ -1,113 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import com.google.zetasql.SimpleTable;
-import java.util.List;
-import java.util.stream.Collectors;
-import org.apache.beam.sdk.extensions.sql.impl.BeamCalciteSchema;
-import org.apache.beam.sdk.extensions.sql.impl.TableName;
-import org.apache.beam.sdk.extensions.sql.meta.CustomTableResolver;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Schema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Table;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
-
-/** Utility methods to resolve a table, given a top-level Calcite schema and a table path. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-public class TableResolution {
-
-  /**
-   * Resolves {@code tablePath} according to the given {@code schemaPlus}.
-   *
-   * <p>{@code tablePath} represents a structured table name where the last component is the name of
-   * the table and all the preceding components are sub-schemas / namespaces within {@code
-   * schemaPlus}.
-   */
-  public static Table resolveCalciteTable(SchemaPlus schemaPlus, List<String> tablePath) {
-    Schema subSchema = schemaPlus;
-
-    // subSchema.getSubschema() for all except last
-    for (int i = 0; i < tablePath.size() - 1; i++) {
-      subSchema = subSchema.getSubSchema(tablePath.get(i));
-      if (subSchema == null) {
-        throw new IllegalStateException(
-            String.format(
-                "While resolving table path %s, no sub-schema found for component %s (\"%s\")",
-                tablePath, i, tablePath.get(i)));
-      }
-    }
-
-    // for the final one call getTable()
-    return subSchema.getTable(Iterables.getLast(tablePath));
-  }
-
-  /**
-   * Registers tables that will be resolved during query analysis, so table providers can eagerly
-   * pre-load metadata.
-   */
-  // TODO(https://issues.apache.org/jira/browse/BEAM-8817): share this logic between dialects
-  public static void registerTables(SchemaPlus schemaPlus, List<List<String>> tables) {
-    Schema defaultSchema = CalciteSchema.from(schemaPlus).schema;
-    if (defaultSchema instanceof BeamCalciteSchema
-        && ((BeamCalciteSchema) defaultSchema).getTableProvider() instanceof CustomTableResolver) {
-      ((CustomTableResolver) ((BeamCalciteSchema) defaultSchema).getTableProvider())
-          .registerKnownTableNames(
-              tables.stream().map(TableName::create).collect(Collectors.toList()));
-    }
-
-    for (String subSchemaName : schemaPlus.getSubSchemaNames()) {
-      Schema subSchema = CalciteSchema.from(schemaPlus.getSubSchema(subSchemaName)).schema;
-
-      if (subSchema instanceof BeamCalciteSchema
-          && ((BeamCalciteSchema) subSchema).getTableProvider() instanceof CustomTableResolver) {
-        ((CustomTableResolver) ((BeamCalciteSchema) subSchema).getTableProvider())
-            .registerKnownTableNames(
-                tables.stream().map(TableName::create).collect(Collectors.toList()));
-      }
-    }
-  }
-
-  /**
-   * Data class to store simple table, its full path (excluding top-level schema), and top-level
-   * schema.
-   */
-  static class SimpleTableWithPath {
-
-    SimpleTable table;
-    List<String> path;
-
-    static SimpleTableWithPath of(List<String> path) {
-      SimpleTableWithPath tableWithPath = new SimpleTableWithPath();
-      tableWithPath.table = new SimpleTable(Iterables.getLast(path));
-      tableWithPath.path = path;
-      return tableWithPath;
-    }
-
-    SimpleTable getTable() {
-      return table;
-    }
-
-    List<String> getPath() {
-      return path;
-    }
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSQLPlannerImpl.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSQLPlannerImpl.java
deleted file mode 100644
index c8a1097b54a2..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSQLPlannerImpl.java
+++ /dev/null
@@ -1,118 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import com.google.zetasql.AnalyzerOptions;
-import com.google.zetasql.LanguageOptions;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedQueryStmt;
-import java.util.List;
-import org.apache.beam.sdk.extensions.sql.impl.QueryPlanner.QueryParameters;
-import org.apache.beam.sdk.extensions.sql.zetasql.translation.ConversionContext;
-import org.apache.beam.sdk.extensions.sql.zetasql.translation.ExpressionConverter;
-import org.apache.beam.sdk.extensions.sql.zetasql.translation.QueryStatementConverter;
-import org.apache.beam.vendor.calcite.v1_28_0.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.java.JavaTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelRoot;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexExecutor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.FrameworkConfig;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Frameworks;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Program;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Util;
-
-/** ZetaSQLPlannerImpl. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-class ZetaSQLPlannerImpl {
-  private final SchemaPlus defaultSchemaPlus;
-
-  // variables that are used in Calcite's planner.
-  private final FrameworkConfig config;
-  private RelOptPlanner planner;
-  private JavaTypeFactory typeFactory;
-  private final RexExecutor executor;
-  private final ImmutableList<Program> programs;
-
-  private String defaultTimezone = "UTC"; // choose UTC (offset 00:00) unless explicitly set
-
-  ZetaSQLPlannerImpl(FrameworkConfig config) {
-    this.config = config;
-    this.executor = config.getExecutor();
-    this.programs = config.getPrograms();
-
-    Frameworks.withPlanner(
-        (cluster, relOptSchema, rootSchema) -> {
-          Util.discard(rootSchema); // use our own defaultSchema
-          typeFactory = (JavaTypeFactory) cluster.getTypeFactory();
-          planner = cluster.getPlanner();
-          planner.setExecutor(executor);
-          return null;
-        },
-        config);
-
-    this.defaultSchemaPlus = config.getDefaultSchema();
-  }
-
-  public RelRoot rel(String sql, QueryParameters params) {
-    RelOptCluster cluster = RelOptCluster.create(planner, new RexBuilder(typeFactory));
-    AnalyzerOptions options = SqlAnalyzer.getAnalyzerOptions(params, defaultTimezone);
-    BeamZetaSqlCatalog catalog =
-        BeamZetaSqlCatalog.create(
-            defaultSchemaPlus, (JavaTypeFactory) cluster.getTypeFactory(), options);
-
-    // Set up table providers that need to be pre-registered
-    SqlAnalyzer analyzer = new SqlAnalyzer();
-    List<List<String>> tables = analyzer.extractTableNames(sql, options);
-    TableResolution.registerTables(this.defaultSchemaPlus, tables);
-    QueryTrait trait = new QueryTrait();
-    catalog.addTables(tables, trait);
-
-    ResolvedQueryStmt statement = analyzer.analyzeQuery(sql, options, catalog);
-
-    ExpressionConverter expressionConverter =
-        new ExpressionConverter(cluster, params, catalog.getUserFunctionDefinitions());
-    ConversionContext context = ConversionContext.of(config, expressionConverter, cluster, trait);
-
-    RelNode convertedNode = QueryStatementConverter.convertRootQuery(context, statement);
-    return RelRoot.of(convertedNode, SqlKind.ALL);
-  }
-
-  RelNode transform(int i, RelTraitSet relTraitSet, RelNode relNode) {
-    Program program = programs.get(i);
-    return program.run(planner, relNode, relTraitSet, ImmutableList.of(), ImmutableList.of());
-  }
-
-  String getDefaultTimezone() {
-    return defaultTimezone;
-  }
-
-  void setDefaultTimezone(String timezone) {
-    defaultTimezone = timezone;
-  }
-
-  static LanguageOptions getLanguageOptions() {
-    return SqlAnalyzer.baseAnalyzerOptions().getLanguageOptions();
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSQLQueryPlanner.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSQLQueryPlanner.java
deleted file mode 100644
index 8210afed8119..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSQLQueryPlanner.java
+++ /dev/null
@@ -1,256 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import com.google.zetasql.LanguageOptions;
-import com.google.zetasql.Value;
-import java.util.Collection;
-import java.util.List;
-import java.util.Map;
-import org.apache.beam.sdk.extensions.sql.impl.BeamSqlPipelineOptions;
-import org.apache.beam.sdk.extensions.sql.impl.CalciteQueryPlanner.NonCumulativeCostImpl;
-import org.apache.beam.sdk.extensions.sql.impl.JdbcConnection;
-import org.apache.beam.sdk.extensions.sql.impl.ParseException;
-import org.apache.beam.sdk.extensions.sql.impl.QueryPlanner;
-import org.apache.beam.sdk.extensions.sql.impl.SqlConversionException;
-import org.apache.beam.sdk.extensions.sql.impl.planner.BeamCostModel;
-import org.apache.beam.sdk.extensions.sql.impl.planner.BeamRelMetadataQuery;
-import org.apache.beam.sdk.extensions.sql.impl.planner.BeamRuleSets;
-import org.apache.beam.sdk.extensions.sql.impl.planner.RelMdNodeStats;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.extensions.sql.impl.rule.BeamCalcRule;
-import org.apache.beam.sdk.extensions.sql.impl.rule.BeamUncollectRule;
-import org.apache.beam.sdk.extensions.sql.impl.rule.BeamUnnestRule;
-import org.apache.beam.sdk.extensions.sql.zetasql.unnest.BeamZetaSqlUncollectRule;
-import org.apache.beam.sdk.extensions.sql.zetasql.unnest.BeamZetaSqlUnnestRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.CalciteConnectionConfig;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.ConventionTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.prepare.CalciteCatalogReader;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelRoot;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.ChainedRelMetadataProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.JaninoRelMetadataProvider;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.metadata.RelMetadataQuery;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.rules.JoinCommuteRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperatorTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParser;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserImplFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.util.SqlOperatorTables;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.FrameworkConfig;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Frameworks;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSets;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-/** ZetaSQLQueryPlanner. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-public class ZetaSQLQueryPlanner implements QueryPlanner {
-  public static final Collection<RelOptRule> DEFAULT_CALC =
-      ImmutableList.<RelOptRule>builder().add(BeamZetaSqlCalcSplittingRule.INSTANCE).build();
-
-  private static final Logger LOG = LoggerFactory.getLogger(ZetaSQLQueryPlanner.class);
-
-  private final ZetaSQLPlannerImpl plannerImpl;
-
-  public ZetaSQLQueryPlanner(FrameworkConfig config) {
-    plannerImpl = new ZetaSQLPlannerImpl(config);
-  }
-
-  /**
-   * Called by {@link org.apache.beam.sdk.extensions.sql.impl.BeamSqlEnv}.instantiatePlanner()
-   * reflectively.
-   */
-  public ZetaSQLQueryPlanner(JdbcConnection jdbcConnection, Collection<RuleSet> ruleSets) {
-    LOG.warn(
-        "Beam ZetaSQL has been deprecated. See https://github.com/apache/beam/issues/34423 for details.");
-    plannerImpl =
-        new ZetaSQLPlannerImpl(
-            defaultConfig(jdbcConnection, modifyRuleSetsForZetaSql(ruleSets, DEFAULT_CALC)));
-    setDefaultTimezone(
-        jdbcConnection
-            .getPipelineOptions()
-            .as(BeamSqlPipelineOptions.class)
-            .getZetaSqlDefaultTimezone());
-  }
-
-  public static final Factory FACTORY = ZetaSQLQueryPlanner::new;
-
-  public static Collection<RuleSet> getZetaSqlRuleSets() {
-    return modifyRuleSetsForZetaSql(BeamRuleSets.getRuleSets(), DEFAULT_CALC);
-  }
-
-  public static Collection<RuleSet> getZetaSqlRuleSets(Collection<RelOptRule> calc) {
-    return modifyRuleSetsForZetaSql(BeamRuleSets.getRuleSets(), calc);
-  }
-
-  private static Collection<RuleSet> modifyRuleSetsForZetaSql(
-      Collection<RuleSet> ruleSets, Collection<RelOptRule> calc) {
-    ImmutableList.Builder<RuleSet> ret = ImmutableList.builder();
-    for (RuleSet ruleSet : ruleSets) {
-      ImmutableList.Builder<RelOptRule> bd = ImmutableList.builder();
-      for (RelOptRule rule : ruleSet) {
-        // TODO[https://github.com/apache/beam/issues/20077]: Fix join re-ordering for ZetaSQL
-        // planner. Currently join re-ordering
-        //  requires the JoinCommuteRule, which doesn't work without struct flattening.
-        if (rule instanceof JoinCommuteRule) {
-          continue;
-        } else if (rule instanceof BeamCalcRule) {
-          bd.addAll(calc);
-        } else if (rule instanceof BeamUnnestRule) {
-          bd.add(BeamZetaSqlUnnestRule.INSTANCE);
-        } else if (rule instanceof BeamUncollectRule) {
-          bd.add(BeamZetaSqlUncollectRule.INSTANCE);
-        } else {
-          bd.add(rule);
-        }
-      }
-      bd.add(BeamZetaSqlCalcMergeRule.INSTANCE);
-      ret.add(RuleSets.ofList(bd.build()));
-    }
-    return ret.build();
-  }
-
-  public String getDefaultTimezone() {
-    return plannerImpl.getDefaultTimezone();
-  }
-
-  public void setDefaultTimezone(String timezone) {
-    plannerImpl.setDefaultTimezone(timezone);
-  }
-
-  public static LanguageOptions getLanguageOptions() {
-    return ZetaSQLPlannerImpl.getLanguageOptions();
-  }
-
-  public BeamRelNode convertToBeamRel(String sqlStatement) {
-    return convertToBeamRel(sqlStatement, QueryParameters.ofNone());
-  }
-
-  public BeamRelNode convertToBeamRel(String sqlStatement, Map<String, Value> queryParams)
-      throws ParseException, SqlConversionException {
-    return convertToBeamRel(sqlStatement, QueryParameters.ofNamed(queryParams));
-  }
-
-  public BeamRelNode convertToBeamRel(String sqlStatement, List<Value> queryParams)
-      throws ParseException, SqlConversionException {
-    return convertToBeamRel(sqlStatement, QueryParameters.ofPositional(queryParams));
-  }
-
-  @Override
-  public BeamRelNode convertToBeamRel(String sqlStatement, QueryParameters queryParameters)
-      throws ParseException, SqlConversionException {
-    return convertToBeamRelInternal(sqlStatement, queryParameters);
-  }
-
-  @Override
-  public SqlNode parse(String sqlStatement) throws ParseException {
-    throw new UnsupportedOperationException(
-        String.format(
-            "%s.parse(String) is not implemented and should need be called",
-            this.getClass().getCanonicalName()));
-  }
-
-  private BeamRelNode convertToBeamRelInternal(String sql, QueryParameters queryParams) {
-    RelRoot root = plannerImpl.rel(sql, queryParams);
-    RelTraitSet desiredTraits =
-        root.rel
-            .getTraitSet()
-            .replace(BeamLogicalConvention.INSTANCE)
-            .replace(root.collation)
-            .simplify();
-    // beam physical plan
-    root.rel
-        .getCluster()
-        .setMetadataProvider(
-            ChainedRelMetadataProvider.of(
-                ImmutableList.of(
-                    NonCumulativeCostImpl.SOURCE,
-                    RelMdNodeStats.SOURCE,
-                    root.rel.getCluster().getMetadataProvider())));
-
-    root.rel.getCluster().setMetadataQuerySupplier(BeamRelMetadataQuery::instance);
-
-    RelMetadataQuery.THREAD_PROVIDERS.set(
-        JaninoRelMetadataProvider.of(root.rel.getCluster().getMetadataProvider()));
-    root.rel.getCluster().invalidateMetadataQuery();
-    try {
-      BeamRelNode beamRelNode = (BeamRelNode) plannerImpl.transform(0, desiredTraits, root.rel);
-      LOG.info("BEAMPlan>\n{}", BeamSqlRelUtils.explainLazily(beamRelNode));
-      return beamRelNode;
-    } catch (RelOptPlanner.CannotPlanException e) {
-      throw new SqlConversionException("Failed to produce plan for query " + sql, e);
-    }
-  }
-
-  @SuppressWarnings({
-    "rawtypes", // Frameworks.ConfigBuilder.traitDefs has method signature of raw type
-  })
-  private static FrameworkConfig defaultConfig(
-      JdbcConnection connection, Collection<RuleSet> ruleSets) {
-    final CalciteConnectionConfig config = connection.config();
-    final SqlParser.ConfigBuilder parserConfig =
-        SqlParser.configBuilder()
-            .setQuotedCasing(config.quotedCasing())
-            .setUnquotedCasing(config.unquotedCasing())
-            .setQuoting(config.quoting())
-            .setConformance(config.conformance())
-            .setCaseSensitive(config.caseSensitive());
-    final SqlParserImplFactory parserFactory =
-        config.parserFactory(SqlParserImplFactory.class, null);
-    if (parserFactory != null) {
-      parserConfig.setParserFactory(parserFactory);
-    }
-
-    final SchemaPlus schema = connection.getRootSchema();
-    final SchemaPlus defaultSchema = connection.getCurrentSchemaPlus();
-
-    final ImmutableList<RelTraitDef> traitDefs = ImmutableList.of(ConventionTraitDef.INSTANCE);
-
-    final CalciteCatalogReader catalogReader =
-        new CalciteCatalogReader(
-            CalciteSchema.from(schema),
-            ImmutableList.of(defaultSchema.getName()),
-            connection.getTypeFactory(),
-            connection.config());
-    final SqlOperatorTable opTab0 =
-        connection.config().fun(SqlOperatorTable.class, SqlStdOperatorTable.instance());
-
-    return Frameworks.newConfigBuilder()
-        .parserConfig(parserConfig.build())
-        .defaultSchema(defaultSchema)
-        .traitDefs(traitDefs)
-        .ruleSets(ruleSets.toArray(new RuleSet[0]))
-        .costFactory(BeamCostModel.FACTORY)
-        .typeSystem(connection.getTypeFactory().getTypeSystem())
-        .operatorTable(SqlOperatorTables.chain(opTab0, catalogReader))
-        .build();
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlBeamTranslationUtils.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlBeamTranslationUtils.java
deleted file mode 100644
index 318da1c41442..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlBeamTranslationUtils.java
+++ /dev/null
@@ -1,296 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import com.google.protobuf.ByteString;
-import com.google.zetasql.ArrayType;
-import com.google.zetasql.StructType;
-import com.google.zetasql.StructType.StructField;
-import com.google.zetasql.Type;
-import com.google.zetasql.TypeFactory;
-import com.google.zetasql.Value;
-import com.google.zetasql.ZetaSQLType.TypeKind;
-import java.math.BigDecimal;
-import java.time.LocalDate;
-import java.time.LocalDateTime;
-import java.time.LocalTime;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.stream.Collectors;
-import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.schemas.Schema.Field;
-import org.apache.beam.sdk.schemas.Schema.FieldType;
-import org.apache.beam.sdk.schemas.logicaltypes.DateTime;
-import org.apache.beam.sdk.schemas.logicaltypes.SqlTypes;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.math.LongMath;
-import org.checkerframework.checker.nullness.qual.Nullable;
-import org.joda.time.Instant;
-
-/**
- * Utility methods for ZetaSQL <=> Beam translation.
- *
- * <p>Unsupported ZetaSQL types: INT32, UINT32, UINT64, FLOAT, ENUM, PROTO, GEOGRAPHY
- */
-@Internal
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-public final class ZetaSqlBeamTranslationUtils {
-
-  private static final long MICROS_PER_MILLI = 1000L;
-
-  private ZetaSqlBeamTranslationUtils() {}
-
-  // Type conversion: Beam => ZetaSQL
-  public static Type toZetaSqlType(FieldType fieldType) {
-    switch (fieldType.getTypeName()) {
-      case INT64:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_INT64);
-      case DOUBLE:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_DOUBLE);
-      case BOOLEAN:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_BOOL);
-      case STRING:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_STRING);
-      case BYTES:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_BYTES);
-      case DECIMAL:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_NUMERIC);
-      case DATETIME:
-        // TODO[https://github.com/apache/beam/issues/20364]: Mapping TIMESTAMP to a Beam
-        // LogicalType instead?
-        return TypeFactory.createSimpleType(TypeKind.TYPE_TIMESTAMP);
-      case LOGICAL_TYPE:
-        String identifier = fieldType.getLogicalType().getIdentifier();
-        if (SqlTypes.DATE.getIdentifier().equals(identifier)) {
-          return TypeFactory.createSimpleType(TypeKind.TYPE_DATE);
-        } else if (SqlTypes.TIME.getIdentifier().equals(identifier)) {
-          return TypeFactory.createSimpleType(TypeKind.TYPE_TIME);
-        } else if (SqlTypes.DATETIME.getIdentifier().equals(identifier)) {
-          return TypeFactory.createSimpleType(TypeKind.TYPE_DATETIME);
-        } else {
-          throw new UnsupportedOperationException("Unknown Beam logical type: " + identifier);
-        }
-      case ARRAY:
-        return toZetaSqlArrayType(fieldType.getCollectionElementType());
-      case ROW:
-        return toZetaSqlStructType(fieldType.getRowSchema());
-      default:
-        throw new UnsupportedOperationException(
-            "Unknown Beam fieldType: " + fieldType.getTypeName());
-    }
-  }
-
-  private static ArrayType toZetaSqlArrayType(FieldType elementFieldType) {
-    return TypeFactory.createArrayType(toZetaSqlType(elementFieldType));
-  }
-
-  public static StructType toZetaSqlStructType(Schema schema) {
-    return TypeFactory.createStructType(
-        schema.getFields().stream()
-            .map(f -> new StructField(f.getName(), toZetaSqlType(f.getType())))
-            .collect(Collectors.toList()));
-  }
-
-  // Value conversion: Beam => ZetaSQL
-  public static Value toZetaSqlValue(@Nullable Object object, FieldType fieldType) {
-    if (object == null) {
-      return Value.createNullValue(toZetaSqlType(fieldType));
-    }
-    switch (fieldType.getTypeName()) {
-      case INT64:
-        return Value.createInt64Value((Long) object);
-      case DOUBLE:
-        return Value.createDoubleValue((Double) object);
-      case BOOLEAN:
-        return Value.createBoolValue((Boolean) object);
-      case STRING:
-        return Value.createStringValue((String) object);
-      case BYTES:
-        return Value.createBytesValue(ByteString.copyFrom((byte[]) object));
-      case DECIMAL:
-        return Value.createNumericValue((BigDecimal) object);
-      case DATETIME:
-        return Value.createTimestampValueFromUnixMicros(
-            LongMath.checkedMultiply(((Instant) object).getMillis(), MICROS_PER_MILLI));
-      case LOGICAL_TYPE:
-        String identifier = fieldType.getLogicalType().getIdentifier();
-        if (SqlTypes.DATE.getIdentifier().equals(identifier)) {
-          if (object instanceof Long) { // base type
-            return Value.createDateValue(((Long) object).intValue());
-          } else { // input type
-            return Value.createDateValue((LocalDate) object);
-          }
-        } else if (SqlTypes.TIME.getIdentifier().equals(identifier)) {
-          LocalTime localTime;
-          if (object instanceof Long) { // base type
-            localTime = LocalTime.ofNanoOfDay((Long) object);
-          } else { // input type
-            localTime = (LocalTime) object;
-          }
-          return Value.createTimeValue(localTime);
-        } else if (SqlTypes.DATETIME.getIdentifier().equals(identifier)) {
-          LocalDateTime datetime;
-          if (object instanceof Row) { // base type
-            datetime =
-                LocalDateTime.of(
-                    LocalDate.ofEpochDay(((Row) object).getInt64(DateTime.DATE_FIELD_NAME)),
-                    LocalTime.ofNanoOfDay(((Row) object).getInt64(DateTime.TIME_FIELD_NAME)));
-          } else { // input type
-            datetime = (LocalDateTime) object;
-          }
-          return Value.createDatetimeValue(datetime);
-        } else {
-          throw new UnsupportedOperationException("Unknown Beam logical type: " + identifier);
-        }
-      case ARRAY:
-        return toZetaSqlArrayValue((List<Object>) object, fieldType.getCollectionElementType());
-      case ROW:
-        return toZetaSqlStructValue((Row) object, fieldType.getRowSchema());
-      default:
-        throw new UnsupportedOperationException(
-            "Unknown Beam fieldType: " + fieldType.getTypeName());
-    }
-  }
-
-  private static Value toZetaSqlArrayValue(List<Object> elements, FieldType elementFieldType) {
-    List<Value> values =
-        elements.stream()
-            .map(e -> toZetaSqlValue(e, elementFieldType))
-            .collect(Collectors.toList());
-    return Value.createArrayValue(toZetaSqlArrayType(elementFieldType), values);
-  }
-
-  public static Value toZetaSqlStructValue(Row row, Schema schema) {
-    List<Value> values = new ArrayList<>(row.getFieldCount());
-
-    for (int i = 0; i < row.getFieldCount(); i++) {
-      values.add(toZetaSqlValue(row.getBaseValue(i, Object.class), schema.getField(i).getType()));
-    }
-    return Value.createStructValue(toZetaSqlStructType(schema), values);
-  }
-
-  // Type conversion: ZetaSQL => Beam
-  public static FieldType toBeamType(Type type) {
-    switch (type.getKind()) {
-      case TYPE_INT64:
-        return FieldType.INT64.withNullable(true);
-      case TYPE_DOUBLE:
-        return FieldType.DOUBLE.withNullable(true);
-      case TYPE_BOOL:
-        return FieldType.BOOLEAN.withNullable(true);
-      case TYPE_STRING:
-        return FieldType.STRING.withNullable(true);
-      case TYPE_BYTES:
-        return FieldType.BYTES.withNullable(true);
-      case TYPE_NUMERIC:
-        return FieldType.DECIMAL.withNullable(true);
-      case TYPE_DATE:
-        return FieldType.logicalType(SqlTypes.DATE).withNullable(true);
-      case TYPE_TIME:
-        return FieldType.logicalType(SqlTypes.TIME).withNullable(true);
-      case TYPE_DATETIME:
-        return FieldType.logicalType(SqlTypes.DATETIME).withNullable(true);
-      case TYPE_TIMESTAMP:
-        return FieldType.DATETIME.withNullable(true);
-      case TYPE_ARRAY:
-        return FieldType.array(toBeamType(type.asArray().getElementType())).withNullable(true);
-      case TYPE_STRUCT:
-        return FieldType.row(
-                type.asStruct().getFieldList().stream()
-                    .map(f -> Field.of(f.getName(), toBeamType(f.getType())))
-                    .collect(Schema.toSchema()))
-            .withNullable(true);
-      default:
-        throw new UnsupportedOperationException("Unknown ZetaSQL type: " + type.getKind());
-    }
-  }
-
-  // Value conversion: ZetaSQL => Beam (target Beam type unknown)
-  public static Object toBeamObject(Value value, boolean verifyValues) {
-    return toBeamObject(value, toBeamType(value.getType()), verifyValues);
-  }
-
-  // Value conversion: ZetaSQL => Beam (target Beam type known)
-  public static Object toBeamObject(Value value, FieldType fieldType, boolean verifyValues) {
-    if (value.isNull()) {
-      return null;
-    }
-    switch (fieldType.getTypeName()) {
-      case INT64:
-        return value.getInt64Value();
-      case DOUBLE:
-        // Floats with a floating part equal to zero are treated as whole (INT64).
-        // Cast to double when that happens.
-        if (value.getType().getKind().equals(TypeKind.TYPE_INT64)) {
-          return (double) value.getInt64Value();
-        }
-        return value.getDoubleValue();
-      case BOOLEAN:
-        return value.getBoolValue();
-      case STRING:
-        return value.getStringValue();
-      case BYTES:
-        return value.getBytesValue().toByteArray();
-      case DECIMAL:
-        return value.getNumericValue();
-      case DATETIME:
-        return Instant.ofEpochMilli(value.getTimestampUnixMicros() / MICROS_PER_MILLI);
-      case LOGICAL_TYPE:
-        String identifier = fieldType.getLogicalType().getIdentifier();
-        if (SqlTypes.DATE.getIdentifier().equals(identifier)) {
-          return value.getLocalDateValue();
-        } else if (SqlTypes.TIME.getIdentifier().equals(identifier)) {
-          return value.getLocalTimeValue();
-        } else if (SqlTypes.DATETIME.getIdentifier().equals(identifier)) {
-          return value.getLocalDateTimeValue();
-        } else {
-          throw new UnsupportedOperationException("Unknown Beam logical type: " + identifier);
-        }
-      case ARRAY:
-        return toBeamList(value, fieldType.getCollectionElementType(), verifyValues);
-      case ROW:
-        return toBeamRow(value, fieldType.getRowSchema(), verifyValues);
-      default:
-        throw new UnsupportedOperationException(
-            "Unknown Beam fieldType: " + fieldType.getTypeName());
-    }
-  }
-
-  private static List<Object> toBeamList(
-      Value arrayValue, FieldType elementType, boolean verifyValues) {
-    return arrayValue.getElementList().stream()
-        .map(e -> toBeamObject(e, elementType, verifyValues))
-        .collect(Collectors.toList());
-  }
-
-  public static Row toBeamRow(Value structValue, Schema schema, boolean verifyValues) {
-    List<Object> objects = new ArrayList<>(schema.getFieldCount());
-    List<Value> values = structValue.getFieldList();
-    for (int i = 0; i < values.size(); i++) {
-      objects.add(toBeamObject(values.get(i), schema.getField(i).getType(), verifyValues));
-    }
-    Row row =
-        verifyValues
-            ? Row.withSchema(schema).addValues(objects).build()
-            : Row.withSchema(schema).attachValues(objects);
-    return row;
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlCalciteTranslationUtils.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlCalciteTranslationUtils.java
deleted file mode 100644
index 965426db287f..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlCalciteTranslationUtils.java
+++ /dev/null
@@ -1,366 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import com.google.zetasql.StructType;
-import com.google.zetasql.StructType.StructField;
-import com.google.zetasql.Type;
-import com.google.zetasql.TypeFactory;
-import com.google.zetasql.Value;
-import com.google.zetasql.ZetaSQLType.TypeKind;
-import com.google.zetasql.functions.ZetaSQLDateTime.DateTimestampPart;
-import java.math.BigDecimal;
-import java.time.LocalDateTime;
-import java.time.LocalTime;
-import java.util.HashSet;
-import java.util.List;
-import java.util.Set;
-import java.util.stream.Collectors;
-import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.sdk.extensions.sql.meta.provider.bigquery.BeamBigQuerySqlDialect;
-import org.apache.beam.sdk.extensions.sql.zetasql.translation.SqlOperators;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.ByteString;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.TimeUnit;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.TimeUnitRange;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.DateString;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.TimeString;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.TimestampString;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-
-/**
- * Utility methods for ZetaSQL <=> Calcite translation.
- *
- * <p>Unsupported ZetaSQL types: INT32, UINT32, UINT64, FLOAT, ENUM (internal), PROTO, GEOGRAPHY
- */
-@Internal
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-public final class ZetaSqlCalciteTranslationUtils {
-  // Maximum and minimum allowed values for the NUMERIC/DECIMAL data type.
-  // https://github.com/google/zetasql/blob/master/docs/data-types.md#decimal-type
-  public static final BigDecimal ZETASQL_NUMERIC_MAX_VALUE =
-      new BigDecimal("99999999999999999999999999999.999999999");
-  public static final BigDecimal ZETASQL_NUMERIC_MIN_VALUE =
-      new BigDecimal("-99999999999999999999999999999.999999999");
-  // Number of digits after the decimal point supported by the NUMERIC data type.
-  public static final int ZETASQL_NUMERIC_SCALE = 9;
-
-  private ZetaSqlCalciteTranslationUtils() {}
-
-  // TODO[BEAM-9178]: support DateTimestampPart.WEEK and "WEEK with weekday"s
-  private static final ImmutableMap<Integer, TimeUnit> TIME_UNIT_CASTING_MAP =
-      ImmutableMap.<Integer, TimeUnit>builder()
-          .put(DateTimestampPart.YEAR.getNumber(), TimeUnit.YEAR)
-          .put(DateTimestampPart.MONTH.getNumber(), TimeUnit.MONTH)
-          .put(DateTimestampPart.DAY.getNumber(), TimeUnit.DAY)
-          .put(DateTimestampPart.DAYOFWEEK.getNumber(), TimeUnit.DOW)
-          .put(DateTimestampPart.DAYOFYEAR.getNumber(), TimeUnit.DOY)
-          .put(DateTimestampPart.QUARTER.getNumber(), TimeUnit.QUARTER)
-          .put(DateTimestampPart.HOUR.getNumber(), TimeUnit.HOUR)
-          .put(DateTimestampPart.MINUTE.getNumber(), TimeUnit.MINUTE)
-          .put(DateTimestampPart.SECOND.getNumber(), TimeUnit.SECOND)
-          .put(DateTimestampPart.MILLISECOND.getNumber(), TimeUnit.MILLISECOND)
-          .put(DateTimestampPart.MICROSECOND.getNumber(), TimeUnit.MICROSECOND)
-          .put(DateTimestampPart.ISOYEAR.getNumber(), TimeUnit.ISOYEAR)
-          .put(DateTimestampPart.ISOWEEK.getNumber(), TimeUnit.WEEK)
-          .build();
-
-  // Type conversion: Calcite => ZetaSQL
-  public static Type toZetaSqlType(RelDataType calciteType) {
-    switch (calciteType.getSqlTypeName()) {
-      case BIGINT:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_INT64);
-      case DOUBLE:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_DOUBLE);
-      case BOOLEAN:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_BOOL);
-      case VARCHAR:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_STRING);
-      case VARBINARY:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_BYTES);
-      case DECIMAL:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_NUMERIC);
-      case DATE:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_DATE);
-      case TIME:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_TIME);
-      case TIMESTAMP_WITH_LOCAL_TIME_ZONE:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_DATETIME);
-      case TIMESTAMP:
-        return TypeFactory.createSimpleType(TypeKind.TYPE_TIMESTAMP);
-      case ARRAY:
-        return TypeFactory.createArrayType(toZetaSqlType(calciteType.getComponentType()));
-      case ROW:
-        return TypeFactory.createStructType(
-            calciteType.getFieldList().stream()
-                .map(f -> new StructField(f.getName(), toZetaSqlType(f.getType())))
-                .collect(Collectors.toList()));
-      default:
-        throw new UnsupportedOperationException(
-            "Unknown Calcite type: " + calciteType.getSqlTypeName().getName());
-    }
-  }
-
-  // Type conversion: ZetaSQL => Calcite
-  public static RelDataType toCalciteType(Type type, boolean nullable, RexBuilder rexBuilder) {
-    RelDataType nonNullType;
-    switch (type.getKind()) {
-      case TYPE_INT64:
-        nonNullType = rexBuilder.getTypeFactory().createSqlType(SqlTypeName.BIGINT);
-        break;
-      case TYPE_DOUBLE:
-        nonNullType = rexBuilder.getTypeFactory().createSqlType(SqlTypeName.DOUBLE);
-        break;
-      case TYPE_BOOL:
-        nonNullType = rexBuilder.getTypeFactory().createSqlType(SqlTypeName.BOOLEAN);
-        break;
-      case TYPE_STRING:
-        nonNullType = rexBuilder.getTypeFactory().createSqlType(SqlTypeName.VARCHAR);
-        break;
-      case TYPE_BYTES:
-        nonNullType = rexBuilder.getTypeFactory().createSqlType(SqlTypeName.VARBINARY);
-        break;
-      case TYPE_NUMERIC:
-        nonNullType = rexBuilder.getTypeFactory().createSqlType(SqlTypeName.DECIMAL);
-        break;
-      case TYPE_DATE:
-        nonNullType = rexBuilder.getTypeFactory().createSqlType(SqlTypeName.DATE);
-        break;
-      case TYPE_TIME:
-        nonNullType = rexBuilder.getTypeFactory().createSqlType(SqlTypeName.TIME);
-        break;
-      case TYPE_DATETIME:
-        nonNullType =
-            rexBuilder.getTypeFactory().createSqlType(SqlTypeName.TIMESTAMP_WITH_LOCAL_TIME_ZONE);
-        break;
-      case TYPE_TIMESTAMP:
-        nonNullType = rexBuilder.getTypeFactory().createSqlType(SqlTypeName.TIMESTAMP);
-        break;
-      case TYPE_ARRAY:
-        // TODO: Should element type has the same nullability as the array type?
-        nonNullType = toCalciteArrayType(type.asArray().getElementType(), nullable, rexBuilder);
-        break;
-      case TYPE_STRUCT:
-        // TODO: Should field type has the same nullability as the struct type?
-        nonNullType = toCalciteStructType(type.asStruct(), nullable, rexBuilder);
-        break;
-      default:
-        throw new UnsupportedOperationException("Unknown ZetaSQL type: " + type.getKind().name());
-    }
-    return rexBuilder.getTypeFactory().createTypeWithNullability(nonNullType, nullable);
-  }
-
-  private static RelDataType toCalciteArrayType(
-      Type elementType, boolean nullable, RexBuilder rexBuilder) {
-    return rexBuilder
-        .getTypeFactory()
-        // -1 cardinality means unlimited array size
-        .createArrayType(toCalciteType(elementType, nullable, rexBuilder), -1);
-  }
-
-  private static RelDataType toCalciteStructType(
-      StructType structType, boolean nullable, RexBuilder rexBuilder) {
-    List<StructField> fields = structType.getFieldList();
-    List<String> fieldNames = getFieldNameList(fields);
-    List<RelDataType> fieldTypes =
-        fields.stream()
-            .map(f -> toCalciteType(f.getType(), nullable, rexBuilder))
-            .collect(Collectors.toList());
-    return rexBuilder.getTypeFactory().createStructType(fieldTypes, fieldNames);
-  }
-
-  private static List<String> getFieldNameList(List<StructField> fields) {
-    ImmutableList.Builder<String> b = ImmutableList.builder();
-    Set<String> usedName = new HashSet<>();
-    for (int i = 0; i < fields.size(); i++) {
-      String name = fields.get(i).getName();
-      // Follow the same way that BigQuery handles unspecified or duplicate field name
-      if ("".equals(name) || name.startsWith("_field_") || usedName.contains(name)) {
-        name = "_field_" + (i + 1); // BigQuery uses 1-based default field name
-      }
-      b.add(name);
-      usedName.add(name);
-    }
-    return b.build();
-  }
-
-  // Value conversion: ZetaSQL => Calcite
-  public static RexNode toRexNode(Value value, RexBuilder rexBuilder) {
-    Type type = value.getType();
-    if (value.isNull()) {
-      return rexBuilder.makeNullLiteral(toCalciteType(type, true, rexBuilder));
-    }
-
-    switch (type.getKind()) {
-      case TYPE_INT64:
-        return rexBuilder.makeExactLiteral(
-            new BigDecimal(value.getInt64Value()), toCalciteType(type, false, rexBuilder));
-      case TYPE_DOUBLE:
-        // Cannot simply call makeApproxLiteral() because +inf, -inf, and NaN cannot be represented
-        // as BigDecimal. So we create wrapper functions here for these three cases such that we can
-        // later recognize it and customize its unparsing in BeamBigQuerySqlDialect.
-        double val = value.getDoubleValue();
-        String wrapperFun = null;
-        if (val == Double.POSITIVE_INFINITY) {
-          wrapperFun = BeamBigQuerySqlDialect.DOUBLE_POSITIVE_INF_WRAPPER;
-        } else if (val == Double.NEGATIVE_INFINITY) {
-          wrapperFun = BeamBigQuerySqlDialect.DOUBLE_NEGATIVE_INF_WRAPPER;
-        } else if (Double.isNaN(val)) {
-          wrapperFun = BeamBigQuerySqlDialect.DOUBLE_NAN_WRAPPER;
-        }
-
-        RelDataType returnType = toCalciteType(type, false, rexBuilder);
-        if (wrapperFun == null) {
-          return rexBuilder.makeApproxLiteral(new BigDecimal(val), returnType);
-        } else if (BeamBigQuerySqlDialect.DOUBLE_NAN_WRAPPER.equals(wrapperFun)) {
-          // TODO[https://github.com/apache/beam/issues/20354]: Update the temporary workaround
-          // below after vendored Calcite version.
-          // Adding an additional random parameter for the wrapper function of NaN, to avoid
-          // triggering Calcite operation simplification. (e.g. 'NaN == NaN' would be simplify to
-          // 'null or NaN is not null' in Calcite. This would miscalculate the expression to be
-          // true, which should be false.)
-          return rexBuilder.makeCall(
-              SqlOperators.createZetaSqlFunction(wrapperFun, returnType.getSqlTypeName()),
-              rexBuilder.makeApproxLiteral(BigDecimal.valueOf(Math.random()), returnType));
-        } else {
-          return rexBuilder.makeCall(
-              SqlOperators.createZetaSqlFunction(wrapperFun, returnType.getSqlTypeName()));
-        }
-      case TYPE_BOOL:
-        return rexBuilder.makeLiteral(value.getBoolValue());
-      case TYPE_STRING:
-        // Has to allow CAST because Calcite create CHAR type first and does a CAST to VARCHAR.
-        // If not allow cast, rexBuilder() will only build a literal with CHAR type.
-        return rexBuilder.makeLiteral(
-            value.getStringValue(), toCalciteType(type, false, rexBuilder), true);
-      case TYPE_BYTES:
-        return rexBuilder.makeBinaryLiteral(new ByteString(value.getBytesValue().toByteArray()));
-      case TYPE_NUMERIC:
-        // Cannot simply call makeExactLiteral() because later it will be unparsed to the string
-        // representation of the BigDecimal itself (e.g. "SELECT NUMERIC '0'" will be unparsed to
-        // "SELECT 0E-9"), and Calcite does not allow customize unparsing of SqlNumericLiteral.
-        // So we create a wrapper function here such that we can later recognize it and customize
-        // its unparsing in BeamBigQuerySqlDialect.
-        return rexBuilder.makeCall(
-            SqlOperators.createZetaSqlFunction(
-                BeamBigQuerySqlDialect.NUMERIC_LITERAL_WRAPPER,
-                toCalciteType(type, false, rexBuilder).getSqlTypeName()),
-            rexBuilder.makeExactLiteral(
-                value.getNumericValue(), toCalciteType(type, false, rexBuilder)));
-      case TYPE_DATE:
-        return rexBuilder.makeDateLiteral(dateValueToDateString(value));
-      case TYPE_TIME:
-        return rexBuilder.makeTimeLiteral(
-            timeValueToTimeString(value),
-            rexBuilder.getTypeFactory().getTypeSystem().getMaxPrecision(SqlTypeName.TIME));
-      case TYPE_DATETIME:
-        return rexBuilder.makeTimestampWithLocalTimeZoneLiteral(
-            datetimeValueToTimestampString(value),
-            rexBuilder
-                .getTypeFactory()
-                .getTypeSystem()
-                .getMaxPrecision(SqlTypeName.TIMESTAMP_WITH_LOCAL_TIME_ZONE));
-      case TYPE_TIMESTAMP:
-        return rexBuilder.makeTimestampLiteral(
-            timestampValueToTimestampString(value),
-            rexBuilder.getTypeFactory().getTypeSystem().getMaxPrecision(SqlTypeName.TIMESTAMP));
-      case TYPE_ARRAY:
-        return arrayValueToRexNode(value, rexBuilder);
-      case TYPE_STRUCT:
-        return structValueToRexNode(value, rexBuilder);
-      case TYPE_ENUM: // internal only, used for DateTimestampPart
-        return enumValueToRexNode(value, rexBuilder);
-      default:
-        throw new UnsupportedOperationException("Unknown ZetaSQL type: " + type.getKind().name());
-    }
-  }
-
-  private static RexNode arrayValueToRexNode(Value value, RexBuilder rexBuilder) {
-    return rexBuilder.makeCall(
-        toCalciteArrayType(
-            value.getType().asArray().getElementType(),
-            value.getElementList().stream().anyMatch(v -> v.isNull()),
-            rexBuilder),
-        SqlStdOperatorTable.ARRAY_VALUE_CONSTRUCTOR,
-        value.getElementList().stream()
-            .map(v -> toRexNode(v, rexBuilder))
-            .collect(Collectors.toList()));
-  }
-
-  private static RexNode structValueToRexNode(Value value, RexBuilder rexBuilder) {
-    return rexBuilder.makeCall(
-        toCalciteStructType(value.getType().asStruct(), false, rexBuilder),
-        SqlStdOperatorTable.ROW,
-        value.getFieldList().stream()
-            .map(v -> toRexNode(v, rexBuilder))
-            .collect(Collectors.toList()));
-  }
-
-  // internal only, used for DateTimestampPart
-  private static RexNode enumValueToRexNode(Value value, RexBuilder rexBuilder) {
-    String enumDescriptorName = value.getType().asEnum().getDescriptor().getFullName();
-    if (!"zetasql.functions.DateTimestampPart".equals(enumDescriptorName)) {
-      throw new UnsupportedOperationException("Unknown ZetaSQL Enum type: " + enumDescriptorName);
-    }
-    TimeUnit timeUnit = TIME_UNIT_CASTING_MAP.get(value.getEnumValue());
-    if (timeUnit == null) {
-      throw new UnsupportedOperationException("Unknown ZetaSQL Enum value: " + value.getEnumName());
-    }
-    return rexBuilder.makeFlag(TimeUnitRange.of(timeUnit, null));
-  }
-
-  private static DateString dateValueToDateString(Value value) {
-    return DateString.fromDaysSinceEpoch(value.getDateValue());
-  }
-
-  private static TimeString timeValueToTimeString(Value value) {
-    LocalTime localTime = value.getLocalTimeValue();
-    return new TimeString(localTime.getHour(), localTime.getMinute(), localTime.getSecond())
-        .withNanos(localTime.getNano());
-  }
-
-  private static TimestampString datetimeValueToTimestampString(Value value) {
-    LocalDateTime dateTime = value.getLocalDateTimeValue();
-    return new TimestampString(
-            dateTime.getYear(),
-            dateTime.getMonthValue(),
-            dateTime.getDayOfMonth(),
-            dateTime.getHour(),
-            dateTime.getMinute(),
-            dateTime.getSecond())
-        .withNanos(dateTime.getNano());
-  }
-
-  private static TimestampString timestampValueToTimestampString(Value value) {
-    long micros = value.getTimestampUnixMicros();
-    if (micros % 1000L != 0) {
-      throw new UnsupportedOperationException(
-          String.format(
-              "%s has sub-millisecond precision, which Beam ZetaSQL does not currently support.",
-              micros));
-    }
-    return TimestampString.fromMillisSinceEpoch(micros / 1000L);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlException.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlException.java
deleted file mode 100644
index 7a948efb4b74..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlException.java
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import com.google.zetasql.io.grpc.Status;
-import com.google.zetasql.io.grpc.StatusRuntimeException;
-
-/**
- * Exception to be thrown by the Beam ZetaSQL planner.
- *
- * <p>Wraps a {@link StatusRuntimeException} containing a GRPC status code.
- */
-public class ZetaSqlException extends RuntimeException {
-
-  public ZetaSqlException(StatusRuntimeException cause) {
-    super(cause);
-  }
-
-  public ZetaSqlException(String message) {
-    this(Status.UNIMPLEMENTED.withDescription(message).asRuntimeException());
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/AggregateScanConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/AggregateScanConverter.java
deleted file mode 100644
index 412cd46001f8..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/AggregateScanConverter.java
+++ /dev/null
@@ -1,283 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_CAST;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_COLUMN_REF;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_GET_STRUCT_FIELD;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_LITERAL;
-
-import com.google.zetasql.FunctionSignature;
-import com.google.zetasql.ZetaSQLResolvedNodeKind;
-import com.google.zetasql.ZetaSQLType.TypeKind;
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedAggregateFunctionCall;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedAggregateScan;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedComputedColumn;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedComputedColumnBase;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedExpr;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.List;
-import java.util.stream.Collectors;
-import java.util.stream.IntStream;
-import org.apache.beam.sdk.extensions.sql.impl.UdafImpl;
-import org.apache.beam.sdk.extensions.sql.zetasql.BeamZetaSqlCatalog;
-import org.apache.beam.sdk.extensions.sql.zetasql.ZetaSqlCalciteTranslationUtils;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollations;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.AggregateCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalAggregate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalProject;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlAggFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlReturnTypeInference;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.ImmutableBitSet;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-
-/** Converts aggregate calls. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-class AggregateScanConverter extends RelConverter<ResolvedAggregateScan> {
-  private static final String AVG_ILLEGAL_LONG_INPUT_TYPE =
-      "AVG(INT64) is not supported. You might want to use AVG(CAST(expression AS FLOAT64).";
-
-  AggregateScanConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedAggregateScan zetaNode) {
-    return Collections.singletonList(zetaNode.getInputScan());
-  }
-
-  @Override
-  public RelNode convert(ResolvedAggregateScan zetaNode, List<RelNode> inputs) {
-    LogicalProject input = convertAggregateScanInputScanToLogicalProject(zetaNode, inputs.get(0));
-
-    // Calcite LogicalAggregate's GroupSet is indexes of group fields starting from 0.
-    int groupFieldsListSize = zetaNode.getGroupByList().size();
-    ImmutableBitSet groupSet;
-    if (groupFieldsListSize != 0) {
-      groupSet =
-          ImmutableBitSet.of(
-              IntStream.rangeClosed(0, groupFieldsListSize - 1)
-                  .boxed()
-                  .collect(Collectors.toList()));
-    } else {
-      groupSet = ImmutableBitSet.of();
-    }
-
-    // TODO: add support for indicator
-
-    List<AggregateCall> aggregateCalls;
-    if (zetaNode.getAggregateList().isEmpty()) {
-      aggregateCalls = ImmutableList.of();
-    } else {
-      aggregateCalls = new ArrayList<>();
-      // For aggregate calls, their input ref follow after GROUP BY input ref.
-      int columnRefoff = groupFieldsListSize;
-      for (ResolvedComputedColumnBase computedColumn : zetaNode.getAggregateList()) {
-        AggregateCall aggCall =
-            convertAggCall(computedColumn, columnRefoff, groupSet.size(), input);
-        aggregateCalls.add(aggCall);
-        if (!aggCall.getArgList().isEmpty()) {
-          // Only increment column reference offset when aggregates use them (BEAM-8042).
-          // Ex: COUNT(*) does not have arguments, while COUNT(`field`) does.
-          columnRefoff++;
-        }
-      }
-    }
-
-    LogicalAggregate logicalAggregate =
-        new LogicalAggregate(
-            getCluster(),
-            input.getTraitSet(),
-            input,
-            groupSet,
-            ImmutableList.of(groupSet),
-            aggregateCalls);
-
-    return logicalAggregate;
-  }
-
-  private LogicalProject convertAggregateScanInputScanToLogicalProject(
-      ResolvedAggregateScan node, RelNode input) {
-    // AggregateScan's input is the source of data (e.g. TableScan), which is different from the
-    // design of CalciteSQL, in which the LogicalAggregate's input is a LogicalProject, whose input
-    // is a LogicalTableScan. When AggregateScan's input is WithRefScan, the WithRefScan is
-    // ebullient to a LogicalTableScan. So it's still required to build another LogicalProject as
-    // the input of LogicalAggregate.
-    List<RexNode> projects = new ArrayList<>();
-    List<String> fieldNames = new ArrayList<>();
-
-    // LogicalProject has a list of expr, which including UDF in GROUP BY clause for
-    // LogicalAggregate.
-    for (ResolvedComputedColumn computedColumn : node.getGroupByList()) {
-      projects.add(
-          getExpressionConverter()
-              .convertRexNodeFromResolvedExpr(
-                  computedColumn.getExpr(),
-                  node.getInputScan().getColumnList(),
-                  input.getRowType().getFieldList(),
-                  ImmutableMap.of()));
-      fieldNames.add(getTrait().resolveAlias(computedColumn.getColumn()));
-    }
-
-    // LogicalProject should also include columns used by aggregate functions. These columns should
-    // follow after GROUP BY columns.
-    // TODO: remove duplicate columns in projects.
-    for (ResolvedComputedColumnBase resolvedComputedColumn : node.getAggregateList()) {
-      // Should create Calcite's RexInputRef from ResolvedColumn from ResolvedComputedColumn.
-      // TODO: handle aggregate function with more than one argument and handle OVER
-      // TODO: is there is general way for column reference tracking and deduplication for
-      // aggregation?
-      ResolvedAggregateFunctionCall aggregateFunctionCall =
-          ((ResolvedAggregateFunctionCall) resolvedComputedColumn.getExpr());
-      if (aggregateFunctionCall.getArgumentList() != null
-          && aggregateFunctionCall.getArgumentList().size() >= 1) {
-        ResolvedExpr resolvedExpr = aggregateFunctionCall.getArgumentList().get(0);
-        for (int i = 0; i < aggregateFunctionCall.getArgumentList().size(); i++) {
-          if (i == 0) {
-            // TODO: assume aggregate function's input is either a ColumnRef or a cast(ColumnRef).
-            // TODO: user might use multiple CAST so we need to handle this rare case.
-            projects.add(
-                getExpressionConverter()
-                    .convertRexNodeFromResolvedExpr(
-                        resolvedExpr,
-                        node.getInputScan().getColumnList(),
-                        input.getRowType().getFieldList(),
-                        ImmutableMap.of()));
-          } else {
-            projects.add(
-                getExpressionConverter()
-                    .convertRexNodeFromResolvedExpr(
-                        aggregateFunctionCall.getArgumentList().get(i)));
-          }
-          fieldNames.add(getTrait().resolveAlias(resolvedComputedColumn.getColumn()));
-        }
-      }
-    }
-
-    return LogicalProject.create(input, ImmutableList.of(), projects, fieldNames);
-  }
-
-  private AggregateCall convertAggCall(
-      ResolvedComputedColumnBase computedColumn, int columnRefOff, int groupCount, RelNode input) {
-    ResolvedAggregateFunctionCall aggregateFunctionCall =
-        (ResolvedAggregateFunctionCall) computedColumn.getExpr();
-
-    // Reject AVG(INT64)
-    if (aggregateFunctionCall.getFunction().getName().equals("avg")) {
-      FunctionSignature signature = aggregateFunctionCall.getSignature();
-      if (signature
-          .getFunctionArgumentList()
-          .get(0)
-          .getType()
-          .getKind()
-          .equals(TypeKind.TYPE_INT64)) {
-        throw new UnsupportedOperationException(AVG_ILLEGAL_LONG_INPUT_TYPE);
-      }
-    }
-
-    // Reject aggregation DISTINCT
-    if (aggregateFunctionCall.getDistinct()) {
-      throw new UnsupportedOperationException(
-          "Does not support "
-              + aggregateFunctionCall.getFunction().getSqlName()
-              + " DISTINCT. 'SELECT DISTINCT' syntax could be used to deduplicate before"
-              + " aggregation.");
-    }
-
-    final SqlAggFunction sqlAggFunction;
-    if (aggregateFunctionCall
-        .getFunction()
-        .getGroup()
-        .equals(BeamZetaSqlCatalog.USER_DEFINED_JAVA_AGGREGATE_FUNCTIONS)) {
-      // Create a new operator for user-defined functions.
-      SqlReturnTypeInference typeInference =
-          x ->
-              ZetaSqlCalciteTranslationUtils.toCalciteType(
-                  aggregateFunctionCall
-                      .getFunction()
-                      .getSignatureList()
-                      .get(0)
-                      .getResultType()
-                      .getType(),
-                  // TODO(BEAM-9514) set nullable=true
-                  false,
-                  getCluster().getRexBuilder());
-      UdafImpl<?, ?, ?> impl =
-          new UdafImpl<>(
-              getExpressionConverter()
-                  .userFunctionDefinitions
-                  .javaAggregateFunctions()
-                  .get(aggregateFunctionCall.getFunction().getNamePath()));
-      sqlAggFunction =
-          SqlOperators.createUdafOperator(
-              aggregateFunctionCall.getFunction().getName(), typeInference, impl);
-    } else {
-      // Look up builtin functions in SqlOperatorMappingTable.
-      sqlAggFunction = (SqlAggFunction) SqlOperatorMappingTable.create(aggregateFunctionCall);
-      if (sqlAggFunction == null) {
-        throw new UnsupportedOperationException(
-            "Does not support ZetaSQL aggregate function: "
-                + aggregateFunctionCall.getFunction().getName());
-      }
-    }
-
-    List<Integer> argList = new ArrayList<>();
-    ResolvedAggregateFunctionCall expr = ((ResolvedAggregateFunctionCall) computedColumn.getExpr());
-    List<ZetaSQLResolvedNodeKind.ResolvedNodeKind> resolvedNodeKinds =
-        Arrays.asList(RESOLVED_CAST, RESOLVED_COLUMN_REF, RESOLVED_GET_STRUCT_FIELD);
-    for (int i = 0; i < expr.getArgumentList().size(); i++) {
-      // Throw an error if aggregate function's input isn't either a ColumnRef or a cast(ColumnRef).
-      // TODO: is there a general way to handle aggregation calls conversion?
-      ZetaSQLResolvedNodeKind.ResolvedNodeKind resolvedNodeKind =
-          expr.getArgumentList().get(i).nodeKind();
-      if (i == 0 && resolvedNodeKinds.contains(resolvedNodeKind)) {
-        argList.add(columnRefOff);
-      } else if (i > 0 && resolvedNodeKind == RESOLVED_LITERAL) {
-        continue;
-      } else {
-        throw new UnsupportedOperationException(
-            "Aggregate function only accepts Column Reference or CAST(Column Reference) as the first argument and "
-                + "Literals as subsequent arguments as its inputs");
-      }
-    }
-
-    String aggName = getTrait().resolveAlias(computedColumn.getColumn());
-    return AggregateCall.create(
-        sqlAggFunction,
-        false,
-        false,
-        false,
-        argList,
-        -1,
-        null,
-        RelCollations.EMPTY,
-        groupCount,
-        input,
-        // When we pass null as the return type, Calcite infers it for us.
-        null,
-        aggName);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ArrayScanColumnRefToUncollect.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ArrayScanColumnRefToUncollect.java
deleted file mode 100644
index ac3de648b52d..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ArrayScanColumnRefToUncollect.java
+++ /dev/null
@@ -1,127 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.sdk.extensions.sql.zetasql.unnest.ZetaSqlUnnest;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.CorrelationId;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.JoinRelType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalCorrelate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalProject;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.ImmutableBitSet;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-
-/**
- * Converts array scan that represents a reference to an array column, or an (possibly nested) array
- * field of an struct column to uncollect.
- */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-class ArrayScanColumnRefToUncollect extends RelConverter<ResolvedNodes.ResolvedArrayScan> {
-  ArrayScanColumnRefToUncollect(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public boolean canConvert(ResolvedNodes.ResolvedArrayScan zetaNode) {
-    return zetaNode.getInputScan() != null
-        && getColumnRef(zetaNode.getArrayExpr()) != null
-        && zetaNode.getJoinExpr() == null;
-  }
-
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedNodes.ResolvedArrayScan zetaNode) {
-    return ImmutableList.of(zetaNode.getInputScan());
-  }
-
-  @Override
-  public RelNode convert(ResolvedNodes.ResolvedArrayScan zetaNode, List<RelNode> inputs) {
-    assert inputs.size() == 1;
-    RelNode input = inputs.get(0);
-    RexInputRef columnRef =
-        (RexInputRef)
-            getExpressionConverter()
-                .convertRexNodeFromResolvedExpr(
-                    getColumnRef(zetaNode.getArrayExpr()),
-                    zetaNode.getInputScan().getColumnList(),
-                    input.getRowType().getFieldList(),
-                    ImmutableMap.of());
-
-    CorrelationId correlationId = new CorrelationId(0);
-    RexNode convertedColumnRef =
-        getCluster()
-            .getRexBuilder()
-            .makeFieldAccess(
-                getCluster().getRexBuilder().makeCorrel(input.getRowType(), correlationId),
-                columnRef.getIndex());
-
-    String fieldName =
-        String.format(
-            "%s%s",
-            zetaNode.getElementColumn().getTableName(), zetaNode.getElementColumn().getName());
-
-    RelNode projectNode =
-        LogicalProject.create(
-            createOneRow(getCluster()),
-            ImmutableList.of(),
-            Collections.singletonList(
-                convertArrayExpr(
-                    zetaNode.getArrayExpr(), getCluster().getRexBuilder(), convertedColumnRef)),
-            ImmutableList.of(fieldName));
-
-    boolean ordinality = (zetaNode.getArrayOffsetColumn() != null);
-    RelNode uncollect = ZetaSqlUnnest.create(projectNode.getTraitSet(), projectNode, ordinality);
-
-    return LogicalCorrelate.create(
-        input,
-        uncollect,
-        correlationId,
-        ImmutableBitSet.of(columnRef.getIndex()),
-        JoinRelType.INNER);
-  }
-
-  private static ResolvedNodes.ResolvedColumnRef getColumnRef(ResolvedNode arrayExpr) {
-    while (arrayExpr instanceof ResolvedNodes.ResolvedGetStructField) {
-      arrayExpr = ((ResolvedNodes.ResolvedGetStructField) arrayExpr).getExpr();
-    }
-    return arrayExpr instanceof ResolvedNodes.ResolvedColumnRef
-        ? (ResolvedNodes.ResolvedColumnRef) arrayExpr
-        : null;
-  }
-
-  private static RexNode convertArrayExpr(
-      ResolvedNodes.ResolvedExpr expr, RexBuilder builder, RexNode convertedColumnRef) {
-    if (expr instanceof ResolvedNodes.ResolvedColumnRef) {
-      return convertedColumnRef;
-    }
-    ResolvedNodes.ResolvedGetStructField getStructField =
-        (ResolvedNodes.ResolvedGetStructField) expr;
-    return builder.makeFieldAccess(
-        convertArrayExpr(getStructField.getExpr(), builder, convertedColumnRef),
-        (int) getStructField.getFieldIdx());
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ArrayScanLiteralToUncollectConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ArrayScanLiteralToUncollectConverter.java
deleted file mode 100644
index 902b7a762fe5..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ArrayScanLiteralToUncollectConverter.java
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedArrayScan;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.sdk.extensions.sql.zetasql.unnest.ZetaSqlUnnest;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalProject;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-
-/** Converts array scan that represents an array literal to uncollect. */
-class ArrayScanLiteralToUncollectConverter extends RelConverter<ResolvedArrayScan> {
-
-  ArrayScanLiteralToUncollectConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public boolean canConvert(ResolvedArrayScan zetaNode) {
-    return zetaNode.getInputScan() == null;
-  }
-
-  @Override
-  public RelNode convert(ResolvedArrayScan zetaNode, List<RelNode> inputs) {
-    RexNode arrayLiteralExpression =
-        getExpressionConverter().convertRexNodeFromResolvedExpr(zetaNode.getArrayExpr());
-
-    String fieldName =
-        String.format(
-            "%s%s",
-            zetaNode.getElementColumn().getTableName(), zetaNode.getElementColumn().getName());
-
-    RelNode projectNode =
-        LogicalProject.create(
-            createOneRow(getCluster()),
-            ImmutableList.of(),
-            Collections.singletonList(arrayLiteralExpression),
-            ImmutableList.of(fieldName));
-
-    boolean ordinality = (zetaNode.getArrayOffsetColumn() != null);
-
-    // These asserts guaranteed by the parser code, but not the data structure.
-    // If they aren't true we need to add a Project to reorder columns.
-    assert zetaNode.getElementColumn().getId() == 1;
-    assert !ordinality || zetaNode.getArrayOffsetColumn().getColumn().getId() == 2;
-    return ZetaSqlUnnest.create(projectNode.getTraitSet(), projectNode, ordinality);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ArrayScanToJoinConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ArrayScanToJoinConverter.java
deleted file mode 100644
index 79984ea877e0..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ArrayScanToJoinConverter.java
+++ /dev/null
@@ -1,126 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedArrayScan;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedColumnRef;
-import java.util.ArrayList;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.sdk.extensions.sql.zetasql.unnest.ZetaSqlUnnest;
-import org.apache.beam.vendor.calcite.v1_28_0.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.CorrelationId;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.JoinRelType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalJoin;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalProject;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableSet;
-
-/** Converts array scan that represents join of an uncollect(array_field) to uncollect. */
-class ArrayScanToJoinConverter extends RelConverter<ResolvedArrayScan> {
-
-  ArrayScanToJoinConverter(ConversionContext context) {
-    super(context);
-  }
-
-  /** This is the case of {@code table [LEFT|INNER] JOIN UNNEST(table.array_field) on join_expr}. */
-  @Override
-  public boolean canConvert(ResolvedArrayScan zetaNode) {
-    return zetaNode.getArrayExpr() instanceof ResolvedColumnRef
-        && zetaNode.getInputScan() != null
-        && zetaNode.getJoinExpr() != null;
-  }
-
-  /** Left input is converted from input scan. */
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedArrayScan zetaNode) {
-    return Collections.singletonList(zetaNode.getInputScan());
-  }
-
-  /** Returns a LogicJoin. */
-  @Override
-  public RelNode convert(ResolvedArrayScan zetaNode, List<RelNode> inputs) {
-    List<RexNode> projects = new ArrayList<>();
-
-    RelNode leftInput = inputs.get(0);
-
-    ResolvedColumnRef columnRef = (ResolvedColumnRef) zetaNode.getArrayExpr();
-    CorrelationId correlationId = getCluster().createCorrel();
-    getCluster().getQuery().mapCorrel(correlationId.getName(), leftInput);
-    String columnName =
-        String.format(
-            "%s%s",
-            zetaNode.getElementColumn().getTableName(), zetaNode.getElementColumn().getName());
-
-    projects.add(
-        getCluster()
-            .getRexBuilder()
-            .makeFieldAccess(
-                getCluster().getRexBuilder().makeCorrel(leftInput.getRowType(), correlationId),
-                getExpressionConverter()
-                    .indexOfProjectionColumnRef(
-                        columnRef.getColumn().getId(), zetaNode.getInputScan().getColumnList())));
-
-    RelNode projectNode =
-        LogicalProject.create(
-            createOneRow(getCluster()), ImmutableList.of(), projects, ImmutableList.of(columnName));
-
-    // Create an UnCollect
-    boolean ordinality = (zetaNode.getArrayOffsetColumn() != null);
-
-    // These asserts guaranteed by the parser code, but not the data structure.
-    // If they aren't true we need the Project to reorder columns.
-    assert zetaNode.getElementColumn().getId() == 1;
-    assert !ordinality || zetaNode.getArrayOffsetColumn().getColumn().getId() == 2;
-    ZetaSqlUnnest uncollectNode =
-        ZetaSqlUnnest.create(projectNode.getTraitSet(), projectNode, ordinality);
-
-    List<RexInputRef> rightProjects = new ArrayList<>();
-    List<String> rightNames = new ArrayList<>();
-    rightProjects.add(getCluster().getRexBuilder().makeInputRef(uncollectNode, 0));
-    rightNames.add(columnName);
-    if (ordinality) {
-      rightProjects.add(getCluster().getRexBuilder().makeInputRef(uncollectNode, 1));
-      rightNames.add(
-          String.format(
-              zetaNode.getArrayOffsetColumn().getColumn().getTableName(),
-              zetaNode.getArrayOffsetColumn().getColumn().getName()));
-    }
-
-    RelNode rightInput =
-        LogicalProject.create(uncollectNode, ImmutableList.of(), rightProjects, rightNames);
-
-    // Join condition should be a RexNode converted from join_expr.
-    RexNode condition =
-        getExpressionConverter().convertRexNodeFromResolvedExpr(zetaNode.getJoinExpr());
-    JoinRelType joinRelType = zetaNode.getIsOuter() ? JoinRelType.LEFT : JoinRelType.INNER;
-
-    return LogicalJoin.create(
-        leftInput,
-        rightInput,
-        ImmutableList.of(),
-        condition,
-        ImmutableSet.of(),
-        joinRelType,
-        false,
-        ImmutableList.of());
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ConversionContext.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ConversionContext.java
deleted file mode 100644
index 01cf2d1c20f5..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ConversionContext.java
+++ /dev/null
@@ -1,96 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNode;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.sdk.extensions.sql.zetasql.QueryTrait;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.FrameworkConfig;
-
-/** Conversion context, some rules need this data to convert the nodes. */
-@Internal
-public class ConversionContext {
-  private final FrameworkConfig config;
-  private final ExpressionConverter expressionConverter;
-  private final RelOptCluster cluster;
-  private final QueryTrait trait;
-
-  // SQL native user-defined table-valued function can be resolved by Analyzer. Its sql body is
-  // converted to ResolvedNode, in which function parameters are replaced with ResolvedArgumentRef.
-  // Meanwhile, Analyzer provides values for function parameters because it looks ahead to find
-  // the SELECT query. Thus keep the argument name to values (converted to RexNode) mapping in
-  // Context for future usage in plan conversion.
-  private Map<String, RexNode> functionArgumentRefMapping;
-
-  public static ConversionContext of(
-      FrameworkConfig config,
-      ExpressionConverter expressionConverter,
-      RelOptCluster cluster,
-      QueryTrait trait) {
-    return new ConversionContext(config, expressionConverter, cluster, trait);
-  }
-
-  private ConversionContext(
-      FrameworkConfig config,
-      ExpressionConverter expressionConverter,
-      RelOptCluster cluster,
-      QueryTrait trait) {
-    this.config = config;
-    this.expressionConverter = expressionConverter;
-    this.cluster = cluster;
-    this.trait = trait;
-    this.functionArgumentRefMapping = new HashMap<>();
-  }
-
-  FrameworkConfig getConfig() {
-    return config;
-  }
-
-  ExpressionConverter getExpressionConverter() {
-    return expressionConverter;
-  }
-
-  RelOptCluster cluster() {
-    return cluster;
-  }
-
-  QueryTrait getTrait() {
-    return trait;
-  }
-
-  Map<List<String>, ResolvedNode> getUserDefinedTableValuedFunctions() {
-    return getExpressionConverter().userFunctionDefinitions.sqlTableValuedFunctions();
-  }
-
-  Map<String, RexNode> getFunctionArgumentRefMapping() {
-    return functionArgumentRefMapping;
-  }
-
-  void addToFunctionArgumentRefMapping(String s, RexNode r) {
-    getFunctionArgumentRefMapping().put(s, r);
-  }
-
-  void clearFunctionArgumentRefMapping() {
-    getFunctionArgumentRefMapping().clear();
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ExpressionConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ExpressionConverter.java
deleted file mode 100644
index 0f32451504b3..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ExpressionConverter.java
+++ /dev/null
@@ -1,965 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_FUNCTION_CALL;
-import static com.google.zetasql.ZetaSQLType.TypeKind.TYPE_BOOL;
-import static com.google.zetasql.ZetaSQLType.TypeKind.TYPE_BYTES;
-import static com.google.zetasql.ZetaSQLType.TypeKind.TYPE_DOUBLE;
-import static com.google.zetasql.ZetaSQLType.TypeKind.TYPE_INT64;
-import static com.google.zetasql.ZetaSQLType.TypeKind.TYPE_NUMERIC;
-import static com.google.zetasql.ZetaSQLType.TypeKind.TYPE_STRING;
-import static com.google.zetasql.ZetaSQLType.TypeKind.TYPE_TIMESTAMP;
-import static org.apache.beam.sdk.extensions.sql.zetasql.BeamZetaSqlCatalog.PRE_DEFINED_WINDOW_FUNCTIONS;
-import static org.apache.beam.sdk.extensions.sql.zetasql.BeamZetaSqlCatalog.USER_DEFINED_JAVA_SCALAR_FUNCTIONS;
-import static org.apache.beam.sdk.extensions.sql.zetasql.BeamZetaSqlCatalog.USER_DEFINED_SQL_FUNCTIONS;
-import static org.apache.beam.sdk.extensions.sql.zetasql.BeamZetaSqlCatalog.ZETASQL_FUNCTION_GROUP_NAME;
-import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
-
-import com.google.common.base.Ascii;
-import com.google.common.base.Preconditions;
-import com.google.common.collect.ImmutableList;
-import com.google.common.collect.ImmutableMap;
-import com.google.common.collect.ImmutableSet;
-import com.google.zetasql.TVFRelation;
-import com.google.zetasql.TVFRelation.Column;
-import com.google.zetasql.TableValuedFunction;
-import com.google.zetasql.TableValuedFunction.FixedOutputSchemaTVF;
-import com.google.zetasql.Type;
-import com.google.zetasql.Value;
-import com.google.zetasql.ZetaSQLType.TypeKind;
-import com.google.zetasql.io.grpc.Status;
-import com.google.zetasql.resolvedast.ResolvedColumn;
-import com.google.zetasql.resolvedast.ResolvedNodes;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedAggregateScan;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedArgumentRef;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedCast;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedColumnRef;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedComputedColumn;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedCreateFunctionStmt;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedExpr;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedFunctionCall;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedGetStructField;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedLiteral;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedOrderByScan;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedParameter;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedProjectScan;
-import java.math.BigDecimal;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.List;
-import java.util.Map;
-import java.util.stream.Collectors;
-import java.util.stream.IntStream;
-import org.apache.beam.repackaged.core.org.apache.commons.lang3.reflect.FieldUtils;
-import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.sdk.extensions.sql.impl.QueryPlanner.QueryParameters;
-import org.apache.beam.sdk.extensions.sql.impl.ZetaSqlUserDefinedSQLNativeTableValuedFunction;
-import org.apache.beam.sdk.extensions.sql.impl.utils.TVFStreamingUtils;
-import org.apache.beam.sdk.extensions.sql.zetasql.ZetaSqlCalciteTranslationUtils;
-import org.apache.beam.sdk.extensions.sql.zetasql.ZetaSqlException;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.avatica.util.TimeUnit;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeField;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFieldImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelRecordType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexInputRef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIntervalQualifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlRowOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.checkerframework.checker.nullness.qual.Nullable;
-
-/**
- * Extracts expressions (function calls, field accesses) from the resolve query nodes, converts them
- * to RexNodes.
- */
-@Internal
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-public class ExpressionConverter {
-
-  // Constants of pre-defined functions.
-  private static final String WINDOW_START = "_START";
-  private static final String WINDOW_END = "_END";
-  private static final String FIXED_WINDOW = "TUMBLE";
-  private static final String FIXED_WINDOW_START = FIXED_WINDOW + WINDOW_START;
-  private static final String FIXED_WINDOW_END = FIXED_WINDOW + WINDOW_END;
-  private static final String SLIDING_WINDOW = "HOP";
-  private static final String SLIDING_WINDOW_START = SLIDING_WINDOW + WINDOW_START;
-  private static final String SLIDING_WINDOW_END = SLIDING_WINDOW + WINDOW_END;
-  private static final String SESSION_WINDOW = "SESSION";
-  private static final String SESSION_WINDOW_START = SESSION_WINDOW + WINDOW_START;
-  private static final String SESSION_WINDOW_END = SESSION_WINDOW + WINDOW_END;
-
-  private static final ImmutableMap<String, String> WINDOW_START_END_TO_WINDOW_MAP =
-      ImmutableMap.<String, String>builder()
-          .put(FIXED_WINDOW_START, FIXED_WINDOW)
-          .put(FIXED_WINDOW_END, FIXED_WINDOW)
-          .put(SLIDING_WINDOW_START, SLIDING_WINDOW)
-          .put(SLIDING_WINDOW_END, SLIDING_WINDOW)
-          .put(SESSION_WINDOW_START, SESSION_WINDOW)
-          .put(SESSION_WINDOW_END, SESSION_WINDOW)
-          .build();
-
-  private static final ImmutableSet<String> WINDOW_START_END_FUNCTION_SET =
-      ImmutableSet.of(
-          FIXED_WINDOW_START,
-          FIXED_WINDOW_END,
-          SLIDING_WINDOW_START,
-          SLIDING_WINDOW_END,
-          SESSION_WINDOW_START,
-          SESSION_WINDOW_END);
-
-  private static final ImmutableMap<TypeKind, ImmutableSet<TypeKind>> UNSUPPORTED_CASTING =
-      ImmutableMap.<TypeKind, ImmutableSet<TypeKind>>builder()
-          .put(TYPE_INT64, ImmutableSet.of(TYPE_DOUBLE))
-          .put(TYPE_BOOL, ImmutableSet.of(TYPE_STRING))
-          .put(TYPE_STRING, ImmutableSet.of(TYPE_BOOL, TYPE_DOUBLE))
-          .build();
-
-  private static final ImmutableSet<String> DATE_PART_UNITS_TO_MILLIS =
-      ImmutableSet.of("DAY", "HOUR", "MINUTE", "SECOND");
-  private static final ImmutableSet<String> DATE_PART_UNITS_TO_MONTHS = ImmutableSet.of("YEAR");
-
-  private static final long ONE_SECOND_IN_MILLIS = 1000L;
-  private static final long ONE_MINUTE_IN_MILLIS = 60L * ONE_SECOND_IN_MILLIS;
-  private static final long ONE_HOUR_IN_MILLIS = 60L * ONE_MINUTE_IN_MILLIS;
-  private static final long ONE_DAY_IN_MILLIS = 24L * ONE_HOUR_IN_MILLIS;
-
-  @SuppressWarnings("unused")
-  private static final long ONE_MONTH_IN_MILLIS = 30L * ONE_DAY_IN_MILLIS;
-
-  @SuppressWarnings("unused")
-  private static final long ONE_YEAR_IN_MILLIS = 365L * ONE_DAY_IN_MILLIS;
-
-  // Constants of error messages.
-  private static final String INTERVAL_DATE_PART_MSG =
-      "YEAR, QUARTER, MONTH, WEEK, DAY, HOUR, MINUTE, SECOND, MILLISECOND";
-  private static final String INTERVAL_FORMAT_MSG =
-      "INTERVAL should be set as a STRING in the specific format: \"INTERVAL int64 date_part\"."
-          + " The date_part includes: "
-          + INTERVAL_DATE_PART_MSG;
-
-  private final RelOptCluster cluster;
-  private final QueryParameters queryParams;
-  private int nullParamCount = 0;
-  final UserFunctionDefinitions userFunctionDefinitions;
-
-  public ExpressionConverter(
-      RelOptCluster cluster,
-      QueryParameters params,
-      UserFunctionDefinitions userFunctionDefinitions) {
-    this.cluster = cluster;
-    this.queryParams = params;
-    this.userFunctionDefinitions = userFunctionDefinitions;
-  }
-
-  /** Extract expressions from a project scan node. */
-  public List<RexNode> retrieveRexNode(ResolvedProjectScan node, List<RelDataTypeField> fieldList) {
-    List<RexNode> ret = new ArrayList<>();
-
-    for (ResolvedColumn column : node.getColumnList()) {
-      int index = -1;
-      if ((index = indexOfResolvedColumnInExprList(node.getExprList(), column)) != -1) {
-        ResolvedComputedColumn computedColumn = node.getExprList().get(index);
-        int windowFieldIndex = -1;
-        if (computedColumn.getExpr().nodeKind() == RESOLVED_FUNCTION_CALL) {
-          String functionName =
-              ((ResolvedFunctionCall) computedColumn.getExpr()).getFunction().getName();
-          if (WINDOW_START_END_FUNCTION_SET.contains(functionName)) {
-            ResolvedAggregateScan resolvedAggregateScan =
-                (ResolvedAggregateScan) node.getInputScan();
-            windowFieldIndex =
-                indexOfWindowField(
-                    resolvedAggregateScan.getGroupByList(),
-                    resolvedAggregateScan.getColumnList(),
-                    WINDOW_START_END_TO_WINDOW_MAP.get(functionName));
-          }
-        }
-        ret.add(
-            convertRexNodeFromComputedColumnWithFieldList(
-                computedColumn, node.getInputScan().getColumnList(), fieldList, windowFieldIndex));
-      } else {
-        // ResolvedColumn is not a expression, which means it has to be an input column reference.
-        index = indexOfProjectionColumnRef(column.getId(), node.getInputScan().getColumnList());
-        if (index < 0 || index >= node.getInputScan().getColumnList().size()) {
-          throw new IllegalStateException(
-              String.format("Cannot find %s in fieldList %s", column, fieldList));
-        }
-
-        ret.add(rexBuilder().makeInputRef(fieldList.get(index).getType(), index));
-      }
-    }
-    return ret;
-  }
-
-  /** Extract expressions from order by scan node. */
-  public List<RexNode> retrieveRexNodeFromOrderByScan(
-      RelOptCluster cluster, ResolvedOrderByScan node, List<RelDataTypeField> fieldList) {
-    final RexBuilder rexBuilder = cluster.getRexBuilder();
-    List<RexNode> ret = new ArrayList<>();
-
-    for (ResolvedColumn column : node.getColumnList()) {
-      int index = indexOfProjectionColumnRef(column.getId(), node.getInputScan().getColumnList());
-      ret.add(rexBuilder.makeInputRef(fieldList.get(index).getType(), index));
-    }
-
-    return ret;
-  }
-
-  private static int indexOfResolvedColumnInExprList(
-      ImmutableList<ResolvedComputedColumn> exprList, ResolvedColumn column) {
-    if (exprList == null || exprList.isEmpty()) {
-      return -1;
-    }
-
-    for (int i = 0; i < exprList.size(); i++) {
-      ResolvedComputedColumn computedColumn = exprList.get(i);
-      if (computedColumn.getColumn().equals(column)) {
-        return i;
-      }
-    }
-
-    return -1;
-  }
-
-  private static int indexOfWindowField(
-      List<ResolvedComputedColumn> groupByList, List<ResolvedColumn> columnList, String windowFn) {
-    for (ResolvedComputedColumn groupByComputedColumn : groupByList) {
-      if (groupByComputedColumn.getExpr().nodeKind() == RESOLVED_FUNCTION_CALL) {
-        ResolvedFunctionCall functionCall = (ResolvedFunctionCall) groupByComputedColumn.getExpr();
-        if (functionCall.getFunction().getName().equals(windowFn)) {
-          int ret =
-              indexOfResolvedColumnInColumnList(columnList, groupByComputedColumn.getColumn());
-          if (ret == -1) {
-            throw new IllegalStateException("Cannot find " + windowFn + " in " + groupByList);
-          } else {
-            return ret;
-          }
-        }
-      }
-    }
-
-    throw new IllegalStateException("Cannot find " + windowFn + " in " + groupByList);
-  }
-
-  private static int indexOfResolvedColumnInColumnList(
-      List<ResolvedColumn> columnList, ResolvedColumn column) {
-    if (columnList == null || columnList.isEmpty()) {
-      return -1;
-    }
-
-    for (int i = 0; i < columnList.size(); i++) {
-      if (columnList.get(i).equals(column)) {
-        return i;
-      }
-    }
-
-    return -1;
-  }
-
-  /** Create a RexNode for a corresponding resolved expression node. */
-  public RexNode convertRexNodeFromResolvedExpr(
-      ResolvedExpr expr,
-      List<ResolvedColumn> columnList,
-      List<RelDataTypeField> fieldList,
-      Map<String, RexNode> functionArguments) {
-    if (columnList == null || fieldList == null) {
-      return convertRexNodeFromResolvedExpr(expr);
-    }
-
-    RexNode ret;
-
-    switch (expr.nodeKind()) {
-      case RESOLVED_LITERAL:
-        ret = convertResolvedLiteral((ResolvedLiteral) expr);
-        break;
-      case RESOLVED_COLUMN_REF:
-        ret = convertResolvedColumnRef((ResolvedColumnRef) expr, columnList, fieldList);
-        break;
-      case RESOLVED_FUNCTION_CALL:
-        ret =
-            convertResolvedFunctionCall(
-                (ResolvedFunctionCall) expr, columnList, fieldList, functionArguments);
-        break;
-      case RESOLVED_CAST:
-        ret = convertResolvedCast((ResolvedCast) expr, columnList, fieldList, functionArguments);
-        break;
-      case RESOLVED_PARAMETER:
-        ret = convertResolvedParameter((ResolvedParameter) expr);
-        break;
-      case RESOLVED_GET_STRUCT_FIELD:
-        ret =
-            convertResolvedStructFieldAccess(
-                (ResolvedGetStructField) expr, columnList, fieldList, functionArguments);
-        break;
-      case RESOLVED_ARGUMENT_REF:
-        ret = convertResolvedArgumentRef((ResolvedArgumentRef) expr, functionArguments);
-        break;
-      default:
-        ret = convertRexNodeFromResolvedExpr(expr);
-    }
-
-    return ret;
-  }
-
-  public RexNode convertRelNodeToRexRangeRef(RelNode rel) {
-    return rexBuilder().makeRangeReference(rel);
-  }
-
-  /** Create a RexNode for a corresponding resolved expression. */
-  public RexNode convertRexNodeFromResolvedExpr(ResolvedExpr expr) {
-    RexNode ret;
-
-    switch (expr.nodeKind()) {
-      case RESOLVED_LITERAL:
-        ret = convertResolvedLiteral((ResolvedLiteral) expr);
-        break;
-      case RESOLVED_COLUMN_REF:
-        ret = convertResolvedColumnRef((ResolvedColumnRef) expr);
-        break;
-      case RESOLVED_FUNCTION_CALL:
-        // TODO: is there a better way to shared code for different cases of
-        // convertResolvedFunctionCall than passing nulls?
-        ret =
-            convertResolvedFunctionCall((ResolvedFunctionCall) expr, null, null, ImmutableMap.of());
-        break;
-      case RESOLVED_CAST:
-        ret = convertResolvedCast((ResolvedCast) expr, null, null, ImmutableMap.of());
-        break;
-      case RESOLVED_PARAMETER:
-        ret = convertResolvedParameter((ResolvedParameter) expr);
-        break;
-      case RESOLVED_GET_STRUCT_FIELD:
-        ret = convertResolvedStructFieldAccess((ResolvedGetStructField) expr);
-        break;
-      case RESOLVED_SUBQUERY_EXPR:
-        throw new UnsupportedOperationException("Does not support sub-queries");
-      default:
-        throw new UnsupportedOperationException(
-            "Does not support expr node kind " + expr.nodeKind());
-    }
-
-    return ret;
-  }
-
-  private RexNode convertRexNodeFromComputedColumnWithFieldList(
-      ResolvedComputedColumn column,
-      List<ResolvedColumn> columnList,
-      List<RelDataTypeField> fieldList,
-      int windowFieldIndex) {
-    if (column.getExpr().nodeKind() != RESOLVED_FUNCTION_CALL) {
-      return convertRexNodeFromResolvedExpr(
-          column.getExpr(), columnList, fieldList, ImmutableMap.of());
-    }
-
-    ResolvedFunctionCall functionCall = (ResolvedFunctionCall) column.getExpr();
-
-    // TODO: is there any other illegal case?
-    if (functionCall.getFunction().getName().equals(FIXED_WINDOW)
-        || functionCall.getFunction().getName().equals(SLIDING_WINDOW)
-        || functionCall.getFunction().getName().equals(SESSION_WINDOW)) {
-      throw new ZetaSqlException(
-          functionCall.getFunction().getName() + " shouldn't appear in SELECT exprlist.");
-    }
-
-    if (!functionCall.getFunction().getGroup().equals(PRE_DEFINED_WINDOW_FUNCTIONS)) {
-      // non-window function should still go through normal FunctionCall conversion process.
-      return convertRexNodeFromResolvedExpr(
-          column.getExpr(), columnList, fieldList, ImmutableMap.of());
-    }
-
-    // ONLY window_start and window_end should arrive here.
-    // TODO: Have extra verification here to make sure window start/end functions have the same
-    // parameter with window function.
-    List<RexNode> operands = new ArrayList<>();
-    switch (functionCall.getFunction().getName()) {
-      case FIXED_WINDOW_START:
-      case SLIDING_WINDOW_START:
-      case SESSION_WINDOW_START:
-        // TODO: in Calcite implementation, session window's start is equal to end. Need to fix it
-        // in Calcite.
-      case SESSION_WINDOW_END:
-        return rexBuilder()
-            .makeInputRef(fieldList.get(windowFieldIndex).getType(), windowFieldIndex);
-      case FIXED_WINDOW_END:
-        operands.add(
-            rexBuilder().makeInputRef(fieldList.get(windowFieldIndex).getType(), windowFieldIndex));
-        // TODO: check window_end 's duration is the same as it's aggregate window.
-        operands.add(
-            convertIntervalToRexIntervalLiteral(
-                (ResolvedLiteral) functionCall.getArgumentList().get(0)));
-        return rexBuilder().makeCall(SqlOperators.ZETASQL_TIMESTAMP_ADD, operands);
-      case SLIDING_WINDOW_END:
-        operands.add(
-            rexBuilder().makeInputRef(fieldList.get(windowFieldIndex).getType(), windowFieldIndex));
-        operands.add(
-            convertIntervalToRexIntervalLiteral(
-                (ResolvedLiteral) functionCall.getArgumentList().get(1)));
-        return rexBuilder().makeCall(SqlOperators.ZETASQL_TIMESTAMP_ADD, operands);
-      default:
-        throw new UnsupportedOperationException(
-            "Does not support window start/end: " + functionCall.getFunction().getName());
-    }
-  }
-
-  public RexNode trueLiteral() {
-    return rexBuilder().makeLiteral(true);
-  }
-
-  /** Convert a resolved literal to a RexNode. */
-  public RexNode convertResolvedLiteral(ResolvedLiteral resolvedLiteral) {
-    return ZetaSqlCalciteTranslationUtils.toRexNode(resolvedLiteral.getValue(), rexBuilder());
-  }
-
-  /** Convert a TableValuedFunction in ZetaSQL to a RexCall in Calcite. */
-  public RexCall convertTableValuedFunction(
-      RelNode input,
-      TableValuedFunction tvf,
-      List<ResolvedNodes.ResolvedFunctionArgument> argumentList,
-      List<ResolvedColumn> inputTableColumns) {
-    ResolvedColumn wmCol;
-    // Handle builtin windowing TVF.
-    switch (tvf.getName()) {
-      case TVFStreamingUtils.FIXED_WINDOW_TVF:
-        // TUMBLE tvf's second argument is descriptor.
-        wmCol = extractWatermarkColumnFromDescriptor(argumentList.get(1).getDescriptorArg());
-
-        return (RexCall)
-            rexBuilder()
-                .makeCall(
-                    new SqlWindowTableFunction(SqlKind.TUMBLE.name()),
-                    convertRelNodeToRexRangeRef(input),
-                    convertResolvedColumnToRexInputRef(wmCol, inputTableColumns),
-                    convertIntervalToRexIntervalLiteral(
-                        (ResolvedLiteral) argumentList.get(2).getExpr()));
-
-      case TVFStreamingUtils.SLIDING_WINDOW_TVF:
-        // HOP tvf's second argument is descriptor.
-        wmCol = extractWatermarkColumnFromDescriptor(argumentList.get(1).getDescriptorArg());
-        return (RexCall)
-            rexBuilder()
-                .makeCall(
-                    new SqlWindowTableFunction(SqlKind.HOP.name()),
-                    convertRelNodeToRexRangeRef(input),
-                    convertResolvedColumnToRexInputRef(wmCol, inputTableColumns),
-                    convertIntervalToRexIntervalLiteral(
-                        (ResolvedLiteral) argumentList.get(2).getExpr()),
-                    convertIntervalToRexIntervalLiteral(
-                        (ResolvedLiteral) argumentList.get(3).getExpr()));
-
-      case TVFStreamingUtils.SESSION_WINDOW_TVF:
-        // SESSION tvf's second argument is descriptor.
-        wmCol = extractWatermarkColumnFromDescriptor(argumentList.get(1).getDescriptorArg());
-        // SESSION tvf's third argument is descriptor.
-        List<ResolvedColumn> keyCol =
-            extractSessionKeyColumnFromDescriptor(argumentList.get(2).getDescriptorArg());
-        List<RexNode> operands = new ArrayList<>();
-        operands.add(convertRelNodeToRexRangeRef(input));
-        operands.add(convertResolvedColumnToRexInputRef(wmCol, inputTableColumns));
-        operands.add(
-            convertIntervalToRexIntervalLiteral((ResolvedLiteral) argumentList.get(3).getExpr()));
-        operands.addAll(convertResolvedColumnsToRexInputRef(keyCol, inputTableColumns));
-        return (RexCall)
-            rexBuilder().makeCall(new SqlWindowTableFunction(SqlKind.SESSION.name()), operands);
-    }
-
-    if (tvf instanceof FixedOutputSchemaTVF) {
-      FixedOutputSchemaTVF fixedOutputSchemaTVF = (FixedOutputSchemaTVF) tvf;
-      return (RexCall)
-          rexBuilder()
-              .makeCall(
-                  new ZetaSqlUserDefinedSQLNativeTableValuedFunction(
-                      new SqlIdentifier(tvf.getName(), SqlParserPos.ZERO),
-                      opBinding -> {
-                        TVFRelation rel = fixedOutputSchemaTVF.getOutputSchema();
-                        // TODO(yathu) revert this workaround when ZetaSQL adds back this API.
-                        List<Column> cols;
-                        try {
-                          cols = (List<Column>) FieldUtils.readField(rel, "columns", true);
-                        } catch (IllegalAccessException e) {
-                          throw new RuntimeException(e);
-                        }
-                        List<RelDataTypeField> relDataTypeFields =
-                            convertTVFRelationColumnsToRelDataTypeFields(cols);
-                        return new RelRecordType(relDataTypeFields);
-                      },
-                      null,
-                      null,
-                      null,
-                      null));
-    }
-
-    throw new UnsupportedOperationException(
-        "Does not support table-valued function: " + tvf.getName());
-  }
-
-  private List<RelDataTypeField> convertTVFRelationColumnsToRelDataTypeFields(
-      List<TVFRelation.Column> columns) {
-    return IntStream.range(0, columns.size())
-        .mapToObj(
-            i ->
-                new RelDataTypeFieldImpl(
-                    columns.get(i).getName(),
-                    i,
-                    ZetaSqlCalciteTranslationUtils.toCalciteType(
-                        columns.get(i).getType(), false, rexBuilder())))
-        .collect(Collectors.toList());
-  }
-
-  private List<RexInputRef> convertResolvedColumnsToRexInputRef(
-      List<ResolvedColumn> columns, List<ResolvedColumn> inputTableColumns) {
-    List<RexInputRef> ret = new ArrayList<>();
-    for (ResolvedColumn column : columns) {
-      ret.add(convertResolvedColumnToRexInputRef(column, inputTableColumns));
-    }
-    return ret;
-  }
-
-  private RexInputRef convertResolvedColumnToRexInputRef(
-      ResolvedColumn column, List<ResolvedColumn> inputTableColumns) {
-    for (int i = 0; i < inputTableColumns.size(); i++) {
-      if (inputTableColumns.get(i).equals(column)) {
-        return rexBuilder()
-            .makeInputRef(
-                ZetaSqlCalciteTranslationUtils.toCalciteType(column.getType(), false, rexBuilder()),
-                i);
-      }
-    }
-
-    throw new IllegalArgumentException(
-        "ZetaSQL parser guarantees that wmCol can be found from inputTableColumns so it shouldn't reach here.");
-  }
-
-  private ResolvedColumn extractWatermarkColumnFromDescriptor(
-      ResolvedNodes.ResolvedDescriptor descriptor) {
-    ResolvedColumn wmCol = descriptor.getDescriptorColumnList().get(0);
-    checkArgument(
-        wmCol.getType().getKind() == TYPE_TIMESTAMP,
-        "Watermarked column should be TIMESTAMP type: %s",
-        descriptor.getDescriptorColumnNameList().get(0));
-    return wmCol;
-  }
-
-  private List<ResolvedColumn> extractSessionKeyColumnFromDescriptor(
-      ResolvedNodes.ResolvedDescriptor descriptor) {
-    checkArgument(
-        descriptor.getDescriptorColumnNameList().size() > 0,
-        "Session key descriptor should not be empty");
-
-    return descriptor.getDescriptorColumnList();
-  }
-
-  private RexNode convertResolvedColumnRef(
-      ResolvedColumnRef columnRef,
-      List<ResolvedColumn> columnList,
-      List<RelDataTypeField> fieldList) {
-    int index = indexOfProjectionColumnRef(columnRef.getColumn().getId(), columnList);
-    if (index < 0 || index >= columnList.size()) {
-      throw new IllegalStateException(
-          String.format("Cannot find %s in fieldList %s", columnRef.getColumn(), fieldList));
-    }
-    return rexBuilder().makeInputRef(fieldList.get(index).getType(), index);
-  }
-
-  private RexNode convertResolvedColumnRef(ResolvedColumnRef columnRef) {
-    // TODO: id - 1 might be only correct if the columns read from TableScan.
-    // What if the columns come from other scans (which means their id are not indexed from 0),
-    // and what if there are some mis-order?
-    // TODO: can join key be NULL?
-    return rexBuilder()
-        .makeInputRef(
-            ZetaSqlCalciteTranslationUtils.toCalciteType(columnRef.getType(), false, rexBuilder()),
-            (int) columnRef.getColumn().getId() - 1);
-  }
-
-  /** Return an index of the projection column reference. */
-  public int indexOfProjectionColumnRef(long colId, List<ResolvedColumn> columnList) {
-    int ret = -1;
-    for (int i = 0; i < columnList.size(); i++) {
-      if (columnList.get(i).getId() == colId) {
-        ret = i;
-        break;
-      }
-    }
-
-    return ret;
-  }
-
-  private RexNode convertResolvedFunctionCall(
-      ResolvedFunctionCall functionCall,
-      @Nullable List<ResolvedColumn> columnList,
-      @Nullable List<RelDataTypeField> fieldList,
-      Map<String, RexNode> outerFunctionArguments) {
-    final String funGroup = functionCall.getFunction().getGroup();
-    final String funName = functionCall.getFunction().getName();
-    SqlOperator op = SqlOperatorMappingTable.create(functionCall);
-    List<RexNode> operands = new ArrayList<>();
-
-    if (PRE_DEFINED_WINDOW_FUNCTIONS.equals(funGroup)) {
-      switch (funName) {
-        case FIXED_WINDOW:
-        case SESSION_WINDOW:
-          // TODO: check size and type of window function argument list.
-          // Add ts column reference to operands.
-          operands.add(
-              convertRexNodeFromResolvedExpr(
-                  functionCall.getArgumentList().get(0),
-                  columnList,
-                  fieldList,
-                  outerFunctionArguments));
-          // Add fixed window size or session window gap to operands.
-          operands.add(
-              convertIntervalToRexIntervalLiteral(
-                  (ResolvedLiteral) functionCall.getArgumentList().get(1)));
-          break;
-        case SLIDING_WINDOW:
-          // Add ts column reference to operands.
-          operands.add(
-              convertRexNodeFromResolvedExpr(
-                  functionCall.getArgumentList().get(0),
-                  columnList,
-                  fieldList,
-                  outerFunctionArguments));
-          // add sliding window emit frequency to operands.
-          operands.add(
-              convertIntervalToRexIntervalLiteral(
-                  (ResolvedLiteral) functionCall.getArgumentList().get(1)));
-          // add sliding window size to operands.
-          operands.add(
-              convertIntervalToRexIntervalLiteral(
-                  (ResolvedLiteral) functionCall.getArgumentList().get(2)));
-          break;
-        default:
-          throw new UnsupportedOperationException(
-              "Unsupported function: " + funName + ". Only support TUMBLE, HOP, and SESSION now.");
-      }
-    } else if (ZETASQL_FUNCTION_GROUP_NAME.equals(funGroup)) {
-      if (op == null) {
-        Type returnType = functionCall.getSignature().getResultType().getType();
-        if (returnType != null) {
-          op =
-              SqlOperators.createZetaSqlFunction(
-                  funName,
-                  ZetaSqlCalciteTranslationUtils.toCalciteType(returnType, false, rexBuilder())
-                      .getSqlTypeName());
-        } else {
-          throw new UnsupportedOperationException("Does not support ZetaSQL function: " + funName);
-        }
-      }
-
-      for (ResolvedExpr expr : functionCall.getArgumentList()) {
-        operands.add(
-            convertRexNodeFromResolvedExpr(expr, columnList, fieldList, outerFunctionArguments));
-      }
-    } else if (USER_DEFINED_JAVA_SCALAR_FUNCTIONS.equals(funGroup)) {
-      UserFunctionDefinitions.JavaScalarFunction javaScalarFunction =
-          userFunctionDefinitions
-              .javaScalarFunctions()
-              .get(functionCall.getFunction().getNamePath());
-      ArrayList<RexNode> innerFunctionArguments = new ArrayList<>();
-      for (int i = 0; i < functionCall.getArgumentList().size(); i++) {
-        ResolvedExpr argExpr = functionCall.getArgumentList().get(i);
-        RexNode argNode =
-            convertRexNodeFromResolvedExpr(argExpr, columnList, fieldList, outerFunctionArguments);
-        innerFunctionArguments.add(argNode);
-      }
-      return rexBuilder()
-          .makeCall(
-              SqlOperators.createUdfOperator(
-                  functionCall.getFunction().getName(),
-                  javaScalarFunction.method(),
-                  USER_DEFINED_JAVA_SCALAR_FUNCTIONS,
-                  javaScalarFunction.jarPath()),
-              innerFunctionArguments);
-    } else if (USER_DEFINED_SQL_FUNCTIONS.equals(funGroup)) {
-      ResolvedCreateFunctionStmt createFunctionStmt =
-          userFunctionDefinitions
-              .sqlScalarFunctions()
-              .get(functionCall.getFunction().getNamePath());
-      ResolvedExpr functionExpression = createFunctionStmt.getFunctionExpression();
-      ImmutableMap.Builder<String, RexNode> innerFunctionArguments = ImmutableMap.builder();
-      for (int i = 0; i < functionCall.getArgumentList().size(); i++) {
-        String argName = createFunctionStmt.getArgumentNameList().get(i);
-        ResolvedExpr argExpr = functionCall.getArgumentList().get(i);
-        RexNode argNode =
-            convertRexNodeFromResolvedExpr(argExpr, columnList, fieldList, outerFunctionArguments);
-        innerFunctionArguments.put(argName, argNode);
-      }
-      return this.convertRexNodeFromResolvedExpr(
-          functionExpression, columnList, fieldList, innerFunctionArguments.build());
-    } else {
-      throw new UnsupportedOperationException("Does not support function group: " + funGroup);
-    }
-
-    SqlOperatorRewriter rewriter =
-        SqlOperatorMappingTable.ZETASQL_FUNCTION_TO_CALCITE_SQL_OPERATOR_REWRITER.get(funName);
-
-    if (rewriter != null) {
-      return rewriter.apply(rexBuilder(), operands);
-    } else {
-      return rexBuilder().makeCall(op, operands);
-    }
-  }
-
-  private RexNode convertIntervalToRexIntervalLiteral(ResolvedLiteral resolvedLiteral) {
-    if (resolvedLiteral.getType().getKind() != TYPE_STRING) {
-      throw new ZetaSqlException(INTERVAL_FORMAT_MSG);
-    }
-
-    String valStr = resolvedLiteral.getValue().getStringValue();
-    List<String> stringList =
-        Arrays.stream(valStr.split(" ")).filter(s -> !s.isEmpty()).collect(Collectors.toList());
-
-    if (stringList.size() != 3) {
-      throw new ZetaSqlException(INTERVAL_FORMAT_MSG);
-    }
-
-    if (!Ascii.toUpperCase(stringList.get(0)).equals("INTERVAL")) {
-      throw new ZetaSqlException(INTERVAL_FORMAT_MSG);
-    }
-
-    long intervalValue;
-    try {
-      intervalValue = Long.parseLong(stringList.get(1));
-    } catch (NumberFormatException e) {
-      throw new ZetaSqlException(
-          Status.UNIMPLEMENTED
-              .withDescription(INTERVAL_FORMAT_MSG)
-              .withCause(e)
-              .asRuntimeException());
-    }
-
-    String intervalDatepart = Ascii.toUpperCase(stringList.get(2));
-    return createCalciteIntervalRexLiteral(intervalValue, intervalDatepart);
-  }
-
-  private RexLiteral createCalciteIntervalRexLiteral(long intervalValue, String intervalTimeUnit) {
-    SqlIntervalQualifier sqlIntervalQualifier =
-        convertIntervalDatepartToSqlIntervalQualifier(intervalTimeUnit);
-    BigDecimal decimalValue;
-    if (DATE_PART_UNITS_TO_MILLIS.contains(intervalTimeUnit)) {
-      decimalValue = convertIntervalValueToMillis(sqlIntervalQualifier, intervalValue);
-    } else if (DATE_PART_UNITS_TO_MONTHS.contains(intervalTimeUnit)) {
-      decimalValue = new BigDecimal(intervalValue * 12);
-    } else {
-      decimalValue = new BigDecimal(intervalValue);
-    }
-    return rexBuilder().makeIntervalLiteral(decimalValue, sqlIntervalQualifier);
-  }
-
-  private static BigDecimal convertIntervalValueToMillis(
-      SqlIntervalQualifier qualifier, long value) {
-    switch (qualifier.typeName()) {
-      case INTERVAL_DAY:
-        return new BigDecimal(value * ONE_DAY_IN_MILLIS);
-      case INTERVAL_HOUR:
-        return new BigDecimal(value * ONE_HOUR_IN_MILLIS);
-      case INTERVAL_MINUTE:
-        return new BigDecimal(value * ONE_MINUTE_IN_MILLIS);
-      case INTERVAL_SECOND:
-        return new BigDecimal(value * ONE_SECOND_IN_MILLIS);
-      default:
-        throw new ZetaSqlException(qualifier.typeName().toString());
-    }
-  }
-
-  private static SqlIntervalQualifier convertIntervalDatepartToSqlIntervalQualifier(
-      String datePart) {
-    switch (datePart) {
-      case "YEAR":
-        return new SqlIntervalQualifier(TimeUnit.YEAR, null, SqlParserPos.ZERO);
-      case "MONTH":
-        return new SqlIntervalQualifier(TimeUnit.MONTH, null, SqlParserPos.ZERO);
-      case "DAY":
-        return new SqlIntervalQualifier(TimeUnit.DAY, null, SqlParserPos.ZERO);
-      case "HOUR":
-        return new SqlIntervalQualifier(TimeUnit.HOUR, null, SqlParserPos.ZERO);
-      case "MINUTE":
-        return new SqlIntervalQualifier(TimeUnit.MINUTE, null, SqlParserPos.ZERO);
-      case "SECOND":
-        return new SqlIntervalQualifier(TimeUnit.SECOND, null, SqlParserPos.ZERO);
-      case "WEEK":
-        return new SqlIntervalQualifier(TimeUnit.WEEK, null, SqlParserPos.ZERO);
-      case "QUARTER":
-        return new SqlIntervalQualifier(TimeUnit.QUARTER, null, SqlParserPos.ZERO);
-      case "MILLISECOND":
-        return new SqlIntervalQualifier(TimeUnit.MILLISECOND, null, SqlParserPos.ZERO);
-      default:
-        throw new ZetaSqlException(
-            String.format(
-                "Received an undefined INTERVAL unit: %s. Please specify unit from the following"
-                    + " list: %s.",
-                datePart, INTERVAL_DATE_PART_MSG));
-    }
-  }
-
-  private RexNode convertResolvedCast(
-      ResolvedCast resolvedCast,
-      List<ResolvedColumn> columnList,
-      List<RelDataTypeField> fieldList,
-      Map<String, RexNode> functionArguments) {
-    return convertResolvedCast(
-        resolvedCast,
-        convertRexNodeFromResolvedExpr(
-            resolvedCast.getExpr(), columnList, fieldList, functionArguments));
-  }
-
-  private RexNode convertResolvedCast(ResolvedCast resolvedCast, RexNode input) {
-    TypeKind fromType = resolvedCast.getExpr().getType().getKind();
-    TypeKind toType = resolvedCast.getType().getKind();
-    isCastingSupported(fromType, toType, input);
-
-    // nullability of the output type should match that of the input node's type
-    RelDataType outputType =
-        ZetaSqlCalciteTranslationUtils.toCalciteType(
-            resolvedCast.getType(), input.getType().isNullable(), rexBuilder());
-
-    if (isZetaSQLCast(fromType, toType)) {
-      return rexBuilder().makeCall(outputType, SqlOperators.CAST_OP, ImmutableList.of(input));
-    } else {
-      return rexBuilder().makeCast(outputType, input);
-    }
-  }
-
-  private static void isCastingSupported(TypeKind fromType, TypeKind toType, RexNode input) {
-    if (UNSUPPORTED_CASTING.containsKey(toType)
-        && UNSUPPORTED_CASTING.get(toType).contains(fromType)) {
-      throw new UnsupportedOperationException(
-          "Does not support CAST(" + fromType + " AS " + toType + ")");
-    }
-    if (fromType.equals(TYPE_DOUBLE)
-        && toType.equals(TYPE_NUMERIC)
-        && input instanceof RexLiteral) {
-      BigDecimal value = (BigDecimal) ((RexLiteral) input).getValue();
-      if (value.compareTo(ZetaSqlCalciteTranslationUtils.ZETASQL_NUMERIC_MAX_VALUE) > 0) {
-        throw new ZetaSqlException(
-            Status.OUT_OF_RANGE
-                .withDescription(
-                    String.format(
-                        "Casting %s as %s would cause overflow of literal %s.",
-                        fromType, toType, value))
-                .asRuntimeException());
-      }
-      if (value.compareTo(ZetaSqlCalciteTranslationUtils.ZETASQL_NUMERIC_MIN_VALUE) < 0) {
-        throw new ZetaSqlException(
-            Status.OUT_OF_RANGE
-                .withDescription(
-                    String.format(
-                        "Casting %s as %s would cause underflow of literal %s.",
-                        fromType, toType, value))
-                .asRuntimeException());
-      }
-      if (value.scale() > ZetaSqlCalciteTranslationUtils.ZETASQL_NUMERIC_SCALE) {
-        throw new ZetaSqlException(
-            Status.OUT_OF_RANGE
-                .withDescription(
-                    String.format(
-                        "Cannot cast %s as %s: scale %d exceeds %d for literal %s.",
-                        fromType,
-                        toType,
-                        value.scale(),
-                        ZetaSqlCalciteTranslationUtils.ZETASQL_NUMERIC_SCALE,
-                        value))
-                .asRuntimeException());
-      }
-    }
-  }
-
-  private static boolean isZetaSQLCast(TypeKind fromType, TypeKind toType) {
-    // TODO: Structure CAST_OP so that we don't have to repeat the supported types
-    // here
-    return (fromType.equals(TYPE_BYTES) && toType.equals(TYPE_STRING))
-        || (fromType.equals(TYPE_INT64) && toType.equals(TYPE_BOOL))
-        || (fromType.equals(TYPE_BOOL) && toType.equals(TYPE_INT64))
-        || (fromType.equals(TYPE_TIMESTAMP) && toType.equals(TYPE_STRING));
-  }
-
-  private RexNode convertResolvedParameter(ResolvedParameter parameter) {
-    Value value;
-    switch (queryParams.getKind()) {
-      case NAMED:
-        value = ((Map<String, Value>) queryParams.named()).get(parameter.getName());
-        break;
-      case POSITIONAL:
-        // parameter is 1-indexed, while parameter list is 0-indexed.
-        value = ((List<Value>) queryParams.positional()).get((int) parameter.getPosition() - 1);
-        break;
-      default:
-        throw new IllegalArgumentException("Found unexpected parameter " + parameter);
-    }
-    Preconditions.checkState(parameter.getType().equals(value.getType()));
-    if (value.isNull()) {
-      // In some cases NULL parameter cannot be substituted with NULL literal
-      // Therefore we create a dynamic parameter placeholder here for each NULL parameter
-      return rexBuilder()
-          .makeDynamicParam(
-              ZetaSqlCalciteTranslationUtils.toCalciteType(value.getType(), true, rexBuilder()),
-              nullParamCount++);
-    } else {
-      // Substitute non-NULL parameter with literal
-      return ZetaSqlCalciteTranslationUtils.toRexNode(value, rexBuilder());
-    }
-  }
-
-  private RexNode convertResolvedArgumentRef(
-      ResolvedArgumentRef resolvedArgumentRef, Map<String, RexNode> functionArguments) {
-    return functionArguments.get(resolvedArgumentRef.getName());
-  }
-
-  private RexNode convertResolvedStructFieldAccess(ResolvedGetStructField resolvedGetStructField) {
-    RexNode referencedExpr = convertRexNodeFromResolvedExpr(resolvedGetStructField.getExpr());
-    return convertResolvedStructFieldAccessInternal(
-        referencedExpr, (int) resolvedGetStructField.getFieldIdx());
-  }
-
-  private RexNode convertResolvedStructFieldAccess(
-      ResolvedGetStructField resolvedGetStructField,
-      List<ResolvedColumn> columnList,
-      List<RelDataTypeField> fieldList,
-      Map<String, RexNode> functionArguments) {
-    RexNode referencedExpr =
-        convertRexNodeFromResolvedExpr(
-            resolvedGetStructField.getExpr(), columnList, fieldList, functionArguments);
-    return convertResolvedStructFieldAccessInternal(
-        referencedExpr, (int) resolvedGetStructField.getFieldIdx());
-  }
-
-  private RexNode convertResolvedStructFieldAccessInternal(RexNode referencedExpr, int fieldIdx) {
-    // Calcite SQL does not allow the ROW constructor to be dereferenced directly, so do it here.
-    if (referencedExpr instanceof RexCall
-        && ((RexCall) referencedExpr).getOperator() instanceof SqlRowOperator) {
-      return ((RexCall) referencedExpr).getOperands().get(fieldIdx);
-    }
-    return rexBuilder().makeFieldAccess(referencedExpr, fieldIdx);
-  }
-
-  private RexBuilder rexBuilder() {
-    return cluster.getRexBuilder();
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/FilterScanConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/FilterScanConverter.java
deleted file mode 100644
index 6a4208e808ca..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/FilterScanConverter.java
+++ /dev/null
@@ -1,53 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedFilterScan;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalFilter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-
-/** Converts filter. */
-class FilterScanConverter extends RelConverter<ResolvedFilterScan> {
-
-  FilterScanConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedFilterScan zetaNode) {
-    return Collections.singletonList(zetaNode.getInputScan());
-  }
-
-  @Override
-  public RelNode convert(ResolvedFilterScan zetaNode, List<RelNode> inputs) {
-    RelNode input = inputs.get(0);
-    RexNode condition =
-        getExpressionConverter()
-            .convertRexNodeFromResolvedExpr(
-                zetaNode.getFilterExpr(),
-                zetaNode.getInputScan().getColumnList(),
-                input.getRowType().getFieldList(),
-                context.getFunctionArgumentRefMapping());
-
-    return LogicalFilter.create(input, condition);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/JoinScanConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/JoinScanConverter.java
deleted file mode 100644
index 085395725fbb..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/JoinScanConverter.java
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedColumn;
-import com.google.zetasql.resolvedast.ResolvedJoinScanEnums.JoinType;
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedJoinScan;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.JoinRelType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalJoin;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeField;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableSet;
-
-/** Converts joins if neither side of the join is a WithRefScan. */
-class JoinScanConverter extends RelConverter<ResolvedJoinScan> {
-
-  private static final ImmutableMap<JoinType, JoinRelType> JOIN_TYPES =
-      ImmutableMap.of(
-          JoinType.INNER,
-          JoinRelType.INNER,
-          JoinType.FULL,
-          JoinRelType.FULL,
-          JoinType.LEFT,
-          JoinRelType.LEFT,
-          JoinType.RIGHT,
-          JoinRelType.RIGHT);
-
-  JoinScanConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public boolean canConvert(ResolvedJoinScan zetaNode) {
-    return true;
-  }
-
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedJoinScan zetaNode) {
-    return ImmutableList.of(zetaNode.getLeftScan(), zetaNode.getRightScan());
-  }
-
-  @Override
-  public RelNode convert(ResolvedJoinScan zetaNode, List<RelNode> inputs) {
-    RelNode convertedLeftInput = inputs.get(0);
-    RelNode convertedRightInput = inputs.get(1);
-
-    List<ResolvedColumn> combinedZetaFieldsList =
-        ImmutableList.<ResolvedColumn>builder()
-            .addAll(zetaNode.getLeftScan().getColumnList())
-            .addAll(zetaNode.getRightScan().getColumnList())
-            .build();
-
-    List<RelDataTypeField> combinedCalciteFieldsList =
-        ImmutableList.<RelDataTypeField>builder()
-            .addAll(convertedLeftInput.getRowType().getFieldList())
-            .addAll(convertedRightInput.getRowType().getFieldList())
-            .build();
-
-    final RexNode condition;
-    if (zetaNode.getJoinExpr() == null) {
-      condition = getExpressionConverter().trueLiteral();
-    } else {
-      condition =
-          getExpressionConverter()
-              .convertRexNodeFromResolvedExpr(
-                  zetaNode.getJoinExpr(),
-                  combinedZetaFieldsList,
-                  combinedCalciteFieldsList,
-                  ImmutableMap.of());
-    }
-
-    return LogicalJoin.create(
-        convertedLeftInput,
-        convertedRightInput,
-        ImmutableList.of(),
-        condition,
-        ImmutableSet.of(),
-        convertResolvedJoinType(zetaNode.getJoinType()));
-  }
-
-  static JoinRelType convertResolvedJoinType(JoinType joinType) {
-    if (!JOIN_TYPES.containsKey(joinType)) {
-      throw new UnsupportedOperationException("JOIN type: " + joinType + " is unsupported.");
-    }
-
-    return JOIN_TYPES.get(joinType);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/LimitOffsetScanToLimitConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/LimitOffsetScanToLimitConverter.java
deleted file mode 100644
index 5cb9569ba074..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/LimitOffsetScanToLimitConverter.java
+++ /dev/null
@@ -1,81 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedLimitOffsetScan;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedOrderByScan;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollations;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalSort;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexDynamicParam;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-
-/** Converts LIMIT without ORDER BY. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-class LimitOffsetScanToLimitConverter extends RelConverter<ResolvedLimitOffsetScan> {
-
-  LimitOffsetScanToLimitConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public boolean canConvert(ResolvedLimitOffsetScan zetaNode) {
-    return !(zetaNode.getInputScan() instanceof ResolvedOrderByScan);
-  }
-
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedLimitOffsetScan zetaNode) {
-    return Collections.singletonList(zetaNode.getInputScan());
-  }
-
-  @Override
-  public RelNode convert(ResolvedLimitOffsetScan zetaNode, List<RelNode> inputs) {
-    RelNode input = inputs.get(0);
-    RelCollation relCollation = RelCollations.of(ImmutableList.of());
-    RexNode offset =
-        zetaNode.getOffset() == null
-            ? null
-            : getExpressionConverter().convertRexNodeFromResolvedExpr(zetaNode.getOffset());
-    RexNode fetch =
-        getExpressionConverter()
-            .convertRexNodeFromResolvedExpr(
-                zetaNode.getLimit(),
-                zetaNode.getColumnList(),
-                input.getRowType().getFieldList(),
-                ImmutableMap.of());
-
-    // offset or fetch being RexDynamicParam means it is NULL (the only param supported currently)
-    if (offset instanceof RexDynamicParam
-        || RexLiteral.isNullLiteral(offset)
-        || fetch instanceof RexDynamicParam
-        || RexLiteral.isNullLiteral(fetch)) {
-      throw new UnsupportedOperationException("Limit requires non-null count and offset");
-    }
-
-    return LogicalSort.create(input, relCollation, offset, fetch);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/LimitOffsetScanToOrderByLimitConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/LimitOffsetScanToOrderByLimitConverter.java
deleted file mode 100644
index 10ff26a9bb4c..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/LimitOffsetScanToOrderByLimitConverter.java
+++ /dev/null
@@ -1,116 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import static java.util.stream.Collectors.toList;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelFieldCollation.Direction.ASCENDING;
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelFieldCollation.Direction.DESCENDING;
-
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedLimitOffsetScan;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedOrderByItem;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedOrderByScan;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelCollationImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelFieldCollation;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelFieldCollation.Direction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalProject;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalSort;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-
-/** Converts ORDER BY LIMIT. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-class LimitOffsetScanToOrderByLimitConverter extends RelConverter<ResolvedLimitOffsetScan> {
-
-  LimitOffsetScanToOrderByLimitConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public boolean canConvert(ResolvedLimitOffsetScan zetaNode) {
-    return zetaNode.getInputScan() instanceof ResolvedOrderByScan;
-  }
-
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedLimitOffsetScan zetaNode) {
-    // The immediate input is the ORDER BY scan which we don't support,
-    // but we can handle the ORDER BY LIMIT if we know the underlying projection, for example.
-    return Collections.singletonList(
-        ((ResolvedOrderByScan) zetaNode.getInputScan()).getInputScan());
-  }
-
-  @Override
-  public RelNode convert(ResolvedLimitOffsetScan zetaNode, List<RelNode> inputs) {
-    ResolvedOrderByScan inputOrderByScan = (ResolvedOrderByScan) zetaNode.getInputScan();
-    RelNode input = inputs.get(0);
-    RelCollation relCollation = getRelCollation(inputOrderByScan);
-
-    RexNode offset =
-        zetaNode.getOffset() == null
-            ? null
-            : getExpressionConverter().convertRexNodeFromResolvedExpr(zetaNode.getOffset());
-    RexNode fetch =
-        getExpressionConverter()
-            .convertRexNodeFromResolvedExpr(
-                zetaNode.getLimit(),
-                zetaNode.getColumnList(),
-                input.getRowType().getFieldList(),
-                ImmutableMap.of());
-
-    if (RexLiteral.isNullLiteral(offset) || RexLiteral.isNullLiteral(fetch)) {
-      throw new UnsupportedOperationException("Limit requires non-null count and offset");
-    }
-
-    RelNode sorted = LogicalSort.create(input, relCollation, offset, fetch);
-    return convertOrderByScanToLogicalScan(inputOrderByScan, sorted);
-  }
-
-  /** Collation is a sort order, as in ORDER BY DESCENDING/ASCENDING. */
-  private static RelCollation getRelCollation(ResolvedOrderByScan node) {
-    final long inputOffset = node.getInputScan().getColumnList().get(0).getId();
-    List<RelFieldCollation> fieldCollations =
-        node.getOrderByItemList().stream()
-            .map(item -> orderByItemToFieldCollation(item, inputOffset))
-            .collect(toList());
-    return RelCollationImpl.of(fieldCollations);
-  }
-
-  private static RelFieldCollation orderByItemToFieldCollation(
-      ResolvedOrderByItem item, long inputOffset) {
-    Direction sortDirection = item.getIsDescending() ? DESCENDING : ASCENDING;
-    final long fieldIndex = item.getColumnRef().getColumn().getId() - inputOffset;
-    return new RelFieldCollation((int) fieldIndex, sortDirection);
-  }
-
-  private RelNode convertOrderByScanToLogicalScan(ResolvedOrderByScan node, RelNode input) {
-    List<RexNode> projects =
-        getExpressionConverter()
-            .retrieveRexNodeFromOrderByScan(getCluster(), node, input.getRowType().getFieldList());
-    List<String> fieldNames = getTrait().retrieveFieldNames(node.getColumnList());
-
-    return LogicalProject.create(input, ImmutableList.of(), projects, fieldNames);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/OrderByScanUnsupportedConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/OrderByScanUnsupportedConverter.java
deleted file mode 100644
index 63bc77cc6812..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/OrderByScanUnsupportedConverter.java
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedOrderByScan;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-
-/**
- * Always throws exception, represents the case when order by is used without limit.
- *
- * <p>Order by limit is a special case that is handled in {@link LimitOffsetScanToLimitConverter}.
- */
-class OrderByScanUnsupportedConverter extends RelConverter<ResolvedOrderByScan> {
-
-  OrderByScanUnsupportedConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public RelNode convert(ResolvedOrderByScan zetaNode, List<RelNode> inputs) {
-    throw new UnsupportedOperationException("ORDER BY without a LIMIT is not supported.");
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ProjectScanConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ProjectScanConverter.java
deleted file mode 100644
index 49a1f2dbd4d9..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ProjectScanConverter.java
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedProjectScan;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalProject;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-
-/** Converts projection. */
-class ProjectScanConverter extends RelConverter<ResolvedProjectScan> {
-
-  ProjectScanConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedProjectScan zetaNode) {
-    return Collections.singletonList(zetaNode.getInputScan());
-  }
-
-  @Override
-  public RelNode convert(ResolvedProjectScan zetaNode, List<RelNode> inputs) {
-    RelNode input = inputs.get(0);
-
-    List<RexNode> projects =
-        getExpressionConverter().retrieveRexNode(zetaNode, input.getRowType().getFieldList());
-    List<String> fieldNames = getTrait().retrieveFieldNames(zetaNode.getColumnList());
-    return LogicalProject.create(input, ImmutableList.of(), projects, fieldNames);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/QueryStatementConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/QueryStatementConverter.java
deleted file mode 100644
index e3d9042dcfe1..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/QueryStatementConverter.java
+++ /dev/null
@@ -1,119 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_AGGREGATE_SCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_ARRAY_SCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_FILTER_SCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_JOIN_SCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_LIMIT_OFFSET_SCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_ORDER_BY_SCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_PROJECT_SCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_SET_OPERATION_SCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_SINGLE_ROW_SCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_TABLE_SCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_TVFSCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_WITH_REF_SCAN;
-import static com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind.RESOLVED_WITH_SCAN;
-import static java.util.stream.Collectors.toList;
-
-import com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind;
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedQueryStmt;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMultimap;
-
-/**
- * Converts a resolved Zeta SQL query represented by a tree to corresponding Calcite representation.
- */
-@SuppressWarnings({
-  "rawtypes" // TODO(https://github.com/apache/beam/issues/20447)
-})
-public class QueryStatementConverter extends RelConverter<ResolvedQueryStmt> {
-
-  /** Conversion rules, multimap from node kind to conversion rule. */
-  private final ImmutableMultimap<ResolvedNodeKind, RelConverter> rules;
-
-  public static RelNode convertRootQuery(ConversionContext context, ResolvedQueryStmt query) {
-    return new QueryStatementConverter(context).convert(query, Collections.emptyList());
-  }
-
-  private QueryStatementConverter(ConversionContext context) {
-    super(context);
-    this.rules =
-        ImmutableMultimap.<ResolvedNodeKind, RelConverter>builder()
-            .put(RESOLVED_AGGREGATE_SCAN, new AggregateScanConverter(context))
-            .put(RESOLVED_ARRAY_SCAN, new ArrayScanToJoinConverter(context))
-            .put(RESOLVED_ARRAY_SCAN, new ArrayScanLiteralToUncollectConverter(context))
-            .put(RESOLVED_ARRAY_SCAN, new ArrayScanColumnRefToUncollect(context))
-            .put(RESOLVED_FILTER_SCAN, new FilterScanConverter(context))
-            .put(RESOLVED_JOIN_SCAN, new JoinScanConverter(context))
-            .put(RESOLVED_LIMIT_OFFSET_SCAN, new LimitOffsetScanToLimitConverter(context))
-            .put(RESOLVED_LIMIT_OFFSET_SCAN, new LimitOffsetScanToOrderByLimitConverter(context))
-            .put(RESOLVED_ORDER_BY_SCAN, new OrderByScanUnsupportedConverter(context))
-            .put(RESOLVED_PROJECT_SCAN, new ProjectScanConverter(context))
-            .put(RESOLVED_SET_OPERATION_SCAN, new SetOperationScanConverter(context))
-            .put(RESOLVED_SINGLE_ROW_SCAN, new SingleRowScanConverter(context))
-            .put(RESOLVED_TABLE_SCAN, new TableScanConverter(context))
-            .put(RESOLVED_WITH_REF_SCAN, new WithRefScanConverter(context))
-            .put(RESOLVED_WITH_SCAN, new WithScanConverter(context))
-            .put(RESOLVED_TVFSCAN, new TVFScanConverter(context))
-            .build();
-  }
-
-  @Override
-  public RelNode convert(ResolvedQueryStmt zetaNode, List<RelNode> inputs) {
-    if (zetaNode.getIsValueTable()) {
-      throw new UnsupportedOperationException("Value Tables are not supported");
-    }
-
-    getTrait().addOutputColumnList(zetaNode.getOutputColumnList());
-
-    return convertNode(zetaNode.getQuery());
-  }
-
-  /**
-   * Convert node.
-   *
-   * <p>Finds a matching rule, uses the rule to extract inputs from the node, then converts the
-   * inputs (recursively), then converts the node using the converted inputs.
-   */
-  private RelNode convertNode(ResolvedNode zetaNode) {
-    RelConverter nodeConverter = getConverterRule(zetaNode);
-    List<ResolvedNode> inputs = nodeConverter.getInputs(zetaNode);
-    List<RelNode> convertedInputs = inputs.stream().map(this::convertNode).collect(toList());
-    return nodeConverter.convert(zetaNode, convertedInputs);
-  }
-
-  private RelConverter getConverterRule(ResolvedNode zetaNode) {
-    if (!rules.containsKey(zetaNode.nodeKind())) {
-      throw new UnsupportedOperationException(
-          String.format("Conversion of %s is not supported", zetaNode.nodeKind()));
-    }
-
-    return rules.get(zetaNode.nodeKind()).stream()
-        .filter(relConverter -> relConverter.canConvert(zetaNode))
-        .findFirst()
-        .orElseThrow(
-            () ->
-                new UnsupportedOperationException(
-                    String.format("Cannot find a conversion rule for: %s", zetaNode)));
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/RelConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/RelConverter.java
deleted file mode 100644
index 3111a8c2e1f3..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/RelConverter.java
+++ /dev/null
@@ -1,100 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNode;
-import java.math.BigDecimal;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.sdk.extensions.sql.zetasql.QueryTrait;
-import org.apache.beam.vendor.calcite.v1_28_0.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalValues;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.FrameworkConfig;
-
-/** A rule that converts Zeta SQL resolved relational node to corresponding Calcite rel node. */
-abstract class RelConverter<T extends ResolvedNode> {
-
-  /**
-   * Conversion context, contains things like FrameworkConfig, QueryTrait and other state used
-   * during conversion.
-   */
-  protected ConversionContext context;
-
-  RelConverter(ConversionContext context) {
-    this.context = context;
-  }
-
-  /** Whether this rule can handle the conversion of the specific node. */
-  public boolean canConvert(T zetaNode) {
-    return true;
-  }
-
-  /** Extract Zeta SQL resolved nodes that correspond to the inputs of the current node. */
-  public List<ResolvedNode> getInputs(T zetaNode) {
-    return Collections.emptyList();
-  }
-
-  /**
-   * Converts given Zeta SQL node to corresponding Calcite node.
-   *
-   * <p>{@code inputs} are node inputs that have already been converter to Calcite versions. They
-   * correspond to the nodes in {@link #getInputs(ResolvedNode)}.
-   */
-  public abstract RelNode convert(T zetaNode, List<RelNode> inputs);
-
-  protected RelOptCluster getCluster() {
-    return context.cluster();
-  }
-
-  protected FrameworkConfig getConfig() {
-    return context.getConfig();
-  }
-
-  protected ExpressionConverter getExpressionConverter() {
-    return context.getExpressionConverter();
-  }
-
-  protected QueryTrait getTrait() {
-    return context.getTrait();
-  }
-
-  // This function creates a single dummy input row for queries that don't read from a table.
-  // For example: SELECT "hello"
-  // The code is copy-pasted from Calcite's LogicalValues.createOneRow() with a single line
-  // change: SqlTypeName.INTEGER replaced by SqlTypeName.BIGINT.
-  // Would like to call LogicalValues.createOneRow() directly, but it uses type SqlTypeName.INTEGER
-  // which corresponds to TypeKind.TYPE_INT32 in ZetaSQL, a type not supported in ZetaSQL
-  // PRODUCT_EXTERNAL mode. See
-  // https://github.com/google/zetasql/blob/c610a21ffdc110293c1c7bd255a2674ebc7ec7a8/java/com/google/zetasql/TypeFactory.java#L61
-  static LogicalValues createOneRow(RelOptCluster cluster) {
-    final RelDataType rowType =
-        cluster.getTypeFactory().builder().add("ZERO", SqlTypeName.BIGINT).nullable(false).build();
-    final ImmutableList<ImmutableList<RexLiteral>> tuples =
-        ImmutableList.of(
-            ImmutableList.of(
-                cluster
-                    .getRexBuilder()
-                    .makeExactLiteral(BigDecimal.ZERO, rowType.getFieldList().get(0).getType())));
-    return LogicalValues.create(cluster, rowType, tuples);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SetOperationScanConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SetOperationScanConverter.java
deleted file mode 100644
index f8ac7ccdd299..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SetOperationScanConverter.java
+++ /dev/null
@@ -1,114 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import static com.google.zetasql.resolvedast.ResolvedSetOperationScanEnums.SetOperationType.EXCEPT_ALL;
-import static com.google.zetasql.resolvedast.ResolvedSetOperationScanEnums.SetOperationType.EXCEPT_DISTINCT;
-import static com.google.zetasql.resolvedast.ResolvedSetOperationScanEnums.SetOperationType.INTERSECT_ALL;
-import static com.google.zetasql.resolvedast.ResolvedSetOperationScanEnums.SetOperationType.INTERSECT_DISTINCT;
-import static com.google.zetasql.resolvedast.ResolvedSetOperationScanEnums.SetOperationType.UNION_ALL;
-import static com.google.zetasql.resolvedast.ResolvedSetOperationScanEnums.SetOperationType.UNION_DISTINCT;
-import static java.util.stream.Collectors.toList;
-
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedSetOperationItem;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedSetOperationScan;
-import com.google.zetasql.resolvedast.ResolvedSetOperationScanEnums.SetOperationType;
-import java.util.List;
-import java.util.function.BiFunction;
-import java.util.function.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalIntersect;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalMinus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalUnion;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-
-/** Converts set operations. */
-class SetOperationScanConverter extends RelConverter<ResolvedSetOperationScan> {
-  private enum Type {
-    DISTINCT,
-    ALL
-  }
-
-  private static final ImmutableMap<SetOperationType, Function<List<RelNode>, RelNode>>
-      SET_OPERATION_FACTORIES =
-          ImmutableMap.<SetOperationType, Function<List<RelNode>, RelNode>>builder()
-              .put(UNION_ALL, createFactoryFor(LogicalUnion::create, Type.ALL))
-              .put(UNION_DISTINCT, createFactoryFor(LogicalUnion::create, Type.DISTINCT))
-              .put(INTERSECT_ALL, createFactoryFor(LogicalIntersect::create, Type.ALL))
-              .put(INTERSECT_DISTINCT, createFactoryFor(LogicalIntersect::create, Type.DISTINCT))
-              .put(EXCEPT_ALL, createFactoryFor(LogicalMinus::create, Type.ALL))
-              .put(EXCEPT_DISTINCT, createFactoryFor(LogicalMinus::create, Type.DISTINCT))
-              .build();
-
-  /**
-   * A little closure to wrap the invocation of the factory method (e.g. LogicalUnion::create) for
-   * the set operation node.
-   */
-  private static Function<List<RelNode>, RelNode> createFactoryFor(
-      BiFunction<List<RelNode>, Boolean, RelNode> setOperationFactory, Type type) {
-    return (List<RelNode> inputs) -> createRel(setOperationFactory, type == Type.ALL, inputs);
-  }
-
-  SetOperationScanConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedSetOperationScan zetaNode) {
-    return zetaNode.getInputItemList().stream()
-        .map(ResolvedSetOperationItem::getScan)
-        .collect(toList());
-  }
-
-  @Override
-  public RelNode convert(ResolvedSetOperationScan zetaNode, List<RelNode> inputs) {
-    if (!SET_OPERATION_FACTORIES.containsKey(zetaNode.getOpType())) {
-      throw new UnsupportedOperationException(
-          "Operation " + zetaNode.getOpType() + " is unsupported");
-    }
-
-    return SET_OPERATION_FACTORIES.get(zetaNode.getOpType()).apply(inputs);
-  }
-
-  /** Beam set operations rel expects two inputs, so we are constructing a binary tree here. */
-  private static RelNode createRel(
-      BiFunction<List<RelNode>, Boolean, RelNode> factory, boolean all, List<RelNode> inputs) {
-    return inputs.stream()
-        .skip(2)
-        .reduce(
-            // start with creating a set node for two first inputs
-            invokeFactory(factory, inputs.get(0), inputs.get(1), all),
-            // create another operation node with previous op node and the next input
-            (setOpNode, nextInput) -> invokeFactory(factory, setOpNode, nextInput, all));
-  }
-
-  /**
-   * Creates a set operation rel with two inputs.
-   *
-   * <p>Factory is, for example, LogicalUnion::create.
-   */
-  private static RelNode invokeFactory(
-      BiFunction<List<RelNode>, Boolean, RelNode> factory,
-      RelNode input1,
-      RelNode input2,
-      boolean all) {
-    return factory.apply(ImmutableList.of(input1, input2), all);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlCaseWithValueOperatorRewriter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlCaseWithValueOperatorRewriter.java
deleted file mode 100644
index 02ce2d57b9c4..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlCaseWithValueOperatorRewriter.java
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
-
-/**
- * Rewrites $case_with_value calls as $case_no_value calls.
- *
- * <p>Turns:
- *
- * <pre><code>CASE x
- *   WHEN w1 THEN t1
- *   WHEN w2 THEN t2
- *   ELSE e
- *   END</code></pre>
- *
- * <p>into:
- *
- * <pre><code>CASE
- *   WHEN x == w1 THEN t1
- *   WHEN x == w2 THEN t2
- *   ELSE expr
- *   END</code></pre>
- *
- * <p>Note that the ELSE statement is actually optional, but we don't need to worry about that here
- * because the ZetaSQL analyzer populates the ELSE argument as a NULL literal if it's not specified.
- */
-class SqlCaseWithValueOperatorRewriter implements SqlOperatorRewriter {
-  @Override
-  public RexNode apply(RexBuilder rexBuilder, List<RexNode> operands) {
-    Preconditions.checkArgument(
-        operands.size() % 2 == 0 && !operands.isEmpty(),
-        "$case_with_value should have an even number of arguments greater than 0 in function call"
-            + " (The value operand, the else operand, and paired when/then operands).");
-    SqlOperator op = SqlStdOperatorTable.CASE;
-
-    List<RexNode> newOperands = new ArrayList<>();
-    RexNode value = operands.get(0);
-
-    for (int i = 1; i < operands.size() - 2; i += 2) {
-      RexNode when = operands.get(i);
-      RexNode then = operands.get(i + 1);
-      newOperands.add(
-          rexBuilder.makeCall(SqlStdOperatorTable.EQUALS, ImmutableList.of(value, when)));
-      newOperands.add(then);
-    }
-
-    RexNode elseOperand = Iterables.getLast(operands);
-    newOperands.add(elseOperand);
-
-    return rexBuilder.makeCall(op, newOperands);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlCoalesceOperatorRewriter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlCoalesceOperatorRewriter.java
deleted file mode 100644
index df1217fa15bd..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlCoalesceOperatorRewriter.java
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Util;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-
-/**
- * Rewrites COALESCE calls as CASE ($case_no_value) calls.
- *
- * <p>Turns <code>COALESCE(a, b, c)</code> into:
- *
- * <pre><code>CASE
- *   WHEN a IS NOT NULL THEN a
- *   WHEN b IS NOT NULL THEN b
- *   ELSE c
- *   END</code></pre>
- *
- * <p>There is also a special case for the single-argument case: <code>COALESCE(a)</code> becomes
- * just <code>a</code>.
- */
-class SqlCoalesceOperatorRewriter implements SqlOperatorRewriter {
-  @Override
-  public RexNode apply(RexBuilder rexBuilder, List<RexNode> operands) {
-    Preconditions.checkArgument(
-        operands.size() >= 1, "COALESCE should have at least one argument in function call.");
-
-    // No need for a case operator if there's only one operand
-    if (operands.size() == 1) {
-      return operands.get(0);
-    }
-
-    SqlOperator op = SqlStdOperatorTable.CASE;
-
-    List<RexNode> newOperands = new ArrayList<>();
-    for (RexNode operand : Util.skipLast(operands)) {
-      newOperands.add(
-          rexBuilder.makeCall(SqlStdOperatorTable.IS_NOT_NULL, ImmutableList.of(operand)));
-      newOperands.add(operand);
-    }
-    newOperands.add(Util.last(operands));
-
-    return rexBuilder.makeCall(op, newOperands);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlIfNullOperatorRewriter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlIfNullOperatorRewriter.java
deleted file mode 100644
index ecab784a5e57..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlIfNullOperatorRewriter.java
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-
-/**
- * Rewrites IFNULL calls as CASE ($case_no_value) calls.
- *
- * <p>Turns <code>IFNULL(expr, null_result)</code> into: <code><pre>CASE
- *   WHEN expr IS NULL THEN null_result
- *   ELSE expr
- *   END</pre></code>
- */
-class SqlIfNullOperatorRewriter implements SqlOperatorRewriter {
-  @Override
-  public RexNode apply(RexBuilder rexBuilder, List<RexNode> operands) {
-    Preconditions.checkArgument(
-        operands.size() == 2, "IFNULL should have two arguments in function call.");
-
-    SqlOperator op = SqlStdOperatorTable.CASE;
-    List<RexNode> newOperands =
-        ImmutableList.of(
-            rexBuilder.makeCall(SqlStdOperatorTable.IS_NULL, ImmutableList.of(operands.get(0))),
-            operands.get(1),
-            operands.get(0));
-
-    return rexBuilder.makeCall(op, newOperands);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlInOperatorRewriter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlInOperatorRewriter.java
deleted file mode 100644
index a73d0abde14d..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlInOperatorRewriter.java
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-
-/** Rewrites $in calls as SEARCH calls. */
-class SqlInOperatorRewriter implements SqlOperatorRewriter {
-  @Override
-  public RexNode apply(RexBuilder rexBuilder, List<RexNode> operands) {
-    Preconditions.checkArgument(
-        operands.size() >= 2, "IN should have at least two arguments in function call.");
-    final RexNode arg = operands.get(0);
-    final List<RexNode> ranges = ImmutableList.copyOf(operands.subList(1, operands.size()));
-
-    // ZetaSQL has weird behavior for NULL...
-    for (RexNode node : ranges) {
-      if (node instanceof RexLiteral && ((RexLiteral) node).isNull()) {
-        throw new UnsupportedOperationException("IN NULL unsupported");
-      }
-    }
-
-    return rexBuilder.makeIn(arg, ranges);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlNullIfOperatorRewriter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlNullIfOperatorRewriter.java
deleted file mode 100644
index 99dd23fff82d..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlNullIfOperatorRewriter.java
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-
-/**
- * Rewrites NULLIF calls as CASE ($case_no_value) calls.
- *
- * <p>Turns <code>NULLIF(expression, expression_to_match)</code> into: <code><pre>CASE
- *   WHEN expression == expression_to_match THEN NULL
- *   ELSE expression
- *   END</pre></code>
- */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-class SqlNullIfOperatorRewriter implements SqlOperatorRewriter {
-  @Override
-  public RexNode apply(RexBuilder rexBuilder, List<RexNode> operands) {
-    Preconditions.checkArgument(
-        operands.size() == 2, "NULLIF should have two arguments in function call.");
-
-    SqlOperator op =
-        SqlOperatorMappingTable.ZETASQL_FUNCTION_TO_CALCITE_SQL_OPERATOR
-            .get("$case_no_value")
-            .apply(null);
-    List<RexNode> newOperands =
-        ImmutableList.of(
-            rexBuilder.makeCall(
-                SqlStdOperatorTable.EQUALS, ImmutableList.of(operands.get(0), operands.get(1))),
-            rexBuilder.makeNullLiteral(operands.get(1).getType()),
-            operands.get(0));
-
-    return rexBuilder.makeCall(op, newOperands);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlOperatorMappingTable.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlOperatorMappingTable.java
deleted file mode 100644
index c3b0c6376871..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlOperatorMappingTable.java
+++ /dev/null
@@ -1,128 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNodes;
-import java.util.Map;
-import java.util.function.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.fun.SqlStdOperatorTable;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-import org.checkerframework.checker.nullness.qual.Nullable;
-
-/** SqlOperatorMappingTable. */
-class SqlOperatorMappingTable {
-
-  // todo: Some of operators defined here are later overridden in ZetaSQLPlannerImpl.
-  // We should remove them from this table and add generic way to provide custom
-  // implementation. (Ex.: timestamp_add)
-  static final Map<String, Function<ResolvedNodes.ResolvedFunctionCallBase, SqlOperator>>
-      ZETASQL_FUNCTION_TO_CALCITE_SQL_OPERATOR =
-          ImmutableMap
-              .<String, Function<ResolvedNodes.ResolvedFunctionCallBase, SqlOperator>>builder()
-              // grouped window function
-              .put("TUMBLE", resolvedFunction -> SqlStdOperatorTable.TUMBLE_OLD)
-              .put("HOP", resolvedFunction -> SqlStdOperatorTable.HOP_OLD)
-              .put("SESSION", resolvedFunction -> SqlStdOperatorTable.SESSION_OLD)
-
-              // ZetaSQL functions
-              .put("$and", resolvedFunction -> SqlStdOperatorTable.AND)
-              .put("$or", resolvedFunction -> SqlStdOperatorTable.OR)
-              .put("$not", resolvedFunction -> SqlStdOperatorTable.NOT)
-              .put("$equal", resolvedFunction -> SqlStdOperatorTable.EQUALS)
-              .put("$not_equal", resolvedFunction -> SqlStdOperatorTable.NOT_EQUALS)
-              .put("$greater", resolvedFunction -> SqlStdOperatorTable.GREATER_THAN)
-              .put(
-                  "$greater_or_equal",
-                  resolvedFunction -> SqlStdOperatorTable.GREATER_THAN_OR_EQUAL)
-              .put("$less", resolvedFunction -> SqlStdOperatorTable.LESS_THAN)
-              .put("$less_or_equal", resolvedFunction -> SqlStdOperatorTable.LESS_THAN_OR_EQUAL)
-              .put("$like", resolvedFunction -> SqlOperators.LIKE)
-              .put("$is_null", resolvedFunction -> SqlStdOperatorTable.IS_NULL)
-              .put("$is_true", resolvedFunction -> SqlStdOperatorTable.IS_TRUE)
-              .put("$is_false", resolvedFunction -> SqlStdOperatorTable.IS_FALSE)
-              .put("$add", resolvedFunction -> SqlStdOperatorTable.PLUS)
-              .put("$subtract", resolvedFunction -> SqlStdOperatorTable.MINUS)
-              .put("$multiply", resolvedFunction -> SqlStdOperatorTable.MULTIPLY)
-              .put("$unary_minus", resolvedFunction -> SqlStdOperatorTable.UNARY_MINUS)
-              .put("$divide", resolvedFunction -> SqlStdOperatorTable.DIVIDE)
-              .put("concat", resolvedFunction -> SqlOperators.CONCAT)
-              .put("substr", resolvedFunction -> SqlOperators.SUBSTR)
-              .put("substring", resolvedFunction -> SqlOperators.SUBSTR)
-              .put("trim", resolvedFunction -> SqlOperators.TRIM)
-              .put("replace", resolvedFunction -> SqlOperators.REPLACE)
-              .put("char_length", resolvedFunction -> SqlOperators.CHAR_LENGTH)
-              .put("starts_with", resolvedFunction -> SqlOperators.START_WITHS)
-              .put("ends_with", resolvedFunction -> SqlOperators.ENDS_WITH)
-              .put("ltrim", resolvedFunction -> SqlOperators.LTRIM)
-              .put("rtrim", resolvedFunction -> SqlOperators.RTRIM)
-              .put("reverse", resolvedFunction -> SqlOperators.REVERSE)
-              .put("$count_star", resolvedFunction -> SqlStdOperatorTable.COUNT)
-              .put("max", resolvedFunction -> SqlStdOperatorTable.MAX)
-              .put("min", resolvedFunction -> SqlStdOperatorTable.MIN)
-              .put("avg", resolvedFunction -> SqlStdOperatorTable.AVG)
-              .put("sum", resolvedFunction -> SqlStdOperatorTable.SUM)
-              .put("any_value", resolvedFunction -> SqlStdOperatorTable.ANY_VALUE)
-              .put("count", resolvedFunction -> SqlStdOperatorTable.COUNT)
-              .put("bit_and", resolvedFunction -> SqlStdOperatorTable.BIT_AND)
-              .put("string_agg", SqlOperators::createStringAggOperator) // NULL values not supported
-              .put("array_agg", resolvedFunction -> SqlOperators.ARRAY_AGG_FN)
-              .put("bit_or", resolvedFunction -> SqlStdOperatorTable.BIT_OR)
-              .put("bit_xor", resolvedFunction -> SqlOperators.BIT_XOR)
-              .put("ceil", resolvedFunction -> SqlStdOperatorTable.CEIL)
-              .put("floor", resolvedFunction -> SqlStdOperatorTable.FLOOR)
-              .put("mod", resolvedFunction -> SqlStdOperatorTable.MOD)
-              .put("timestamp", resolvedFunction -> SqlOperators.TIMESTAMP_OP)
-              .put("$case_no_value", resolvedFunction -> SqlStdOperatorTable.CASE)
-
-              // if operator - IF(cond, pos, neg) can actually be mapped directly to `CASE WHEN cond
-              // THEN pos ELSE neg`
-              .put("if", resolvedFunction -> SqlStdOperatorTable.CASE)
-
-              // $case_no_value specializations
-              // all of these operators can have their operands adjusted to achieve the same thing
-              // with
-              // a call to $case_with_value
-              .put("$case_with_value", resolvedFunction -> SqlStdOperatorTable.CASE)
-              .put("coalesce", resolvedFunction -> SqlStdOperatorTable.CASE)
-              .put("ifnull", resolvedFunction -> SqlStdOperatorTable.CASE)
-              .put("nullif", resolvedFunction -> SqlStdOperatorTable.CASE)
-              .put("countif", resolvedFunction -> SqlOperators.COUNTIF)
-              .build();
-
-  static final Map<String, SqlOperatorRewriter> ZETASQL_FUNCTION_TO_CALCITE_SQL_OPERATOR_REWRITER =
-      ImmutableMap.<String, SqlOperatorRewriter>builder()
-          .put("$case_with_value", new SqlCaseWithValueOperatorRewriter())
-          .put("coalesce", new SqlCoalesceOperatorRewriter())
-          .put("ifnull", new SqlIfNullOperatorRewriter())
-          .put("nullif", new SqlNullIfOperatorRewriter())
-          .put("$in", new SqlInOperatorRewriter())
-          .build();
-
-  static @Nullable SqlOperator create(
-      ResolvedNodes.ResolvedFunctionCallBase aggregateFunctionCall) {
-
-    Function<ResolvedNodes.ResolvedFunctionCallBase, SqlOperator> sqlOperatorFactory =
-        ZETASQL_FUNCTION_TO_CALCITE_SQL_OPERATOR.get(aggregateFunctionCall.getFunction().getName());
-
-    if (sqlOperatorFactory != null) {
-      return sqlOperatorFactory.apply(aggregateFunctionCall);
-    }
-    return null;
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlOperatorRewriter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlOperatorRewriter.java
deleted file mode 100644
index e283a7238727..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlOperatorRewriter.java
+++ /dev/null
@@ -1,35 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexBuilder;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-
-/** Interface for rewriting calls a specific ZetaSQL operator. */
-interface SqlOperatorRewriter {
-  /**
-   * Create and return a new {@link RexNode} that represents a call to this operator with the
-   * specified operands.
-   *
-   * @param rexBuilder A {@link RexBuilder} instance to use for creating new {@link RexNode}s
-   * @param operands The original list of {@link RexNode} operands passed to this operator call
-   * @return The created RexNode
-   */
-  RexNode apply(RexBuilder rexBuilder, List<RexNode> operands);
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlOperators.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlOperators.java
deleted file mode 100644
index 0f6dcea2f692..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlOperators.java
+++ /dev/null
@@ -1,352 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import static org.apache.beam.sdk.extensions.sql.zetasql.BeamZetaSqlCatalog.ZETASQL_FUNCTION_GROUP_NAME;
-
-import com.google.zetasql.Value;
-import com.google.zetasql.io.grpc.Status;
-import com.google.zetasql.io.grpc.StatusRuntimeException;
-import com.google.zetasql.resolvedast.ResolvedNodes;
-import java.lang.reflect.Method;
-import java.nio.charset.StandardCharsets;
-import java.util.ArrayList;
-import java.util.List;
-import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.sdk.extensions.sql.impl.ScalarFunctionImpl;
-import org.apache.beam.sdk.extensions.sql.impl.UdafImpl;
-import org.apache.beam.sdk.extensions.sql.impl.planner.BeamRelDataTypeSystem;
-import org.apache.beam.sdk.extensions.sql.impl.transform.BeamBuiltinAggregations;
-import org.apache.beam.sdk.extensions.sql.impl.transform.agg.CountIf;
-import org.apache.beam.sdk.extensions.sql.impl.udaf.ArrayAgg;
-import org.apache.beam.sdk.extensions.sql.impl.udaf.StringAgg;
-import org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils;
-import org.apache.beam.sdk.extensions.sql.zetasql.ZetaSqlException;
-import org.apache.beam.sdk.extensions.sql.zetasql.translation.impl.BeamBuiltinMethods;
-import org.apache.beam.sdk.extensions.sql.zetasql.translation.impl.CastFunctionImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.JavaTypeFactoryImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactoryImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.AggregateFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.FunctionParameter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.ScalarFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlFunctionCategory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlSyntax;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParserPos;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.ArraySqlType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.FamilyOperandTypeChecker;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.InferTypes;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.OperandTypes;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlReturnTypeInference;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeFactoryImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeFamily;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlUserDefinedAggFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlUserDefinedFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Optionality;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.util.Util;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
-
-/**
- * A separate SqlOperators table for those functions that do not exist or not compatible with
- * Calcite. Most of functions within this class is copied from Calcite.
- */
-@Internal
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-public class SqlOperators {
-  public static final SqlOperator ZETASQL_TIMESTAMP_ADD =
-      createZetaSqlFunction("timestamp_add", SqlTypeName.TIMESTAMP);
-
-  private static final RelDataType OTHER = createSqlType(SqlTypeName.OTHER, false);
-  private static final RelDataType TIMESTAMP = createSqlType(SqlTypeName.TIMESTAMP, false);
-  private static final RelDataType NULLABLE_TIMESTAMP = createSqlType(SqlTypeName.TIMESTAMP, true);
-  private static final RelDataType BIGINT = createSqlType(SqlTypeName.BIGINT, false);
-  private static final RelDataType NULLABLE_BIGINT = createSqlType(SqlTypeName.BIGINT, true);
-
-  public static final SqlOperator ARRAY_AGG_FN =
-      createUdafOperator(
-          "array_agg",
-          x -> new ArraySqlType(x.getOperandType(0), true),
-          new UdafImpl<>(new ArrayAgg.ArrayAggArray<>()));
-
-  public static final SqlOperator START_WITHS =
-      createUdfOperator(
-          "STARTS_WITH", BeamBuiltinMethods.STARTS_WITH_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator CONCAT =
-      createUdfOperator("CONCAT", BeamBuiltinMethods.CONCAT_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator REPLACE =
-      createUdfOperator("REPLACE", BeamBuiltinMethods.REPLACE_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator TRIM =
-      createUdfOperator("TRIM", BeamBuiltinMethods.TRIM_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator LTRIM =
-      createUdfOperator("LTRIM", BeamBuiltinMethods.LTRIM_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator RTRIM =
-      createUdfOperator("RTRIM", BeamBuiltinMethods.RTRIM_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator SUBSTR =
-      createUdfOperator("SUBSTR", BeamBuiltinMethods.SUBSTR_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator REVERSE =
-      createUdfOperator("REVERSE", BeamBuiltinMethods.REVERSE_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator CHAR_LENGTH =
-      createUdfOperator(
-          "CHAR_LENGTH", BeamBuiltinMethods.CHAR_LENGTH_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator ENDS_WITH =
-      createUdfOperator(
-          "ENDS_WITH", BeamBuiltinMethods.ENDS_WITH_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator LIKE =
-      createUdfOperator(
-          "LIKE",
-          BeamBuiltinMethods.LIKE_METHOD,
-          SqlSyntax.BINARY,
-          ZETASQL_FUNCTION_GROUP_NAME,
-          "");
-
-  public static final SqlOperator VALIDATE_TIMESTAMP =
-      createUdfOperator(
-          "validateTimestamp",
-          DateTimeUtils.class,
-          "validateTimestamp",
-          x -> NULLABLE_TIMESTAMP,
-          ImmutableList.of(TIMESTAMP),
-          ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator VALIDATE_TIME_INTERVAL =
-      createUdfOperator(
-          "validateIntervalArgument",
-          DateTimeUtils.class,
-          "validateTimeInterval",
-          x -> NULLABLE_BIGINT,
-          ImmutableList.of(BIGINT, OTHER),
-          ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator TIMESTAMP_OP =
-      createUdfOperator(
-          "TIMESTAMP", BeamBuiltinMethods.TIMESTAMP_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator DATE_OP =
-      createUdfOperator("DATE", BeamBuiltinMethods.DATE_METHOD, ZETASQL_FUNCTION_GROUP_NAME);
-
-  public static final SqlOperator BIT_XOR =
-      createUdafOperator(
-          "BIT_XOR",
-          x -> NULLABLE_BIGINT,
-          new UdafImpl<>(new BeamBuiltinAggregations.BitXOr<Number>()));
-
-  public static final SqlOperator COUNTIF =
-      createUdafOperator(
-          "countif",
-          x -> createTypeFactory().createSqlType(SqlTypeName.BIGINT),
-          new UdafImpl<>(new CountIf.CountIfFn()));
-
-  public static final SqlUserDefinedFunction CAST_OP =
-      new SqlUserDefinedFunction(
-          new SqlIdentifier("CAST", SqlParserPos.ZERO),
-          SqlKind.OTHER_FUNCTION,
-          null,
-          null,
-          null,
-          new CastFunctionImpl());
-
-  public static SqlOperator createStringAggOperator(
-      ResolvedNodes.ResolvedFunctionCallBase aggregateFunctionCall) {
-    List<ResolvedNodes.ResolvedExpr> args = aggregateFunctionCall.getArgumentList();
-    String inputType = args.get(0).getType().typeName();
-    Value delimiter = null;
-    if (args.size() == 2) {
-      ResolvedNodes.ResolvedExpr resolvedExpr = args.get(1);
-      if (resolvedExpr instanceof ResolvedNodes.ResolvedLiteral) {
-        delimiter = ((ResolvedNodes.ResolvedLiteral) resolvedExpr).getValue();
-      } else {
-        // TODO(https://github.com/apache/beam/issues/21283) Add support for params
-        throw new ZetaSqlException(
-            new StatusRuntimeException(
-                Status.INVALID_ARGUMENT.withDescription(
-                    String.format(
-                        "STRING_AGG only supports ResolvedLiteral as delimiter, provided %s",
-                        resolvedExpr.getClass().getName()))));
-      }
-    }
-
-    switch (inputType) {
-      case "BYTES":
-        return SqlOperators.createUdafOperator(
-            "string_agg",
-            x -> SqlOperators.createTypeFactory().createSqlType(SqlTypeName.VARBINARY),
-            new UdafImpl<>(
-                new StringAgg.StringAggByte(
-                    delimiter == null
-                        ? ",".getBytes(StandardCharsets.UTF_8)
-                        : delimiter.getBytesValue().toByteArray())));
-      case "STRING":
-        return SqlOperators.createUdafOperator(
-            "string_agg",
-            x -> SqlOperators.createTypeFactory().createSqlType(SqlTypeName.VARCHAR),
-            new UdafImpl<>(
-                new StringAgg.StringAggString(
-                    delimiter == null ? "," : delimiter.getStringValue())));
-      default:
-        throw new UnsupportedOperationException(
-            String.format("[%s] is not supported in STRING_AGG", inputType));
-    }
-  }
-
-  /**
-   * Create a dummy SqlFunction of type OTHER_FUNCTION from given function name and return type.
-   * These functions will be unparsed in either {@link
-   * org.apache.beam.sdk.extensions.sql.zetasql.BeamZetaSqlCalcRel} (for built-in functions) or
-   * {@link org.apache.beam.sdk.extensions.sql.impl.rel.BeamCalcRel} (for user-defined functions).
-   */
-  public static SqlFunction createZetaSqlFunction(String name, SqlTypeName returnType) {
-    return new SqlFunction(
-        name,
-        SqlKind.OTHER_FUNCTION,
-        x -> createSqlType(returnType, true),
-        null, // operandTypeInference
-        null, // operandTypeChecker
-        SqlFunctionCategory.USER_DEFINED_FUNCTION);
-  }
-
-  static SqlUserDefinedAggFunction createUdafOperator(
-      String name, SqlReturnTypeInference returnTypeInference, AggregateFunction function) {
-    return new SqlUserDefinedAggFunction(
-        new SqlIdentifier(name, SqlParserPos.ZERO),
-        returnTypeInference,
-        null,
-        null,
-        function,
-        false,
-        false,
-        Optionality.FORBIDDEN,
-        createTypeFactory());
-  }
-
-  private static SqlUserDefinedFunction createUdfOperator(
-      String name,
-      Class<?> methodClass,
-      String methodName,
-      SqlReturnTypeInference returnTypeInference,
-      List<RelDataType> paramTypes,
-      String funGroup) {
-    return new SqlUserDefinedFunction(
-        new SqlIdentifier(name, SqlParserPos.ZERO),
-        returnTypeInference,
-        null,
-        null,
-        paramTypes,
-        ZetaSqlScalarFunctionImpl.create(methodClass, methodName, funGroup, ""));
-  }
-
-  static SqlUserDefinedFunction createUdfOperator(
-      String name, Method method, String funGroup, String jarPath) {
-    return createUdfOperator(name, method, SqlSyntax.FUNCTION, funGroup, jarPath);
-  }
-
-  static SqlUserDefinedFunction createUdfOperator(String name, Method method, String funGroup) {
-    return createUdfOperator(name, method, SqlSyntax.FUNCTION, funGroup, "");
-  }
-
-  private static SqlUserDefinedFunction createUdfOperator(
-      String name, Method method, final SqlSyntax syntax, String funGroup, String jarPath) {
-    Function function = ZetaSqlScalarFunctionImpl.create(method, funGroup, jarPath);
-    final RelDataTypeFactory typeFactory = createTypeFactory();
-
-    List<RelDataType> argTypes = new ArrayList<>();
-    List<SqlTypeFamily> typeFamilies = new ArrayList<>();
-    for (FunctionParameter o : function.getParameters()) {
-      final RelDataType type = o.getType(typeFactory);
-      argTypes.add(type);
-      typeFamilies.add(Util.first(type.getSqlTypeName().getFamily(), SqlTypeFamily.ANY));
-    }
-
-    final FamilyOperandTypeChecker typeChecker =
-        OperandTypes.family(typeFamilies, i -> function.getParameters().get(i).isOptional());
-
-    final List<RelDataType> paramTypes = toSql(typeFactory, argTypes);
-
-    return new SqlUserDefinedFunction(
-        new SqlIdentifier(name, SqlParserPos.ZERO),
-        infer((ScalarFunction) function),
-        InferTypes.explicit(argTypes),
-        typeChecker,
-        paramTypes,
-        function) {
-      @Override
-      public SqlSyntax getSyntax() {
-        return syntax;
-      }
-    };
-  }
-
-  private static RelDataType createSqlType(SqlTypeName typeName, boolean withNullability) {
-    final RelDataTypeFactory typeFactory = createTypeFactory();
-    RelDataType type = typeFactory.createSqlType(typeName);
-    if (withNullability) {
-      type = typeFactory.createTypeWithNullability(type, true);
-    }
-    return type;
-  }
-
-  private static RelDataTypeFactory createTypeFactory() {
-    return new SqlTypeFactoryImpl(BeamRelDataTypeSystem.INSTANCE);
-  }
-
-  private static SqlReturnTypeInference infer(final ScalarFunction function) {
-    return opBinding -> {
-      final RelDataTypeFactory typeFactory = opBinding.getTypeFactory();
-      final RelDataType type;
-      if (function instanceof ScalarFunctionImpl) {
-        type = ((ScalarFunctionImpl) function).getReturnType(typeFactory, opBinding);
-      } else {
-        type = function.getReturnType(typeFactory);
-      }
-      return toSql(typeFactory, type);
-    };
-  }
-
-  private static List<RelDataType> toSql(
-      final RelDataTypeFactory typeFactory, List<RelDataType> types) {
-    return Lists.transform(types, type -> toSql(typeFactory, type));
-  }
-
-  private static RelDataType toSql(RelDataTypeFactory typeFactory, RelDataType type) {
-    if (type instanceof RelDataTypeFactoryImpl.JavaType
-        && ((RelDataTypeFactoryImpl.JavaType) type).getJavaClass() == Object.class) {
-      return typeFactory.createTypeWithNullability(
-          typeFactory.createSqlType(SqlTypeName.ANY), true);
-    }
-    return JavaTypeFactoryImpl.toSql(typeFactory, type);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlWindowTableFunction.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlWindowTableFunction.java
deleted file mode 100644
index cc40c43b91d7..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/SqlWindowTableFunction.java
+++ /dev/null
@@ -1,119 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.apache.beam.sdk.extensions.sql.impl.utils.TVFStreamingUtils;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeField;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFieldImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelRecordType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlCallBinding;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlFunctionCategory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperandCountRange;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlOperandCountRanges;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlReturnTypeInference;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlValidator;
-
-/** Base class for table-valued function windowing operator (TUMBLE, HOP and SESSION). */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-class SqlWindowTableFunction extends SqlFunction {
-  public SqlWindowTableFunction(String name) {
-    super(
-        name,
-        SqlKind.OTHER_FUNCTION,
-        ARG0_TABLE_FUNCTION_WINDOWING,
-        null,
-        null,
-        SqlFunctionCategory.SYSTEM);
-  }
-
-  @Override
-  public SqlOperandCountRange getOperandCountRange() {
-    return SqlOperandCountRanges.of(3);
-  }
-
-  @Override
-  public boolean checkOperandTypes(SqlCallBinding callBinding, boolean throwOnFailure) {
-    // There should only be three operands, and number of operands are checked before
-    // this call.
-    final SqlNode operand0 = callBinding.operand(0);
-    final SqlValidator validator = callBinding.getValidator();
-    final RelDataType type = validator.getValidatedNodeType(operand0);
-    if (type.getSqlTypeName() != SqlTypeName.ROW) {
-      return throwValidationSignatureErrorOrReturnFalse(callBinding, throwOnFailure);
-    }
-    return true;
-  }
-
-  private boolean throwValidationSignatureErrorOrReturnFalse(
-      SqlCallBinding callBinding, boolean throwOnFailure) {
-    if (throwOnFailure) {
-      throw callBinding.newValidationSignatureError();
-    } else {
-      return false;
-    }
-  }
-
-  @Override
-  public String getAllowedSignatures(String opNameToUse) {
-    return getName() + "(TABLE table_name, DESCRIPTOR(col1, col2 ...), datetime interval)";
-  }
-
-  /**
-   * The first parameter of table-value function windowing is a TABLE parameter, which is not
-   * scalar. So need to override SqlOperator.argumentMustBeScalar.
-   */
-  @Override
-  public boolean argumentMustBeScalar(int ordinal) {
-    return ordinal != 0;
-  }
-
-  /**
-   * Type-inference strategy whereby the result type of a table function call is a ROW, which is
-   * combined from the operand #0(TABLE parameter)'s schema and two additional fields:
-   *
-   * <ol>
-   *   <li>window_start. TIMESTAMP type to indicate a window's start.
-   *   <li>window_end. TIMESTAMP type to indicate a window's end.
-   * </ol>
-   */
-  public static final SqlReturnTypeInference ARG0_TABLE_FUNCTION_WINDOWING =
-      opBinding -> {
-        RelDataType inputRowType = opBinding.getOperandType(0);
-        List<RelDataTypeField> newFields = new ArrayList<>(inputRowType.getFieldList());
-        RelDataType timestampType = opBinding.getTypeFactory().createSqlType(SqlTypeName.TIMESTAMP);
-
-        RelDataTypeField windowStartField =
-            new RelDataTypeFieldImpl(
-                TVFStreamingUtils.WINDOW_START, newFields.size(), timestampType);
-        newFields.add(windowStartField);
-        RelDataTypeField windowEndField =
-            new RelDataTypeFieldImpl(TVFStreamingUtils.WINDOW_END, newFields.size(), timestampType);
-        newFields.add(windowEndField);
-
-        return new RelRecordType(inputRowType.getStructKind(), newFields);
-      };
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/TVFScanConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/TVFScanConverter.java
deleted file mode 100644
index 298e7ab35a88..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/TVFScanConverter.java
+++ /dev/null
@@ -1,105 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.FileDescriptorSetsBuilder;
-import com.google.zetasql.FunctionProtos.TableValuedFunctionProto;
-import com.google.zetasql.TableValuedFunction.FixedOutputSchemaTVF;
-import com.google.zetasql.ZetaSQLResolvedNodeKind.ResolvedNodeKind;
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedFunctionArgument;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedLiteral;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedTVFScan;
-import java.util.ArrayList;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalTableFunctionScan;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-
-/** Converts TVFScan. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-class TVFScanConverter extends RelConverter<ResolvedTVFScan> {
-
-  TVFScanConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public RelNode convert(ResolvedTVFScan zetaNode, List<RelNode> inputs) {
-    RelNode input = inputs.get(0);
-    RexCall call =
-        getExpressionConverter()
-            .convertTableValuedFunction(
-                input,
-                zetaNode.getTvf(),
-                zetaNode.getArgumentList(),
-                zetaNode.getArgumentList().size() > 0
-                        && zetaNode.getArgumentList().get(0).getScan() != null
-                    ? zetaNode.getArgumentList().get(0).getScan().getColumnList()
-                    : Collections.emptyList());
-    RelNode tableFunctionScan =
-        LogicalTableFunctionScan.create(
-            getCluster(), inputs, call, null, call.getType(), Collections.EMPTY_SET);
-
-    // Pure SQL UDF's language body is built bottom up, so FunctionArgumentRefMapping should be
-    // already consumed thus it can be cleared now.
-    context.clearFunctionArgumentRefMapping();
-    return tableFunctionScan;
-  }
-
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedTVFScan zetaNode) {
-    List<ResolvedNode> inputs = new ArrayList<>();
-    if (zetaNode.getTvf() != null
-        && context
-            .getUserDefinedTableValuedFunctions()
-            .containsKey(zetaNode.getTvf().getNamePath())) {
-      inputs.add(context.getUserDefinedTableValuedFunctions().get(zetaNode.getTvf().getNamePath()));
-    }
-
-    for (ResolvedFunctionArgument argument : zetaNode.getArgumentList()) {
-      if (argument.getScan() != null) {
-        inputs.add(argument.getScan());
-      }
-    }
-
-    // Extract ResolvedArguments for solving ResolvedArgumentRef in later conversion.
-    if (zetaNode.getTvf() instanceof FixedOutputSchemaTVF) {
-      FileDescriptorSetsBuilder temp = new FileDescriptorSetsBuilder();
-      // TODO: migrate to public Java API to retrieve FunctionSignature.
-      TableValuedFunctionProto tableValuedFunctionProto = zetaNode.getTvf().serialize(temp);
-      for (int i = 0; i < tableValuedFunctionProto.getSignature().getArgumentList().size(); i++) {
-        String argumentName =
-            tableValuedFunctionProto.getSignature().getArgument(i).getOptions().getArgumentName();
-        if (zetaNode.getArgumentList().get(i).nodeKind()
-            == ResolvedNodeKind.RESOLVED_FUNCTION_ARGUMENT) {
-          ResolvedFunctionArgument resolvedTVFArgument = zetaNode.getArgumentList().get(i);
-          if (resolvedTVFArgument.getExpr().nodeKind() == ResolvedNodeKind.RESOLVED_LITERAL) {
-            ResolvedLiteral literal = (ResolvedLiteral) resolvedTVFArgument.getExpr();
-            context.addToFunctionArgumentRefMapping(
-                argumentName, getExpressionConverter().convertResolvedLiteral(literal));
-          }
-        }
-      }
-    }
-    return inputs;
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/TableScanConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/TableScanConverter.java
deleted file mode 100644
index 8b7af956ec9a..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/TableScanConverter.java
+++ /dev/null
@@ -1,120 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkNotNull;
-
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedTableScan;
-import java.util.List;
-import java.util.Properties;
-import org.apache.beam.sdk.extensions.sql.zetasql.TableResolution;
-import org.apache.beam.vendor.calcite.v1_28_0.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.config.CalciteConnectionConfigImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.jdbc.CalciteSchema;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.prepare.CalciteCatalogReader;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.prepare.RelOptTableImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelRoot;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.hint.RelHint;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Table;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.TranslatableTable;
-import org.checkerframework.checker.nullness.qual.Nullable;
-
-/** Converts table scan. */
-class TableScanConverter extends RelConverter<ResolvedTableScan> {
-
-  TableScanConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public RelNode convert(ResolvedTableScan zetaNode, List<RelNode> inputs) {
-
-    List<String> tablePath = getTablePath(zetaNode.getTable());
-
-    SchemaPlus defaultSchemaPlus = getConfig().getDefaultSchema();
-    if (defaultSchemaPlus == null) {
-      throw new AssertionError("Default schema is null.");
-    }
-    // TODO: reject incorrect top-level schema
-
-    Table calciteTable = TableResolution.resolveCalciteTable(defaultSchemaPlus, tablePath);
-
-    // we already resolved the table before passing the query to Analyzer, so it should be there
-    checkNotNull(
-        calciteTable,
-        "Unable to resolve the table path %s in schema %s",
-        tablePath,
-        defaultSchemaPlus.getName());
-
-    String defaultSchemaName = defaultSchemaPlus.getName();
-
-    final CalciteCatalogReader catalogReader =
-        new CalciteCatalogReader(
-            CalciteSchema.from(defaultSchemaPlus),
-            ImmutableList.of(defaultSchemaName),
-            getCluster().getTypeFactory(),
-            new CalciteConnectionConfigImpl(new Properties()));
-
-    RelOptTableImpl relOptTable =
-        RelOptTableImpl.create(
-            catalogReader,
-            calciteTable.getRowType(getCluster().getTypeFactory()),
-            calciteTable,
-            ImmutableList.<String>builder().add(defaultSchemaName).addAll(tablePath).build());
-
-    if (calciteTable instanceof TranslatableTable) {
-      return ((TranslatableTable) calciteTable).toRel(createToRelContext(), relOptTable);
-    } else {
-      throw new UnsupportedOperationException("Does not support non TranslatableTable type table!");
-    }
-  }
-
-  private List<String> getTablePath(com.google.zetasql.Table table) {
-    if (!getTrait().isTableResolved(table)) {
-      throw new IllegalArgumentException(
-          "Unexpected table found when converting to Calcite rel node: " + table);
-    }
-
-    return getTrait().getTablePath(table);
-  }
-
-  private RelOptTable.ToRelContext createToRelContext() {
-    return new RelOptTable.ToRelContext() {
-      @Override
-      public RelRoot expandView(
-          RelDataType relDataType, String s, List<String> list, @Nullable List<String> list1) {
-        throw new UnsupportedOperationException("This RelContext does not support expandView");
-      }
-
-      @Override
-      public RelOptCluster getCluster() {
-        return TableScanConverter.this.getCluster();
-      }
-
-      @Override
-      public List<RelHint> getTableHints() {
-        return ImmutableList.of();
-      }
-    };
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/UserFunctionDefinitions.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/UserFunctionDefinitions.java
deleted file mode 100644
index b1891337a550..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/UserFunctionDefinitions.java
+++ /dev/null
@@ -1,81 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.auto.value.AutoValue;
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes;
-import java.lang.reflect.Method;
-import java.util.List;
-import org.apache.beam.sdk.transforms.Combine;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-
-/** Holds user defined function definitions. */
-@AutoValue
-public abstract class UserFunctionDefinitions {
-  public abstract ImmutableMap<List<String>, ResolvedNodes.ResolvedCreateFunctionStmt>
-      sqlScalarFunctions();
-
-  /**
-   * SQL native user-defined table-valued function can be resolved by Analyzer. Keeping the function
-   * name to its ResolvedNode mapping so during Plan conversion, UDTVF implementation can replace
-   * inputs of TVFScanConverter.
-   */
-  public abstract ImmutableMap<List<String>, ResolvedNode> sqlTableValuedFunctions();
-
-  public abstract ImmutableMap<List<String>, JavaScalarFunction> javaScalarFunctions();
-
-  public abstract ImmutableMap<List<String>, Combine.CombineFn<?, ?, ?>> javaAggregateFunctions();
-
-  @AutoValue
-  public abstract static class JavaScalarFunction {
-    public static JavaScalarFunction create(Method method, String jarPath) {
-      return new AutoValue_UserFunctionDefinitions_JavaScalarFunction(method, jarPath);
-    }
-
-    public abstract Method method();
-
-    /** The Beam filesystem path to the jar where the method was defined. */
-    public abstract String jarPath();
-  }
-
-  @AutoValue.Builder
-  public abstract static class Builder {
-    public abstract Builder setSqlScalarFunctions(
-        ImmutableMap<List<String>, ResolvedNodes.ResolvedCreateFunctionStmt> sqlScalarFunctions);
-
-    public abstract Builder setSqlTableValuedFunctions(
-        ImmutableMap<List<String>, ResolvedNode> sqlTableValuedFunctions);
-
-    public abstract Builder setJavaScalarFunctions(
-        ImmutableMap<List<String>, JavaScalarFunction> javaScalarFunctions);
-
-    public abstract Builder setJavaAggregateFunctions(
-        ImmutableMap<List<String>, Combine.CombineFn<?, ?, ?>> javaAggregateFunctions);
-
-    public abstract UserFunctionDefinitions build();
-  }
-
-  public static Builder newBuilder() {
-    return new AutoValue_UserFunctionDefinitions.Builder()
-        .setSqlScalarFunctions(ImmutableMap.of())
-        .setSqlTableValuedFunctions(ImmutableMap.of())
-        .setJavaScalarFunctions(ImmutableMap.of())
-        .setJavaAggregateFunctions(ImmutableMap.of());
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/WithRefScanConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/WithRefScanConverter.java
deleted file mode 100644
index f33b10f70586..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/WithRefScanConverter.java
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedWithRefScan;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-
-/** Converts a call-site reference to a named WITH subquery. */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-class WithRefScanConverter extends RelConverter<ResolvedWithRefScan> {
-
-  WithRefScanConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedWithRefScan zetaNode) {
-    // WithRefScan contains only a name of a WITH query,
-    // but to actually convert it to the node we need to get the resolved node representation
-    // of the query. Here we take it from the trait, where it was persisted previously
-    // in WithScanConverter that actually parses the WITH query part.
-    //
-    // This query node returned from here will be converted by some other converter,
-    // (e.g. if the WITH query root is a projection it will go through ProjectScanConverter)
-    // and will reach the convert() method below as an already converted rel node.
-    return Collections.singletonList(
-        getTrait().withEntries.get(zetaNode.getWithQueryName()).getWithSubquery());
-  }
-
-  @Override
-  public RelNode convert(ResolvedWithRefScan zetaNode, List<RelNode> inputs) {
-    // Here the actual WITH query body has already been converted by, e.g. a ProjectScnaConverter,
-    // so to resolve the reference we just return that converter rel node.
-    return inputs.get(0);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/WithScanConverter.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/WithScanConverter.java
deleted file mode 100644
index 0e80ddbdb260..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/WithScanConverter.java
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import com.google.zetasql.resolvedast.ResolvedNode;
-import com.google.zetasql.resolvedast.ResolvedNodes.ResolvedWithScan;
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-
-/** Converts a named WITH. */
-class WithScanConverter extends RelConverter<ResolvedWithScan> {
-
-  WithScanConverter(ConversionContext context) {
-    super(context);
-  }
-
-  @Override
-  public List<ResolvedNode> getInputs(ResolvedWithScan zetaNode) {
-    // We must persist the named WITH queries nodes,
-    // so that when they are referenced by name (e.g. in FROM/JOIN), we can
-    // resolve them. We need this because the nodes that represent the references (WithRefScan)
-    // only contain the names of the queries, so we need to keep this map for resolution of the
-    // names.
-    zetaNode
-        .getWithEntryList()
-        .forEach(withEntry -> getTrait().withEntries.put(withEntry.getWithQueryName(), withEntry));
-
-    // Returning the body of the query, it is something like ProjectScan that will be converted
-    // by ProjectScanConverter before it reaches the convert() method below.
-    return Collections.singletonList(zetaNode.getQuery());
-  }
-
-  @Override
-  public RelNode convert(ResolvedWithScan zetaNode, List<RelNode> inputs) {
-    // The body of the WITH query is already converted at this point so we just
-    // return it, nothing else is needed.
-    return inputs.get(0);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ZetaSqlScalarFunctionImpl.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ZetaSqlScalarFunctionImpl.java
deleted file mode 100644
index 1ed6939f9549..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/ZetaSqlScalarFunctionImpl.java
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation;
-
-import java.lang.reflect.Method;
-import org.apache.beam.sdk.extensions.sql.impl.ScalarFunctionImpl;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.CallImplementor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.ScalarFunction;
-
-/** ZetaSQL-specific extension to {@link ScalarFunctionImpl}. */
-public class ZetaSqlScalarFunctionImpl extends ScalarFunctionImpl {
-  /**
-   * ZetaSQL function group identifier. Different function groups may have divergent translation
-   * paths.
-   */
-  public final String functionGroup;
-
-  private ZetaSqlScalarFunctionImpl(
-      Method method, CallImplementor implementor, String functionGroup, String jarPath) {
-    super(method, implementor, jarPath);
-    this.functionGroup = functionGroup;
-  }
-
-  /**
-   * Creates {@link org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function} from
-   * given class.
-   *
-   * <p>If a method of the given name is not found or it does not suit, returns {@code null}.
-   *
-   * @param clazz class that is used to implement the function
-   * @param methodName Method name (typically "eval")
-   * @param functionGroup ZetaSQL function group identifier. Different function groups may have
-   *     divergent translation paths.
-   * @return created {@link ScalarFunction} or null
-   */
-  public static Function create(
-      Class<?> clazz, String methodName, String functionGroup, String jarPath) {
-    return create(findMethod(clazz, methodName), functionGroup, jarPath);
-  }
-
-  /**
-   * Creates {@link org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Function} from
-   * given method. When {@code eval} method does not suit, {@code null} is returned.
-   *
-   * @param method method that is used to implement the function
-   * @param functionGroup ZetaSQL function group identifier. Different function groups may have
-   *     divergent translation paths.
-   * @return created {@link Function} or null
-   */
-  public static Function create(Method method, String functionGroup, String jarPath) {
-    validateMethod(method);
-    CallImplementor implementor = createImplementor(method);
-    return new ZetaSqlScalarFunctionImpl(method, implementor, functionGroup, jarPath);
-  }
-
-  /*
-   * Finds a method in a given class by name.
-   * @param clazz class to search method in
-   * @param name name of the method to find
-   * @return the first method with matching name or null when no method found
-   */
-  private static Method findMethod(Class<?> clazz, String name) {
-    for (Method method : clazz.getMethods()) {
-      if (method.getName().equals(name) && !method.isBridge()) {
-        return method;
-      }
-    }
-    throw new NoSuchMethodError(
-        String.format("Method %s not found in class %s.", name, clazz.getName()));
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/BeamBuiltinMethods.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/BeamBuiltinMethods.java
deleted file mode 100644
index 79adab4a01e8..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/BeamBuiltinMethods.java
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation.impl;
-
-import java.lang.reflect.Method;
-import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Types;
-
-/** BeamBuiltinMethods. */
-@Internal
-public class BeamBuiltinMethods {
-  public static final Method STARTS_WITH_METHOD =
-      Types.lookupMethod(StringFunctions.class, "startsWith", String.class, String.class);
-
-  public static final Method ENDS_WITH_METHOD =
-      Types.lookupMethod(StringFunctions.class, "endsWith", String.class, String.class);
-
-  public static final Method LIKE_METHOD =
-      Types.lookupMethod(StringFunctions.class, "like", String.class, String.class);
-
-  public static final Method CONCAT_METHOD =
-      Types.lookupMethod(
-          StringFunctions.class,
-          "concat",
-          String.class,
-          String.class,
-          String.class,
-          String.class,
-          String.class);
-
-  public static final Method REPLACE_METHOD =
-      Types.lookupMethod(
-          StringFunctions.class, "replace", String.class, String.class, String.class);
-
-  public static final Method TRIM_METHOD =
-      Types.lookupMethod(StringFunctions.class, "trim", String.class, String.class);
-
-  public static final Method LTRIM_METHOD =
-      Types.lookupMethod(StringFunctions.class, "ltrim", String.class, String.class);
-
-  public static final Method RTRIM_METHOD =
-      Types.lookupMethod(StringFunctions.class, "rtrim", String.class, String.class);
-
-  public static final Method SUBSTR_METHOD =
-      Types.lookupMethod(StringFunctions.class, "substr", String.class, long.class, long.class);
-
-  public static final Method REVERSE_METHOD =
-      Types.lookupMethod(StringFunctions.class, "reverse", String.class);
-
-  public static final Method CHAR_LENGTH_METHOD =
-      Types.lookupMethod(StringFunctions.class, "charLength", String.class);
-
-  public static final Method TIMESTAMP_METHOD =
-      Types.lookupMethod(TimestampFunctions.class, "timestamp", String.class, String.class);
-
-  public static final Method DATE_METHOD =
-      Types.lookupMethod(DateFunctions.class, "date", Integer.class, Integer.class, Integer.class);
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/CastFunctionImpl.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/CastFunctionImpl.java
deleted file mode 100644
index 1bcd73479bce..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/CastFunctionImpl.java
+++ /dev/null
@@ -1,107 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation.impl;
-
-import static org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.RexImpTable.createImplementor;
-
-import java.util.Collections;
-import java.util.List;
-import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.CallImplementor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.NotNullImplementor;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.NullPolicy;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.RexImpTable;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.adapter.enumerable.RexToLixTranslator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Expression;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.tree.Expressions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.FunctionParameter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.ImplementableFunction;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
-
-/** ZetaSQLCastFunctionImpl. */
-@Internal
-public class CastFunctionImpl implements ImplementableFunction {
-  @Override
-  public CallImplementor getImplementor() {
-    return createImplementor(new ZetaSQLCastCallNotNullImplementor(), NullPolicy.STRICT, false);
-  }
-
-  @Override
-  public List<FunctionParameter> getParameters() {
-    return Collections.emptyList();
-  }
-
-  private static class ZetaSQLCastCallNotNullImplementor implements NotNullImplementor {
-
-    @Override
-    public Expression implement(
-        RexToLixTranslator rexToLixTranslator, RexCall rexCall, List<Expression> list) {
-      if (rexCall.getOperands().size() != 1 || list.size() != 1) {
-        throw new IllegalArgumentException("CAST should have one operand.");
-      }
-      SqlTypeName toType = rexCall.getType().getSqlTypeName();
-      SqlTypeName fromType = rexCall.getOperands().get(0).getType().getSqlTypeName();
-
-      Expression translatedOperand = list.get(0);
-      Expression convertedOperand;
-      // CAST(BYTES AS STRING) - BINARY to VARCHAR in Calcite
-      if (fromType == SqlTypeName.BINARY && toType == SqlTypeName.VARCHAR) {
-        // operand is literal, which is bytes wrapped in ByteString.
-        // this piece of code is same as
-        // BeamCodegenUtils.toStringUTF8(ByeString.getBytes());
-        convertedOperand =
-            Expressions.call(
-                BeamCodegenUtils.class,
-                "toStringUTF8",
-                Expressions.call(translatedOperand, "getBytes"));
-      } else if (fromType == SqlTypeName.VARBINARY && toType == SqlTypeName.VARCHAR) {
-        // translatedOperand is a byte[]
-        // this piece of code is same as
-        // BeamCodegenUtils.toStringUTF8(byte[]);
-        convertedOperand =
-            Expressions.call(BeamCodegenUtils.class, "toStringUTF8", translatedOperand);
-      } else if (fromType == SqlTypeName.BOOLEAN && toType == SqlTypeName.BIGINT) {
-        convertedOperand =
-            Expressions.condition(
-                translatedOperand,
-                Expressions.constant(1L, Long.class),
-                Expressions.constant(0L, Long.class));
-      } else if (fromType == SqlTypeName.BIGINT && toType == SqlTypeName.BOOLEAN) {
-        convertedOperand = Expressions.notEqual(translatedOperand, Expressions.constant(0));
-      } else if (fromType == SqlTypeName.TIMESTAMP && toType == SqlTypeName.VARCHAR) {
-        convertedOperand =
-            Expressions.call(BeamCodegenUtils.class, "toStringTimestamp", translatedOperand);
-      } else {
-        throw new UnsupportedOperationException(
-            "Unsupported CAST: " + fromType.name() + " to " + toType.name());
-      }
-
-      // If operand is nullable, wrap in a null check
-      if (rexCall.getOperands().get(0).getType().isNullable()) {
-        convertedOperand =
-            Expressions.condition(
-                Expressions.equal(translatedOperand, RexImpTable.NULL_EXPR),
-                RexImpTable.NULL_EXPR,
-                convertedOperand);
-      }
-
-      return convertedOperand;
-    }
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/DateFunctions.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/DateFunctions.java
deleted file mode 100644
index e5fda88327d3..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/DateFunctions.java
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation.impl;
-
-import java.util.TimeZone;
-import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils;
-import org.joda.time.DateTime;
-import org.joda.time.DateTimeZone;
-
-/** DateFunctions. */
-@Internal
-public class DateFunctions {
-  public DateTime date(Integer year, Integer month, Integer day) {
-    return DateTimeUtils.parseDate(
-        String.join("-", year.toString(), month.toString(), day.toString()));
-  }
-
-  public DateTime date(DateTime ts) {
-    return date(ts, "UTC");
-  }
-
-  public DateTime date(DateTime ts, String timezone) {
-    return ts.withZoneRetainFields(DateTimeZone.forTimeZone(TimeZone.getTimeZone(timezone)));
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/StringFunctions.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/StringFunctions.java
deleted file mode 100644
index ac32fd32c46e..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/StringFunctions.java
+++ /dev/null
@@ -1,184 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation.impl;
-
-import java.util.regex.Pattern;
-import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.function.Strict;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.runtime.SqlFunctions;
-
-/** StringFunctions. */
-@Internal
-public class StringFunctions {
-  public static final String SUBSTR_PARAMETER_EXCEED_INTEGER =
-      "SUBSTR function only allows: "
-          + Integer.MIN_VALUE
-          + " <= position or length <= "
-          + Integer.MAX_VALUE;
-
-  @Strict
-  public static Boolean startsWith(String str1, String str2) {
-    return str1.startsWith(str2);
-  }
-
-  @Strict
-  public static Boolean endsWith(String str1, String str2) {
-    return str1.endsWith(str2);
-  }
-
-  @Strict
-  public static String concat(String arg) {
-    return arg;
-  }
-
-  @Strict
-  public static String concat(String arg1, String arg2) {
-    return concatIfNotIncludeNull(arg1, arg2);
-  }
-
-  @Strict
-  public static String concat(String arg1, String arg2, String arg3) {
-    return concatIfNotIncludeNull(arg1, arg2, arg3);
-  }
-
-  @Strict
-  public static String concat(String arg1, String arg2, String arg3, String arg4) {
-    return concatIfNotIncludeNull(arg1, arg2, arg3, arg4);
-  }
-
-  @Strict
-  public static String concat(String arg1, String arg2, String arg3, String arg4, String arg5) {
-    return concatIfNotIncludeNull(arg1, arg2, arg3, arg4, arg5);
-  }
-
-  @Strict
-  private static String concatIfNotIncludeNull(String... args) {
-    return String.join("", args);
-  }
-
-  // https://jira.apache.org/jira/browse/CALCITE-2889
-  // public static String concat(String... args) {
-  //   StringBuilder stringBuilder = new StringBuilder();
-  //   for (String arg : args) {
-  //     stringBuilder.append(arg);
-  //   }
-  //   return stringBuilder.toString();
-  // }
-
-  @Strict
-  public static String replace(String origin, String target, String replacement) {
-    // Java's string.replace behaves differently when target = "". When target = "",
-    // Java's replace function replace every character in origin with replacement,
-    // while origin value should not be changed is expected in SQL.
-    if (target.length() == 0) {
-      return origin;
-    }
-
-    return origin.replace(target, replacement);
-  }
-
-  public static String trim(String str) {
-    return trim(str, " ");
-  }
-
-  @Strict
-  public static String trim(String str, String seek) {
-    return SqlFunctions.trim(true, true, seek, str, false);
-  }
-
-  public static String ltrim(String str) {
-    return ltrim(str, " ");
-  }
-
-  @Strict
-  public static String ltrim(String str, String seek) {
-    return SqlFunctions.trim(true, false, seek, str, false);
-  }
-
-  public static String rtrim(String str) {
-    return rtrim(str, " ");
-  }
-
-  @Strict
-  public static String rtrim(String str, String seek) {
-    return SqlFunctions.trim(false, true, seek, str, false);
-  }
-
-  public static String substr(String str, long from, long len) {
-    if (from > Integer.MAX_VALUE
-        || len > Integer.MAX_VALUE
-        || from < Integer.MIN_VALUE
-        || len < Integer.MIN_VALUE) {
-      throw new RuntimeException(SUBSTR_PARAMETER_EXCEED_INTEGER);
-    }
-    return SqlFunctions.substring(str, (int) from, (int) len);
-  }
-
-  @Strict
-  public static String reverse(String str) {
-    return new StringBuilder(str).reverse().toString();
-  }
-
-  @Strict
-  public static Long charLength(String str) {
-    return (long) str.length();
-  }
-
-  // ZetaSQL's LIKE statement does not support the ESCAPE clause. Instead it
-  // always uses \ as an escape character.
-  @Strict
-  public static Boolean like(String s, String pattern) {
-    String regex = sqlToRegexLike(pattern, '\\');
-    return Pattern.matches(regex, s);
-  }
-
-  private static final String JAVA_REGEX_SPECIALS = "[]()|^-+*?{}$\\.";
-
-  /**
-   * Translates a SQL LIKE pattern to Java regex pattern. Modified from Apache Calcite's
-   * Like.sqlToRegexLike
-   */
-  private static String sqlToRegexLike(String sqlPattern, char escapeChar) {
-    int i;
-    final int len = sqlPattern.length();
-    final StringBuilder javaPattern = new StringBuilder(len + len);
-    for (i = 0; i < len; i++) {
-      char c = sqlPattern.charAt(i);
-      if (c == escapeChar) {
-        if (i == (sqlPattern.length() - 1)) {
-          throw new IllegalArgumentException("LIKE pattern ends with a backslash");
-        }
-        char nextChar = sqlPattern.charAt(++i);
-        if (JAVA_REGEX_SPECIALS.indexOf(nextChar) >= 0) {
-          javaPattern.append('\\');
-        }
-        javaPattern.append(nextChar);
-      } else if (c == '_') {
-        javaPattern.append('.');
-      } else if (c == '%') {
-        javaPattern.append("(?s:.*)");
-      } else {
-        if (JAVA_REGEX_SPECIALS.indexOf(c) >= 0) {
-          javaPattern.append('\\');
-        }
-        javaPattern.append(c);
-      }
-    }
-    return javaPattern.toString();
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/TimestampFunctions.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/TimestampFunctions.java
deleted file mode 100644
index 904b192b2a5b..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/TimestampFunctions.java
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation.impl;
-
-import java.util.TimeZone;
-import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.linq4j.function.Strict;
-import org.joda.time.DateTime;
-import org.joda.time.DateTimeZone;
-
-/** TimestampFunctions. */
-@Internal
-public class TimestampFunctions {
-  public static DateTime timestamp(String timestampStr) {
-    return timestamp(timestampStr, "UTC");
-  }
-
-  @Strict
-  public static DateTime timestamp(String timestampStr, String timezone) {
-    return DateTimeUtils.findDateTimePattern(timestampStr)
-        .withZone(DateTimeZone.forTimeZone(TimeZone.getTimeZone(timezone)))
-        .parseDateTime(timestampStr);
-  }
-
-  @Strict
-  public static DateTime timestamp(Integer numOfDays) {
-    return timestamp(numOfDays, "UTC");
-  }
-
-  @Strict
-  public static DateTime timestamp(Integer numOfDays, String timezone) {
-    return new DateTime((long) numOfDays * DateTimeUtils.MILLIS_PER_DAY, DateTimeZone.UTC)
-        .withZoneRetainFields(DateTimeZone.forTimeZone(TimeZone.getTimeZone(timezone)));
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUncollectRel.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUncollectRel.java
deleted file mode 100644
index 4cce106c6279..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUncollectRel.java
+++ /dev/null
@@ -1,120 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.unnest;
-
-import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
-
-import org.apache.beam.sdk.extensions.sql.impl.planner.BeamCostModel;
-import org.apache.beam.sdk.extensions.sql.impl.planner.BeamRelMetadataQuery;
-import org.apache.beam.sdk.extensions.sql.impl.planner.NodeStats;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.extensions.sql.impl.utils.CalciteUtils;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.transforms.DoFn;
-import org.apache.beam.sdk.transforms.PTransform;
-import org.apache.beam.sdk.transforms.ParDo;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.PCollectionList;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-
-/**
- * {@link BeamRelNode} to implement an uncorrelated {@link ZetaSqlUnnest}, aka UNNEST.
- *
- * <p>This class is a copy of {@link org.apache.beam.sdk.extensions.sql.impl.rel.BeamUncollectRel}
- * except that in UncollectDoFn it does not treat elements of struct type differently.
- *
- * <p>Details of why unwrapping structs breaks ZetaSQL UNNEST syntax is in
- * https://issues.apache.org/jira/browse/BEAM-10896.
- */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-public class BeamZetaSqlUncollectRel extends ZetaSqlUnnest implements BeamRelNode {
-
-  public BeamZetaSqlUncollectRel(
-      RelOptCluster cluster, RelTraitSet traitSet, RelNode input, boolean withOrdinality) {
-    super(cluster, traitSet, input, withOrdinality);
-  }
-
-  @Override
-  public RelNode copy(RelTraitSet traitSet, RelNode input) {
-    return new BeamZetaSqlUncollectRel(getCluster(), traitSet, input, withOrdinality);
-  }
-
-  @Override
-  public PTransform<PCollectionList<Row>, PCollection<Row>> buildPTransform() {
-    return new Transform();
-  }
-
-  private class Transform extends PTransform<PCollectionList<Row>, PCollection<Row>> {
-
-    @Override
-    public PCollection<Row> expand(PCollectionList<Row> pinput) {
-      checkArgument(
-          pinput.size() == 1,
-          "Wrong number of inputs for %s: %s",
-          BeamZetaSqlUncollectRel.class.getSimpleName(),
-          pinput);
-      PCollection<Row> upstream = pinput.get(0);
-
-      // Each row of the input contains a single array of things to be emitted; Calcite knows
-      // what the row looks like
-      Schema outputSchema = CalciteUtils.toSchema(getRowType());
-
-      PCollection<Row> uncollected =
-          upstream.apply(ParDo.of(new UncollectDoFn(outputSchema))).setRowSchema(outputSchema);
-
-      return uncollected;
-    }
-  }
-
-  @Override
-  public NodeStats estimateNodeStats(BeamRelMetadataQuery mq) {
-    // We estimate the average length of each array by a constant.
-    // We might be able to get an estimate of the length by making a MetadataHandler for this
-    // purpose, and get the estimate by reading the first couple of the rows in the source.
-    return BeamSqlRelUtils.getNodeStats(this.input, mq).multiply(2);
-  }
-
-  @Override
-  public BeamCostModel beamComputeSelfCost(RelOptPlanner planner, BeamRelMetadataQuery mq) {
-    NodeStats estimates = BeamSqlRelUtils.getNodeStats(this, mq);
-    return BeamCostModel.FACTORY.makeCost(estimates.getRowCount(), estimates.getRate());
-  }
-
-  private static class UncollectDoFn extends DoFn<Row, Row> {
-
-    private final Schema schema;
-
-    private UncollectDoFn(Schema schema) {
-      this.schema = schema;
-    }
-
-    @ProcessElement
-    public void process(@Element Row inputRow, OutputReceiver<Row> output) {
-      for (Object element : inputRow.getArray(0)) {
-        output.output(Row.withSchema(schema).addValue(element).build());
-      }
-    }
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUncollectRule.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUncollectRule.java
deleted file mode 100644
index 61e2f07440b7..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUncollectRule.java
+++ /dev/null
@@ -1,54 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.unnest;
-
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.convert.ConverterRule;
-
-/**
- * A {@code ConverterRule} to replace {@link ZetaSqlUnnest} with {@link BeamZetaSqlUncollectRel}.
- *
- * <p>This class is a copy of {@link org.apache.beam.sdk.extensions.sql.impl.rel.BeamUncollectRel}
- * except that it works on {@link ZetaSqlUnnest} instead of Calcite Uncollect.
- *
- * <p>Details of why unwrapping structs breaks ZetaSQL UNNEST syntax is in
- * https://issues.apache.org/jira/browse/BEAM-10896.
- */
-public class BeamZetaSqlUncollectRule extends ConverterRule {
-  public static final BeamZetaSqlUncollectRule INSTANCE = new BeamZetaSqlUncollectRule();
-
-  private BeamZetaSqlUncollectRule() {
-    super(
-        ZetaSqlUnnest.class, Convention.NONE, BeamLogicalConvention.INSTANCE, "BeamUncollectRule");
-  }
-
-  @Override
-  public RelNode convert(RelNode rel) {
-    ZetaSqlUnnest uncollect = (ZetaSqlUnnest) rel;
-
-    return new BeamZetaSqlUncollectRel(
-        uncollect.getCluster(),
-        uncollect.getTraitSet().replace(BeamLogicalConvention.INSTANCE),
-        convert(
-            uncollect.getInput(),
-            uncollect.getInput().getTraitSet().replace(BeamLogicalConvention.INSTANCE)),
-        uncollect.withOrdinality);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUnnestRel.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUnnestRel.java
deleted file mode 100644
index 54a0cc5ad919..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUnnestRel.java
+++ /dev/null
@@ -1,164 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.unnest;
-
-import java.util.Collection;
-import java.util.List;
-import org.apache.beam.sdk.extensions.sql.impl.planner.BeamCostModel;
-import org.apache.beam.sdk.extensions.sql.impl.planner.BeamRelMetadataQuery;
-import org.apache.beam.sdk.extensions.sql.impl.planner.NodeStats;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.extensions.sql.impl.utils.CalciteUtils;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.transforms.DoFn;
-import org.apache.beam.sdk.transforms.PTransform;
-import org.apache.beam.sdk.transforms.ParDo;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.PCollectionList;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Correlate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.JoinRelType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.validate.SqlValidatorUtil;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.checkerframework.checker.nullness.qual.Nullable;
-
-/**
- * {@link BeamRelNode} to implement UNNEST, supporting specifically only {@link Correlate} with
- * {@link ZetaSqlUnnest}.
- *
- * <p>This class is a copy of {@link org.apache.beam.sdk.extensions.sql.impl.rel.BeamUnnestRel}
- * except that in UnnestFn it does not treat elements of struct type differently.
- *
- * <p>Details of why unwrapping structs breaks ZetaSQL UNNEST syntax is in
- * https://issues.apache.org/jira/browse/BEAM-10896.
- */
-@SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
-})
-public class BeamZetaSqlUnnestRel extends ZetaSqlUnnest implements BeamRelNode {
-
-  private final RelDataType unnestType;
-  private final List<Integer> unnestIndices;
-
-  public BeamZetaSqlUnnestRel(
-      RelOptCluster cluster,
-      RelTraitSet traitSet,
-      RelNode input,
-      RelDataType unnestType,
-      List<Integer> unnestIndices) {
-    super(cluster, traitSet, input, false);
-    this.unnestType = unnestType;
-    this.unnestIndices = unnestIndices;
-  }
-
-  @Override
-  public ZetaSqlUnnest copy(RelTraitSet traitSet, RelNode input) {
-    return new BeamZetaSqlUnnestRel(getCluster(), traitSet, input, unnestType, unnestIndices);
-  }
-
-  @Override
-  protected RelDataType deriveRowType() {
-    return SqlValidatorUtil.deriveJoinRowType(
-        input.getRowType(),
-        unnestType,
-        JoinRelType.INNER,
-        getCluster().getTypeFactory(),
-        null,
-        ImmutableList.of());
-  }
-
-  @Override
-  public NodeStats estimateNodeStats(BeamRelMetadataQuery mq) {
-    // We estimate the average length of each array by a constant.
-    // We might be able to get an estimate of the length by making a MetadataHandler for this
-    // purpose, and get the estimate by reading the first couple of the rows in the source.
-    return BeamSqlRelUtils.getNodeStats(this.input, mq).multiply(2);
-  }
-
-  @Override
-  public BeamCostModel beamComputeSelfCost(RelOptPlanner planner, BeamRelMetadataQuery mq) {
-    NodeStats estimates = BeamSqlRelUtils.getNodeStats(this, mq);
-    return BeamCostModel.FACTORY.makeCost(estimates.getRowCount(), estimates.getRate());
-  }
-
-  @Override
-  public RelWriter explainTerms(RelWriter pw) {
-    return super.explainTerms(pw).item("unnestIndices", unnestIndices);
-  }
-
-  @Override
-  public PTransform<PCollectionList<Row>, PCollection<Row>> buildPTransform() {
-    return new Transform();
-  }
-
-  private class Transform extends PTransform<PCollectionList<Row>, PCollection<Row>> {
-    @Override
-    public PCollection<Row> expand(PCollectionList<Row> pinput) {
-      // The set of rows where we run the correlated unnest for each row
-      PCollection<Row> outer = pinput.get(0);
-
-      Schema joinedSchema = CalciteUtils.toSchema(getRowType());
-
-      return outer
-          .apply(ParDo.of(new UnnestFn(joinedSchema, unnestIndices)))
-          .setRowSchema(joinedSchema);
-    }
-  }
-
-  private static class UnnestFn extends DoFn<Row, Row> {
-
-    private final Schema outputSchema;
-    private final List<Integer> unnestIndices;
-
-    private UnnestFn(Schema outputSchema, List<Integer> unnestIndices) {
-      this.outputSchema = outputSchema;
-      this.unnestIndices = unnestIndices;
-    }
-
-    @ProcessElement
-    public void process(@Element Row row, OutputReceiver<Row> out) {
-      Row rowWithArrayField = row;
-      Schema schemaWithArrayField = outputSchema;
-      for (int i = unnestIndices.size() - 1; i > 0; i--) {
-        rowWithArrayField = rowWithArrayField.getRow(unnestIndices.get(i));
-        schemaWithArrayField =
-            schemaWithArrayField.getField(unnestIndices.get(i)).getType().getRowSchema();
-      }
-      @Nullable Collection<Object> rawValues = rowWithArrayField.getArray(unnestIndices.get(0));
-
-      if (rawValues == null) {
-        return;
-      }
-
-      for (Object uncollectedValue : rawValues) {
-        out.output(
-            Row.withSchema(outputSchema)
-                .addValues(row.getBaseValues())
-                .addValue(uncollectedValue)
-                .build());
-      }
-    }
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUnnestRule.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUnnestRule.java
deleted file mode 100644
index d9fed7f6ae6b..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/BeamZetaSqlUnnestRule.java
+++ /dev/null
@@ -1,116 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.unnest;
-
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamLogicalConvention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRule;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptRuleCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.volcano.RelSubset;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.SingleRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.Correlate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.core.JoinRelType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalCorrelate;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.logical.LogicalProject;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexFieldAccess;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rex.RexNode;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-
-/**
- * A {@code ConverterRule} to replace {@link Correlate} {@link ZetaSqlUnnest} with {@link
- * BeamZetaSqlUnnestRel}.
- *
- * <p>This class is a copy of {@link org.apache.beam.sdk.extensions.sql.impl.rule.BeamUnnestRule}
- * except that it works on {@link ZetaSqlUnnest} instead of Calcite Uncollect.
- *
- * <p>Details of why unwrapping structs breaks ZetaSQL UNNEST syntax is in
- * https://issues.apache.org/jira/browse/BEAM-10896.
- */
-public class BeamZetaSqlUnnestRule extends RelOptRule {
-  public static final BeamZetaSqlUnnestRule INSTANCE = new BeamZetaSqlUnnestRule();
-
-  // TODO: more general Correlate
-  private BeamZetaSqlUnnestRule() {
-    super(
-        operand(
-            LogicalCorrelate.class, operand(RelNode.class, any()), operand(SingleRel.class, any())),
-        "BeamZetaSqlUnnestRule");
-  }
-
-  @Override
-  public void onMatch(RelOptRuleCall call) {
-    LogicalCorrelate correlate = call.rel(0);
-    RelNode outer = call.rel(1);
-    RelNode uncollect = call.rel(2);
-
-    if (correlate.getRequiredColumns().cardinality() != 1) {
-      // can only unnest a single column
-      return;
-    }
-    if (correlate.getJoinType() != JoinRelType.INNER) {
-      return;
-    }
-
-    if (!(uncollect instanceof ZetaSqlUnnest)) {
-      // Drop projection
-      uncollect = ((SingleRel) uncollect).getInput();
-      if (uncollect instanceof RelSubset) {
-        uncollect = ((RelSubset) uncollect).getOriginal();
-      }
-      if (!(uncollect instanceof ZetaSqlUnnest)) {
-        return;
-      }
-    }
-
-    RelNode project = ((ZetaSqlUnnest) uncollect).getInput();
-    if (project instanceof RelSubset) {
-      project = ((RelSubset) project).getOriginal();
-    }
-    if (!(project instanceof LogicalProject)) {
-      return;
-    }
-
-    if (((LogicalProject) project).getProjects().size() != 1) {
-      // can only unnest a single column
-      return;
-    }
-
-    RexNode exp = ((LogicalProject) project).getProjects().get(0);
-    if (!(exp instanceof RexFieldAccess)) {
-      return;
-    }
-    RexFieldAccess fieldAccess = (RexFieldAccess) exp;
-    // Innermost field index comes first (e.g. struct.field1.field2 => [2, 1])
-    ImmutableList.Builder<Integer> fieldAccessIndices = ImmutableList.builder();
-    while (true) {
-      fieldAccessIndices.add(fieldAccess.getField().getIndex());
-      if (!(fieldAccess.getReferenceExpr() instanceof RexFieldAccess)) {
-        break;
-      }
-      fieldAccess = (RexFieldAccess) fieldAccess.getReferenceExpr();
-    }
-
-    call.transformTo(
-        new BeamZetaSqlUnnestRel(
-            correlate.getCluster(),
-            correlate.getTraitSet().replace(BeamLogicalConvention.INSTANCE),
-            convert(outer, outer.getTraitSet().replace(BeamLogicalConvention.INSTANCE)),
-            call.rel(2).getRowType(),
-            fieldAccessIndices.build()));
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/ZetaSqlUnnest.java b/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/ZetaSqlUnnest.java
deleted file mode 100644
index 1c6f47ab0372..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/ZetaSqlUnnest.java
+++ /dev/null
@@ -1,156 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql.unnest;
-
-import java.util.List;
-import org.apache.beam.sdk.util.Preconditions;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Convention;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptCluster;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitSet;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelInput;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.RelWriter;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.SingleRel;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.rel.type.RelDataTypeField;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlUnnestOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlUtil;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.ArraySqlType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.MapSqlType;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.type.SqlTypeName;
-
-/**
- * This class is a copy of Uncollect.java in Calcite:
- * https://github.com/apache/calcite/blob/calcite-1.20.0/core/src/main/java/org/apache/calcite/rel/core/Uncollect.java
- * except that in deriveUncollectRowType() it does not unwrap array elements of struct type.
- *
- * <p>Details of why unwrapping structs breaks ZetaSQL UNNEST syntax is in
- * https://issues.apache.org/jira/browse/BEAM-10896.
- */
-public class ZetaSqlUnnest extends SingleRel {
-  public final boolean withOrdinality;
-
-  // ~ Constructors -----------------------------------------------------------
-
-  /**
-   * Creates an Uncollect.
-   *
-   * <p>Use {@link #create} unless you know what you're doing.
-   */
-  public ZetaSqlUnnest(
-      RelOptCluster cluster, RelTraitSet traitSet, RelNode input, boolean withOrdinality) {
-    super(cluster, traitSet, input);
-    this.withOrdinality = withOrdinality;
-  }
-
-  /** Creates an Uncollect by parsing serialized output. */
-  public ZetaSqlUnnest(RelInput input) {
-    this(
-        input.getCluster(),
-        input.getTraitSet(),
-        input.getInput(),
-        input.getBoolean("withOrdinality", false));
-  }
-
-  /**
-   * Creates an Uncollect.
-   *
-   * <p>Each field of the input relational expression must be an array or multiset.
-   *
-   * @param traitSet Trait set
-   * @param input Input relational expression
-   * @param withOrdinality Whether output should contain an ORDINALITY column
-   */
-  public static ZetaSqlUnnest create(RelTraitSet traitSet, RelNode input, boolean withOrdinality) {
-    final RelOptCluster cluster = input.getCluster();
-    return new ZetaSqlUnnest(cluster, traitSet, input, withOrdinality);
-  }
-
-  // ~ Methods ----------------------------------------------------------------
-
-  @Override
-  public RelWriter explainTerms(RelWriter pw) {
-    return super.explainTerms(pw).itemIf("withOrdinality", withOrdinality, withOrdinality);
-  }
-
-  @Override
-  public final RelNode copy(RelTraitSet traitSet, List<RelNode> inputs) {
-    return copy(traitSet, sole(inputs));
-  }
-
-  public RelNode copy(RelTraitSet traitSet, RelNode input) {
-    assert traitSet.containsIfApplicable(Convention.NONE);
-    return new ZetaSqlUnnest(getCluster(), traitSet, input, withOrdinality);
-  }
-
-  @Override
-  protected RelDataType deriveRowType() {
-    return deriveUncollectRowType(input, withOrdinality);
-  }
-
-  /**
-   * Returns the row type returned by applying the 'UNNEST' operation to a relational expression.
-   *
-   * <p>Each column in the relational expression must be a multiset of structs or an array. The
-   * return type is the type of that column, plus an ORDINALITY column if {@code withOrdinality}.
-   */
-  public static RelDataType deriveUncollectRowType(RelNode rel, boolean withOrdinality) {
-    RelDataType inputType = rel.getRowType();
-    assert inputType.isStruct() : inputType + " is not a struct";
-    final List<RelDataTypeField> fields = inputType.getFieldList();
-    final RelDataTypeFactory typeFactory = rel.getCluster().getTypeFactory();
-    final RelDataTypeFactory.Builder builder = typeFactory.builder();
-
-    if (fields.size() == 1 && fields.get(0).getType().getSqlTypeName() == SqlTypeName.ANY) {
-      // Component type is unknown to Uncollect, build a row type with input column name
-      // and Any type.
-      return builder.add(fields.get(0).getName(), SqlTypeName.ANY).nullable(true).build();
-    }
-
-    for (RelDataTypeField field : fields) {
-      if (field.getType() instanceof MapSqlType) {
-        builder.add(
-            SqlUnnestOperator.MAP_KEY_COLUMN_NAME,
-            Preconditions.checkArgumentNotNull(
-                field.getType().getKeyType(),
-                "Encountered MAP type with null key type in field %s",
-                field));
-        builder.add(
-            SqlUnnestOperator.MAP_VALUE_COLUMN_NAME,
-            Preconditions.checkArgumentNotNull(
-                field.getType().getValueType(),
-                "Encountered MAP type with null value type in field %s",
-                field));
-      } else {
-        assert field.getType() instanceof ArraySqlType;
-        RelDataType ret =
-            Preconditions.checkArgumentNotNull(
-                field.getType().getComponentType(),
-                "Encountered ARRAY type with null component type in field %s",
-                field);
-        // Only difference than Uncollect.java: treats record type and scalar type equally
-        builder.add(SqlUtil.deriveAliasFromOrdinal(field.getIndex()), ret);
-      }
-    }
-    if (withOrdinality) {
-      builder.add(SqlUnnestOperator.ORDINALITY_COLUMN_NAME, SqlTypeName.INTEGER);
-    }
-    return builder.build();
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamJavaUdfCalcRuleTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamJavaUdfCalcRuleTest.java
deleted file mode 100644
index a6ca07b307ea..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamJavaUdfCalcRuleTest.java
+++ /dev/null
@@ -1,84 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.hamcrest.Matchers.isA;
-
-import org.apache.beam.sdk.extensions.sql.impl.SqlConversionException;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.testing.PAssert;
-import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelOptPlanner;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Frameworks;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSet;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.joda.time.Duration;
-import org.junit.Before;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.rules.ExpectedException;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-/** Tests for {@link BeamJavaUdfCalcRule}. */
-@RunWith(JUnit4.class)
-public class BeamJavaUdfCalcRuleTest extends ZetaSqlTestBase {
-  @Rule public transient TestPipeline pipeline = TestPipeline.create();
-  @Rule public ExpectedException thrown = ExpectedException.none();
-
-  @Before
-  public void setUp() {
-    initialize();
-
-    this.config =
-        Frameworks.newConfigBuilder(config)
-            .ruleSets(
-                ZetaSQLQueryPlanner.getZetaSqlRuleSets(
-                        ImmutableList.of(BeamJavaUdfCalcRule.INSTANCE))
-                    .toArray(new RuleSet[0]))
-            .build();
-  }
-
-  @Test
-  public void testSelectLiteral() {
-    String sql = "SELECT 1;";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema singleField = Schema.builder().addInt64Field("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(singleField).addValues(1L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testBuiltinFunctionThrowsSqlConversionException() {
-    String sql = "SELECT abs(1);";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-
-    thrown.expect(SqlConversionException.class);
-    thrown.expectCause(isA(RelOptPlanner.CannotPlanException.class));
-
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcRelTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcRelTest.java
deleted file mode 100644
index 2cb8501eb276..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCalcRelTest.java
+++ /dev/null
@@ -1,120 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import org.apache.beam.sdk.Pipeline;
-import org.apache.beam.sdk.extensions.sql.impl.QueryPlanner.QueryParameters;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.runners.TransformHierarchy;
-import org.apache.beam.sdk.schemas.FieldAccessDescriptor;
-import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.sdk.transforms.DoFnSchemaInformation;
-import org.apache.beam.sdk.transforms.ParDo;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.PValue;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
-import org.junit.Assert;
-import org.junit.Before;
-import org.junit.Rule;
-import org.junit.Test;
-
-/** Tests related to {@code BeamZetaSqlCalcRel}. */
-public class BeamZetaSqlCalcRelTest extends ZetaSqlTestBase {
-
-  private PCollection<Row> compile(String sql) {
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql, QueryParameters.ofNone());
-    return BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-  }
-
-  @Rule public final TestPipeline pipeline = TestPipeline.create();
-
-  @Before
-  public void setUp() {
-    initialize();
-  }
-
-  private static class NodeGetter extends Pipeline.PipelineVisitor.Defaults {
-
-    private final PValue target;
-    private TransformHierarchy.Node producer;
-
-    private NodeGetter(PValue target) {
-      this.target = target;
-    }
-
-    @Override
-    public void visitValue(PValue value, TransformHierarchy.Node producer) {
-      if (value == target) {
-        assert this.producer == null;
-        this.producer = producer;
-      }
-    }
-  }
-
-  @Test
-  public void testSingleFieldAccess() throws IllegalAccessException {
-    String sql = "SELECT Key FROM KeyValue";
-
-    PCollection<Row> rows = compile(sql);
-
-    final NodeGetter nodeGetter = new NodeGetter(rows);
-    pipeline.traverseTopologically(nodeGetter);
-
-    ParDo.MultiOutput<Row, Row> pardo =
-        (ParDo.MultiOutput<Row, Row>) nodeGetter.producer.getTransform();
-    PCollection<Row> input =
-        (PCollection<Row>) Iterables.getOnlyElement(nodeGetter.producer.getInputs().values());
-
-    DoFnSchemaInformation info = ParDo.getDoFnSchemaInformation(pardo.getFn(), input);
-
-    FieldAccessDescriptor fieldAccess = info.getFieldAccessDescriptor();
-
-    Assert.assertTrue(fieldAccess.referencesSingleField());
-    Assert.assertEquals("Key", Iterables.getOnlyElement(fieldAccess.fieldNamesAccessed()));
-
-    pipeline.run().waitUntilFinish();
-  }
-
-  @Test
-  public void testNoFieldAccess() throws IllegalAccessException {
-    String sql = "SELECT 1 FROM KeyValue";
-
-    PCollection<Row> rows = compile(sql);
-
-    final NodeGetter nodeGetter = new NodeGetter(rows);
-    pipeline.traverseTopologically(nodeGetter);
-
-    ParDo.MultiOutput<Row, Row> pardo =
-        (ParDo.MultiOutput<Row, Row>) nodeGetter.producer.getTransform();
-    PCollection<Row> input =
-        (PCollection<Row>) Iterables.getOnlyElement(nodeGetter.producer.getInputs().values());
-
-    DoFnSchemaInformation info = ParDo.getDoFnSchemaInformation(pardo.getFn(), input);
-
-    FieldAccessDescriptor fieldAccess = info.getFieldAccessDescriptor();
-
-    Assert.assertFalse(fieldAccess.getAllFields());
-    Assert.assertTrue(fieldAccess.getFieldsAccessed().isEmpty());
-    Assert.assertTrue(fieldAccess.getNestedFieldsAccessed().isEmpty());
-
-    pipeline.run().waitUntilFinish();
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCatalogTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCatalogTest.java
deleted file mode 100644
index 733de268c88b..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/BeamZetaSqlCatalogTest.java
+++ /dev/null
@@ -1,165 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.apache.beam.sdk.extensions.sql.zetasql.BeamZetaSqlCatalog.USER_DEFINED_JAVA_SCALAR_FUNCTIONS;
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNotNull;
-
-import com.google.zetasql.Analyzer;
-import com.google.zetasql.AnalyzerOptions;
-import com.google.zetasql.resolvedast.ResolvedNodes;
-import java.lang.reflect.Method;
-import java.sql.Time;
-import java.util.List;
-import org.apache.beam.sdk.extensions.sql.BeamSqlUdf;
-import org.apache.beam.sdk.extensions.sql.impl.JdbcConnection;
-import org.apache.beam.sdk.extensions.sql.impl.JdbcDriver;
-import org.apache.beam.sdk.extensions.sql.impl.ScalarFunctionImpl;
-import org.apache.beam.sdk.extensions.sql.meta.provider.ReadOnlyTableProvider;
-import org.apache.beam.sdk.extensions.sql.zetasql.translation.UserFunctionDefinitions;
-import org.apache.beam.sdk.options.PipelineOptionsFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.rules.ExpectedException;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-/** Tests for {@link BeamZetaSqlCatalog}. */
-@RunWith(JUnit4.class)
-public class BeamZetaSqlCatalogTest {
-  @Rule public ExpectedException thrown = ExpectedException.none();
-
-  public static class IncrementFn implements BeamSqlUdf {
-    public Long eval(Long i) {
-      return i + 1;
-    }
-  }
-
-  public static class ReturnsArrayTimeFn implements BeamSqlUdf {
-    public List<Time> eval() {
-      return ImmutableList.of(new Time(0));
-    }
-  }
-
-  public static class TakesArrayTimeFn implements BeamSqlUdf {
-    public Long eval(List<Time> ls) {
-      return 0L;
-    }
-  }
-
-  @Test
-  public void loadsUserDefinedFunctionsFromSchema() throws NoSuchMethodException {
-    JdbcConnection jdbcConnection = createJdbcConnection();
-    SchemaPlus calciteSchema = jdbcConnection.getCurrentSchemaPlus();
-    Method method = IncrementFn.class.getMethod("eval", Long.class);
-    calciteSchema.add("increment", ScalarFunctionImpl.create(method));
-    BeamZetaSqlCatalog beamCatalog =
-        BeamZetaSqlCatalog.create(
-            calciteSchema, jdbcConnection.getTypeFactory(), SqlAnalyzer.baseAnalyzerOptions());
-    assertNotNull(
-        "ZetaSQL catalog contains function signature.",
-        beamCatalog
-            .getZetaSqlCatalog()
-            .getFunctionByFullName(USER_DEFINED_JAVA_SCALAR_FUNCTIONS + ":increment"));
-    assertEquals(
-        "Beam catalog contains function definition.",
-        UserFunctionDefinitions.JavaScalarFunction.create(method, ""),
-        beamCatalog
-            .getUserFunctionDefinitions()
-            .javaScalarFunctions()
-            .get(ImmutableList.of("increment")));
-  }
-
-  @Test
-  public void rejectsScalarFunctionImplWithUnsupportedReturnType() throws NoSuchMethodException {
-    JdbcConnection jdbcConnection = createJdbcConnection();
-    SchemaPlus calciteSchema = jdbcConnection.getCurrentSchemaPlus();
-    Method method = ReturnsArrayTimeFn.class.getMethod("eval");
-    calciteSchema.add("return_array", ScalarFunctionImpl.create(method));
-    thrown.expect(UnsupportedOperationException.class);
-    thrown.expectMessage("Calcite type TIME not allowed in function return_array");
-    BeamZetaSqlCatalog.create(
-        calciteSchema, jdbcConnection.getTypeFactory(), SqlAnalyzer.baseAnalyzerOptions());
-  }
-
-  @Test
-  public void rejectsScalarFunctionImplWithUnsupportedParameterType() throws NoSuchMethodException {
-    JdbcConnection jdbcConnection = createJdbcConnection();
-    SchemaPlus calciteSchema = jdbcConnection.getCurrentSchemaPlus();
-    Method method = TakesArrayTimeFn.class.getMethod("eval", List.class);
-    calciteSchema.add("take_array", ScalarFunctionImpl.create(method));
-    thrown.expect(UnsupportedOperationException.class);
-    thrown.expectMessage("Calcite type TIME not allowed in function take_array");
-    BeamZetaSqlCatalog.create(
-        calciteSchema, jdbcConnection.getTypeFactory(), SqlAnalyzer.baseAnalyzerOptions());
-  }
-
-  @Test
-  public void rejectsCreateFunctionStmtWithUnsupportedReturnType() {
-    JdbcConnection jdbcConnection = createJdbcConnection();
-    AnalyzerOptions analyzerOptions = SqlAnalyzer.baseAnalyzerOptions();
-    BeamZetaSqlCatalog beamCatalog =
-        BeamZetaSqlCatalog.create(
-            jdbcConnection.getCurrentSchemaPlus(),
-            jdbcConnection.getTypeFactory(),
-            analyzerOptions);
-
-    String sql =
-        "CREATE FUNCTION foo() RETURNS ARRAY<TIME> LANGUAGE java OPTIONS (path='/does/not/exist');";
-    ResolvedNodes.ResolvedStatement resolvedStatement =
-        Analyzer.analyzeStatement(sql, analyzerOptions, beamCatalog.getZetaSqlCatalog());
-    ResolvedNodes.ResolvedCreateFunctionStmt createFunctionStmt =
-        (ResolvedNodes.ResolvedCreateFunctionStmt) resolvedStatement;
-
-    thrown.expect(UnsupportedOperationException.class);
-    thrown.expectMessage("ZetaSQL type TYPE_TIME not allowed in function foo");
-    beamCatalog.addFunction(createFunctionStmt);
-  }
-
-  @Test
-  public void rejectsCreateFunctionStmtWithUnsupportedParameterType() {
-    JdbcConnection jdbcConnection = createJdbcConnection();
-    AnalyzerOptions analyzerOptions = SqlAnalyzer.baseAnalyzerOptions();
-    BeamZetaSqlCatalog beamCatalog =
-        BeamZetaSqlCatalog.create(
-            jdbcConnection.getCurrentSchemaPlus(),
-            jdbcConnection.getTypeFactory(),
-            analyzerOptions);
-
-    String sql =
-        "CREATE FUNCTION foo(a ARRAY<TIME>) RETURNS INT64 LANGUAGE java OPTIONS (path='/does/not/exist');";
-    ResolvedNodes.ResolvedStatement resolvedStatement =
-        Analyzer.analyzeStatement(sql, analyzerOptions, beamCatalog.getZetaSqlCatalog());
-    ResolvedNodes.ResolvedCreateFunctionStmt createFunctionStmt =
-        (ResolvedNodes.ResolvedCreateFunctionStmt) resolvedStatement;
-
-    thrown.expect(UnsupportedOperationException.class);
-    thrown.expectMessage("ZetaSQL type TYPE_TIME not allowed in function foo");
-    beamCatalog.addFunction(createFunctionStmt);
-  }
-
-  private JdbcConnection createJdbcConnection() {
-    return JdbcDriver.connect(
-        new ReadOnlyTableProvider("empty_table_provider", ImmutableMap.of()),
-        PipelineOptionsFactory.create());
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/StreamingSqlTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/StreamingSqlTest.java
deleted file mode 100644
index 4fd3b35724f4..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/StreamingSqlTest.java
+++ /dev/null
@@ -1,550 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils.parseTimestampWithUTCTimeZone;
-
-import java.util.Arrays;
-import java.util.List;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.testing.PAssert;
-import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.Row;
-import org.joda.time.DateTime;
-import org.joda.time.Duration;
-import org.joda.time.chrono.ISOChronology;
-import org.junit.Before;
-import org.junit.Ignore;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.rules.ExpectedException;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-/** Tests for ZetaSQL windowing functions (TUMBLE, HOP, and SESSION). */
-@RunWith(JUnit4.class)
-public class StreamingSqlTest extends ZetaSqlTestBase {
-  @Rule public transient TestPipeline pipeline = TestPipeline.create();
-  @Rule public ExpectedException thrown = ExpectedException.none();
-
-  @Before
-  public void setUp() {
-    initialize();
-  }
-
-  @Test
-  public void testZetaSQLBasicSlidingWindowing() {
-    String sql =
-        "SELECT "
-            + "COUNT(*) as field_count, "
-            + "HOP_START(\"INTERVAL 1 SECOND\", \"INTERVAL 2 SECOND\") as window_start, "
-            + "HOP_END(\"INTERVAL 1 SECOND\", \"INTERVAL 2 SECOND\") as window_end "
-            + "FROM window_test_table "
-            + "GROUP BY HOP(ts, \"INTERVAL 1 SECOND\", \"INTERVAL 2 SECOND\");";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("count_star")
-            .addDateTimeField("field1")
-            .addDateTimeField("field2")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    2L,
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 9, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    1L,
-                    new DateTime(2018, 7, 1, 21, 26, 5, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    2L,
-                    new DateTime(2018, 7, 1, 21, 26, 6, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 8, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    2L,
-                    new DateTime(2018, 7, 1, 21, 26, 8, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 10, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    1L,
-                    new DateTime(2018, 7, 1, 21, 26, 9, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 11, ISOChronology.getInstanceUTC()))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBasicSessionWindowing() {
-    String sql =
-        "SELECT "
-            + "COUNT(*) as field_count, "
-            + "SESSION_START(\"INTERVAL 3 SECOND\") as window_start, "
-            + "SESSION_END(\"INTERVAL 3 SECOND\") as window_end "
-            + "FROM window_test_table_two "
-            + "GROUP BY SESSION(ts, \"INTERVAL 3 SECOND\");";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("count_star")
-            .addDateTimeField("field1")
-            .addDateTimeField("field2")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    2L,
-                    new DateTime(2018, 7, 1, 21, 26, 12, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 12, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    2L,
-                    new DateTime(2018, 7, 1, 21, 26, 6, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 6, ISOChronology.getInstanceUTC()))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLNestedQueryFour() {
-    String sql =
-        "SELECT t1.Value, TUMBLE_START('INTERVAL 1 SECOND') AS period_start, MIN(t2.Value) as"
-            + " min_v FROM KeyValue AS t1 INNER JOIN BigTable AS t2 on t1.Key = t2.RowKey GROUP BY"
-            + " t1.Value, TUMBLE(t2.ts, 'INTERVAL 1 SECOND')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addStringField("value")
-                        .addDateTimeField("min_v")
-                        .addStringField("period_start")
-                        .build())
-                .addValues(
-                    "KeyValue235",
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()),
-                    "BigTable235")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testWithQuerySeven() {
-    String sql =
-        "WITH T1 AS (SELECT * FROM KeyValue) SELECT "
-            + "COUNT(*) as field_count, "
-            + "TUMBLE_START(\"INTERVAL 1 SECOND\") as window_start, "
-            + "TUMBLE_END(\"INTERVAL 1 SECOND\") as window_end "
-            + "FROM T1 "
-            + "GROUP BY TUMBLE(ts, \"INTERVAL 1 SECOND\");";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("count_start")
-            .addDateTimeField("field1")
-            .addDateTimeField("field2")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    1L,
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 8, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    1L,
-                    new DateTime(2018, 7, 1, 21, 26, 6, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTVFTumbleAggregation() {
-    String sql =
-        "SELECT COUNT(*) as field_count, "
-            + "window_start "
-            + "FROM TUMBLE((select * from KeyValue), descriptor(ts), 'INTERVAL 1 SECOND') "
-            + "GROUP BY window_start";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder().addInt64Field("field_count").addDateTimeField("window_start").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(1L, new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(1L, new DateTime(2018, 7, 1, 21, 26, 6, ISOChronology.getInstanceUTC()))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testHopAsTVFAggregation() {
-    String sql =
-        "SELECT COUNT(*) as field_count, window_start, window_end "
-            + "FROM HOP((select * from window_test_table), "
-            + "descriptor(ts), \"INTERVAL 1 SECOND\", \"INTERVAL 2 SECOND\") "
-            + "GROUP BY window_start, window_end;";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field_count")
-            .addDateTimeField("field1")
-            .addDateTimeField("field2")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    2L,
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 9, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    1L,
-                    new DateTime(2018, 7, 1, 21, 26, 5, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    2L,
-                    new DateTime(2018, 7, 1, 21, 26, 6, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 8, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    2L,
-                    new DateTime(2018, 7, 1, 21, 26, 8, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 10, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    1L,
-                    new DateTime(2018, 7, 1, 21, 26, 9, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 11, ISOChronology.getInstanceUTC()))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void runTumbleWindow() throws Exception {
-    String sql =
-        "SELECT f_long, COUNT(*) AS getFieldCount,"
-            + " window_start, "
-            + " window_end "
-            + " FROM TUMBLE((select * from streaming_sql_test_table_a), descriptor(f_timestamp), \"INTERVAL 1 HOUR\") "
-            + " GROUP BY window_start, window_end, f_long";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema resultType =
-        Schema.builder()
-            .addInt64Field("f_long")
-            .addInt64Field("size")
-            .addDateTimeField("window_start")
-            .addDateTimeField("window_end")
-            .build();
-
-    List<Row> expectedRows =
-        Arrays.asList(
-            Row.withSchema(resultType)
-                .addValues(
-                    1000L,
-                    3L,
-                    parseTimestampWithUTCTimeZone("2017-01-01 01:00:00"),
-                    parseTimestampWithUTCTimeZone("2017-01-01 02:00:00"))
-                .build(),
-            Row.withSchema(resultType)
-                .addValues(
-                    4000L,
-                    1L,
-                    parseTimestampWithUTCTimeZone("2017-01-01 02:00:00"),
-                    parseTimestampWithUTCTimeZone("2017-01-01 03:00:00"))
-                .build());
-
-    PAssert.that(stream).containsInAnyOrder(expectedRows);
-
-    pipeline.run().waitUntilFinish();
-  }
-
-  @Test
-  public void runTumbleWindowFor31Days() throws Exception {
-    String sql =
-        "SELECT f_long, COUNT(*) AS getFieldCount,"
-            + " window_start, "
-            + " window_end "
-            + " FROM TUMBLE((select * from streaming_sql_test_table_b), descriptor(f_timestamp), \"INTERVAL 31 DAY\") "
-            + " GROUP BY f_long, window_start, window_end";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema resultType =
-        Schema.builder()
-            .addInt64Field("f_long")
-            .addInt64Field("size")
-            .addDateTimeField("window_start")
-            .addDateTimeField("window_end")
-            .build();
-
-    List<Row> expectedRows =
-        Arrays.asList(
-            Row.withSchema(resultType)
-                .addValues(
-                    1000L,
-                    1L,
-                    parseTimestampWithUTCTimeZone("2016-12-08 00:00:00"),
-                    parseTimestampWithUTCTimeZone("2017-01-08 00:00:00"))
-                .build(),
-            Row.withSchema(resultType)
-                .addValues(
-                    2000L,
-                    1L,
-                    parseTimestampWithUTCTimeZone("2017-01-08 00:00:00"),
-                    parseTimestampWithUTCTimeZone("2017-02-08 00:00:00"))
-                .build(),
-            Row.withSchema(resultType)
-                .addValues(
-                    3000L,
-                    1L,
-                    parseTimestampWithUTCTimeZone("2017-02-08 00:00:00"),
-                    parseTimestampWithUTCTimeZone("2017-03-11 00:00:00"))
-                .build());
-
-    PAssert.that(stream).containsInAnyOrder(expectedRows);
-
-    pipeline.run().waitUntilFinish();
-  }
-
-  @Test
-  public void runHopWindow() throws Exception {
-    String sql =
-        "SELECT f_long, COUNT(*) AS `getFieldCount`,"
-            + "  `window_start`, "
-            + "  `window_end` "
-            + " FROM HOP((select * from streaming_sql_test_table_a), descriptor(f_timestamp), "
-            + " \"INTERVAL 30 MINUTE\", \"INTERVAL 1 HOUR\")"
-            + " GROUP BY f_long, window_start, window_end";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema resultType =
-        Schema.builder()
-            .addInt64Field("f_long")
-            .addInt64Field("size")
-            .addDateTimeField("window_start")
-            .addDateTimeField("window_end")
-            .build();
-
-    List<Row> expectedRows =
-        Arrays.asList(
-            Row.withSchema(resultType)
-                .addValues(
-                    1000L,
-                    3L,
-                    parseTimestampWithUTCTimeZone("2017-01-01 00:30:00"),
-                    parseTimestampWithUTCTimeZone("2017-01-01 01:30:00"))
-                .build(),
-            Row.withSchema(resultType)
-                .addValues(
-                    1000L,
-                    3L,
-                    parseTimestampWithUTCTimeZone("2017-01-01 01:00:00"),
-                    parseTimestampWithUTCTimeZone("2017-01-01 02:00:00"))
-                .build(),
-            Row.withSchema(resultType)
-                .addValues(
-                    4000L,
-                    1L,
-                    parseTimestampWithUTCTimeZone("2017-01-01 01:30:00"),
-                    parseTimestampWithUTCTimeZone("2017-01-01 02:30:00"))
-                .build(),
-            Row.withSchema(resultType)
-                .addValues(
-                    4000L,
-                    1L,
-                    parseTimestampWithUTCTimeZone("2017-01-01 02:00:00"),
-                    parseTimestampWithUTCTimeZone("2017-01-01 03:00:00"))
-                .build());
-
-    PAssert.that(stream).containsInAnyOrder(expectedRows);
-
-    pipeline.run().waitUntilFinish();
-  }
-
-  @Test
-  public void runSessionWindow() throws Exception {
-    String sql =
-        "SELECT f_long, COUNT(*) AS `getFieldCount`,"
-            + " `window_start`, "
-            + " `window_end` "
-            + " FROM SESSION((select * from streaming_sql_test_table_a), descriptor(f_timestamp), "
-            + " descriptor(f_long), \"INTERVAL 5 MINUTE\")"
-            + " GROUP BY f_long, window_start, window_end";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema resultType =
-        Schema.builder()
-            .addInt64Field("f_long")
-            .addInt64Field("size")
-            .addDateTimeField("window_start")
-            .addDateTimeField("window_end")
-            .build();
-
-    List<Row> expectedRows =
-        Arrays.asList(
-            Row.withSchema(resultType)
-                .addValues(
-                    1000L,
-                    3L,
-                    parseTimestampWithUTCTimeZone("2017-01-01 01:01:03"),
-                    parseTimestampWithUTCTimeZone("2017-01-01 01:11:03"))
-                .build(),
-            Row.withSchema(resultType)
-                .addValues(
-                    4000L,
-                    1L,
-                    parseTimestampWithUTCTimeZone("2017-01-01 02:04:03"),
-                    parseTimestampWithUTCTimeZone("2017-01-01 02:09:03"))
-                .build());
-
-    PAssert.that(stream).containsInAnyOrder(expectedRows);
-
-    pipeline.run().waitUntilFinish();
-  }
-
-  @Test
-  public void runSessionWindow2() throws Exception {
-    String sql =
-        "SELECT f_long, f_string, COUNT(*) AS `getFieldCount`,"
-            + " `window_start`, `window_end` "
-            + " FROM SESSION((select * from streaming_sql_test_table_a), descriptor(f_timestamp), "
-            + " descriptor(f_long, f_string), \"INTERVAL 5 MINUTE\")"
-            + " GROUP BY f_long, f_string, window_start, window_end";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema resultType =
-        Schema.builder()
-            .addInt64Field("f_long")
-            .addStringField("f_string")
-            .addInt64Field("size")
-            .addDateTimeField("window_start")
-            .addDateTimeField("window_end")
-            .build();
-
-    List<Row> expectedRows =
-        Arrays.asList(
-            Row.withSchema(resultType)
-                .addValues(
-                    1000L,
-                    "string_row1",
-                    2L,
-                    parseTimestampWithUTCTimeZone("2017-01-01 01:01:03"),
-                    parseTimestampWithUTCTimeZone("2017-01-01 01:07:03"))
-                .build(),
-            Row.withSchema(resultType)
-                .addValues(
-                    1000L,
-                    "string_row3",
-                    1L,
-                    parseTimestampWithUTCTimeZone("2017-01-01 01:06:03"),
-                    parseTimestampWithUTCTimeZone("2017-01-01 01:11:03"))
-                .build(),
-            Row.withSchema(resultType)
-                .addValues(
-                    4000L,
-                    "第四行",
-                    1L,
-                    parseTimestampWithUTCTimeZone("2017-01-01 02:04:03"),
-                    parseTimestampWithUTCTimeZone("2017-01-01 02:09:03"))
-                .build());
-
-    PAssert.that(stream).containsInAnyOrder(expectedRows);
-
-    pipeline.run().waitUntilFinish();
-  }
-
-  @Test
-  @Ignore(
-      "[https://github.com/apache/beam/issues/20101] CAST operator does not work fully due to bugs in unparsing")
-  public void testZetaSQLStructFieldAccessInTumble() {
-    String sql =
-        "SELECT TUMBLE_START('INTERVAL 1 MINUTE') FROM table_with_struct_ts_string AS A GROUP BY "
-            + "TUMBLE(CAST(A.struct_col.struct_col_str AS TIMESTAMP), 'INTERVAL 1 MINUTE')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    final Schema schema = Schema.builder().addDateTimeField("field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValue(parseTimestampWithUTCTimeZone("2019-01-15 13:21:00"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/TableResolutionTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/TableResolutionTest.java
deleted file mode 100644
index fdb3db8832ed..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/TableResolutionTest.java
+++ /dev/null
@@ -1,128 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.hamcrest.MatcherAssert.assertThat;
-import static org.mockito.Mockito.when;
-
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.Table;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.hamcrest.Matchers;
-import org.junit.Assert;
-import org.junit.Before;
-import org.junit.Test;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-import org.mockito.Mock;
-import org.mockito.MockitoAnnotations;
-
-/** Unit tests for {@link TableResolution}. */
-@RunWith(JUnit4.class)
-public class TableResolutionTest {
-
-  // A simple in-memory SchemaPlus would be fine
-  @Mock SchemaPlus mockSchemaPlus;
-  @Mock SchemaPlus innerSchemaPlus;
-
-  // A table whose identity is not important
-  @Mock Table mockTable;
-
-  @Before
-  public void setUp() {
-    MockitoAnnotations.initMocks(this);
-  }
-
-  /** Unit test for resolving a table with no hierarchy. */
-  @Test
-  public void testResolveFlat() {
-    String tableName = "fake_table";
-    when(mockSchemaPlus.getTable(tableName)).thenReturn(mockTable);
-    Table table = TableResolution.resolveCalciteTable(mockSchemaPlus, ImmutableList.of(tableName));
-    assertThat(table, Matchers.is(mockTable));
-  }
-
-  /** Unit test for resolving a table with no hierarchy but dots in its actual name. */
-  @Test
-  public void testResolveWithDots() {
-    String tableName = "fake.table";
-    when(mockSchemaPlus.getTable(tableName)).thenReturn(mockTable);
-    Table table = TableResolution.resolveCalciteTable(mockSchemaPlus, ImmutableList.of(tableName));
-    assertThat(table, Matchers.is(mockTable));
-  }
-
-  /** Unit test for failing to resolve a table with no subschemas. */
-  @Test
-  public void testMissingFlat() {
-    String tableName = "fake_table";
-    when(mockSchemaPlus.getTable(tableName)).thenReturn(null);
-    Table table = TableResolution.resolveCalciteTable(mockSchemaPlus, ImmutableList.of(tableName));
-    assertThat(table, Matchers.nullValue());
-  }
-
-  /** Unit test for resolving a table with some hierarchy. */
-  @Test
-  public void testResolveNested() {
-    String subSchema = "fake_schema";
-    String tableName = "fake_table";
-    when(mockSchemaPlus.getSubSchema(subSchema)).thenReturn(innerSchemaPlus);
-    when(innerSchemaPlus.getTable(tableName)).thenReturn(mockTable);
-    Table table =
-        TableResolution.resolveCalciteTable(mockSchemaPlus, ImmutableList.of(subSchema, tableName));
-    assertThat(table, Matchers.is(mockTable));
-  }
-
-  /** Unit test for resolving a table with dots in the subschema names and the table name. */
-  @Test
-  public void testResolveNestedWithDots() {
-    String subSchema = "fake.schema";
-    String tableName = "fake.table";
-    when(mockSchemaPlus.getSubSchema(subSchema)).thenReturn(innerSchemaPlus);
-    when(innerSchemaPlus.getTable(tableName)).thenReturn(mockTable);
-    Table table =
-        TableResolution.resolveCalciteTable(mockSchemaPlus, ImmutableList.of(subSchema, tableName));
-    assertThat(table, Matchers.is(mockTable));
-  }
-
-  /** Unit test for resolving a table with some hierarchy that is missing. */
-  @Test
-  public void testMissingSubschema() {
-    String subSchema = "fake_schema";
-    String tableName = "fake_table";
-    when(mockSchemaPlus.getSubSchema(subSchema)).thenReturn(null);
-
-    Assert.assertThrows(
-        IllegalStateException.class,
-        () -> {
-          TableResolution.resolveCalciteTable(
-              mockSchemaPlus, ImmutableList.of(subSchema, tableName));
-        });
-  }
-
-  /** Unit test for resolving a table with some hierarchy and the table is missing. */
-  @Test
-  public void testMissingTableInSubschema() {
-    String subSchema = "fake_schema";
-    String tableName = "fake_table";
-    when(mockSchemaPlus.getSubSchema(subSchema)).thenReturn(innerSchemaPlus);
-    when(innerSchemaPlus.getTable(tableName)).thenReturn(null);
-    Table table =
-        TableResolution.resolveCalciteTable(mockSchemaPlus, ImmutableList.of(subSchema, tableName));
-    assertThat(table, Matchers.nullValue());
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/TestInput.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/TestInput.java
deleted file mode 100644
index 802299fc73ee..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/TestInput.java
+++ /dev/null
@@ -1,397 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils.parseTimestampWithUTCTimeZone;
-
-import java.nio.charset.StandardCharsets;
-import java.time.LocalDate;
-import java.time.LocalDateTime;
-import java.time.LocalTime;
-import java.util.Arrays;
-import org.apache.beam.sdk.extensions.sql.meta.provider.test.TestBoundedTable;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.schemas.Schema.FieldType;
-import org.apache.beam.sdk.schemas.logicaltypes.SqlTypes;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-
-/** TestInput. */
-class TestInput {
-
-  public static final TestBoundedTable BASIC_TABLE_ONE =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("Key")
-                  .addStringField("Value")
-                  .addDateTimeField("ts")
-                  .build())
-          .addRows(
-              14L,
-              "KeyValue234",
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:06"),
-              15L,
-              "KeyValue235",
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:07"));
-
-  public static final TestBoundedTable BASIC_TABLE_TWO =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("RowKey")
-                  .addStringField("Value")
-                  .addDateTimeField("ts")
-                  .build())
-          .addRows(
-              15L,
-              "BigTable235",
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:07"),
-              16L,
-              "BigTable236",
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:08"));
-
-  public static final TestBoundedTable BASIC_TABLE_THREE =
-      TestBoundedTable.of(Schema.builder().addInt64Field("ColId").addStringField("Value").build())
-          .addRows(15L, "Spanner235", 16L, "Spanner236", 17L, "Spanner237");
-
-  public static final TestBoundedTable AGGREGATE_TABLE_ONE =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("Key")
-                  .addInt64Field("Key2")
-                  .addInt64Field("f_int_1")
-                  .addStringField("f_str_1")
-                  .addDoubleField("f_double_1")
-                  .build())
-          .addRows(1L, 10L, 1L, "1", 1.0)
-          .addRows(1L, 11L, 2L, "2", 2.0)
-          .addRows(2L, 11L, 3L, "3", 3.0)
-          .addRows(2L, 11L, 4L, "4", 4.0)
-          .addRows(2L, 12L, 5L, "5", 5.0)
-          .addRows(3L, 13L, 6L, "6", 6.0)
-          .addRows(3L, 13L, 7L, "7", 7.0);
-
-  public static final TestBoundedTable AGGREGATE_TABLE_TWO =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("Key")
-                  .addInt64Field("Key2")
-                  .addInt64Field("f_int_1")
-                  .addStringField("f_str_1")
-                  .build())
-          .addRows(1L, 10L, 1L, "1")
-          .addRows(2L, 11L, 3L, "3")
-          .addRows(2L, 11L, 4L, "4")
-          .addRows(2L, 12L, 5L, "5")
-          .addRows(2L, 13L, 6L, "6")
-          .addRows(3L, 13L, 7L, "7");
-
-  public static final TestBoundedTable TABLE_ALL_TYPES =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("row_id")
-                  .addBooleanField("bool_col")
-                  .addInt64Field("int64_col")
-                  .addDoubleField("double_col")
-                  .addStringField("str_col")
-                  .addByteArrayField("bytes_col")
-                  .build())
-          .addRows(1L, true, -1L, 0.125d, "1", stringToBytes("1"))
-          .addRows(2L, false, -2L, Math.pow(0.1, 324.0), "2", stringToBytes("2"))
-          .addRows(3L, true, -3L, 0.375d, "3", stringToBytes("3"))
-          .addRows(4L, false, -4L, 0.5d, "4", stringToBytes("4"))
-          .addRows(5L, false, -5L, 0.5d, "5", stringToBytes("5"));
-
-  public static final TestBoundedTable TABLE_ALL_TYPES_2 =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("row_id")
-                  .addBooleanField("bool_col")
-                  .addInt64Field("int64_col")
-                  .addDoubleField("double_col")
-                  .addStringField("str_col")
-                  .addByteArrayField("bytes_col")
-                  .build())
-          .addRows(6L, true, -6L, 0.125d, "6", stringToBytes("6"))
-          .addRows(7L, false, -7L, Math.pow(0.1, 324.0), "7", stringToBytes("7"))
-          .addRows(8L, true, -8L, 0.375d, "8", stringToBytes("8"))
-          .addRows(9L, false, -9L, 0.5d, "9", stringToBytes("9"))
-          .addRows(10L, false, -10L, 0.5d, "10", stringToBytes("10"));
-
-  public static final TestBoundedTable TIMESTAMP_TABLE_ONE =
-      TestBoundedTable.of(Schema.builder().addDateTimeField("ts").addInt64Field("value").build())
-          .addRows(
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:06"),
-              3L,
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:07"),
-              4L,
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:08"),
-              6L,
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:09"),
-              7L);
-
-  public static final TestBoundedTable TIMESTAMP_TABLE_TWO =
-      TestBoundedTable.of(Schema.builder().addDateTimeField("ts").addInt64Field("value").build())
-          .addRows(
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:06"),
-              3L,
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:07"),
-              4L,
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:12"),
-              6L,
-              parseTimestampWithUTCTimeZone("2018-07-01 21:26:13"),
-              7L);
-
-  public static final TestBoundedTable TABLE_ALL_NULL =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addNullableField("primary_key", FieldType.INT64)
-                  .addNullableField("bool_val", FieldType.BOOLEAN)
-                  .addNullableField("double_val", FieldType.DOUBLE)
-                  .addNullableField("int64_val", FieldType.INT64)
-                  .addNullableField("str_val", FieldType.STRING)
-                  .build())
-          .addRows(1L, null, null, null, null);
-
-  private static final Schema TABLE_WITH_STRUCT_ROW_SCHEMA =
-      Schema.builder().addInt64Field("struct_col_long").addStringField("struct_col_str").build();
-
-  public static final TestBoundedTable TABLE_WITH_STRUCT =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addField("id", FieldType.INT64)
-                  .addField("struct_col", FieldType.row(TABLE_WITH_STRUCT_ROW_SCHEMA))
-                  .build())
-          .addRows(
-              1L,
-              Row.withSchema(TABLE_WITH_STRUCT_ROW_SCHEMA).addValues(16L, "row_one").build(),
-              2L,
-              Row.withSchema(TABLE_WITH_STRUCT_ROW_SCHEMA).addValues(17L, "row_two").build());
-
-  public static final TestBoundedTable TABLE_WITH_STRUCT_TIMESTAMP_STRING =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addField("struct_col", FieldType.row(TABLE_WITH_STRUCT_ROW_SCHEMA))
-                  .build())
-          .addRows(
-              Row.withSchema(TABLE_WITH_STRUCT_ROW_SCHEMA)
-                  .addValues(3L, "2019-01-15 13:21:03")
-                  .build());
-
-  public static final Schema STRUCT_SCHEMA =
-      Schema.builder().addInt64Field("row_id").addStringField("data").build();
-  private static final Schema structTableSchema =
-      Schema.builder().addRowField("rowCol", STRUCT_SCHEMA).build();
-
-  public static final TestBoundedTable TABLE_WITH_STRUCT_TWO =
-      TestBoundedTable.of(structTableSchema)
-          .addRows(Row.withSchema(STRUCT_SCHEMA).addValues(1L, "data1").build())
-          .addRows(Row.withSchema(STRUCT_SCHEMA).addValues(2L, "data2").build())
-          .addRows(Row.withSchema(STRUCT_SCHEMA).addValues(3L, "data2").build())
-          .addRows(Row.withSchema(STRUCT_SCHEMA).addValues(3L, "data3").build());
-
-  public static final TestBoundedTable TABLE_WITH_ARRAY =
-      TestBoundedTable.of(Schema.builder().addArrayField("array_col", FieldType.STRING).build())
-          .addRows(Arrays.asList("1", "2", "3"), ImmutableList.of());
-
-  public static final TestBoundedTable TABLE_WITH_ARRAY_FOR_UNNEST =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("int_col")
-                  .addArrayField("int_array_col", FieldType.INT64)
-                  .build())
-          .addRows(14L, Arrays.asList(14L, 18L))
-          .addRows(18L, Arrays.asList(22L, 24L));
-
-  public static final TestBoundedTable TABLE_WITH_ARRAY_OF_STRUCT =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("int_col")
-                  .addArrayField("array_col", FieldType.row(STRUCT_SCHEMA))
-                  .build())
-          .addRows(10L, ImmutableList.of(Row.withSchema(STRUCT_SCHEMA).addValues(1L, "1").build()))
-          .addRows(
-              20L,
-              Arrays.asList(
-                  Row.withSchema(STRUCT_SCHEMA).addValues(2L, "2").build(),
-                  Row.withSchema(STRUCT_SCHEMA).addValues(3L, "3").build()));
-
-  private static final Schema STRUCT_OF_ARRAY =
-      Schema.builder().addArrayField("arr", FieldType.STRING).build();
-  private static final Schema STRUCT_OF_STRUCT_OF_ARRAY =
-      Schema.builder().addRowField("struct", STRUCT_OF_ARRAY).build();
-  public static final TestBoundedTable TABLE_WITH_STRUCT_OF_STRUCT_OF_ARRAY =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("int_col")
-                  .addRowField("struct_col", STRUCT_OF_STRUCT_OF_ARRAY)
-                  .build())
-          .addRows(
-              10L,
-              Row.withSchema(STRUCT_OF_STRUCT_OF_ARRAY)
-                  .addValue(Row.withSchema(STRUCT_OF_ARRAY).addArray("1").build())
-                  .build())
-          .addRows(
-              20L,
-              Row.withSchema(STRUCT_OF_STRUCT_OF_ARRAY)
-                  .addValue(Row.withSchema(STRUCT_OF_ARRAY).addArray("2", "3").build())
-                  .build());
-
-  public static final Schema STRUCT_OF_STRUCT =
-      Schema.builder().addRowField("row", STRUCT_SCHEMA).build();
-  public static final TestBoundedTable TABLE_WITH_STRUCT_OF_STRUCT =
-      TestBoundedTable.of(STRUCT_OF_STRUCT)
-          .addRows(Row.withSchema(STRUCT_SCHEMA).attachValues(1L, "1"))
-          .addRows(Row.withSchema(STRUCT_SCHEMA).attachValues(2L, "2"));
-  public static final TestBoundedTable TABLE_WITH_ARRAY_OF_STRUCT_OF_STRUCT =
-      TestBoundedTable.of(
-              Schema.builder().addArrayField("array_col", FieldType.row(STRUCT_OF_STRUCT)).build())
-          .addRows(
-              Arrays.asList(
-                  Row.withSchema(STRUCT_OF_STRUCT)
-                      .addValues(Row.withSchema(STRUCT_SCHEMA).addValues(1L, "1").build())
-                      .build(),
-                  Row.withSchema(STRUCT_OF_STRUCT)
-                      .addValues(Row.withSchema(STRUCT_SCHEMA).addValues(2L, "2").build())
-                      .build()));
-
-  private static final Schema STRUCT_OF_ARRAY_OF_STRUCT =
-      Schema.builder().addArrayField("arr", FieldType.row(STRUCT_SCHEMA)).build();
-  public static final TestBoundedTable TABLE_WITH_STRUCT_OF_ARRAY_OF_STRUCT =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("int_col")
-                  .addRowField("struct_col", STRUCT_OF_ARRAY_OF_STRUCT)
-                  .build())
-          .addRows(
-              10L,
-              Row.withSchema(STRUCT_OF_ARRAY_OF_STRUCT)
-                  .addArray(Row.withSchema(STRUCT_SCHEMA).addValues(1L, "1").build())
-                  .build())
-          .addRows(
-              20L,
-              Row.withSchema(STRUCT_OF_ARRAY_OF_STRUCT)
-                  .addArray(
-                      Row.withSchema(STRUCT_SCHEMA).addValues(2L, "2").build(),
-                      Row.withSchema(STRUCT_SCHEMA).addValues(3L, "3").build())
-                  .build());
-
-  public static final TestBoundedTable TABLE_WITH_ARRAY_OF_STRUCT_OF_ARRAY =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("int_col")
-                  .addArrayField("array_col", FieldType.row(STRUCT_OF_ARRAY))
-                  .build())
-          .addRows(10L, ImmutableList.of(Row.withSchema(STRUCT_OF_ARRAY).addArray("a").build()))
-          .addRows(
-              20L,
-              Arrays.asList(
-                  Row.withSchema(STRUCT_OF_ARRAY).addArray("b").build(),
-                  Row.withSchema(STRUCT_OF_ARRAY).addArray("c", "d").build()));
-
-  public static final TestBoundedTable TABLE_FOR_CASE_WHEN =
-      TestBoundedTable.of(
-              Schema.builder().addInt64Field("f_int").addStringField("f_string").build())
-          .addRows(1L, "20181018");
-
-  public static final TestBoundedTable TABLE_EMPTY =
-      TestBoundedTable.of(Schema.builder().addInt64Field("ColId").addStringField("Value").build());
-
-  private static final Schema TABLE_WITH_MAP_SCHEMA =
-      Schema.builder()
-          .addMapField("map_field", FieldType.STRING, FieldType.STRING)
-          .addRowField("row_field", STRUCT_SCHEMA)
-          .build();
-  public static final TestBoundedTable TABLE_WITH_MAP =
-      TestBoundedTable.of(TABLE_WITH_MAP_SCHEMA)
-          .addRows(
-              ImmutableMap.of("MAP_KEY_1", "MAP_VALUE_1"),
-              Row.withSchema(STRUCT_SCHEMA).addValues(1L, "data1").build());
-
-  private static final Schema TABLE_WITH_DATE_SCHEMA =
-      Schema.builder()
-          .addLogicalTypeField("date_field", SqlTypes.DATE)
-          .addStringField("str_field")
-          .build();
-
-  public static final TestBoundedTable TABLE_WITH_DATE =
-      TestBoundedTable.of(TABLE_WITH_DATE_SCHEMA)
-          .addRows(LocalDate.of(2008, 12, 25), "s")
-          .addRows(LocalDate.of(2020, 4, 7), "s");
-
-  private static final Schema TABLE_WITH_TIME_SCHEMA =
-      Schema.builder()
-          .addLogicalTypeField("time_field", SqlTypes.TIME)
-          .addStringField("str_field")
-          .build();
-
-  public static final TestBoundedTable TABLE_WITH_TIME =
-      TestBoundedTable.of(TABLE_WITH_TIME_SCHEMA)
-          .addRows(LocalTime.of(15, 30, 0), "s")
-          .addRows(LocalTime.of(23, 35, 59), "s");
-
-  private static final Schema TABLE_WITH_NUMERIC_SCHEMA =
-      Schema.builder().addDecimalField("numeric_field").addStringField("str_field").build();
-
-  public static final TestBoundedTable TABLE_WITH_NUMERIC =
-      TestBoundedTable.of(TABLE_WITH_NUMERIC_SCHEMA)
-          .addRows(ZetaSqlTypesUtils.bigDecimalAsNumeric("123.4567"), "str1")
-          .addRows(ZetaSqlTypesUtils.bigDecimalAsNumeric("765.4321"), "str2")
-          .addRows(ZetaSqlTypesUtils.bigDecimalAsNumeric("-555.5555"), "str3");
-
-  private static final Schema TABLE_WITH_DATETIME_SCHEMA =
-      Schema.builder()
-          .addLogicalTypeField("datetime_field", SqlTypes.DATETIME)
-          .addStringField("str_field")
-          .build();
-
-  public static final TestBoundedTable TABLE_WITH_DATETIME =
-      TestBoundedTable.of(TABLE_WITH_DATETIME_SCHEMA)
-          .addRows(LocalDateTime.of(2008, 12, 25, 15, 30, 0).withNano(123456000), "s")
-          .addRows(LocalDateTime.of(2012, 10, 6, 11, 45, 0).withNano(987654000), "s");
-
-  private static byte[] stringToBytes(String s) {
-    return s.getBytes(StandardCharsets.UTF_8);
-  }
-
-  public static final TestBoundedTable STREAMING_SQL_TABLE_A =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("f_long")
-                  .addDoubleField("f_double")
-                  .addStringField("f_string")
-                  .addDateTimeField("f_timestamp")
-                  .build())
-          .addRows(1000L, 1.0d, "string_row1", parseTimestampWithUTCTimeZone("2017-01-01 01:01:03"))
-          .addRows(1000L, 2.0d, "string_row1", parseTimestampWithUTCTimeZone("2017-01-01 01:02:03"))
-          .addRows(1000L, 3.0d, "string_row3", parseTimestampWithUTCTimeZone("2017-01-01 01:06:03"))
-          .addRows(4000L, 4.0d, "第四行", parseTimestampWithUTCTimeZone("2017-01-01 02:04:03"));
-
-  public static final TestBoundedTable STREAMING_SQL_TABLE_B =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addInt64Field("f_long")
-                  .addDoubleField("f_double")
-                  .addStringField("f_string")
-                  .addDateTimeField("f_timestamp")
-                  .build())
-          .addRows(1000L, 1.0d, "string_row1", parseTimestampWithUTCTimeZone("2017-01-01 01:01:03"))
-          .addRows(2000L, 2.0d, "string_row2", parseTimestampWithUTCTimeZone("2017-02-01 01:02:03"))
-          .addRows(
-              3000L, 3.0d, "string_row3", parseTimestampWithUTCTimeZone("2017-03-01 01:06:03"));
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSQLPushDownTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSQLPushDownTest.java
deleted file mode 100644
index fd2b7dc7ca5e..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSQLPushDownTest.java
+++ /dev/null
@@ -1,228 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.apache.beam.sdk.extensions.sql.meta.provider.test.TestTableProvider.PUSH_DOWN_OPTION;
-import static org.hamcrest.MatcherAssert.assertThat;
-import static org.hamcrest.Matchers.instanceOf;
-import static org.junit.Assert.assertEquals;
-
-import org.apache.beam.sdk.extensions.sql.TableUtils;
-import org.apache.beam.sdk.extensions.sql.impl.BeamSqlEnv;
-import org.apache.beam.sdk.extensions.sql.impl.JdbcConnection;
-import org.apache.beam.sdk.extensions.sql.impl.JdbcDriver;
-import org.apache.beam.sdk.extensions.sql.impl.planner.BeamCostModel;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamIOSourceRel;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.meta.Table;
-import org.apache.beam.sdk.extensions.sql.meta.provider.test.TestTableProvider;
-import org.apache.beam.sdk.extensions.sql.meta.provider.test.TestTableProvider.PushDownOptions;
-import org.apache.beam.sdk.options.PipelineOptionsFactory;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Context;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Contexts;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.ConventionTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.RelTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.FrameworkConfig;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Frameworks;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSet;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.joda.time.Duration;
-import org.junit.BeforeClass;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-@RunWith(JUnit4.class)
-public class ZetaSQLPushDownTest {
-  private static final Long PIPELINE_EXECUTION_WAITTIME_MINUTES = 2L;
-  private static final Schema BASIC_SCHEMA =
-      Schema.builder()
-          .addInt64Field("unused1")
-          .addInt64Field("id")
-          .addStringField("name")
-          .addInt64Field("unused2")
-          .build();
-
-  private static TestTableProvider tableProvider;
-  private static FrameworkConfig config;
-  private static ZetaSQLQueryPlanner zetaSQLQueryPlanner;
-  private static BeamSqlEnv sqlEnv;
-
-  @Rule public transient TestPipeline pipeline = TestPipeline.create();
-
-  @BeforeClass
-  public static void setUp() {
-    initializeBeamTableProvider();
-    initializeCalciteEnvironment();
-    zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    sqlEnv =
-        BeamSqlEnv.builder(tableProvider)
-            .setPipelineOptions(PipelineOptionsFactory.create())
-            .build();
-  }
-
-  @Test
-  public void testProjectPushDown_withoutPredicate() {
-    String sql = "SELECT name, id, unused1 FROM InMemoryTableProject";
-
-    BeamRelNode zetaSqlNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    BeamRelNode calciteSqlNode = sqlEnv.parseQuery(sql);
-
-    assertThat(zetaSqlNode, instanceOf(BeamIOSourceRel.class));
-    assertThat(calciteSqlNode, instanceOf(BeamIOSourceRel.class));
-    assertEquals(calciteSqlNode.getDigest(), zetaSqlNode.getDigest());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testProjectPushDown_withoutPredicate_withComplexSelect() {
-    String sql = "SELECT id+1 FROM InMemoryTableProject";
-
-    BeamRelNode zetaSqlNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    BeamRelNode calciteSqlNode = sqlEnv.parseQuery(sql);
-
-    assertThat(zetaSqlNode.getInput(0), instanceOf(BeamIOSourceRel.class));
-    assertThat(calciteSqlNode.getInput(0), instanceOf(BeamIOSourceRel.class));
-    assertEquals(calciteSqlNode.getInput(0).getDigest(), zetaSqlNode.getInput(0).getDigest());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testProjectPushDown_withPredicate() {
-    String sql = "SELECT name FROM InMemoryTableProject where id=2";
-
-    BeamRelNode zetaSqlNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    BeamRelNode calciteSqlNode = sqlEnv.parseQuery(sql);
-
-    assertThat(zetaSqlNode.getInput(0), instanceOf(BeamIOSourceRel.class));
-    assertThat(calciteSqlNode.getInput(0), instanceOf(BeamIOSourceRel.class));
-    assertEquals(calciteSqlNode.getInput(0).getDigest(), zetaSqlNode.getInput(0).getDigest());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testProjectFilterPushDown_withoutPredicate() {
-    String sql = "SELECT name, id, unused1 FROM InMemoryTableBoth";
-
-    BeamRelNode zetaSqlNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    BeamRelNode calciteSqlNode = sqlEnv.parseQuery(sql);
-
-    assertThat(zetaSqlNode, instanceOf(BeamIOSourceRel.class));
-    assertThat(calciteSqlNode, instanceOf(BeamIOSourceRel.class));
-    assertEquals(calciteSqlNode.getDigest(), zetaSqlNode.getDigest());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testProjectFilterPushDown_withSupportedPredicate() {
-    String sql = "SELECT name FROM InMemoryTableBoth where id=2";
-
-    BeamRelNode zetaSqlNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    BeamRelNode calciteSqlNode = sqlEnv.parseQuery(sql);
-
-    assertThat(zetaSqlNode, instanceOf(BeamIOSourceRel.class));
-    assertThat(calciteSqlNode, instanceOf(BeamIOSourceRel.class));
-    assertEquals(calciteSqlNode.getDigest(), zetaSqlNode.getDigest());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testProjectFilterPushDown_withUnsupportedPredicate() {
-    String sql = "SELECT name FROM InMemoryTableBoth where id=2 or unused1=200";
-
-    BeamRelNode zetaSqlNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    BeamRelNode calciteSqlNode = sqlEnv.parseQuery(sql);
-
-    assertThat(zetaSqlNode.getInput(0), instanceOf(BeamIOSourceRel.class));
-    assertThat(calciteSqlNode.getInput(0), instanceOf(BeamIOSourceRel.class));
-    assertEquals(calciteSqlNode.getInput(0).getDigest(), zetaSqlNode.getInput(0).getDigest());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  private static void initializeCalciteEnvironment() {
-    initializeCalciteEnvironmentWithContext();
-  }
-
-  @SuppressWarnings({
-    "rawtypes", // Frameworks.ConfigBuilder.traitDefs has method signature of raw type
-  })
-  private static void initializeCalciteEnvironmentWithContext(Context... extraContext) {
-    JdbcConnection jdbcConnection =
-        JdbcDriver.connect(tableProvider, PipelineOptionsFactory.create());
-    SchemaPlus defaultSchemaPlus = jdbcConnection.getCurrentSchemaPlus();
-    final ImmutableList<RelTraitDef> traitDefs = ImmutableList.of(ConventionTraitDef.INSTANCE);
-
-    Object[] contexts =
-        ImmutableList.<Context>builder()
-            .add(Contexts.of(jdbcConnection.config()))
-            .add(extraContext)
-            .build()
-            .toArray();
-
-    config =
-        Frameworks.newConfigBuilder()
-            .defaultSchema(defaultSchemaPlus)
-            .traitDefs(traitDefs)
-            .context(Contexts.of(contexts))
-            .ruleSets(ZetaSQLQueryPlanner.getZetaSqlRuleSets().toArray(new RuleSet[0]))
-            .costFactory(BeamCostModel.FACTORY)
-            .typeSystem(jdbcConnection.getTypeFactory().getTypeSystem())
-            .build();
-  }
-
-  private static void initializeBeamTableProvider() {
-    Table projectTable = getTable("InMemoryTableProject", PushDownOptions.PROJECT);
-    Table bothTable = getTable("InMemoryTableBoth", PushDownOptions.BOTH);
-    Row[] rows = {
-      row(BASIC_SCHEMA, 100L, 1L, "one", 100L), row(BASIC_SCHEMA, 200L, 2L, "two", 200L)
-    };
-
-    tableProvider = new TestTableProvider();
-    tableProvider.createTable(projectTable);
-    tableProvider.createTable(bothTable);
-    tableProvider.addRows(projectTable.getName(), rows);
-    tableProvider.addRows(bothTable.getName(), rows);
-  }
-
-  private static Row row(Schema schema, Object... objects) {
-    return Row.withSchema(schema).addValues(objects).build();
-  }
-
-  private static Table getTable(String name, PushDownOptions options) {
-    return Table.builder()
-        .name(name)
-        .comment(name + " table")
-        .schema(BASIC_SCHEMA)
-        .properties(
-            TableUtils.parseProperties(
-                "{ " + PUSH_DOWN_OPTION + ": " + "\"" + options.toString() + "\" }"))
-        .type("test")
-        .build();
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlBeamTranslationUtilsTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlBeamTranslationUtilsTest.java
deleted file mode 100644
index 27b3c8ee884e..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlBeamTranslationUtilsTest.java
+++ /dev/null
@@ -1,149 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.junit.Assert.assertEquals;
-
-import com.google.protobuf.ByteString;
-import com.google.zetasql.ArrayType;
-import com.google.zetasql.StructType;
-import com.google.zetasql.StructType.StructField;
-import com.google.zetasql.TypeFactory;
-import com.google.zetasql.Value;
-import com.google.zetasql.ZetaSQLType.TypeKind;
-import java.time.LocalDate;
-import java.time.LocalDateTime;
-import java.time.LocalTime;
-import java.util.Arrays;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.schemas.Schema.FieldType;
-import org.apache.beam.sdk.schemas.logicaltypes.SqlTypes;
-import org.apache.beam.sdk.values.Row;
-import org.joda.time.Instant;
-import org.junit.Test;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-/** Tests for utility methods for ZetaSQL related operations. */
-@RunWith(JUnit4.class)
-public class ZetaSqlBeamTranslationUtilsTest {
-
-  private static final Schema TEST_INNER_SCHEMA =
-      Schema.builder().addField("i1", FieldType.INT64).addField("i2", FieldType.STRING).build();
-
-  private static final Schema TEST_SCHEMA =
-      Schema.builder()
-          .addField("f_int64", FieldType.INT64)
-          .addField("f_float64", FieldType.DOUBLE)
-          .addField("f_boolean", FieldType.BOOLEAN)
-          .addField("f_string", FieldType.STRING)
-          .addField("f_bytes", FieldType.BYTES)
-          .addLogicalTypeField("f_date", SqlTypes.DATE)
-          .addLogicalTypeField("f_datetime", SqlTypes.DATETIME)
-          .addLogicalTypeField("f_time", SqlTypes.TIME)
-          .addField("f_timestamp", FieldType.DATETIME)
-          .addArrayField("f_array", FieldType.DOUBLE)
-          .addRowField("f_struct", TEST_INNER_SCHEMA)
-          .addField("f_numeric", FieldType.DECIMAL)
-          .addNullableField("f_null", FieldType.INT64)
-          .build();
-
-  private static final FieldType TEST_FIELD_TYPE = FieldType.row(TEST_SCHEMA);
-
-  private static final ArrayType TEST_INNER_ARRAY_TYPE =
-      TypeFactory.createArrayType(TypeFactory.createSimpleType(TypeKind.TYPE_DOUBLE));
-
-  private static final StructType TEST_INNER_STRUCT_TYPE =
-      TypeFactory.createStructType(
-          Arrays.asList(
-              new StructField("i1", TypeFactory.createSimpleType(TypeKind.TYPE_INT64)),
-              new StructField("i2", TypeFactory.createSimpleType(TypeKind.TYPE_STRING))));
-
-  private static final StructType TEST_TYPE =
-      TypeFactory.createStructType(
-          Arrays.asList(
-              new StructField("f_int64", TypeFactory.createSimpleType(TypeKind.TYPE_INT64)),
-              new StructField("f_float64", TypeFactory.createSimpleType(TypeKind.TYPE_DOUBLE)),
-              new StructField("f_boolean", TypeFactory.createSimpleType(TypeKind.TYPE_BOOL)),
-              new StructField("f_string", TypeFactory.createSimpleType(TypeKind.TYPE_STRING)),
-              new StructField("f_bytes", TypeFactory.createSimpleType(TypeKind.TYPE_BYTES)),
-              new StructField("f_date", TypeFactory.createSimpleType(TypeKind.TYPE_DATE)),
-              new StructField("f_datetime", TypeFactory.createSimpleType(TypeKind.TYPE_DATETIME)),
-              new StructField("f_time", TypeFactory.createSimpleType(TypeKind.TYPE_TIME)),
-              new StructField("f_timestamp", TypeFactory.createSimpleType(TypeKind.TYPE_TIMESTAMP)),
-              new StructField("f_array", TEST_INNER_ARRAY_TYPE),
-              new StructField("f_struct", TEST_INNER_STRUCT_TYPE),
-              new StructField("f_numeric", TypeFactory.createSimpleType(TypeKind.TYPE_NUMERIC)),
-              new StructField("f_null", TypeFactory.createSimpleType(TypeKind.TYPE_INT64))));
-
-  private static final Row TEST_ROW =
-      Row.withSchema(TEST_SCHEMA)
-          .addValue(64L)
-          .addValue(5.15)
-          .addValue(false)
-          .addValue("Hello")
-          .addValue(new byte[] {0x11, 0x22})
-          .addValue(LocalDate.of(2020, 6, 4))
-          .addValue(LocalDateTime.of(2008, 12, 25, 15, 30, 0).withNano(123456000))
-          .addValue(LocalTime.of(15, 30, 45).withNano(123456000))
-          .addValue(Instant.ofEpochMilli(12345678L))
-          .addArray(3.0, 6.5)
-          .addValue(Row.withSchema(TEST_INNER_SCHEMA).addValues(0L, "world").build())
-          .addValue(ZetaSqlTypesUtils.bigDecimalAsNumeric("12346"))
-          .addValue(null)
-          .build();
-
-  private static final Value TEST_VALUE =
-      Value.createStructValue(
-          TEST_TYPE,
-          Arrays.asList(
-              Value.createInt64Value(64L),
-              Value.createDoubleValue(5.15),
-              Value.createBoolValue(false),
-              Value.createStringValue("Hello"),
-              Value.createBytesValue(ByteString.copyFrom(new byte[] {0x11, 0x22})),
-              Value.createDateValue(LocalDate.of(2020, 6, 4)),
-              Value.createDatetimeValue(
-                  LocalDateTime.of(2008, 12, 25, 15, 30, 0).withNano(123456000)),
-              Value.createTimeValue(LocalTime.of(15, 30, 45).withNano(123456000)),
-              Value.createTimestampValueFromUnixMicros(12345678000L),
-              Value.createArrayValue(
-                  TEST_INNER_ARRAY_TYPE,
-                  Arrays.asList(Value.createDoubleValue(3.0), Value.createDoubleValue(6.5))),
-              Value.createStructValue(
-                  TEST_INNER_STRUCT_TYPE,
-                  Arrays.asList(Value.createInt64Value(0L), Value.createStringValue("world"))),
-              Value.createNumericValue(ZetaSqlTypesUtils.bigDecimalAsNumeric("12346")),
-              Value.createNullValue(TypeFactory.createSimpleType(TypeKind.TYPE_INT64))));
-
-  @Test
-  public void testBeamFieldTypeToZetaSqlType() {
-    assertEquals(ZetaSqlBeamTranslationUtils.toZetaSqlType(TEST_FIELD_TYPE), TEST_TYPE);
-  }
-
-  @Test
-  public void testJavaObjectToZetaSqlValue() {
-    assertEquals(ZetaSqlBeamTranslationUtils.toZetaSqlValue(TEST_ROW, TEST_FIELD_TYPE), TEST_VALUE);
-  }
-
-  @Test
-  public void testZetaSqlValueToJavaObject() {
-    assertEquals(
-        ZetaSqlBeamTranslationUtils.toBeamObject(TEST_VALUE, TEST_FIELD_TYPE, true), TEST_ROW);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlDialectSpecTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlDialectSpecTest.java
deleted file mode 100644
index a62ce2dc9a52..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlDialectSpecTest.java
+++ /dev/null
@@ -1,4122 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils.parseTimestampWithUTCTimeZone;
-import static org.apache.beam.sdk.schemas.Schema.FieldType.DATETIME;
-import static org.junit.Assert.assertTrue;
-
-import com.google.protobuf.ByteString;
-import com.google.zetasql.StructType;
-import com.google.zetasql.StructType.StructField;
-import com.google.zetasql.TypeFactory;
-import com.google.zetasql.Value;
-import com.google.zetasql.ZetaSQLType.TypeKind;
-import java.nio.charset.StandardCharsets;
-import java.time.LocalDate;
-import java.time.LocalTime;
-import java.util.Arrays;
-import java.util.HashMap;
-import java.util.Iterator;
-import java.util.List;
-import java.util.Map;
-import org.apache.beam.sdk.extensions.sql.SqlTransform;
-import org.apache.beam.sdk.extensions.sql.impl.BeamSqlPipelineOptions;
-import org.apache.beam.sdk.extensions.sql.impl.QueryPlanner.QueryParameters;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.schemas.Schema.Field;
-import org.apache.beam.sdk.schemas.Schema.FieldType;
-import org.apache.beam.sdk.schemas.logicaltypes.SqlTypes;
-import org.apache.beam.sdk.testing.PAssert;
-import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-import org.joda.time.DateTime;
-import org.joda.time.Duration;
-import org.joda.time.chrono.ISOChronology;
-import org.junit.Assert;
-import org.junit.Before;
-import org.junit.Ignore;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.rules.ExpectedException;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-/** Tests for various operations/functions defined by ZetaSQL dialect. */
-@RunWith(JUnit4.class)
-public class ZetaSqlDialectSpecTest extends ZetaSqlTestBase {
-
-  @Rule public transient TestPipeline pipeline = TestPipeline.create();
-  @Rule public ExpectedException thrown = ExpectedException.none();
-
-  private PCollection<Row> execute(String sql, QueryParameters params) {
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-    return BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-  }
-
-  private PCollection<Row> execute(String sql) {
-    return execute(sql, QueryParameters.ofNone());
-  }
-
-  private PCollection<Row> execute(String sql, Map<String, Value> params) {
-    return execute(sql, QueryParameters.ofNamed(params));
-  }
-
-  private PCollection<Row> execute(String sql, List<Value> params) {
-    return execute(sql, QueryParameters.ofPositional(params));
-  }
-
-  @Before
-  public void setUp() {
-    initialize();
-  }
-
-  @Test
-  public void testSimpleSelect() {
-    String sql =
-        "SELECT CAST (1243 as INT64), "
-            + "CAST ('2018-09-15 12:59:59.000000+00' as TIMESTAMP), "
-            + "CAST ('string' as STRING);";
-
-    PCollection<Row> stream = execute(sql);
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addDateTimeField("field2")
-            .addStringField("field3")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    1243L,
-                    new DateTime(2018, 9, 15, 12, 59, 59, ISOChronology.getInstanceUTC()),
-                    "string")
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  // Verify that we can set the query planner via withQueryPlannerClass
-  @Test
-  public void testWithQueryPlannerClass() {
-    String sql =
-        "SELECT CAST (1243 as INT64), "
-            + "CAST ('2018-09-15 12:59:59.000000+00' as TIMESTAMP), "
-            + "CAST ('string' as STRING);";
-
-    PCollection<Row> stream =
-        pipeline.apply(SqlTransform.query(sql).withQueryPlannerClass(ZetaSQLQueryPlanner.class));
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addDateTimeField("field2")
-            .addStringField("field3")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    1243L,
-                    new DateTime(2018, 9, 15, 12, 59, 59, ISOChronology.getInstanceUTC()),
-                    "string")
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  // Verify that we can set the query planner via pipeline options
-  @Test
-  public void testPlannerNamePipelineOption() {
-    pipeline
-        .getOptions()
-        .as(BeamSqlPipelineOptions.class)
-        .setPlannerName("org.apache.beam.sdk.extensions.sql.zetasql.ZetaSQLQueryPlanner");
-
-    String sql =
-        "SELECT CAST (1243 as INT64), "
-            + "CAST ('2018-09-15 12:59:59.000000+00' as TIMESTAMP), "
-            + "CAST ('string' as STRING);";
-
-    PCollection<Row> stream = pipeline.apply(SqlTransform.query(sql));
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addDateTimeField("field2")
-            .addStringField("field3")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    1243L,
-                    new DateTime(2018, 9, 15, 12, 59, 59, ISOChronology.getInstanceUTC()),
-                    "string")
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testByteLiterals() {
-    String sql = "SELECT b'abc'";
-
-    byte[] byteString = new byte[] {'a', 'b', 'c'};
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addNullableField("ColA", FieldType.BYTES).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(byteString).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testByteString() {
-    String sql = "SELECT @p0 IS NULL AS ColA";
-
-    ByteString byteString = ByteString.copyFrom(new byte[] {0x62});
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.<String, Value>builder().put("p0", Value.createBytesValue(byteString)).build();
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("ColA", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(false).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStringLiterals() {
-    String sql = "SELECT '\"America/Los_Angeles\"\\n'";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addNullableField("ColA", FieldType.STRING).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues("\"America/Los_Angeles\"\n").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testParameterString() {
-    String sql = "SELECT ?";
-    ImmutableList<Value> params = ImmutableList.of(Value.createStringValue("abc\n"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("ColA", FieldType.STRING).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("abc\n").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEQ1() {
-    String sql = "SELECT @p0 = @p1 AS ColA";
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.<String, Value>builder()
-            .put("p0", Value.createSimpleNullValue(TypeKind.TYPE_BOOL))
-            .put("p1", Value.createBoolValue(true))
-            .build();
-
-    PCollection<Row> stream = execute(sql, params);
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((Boolean) null).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEQ2() {
-    String sql = "SELECT @p0 = @p1 AS ColA";
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.<String, Value>builder()
-            .put("p0", Value.createDoubleValue(0))
-            .put("p1", Value.createDoubleValue(Double.POSITIVE_INFINITY))
-            .build();
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addBooleanField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(false).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEQ3() {
-    String sql = "SELECT @p0 = @p1 AS ColA";
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.<String, Value>builder()
-            .put("p0", Value.createSimpleNullValue(TypeKind.TYPE_DOUBLE))
-            .put("p1", Value.createDoubleValue(3.14))
-            .build();
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((Boolean) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEQ4() {
-    String sql = "SELECT @p0 = @p1 AS ColA";
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.<String, Value>builder()
-            .put("p0", Value.createBytesValue(ByteString.copyFromUtf8("hello")))
-            .put("p1", Value.createBytesValue(ByteString.copyFromUtf8("hello")))
-            .build();
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(true).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEQ5() {
-    String sql = "SELECT b'hello' = b'hello' AS ColA";
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(true).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEQ6() {
-    String sql = "SELECT ? = ? AS ColA";
-    ImmutableList<Value> params =
-        ImmutableList.of(Value.createInt64Value(4L), Value.createInt64Value(5L));
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(false).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIn() {
-    String sql = "SELECT 'b' IN ('a', 'b', 'c')";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addBooleanField("f_bool").build())
-                .addValues(true)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testInArray() {
-    String sql = "SELECT 'b' IN UNNEST(['a', 'b', 'c'])";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addBooleanField("f_bool").build())
-                .addValues(true)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIsNotNull1() {
-    String sql = "SELECT @p0 IS NOT NULL AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of("p0", Value.createSimpleNullValue(TypeKind.TYPE_STRING));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(false).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIsNotNull2() {
-    String sql = "SELECT @p0 IS NOT NULL AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createNullValue(
-                TypeFactory.createArrayType(TypeFactory.createSimpleType(TypeKind.TYPE_INT64))));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(false).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIsNotNull3() {
-    String sql = "SELECT @p0 IS NOT NULL AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createNullValue(
-                TypeFactory.createStructType(
-                    Arrays.asList(
-                        new StructField(
-                            "a", TypeFactory.createSimpleType(TypeKind.TYPE_STRING))))));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(false).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIfBasic() {
-    String sql = "SELECT IF(@p0, @p1, @p2) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createBoolValue(true),
-            "p1",
-            Value.createInt64Value(1),
-            "p2",
-            Value.createInt64Value(2));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.INT64).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(1L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIfPositional() {
-    String sql = "SELECT IF(?, ?, ?) AS ColA";
-
-    ImmutableList<Value> params =
-        ImmutableList.of(
-            Value.createBoolValue(true), Value.createInt64Value(1), Value.createInt64Value(2));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.INT64).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(1L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCoalesceBasic() {
-    String sql = "SELECT COALESCE(@p0, @p1, @p2) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createSimpleNullValue(TypeKind.TYPE_STRING),
-            "p1",
-            Value.createStringValue("yay"),
-            "p2",
-            Value.createStringValue("nay"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("yay").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCoalesceSingleArgument() {
-    String sql = "SELECT COALESCE(@p0) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of("p0", Value.createSimpleNullValue(TypeKind.TYPE_INT64));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema =
-        Schema.builder().addNullableField("field1", FieldType.array(FieldType.INT64)).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue(null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCoalesceNullArray() {
-    String sql = "SELECT COALESCE(@p0, @p1) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createNullValue(
-                TypeFactory.createArrayType(TypeFactory.createSimpleType(TypeKind.TYPE_INT64))),
-            "p1",
-            Value.createNullValue(
-                TypeFactory.createArrayType(TypeFactory.createSimpleType(TypeKind.TYPE_INT64))));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema =
-        Schema.builder().addNullableField("field1", FieldType.array(FieldType.INT64)).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue(null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testNullIfCoercion() {
-    String sql = "SELECT NULLIF(@p0, @p1) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createInt64Value(3L),
-            "p1",
-            Value.createSimpleNullValue(TypeKind.TYPE_DOUBLE));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.DOUBLE).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue(3.0).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCoalesceNullStruct() {
-    String sql = "SELECT COALESCE(NULL, STRUCT(\"a\" AS s, -33 AS i))";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema innerSchema =
-        Schema.of(Field.of("s", FieldType.STRING), Field.of("i", FieldType.INT64));
-    final Schema schema =
-        Schema.builder().addNullableField("field1", FieldType.row(innerSchema)).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValue(Row.withSchema(innerSchema).addValues("a", -33L).build())
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIfTimestamp() {
-    String sql = "SELECT IF(@p0, @p1, @p2) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createBoolValue(false),
-            "p1",
-            Value.createTimestampValueFromUnixMicros(0),
-            "p2",
-            Value.createTimestampValueFromUnixMicros(
-                DateTime.parse("2019-01-01T00:00:00Z").getMillis() * 1000));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", DATETIME).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(DateTime.parse("2019-01-01T00:00:00Z")).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  @Ignore("$make_array is not implemented")
-  public void testMakeArray() {
-    String sql = "SELECT [s3, s1, s2] FROM (SELECT \"foo\" AS s1, \"bar\" AS s2, \"baz\" AS s3);";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder().addNullableField("field1", FieldType.array(FieldType.STRING)).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValue(ImmutableList.of("baz", "foo", "bar")).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testNullIfPositive() {
-    String sql = "SELECT NULLIF(@p0, @p1) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("null"), "p1", Value.createStringValue("null"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue(null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testNullIfNegative() {
-    String sql = "SELECT NULLIF(@p0, @p1) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("foo"), "p1", Value.createStringValue("null"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("foo").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIfNullPositive() {
-    String sql = "SELECT IFNULL(@p0, @p1) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("foo"), "p1", Value.createStringValue("default"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("foo").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIfNullNegative() {
-    String sql = "SELECT IFNULL(@p0, @p1) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createSimpleNullValue(TypeKind.TYPE_STRING),
-            "p1",
-            Value.createStringValue("yay"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("yay").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEmptyArrayParameter() {
-    String sql = "SELECT @p0 AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createArrayValue(
-                TypeFactory.createArrayType(TypeFactory.createSimpleType(TypeKind.TYPE_INT64)),
-                ImmutableList.of()));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addArrayField("field1", FieldType.INT64).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValue(ImmutableList.of()).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEmptyArrayLiteral() {
-    String sql = "SELECT ARRAY<STRING>[];";
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addArrayField("field1", FieldType.STRING).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValue(ImmutableList.of()).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLike1() {
-    String sql = "SELECT @p0 LIKE @p1 AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("ab%"), "p1", Value.createStringValue("ab\\%"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(true).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLikeNullPattern() {
-    String sql = "SELECT @p0 LIKE @p1 AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createStringValue("ab%"),
-            "p1",
-            Value.createSimpleNullValue(TypeKind.TYPE_STRING));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((Object) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLikeAllowsEscapingNonSpecialCharacter() {
-    String sql = "SELECT @p0 LIKE  @p1 AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of("p0", Value.createStringValue("ab"), "p1", Value.createStringValue("\\ab"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(true).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLikeAllowsEscapingBackslash() {
-    String sql = "SELECT @p0 LIKE  @p1 AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("a\\c"), "p1", Value.createStringValue("a\\\\c"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(true).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLikeBytes() {
-    String sql = "SELECT @p0 LIKE  @p1 AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createBytesValue(ByteString.copyFromUtf8("abcd")),
-            "p1",
-            Value.createBytesValue(ByteString.copyFromUtf8("__%")));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(true).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSimpleUnionAll() {
-    String sql =
-        "SELECT CAST (1243 as INT64), "
-            + "CAST ('2018-09-15 12:59:59.000000+00' as TIMESTAMP), "
-            + "CAST ('string' as STRING) "
-            + " UNION ALL "
-            + " SELECT CAST (1243 as INT64), "
-            + "CAST ('2018-09-15 12:59:59.000000+00' as TIMESTAMP), "
-            + "CAST ('string' as STRING);";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addDateTimeField("field2")
-            .addStringField("field3")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    1243L,
-                    new DateTime(2018, 9, 15, 12, 59, 59, ISOChronology.getInstanceUTC()),
-                    "string")
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    1243L,
-                    new DateTime(2018, 9, 15, 12, 59, 59, ISOChronology.getInstanceUTC()),
-                    "string")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testThreeWayUnionAll() {
-    String sql = "SELECT a FROM (SELECT 1 a UNION ALL SELECT 2 UNION ALL SELECT 3)";
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L).build(),
-            Row.withSchema(schema).addValues(2L).build(),
-            Row.withSchema(schema).addValues(3L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSimpleUnionDISTINCT() {
-    String sql =
-        "SELECT CAST (1243 as INT64), "
-            + "CAST ('2018-09-15 12:59:59.000000+00' as TIMESTAMP), "
-            + "CAST ('string' as STRING) "
-            + " UNION DISTINCT "
-            + " SELECT CAST (1243 as INT64), "
-            + "CAST ('2018-09-15 12:59:59.000000+00' as TIMESTAMP), "
-            + "CAST ('string' as STRING);";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addDateTimeField("field2")
-            .addStringField("field3")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    1243L,
-                    new DateTime(2018, 9, 15, 12, 59, 59, ISOChronology.getInstanceUTC()),
-                    "string")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLInnerJoin() {
-    String sql =
-        "SELECT t1.Key "
-            + "FROM KeyValue AS t1"
-            + " INNER JOIN BigTable AS t2"
-            + " on "
-            + " t1.Key = t2.RowKey AND t1.ts = t2.ts";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("field1").build())
-                .addValues(15L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  // JOIN USING(col) is equivalent to JOIN on left.col = right.col.
-  public void testZetaSQLInnerJoinWithUsing() {
-    String sql = "SELECT t1.Key " + "FROM KeyValue AS t1" + " INNER JOIN BigTable AS t2 USING(ts)";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("field1").build())
-                .addValues(15L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  // testing ordering of the JOIN conditions.
-  public void testZetaSQLInnerJoinTwo() {
-    String sql =
-        "SELECT t2.RowKey "
-            + "FROM KeyValue AS t1"
-            + " INNER JOIN BigTable AS t2"
-            + " on "
-            + " t2.RowKey = t1.Key AND t2.ts = t1.ts";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("field1").build())
-                .addValues(15L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLLeftOuterJoin() {
-    String sql =
-        "SELECT * "
-            + "FROM KeyValue AS t1"
-            + " LEFT JOIN BigTable AS t2"
-            + " on "
-            + " t1.Key = t2.RowKey";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schemaOne =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addStringField("field2")
-            .addDateTimeField("field3")
-            .addNullableField("field4", FieldType.INT64)
-            .addNullableField("field5", FieldType.STRING)
-            .addNullableField("field6", DATETIME)
-            .build();
-
-    final Schema schemaTwo =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addStringField("field2")
-            .addDateTimeField("field3")
-            .addInt64Field("field4")
-            .addStringField("field5")
-            .addDateTimeField("field6")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schemaOne)
-                .addValues(
-                    14L,
-                    "KeyValue234",
-                    new DateTime(2018, 7, 1, 21, 26, 6, ISOChronology.getInstanceUTC()),
-                    null,
-                    null,
-                    null)
-                .build(),
-            Row.withSchema(schemaTwo)
-                .addValues(
-                    15L,
-                    "KeyValue235",
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()),
-                    15L,
-                    "BigTable235",
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLRightOuterJoin() {
-    String sql =
-        "SELECT * "
-            + "FROM KeyValue AS t1"
-            + " RIGHT JOIN BigTable AS t2"
-            + " on "
-            + " t1.Key = t2.RowKey";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schemaOne =
-        Schema.builder()
-            .addNullableField("field1", FieldType.INT64)
-            .addNullableField("field2", FieldType.STRING)
-            .addNullableField("field3", DATETIME)
-            .addInt64Field("field4")
-            .addStringField("field5")
-            .addDateTimeField("field6")
-            .build();
-
-    final Schema schemaTwo =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addStringField("field2")
-            .addDateTimeField("field3")
-            .addInt64Field("field4")
-            .addStringField("field5")
-            .addDateTimeField("field6")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schemaOne)
-                .addValues(
-                    null,
-                    null,
-                    null,
-                    16L,
-                    "BigTable236",
-                    new DateTime(2018, 7, 1, 21, 26, 8, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schemaTwo)
-                .addValues(
-                    15L,
-                    "KeyValue235",
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()),
-                    15L,
-                    "BigTable235",
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLFullOuterJoin() {
-    String sql =
-        "SELECT * "
-            + "FROM KeyValue AS t1"
-            + " FULL JOIN BigTable AS t2"
-            + " on "
-            + " t1.Key = t2.RowKey";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schemaOne =
-        Schema.builder()
-            .addNullableField("field1", FieldType.INT64)
-            .addNullableField("field2", FieldType.STRING)
-            .addNullableField("field3", DATETIME)
-            .addInt64Field("field4")
-            .addStringField("field5")
-            .addDateTimeField("field6")
-            .build();
-
-    final Schema schemaTwo =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addStringField("field2")
-            .addDateTimeField("field3")
-            .addInt64Field("field4")
-            .addStringField("field5")
-            .addDateTimeField("field6")
-            .build();
-
-    final Schema schemaThree =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addStringField("field2")
-            .addDateTimeField("field3")
-            .addNullableField("field4", FieldType.INT64)
-            .addNullableField("field5", FieldType.STRING)
-            .addNullableField("field6", DATETIME)
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schemaOne)
-                .addValues(
-                    null,
-                    null,
-                    null,
-                    16L,
-                    "BigTable236",
-                    new DateTime(2018, 7, 1, 21, 26, 8, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schemaTwo)
-                .addValues(
-                    15L,
-                    "KeyValue235",
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()),
-                    15L,
-                    "BigTable235",
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schemaThree)
-                .addValues(
-                    14L,
-                    "KeyValue234",
-                    new DateTime(2018, 7, 1, 21, 26, 6, ISOChronology.getInstanceUTC()),
-                    null,
-                    null,
-                    null)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  @Ignore("BeamSQL only supports equal join")
-  public void testZetaSQLFullOuterJoinTwo() {
-    String sql =
-        "SELECT * "
-            + "FROM KeyValue AS t1"
-            + " FULL JOIN BigTable AS t2"
-            + " on "
-            + " t1.Key + t2.RowKey = 30";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLFullOuterJoinFalse() {
-    String sql = "SELECT * FROM KeyValue AS t1 FULL JOIN BigTable AS t2 ON false";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    thrown.expect(UnsupportedOperationException.class);
-    BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-  }
-
-  @Test
-  public void testZetaSQLThreeWayInnerJoin() {
-    String sql =
-        "SELECT t3.Value, t2.Value, t1.Value, t1.Key, t3.ColId FROM KeyValue as t1 "
-            + "JOIN BigTable as t2 "
-            + "ON (t1.Key = t2.RowKey) "
-            + "JOIN Spanner as t3 "
-            + "ON (t3.ColId = t1.Key)";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addStringField("t3.Value")
-                        .addStringField("t2.Value")
-                        .addStringField("t1.Value")
-                        .addInt64Field("t1.Key")
-                        .addInt64Field("t3.ColId")
-                        .build())
-                .addValues("Spanner235", "BigTable235", "KeyValue235", 15L, 15L)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLTableJoinOnItselfWithFiltering() {
-    String sql =
-        "SELECT * FROM Spanner as t1 "
-            + "JOIN Spanner as t2 "
-            + "ON (t1.ColId = t2.ColId) WHERE t1.ColId = 17";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addInt64Field("field1")
-                        .addStringField("field2")
-                        .addInt64Field("field3")
-                        .addStringField("field4")
-                        .build())
-                .addValues(17L, "Spanner237", 17L, "Spanner237")
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLSelectFromSelect() {
-    String sql = "SELECT * FROM (SELECT \"apple\" AS fruit, \"carrot\" AS vegetable);";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema =
-        Schema.builder().addStringField("field1").addStringField("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues("apple", "carrot").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-
-    Schema outputSchema = stream.getSchema();
-    Assert.assertEquals(2, outputSchema.getFieldCount());
-    Assert.assertEquals("fruit", outputSchema.getField(0).getName());
-    Assert.assertEquals("vegetable", outputSchema.getField(1).getName());
-  }
-
-  @Test
-  public void testZetaSQLSelectFromTable() {
-    String sql = "SELECT Key, Value FROM KeyValue;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addStringField("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(14L, "KeyValue234").build(),
-            Row.withSchema(schema).addValues(15L, "KeyValue235").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLSelectFromTableLimit() {
-    String sql = "SELECT Key, Value FROM KeyValue LIMIT 2;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addStringField("field2").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(14L, "KeyValue234").build(),
-            Row.withSchema(schema).addValues(15L, "KeyValue235").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLSelectFromTableLimit0() {
-    String sql = "SELECT Key, Value FROM KeyValue LIMIT 0;";
-    PCollection<Row> stream = execute(sql);
-    PAssert.that(stream).empty();
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLSelectNullLimitParam() {
-    String sql = "SELECT Key, Value FROM KeyValue LIMIT @lmt;";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "lmt", Value.createNullValue(TypeFactory.createSimpleType(TypeKind.TYPE_INT64)));
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(RuntimeException.class);
-    thrown.expectMessage("Limit requires non-null count and offset");
-    zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-  }
-
-  @Test
-  public void testZetaSQLSelectNullOffsetParam() {
-    String sql = "SELECT Key, Value FROM KeyValue LIMIT 1 OFFSET @lmt;";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "lmt", Value.createNullValue(TypeFactory.createSimpleType(TypeKind.TYPE_INT64)));
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(RuntimeException.class);
-    thrown.expectMessage("Limit requires non-null count and offset");
-    zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-  }
-
-  @Test
-  public void testZetaSQLSelectFromTableOrderLimit() {
-    String sql =
-        "SELECT x, y FROM (SELECT 1 as x, 0 as y UNION ALL SELECT 0, 0 "
-            + "UNION ALL SELECT 1, 0 UNION ALL SELECT 1, 1) ORDER BY x LIMIT 1";
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addInt64Field("field1").addInt64Field("field2").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(0L, 0L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLSelectFromTableLimitOffset() {
-    String sql =
-        "SELECT COUNT(a) FROM (\n"
-            + "SELECT a FROM (SELECT 1 a UNION ALL SELECT 2 UNION ALL SELECT 3) LIMIT 3 OFFSET 1);";
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(2L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  // There is really no order for a PCollection, so this query does not test
-  // ORDER BY but just a test to see if ORDER BY LIMIT can work.
-  @Test
-  public void testZetaSQLSelectFromTableOrderByLimit() {
-    String sql = "SELECT Key, Value FROM KeyValue ORDER BY Key DESC LIMIT 2;";
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addStringField("field2").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(14L, "KeyValue234").build(),
-            Row.withSchema(schema).addValues(15L, "KeyValue235").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLSelectFromTableOrderByNoSelectLimit() {
-    String sql = "SELECT Value FROM KeyValue ORDER BY Key DESC LIMIT 2;";
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addStringField("field2").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("KeyValue234").build(),
-            Row.withSchema(schema).addValues("KeyValue235").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLSelectFromTableOrderBy() {
-    String sql = "SELECT Key, Value FROM KeyValue ORDER BY Key DESC;";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(RuntimeException.class);
-    thrown.expectMessage("ORDER BY without a LIMIT is not supported.");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testZetaSQLSelectFromTableWithStructType2() {
-    String sql =
-        "SELECT table_with_struct.struct_col.struct_col_str FROM table_with_struct WHERE id = 1;";
-
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addStringField("field").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue("row_one").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLStructFieldAccessInFilter() {
-    String sql =
-        "SELECT table_with_struct.id FROM table_with_struct WHERE"
-            + " table_with_struct.struct_col.struct_col_str = 'row_one';";
-
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addInt64Field("field").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue(1L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLStructFieldAccessInCast() {
-    String sql =
-        "SELECT CAST(table_with_struct.id AS STRING) FROM table_with_struct WHERE"
-            + " table_with_struct.struct_col.struct_col_str = 'row_one';";
-
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addStringField("field").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue("1").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  @Ignore(
-      "[https://github.com/apache/beam/issues/20101] CAST operator does not work fully due to bugs in unparsing")
-  public void testZetaSQLStructFieldAccessInCast2() {
-    String sql =
-        "SELECT CAST(A.struct_col.struct_col_str AS TIMESTAMP) FROM table_with_struct_ts_string AS"
-            + " A";
-
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addDateTimeField("field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValue(parseTimestampWithUTCTimeZone("2019-01-15 13:21:03"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  // Used to validate fix for [BEAM-8042].
-  public void testAggregateWithAndWithoutColumnRefs() {
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-
-    String sql =
-        "SELECT \n"
-            + "  id, \n"
-            + "  SUM(has_f1) as f1_count, \n"
-            + "  SUM(has_f2) as f2_count, \n"
-            + "  SUM(has_f3) as f3_count, \n"
-            + "  SUM(has_f4) as f4_count, \n"
-            + "  SUM(has_f5) as f5_count, \n"
-            + "  COUNT(*) as count, \n"
-            + "  SUM(has_f6) as f6_count  \n"
-            + "FROM (select 0 as id, 1 as has_f1, 2 as has_f2, 3 as has_f3, 4 as has_f4, 5 as has_f5, 6 as has_f6)\n"
-            + "GROUP BY id";
-
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("id")
-            .addInt64Field("f1_count")
-            .addInt64Field("f2_count")
-            .addInt64Field("f3_count")
-            .addInt64Field("f4_count")
-            .addInt64Field("f5_count")
-            .addInt64Field("count")
-            .addInt64Field("f6_count")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(0L, 1L, 2L, 3L, 4L, 5L, 1L, 6L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLStructFieldAccessInGroupBy() {
-    String sql = "SELECT rowCol.row_id, COUNT(*) FROM table_with_struct_two GROUP BY rowCol.row_id";
-
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addInt64Field("field1").addInt64Field("field2").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L, 1L).build(),
-            Row.withSchema(schema).addValues(2L, 1L).build(),
-            Row.withSchema(schema).addValues(3L, 2L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLAnyValueInGroupBy() {
-    String sql =
-        "SELECT rowCol.row_id as key, ANY_VALUE(rowCol.data) as any_value FROM table_with_struct_two GROUP BY rowCol.row_id";
-
-    PCollection<Row> stream = execute(sql);
-    Map<Long, List<String>> allowedTuples = new HashMap<>();
-    allowedTuples.put(1L, Arrays.asList("data1"));
-    allowedTuples.put(2L, Arrays.asList("data2"));
-    allowedTuples.put(3L, Arrays.asList("data2", "data3"));
-
-    PAssert.that(stream)
-        .satisfies(
-            input -> {
-              Iterator<Row> iter = input.iterator();
-              while (iter.hasNext()) {
-                Row row = iter.next();
-                List<String> values = allowedTuples.remove(row.getInt64("key"));
-                assertTrue(values != null);
-                assertTrue(values.contains(row.getString("any_value")));
-              }
-              assertTrue(allowedTuples.isEmpty());
-              return null;
-            });
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLStructFieldAccessInGroupBy2() {
-    String sql =
-        "SELECT rowCol.data, MAX(rowCol.row_id), MIN(rowCol.row_id) FROM table_with_struct_two"
-            + " GROUP BY rowCol.data";
-
-    PCollection<Row> stream = execute(sql);
-    final Schema schema =
-        Schema.builder()
-            .addStringField("field1")
-            .addInt64Field("field2")
-            .addInt64Field("field3")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("data1", 1L, 1L).build(),
-            Row.withSchema(schema).addValues("data2", 3L, 2L).build(),
-            Row.withSchema(schema).addValues("data3", 3L, 3L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLStructFieldAccessInnerJoin() {
-    String sql =
-        "SELECT A.rowCol.data FROM table_with_struct_two AS A INNER JOIN "
-            + "table_with_struct AS B "
-            + "ON A.rowCol.row_id = B.id";
-
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addStringField("field1").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValue("data1").build(),
-            Row.withSchema(schema).addValue("data2").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLSelectFromTableWithArrayType() {
-    String sql = "SELECT array_col FROM table_with_array;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addArrayField("field", FieldType.STRING).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValue(Arrays.asList("1", "2", "3")).build(),
-            Row.withSchema(schema).addValue(ImmutableList.of()).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLSelectStarFromTable() {
-    String sql = "SELECT * FROM BigTable;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addStringField("field2")
-            .addDateTimeField("field3")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    15L,
-                    "BigTable235",
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    16L,
-                    "BigTable236",
-                    new DateTime(2018, 7, 1, 21, 26, 8, ISOChronology.getInstanceUTC()))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBasicFiltering() {
-    String sql = "SELECT Key, Value FROM KeyValue WHERE Key = 14;";
-
-    PCollection<Row> stream = execute(sql);
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addInt64Field("field1").addStringField("field2").build())
-                .addValues(14L, "KeyValue234")
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBasicFilteringTwo() {
-    String sql = "SELECT Key, Value FROM KeyValue WHERE Key = 14 AND Value = 'non-existing';";
-
-    PCollection<Row> stream = execute(sql);
-    PAssert.that(stream).empty();
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBasicFilteringThree() {
-    String sql = "SELECT Key, Value FROM KeyValue WHERE Key = 14 OR Key = 15;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addStringField("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(14L, "KeyValue234").build(),
-            Row.withSchema(schema).addValues(15L, "KeyValue235").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLCountOnAColumn() {
-    String sql = "SELECT COUNT(Key) FROM KeyValue";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(2L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLAggDistinct() {
-    String sql = "SELECT Key, COUNT(DISTINCT Value) FROM KeyValue GROUP BY Key";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(RuntimeException.class);
-    thrown.expectMessage("Does not support COUNT DISTINCT");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testZetaSQLBasicAgg() {
-    String sql = "SELECT Key, COUNT(*) FROM KeyValue GROUP BY Key";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addInt64Field("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(14L, 1L).build(),
-            Row.withSchema(schema).addValues(15L, 1L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLColumnAlias1() {
-    String sql = "SELECT Key, COUNT(*) AS count_col FROM KeyValue GROUP BY Key";
-
-    PCollection<Row> stream = execute(sql);
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-
-    Schema outputSchema = stream.getSchema();
-    Assert.assertEquals(2, outputSchema.getFieldCount());
-    Assert.assertEquals("Key", outputSchema.getField(0).getName());
-    Assert.assertEquals("count_col", outputSchema.getField(1).getName());
-  }
-
-  @Test
-  public void testZetaSQLColumnAlias2() {
-    String sql =
-        "SELECT Key AS k1, (count_col + 1) AS k2 FROM (SELECT Key, COUNT(*) AS count_col FROM"
-            + " KeyValue GROUP BY Key)";
-
-    PCollection<Row> stream = execute(sql);
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-
-    Schema outputSchema = stream.getSchema();
-    Assert.assertEquals(2, outputSchema.getFieldCount());
-    Assert.assertEquals("k1", outputSchema.getField(0).getName());
-    Assert.assertEquals("k2", outputSchema.getField(1).getName());
-  }
-
-  @Test
-  public void testZetaSQLColumnAlias3() {
-    String sql = "SELECT Key AS v1, Value AS v2, ts AS v3 FROM KeyValue";
-
-    PCollection<Row> stream = execute(sql);
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-
-    Schema outputSchema = stream.getSchema();
-    Assert.assertEquals(3, outputSchema.getFieldCount());
-    Assert.assertEquals("v1", outputSchema.getField(0).getName());
-    Assert.assertEquals("v2", outputSchema.getField(1).getName());
-    Assert.assertEquals("v3", outputSchema.getField(2).getName());
-  }
-
-  @Test
-  public void testZetaSQLColumnAlias4() {
-    String sql = "SELECT CAST(123 AS INT64) AS cast_col";
-
-    PCollection<Row> stream = execute(sql);
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-
-    Schema outputSchema = stream.getSchema();
-    Assert.assertEquals(1, outputSchema.getFieldCount());
-    Assert.assertEquals("cast_col", outputSchema.getField(0).getName());
-  }
-
-  @Test
-  public void testZetaSQLAmbiguousAlias() {
-    String sql = "SELECT row_id as ID, int64_col as ID FROM table_all_types GROUP BY ID;";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-
-    thrown.expectMessage(
-        "Name ID in GROUP BY clause is ambiguous; it may refer to multiple columns in the"
-            + " SELECT-list [at 1:68]");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testZetaSQLAggWithOrdinalReference() {
-    String sql = "SELECT Key, COUNT(*) FROM aggregate_test_table GROUP BY 1";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addInt64Field("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L, 2L).build(),
-            Row.withSchema(schema).addValues(2L, 3L).build(),
-            Row.withSchema(schema).addValues(3L, 2L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLAggWithAliasReference() {
-    String sql = "SELECT Key AS K, COUNT(*) FROM aggregate_test_table GROUP BY K";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addInt64Field("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L, 2L).build(),
-            Row.withSchema(schema).addValues(2L, 3L).build(),
-            Row.withSchema(schema).addValues(3L, 2L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBasicAgg2() {
-    String sql = "SELECT Key, COUNT(*) FROM aggregate_test_table GROUP BY Key";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addInt64Field("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L, 2L).build(),
-            Row.withSchema(schema).addValues(2L, 3L).build(),
-            Row.withSchema(schema).addValues(3L, 2L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBasicAgg3() {
-    String sql = "SELECT Key, Key2, COUNT(*) FROM aggregate_test_table GROUP BY Key2, Key";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addInt64Field("field3")
-            .addInt64Field("field2")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L, 10L, 1L).build(),
-            Row.withSchema(schema).addValues(1L, 11L, 1L).build(),
-            Row.withSchema(schema).addValues(2L, 11L, 2L).build(),
-            Row.withSchema(schema).addValues(2L, 12L, 1L).build(),
-            Row.withSchema(schema).addValues(3L, 13L, 2L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBasicAgg4() {
-    String sql =
-        "SELECT Key, Key2, MAX(f_int_1), MIN(f_int_1), SUM(f_int_1), SUM(f_double_1) "
-            + "FROM aggregate_test_table GROUP BY Key2, Key";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addInt64Field("field3")
-            .addInt64Field("field2")
-            .addInt64Field("field4")
-            .addInt64Field("field5")
-            .addDoubleField("field6")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L, 10L, 1L, 1L, 1L, 1.0).build(),
-            Row.withSchema(schema).addValues(1L, 11L, 2L, 2L, 2L, 2.0).build(),
-            Row.withSchema(schema).addValues(2L, 11L, 4L, 3L, 7L, 7.0).build(),
-            Row.withSchema(schema).addValues(2L, 12L, 5L, 5L, 5L, 5.0).build(),
-            Row.withSchema(schema).addValues(3L, 13L, 7L, 6L, 13L, 13.0).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBasicAgg5() {
-    String sql =
-        "SELECT Key, Key2, AVG(CAST(f_int_1 AS FLOAT64)), AVG(f_double_1) "
-            + "FROM aggregate_test_table GROUP BY Key2, Key";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addInt64Field("field2")
-            .addDoubleField("field3")
-            .addDoubleField("field4")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L, 10L, 1.0, 1.0).build(),
-            Row.withSchema(schema).addValues(1L, 11L, 2.0, 2.0).build(),
-            Row.withSchema(schema).addValues(2L, 11L, 3.5, 3.5).build(),
-            Row.withSchema(schema).addValues(2L, 12L, 5.0, 5.0).build(),
-            Row.withSchema(schema).addValues(3L, 13L, 6.5, 6.5).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  @Ignore(
-      "Calcite infers return type of AVG(int64) as BIGINT while ZetaSQL requires it as either"
-          + " NUMERIC or DOUBLE/FLOAT64")
-  public void testZetaSQLTestAVG() {
-    String sql = "SELECT Key, AVG(f_int_1)" + "FROM aggregate_test_table GROUP BY Key";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addInt64Field("field2")
-            .addInt64Field("field3")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L, 10L, 1L).build(),
-            Row.withSchema(schema).addValues(1L, 11L, 6L).build(),
-            Row.withSchema(schema).addValues(2L, 11L, 6L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLGroupByExprInSelect() {
-    String sql = "SELECT int64_col + 1 FROM table_all_types GROUP BY int64_col + 1;";
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValue(0L).build(),
-            Row.withSchema(schema).addValue(-1L).build(),
-            Row.withSchema(schema).addValue(-2L).build(),
-            Row.withSchema(schema).addValue(-3L).build(),
-            Row.withSchema(schema).addValue(-4L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLGroupByAndFiltering() {
-    String sql = "SELECT int64_col FROM table_all_types WHERE int64_col = 1 GROUP BY int64_col;";
-    PCollection<Row> stream = execute(sql);
-    PAssert.that(stream).empty();
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLGroupByAndFilteringOnNonGroupByColumn() {
-    String sql = "SELECT int64_col FROM table_all_types WHERE double_col = 0.5 GROUP BY int64_col;";
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addInt64Field("field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValue(-5L).build(),
-            Row.withSchema(schema).addValue(-4L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBasicHaving() {
-    String sql = "SELECT Key, COUNT(*) FROM aggregate_test_table GROUP BY Key HAVING COUNT(*) > 2";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addInt64Field("field2").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(2L, 3L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLHavingNull() {
-    String sql = "SELECT SUM(int64_val) FROM all_null_table GROUP BY primary_key HAVING false";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema.builder().addInt64Field("field").build();
-
-    PAssert.that(stream).empty();
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBasicFixedWindowing() {
-    String sql =
-        "SELECT "
-            + "COUNT(*) as field_count, "
-            + "TUMBLE_START(\"INTERVAL 1 SECOND\") as window_start, "
-            + "TUMBLE_END(\"INTERVAL 1 SECOND\") as window_end "
-            + "FROM KeyValue "
-            + "GROUP BY TUMBLE(ts, \"INTERVAL 1 SECOND\");";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("count_start")
-            .addDateTimeField("field1")
-            .addDateTimeField("field2")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    1L,
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 8, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    1L,
-                    new DateTime(2018, 7, 1, 21, 26, 6, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  // Test nested selection
-  @Test
-  public void testZetaSQLNestedQueryOne() {
-    String sql =
-        "SELECT a.Value, a.Key FROM (SELECT Key, Value FROM KeyValue WHERE Key = 14 OR Key = 15)"
-            + " as a;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addStringField("field2").addInt64Field("field1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("KeyValue234", 14L).build(),
-            Row.withSchema(schema).addValues("KeyValue235", 15L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  // Test selection, filtering and aggregation combined query.
-  @Test
-  public void testZetaSQLNestedQueryTwo() {
-    String sql =
-        "SELECT a.Key, a.Key2, COUNT(*) FROM "
-            + " (SELECT * FROM aggregate_test_table WHERE Key != 10) as a "
-            + " GROUP BY a.Key2, a.Key";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addInt64Field("field3")
-            .addInt64Field("field2")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L, 10L, 1L).build(),
-            Row.withSchema(schema).addValues(1L, 11L, 1L).build(),
-            Row.withSchema(schema).addValues(2L, 11L, 2L).build(),
-            Row.withSchema(schema).addValues(2L, 12L, 1L).build(),
-            Row.withSchema(schema).addValues(3L, 13L, 2L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  // test selection and join combined query
-  @Test
-  public void testZetaSQLNestedQueryThree() {
-    String sql =
-        "SELECT * FROM (SELECT * FROM KeyValue) AS t1 INNER JOIN (SELECT * FROM BigTable) AS t2 on"
-            + " t1.Key = t2.RowKey";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addInt64Field("Key")
-                        .addStringField("Value")
-                        .addDateTimeField("ts")
-                        .addInt64Field("RowKey")
-                        .addStringField("Value2")
-                        .addDateTimeField("ts2")
-                        .build())
-                .addValues(
-                    15L,
-                    "KeyValue235",
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()),
-                    15L,
-                    "BigTable235",
-                    new DateTime(2018, 7, 1, 21, 26, 7, ISOChronology.getInstanceUTC()))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  // Test nested select with out of order columns.
-  @Test
-  public void testZetaSQLNestedQueryFive() {
-    String sql =
-        "SELECT a.Value, a.Key FROM (SELECT Value, Key FROM KeyValue WHERE Key = 14 OR Key = 15)"
-            + " as a;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addStringField("field2").addInt64Field("field1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("KeyValue234", 14L).build(),
-            Row.withSchema(schema).addValues("KeyValue235", 15L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testMultipleSelectStatementsThrowsException() {
-    String sql = "SELECT 1; SELECT 2;";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(UnsupportedOperationException.class);
-    thrown.expectMessage("No additional statements are allowed after a SELECT statement.");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testDistinct() {
-    String sql = "SELECT DISTINCT Key2 FROM aggregate_test_table";
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addInt64Field("Key2").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(10L).build(),
-            Row.withSchema(schema).addValues(11L).build(),
-            Row.withSchema(schema).addValues(12L).build(),
-            Row.withSchema(schema).addValues(13L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDistinctOnNull() {
-    String sql = "SELECT DISTINCT str_val FROM all_null_table";
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addNullableField("str_val", FieldType.DOUBLE).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((Object) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testAnyValue() {
-    String sql = "SELECT ANY_VALUE(double_val) FROM all_null_table";
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addNullableField("double_val", FieldType.DOUBLE).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((Object) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectNULL() {
-    String sql = "SELECT NULL";
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addNullableField("long_val", FieldType.INT64).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((Object) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testWithQueryOne() {
-    String sql =
-        "With T1 AS (SELECT * FROM KeyValue), T2 AS (SELECT * FROM BigTable) SELECT T2.RowKey FROM"
-            + " T1 INNER JOIN T2 on T1.Key = T2.RowKey;";
-    PCollection<Row> stream = execute(sql);
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("field1").build())
-                .addValues(15L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testWithQueryTwo() {
-    String sql =
-        "WITH T1 AS (SELECT Key, COUNT(*) as value FROM KeyValue GROUP BY Key) SELECT T1.Key,"
-            + " T1.value FROM T1";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addInt64Field("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(14L, 1L).build(),
-            Row.withSchema(schema).addValues(15L, 1L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testWithQueryThree() {
-    String sql =
-        "WITH T1 as (SELECT Value, Key FROM KeyValue WHERE Key = 14 OR Key = 15) SELECT T1.Value,"
-            + " T1.Key FROM T1;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addStringField("field1").addInt64Field("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("KeyValue234", 14L).build(),
-            Row.withSchema(schema).addValues("KeyValue235", 15L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testWithQueryFour() {
-    String sql =
-        "WITH T1 as (SELECT Value, Key FROM KeyValue) SELECT T1.Value, T1.Key FROM T1 WHERE T1.Key"
-            + " = 14 OR T1.Key = 15;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addStringField("field2").addInt64Field("field1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("KeyValue234", 14L).build(),
-            Row.withSchema(schema).addValues("KeyValue235", 15L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testWithQueryFive() {
-    String sql =
-        "WITH T1 AS (SELECT * FROM KeyValue) SELECT T1.Key, COUNT(*) FROM T1 GROUP BY T1.Key";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addInt64Field("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(14L, 1L).build(),
-            Row.withSchema(schema).addValues(15L, 1L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testWithQuerySix() {
-    String sql =
-        "WITH T1 AS (SELECT * FROM window_test_table_two) SELECT "
-            + "COUNT(*) as field_count, "
-            + "SESSION_START(\"INTERVAL 3 SECOND\") as window_start, "
-            + "SESSION_END(\"INTERVAL 3 SECOND\") as window_end "
-            + "FROM T1 "
-            + "GROUP BY SESSION(ts, \"INTERVAL 3 SECOND\");";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("count_star")
-            .addDateTimeField("field1")
-            .addDateTimeField("field2")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    2L,
-                    new DateTime(2018, 7, 1, 21, 26, 12, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 12, ISOChronology.getInstanceUTC()))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    2L,
-                    new DateTime(2018, 7, 1, 21, 26, 6, ISOChronology.getInstanceUTC()),
-                    new DateTime(2018, 7, 1, 21, 26, 6, ISOChronology.getInstanceUTC()))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testWithQuerySeven() {
-    String sql =
-        "WITH t1 AS (select 1 AS k), t2 AS (select 1 AS k), t3 AS (select 1 AS k) "
-            + "SELECT COUNT(*) "
-            + "FROM t1 JOIN t3 USING (k)";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("count").build()).addValues(1L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testWithQueryEight() {
-    String sql =
-        "WITH T AS (SELECT k, 'hello' AS s FROM UNNEST([1, 2, 3]) k) "
-            + "SELECT COUNT(*) "
-            + "FROM T t1 JOIN T t2 USING (k)";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("count").build()).addValues(3L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUNNESTLiteral() {
-    String sql = "SELECT * FROM UNNEST(ARRAY<STRING>['foo', 'bar']);";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    Schema schema = Schema.builder().addStringField("str_field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("foo").build(),
-            Row.withSchema(schema).addValues("bar").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnnestLiteralWithNullElements() {
-    String sql = "SELECT * FROM UNNEST(ARRAY<STRING>['foo', NULL, 'bar']);";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    Schema schema = Schema.builder().addNullableField("str_field", FieldType.STRING).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("foo").build(),
-            Row.withSchema(schema).addValues((String) null).build(),
-            Row.withSchema(schema).addValues("bar").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUNNESTParameters() {
-    String sql = "SELECT * FROM UNNEST(@p0);";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createArrayValue(
-                TypeFactory.createArrayType(TypeFactory.createSimpleType(TypeKind.TYPE_STRING)),
-                ImmutableList.of(Value.createStringValue("foo"), Value.createStringValue("bar"))));
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    Schema schema = Schema.builder().addStringField("str_field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("foo").build(),
-            Row.withSchema(schema).addValues("bar").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  @Ignore(
-      "[https://github.com/apache/beam/issues/20139] ArrayScanToUncollectConverter Unnest does not support sub-queries")
-  public void testUNNESTExpression() {
-    String sql = "SELECT * FROM UNNEST(ARRAY(SELECT Value FROM KeyValue));";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    Schema schema = Schema.builder().addStringField("str_field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("KeyValue234").build(),
-            Row.withSchema(schema).addValues("KeyValue235").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testNamedUNNESTLiteral() {
-    String sql = "SELECT *, T1 FROM UNNEST(ARRAY<STRING>['foo', 'bar']) AS T1";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    Schema schema =
-        Schema.builder().addStringField("str_field").addStringField("str2_field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("foo", "foo").build(),
-            Row.withSchema(schema).addValues("bar", "bar").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testNamedUNNESTLiteralOffset() {
-    String sql = "SELECT x, p FROM UNNEST([3, 4]) AS x WITH OFFSET p";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    thrown.expect(UnsupportedOperationException.class);
-    BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-  }
-
-  @Test
-  public void testUnnestArrayColumn() {
-    String sql =
-        "SELECT p FROM table_with_array_for_unnest, UNNEST(table_with_array_for_unnest.int_array_col) as p";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addInt64Field("int_field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValue(14L).build(),
-            Row.withSchema(schema).addValue(18L).build(),
-            Row.withSchema(schema).addValue(22L).build(),
-            Row.withSchema(schema).addValue(24L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnnestArrayOfStructColumn() {
-    String sql = "SELECT int_col, data FROM table_with_array_of_struct, UNNEST(array_col) AS s";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addInt64Field("int_col").addStringField("data").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(10L, "1").build(),
-            Row.withSchema(schema).addValues(20L, "2").build(),
-            Row.withSchema(schema).addValues(20L, "3").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnnestArrayOfStructLiteral() {
-    String sql = "SELECT a, b FROM UNNEST([STRUCT(1 AS a, '1' AS b), STRUCT(2, '2')])";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addInt64Field("a").addStringField("b").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L, "1").build(),
-            Row.withSchema(schema).addValues(2L, "2").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStructOfStructPassthrough() {
-    String sql = "SELECT * FROM table_with_struct_of_struct";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(TestInput.STRUCT_OF_STRUCT)
-                .attachValues(Row.withSchema(TestInput.STRUCT_SCHEMA).attachValues(1L, "1")),
-            Row.withSchema(TestInput.STRUCT_OF_STRUCT)
-                .attachValues(Row.withSchema(TestInput.STRUCT_SCHEMA).attachValues(2L, "2")));
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStructOfStructSimpleRename() {
-    String sql = "SELECT row as not_row FROM table_with_struct_of_struct";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addRowField("not_row", TestInput.STRUCT_SCHEMA).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .attachValues(Row.withSchema(TestInput.STRUCT_SCHEMA).attachValues(1L, "1")),
-            Row.withSchema(schema)
-                .attachValues(Row.withSchema(TestInput.STRUCT_SCHEMA).attachValues(2L, "2")));
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  @Ignore("[BEAM-9378] This should work, but is currently unimplemented.")
-  public void testStructOfStructRemap() {
-    String sql =
-        "SELECT STRUCT(row.row_id AS int_value_remapped) AS remapped FROM table_with_struct_of_struct";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema nested = Schema.builder().addInt64Field("int_value_remapped").build();
-    Schema schema = Schema.builder().addRowField("remapped", nested).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).attachValues(Row.withSchema(nested).attachValues(1L)),
-            Row.withSchema(schema).attachValues(Row.withSchema(nested).attachValues(2L)));
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnnestStructOfStructOfArray() {
-    String sql =
-        "SELECT int_col, s FROM table_with_struct_of_struct_of_array, UNNEST(struct_col.struct.arr) as s";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addInt64Field("int_col").addStringField("p").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(10L, "1").build(),
-            Row.withSchema(schema).addValues(20L, "2").build(),
-            Row.withSchema(schema).addValues(20L, "3").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnnestArrayOfStructOfStructColumn() {
-    String sql = "SELECT s.row FROM table_with_array_of_struct_of_struct, UNNEST(array_col) as s";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addRowField("row", TestInput.STRUCT_SCHEMA).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(Row.withSchema(TestInput.STRUCT_SCHEMA).addValues(1L, "1").build())
-                .build(),
-            Row.withSchema(schema)
-                .addValues(Row.withSchema(TestInput.STRUCT_SCHEMA).addValues(2L, "2").build())
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnnestArrayOfStructOfStructLiteral() {
-    String sql =
-        "SELECT s.row FROM UNNEST([STRUCT(STRUCT(1, '1') as row), STRUCT(STRUCT(2, '2'))]) as s";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addRowField("row", TestInput.STRUCT_SCHEMA).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(Row.withSchema(TestInput.STRUCT_SCHEMA).addValues(1L, "1").build())
-                .build(),
-            Row.withSchema(schema)
-                .addValues(Row.withSchema(TestInput.STRUCT_SCHEMA).addValues(2L, "2").build())
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnnestStructOfArrayOfStructColumn() {
-    String sql =
-        "SELECT int_col, data FROM table_with_struct_of_array_of_struct, UNNEST(struct_col.arr) as s";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addInt64Field("int_col").addStringField("p").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(10L, "1").build(),
-            Row.withSchema(schema).addValues(20L, "2").build(),
-            Row.withSchema(schema).addValues(20L, "3").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnnestArrayOfStructOfArrayColumn() {
-    String sql =
-        "SELECT s FROM table_with_array_of_struct_of_array, UNNEST(array_col), UNNEST(arr) AS s";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema schema = Schema.builder().addStringField("s").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("a").build(),
-            Row.withSchema(schema).addValues("b").build(),
-            Row.withSchema(schema).addValues("c").build(),
-            Row.withSchema(schema).addValues("d").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnnestArrayOfStructOfArrayLiteral() {
-    String sql = "SELECT b FROM UNNEST([STRUCT([1, 2, 3] AS a)]), UNNEST(a) AS b";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema schema = Schema.builder().addInt64Field("int").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L).build(),
-            Row.withSchema(schema).addValues(2L).build(),
-            Row.withSchema(schema).addValues(3L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStringAggregation() {
-    String sql =
-        "SELECT STRING_AGG(fruit) AS string_agg"
-            + " FROM UNNEST([\"apple\", \"pear\", \"banana\", \"pear\"]) AS fruit";
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addStringField("string_field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValue("apple,pear,banana,pear").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStringAggregationBytes() {
-    String sql =
-        "SELECT STRING_AGG(CAST(fruit as bytes)) AS string_agg"
-            + " FROM UNNEST([\"apple\", \"pear\", \"banana\", \"pear\"]) AS fruit";
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addByteArrayField("bytearray_field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValue("apple,pear,banana,pear".getBytes(StandardCharsets.UTF_8))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStringAggregationDelimiter() {
-    String sql =
-        "SELECT STRING_AGG(fruit, \"&\") AS string_agg"
-            + " FROM UNNEST([\"apple\", \"pear\", \"banana\", \"pear\"]) AS fruit";
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addStringField("string_field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValue("apple&pear&banana&pear").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStringAggregationBytesDelimiter() {
-    String sql =
-        "SELECT STRING_AGG(CAST(fruit as bytes), b\"&\") AS string_agg"
-            + " FROM UNNEST([\"apple\", \"pear\", \"banana\", \"pear\"]) AS fruit";
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addByteArrayField("bytearray_field").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValue("apple&pear&banana&pear".getBytes(StandardCharsets.UTF_8))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStringAggregationParamsDelimiter() {
-    String sql = "SELECT string_agg(\"s\", @separator) FROM (SELECT 1)";
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.<String, Value>builder()
-            .put("separator", Value.createStringValue(","))
-            .build();
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(ZetaSqlException.class); // BEAM-13673
-    zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-  }
-
-  @Test
-  @Ignore("Seeing exception in Beam, need further investigation on the cause of this failed query.")
-  public void testNamedUNNESTJoin() {
-    String sql =
-        "SELECT * "
-            + "FROM table_with_array_for_unnest AS t1"
-            + " LEFT JOIN UNNEST(t1.int_array_col) AS t2"
-            + " on "
-            + " t1.int_col = t2";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream).empty();
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnnestJoinStruct() {
-    String sql =
-        "SELECT b, x FROM UNNEST("
-            + "[STRUCT(true AS b, [3, 5] AS arr), STRUCT(false AS b, [7, 9] AS arr)]) t "
-            + "LEFT JOIN UNNEST(t.arr) x ON b";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(UnsupportedOperationException.class);
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testUnnestJoinLiteral() {
-    String sql =
-        "SELECT a, b "
-            + "FROM UNNEST([1, 1, 2, 3, 5, 8, 13, NULL]) a "
-            + "JOIN UNNEST([1, 2, 3, 5, 7, 11, 13, NULL]) b "
-            + "ON a = b";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(UnsupportedOperationException.class);
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testUnnestJoinSubquery() {
-    String sql =
-        "SELECT a, b "
-            + "FROM UNNEST([1, 2, 3]) a "
-            + "JOIN UNNEST(ARRAY(SELECT b FROM UNNEST([3, 2, 1]) b)) b "
-            + "ON a = b";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(UnsupportedOperationException.class);
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testCaseNoValue() {
-    String sql = "SELECT CASE WHEN 1 > 2 THEN 'not possible' ELSE 'seems right' END";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("str_field").build())
-                .addValue("seems right")
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCaseWithValue() {
-    String sql = "SELECT CASE 1 WHEN 2 THEN 'not possible' ELSE 'seems right' END";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("str_field").build())
-                .addValue("seems right")
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCaseWithValueMultipleCases() {
-    String sql =
-        "SELECT CASE 2 WHEN 1 THEN 'not possible' WHEN 2 THEN 'seems right' ELSE 'also not"
-            + " possible' END";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("str_field").build())
-                .addValue("seems right")
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCaseWithValueNoElse() {
-    String sql = "SELECT CASE 2 WHEN 1 THEN 'not possible' WHEN 2 THEN 'seems right' END";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("str_field").build())
-                .addValue("seems right")
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCaseNoValueNoElseNoMatch() {
-    String sql = "SELECT CASE WHEN 'abc' = '123' THEN 'not possible' END";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addNullableField("str_field", FieldType.STRING).build())
-                .addValue(null)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCaseWithValueNoElseNoMatch() {
-    String sql = "SELECT CASE 2 WHEN 1 THEN 'not possible' END";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addNullableField("str_field", FieldType.STRING).build())
-                .addValue(null)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCastToDateWithCase() {
-    String sql =
-        "SELECT f_int, \n"
-            + "CASE WHEN CHAR_LENGTH(TRIM(f_string)) = 8 \n"
-            + "    THEN CAST (CONCAT(\n"
-            + "       SUBSTR(TRIM(f_string), 1, 4) \n"
-            + "        , '-' \n"
-            + "        , SUBSTR(TRIM(f_string), 5, 2) \n"
-            + "        , '-' \n"
-            + "        , SUBSTR(TRIM(f_string), 7, 2)) AS DATE)\n"
-            + "    ELSE NULL\n"
-            + "END \n"
-            + "FROM table_for_case_when";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema resultType =
-        Schema.builder()
-            .addInt64Field("f_long")
-            .addNullableField("f_date", FieldType.logicalType(SqlTypes.DATE))
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(resultType).addValues(1L, LocalDate.parse("2018-10-18")).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIntersectAll() {
-    String sql =
-        "SELECT Key FROM aggregate_test_table "
-            + "INTERSECT ALL "
-            + "SELECT Key FROM aggregate_test_table_two";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema resultType = Schema.builder().addInt64Field("field").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(resultType).addValues(1L).build(),
-            Row.withSchema(resultType).addValues(2L).build(),
-            Row.withSchema(resultType).addValues(2L).build(),
-            Row.withSchema(resultType).addValues(2L).build(),
-            Row.withSchema(resultType).addValues(3L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIntersectDistinct() {
-    String sql =
-        "SELECT Key FROM aggregate_test_table "
-            + "INTERSECT DISTINCT "
-            + "SELECT Key FROM aggregate_test_table_two";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema resultType = Schema.builder().addInt64Field("field").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(resultType).addValues(1L).build(),
-            Row.withSchema(resultType).addValues(2L).build(),
-            Row.withSchema(resultType).addValues(3L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testExceptAll() {
-    String sql =
-        "SELECT Key FROM aggregate_test_table "
-            + "EXCEPT ALL "
-            + "SELECT Key FROM aggregate_test_table_two";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema resultType = Schema.builder().addInt64Field("field").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(resultType).addValues(1L).build(),
-            Row.withSchema(resultType).addValues(3L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectNullIntersectDistinct() {
-    String sql = "SELECT NULL INTERSECT DISTINCT SELECT 2";
-
-    PCollection<Row> stream = execute(sql);
-    System.err.println("SCHEMA " + stream.getSchema());
-
-    PAssert.that(stream).empty();
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectNullIntersectAll() {
-    String sql = "SELECT NULL INTERSECT ALL SELECT 2";
-
-    PCollection<Row> stream = execute(sql);
-    System.err.println("SCHEMA " + stream.getSchema());
-
-    PAssert.that(stream).empty();
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectNullExceptDistinct() {
-    String sql = "SELECT NULL EXCEPT DISTINCT SELECT 2";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream).containsInAnyOrder(Row.nullRow(stream.getSchema()));
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectNullExceptAll() {
-    String sql = "SELECT NULL EXCEPT ALL SELECT 2";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream).containsInAnyOrder(Row.nullRow(stream.getSchema()));
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectFromEmptyTable() {
-    String sql = "SELECT * FROM table_empty;";
-    PCollection<Row> stream = execute(sql);
-    PAssert.that(stream).empty();
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStartsWithString() {
-    String sql = "SELECT STARTS_WITH('string1', 'stri')";
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(true).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStartsWithString2() {
-    String sql = "SELECT STARTS_WITH(@p0, @p1)";
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.<String, Value>builder()
-            .put("p0", Value.createSimpleNullValue(TypeKind.TYPE_STRING))
-            .put("p1", Value.createStringValue(""))
-            .build();
-
-    PCollection<Row> stream = execute(sql, params);
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((Boolean) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStartsWithString3() {
-    String sql = "SELECT STARTS_WITH(@p0, @p1)";
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.<String, Value>builder()
-            .put("p0", Value.createSimpleNullValue(TypeKind.TYPE_STRING))
-            .put("p1", Value.createSimpleNullValue(TypeKind.TYPE_STRING))
-            .build();
-
-    PCollection<Row> stream = execute(sql, params);
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((Boolean) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEndsWithString() {
-    String sql = "SELECT STARTS_WITH('string1', 'ng0')";
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(false).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEndsWithString2() {
-    String sql = "SELECT STARTS_WITH(@p0, @p1)";
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.<String, Value>builder()
-            .put("p0", Value.createSimpleNullValue(TypeKind.TYPE_STRING))
-            .put("p1", Value.createStringValue(""))
-            .build();
-
-    PCollection<Row> stream = execute(sql, params);
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((Boolean) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEndsWithString3() {
-    String sql = "SELECT STARTS_WITH(@p0, @p1)";
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.<String, Value>builder()
-            .put("p0", Value.createSimpleNullValue(TypeKind.TYPE_STRING))
-            .put("p1", Value.createSimpleNullValue(TypeKind.TYPE_STRING))
-            .build();
-
-    PCollection<Row> stream = execute(sql, params);
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.BOOLEAN).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((Boolean) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testConcatWithOneParameters() {
-    String sql = "SELECT concat('abc')";
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addStringField("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("abc").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testConcatWithTwoParameters() {
-    String sql = "SELECT concat('abc', 'def')";
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addStringField("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("abcdef").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testConcatWithThreeParameters() {
-    String sql = "SELECT concat('abc', 'def', 'xyz')";
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addStringField("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("abcdefxyz").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testConcatWithFourParameters() {
-    String sql = "SELECT concat('abc', 'def', '  ', 'xyz')";
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addStringField("field1").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues("abcdef  xyz").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testConcatWithFiveParameters() {
-    String sql = "SELECT concat('abc', 'def', '  ', 'xyz', 'kkk')";
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addStringField("field1").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues("abcdef  xyzkkk").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testConcatWithSixParameters() {
-    String sql = "SELECT concat('abc', 'def', '  ', 'xyz', 'kkk', 'ttt')";
-    PCollection<Row> stream = execute(sql);
-    final Schema schema = Schema.builder().addStringField("field1").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues("abcdef  xyzkkkttt").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testConcatWithNull1() {
-    String sql = "SELECT CONCAT(@p0, @p1) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createStringValue(""),
-            "p1",
-            Value.createSimpleNullValue(TypeKind.TYPE_STRING));
-    PCollection<Row> stream = execute(sql, params);
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((String) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testConcatWithNull2() {
-    String sql = "SELECT CONCAT(@p0, @p1) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0",
-            Value.createSimpleNullValue(TypeKind.TYPE_STRING),
-            "p1",
-            Value.createSimpleNullValue(TypeKind.TYPE_STRING));
-    PCollection<Row> stream = execute(sql, params);
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((String) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testNamedParameterQuery() {
-    String sql = "SELECT @ColA AS ColA";
-    ImmutableMap<String, Value> params = ImmutableMap.of("ColA", Value.createInt64Value(5));
-
-    PCollection<Row> stream = execute(sql, params);
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(5L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testArrayStructLiteral() {
-    String sql = "SELECT ARRAY<STRUCT<INT64, INT64>>[(11, 12)];";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    final Schema innerSchema =
-        Schema.of(Field.of("s", FieldType.INT64), Field.of("i", FieldType.INT64));
-    final Schema schema =
-        Schema.of(Field.of("field1", FieldType.array(FieldType.row(innerSchema))));
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValue(ImmutableList.of(Row.withSchema(innerSchema).addValues(11L, 12L).build()))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testParameterStruct() {
-    String sql = "SELECT @p as ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p",
-            Value.createStructValue(
-                TypeFactory.createStructType(
-                    ImmutableList.of(
-                        new StructType.StructField(
-                            "s", TypeFactory.createSimpleType(TypeKind.TYPE_STRING)),
-                        new StructType.StructField(
-                            "i", TypeFactory.createSimpleType(TypeKind.TYPE_INT64)))),
-                ImmutableList.of(Value.createStringValue("foo"), Value.createInt64Value(1L))));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema innerSchema =
-        Schema.of(Field.of("s", FieldType.STRING), Field.of("i", FieldType.INT64));
-    final Schema schema = Schema.of(Field.of("field1", FieldType.row(innerSchema)));
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValue(Row.withSchema(innerSchema).addValues("foo", 1L).build())
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testParameterStructNested() {
-    String sql = "SELECT @outer_struct.inner_struct.s as ColA";
-    StructType innerStructType =
-        TypeFactory.createStructType(
-            ImmutableList.of(
-                new StructType.StructField(
-                    "s", TypeFactory.createSimpleType(TypeKind.TYPE_STRING))));
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "outer_struct",
-            Value.createStructValue(
-                TypeFactory.createStructType(
-                    ImmutableList.of(new StructType.StructField("inner_struct", innerStructType))),
-                ImmutableList.of(
-                    Value.createStructValue(
-                        innerStructType, ImmutableList.of(Value.createStringValue("foo"))))));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue("foo").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testConcatNamedParameterQuery() {
-    String sql = "SELECT CONCAT(@p0, @p1) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of("p0", Value.createStringValue(""), "p1", Value.createStringValue("A"));
-
-    PCollection<Row> stream = execute(sql, params);
-    final Schema schema = Schema.builder().addStringField("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("A").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testConcatPositionalParameterQuery() {
-    String sql = "SELECT CONCAT(?, ?, ?) AS ColA";
-    ImmutableList<Value> params =
-        ImmutableList.of(
-            Value.createStringValue("a"),
-            Value.createStringValue("b"),
-            Value.createStringValue("c"));
-
-    PCollection<Row> stream = execute(sql, params);
-    final Schema schema = Schema.builder().addStringField("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("abc").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testReplace1() {
-    String sql = "SELECT REPLACE(@p0, @p1, @p2) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue(""),
-            "p1", Value.createStringValue(""),
-            "p2", Value.createStringValue("a"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testReplace2() {
-    String sql = "SELECT REPLACE(@p0, @p1, @p2) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("abc"),
-            "p1", Value.createStringValue(""),
-            "p2", Value.createStringValue("xyz"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("abc").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testReplace3() {
-    String sql = "SELECT REPLACE(@p0, @p1, @p2) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue(""),
-            "p1", Value.createStringValue(""),
-            "p2", Value.createSimpleNullValue(TypeKind.TYPE_STRING));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((String) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testReplace4() {
-    String sql = "SELECT REPLACE(@p0, @p1, @p2) AS ColA";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createSimpleNullValue(TypeKind.TYPE_STRING),
-            "p1", Value.createSimpleNullValue(TypeKind.TYPE_STRING),
-            "p2", Value.createStringValue(""));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((String) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTrim1() {
-    String sql = "SELECT trim(@p0)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of("p0", Value.createStringValue("   a b c   "));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("a b c").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTrim2() {
-    String sql = "SELECT trim(@p0, @p1)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("abxyzab"), "p1", Value.createStringValue("ab"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("xyz").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTrim3() {
-    String sql = "SELECT trim(@p0, @p1)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createSimpleNullValue(TypeKind.TYPE_STRING),
-            "p1", Value.createSimpleNullValue(TypeKind.TYPE_STRING));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((String) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLTrim1() {
-    String sql = "SELECT ltrim(@p0)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of("p0", Value.createStringValue("   a b c   "));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("a b c   ").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLTrim2() {
-    String sql = "SELECT ltrim(@p0, @p1)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("abxyzab"), "p1", Value.createStringValue("ab"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("xyzab").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLTrim3() {
-    String sql = "SELECT ltrim(@p0, @p1)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createSimpleNullValue(TypeKind.TYPE_STRING),
-            "p1", Value.createSimpleNullValue(TypeKind.TYPE_STRING));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((String) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testRTrim1() {
-    String sql = "SELECT rtrim(@p0)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of("p0", Value.createStringValue("   a b c   "));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("   a b c").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testRTrim2() {
-    String sql = "SELECT rtrim(@p0, @p1)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("abxyzab"), "p1", Value.createStringValue("ab"));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("abxyz").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testRTrim3() {
-    String sql = "SELECT rtrim(@p0, @p1)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createSimpleNullValue(TypeKind.TYPE_STRING),
-            "p1", Value.createSimpleNullValue(TypeKind.TYPE_STRING));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.STRING).build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((String) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  @Ignore("https://github.com/apache/beam/issues/20101")
-  public void testCastBytesToString1() {
-    String sql = "SELECT CAST(@p0 AS STRING)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of("p0", Value.createBytesValue(ByteString.copyFromUtf8("`")));
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("`").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCastBytesToString2() {
-    String sql = "SELECT CAST(b'b' AS STRING)";
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("b").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  @Ignore("https://github.com/apache/beam/issues/20101")
-  public void testCastBytesToStringFromTable() {
-    String sql = "SELECT CAST(bytes_col AS STRING) FROM table_all_types";
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("1").build(),
-            Row.withSchema(schema).addValues("2").build(),
-            Row.withSchema(schema).addValues("3").build(),
-            Row.withSchema(schema).addValues("4").build(),
-            Row.withSchema(schema).addValues("5").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCastStringToTimestamp() {
-    String sql = "SELECT CAST('2019-01-15 13:21:03' AS TIMESTAMP)";
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addDateTimeField("field_1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(parseTimestampWithUTCTimeZone("2019-01-15 13:21:03"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  // test default timezone works properly in query analysis stage
-  public void testCastStringToTimestampWithDefaultTimezoneSet() {
-    String sql = "SELECT CAST('2014-12-01 12:34:56+07:30' AS TIMESTAMP)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    zetaSQLQueryPlanner.setDefaultTimezone("Pacific/Chatham");
-    pipeline
-        .getOptions()
-        .as(BeamSqlPipelineOptions.class)
-        .setZetaSqlDefaultTimezone("Pacific/Chatham");
-
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDateTimeField("field_1").build())
-                .addValues(parseTimestampWithUTCTimeZone("2014-12-01 05:04:56"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  @Ignore("https://github.com/apache/beam/issues/20351")
-  public void testCastBetweenTimeAndString() {
-    String sql =
-        "SELECT CAST(s1 as TIME) as t2, CAST(t1 as STRING) as s2 FROM "
-            + "(SELECT '12:34:56.123456' as s1, TIME '12:34:56.123456' as t1)";
-
-    PCollection<Row> stream = execute(sql);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addLogicalTypeField("t2", SqlTypes.TIME)
-                        .addStringField("s2")
-                        .build())
-                .addValues(LocalTime.of(12, 34, 56, 123456000), "12:34:56.123456")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCastStringToString() {
-    String sql = "SELECT CAST(@p0 AS STRING)";
-    ImmutableMap<String, Value> params = ImmutableMap.of("p0", Value.createStringValue(""));
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCastStringToInt64() {
-    String sql = "SELECT CAST(@p0 AS INT64)";
-    ImmutableMap<String, Value> params = ImmutableMap.of("p0", Value.createStringValue("123"));
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(123L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectConstant() {
-    String sql = "SELECT 'hi'";
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("hi").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  @Ignore("[https://github.com/apache/beam/issues/19963] ZetaSQL does not support Map type")
-  public void testSelectFromTableWithMap() {
-    String sql = "SELECT row_field FROM table_with_map";
-    PCollection<Row> stream = execute(sql);
-    Schema rowSchema = Schema.builder().addInt64Field("row_id").addStringField("data").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addRowField("row_field", rowSchema).build())
-                .addValues(Row.withSchema(rowSchema).addValues(1L, "data1").build())
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSubQuery() {
-    String sql = "select sum(Key) from KeyValue\n" + "group by (select Key)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(UnsupportedOperationException.class);
-    thrown.expectMessage("Does not support sub-queries");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testSubstr() {
-    String sql = "SELECT substr(@p0, @p1, @p2)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("abc"),
-            "p1", Value.createInt64Value(-2L),
-            "p2", Value.createInt64Value(1L));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("b").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSubstring() {
-    String sql = "SELECT substring(@p0, @p1, @p2)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("abc"),
-            "p1", Value.createInt64Value(-2L),
-            "p2", Value.createInt64Value(1L));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addStringField("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("b").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSubstrWithLargeValueExpectException() {
-    String sql = "SELECT substr(@p0, @p1, @p2)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createStringValue("abc"),
-            "p1", Value.createInt64Value(Integer.MAX_VALUE + 1L),
-            "p2", Value.createInt64Value(Integer.MIN_VALUE - 1L));
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-    BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    thrown.expect(RuntimeException.class);
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectAll() {
-    String sql = "SELECT ALL Key, Value FROM KeyValue;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").addStringField("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(14L, "KeyValue234").build(),
-            Row.withSchema(schema).addValues(15L, "KeyValue235").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectDistinct() {
-    String sql = "SELECT DISTINCT Key FROM aggregate_test_table;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(1L).build(),
-            Row.withSchema(schema).addValues(2L).build(),
-            Row.withSchema(schema).addValues(3L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectDistinct2() {
-    String sql =
-        "SELECT DISTINCT val.BYTES\n"
-            + "from (select b\"BYTES\" BYTES union all\n"
-            + "      select b\"bytes\" union all\n"
-            + "      select b\"ByTeS\") val";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addByteArrayField("field1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("BYTES".getBytes(StandardCharsets.UTF_8)).build(),
-            Row.withSchema(schema).addValues("ByTeS".getBytes(StandardCharsets.UTF_8)).build(),
-            Row.withSchema(schema).addValues("bytes".getBytes(StandardCharsets.UTF_8)).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectBytes() {
-    String sql = "SELECT b\"ByTes\"";
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addByteArrayField("field1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("ByTes".getBytes(StandardCharsets.UTF_8)).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectExcept() {
-    String sql = "SELECT * EXCEPT (Key, ts) FROM KeyValue;";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addStringField("field2").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues("KeyValue234").build(),
-            Row.withSchema(schema).addValues("KeyValue235").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSelectReplace() {
-    String sql =
-        "WITH orders AS\n"
-            + "  (SELECT 5 as order_id,\n"
-            + "  \"sprocket\" as item_name,\n"
-            + "  200 as quantity)\n"
-            + "SELECT * REPLACE (\"widget\" AS item_name)\n"
-            + "FROM orders";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("field1")
-            .addStringField("field2")
-            .addInt64Field("field3")
-            .build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues(5L, "widget", 200L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnionAllBasic() {
-    String sql =
-        "SELECT row_id FROM table_all_types UNION ALL SELECT row_id FROM table_all_types_2";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValue(1L).build(),
-            Row.withSchema(schema).addValue(2L).build(),
-            Row.withSchema(schema).addValue(3L).build(),
-            Row.withSchema(schema).addValue(4L).build(),
-            Row.withSchema(schema).addValue(5L).build(),
-            Row.withSchema(schema).addValue(6L).build(),
-            Row.withSchema(schema).addValue(7L).build(),
-            Row.withSchema(schema).addValue(8L).build(),
-            Row.withSchema(schema).addValue(9L).build(),
-            Row.withSchema(schema).addValue(10L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testAVGWithLongInput() {
-    String sql = "SELECT AVG(f_int_1) FROM aggregate_test_table;";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(RuntimeException.class);
-    thrown.expectMessage(
-        "AVG(INT64) is not supported. You might want to use AVG(CAST(expression AS FLOAT64).");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testReverseString() {
-    String sql = "SELECT REVERSE('abc');";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addStringField("field2").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues("cba").build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCharLength() {
-    String sql = "SELECT CHAR_LENGTH('abc');";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(3L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCharLengthNull() {
-    String sql = "SELECT CHAR_LENGTH(@p0);";
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of("p0", Value.createSimpleNullValue(TypeKind.TYPE_STRING));
-
-    PCollection<Row> stream = execute(sql, params);
-
-    final Schema schema = Schema.builder().addNullableField("field", FieldType.INT64).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues((Object) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTumbleAsTVF() {
-    String sql =
-        "select Key, Value, ts, window_start, window_end from "
-            + "TUMBLE((select * from KeyValue), descriptor(ts), 'INTERVAL 1 SECOND')";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    ImmutableMap<String, Value> params = ImmutableMap.of();
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("Key")
-            .addStringField("Value")
-            .addDateTimeField("ts")
-            .addDateTimeField("window_start")
-            .addDateTimeField("window_end")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    14L,
-                    "KeyValue234",
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2018-07-01 21:26:06"),
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2018-07-01 21:26:06"),
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2018-07-01 21:26:07"))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(
-                    15L,
-                    "KeyValue235",
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2018-07-01 21:26:07"),
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2018-07-01 21:26:07"),
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2018-07-01T21:26:08"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIsNullTrueFalse() {
-    String sql =
-        "WITH Src AS (\n"
-            + "  SELECT NULL as data UNION ALL\n"
-            + "  SELECT TRUE UNION ALL\n"
-            + "  SELECT FALSE\n"
-            + ")\n"
-            + "SELECT\n"
-            + "  data IS NULL as isnull,\n"
-            + "  data IS NOT NULL as isnotnull,\n"
-            + "  data IS TRUE as istrue,\n"
-            + "  data IS NOT TRUE as isnottrue,\n"
-            + "  data IS FALSE as isfalse,\n"
-            + "  data IS NOT FALSE as isnotfalse\n"
-            + "FROM Src\n";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    ImmutableMap<String, Value> params = ImmutableMap.of();
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addField("isnull", FieldType.BOOLEAN)
-            .addField("isnotnull", FieldType.BOOLEAN)
-            .addField("istrue", FieldType.BOOLEAN)
-            .addField("isnottrue", FieldType.BOOLEAN)
-            .addField("isfalse", FieldType.BOOLEAN)
-            .addField("isnotfalse", FieldType.BOOLEAN)
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(true, false, false, true, false, true).build(),
-            Row.withSchema(schema).addValues(false, true, true, false, false, true).build(),
-            Row.withSchema(schema).addValues(false, true, false, true, true, false).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBitOr() {
-    String sql = "SELECT BIT_OR(row_id) FROM table_all_types GROUP BY bool_col";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(3L).build(),
-            Row.withSchema(schema).addValue(7L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBitOrNull() {
-    String sql =
-        "SELECT bit_or(CAST(x as int64)) FROM "
-            + "(SELECT NULL x UNION ALL SELECT 5 UNION ALL SELECT 6);";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue(7L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBitAnd() {
-    String sql = "SELECT BIT_AND(row_id) FROM table_all_types GROUP BY bool_col";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValue(1L).build(),
-            Row.withSchema(schema).addValue(0L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBitAndInt64() {
-    String sql = "SELECT bit_and(CAST(x as int64)) FROM (SELECT 1 x FROM (SELECT 1) WHERE false)";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addNullableField("field1", FieldType.INT64).build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue((Long) null).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBitAndNulls() {
-    String sql =
-        "SELECT bit_and(CAST(x as int64)) FROM "
-            + "(SELECT NULL x UNION ALL SELECT 5 UNION ALL SELECT 6)";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue(4L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCountEmpty() {
-    String sql = "SELECT COUNT(x) FROM UNNEST([]) AS x";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue(0L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testBitwiseOrEmpty() {
-    String sql = "SELECT BIT_OR(x) FROM UNNEST([]) AS x";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addNullableField("field1", FieldType.INT64).build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue((Long) null).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testArrayAggNulls() {
-    String sql = "SELECT ARRAY_AGG(x) FROM UNNEST([1, NULL, 3]) AS x";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema =
-        Schema.builder()
-            .addField(
-                Field.of(
-                    "field1",
-                    FieldType.array(FieldType.of(Schema.TypeName.INT64).withNullable(true))))
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addArray(1L, (Long) null, 3L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testArrayAggEmpty() {
-    String sql = "SELECT ARRAY_AGG(x) FROM UNNEST([]) AS x";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addNullableField("field1", FieldType.INT64).build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue((Long) null).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testInt64SumOverflow() {
-    String sql =
-        "SELECT SUM(col1)\n"
-            + "FROM (SELECT CAST(9223372036854775807 as int64) as col1 UNION ALL\n"
-            + "      SELECT CAST(1 as int64))\n";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    thrown.expect(RuntimeException.class);
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testInt64SumUnderflow() {
-    String sql =
-        "SELECT SUM(col1)\n"
-            + "FROM (SELECT CAST(-9223372036854775808 as int64) as col1 UNION ALL\n"
-            + "      SELECT CAST(-1 as int64))\n";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    thrown.expect(RuntimeException.class);
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLSumNulls() {
-    String sql = "SELECT SUM(x) AS sum FROM UNNEST([null, null, null]) AS x";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addNullableField("field1", FieldType.INT64).build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue((Long) null).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSimpleTableName() {
-    String sql = "SELECT Key FROM KeyValue";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema singleField = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(singleField).addValues(14L).build(),
-            Row.withSchema(singleField).addValues(15L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBitXor() {
-    String sql = "SELECT BIT_XOR(x) AS bit_xor FROM UNNEST([5678, 1234]) AS x";
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue(4860L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBitXorEmpty() {
-    String sql = "SELECT bit_xor(CAST(x as int64)) FROM (SELECT 1 x FROM (SELECT 1) WHERE false);";
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addNullableField("field1", FieldType.INT64).build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue((Long) null).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testZetaSQLBitXorNull() {
-    String sql = "SELECT bit_xor(x) FROM (SELECT CAST(NULL AS int64) x);";
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addNullableField("field1", FieldType.INT64).build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue((Long) null).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCountIfZetaSQLDialect() {
-    String sql =
-        "WITH is_positive AS ( SELECT x > 0 flag FROM UNNEST([5, -2, 3, 6, -10, -7, 4, 0]) AS x) "
-            + "SELECT COUNTIF(flag) FROM is_positive";
-
-    PCollection<Row> stream = execute(sql);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValue(4L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testArrayAggZetasql() {
-    String sql = "SELECT ARRAY_AGG(x) AS array_agg " + "FROM UNNEST([1, 2, 3, 4, 5]) AS x";
-
-    PCollection<Row> stream = execute(sql);
-
-    Schema schema = Schema.builder().addArrayField("array_field", FieldType.INT64).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addArray(1L, 2L, 3L, 4L, 5L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlJavaUdfTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlJavaUdfTest.java
deleted file mode 100644
index 8b21bec08be6..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlJavaUdfTest.java
+++ /dev/null
@@ -1,460 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.hamcrest.Matchers.allOf;
-import static org.hamcrest.Matchers.containsString;
-import static org.hamcrest.Matchers.hasProperty;
-import static org.hamcrest.Matchers.isA;
-import static org.junit.Assert.fail;
-
-import com.google.zetasql.SqlException;
-import java.lang.reflect.Method;
-import java.time.LocalDate;
-import org.apache.beam.sdk.Pipeline;
-import org.apache.beam.sdk.extensions.sql.BeamSqlUdf;
-import org.apache.beam.sdk.extensions.sql.SqlTransform;
-import org.apache.beam.sdk.extensions.sql.impl.JdbcConnection;
-import org.apache.beam.sdk.extensions.sql.impl.JdbcDriver;
-import org.apache.beam.sdk.extensions.sql.impl.ScalarFunctionImpl;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.extensions.sql.meta.provider.ReadOnlyTableProvider;
-import org.apache.beam.sdk.options.PipelineOptionsFactory;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.schemas.logicaltypes.SqlTypes;
-import org.apache.beam.sdk.testing.PAssert;
-import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.sdk.transforms.Sum;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Frameworks;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-import org.checkerframework.checker.nullness.qual.Nullable;
-import org.codehaus.commons.compiler.CompileException;
-import org.joda.time.Duration;
-import org.junit.Before;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.rules.ExpectedException;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-/**
- * Tests for ZetaSQL UDFs written in Java.
- *
- * <p>System properties <code>beam.sql.udf.test.jarpath</code> and <code>
- * beam.sql.udf.test.empty_jar_path</code> must be set.
- */
-@RunWith(JUnit4.class)
-public class ZetaSqlJavaUdfTest extends ZetaSqlTestBase {
-  @Rule public transient TestPipeline pipeline = TestPipeline.create();
-  @Rule public ExpectedException thrown = ExpectedException.none();
-
-  private final String jarPathProperty = "beam.sql.udf.test.jar_path";
-  private final String emptyJarPathProperty = "beam.sql.udf.test.empty_jar_path";
-
-  private final @Nullable String jarPath = System.getProperty(jarPathProperty);
-  private final @Nullable String emptyJarPath = System.getProperty(emptyJarPathProperty);
-
-  @Before
-  public void setUp() {
-    if (jarPath == null) {
-      fail(
-          String.format(
-              "System property %s must be set to run %s.",
-              jarPathProperty, ZetaSqlJavaUdfTest.class.getSimpleName()));
-    }
-    if (emptyJarPath == null) {
-      fail(
-          String.format(
-              "System property %s must be set to run %s.",
-              emptyJarPathProperty, ZetaSqlJavaUdfTest.class.getSimpleName()));
-    }
-    initialize();
-  }
-
-  @Test
-  public void testNullaryJavaUdf() {
-    String sql =
-        String.format(
-            "CREATE FUNCTION helloWorld() RETURNS STRING LANGUAGE java "
-                + "OPTIONS (path='%s'); "
-                + "SELECT helloWorld();",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema singleField = Schema.builder().addStringField("field1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(singleField).addValues("Hello world!").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnaryJavaUdf() {
-    String sql =
-        String.format(
-            "CREATE FUNCTION increment(i INT64) RETURNS INT64 LANGUAGE java "
-                + "OPTIONS (path='%s'); "
-                + "SELECT increment(1);",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema singleField = Schema.builder().addInt64Field("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(singleField).addValues(2L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testJavaUdfColumnReference() {
-    String sql =
-        String.format(
-            "CREATE FUNCTION increment(i INT64) RETURNS INT64 LANGUAGE java "
-                + "OPTIONS (path='%s'); "
-                + "SELECT increment(int64_col) FROM table_all_types;",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema singleField = Schema.builder().addInt64Field("field1").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(singleField).addValues(0L).build(),
-            Row.withSchema(singleField).addValues(-1L).build(),
-            Row.withSchema(singleField).addValues(-2L).build(),
-            Row.withSchema(singleField).addValues(-3L).build(),
-            Row.withSchema(singleField).addValues(-4L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testNestedJavaUdf() {
-    String sql =
-        String.format(
-            "CREATE FUNCTION increment(i INT64) RETURNS INT64 LANGUAGE java "
-                + "OPTIONS (path='%s'); "
-                + "SELECT increment(increment(1));",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema singleField = Schema.builder().addInt64Field("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(singleField).addValues(3L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnexpectedNullArgumentThrowsRuntimeException() {
-    String sql =
-        String.format(
-            "CREATE FUNCTION increment(i INT64) RETURNS INT64 LANGUAGE java "
-                + "OPTIONS (path='%s'); "
-                + "SELECT increment(NULL);",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    thrown.expect(Pipeline.PipelineExecutionException.class);
-    thrown.expectMessage("CalcFn failed to evaluate");
-    thrown.expectCause(
-        allOf(isA(RuntimeException.class), hasProperty("cause", isA(NullPointerException.class))));
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testExpectedNullArgument() {
-    String sql =
-        String.format(
-            "CREATE FUNCTION isNull(s STRING) RETURNS BOOLEAN LANGUAGE java "
-                + "OPTIONS (path='%s'); "
-                + "SELECT isNull(NULL);",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema singleField = Schema.builder().addBooleanField("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(singleField).addValues(true).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  public static class IncrementFn implements BeamSqlUdf {
-    public Long eval(Long i) {
-      return i + 1;
-    }
-  }
-
-  @Test
-  public void testSqlTransformRegisterUdf() {
-    String sql = "SELECT increment(0);";
-    PCollection<Row> stream =
-        pipeline.apply(
-            SqlTransform.query(sql)
-                .withQueryPlannerClass(ZetaSQLQueryPlanner.class)
-                .registerUdf("increment", IncrementFn.class));
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(1L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  /** This tests a subset of the code path used by {@link #testSqlTransformRegisterUdf()}. */
-  @Test
-  public void testUdfFromCatalog() throws NoSuchMethodException {
-    // Add IncrementFn to Calcite schema.
-    JdbcConnection jdbcConnection =
-        JdbcDriver.connect(
-            new ReadOnlyTableProvider("empty_table_provider", ImmutableMap.of()),
-            PipelineOptionsFactory.create());
-    Method method = IncrementFn.class.getMethod("eval", Long.class);
-    jdbcConnection.getCurrentSchemaPlus().add("increment", ScalarFunctionImpl.create(method));
-    this.config =
-        Frameworks.newConfigBuilder(config)
-            .defaultSchema(jdbcConnection.getCurrentSchemaPlus())
-            .build();
-
-    String sql = "SELECT increment(0);";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(1L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testNullArgumentIsTypeChecked() {
-    // The Java definition for isNull takes a String, but here we declare it in SQL with INT64.
-    String sql =
-        String.format(
-            "CREATE FUNCTION isNull(i INT64) RETURNS INT64 LANGUAGE java "
-                + "OPTIONS (path='%s'); "
-                + "SELECT isNull(NULL);",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    // TODO(https://github.com/apache/beam/issues/20614) This should fail earlier, before compiling
-    // the CalcFn.
-    thrown.expect(UnsupportedOperationException.class);
-    thrown.expectMessage("Could not compile CalcFn");
-    thrown.expectCause(
-        allOf(
-            isA(CompileException.class),
-            hasProperty(
-                "message",
-                containsString(
-                    "No applicable constructor/method found for actual parameters \"java.lang.Long\""))));
-    BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-  }
-
-  @Test
-  public void testFunctionSignatureTypeMismatchFailsPipelineConstruction() {
-    // The Java definition for isNull takes a String, but here we pass it a Long.
-    String sql =
-        String.format(
-            "CREATE FUNCTION isNull(i INT64) RETURNS BOOLEAN LANGUAGE java "
-                + "OPTIONS (path='%s'); "
-                + "SELECT isNull(0);",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    // TODO(https://github.com/apache/beam/issues/20614) This should fail earlier, before compiling
-    // the CalcFn.
-    thrown.expect(UnsupportedOperationException.class);
-    thrown.expectMessage("Could not compile CalcFn");
-    thrown.expectCause(
-        allOf(
-            isA(CompileException.class),
-            hasProperty(
-                "message",
-                containsString(
-                    "No applicable constructor/method found for actual parameters \"long\""))));
-    BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-  }
-
-  @Test
-  public void testJavaUdfWithNoReturnTypeIsRejected() {
-    String sql =
-        String.format(
-            "CREATE FUNCTION helloWorld() LANGUAGE java "
-                + "OPTIONS (path='%s'); "
-                + "SELECT helloWorld();",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(SqlException.class);
-    thrown.expectMessage("Non-SQL functions must specify a return type");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testProjectUdfAndBuiltin() {
-    String sql =
-        String.format(
-            "CREATE FUNCTION matches(str STRING, regStr STRING) RETURNS BOOLEAN LANGUAGE java OPTIONS (path='%s'); "
-                + "SELECT matches(\"a\", \"a\"), 'apple'='beta'",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema schema = Schema.builder().addBooleanField("field1").addBooleanField("field2").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(true, false).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testProjectNestedUdfAndBuiltin() {
-    String sql =
-        String.format(
-            "CREATE FUNCTION increment(i INT64) RETURNS INT64 LANGUAGE java OPTIONS (path='%s'); "
-                + "SELECT increment(increment(0) + 1);",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema schema = Schema.builder().addInt64Field("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(schema).addValues(3L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testJavaUdfEmptyPath() {
-    String sql =
-        "CREATE FUNCTION foo() RETURNS STRING LANGUAGE java OPTIONS (path=''); SELECT foo();";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(RuntimeException.class);
-    thrown.expectMessage("Failed to define function 'foo'");
-    thrown.expectCause(
-        allOf(
-            isA(IllegalArgumentException.class),
-            hasProperty("message", containsString("No jar was provided to define function foo."))));
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testJavaUdfNoJarProvided() {
-    String sql = "CREATE FUNCTION foo() RETURNS STRING LANGUAGE java; SELECT foo();";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(RuntimeException.class);
-    thrown.expectMessage("Failed to define function 'foo'");
-    thrown.expectCause(
-        allOf(
-            isA(IllegalArgumentException.class),
-            hasProperty("message", containsString("No jar was provided to define function foo."))));
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testPathOptionNotString() {
-    String sql =
-        "CREATE FUNCTION foo() RETURNS STRING LANGUAGE java OPTIONS (path=23); SELECT foo();";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(RuntimeException.class);
-    thrown.expectMessage("Failed to define function 'foo'");
-    thrown.expectCause(
-        allOf(
-            isA(IllegalArgumentException.class),
-            hasProperty(
-                "message",
-                containsString("Option 'path' has type TYPE_INT64 (expected TYPE_STRING)."))));
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testUdaf() {
-    String sql =
-        String.format(
-            "CREATE AGGREGATE FUNCTION my_sum(f INT64) RETURNS INT64 LANGUAGE java OPTIONS (path='%s'); "
-                + "SELECT my_sum(f_int_1) from aggregate_test_table",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema singleField = Schema.builder().addInt64Field("field1").build();
-
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(singleField).addValues(28L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUdafNotFoundFailsToParse() {
-    String sql =
-        String.format(
-            "CREATE AGGREGATE FUNCTION nonexistent(f INT64) RETURNS INT64 LANGUAGE java OPTIONS (path='%s'); "
-                + "SELECT nonexistent(f_int_1) from aggregate_test_table",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-
-    thrown.expect(RuntimeException.class);
-    thrown.expectMessage("Failed to define function 'nonexistent'");
-    thrown.expectCause(
-        allOf(
-            isA(IllegalArgumentException.class),
-            hasProperty(
-                "message",
-                containsString("No implementation of aggregate function nonexistent found"))));
-
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testRegisterUdaf() {
-    String sql = "SELECT my_sum(k) FROM UNNEST([1, 2, 3]) k;";
-    PCollection<Row> stream =
-        pipeline.apply(
-            SqlTransform.query(sql)
-                .withQueryPlannerClass(ZetaSQLQueryPlanner.class)
-                .registerUdaf("my_sum", Sum.ofLongs()));
-    Schema singleField = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(singleField).addValues(6L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateUdf() {
-    String sql =
-        String.format(
-            "CREATE FUNCTION dateIncrementAll(d DATE) RETURNS DATE LANGUAGE java "
-                + "OPTIONS (path='%s'); "
-                + "SELECT dateIncrementAll('2020-04-04');",
-            jarPath);
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-    Schema singleField = Schema.builder().addLogicalTypeField("field1", SqlTypes.DATE).build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(singleField).addValues(LocalDate.of(2021, 5, 5)).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlJavaUdfTypeTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlJavaUdfTypeTest.java
deleted file mode 100644
index 74569afd308b..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlJavaUdfTypeTest.java
+++ /dev/null
@@ -1,586 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import java.math.BigDecimal;
-import java.sql.Date;
-import java.sql.Timestamp;
-import java.time.LocalDate;
-import java.util.List;
-import org.apache.beam.sdk.extensions.sql.BeamSqlUdf;
-import org.apache.beam.sdk.extensions.sql.impl.JdbcConnection;
-import org.apache.beam.sdk.extensions.sql.impl.JdbcDriver;
-import org.apache.beam.sdk.extensions.sql.impl.ScalarFunctionImpl;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.extensions.sql.meta.provider.ReadOnlyTableProvider;
-import org.apache.beam.sdk.extensions.sql.meta.provider.test.TestBoundedTable;
-import org.apache.beam.sdk.options.PipelineOptionsFactory;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.schemas.logicaltypes.SqlTypes;
-import org.apache.beam.sdk.testing.PAssert;
-import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.schema.SchemaPlus;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Frameworks;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-import org.joda.time.DateTime;
-import org.joda.time.DateTimeZone;
-import org.joda.time.Duration;
-import org.junit.Before;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.rules.ExpectedException;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-/** Tests verifying that various data types can be passed through Java UDFs without data loss. */
-@RunWith(JUnit4.class)
-public class ZetaSqlJavaUdfTypeTest extends ZetaSqlTestBase {
-  @Rule public transient TestPipeline pipeline = TestPipeline.create();
-  @Rule public ExpectedException thrown = ExpectedException.none();
-
-  private static final TestBoundedTable table =
-      TestBoundedTable.of(
-              Schema.builder()
-                  .addBooleanField("boolean_true")
-                  .addBooleanField("boolean_false")
-                  .addInt64Field("int64_0")
-                  .addInt64Field("int64_pos")
-                  .addInt64Field("int64_neg")
-                  .addInt64Field("int64_max")
-                  .addInt64Field("int64_min")
-                  .addStringField("string_empty")
-                  .addStringField("string_ascii")
-                  .addStringField("string_unicode")
-                  .addByteArrayField("bytes_empty")
-                  .addByteArrayField("bytes_ascii")
-                  .addByteArrayField("bytes_unicode")
-                  .addDoubleField("float64_0")
-                  .addDoubleField("float64_noninteger")
-                  .addDoubleField("float64_pos")
-                  .addDoubleField("float64_neg")
-                  .addDoubleField("float64_max")
-                  .addDoubleField("float64_min_pos")
-                  .addDoubleField("float64_inf")
-                  .addDoubleField("float64_neg_inf")
-                  .addDoubleField("float64_nan")
-                  .addLogicalTypeField("f_date", SqlTypes.DATE)
-                  .addDateTimeField("f_timestamp")
-                  .addArrayField("array_int64", Schema.FieldType.INT64)
-                  .addDecimalField("numeric_one")
-                  .addDecimalField("numeric_max")
-                  .addDecimalField("numeric_min")
-                  .build())
-          .addRows(
-              true /* boolean_true */,
-              false /* boolean_false */,
-              0L /* int64_0 */,
-              123L /* int64_pos */,
-              -123L /* int64_neg */,
-              9223372036854775807L /* int64_max */,
-              -9223372036854775808L /* int64_min */,
-              "" /* string_empty */,
-              "abc" /* string_ascii */,
-              "スタリング" /* string_unicode */,
-              new byte[] {} /* bytes_empty */,
-              new byte[] {'a', 'b', 'c'} /* bytes_ascii */,
-              new byte[] {-29, -126, -71} /* bytes_unicode */,
-              0.0 /* float64_0 */,
-              0.123 /* float64_noninteger */,
-              123.0 /* float64_pos */,
-              -123.0 /* float64_neg */,
-              1.7976931348623157e+308 /* float64_max */,
-              2.2250738585072014e-308 /* float64_min_pos */,
-              Double.POSITIVE_INFINITY /* float64_inf */,
-              Double.NEGATIVE_INFINITY /* float64_neg_inf */,
-              Double.NaN /* float64_nan */,
-              LocalDate.of(2021, 4, 26) /* f_date */,
-              new DateTime(2021, 5, 6, 3, 48, 32, DateTimeZone.UTC) /* f_timestamp */,
-              ImmutableList.of(1L, 2L, 3L) /* array_int64 */,
-              new BigDecimal("1.000000000" /* numeric_one */),
-              new BigDecimal("99999999999999999999999999999.999999999" /* numeric_max */),
-              new BigDecimal("-99999999999999999999999999999.999999999" /* numeric_min */));
-
-  @Before
-  public void setUp() throws NoSuchMethodException {
-    initialize();
-
-    // Register test table.
-    JdbcConnection jdbcConnection =
-        JdbcDriver.connect(
-            new ReadOnlyTableProvider("table_provider", ImmutableMap.of("table", table)),
-            PipelineOptionsFactory.create());
-
-    // Register UDFs.
-    SchemaPlus schema = jdbcConnection.getCurrentSchemaPlus();
-    schema.add(
-        "test_boolean",
-        ScalarFunctionImpl.create(BooleanIdentityFn.class.getMethod("eval", Boolean.class)));
-    schema.add(
-        "test_int64",
-        ScalarFunctionImpl.create(Int64IdentityFn.class.getMethod("eval", Long.class)));
-    schema.add(
-        "test_string",
-        ScalarFunctionImpl.create(StringIdentityFn.class.getMethod("eval", String.class)));
-    schema.add(
-        "test_bytes",
-        ScalarFunctionImpl.create(BytesIdentityFn.class.getMethod("eval", byte[].class)));
-    schema.add(
-        "test_float64",
-        ScalarFunctionImpl.create(DoubleIdentityFn.class.getMethod("eval", Double.class)));
-    schema.add(
-        "test_date", ScalarFunctionImpl.create(DateIdentityFn.class.getMethod("eval", Date.class)));
-    schema.add(
-        "test_timestamp",
-        ScalarFunctionImpl.create(TimestampIdentityFn.class.getMethod("eval", Timestamp.class)));
-    schema.add(
-        "test_array",
-        ScalarFunctionImpl.create(ListIdentityFn.class.getMethod("eval", List.class)));
-    schema.add(
-        "test_numeric",
-        ScalarFunctionImpl.create(BigDecimalIdentityFn.class.getMethod("eval", BigDecimal.class)));
-
-    this.config = Frameworks.newConfigBuilder(config).defaultSchema(schema).build();
-  }
-
-  public static class BooleanIdentityFn implements BeamSqlUdf {
-    public Boolean eval(Boolean input) {
-      return input;
-    }
-  }
-
-  public static class Int64IdentityFn implements BeamSqlUdf {
-    public Long eval(Long input) {
-      return input;
-    }
-  }
-
-  public static class StringIdentityFn implements BeamSqlUdf {
-    public String eval(String input) {
-      return input;
-    }
-  }
-
-  public static class BytesIdentityFn implements BeamSqlUdf {
-    public byte[] eval(byte[] input) {
-      return input;
-    }
-  }
-
-  public static class DoubleIdentityFn implements BeamSqlUdf {
-    public Double eval(Double input) {
-      return input;
-    }
-  }
-
-  public static class DateIdentityFn implements BeamSqlUdf {
-    public Date eval(Date input) {
-      return input;
-    }
-  }
-
-  public static class TimestampIdentityFn implements BeamSqlUdf {
-    public Timestamp eval(Timestamp input) {
-      return input;
-    }
-  }
-
-  public static class ListIdentityFn implements BeamSqlUdf {
-    public List<Long> eval(List<Long> input) {
-      return input;
-    }
-  }
-
-  public static class BigDecimalIdentityFn implements BeamSqlUdf {
-    public BigDecimal eval(BigDecimal input) {
-      return input;
-    }
-  }
-
-  private void runUdfTypeTest(String query, Object result, Schema.TypeName typeName) {
-    runUdfTypeTest(query, result, Schema.FieldType.of(typeName));
-  }
-
-  private void runUdfTypeTest(String query, Object result, Schema.LogicalType<?, ?> logicalType) {
-    runUdfTypeTest(query, result, Schema.FieldType.logicalType(logicalType));
-  }
-
-  private void runUdfTypeTest(String query, Object result, Schema.FieldType fieldType) {
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(query);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema outputSchema = Schema.builder().addField("res", fieldType).build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(outputSchema).addValues(result).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTrueLiteral() {
-    runUdfTypeTest("SELECT test_boolean(true);", true, Schema.TypeName.BOOLEAN);
-  }
-
-  @Test
-  public void testTrueInput() {
-    runUdfTypeTest("SELECT test_boolean(boolean_true) FROM table;", true, Schema.TypeName.BOOLEAN);
-  }
-
-  @Test
-  public void testFalseLiteral() {
-    runUdfTypeTest("SELECT test_boolean(false);", false, Schema.TypeName.BOOLEAN);
-  }
-
-  @Test
-  public void testFalseInput() {
-    runUdfTypeTest(
-        "SELECT test_boolean(boolean_false) FROM table;", false, Schema.TypeName.BOOLEAN);
-  }
-
-  @Test
-  public void testZeroInt64Literal() {
-    runUdfTypeTest("SELECT test_int64(0);", 0L, Schema.TypeName.INT64);
-  }
-
-  @Test
-  public void testZeroInt64Input() {
-    runUdfTypeTest("SELECT test_int64(int64_0) FROM table;", 0L, Schema.TypeName.INT64);
-  }
-
-  @Test
-  public void testPosInt64Literal() {
-    runUdfTypeTest("SELECT test_int64(123);", 123L, Schema.TypeName.INT64);
-  }
-
-  @Test
-  public void testPosInt64Input() {
-    runUdfTypeTest("SELECT test_int64(int64_pos) FROM table;", 123L, Schema.TypeName.INT64);
-  }
-
-  @Test
-  public void testNegInt64Literal() {
-    runUdfTypeTest("SELECT test_int64(-123);", -123L, Schema.TypeName.INT64);
-  }
-
-  @Test
-  public void testNegInt64Input() {
-    runUdfTypeTest("SELECT test_int64(int64_neg) FROM table;", -123L, Schema.TypeName.INT64);
-  }
-
-  @Test
-  public void testMaxInt64Literal() {
-    runUdfTypeTest(
-        "SELECT test_int64(9223372036854775807);", 9223372036854775807L, Schema.TypeName.INT64);
-  }
-
-  @Test
-  public void testMaxInt64Input() {
-    runUdfTypeTest(
-        "SELECT test_int64(int64_max) FROM table;", 9223372036854775807L, Schema.TypeName.INT64);
-  }
-
-  @Test
-  public void testMinInt64Literal() {
-    runUdfTypeTest(
-        "SELECT test_int64(-9223372036854775808);", -9223372036854775808L, Schema.TypeName.INT64);
-  }
-
-  @Test
-  public void testMinInt64Input() {
-    runUdfTypeTest(
-        "SELECT test_int64(int64_min) FROM table;", -9223372036854775808L, Schema.TypeName.INT64);
-  }
-
-  @Test
-  public void testEmptyStringLiteral() {
-    runUdfTypeTest("SELECT test_string('');", "", Schema.TypeName.STRING);
-  }
-
-  @Test
-  public void testEmptyStringInput() {
-    runUdfTypeTest("SELECT test_string(string_empty) FROM table;", "", Schema.TypeName.STRING);
-  }
-
-  @Test
-  public void testAsciiStringLiteral() {
-    runUdfTypeTest("SELECT test_string('abc');", "abc", Schema.TypeName.STRING);
-  }
-
-  @Test
-  public void testAsciiStringInput() {
-    runUdfTypeTest("SELECT test_string(string_ascii) FROM table;", "abc", Schema.TypeName.STRING);
-  }
-
-  @Test
-  public void testUnicodeStringLiteral() {
-    runUdfTypeTest("SELECT test_string('スタリング');", "スタリング", Schema.TypeName.STRING);
-  }
-
-  @Test
-  public void testUnicodeStringInput() {
-    runUdfTypeTest(
-        "SELECT test_string(string_unicode) FROM table;", "スタリング", Schema.TypeName.STRING);
-  }
-
-  @Test
-  public void testEmptyBytesLiteral() {
-    runUdfTypeTest("SELECT test_bytes(b'');", new byte[] {}, Schema.TypeName.BYTES);
-  }
-
-  @Test
-  public void testEmptyBytesInput() {
-    runUdfTypeTest(
-        "SELECT test_bytes(bytes_empty) FROM table;", new byte[] {}, Schema.TypeName.BYTES);
-  }
-
-  @Test
-  public void testAsciiBytesLiteral() {
-    runUdfTypeTest("SELECT test_bytes(b'abc');", new byte[] {'a', 'b', 'c'}, Schema.TypeName.BYTES);
-  }
-
-  @Test
-  public void testAsciiBytesInput() {
-    runUdfTypeTest(
-        "SELECT test_bytes(bytes_ascii) FROM table;",
-        new byte[] {'a', 'b', 'c'},
-        Schema.TypeName.BYTES);
-  }
-
-  @Test
-  public void testUnicodeBytesLiteral() {
-    runUdfTypeTest("SELECT test_bytes(b'ス');", new byte[] {-29, -126, -71}, Schema.TypeName.BYTES);
-  }
-
-  @Test
-  public void testUnicodeBytesInput() {
-    runUdfTypeTest(
-        "SELECT test_bytes(bytes_unicode) FROM table;",
-        new byte[] {-29, -126, -71},
-        Schema.TypeName.BYTES);
-  }
-
-  @Test
-  public void testZeroFloat64Literal() {
-    runUdfTypeTest("SELECT test_float64(0.0);", 0.0, Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testZeroFloat64Input() {
-    runUdfTypeTest("SELECT test_float64(float64_0) FROM table;", 0.0, Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testNonIntegerFloat64Literal() {
-    runUdfTypeTest("SELECT test_float64(0.123);", 0.123, Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testNonIntegerFloat64Input() {
-    runUdfTypeTest(
-        "SELECT test_float64(float64_noninteger) FROM table;", 0.123, Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testPosFloat64Literal() {
-    runUdfTypeTest("SELECT test_float64(123.0);", 123.0, Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testPosFloat64Input() {
-    runUdfTypeTest("SELECT test_float64(float64_pos) FROM table;", 123.0, Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testNegFloat64Literal() {
-    runUdfTypeTest("SELECT test_float64(-123.0);", -123.0, Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testNegFloat64Input() {
-    runUdfTypeTest("SELECT test_float64(float64_neg) FROM table;", -123.0, Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testMaxFloat64Literal() {
-    runUdfTypeTest(
-        "SELECT test_float64(1.7976931348623157e+308);",
-        1.7976931348623157e+308,
-        Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testMaxFloat64Input() {
-    runUdfTypeTest(
-        "SELECT test_float64(float64_max) FROM table;",
-        1.7976931348623157e+308,
-        Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testMinPosFloat64Literal() {
-    runUdfTypeTest(
-        "SELECT test_float64(2.2250738585072014e-308);",
-        2.2250738585072014e-308,
-        Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testMinPosFloat64Input() {
-    runUdfTypeTest(
-        "SELECT test_float64(float64_min_pos) FROM table;",
-        2.2250738585072014e-308,
-        Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testPosInfFloat64Literal() {
-    runUdfTypeTest(
-        "SELECT test_float64(CAST('+inf' AS FLOAT64));",
-        Double.POSITIVE_INFINITY,
-        Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testPosInfFloat64Input() {
-    runUdfTypeTest(
-        "SELECT test_float64(float64_inf) FROM table;",
-        Double.POSITIVE_INFINITY,
-        Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testNegInfFloat64Literal() {
-    runUdfTypeTest(
-        "SELECT test_float64(CAST('-inf' AS FLOAT64));",
-        Double.NEGATIVE_INFINITY,
-        Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testNegInfFloat64Input() {
-    runUdfTypeTest(
-        "SELECT test_float64(float64_neg_inf) FROM table;",
-        Double.NEGATIVE_INFINITY,
-        Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testNaNFloat64Literal() {
-    runUdfTypeTest(
-        "SELECT test_float64(CAST('NaN' AS FLOAT64));", Double.NaN, Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testNaNFloat64Input() {
-    runUdfTypeTest(
-        "SELECT test_float64(float64_nan) FROM table;", Double.NaN, Schema.TypeName.DOUBLE);
-  }
-
-  @Test
-  public void testDateLiteral() {
-    runUdfTypeTest("SELECT test_date('2021-04-26');", LocalDate.of(2021, 4, 26), SqlTypes.DATE);
-  }
-
-  @Test
-  public void testDateInput() {
-    runUdfTypeTest(
-        "SELECT test_date(f_date) FROM table;", LocalDate.of(2021, 4, 26), SqlTypes.DATE);
-  }
-
-  @Test
-  public void testTimestampLiteral() {
-    runUdfTypeTest(
-        "SELECT test_timestamp('2021-05-06 03:48:32Z');",
-        new DateTime(2021, 5, 6, 3, 48, 32, DateTimeZone.UTC),
-        Schema.TypeName.DATETIME);
-  }
-
-  @Test
-  public void testTimestampInput() {
-    runUdfTypeTest(
-        "SELECT test_timestamp(f_timestamp) FROM table;",
-        new DateTime(2021, 5, 6, 3, 48, 32, DateTimeZone.UTC),
-        Schema.TypeName.DATETIME);
-  }
-
-  @Test
-  public void testArrayLiteral() {
-    runUdfTypeTest(
-        "SELECT test_array(ARRAY<INT64>[1, 2, 3]);",
-        ImmutableList.of(1L, 2L, 3L),
-        Schema.FieldType.array(Schema.FieldType.INT64));
-  }
-
-  @Test
-  public void testArrayInput() {
-    runUdfTypeTest(
-        "SELECT test_array(array_int64) FROM table;",
-        ImmutableList.of(1L, 2L, 3L),
-        Schema.FieldType.array(Schema.FieldType.INT64));
-  }
-
-  @Test
-  public void testNumericOneLiteral() {
-    runUdfTypeTest(
-        "SELECT test_numeric(1.000000000);",
-        new BigDecimal("1.000000000"),
-        Schema.FieldType.DECIMAL);
-  }
-
-  @Test
-  public void testNumericMaxLiteral() {
-    runUdfTypeTest(
-        "SELECT test_numeric(99999999999999999999999999999.999999999);",
-        new BigDecimal("99999999999999999999999999999.999999999"),
-        Schema.FieldType.DECIMAL);
-  }
-
-  @Test
-  public void testNumericMinLiteral() {
-    runUdfTypeTest(
-        "SELECT test_numeric(-99999999999999999999999999999.999999999);",
-        new BigDecimal("-99999999999999999999999999999.999999999"),
-        Schema.FieldType.DECIMAL);
-  }
-
-  @Test
-  public void testNumericOneInput() {
-    runUdfTypeTest(
-        "SELECT test_numeric(numeric_one) FROM table;",
-        new BigDecimal("1.000000000"),
-        Schema.FieldType.DECIMAL);
-  }
-
-  @Test
-  public void testNumericMaxInput() {
-    runUdfTypeTest(
-        "SELECT test_numeric(numeric_max) FROM table;",
-        new BigDecimal("99999999999999999999999999999.999999999"),
-        Schema.FieldType.DECIMAL);
-  }
-
-  @Test
-  public void testNumericMinInput() {
-    runUdfTypeTest(
-        "SELECT test_numeric(numeric_min) FROM table;",
-        new BigDecimal("-99999999999999999999999999999.999999999"),
-        Schema.FieldType.DECIMAL);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlMathFunctionsTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlMathFunctionsTest.java
deleted file mode 100644
index 92e509c00d30..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlMathFunctionsTest.java
+++ /dev/null
@@ -1,1034 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.testing.PAssert;
-import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.Row;
-import org.joda.time.Duration;
-import org.junit.Before;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.rules.ExpectedException;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-/** Tests for ZetaSQL Math functions (on INT64, DOUBLE, NUMERIC types). */
-@RunWith(JUnit4.class)
-public class ZetaSqlMathFunctionsTest extends ZetaSqlTestBase {
-
-  @Rule public transient TestPipeline pipeline = TestPipeline.create();
-  @Rule public ExpectedException thrown = ExpectedException.none();
-
-  @Before
-  public void setUp() {
-    initialize();
-  }
-
-  /////////////////////////////////////////////////////////////////////////////
-  // INT64 type tests
-  /////////////////////////////////////////////////////////////////////////////
-
-  @Test
-  public void testArithmeticOperatorsInt64() {
-    String sql = "SELECT -1, 1 + 2, 1 - 2, 1 * 2, 1 / 2";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addInt64Field("f_int64_1")
-                        .addInt64Field("f_int64_2")
-                        .addInt64Field("f_int64_3")
-                        .addInt64Field("f_int64_4")
-                        .addDoubleField("f_double")
-                        .build())
-                .addValues(-1L, 3L, -1L, 2L, 0.5)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testAbsInt64() {
-    String sql = "SELECT ABS(1), ABS(-1)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addInt64Field("f_int64_1").addInt64Field("f_int64_2").build())
-                .addValues(1L, 1L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSignInt64() {
-    String sql = "SELECT SIGN(0), SIGN(5), SIGN(-5)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addInt64Field("f_int64_1")
-                        .addInt64Field("f_int64_2")
-                        .addInt64Field("f_int64_3")
-                        .build())
-                .addValues(0L, 1L, -1L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testModInt64() {
-    String sql = "SELECT MOD(4, 2)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("f_int64").build())
-                .addValues(0L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDivInt64() {
-    String sql = "SELECT DIV(1, 2), DIV(2, 1)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addInt64Field("f_int64_1").addInt64Field("f_int64_2").build())
-                .addValues(0L, 2L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSafeArithmeticFunctionsInt64() {
-    String sql =
-        "SELECT SAFE_ADD(9223372036854775807, 1), "
-            + "SAFE_SUBTRACT(-9223372036854775808, 1), "
-            + "SAFE_MULTIPLY(9223372036854775807, 2), "
-            + "SAFE_DIVIDE(1, 0), "
-            + "SAFE_NEGATE(-9223372036854775808)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addNullableField("f_int64_1", Schema.FieldType.INT64)
-                        .addNullableField("f_int64_2", Schema.FieldType.INT64)
-                        .addNullableField("f_int64_3", Schema.FieldType.INT64)
-                        .addNullableField("f_int64_4", Schema.FieldType.INT64)
-                        .addNullableField("f_int64_5", Schema.FieldType.INT64)
-                        .build())
-                .addValues(null, null, null, null, null)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  /////////////////////////////////////////////////////////////////////////////
-  // DOUBLE (FLOAT64) type tests
-  /////////////////////////////////////////////////////////////////////////////
-
-  @Test
-  public void testDoubleLiteral() {
-    String sql =
-        "SELECT 3.0, CAST('+inf' AS FLOAT64), CAST('-inf' AS FLOAT64), CAST('NaN' AS FLOAT64)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDoubleField("f_double1")
-                        .addDoubleField("f_double2")
-                        .addDoubleField("f_double3")
-                        .addDoubleField("f_double4")
-                        .build())
-                .addValues(3.0, Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, Double.NaN)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testArithmeticOperatorsDouble() {
-    String sql = "SELECT -1.5, 1.5 + 2.5, 1.5 - 2.5, 1.5 * 2.5, 1.5 / 2.5";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDoubleField("f_double1")
-                        .addDoubleField("f_double2")
-                        .addDoubleField("f_double3")
-                        .addDoubleField("f_double4")
-                        .addDoubleField("f_double5")
-                        .build())
-                .addValues(-1.5, 4.0, -1.0, 3.75, 0.6)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEqualsInf() {
-    String sql =
-        "SELECT CAST('+inf' AS FLOAT64) = CAST('+inf' AS FLOAT64), "
-            + "CAST('+inf' AS FLOAT64) = CAST('-inf' AS FLOAT64)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addBooleanField("f_boolean1")
-                        .addBooleanField("f_boolean2")
-                        .build())
-                .addValues(true, false)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testEqualsNaN() {
-    String sql = "SELECT CAST('NaN' AS FLOAT64) = CAST('NaN' AS FLOAT64)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addBooleanField("f_boolean").build())
-                .addValues(false)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testAbsDouble() {
-    String sql = "SELECT ABS(1.5), ABS(-1.0), ABS(CAST('NaN' AS FLOAT64))";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDoubleField("f_double1")
-                        .addDoubleField("f_double2")
-                        .addDoubleField("f_double3")
-                        .build())
-                .addValues(1.5, 1.0, Double.NaN)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSignDouble() {
-    String sql = "SELECT SIGN(-0.0), SIGN(1.5), SIGN(-1.5), SIGN(CAST('NaN' AS FLOAT64))";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDoubleField("f_double1")
-                        .addDoubleField("f_double2")
-                        .addDoubleField("f_double3")
-                        .addDoubleField("f_double4")
-                        .build())
-                .addValues(0.0, 1.0, -1.0, Double.NaN)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testRoundDouble() {
-    String sql = "SELECT ROUND(1.23), ROUND(-1.27, 1)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDoubleField("f_double1")
-                        .addDoubleField("f_double2")
-                        .build())
-                .addValues(1.0, -1.3)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTruncDouble() {
-    String sql = "SELECT TRUNC(1.23), TRUNC(-1.27, 1)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDoubleField("f_double1")
-                        .addDoubleField("f_double2")
-                        .build())
-                .addValues(1.0, -1.2)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCeilDouble() {
-    String sql = "SELECT CEIL(1.2), CEIL(-1.2)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDoubleField("f_double1")
-                        .addDoubleField("f_double2")
-                        .build())
-                .addValues(2.0, -1.0)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testFloorDouble() {
-    String sql = "SELECT FLOOR(1.2), FLOOR(-1.2)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDoubleField("f_double1")
-                        .addDoubleField("f_double2")
-                        .build())
-                .addValues(1.0, -2.0)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIsInf() {
-    String sql =
-        "SELECT IS_INF(CAST('+inf' AS FLOAT64)), IS_INF(CAST('-inf' AS FLOAT64)), IS_INF(3.0)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addBooleanField("f_boolean1")
-                        .addBooleanField("f_boolean2")
-                        .addBooleanField("f_boolean3")
-                        .build())
-                .addValues(true, true, false)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIsNaN() {
-    String sql = "SELECT IS_NAN(CAST('NaN' AS FLOAT64)), IS_NAN(3.0)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addBooleanField("f_boolean1")
-                        .addBooleanField("f_boolean2")
-                        .build())
-                .addValues(true, false)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testIeeeDivide() {
-    String sql = "SELECT IEEE_DIVIDE(1.0, 0.0)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDoubleField("f_double").build())
-                .addValues(Double.POSITIVE_INFINITY)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSafeDivide() {
-    String sql = "SELECT SAFE_DIVIDE(1.0, 0.0)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addNullableField("f_double", Schema.FieldType.DOUBLE).build())
-                .addValue(null)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSqrtDouble() {
-    String sql = "SELECT SQRT(4.0)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDoubleField("f_double").build())
-                .addValues(2.0)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testPowDouble() {
-    String sql = "SELECT POW(2.0, 3.0)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDoubleField("f_double").build())
-                .addValues(8.0)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testExpDouble() {
-    String sql = "SELECT EXP(2.0)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDoubleField("f_double").build())
-                .addValues(7.38905609893065)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLnDouble() {
-    String sql = "SELECT LN(7.38905609893065)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDoubleField("f_double").build())
-                .addValues(2.0)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLog10Double() {
-    String sql = "SELECT LOG10(100.0)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDoubleField("f_double").build())
-                .addValues(2.0)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLogDouble() {
-    String sql = "SELECT LOG(2.25, 1.5)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDoubleField("f_double").build())
-                .addValues(2.0)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTrigonometricFunctions() {
-    String sql =
-        "SELECT COS(0.0), COSH(0.0), ACOS(1.0), ACOSH(1.0), "
-            + "SIN(0.0), SINH(0.0), ASIN(0.0), ASINH(0.0), "
-            + "TAN(0.0), TANH(0.0), ATAN(0.0), ATANH(0.0), ATAN2(0.0, 0.0)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDoubleField("f_double1")
-                        .addDoubleField("f_double2")
-                        .addDoubleField("f_double3")
-                        .addDoubleField("f_double4")
-                        .addDoubleField("f_double5")
-                        .addDoubleField("f_double6")
-                        .addDoubleField("f_double7")
-                        .addDoubleField("f_double8")
-                        .addDoubleField("f_double9")
-                        .addDoubleField("f_double10")
-                        .addDoubleField("f_double11")
-                        .addDoubleField("f_double12")
-                        .addDoubleField("f_double13")
-                        .build())
-                .addValues(1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  /////////////////////////////////////////////////////////////////////////////
-  // NUMERIC type tests
-  /////////////////////////////////////////////////////////////////////////////
-
-  @Test
-  public void testNumericLiteral() {
-    String sql =
-        "SELECT NUMERIC '0', "
-            + "NUMERIC '123456', "
-            + "NUMERIC '-3.14', "
-            + "NUMERIC '-0.54321', "
-            + "NUMERIC '1.23456e05', "
-            + "NUMERIC '-9.876e-3', "
-            // min value for ZetaSQL NUMERIC type
-            + "NUMERIC '-99999999999999999999999999999.999999999', "
-            // max value for ZetaSQL NUMERIC type
-            + "NUMERIC '99999999999999999999999999999.999999999'";
-    ;
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDecimalField("f_numeric1")
-                        .addDecimalField("f_numeric2")
-                        .addDecimalField("f_numeric3")
-                        .addDecimalField("f_numeric4")
-                        .addDecimalField("f_numeric5")
-                        .addDecimalField("f_numeric6")
-                        .addDecimalField("f_numeric7")
-                        .addDecimalField("f_numeric8")
-                        .build())
-                .addValues(
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("0"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("123456"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("-3.14"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("-0.54321"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("123456"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("-0.009876"),
-                    ZetaSqlCalciteTranslationUtils.ZETASQL_NUMERIC_MIN_VALUE,
-                    ZetaSqlCalciteTranslationUtils.ZETASQL_NUMERIC_MAX_VALUE)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testNumericColumn() {
-    String sql = "SELECT numeric_field FROM table_with_numeric";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema = Schema.builder().addDecimalField("f_numeric").build();
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("123.4567"))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("765.4321"))
-                .build(),
-            Row.withSchema(schema)
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("-555.5555"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testArithmeticOperatorsNumeric() {
-    String sql =
-        "SELECT - NUMERIC '1.23456e05', "
-            + "NUMERIC '1.23456e05' + NUMERIC '9.876e-3', "
-            + "NUMERIC '1.23456e05' - NUMERIC '-9.876e-3', "
-            + "NUMERIC '1.23e02' * NUMERIC '-1.001e-3', "
-            + "NUMERIC '-1.23123e-1' / NUMERIC '-1.001e-3', ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDecimalField("f_numeric1")
-                        .addDecimalField("f_numeric2")
-                        .addDecimalField("f_numeric3")
-                        .addDecimalField("f_numeric4")
-                        .addDecimalField("f_numeric5")
-                        .build())
-                .addValues(
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("-123456"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("123456.009876"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("123456.009876"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("-0.123123"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("123"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testAbsNumeric() {
-    String sql = "SELECT ABS(NUMERIC '1.23456e04'), ABS(NUMERIC '-1.23456e04')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDecimalField("f_numeric1")
-                        .addDecimalField("f_numeric2")
-                        .build())
-                .addValues(
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("12345.6"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("12345.6"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSignNumeric() {
-    String sql = "SELECT SIGN(NUMERIC '0'), SIGN(NUMERIC '1.23e01'), SIGN(NUMERIC '-1.23e01')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDecimalField("f_numeric1")
-                        .addDecimalField("f_numeric2")
-                        .addDecimalField("f_numeric3")
-                        .build())
-                .addValues(
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("0"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("1"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("-1"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testRoundNumeric() {
-    String sql = "SELECT ROUND(NUMERIC '1.23456e04'), ROUND(NUMERIC '-1.234567e04', 1)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDecimalField("f_numeric1")
-                        .addDecimalField("f_numeric2")
-                        .build())
-                .addValues(
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("12346"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("-12345.7"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTruncNumeric() {
-    String sql = "SELECT TRUNC(NUMERIC '1.23456e04'), TRUNC(NUMERIC '-1.234567e04', 1)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDecimalField("f_numeric1")
-                        .addDecimalField("f_numeric2")
-                        .build())
-                .addValues(
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("12345"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("-12345.6"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testCeilNumeric() {
-    String sql = "SELECT CEIL(NUMERIC '1.23456e04'), CEIL(NUMERIC '-1.23456e04')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDecimalField("f_numeric1")
-                        .addDecimalField("f_numeric2")
-                        .build())
-                .addValues(
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("12346"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("-12345"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testFloorNumeric() {
-    String sql = "SELECT FLOOR(NUMERIC '1.23456e04'), FLOOR(NUMERIC '-1.23456e04')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDecimalField("f_numeric1")
-                        .addDecimalField("f_numeric2")
-                        .build())
-                .addValues(
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("12345"),
-                    ZetaSqlTypesUtils.bigDecimalAsNumeric("-12346"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testModNumeric() {
-    String sql = "SELECT MOD(NUMERIC '1.23456e05', NUMERIC '5')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDecimalField("f_numeric").build())
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("1"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDivNumeric() {
-    String sql = "SELECT DIV(NUMERIC '1.23456e05', NUMERIC '5')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDecimalField("f_numeric").build())
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("24691"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSafeArithmeticFunctionsNumeric() {
-    String sql =
-        "SELECT SAFE_ADD(NUMERIC '99999999999999999999999999999.999999999', NUMERIC '1'), "
-            + "SAFE_SUBTRACT(NUMERIC '-99999999999999999999999999999.999999999', NUMERIC '1'), "
-            + "SAFE_MULTIPLY(NUMERIC '99999999999999999999999999999.999999999', NUMERIC '2'), "
-            + "SAFE_DIVIDE(NUMERIC '1.23456e05', NUMERIC '0'), "
-            + "SAFE_NEGATE(NUMERIC '99999999999999999999999999999.999999999')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addNullableField("f_numeric1", Schema.FieldType.DECIMAL)
-                        .addNullableField("f_numeric2", Schema.FieldType.DECIMAL)
-                        .addNullableField("f_numeric3", Schema.FieldType.DECIMAL)
-                        .addNullableField("f_numeric4", Schema.FieldType.DECIMAL)
-                        .addNullableField("f_numeric5", Schema.FieldType.DECIMAL)
-                        .build())
-                .addValues(
-                    null,
-                    null,
-                    null,
-                    null,
-                    ZetaSqlCalciteTranslationUtils.ZETASQL_NUMERIC_MIN_VALUE)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSqrtNumeric() {
-    String sql = "SELECT SQRT(NUMERIC '4')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDecimalField("f_numeric").build())
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("2"))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testPowNumeric() {
-    String sql = "SELECT POW(NUMERIC '2', NUMERIC '3')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDecimalField("f_numeric").build())
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("8"))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testExpNumeric() {
-    String sql = "SELECT EXP(NUMERIC '2')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDecimalField("f_numeric").build())
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("7.389056099"))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLnNumeric() {
-    String sql = "SELECT LN(NUMERIC '7.389056099')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDecimalField("f_numeric").build())
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("2"))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLog10Numeric() {
-    String sql = "SELECT LOG10(NUMERIC '100')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDecimalField("f_numeric").build())
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("2"))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testLogNumeric() {
-    String sql = "SELECT LOG(NUMERIC '2.25', NUMERIC '1.5')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDecimalField("f_numeric").build())
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("2"))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testSumNumeric() {
-    String sql = "SELECT SUM(numeric_field) FROM table_with_numeric";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDecimalField("f_numeric").build())
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("333.3333"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testAvgNumeric() {
-    String sql = "SELECT AVG(numeric_field) FROM table_with_numeric";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDecimalField("f_numeric").build())
-                .addValues(ZetaSqlTypesUtils.bigDecimalAsNumeric("111.1111"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlNativeUdfTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlNativeUdfTest.java
deleted file mode 100644
index 9f37472cfae1..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlNativeUdfTest.java
+++ /dev/null
@@ -1,264 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.hamcrest.Matchers.isA;
-
-import com.google.zetasql.SqlException;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.testing.PAssert;
-import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.Row;
-import org.joda.time.Duration;
-import org.junit.Before;
-import org.junit.Ignore;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.rules.ExpectedException;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-/** Tests for SQL-native user defined functions in the ZetaSQL dialect. */
-@RunWith(JUnit4.class)
-public class ZetaSqlNativeUdfTest extends ZetaSqlTestBase {
-  @Rule public transient TestPipeline pipeline = TestPipeline.create();
-  @Rule public ExpectedException thrown = ExpectedException.none();
-
-  @Before
-  public void setUp() {
-    initialize();
-  }
-
-  @Test
-  public void testAlreadyDefinedUDFThrowsException() {
-    String sql = "CREATE FUNCTION foo() AS (0); CREATE FUNCTION foo() AS (1); SELECT foo();";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(RuntimeException.class);
-    thrown.expectMessage("Failed to define function 'foo'");
-    thrown.expectCause(isA(IllegalArgumentException.class));
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testCreateFunctionNoSelectThrowsException() {
-    String sql = "CREATE FUNCTION plusOne(x INT64) AS (x + 1);";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(UnsupportedOperationException.class);
-    thrown.expectMessage("Statement list must end in a SELECT statement, not CreateFunctionStmt");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testNullaryUdf() {
-    String sql = "CREATE FUNCTION zero() AS (0); SELECT zero();";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("x").build()).addValue(0L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testQualifiedNameUdfUnqualifiedCall() {
-    String sql = "CREATE FUNCTION foo.bar.baz() AS (\"uwu\"); SELECT baz();";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("x").build()).addValue("uwu").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  @Ignore(
-      "Qualified paths can't be resolved due to a bug in ZetaSQL: "
-          + "https://github.com/google/zetasql/issues/42")
-  public void testQualifiedNameUdfQualifiedCallThrowsException() {
-    String sql = "CREATE FUNCTION foo.bar.baz() AS (\"uwu\"); SELECT foo.bar.baz();";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("x").build()).addValue("uwu").build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUnaryUdf() {
-    String sql = "CREATE FUNCTION triple(x INT64) AS (3 * x); SELECT triple(triple(1));";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("x").build()).addValue(9L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUdfWithinUdf() {
-    String sql =
-        "CREATE FUNCTION triple(x INT64) AS (3 * x);"
-            + " CREATE FUNCTION nonuple(x INT64) as (triple(triple(x)));"
-            + " SELECT nonuple(1);";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("x").build()).addValue(9L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUndefinedUdfThrowsException() {
-    String sql =
-        "CREATE FUNCTION foo() AS (bar()); "
-            + "CREATE FUNCTION bar() AS (foo()); "
-            + "SELECT foo();";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(SqlException.class);
-    thrown.expectMessage("Function not found: bar");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testRecursiveUdfThrowsException() {
-    String sql = "CREATE FUNCTION omega() AS (omega()); SELECT omega();";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(SqlException.class);
-    thrown.expectMessage("Function not found: omega");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testUDTVF() {
-    String sql =
-        "CREATE TABLE FUNCTION CustomerRange(MinID INT64, MaxID INT64)\n"
-            + "  AS\n"
-            + "    SELECT *\n"
-            + "    FROM KeyValue\n"
-            + "    WHERE key >= MinId AND key <= MaxId; \n"
-            + " SELECT key FROM CustomerRange(10, 14)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema singleField = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream).containsInAnyOrder(Row.withSchema(singleField).addValues(14L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testNullaryUdtvf() {
-    String sql =
-        "CREATE TABLE FUNCTION CustomerRange()\n"
-            + "  AS\n"
-            + "    SELECT *\n"
-            + "    FROM KeyValue;\n"
-            + " SELECT key FROM CustomerRange()";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    Schema singleField = Schema.builder().addInt64Field("field1").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(singleField).addValues(14L).build(),
-            Row.withSchema(singleField).addValues(15L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testUDTVFTableNotFound() {
-    String sql =
-        "CREATE TABLE FUNCTION CustomerRange(MinID INT64, MaxID INT64)\n"
-            + "  AS\n"
-            + "    SELECT *\n"
-            + "    FROM TableNotExist\n"
-            + "    WHERE key >= MinId AND key <= MaxId; \n"
-            + " SELECT key FROM CustomerRange(10, 14)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(ZetaSqlException.class);
-    thrown.expectMessage("Wasn't able to resolve the path [TableNotExist] in schema: beam");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testUDTVFFunctionNotFound() {
-    String sql =
-        "CREATE TABLE FUNCTION CustomerRange(MinID INT64, MaxID INT64)\n"
-            + "  AS\n"
-            + "    SELECT *\n"
-            + "    FROM KeyValue\n"
-            + "    WHERE key >= MinId AND key <= MaxId; \n"
-            + " SELECT key FROM FunctionNotFound(10, 14)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(SqlException.class);
-    thrown.expectMessage("Table-valued function not found: FunctionNotFound");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testJavascriptUdfUnsupported() {
-    String sql = "CREATE FUNCTION foo() RETURNS STRING LANGUAGE js; SELECT foo();";
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(UnsupportedOperationException.class);
-    thrown.expectMessage("Function foo uses unsupported language js.");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testSqlNativeAggregateFunctionNotSupported() {
-    String sql =
-        "CREATE AGGREGATE FUNCTION double_sum(col FLOAT64)\n"
-            + "AS (2 * SUM(col));\n"
-            + "SELECT double_sum(col1) AS doubled_sum\n"
-            + "FROM (SELECT 1 AS col1 UNION ALL\n"
-            + "      SELECT 3 AS col1 UNION ALL\n"
-            + "      SELECT 5 AS col1\n"
-            + ");";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(UnsupportedOperationException.class);
-    thrown.expectMessage(
-        "Native SQL aggregate functions are not supported (https://github.com/apache/beam/issues/20193).");
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlNumberTypesTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlNumberTypesTest.java
deleted file mode 100644
index a3178c3031c2..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlNumberTypesTest.java
+++ /dev/null
@@ -1,76 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import com.google.zetasql.Value;
-import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-import org.junit.Before;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.rules.ExpectedException;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-/** Tests for ZetaSQL number type handling (on INT64, DOUBLE, NUMERIC types). */
-@RunWith(JUnit4.class)
-public class ZetaSqlNumberTypesTest extends ZetaSqlTestBase {
-  @Rule public transient TestPipeline pipeline = TestPipeline.create();
-  @Rule public ExpectedException thrown = ExpectedException.none();
-
-  @Before
-  public void setUp() {
-    initialize();
-  }
-
-  @Test
-  public void testCastDoubleAsNumericOverflow() {
-    double val = 1.7976931348623157e+308;
-    String sql = "SELECT CAST(@p0 AS NUMERIC) AS ColA";
-
-    thrown.expect(ZetaSqlException.class);
-    thrown.expectMessage("Casting TYPE_DOUBLE as TYPE_NUMERIC would cause overflow of literal");
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    zetaSQLQueryPlanner.convertToBeamRel(sql, ImmutableMap.of("p0", Value.createDoubleValue(val)));
-  }
-
-  @Test
-  public void testCastDoubleAsNumericUnderflow() {
-    double val = -1.7976931348623157e+308;
-    String sql = "SELECT CAST(@p0 AS NUMERIC) AS ColA";
-
-    thrown.expect(ZetaSqlException.class);
-    thrown.expectMessage("Casting TYPE_DOUBLE as TYPE_NUMERIC would cause underflow of literal");
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    zetaSQLQueryPlanner.convertToBeamRel(sql, ImmutableMap.of("p0", Value.createDoubleValue(val)));
-  }
-
-  @Test
-  public void testCastDoubleAsNumericScaleTooLarge() {
-    double val = 2.2250738585072014e-308;
-    String sql = "SELECT CAST(@p0 AS NUMERIC) AS ColA";
-
-    thrown.expect(ZetaSqlException.class);
-    thrown.expectMessage("Cannot cast TYPE_DOUBLE as TYPE_NUMERIC: scale 1022 exceeds 9");
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    zetaSQLQueryPlanner.convertToBeamRel(sql, ImmutableMap.of("p0", Value.createDoubleValue(val)));
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlTestBase.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlTestBase.java
deleted file mode 100644
index 5b67ad76fd9d..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlTestBase.java
+++ /dev/null
@@ -1,97 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import java.util.HashMap;
-import java.util.Map;
-import org.apache.beam.sdk.extensions.sql.impl.JdbcConnection;
-import org.apache.beam.sdk.extensions.sql.impl.JdbcDriver;
-import org.apache.beam.sdk.extensions.sql.impl.planner.BeamCostModel;
-import org.apache.beam.sdk.extensions.sql.meta.BeamSqlTable;
-import org.apache.beam.sdk.extensions.sql.meta.provider.ReadOnlyTableProvider;
-import org.apache.beam.sdk.extensions.sql.meta.provider.TableProvider;
-import org.apache.beam.sdk.options.PipelineOptionsFactory;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.Contexts;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.plan.ConventionTraitDef;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.FrameworkConfig;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.Frameworks;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.tools.RuleSet;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
-
-/** Common setup for ZetaSQL tests. */
-public abstract class ZetaSqlTestBase {
-  protected static final long PIPELINE_EXECUTION_WAITTIME_MINUTES = 2L;
-
-  protected FrameworkConfig config;
-
-  private TableProvider createBeamTableProvider() {
-    Map<String, BeamSqlTable> testBoundedTableMap = new HashMap<>();
-    testBoundedTableMap.put("KeyValue", TestInput.BASIC_TABLE_ONE);
-    testBoundedTableMap.put("BigTable", TestInput.BASIC_TABLE_TWO);
-    testBoundedTableMap.put("Spanner", TestInput.BASIC_TABLE_THREE);
-    testBoundedTableMap.put("aggregate_test_table", TestInput.AGGREGATE_TABLE_ONE);
-    testBoundedTableMap.put("window_test_table", TestInput.TIMESTAMP_TABLE_ONE);
-    testBoundedTableMap.put("window_test_table_two", TestInput.TIMESTAMP_TABLE_TWO);
-    testBoundedTableMap.put("all_null_table", TestInput.TABLE_ALL_NULL);
-    testBoundedTableMap.put("table_with_struct", TestInput.TABLE_WITH_STRUCT);
-    testBoundedTableMap.put("table_with_struct_two", TestInput.TABLE_WITH_STRUCT_TWO);
-    testBoundedTableMap.put("table_with_array", TestInput.TABLE_WITH_ARRAY);
-    testBoundedTableMap.put("table_with_array_for_unnest", TestInput.TABLE_WITH_ARRAY_FOR_UNNEST);
-    testBoundedTableMap.put("table_with_array_of_struct", TestInput.TABLE_WITH_ARRAY_OF_STRUCT);
-    testBoundedTableMap.put("table_with_struct_of_struct", TestInput.TABLE_WITH_STRUCT_OF_STRUCT);
-    testBoundedTableMap.put(
-        "table_with_struct_of_struct_of_array", TestInput.TABLE_WITH_STRUCT_OF_STRUCT_OF_ARRAY);
-    testBoundedTableMap.put(
-        "table_with_array_of_struct_of_struct", TestInput.TABLE_WITH_ARRAY_OF_STRUCT_OF_STRUCT);
-    testBoundedTableMap.put(
-        "table_with_struct_of_array_of_struct", TestInput.TABLE_WITH_STRUCT_OF_ARRAY_OF_STRUCT);
-    testBoundedTableMap.put(
-        "table_with_array_of_struct_of_array", TestInput.TABLE_WITH_ARRAY_OF_STRUCT_OF_ARRAY);
-    testBoundedTableMap.put("table_for_case_when", TestInput.TABLE_FOR_CASE_WHEN);
-    testBoundedTableMap.put("aggregate_test_table_two", TestInput.AGGREGATE_TABLE_TWO);
-    testBoundedTableMap.put("table_empty", TestInput.TABLE_EMPTY);
-    testBoundedTableMap.put("table_all_types", TestInput.TABLE_ALL_TYPES);
-    testBoundedTableMap.put("table_all_types_2", TestInput.TABLE_ALL_TYPES_2);
-    testBoundedTableMap.put("table_with_map", TestInput.TABLE_WITH_MAP);
-    testBoundedTableMap.put("table_with_date", TestInput.TABLE_WITH_DATE);
-    testBoundedTableMap.put("table_with_time", TestInput.TABLE_WITH_TIME);
-    testBoundedTableMap.put("table_with_numeric", TestInput.TABLE_WITH_NUMERIC);
-    testBoundedTableMap.put("table_with_datetime", TestInput.TABLE_WITH_DATETIME);
-    testBoundedTableMap.put(
-        "table_with_struct_ts_string", TestInput.TABLE_WITH_STRUCT_TIMESTAMP_STRING);
-    testBoundedTableMap.put("streaming_sql_test_table_a", TestInput.STREAMING_SQL_TABLE_A);
-    testBoundedTableMap.put("streaming_sql_test_table_b", TestInput.STREAMING_SQL_TABLE_B);
-
-    return new ReadOnlyTableProvider("test_table_provider", testBoundedTableMap);
-  }
-
-  protected void initialize() {
-    JdbcConnection jdbcConnection =
-        JdbcDriver.connect(createBeamTableProvider(), PipelineOptionsFactory.create());
-
-    this.config =
-        Frameworks.newConfigBuilder()
-            .defaultSchema(jdbcConnection.getCurrentSchemaPlus())
-            .traitDefs(ImmutableList.of(ConventionTraitDef.INSTANCE))
-            .context(Contexts.of(jdbcConnection.config()))
-            .ruleSets(ZetaSQLQueryPlanner.getZetaSqlRuleSets().toArray(new RuleSet[0]))
-            .costFactory(BeamCostModel.FACTORY)
-            .typeSystem(jdbcConnection.getTypeFactory().getTypeSystem())
-            .build();
-  }
-}
diff --git a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlTimeFunctionsTest.java b/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlTimeFunctionsTest.java
deleted file mode 100644
index cfc0fce737bc..000000000000
--- a/sdks/java/extensions/sql/zetasql/src/test/java/org/apache/beam/sdk/extensions/sql/zetasql/ZetaSqlTimeFunctionsTest.java
+++ /dev/null
@@ -1,1779 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.extensions.sql.zetasql;
-
-import static org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils.parseDateToValue;
-import static org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils.parseTimeToValue;
-import static org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils.parseTimestampWithTZToValue;
-import static org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils.parseTimestampWithTimeZone;
-import static org.apache.beam.sdk.extensions.sql.zetasql.DateTimeUtils.parseTimestampWithUTCTimeZone;
-
-import com.google.zetasql.Value;
-import com.google.zetasql.ZetaSQLType.TypeKind;
-import java.time.LocalDate;
-import java.time.LocalDateTime;
-import java.time.LocalTime;
-import org.apache.beam.sdk.extensions.sql.impl.BeamSqlPipelineOptions;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamRelNode;
-import org.apache.beam.sdk.extensions.sql.impl.rel.BeamSqlRelUtils;
-import org.apache.beam.sdk.schemas.Schema;
-import org.apache.beam.sdk.schemas.Schema.FieldType;
-import org.apache.beam.sdk.schemas.logicaltypes.SqlTypes;
-import org.apache.beam.sdk.testing.PAssert;
-import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.sdk.values.PCollection;
-import org.apache.beam.sdk.values.Row;
-import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
-import org.joda.time.Duration;
-import org.junit.Before;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.rules.ExpectedException;
-import org.junit.runner.RunWith;
-import org.junit.runners.JUnit4;
-
-/** Tests for ZetaSQL time functions (DATE, TIME, DATETIME, and TIMESTAMP functions). */
-@RunWith(JUnit4.class)
-public class ZetaSqlTimeFunctionsTest extends ZetaSqlTestBase {
-
-  @Rule public transient TestPipeline pipeline = TestPipeline.create();
-  @Rule public ExpectedException thrown = ExpectedException.none();
-
-  @Before
-  public void setUp() {
-    initialize();
-  }
-
-  /////////////////////////////////////////////////////////////////////////////
-  // DATE type tests
-  /////////////////////////////////////////////////////////////////////////////
-
-  @Test
-  public void testDateLiteral() {
-    String sql = "SELECT DATE '2020-3-30'";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("f_date", SqlTypes.DATE).build())
-                .addValues(LocalDate.of(2020, 3, 30))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateColumn() {
-    // NOTE: Do not use textual format parameters (%b or %h: The abbreviated month name) as these
-    // are locale dependent.
-    String sql = "SELECT FORMAT_DATE('%m-%d-%Y', date_field) FROM table_with_date";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("f_date_str").build())
-                .addValues("12-25-2008")
-                .build(),
-            Row.withSchema(Schema.builder().addStringField("f_date_str").build())
-                .addValues("04-07-2020")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testGroupByDate() {
-    String sql = "SELECT date_field, COUNT(*) FROM table_with_date GROUP BY date_field";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addLogicalTypeField("date_field", SqlTypes.DATE)
-            .addInt64Field("count")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(LocalDate.of(2008, 12, 25), 1L).build(),
-            Row.withSchema(schema).addValues(LocalDate.of(2020, 4, 7), 1L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testAggregateOnDate() {
-    String sql = "SELECT MAX(date_field) FROM table_with_date GROUP BY str_field";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("date_field", SqlTypes.DATE).build())
-                .addValues(LocalDate.of(2020, 4, 7))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  // TODO[https://github.com/apache/beam/issues/19980]: Add a test for CURRENT_DATE function
-  // ("SELECT CURRENT_DATE()")
-
-  @Test
-  public void testExtractFromDate() {
-    String sql =
-        "WITH Dates AS (\n"
-            + "  SELECT DATE '2015-12-31' AS date UNION ALL\n"
-            + "  SELECT DATE '2016-01-01'\n"
-            + ")\n"
-            + "SELECT\n"
-            + "  EXTRACT(ISOYEAR FROM date) AS isoyear,\n"
-            + "  EXTRACT(YEAR FROM date) AS year,\n"
-            + "  EXTRACT(ISOWEEK FROM date) AS isoweek,\n"
-            // TODO[https://github.com/apache/beam/issues/20338]: Add tests for DATE_TRUNC and
-            // EXTRACT with "week with weekday" date
-            //  parts once they are supported
-            // + "  EXTRACT(WEEK FROM date) AS week,\n"
-            + "  EXTRACT(MONTH FROM date) AS month,\n"
-            + "  EXTRACT(QUARTER FROM date) AS quarter,\n"
-            + "  EXTRACT(DAY FROM date) AS day,\n"
-            + "  EXTRACT(DAYOFYEAR FROM date) AS dayofyear,\n"
-            + "  EXTRACT(DAYOFWEEK FROM date) AS dayofweek\n"
-            + "FROM Dates";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("isoyear")
-            .addInt64Field("year")
-            .addInt64Field("isoweek")
-            // .addInt64Field("week")
-            .addInt64Field("month")
-            .addInt64Field("quarter")
-            .addInt64Field("day")
-            .addInt64Field("dayofyear")
-            .addInt64Field("dayofweek")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(2015L, 2015L, 53L /* , 52L */, 12L, 4L, 31L, 365L, 5L)
-                .build(),
-            Row.withSchema(schema)
-                .addValues(2015L, 2016L, 53L /* , 0L */, 1L, 1L, 1L, 1L, 6L)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateFromYearMonthDay() {
-    String sql = "SELECT DATE(2008, 12, 25)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("f_date", SqlTypes.DATE).build())
-                .addValues(LocalDate.of(2008, 12, 25))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateFromTimestamp() {
-    String sql = "SELECT DATE(TIMESTAMP '2016-12-25 05:30:00+07', 'America/Los_Angeles')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("f_date", SqlTypes.DATE).build())
-                .addValues(LocalDate.of(2016, 12, 24))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateFromDateTime() {
-    String sql = "SELECT DATE(DATETIME '2008-12-25 15:30:00.123456')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("f_date", SqlTypes.DATE).build())
-                .addValues(LocalDate.of(2008, 12, 25))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateAdd() {
-    String sql =
-        "SELECT "
-            + "DATE_ADD(DATE '2008-12-25', INTERVAL 5 DAY), "
-            + "DATE_ADD(DATE '2008-12-25', INTERVAL 1 MONTH), "
-            + "DATE_ADD(DATE '2008-12-25', INTERVAL 1 YEAR), ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addLogicalTypeField("f_date1", SqlTypes.DATE)
-                        .addLogicalTypeField("f_date2", SqlTypes.DATE)
-                        .addLogicalTypeField("f_date3", SqlTypes.DATE)
-                        .build())
-                .addValues(
-                    LocalDate.of(2008, 12, 30),
-                    LocalDate.of(2009, 1, 25),
-                    LocalDate.of(2009, 12, 25))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateAddWithParameter() {
-    String sql =
-        "SELECT "
-            + "DATE_ADD(@p0, INTERVAL @p1 DAY), "
-            + "DATE_ADD(@p2, INTERVAL @p3 DAY), "
-            + "DATE_ADD(@p4, INTERVAL @p5 YEAR), "
-            + "DATE_ADD(@p6, INTERVAL @p7 DAY), "
-            + "DATE_ADD(@p8, INTERVAL @p9 MONTH)";
-
-    ImmutableMap<String, Value> params =
-        ImmutableMap.<String, Value>builder()
-            .put("p0", Value.createDateValue(0)) // 1970-01-01
-            .put("p1", Value.createInt64Value(2L))
-            .put("p2", parseDateToValue("2019-01-01"))
-            .put("p3", Value.createInt64Value(2L))
-            .put("p4", Value.createSimpleNullValue(TypeKind.TYPE_DATE))
-            .put("p5", Value.createInt64Value(1L))
-            .put("p6", parseDateToValue("2000-02-29"))
-            .put("p7", Value.createInt64Value(-365L))
-            .put("p8", parseDateToValue("1999-03-31"))
-            .put("p9", Value.createInt64Value(-1L))
-            .build();
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addLogicalTypeField("f_date1", SqlTypes.DATE)
-            .addLogicalTypeField("f_date2", SqlTypes.DATE)
-            .addNullableField("f_date3", FieldType.logicalType(SqlTypes.DATE))
-            .addLogicalTypeField("f_date4", SqlTypes.DATE)
-            .addLogicalTypeField("f_date5", SqlTypes.DATE)
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    LocalDate.of(1970, 1, 3),
-                    LocalDate.of(2019, 1, 3),
-                    null,
-                    LocalDate.of(1999, 3, 1),
-                    LocalDate.of(1999, 2, 28))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateSub() {
-    String sql =
-        "SELECT "
-            + "DATE_SUB(DATE '2008-12-25', INTERVAL 5 DAY), "
-            + "DATE_SUB(DATE '2008-12-25', INTERVAL 1 MONTH), "
-            + "DATE_SUB(DATE '2008-12-25', INTERVAL 1 YEAR), ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addLogicalTypeField("f_date1", SqlTypes.DATE)
-                        .addLogicalTypeField("f_date2", SqlTypes.DATE)
-                        .addLogicalTypeField("f_date3", SqlTypes.DATE)
-                        .build())
-                .addValues(
-                    LocalDate.of(2008, 12, 20),
-                    LocalDate.of(2008, 11, 25),
-                    LocalDate.of(2007, 12, 25))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateDiff() {
-    String sql = "SELECT DATE_DIFF(DATE '2010-07-07', DATE '2008-12-25', DAY)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("f_date_diff").build())
-                .addValues(559L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateDiffNegativeResult() {
-    String sql = "SELECT DATE_DIFF(DATE '2017-12-17', DATE '2017-12-18', ISOWEEK)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("f_date_diff").build())
-                .addValues(-1L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTrunc() {
-    String sql = "SELECT DATE_TRUNC(DATE '2015-06-15', ISOYEAR)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("f_date_trunc", SqlTypes.DATE).build())
-                .addValues(LocalDate.of(2014, 12, 29))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testFormatDate() {
-    // NOTE: Do not use textual format parameters (%b or %h: The abbreviated month name) as these
-    // are locale dependent.
-    String sql = "SELECT FORMAT_DATE('%m-%d-%Y', DATE '2008-12-25')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("f_date_str").build())
-                .addValues("12-25-2008")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testParseDate() {
-    String sql = "SELECT PARSE_DATE('%m %d %y', '10 14 18')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("f_date", SqlTypes.DATE).build())
-                .addValues(LocalDate.of(2018, 10, 14))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateToUnixInt64() {
-    String sql = "SELECT UNIX_DATE(DATE '2008-12-25')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("f_unix_date").build())
-                .addValues(14238L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateFromUnixInt64() {
-    String sql = "SELECT DATE_FROM_UNIX_DATE(14238)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("f_date", SqlTypes.DATE).build())
-                .addValues(LocalDate.of(2008, 12, 25))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  /////////////////////////////////////////////////////////////////////////////
-  // TIME type tests
-  /////////////////////////////////////////////////////////////////////////////
-
-  @Test
-  public void testTimeLiteral() {
-    String sql = "SELECT TIME '15:30:00', TIME '15:30:00.135246' ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addLogicalTypeField("f_time1", SqlTypes.TIME)
-                        .addLogicalTypeField("f_time2", SqlTypes.TIME)
-                        .build())
-                .addValues(LocalTime.of(15, 30, 0))
-                .addValues(LocalTime.of(15, 30, 0, 135246000))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimeColumn() {
-    String sql = "SELECT FORMAT_TIME('%T', time_field) FROM table_with_time";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("f_time_str").build())
-                .addValues("15:30:00")
-                .build(),
-            Row.withSchema(Schema.builder().addStringField("f_time_str").build())
-                .addValues("23:35:59")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testGroupByTime() {
-    String sql = "SELECT time_field, COUNT(*) FROM table_with_time GROUP BY time_field";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addLogicalTypeField("time_field", SqlTypes.TIME)
-            .addInt64Field("count")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema).addValues(LocalTime.of(15, 30, 0), 1L).build(),
-            Row.withSchema(schema).addValues(LocalTime.of(23, 35, 59), 1L).build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testAggregateOnTime() {
-    String sql = "SELECT MAX(time_field) FROM table_with_time GROUP BY str_field";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("time_field", SqlTypes.TIME).build())
-                .addValues(LocalTime.of(23, 35, 59))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  // TODO[https://github.com/apache/beam/issues/19980]: Add a test for CURRENT_TIME function
-  // ("SELECT CURRENT_TIME()")
-
-  @Test
-  public void testExtractFromTime() {
-    String sql =
-        "SELECT "
-            + "EXTRACT(HOUR FROM TIME '15:30:35.123456') as hour, "
-            + "EXTRACT(MINUTE FROM TIME '15:30:35.123456') as minute, "
-            + "EXTRACT(SECOND FROM TIME '15:30:35.123456') as second, "
-            + "EXTRACT(MILLISECOND FROM TIME '15:30:35.123456') as millisecond, "
-            + "EXTRACT(MICROSECOND FROM TIME '15:30:35.123456') as microsecond ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("hour")
-            .addInt64Field("minute")
-            .addInt64Field("second")
-            .addInt64Field("millisecond")
-            .addInt64Field("microsecond")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(Row.withSchema(schema).addValues(15L, 30L, 35L, 123L, 123456L).build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimeFromHourMinuteSecond() {
-    String sql = "SELECT TIME(15, 30, 0)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("f_time", SqlTypes.TIME).build())
-                .addValues(LocalTime.of(15, 30, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimeFromTimestamp() {
-    String sql = "SELECT TIME(TIMESTAMP '2008-12-25 15:30:00+08', 'America/Los_Angeles')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("f_time", SqlTypes.TIME).build())
-                .addValues(LocalTime.of(23, 30, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimeFromDateTime() {
-    String sql = "SELECT TIME(DATETIME '2008-12-25 15:30:00.123456')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("f_time", SqlTypes.TIME).build())
-                .addValues(LocalTime.of(15, 30, 0, 123456000))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimeAdd() {
-    String sql =
-        "SELECT "
-            + "TIME_ADD(TIME '15:30:00', INTERVAL 10 MICROSECOND), "
-            + "TIME_ADD(TIME '15:30:00', INTERVAL 10 MILLISECOND), "
-            + "TIME_ADD(TIME '15:30:00', INTERVAL 10 SECOND), "
-            + "TIME_ADD(TIME '15:30:00', INTERVAL 10 MINUTE), "
-            + "TIME_ADD(TIME '15:30:00', INTERVAL 10 HOUR) ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addLogicalTypeField("f_time1", SqlTypes.TIME)
-                        .addLogicalTypeField("f_time2", SqlTypes.TIME)
-                        .addLogicalTypeField("f_time3", SqlTypes.TIME)
-                        .addLogicalTypeField("f_time4", SqlTypes.TIME)
-                        .addLogicalTypeField("f_time5", SqlTypes.TIME)
-                        .build())
-                .addValues(
-                    LocalTime.of(15, 30, 0, 10000),
-                    LocalTime.of(15, 30, 0, 10000000),
-                    LocalTime.of(15, 30, 10, 0),
-                    LocalTime.of(15, 40, 0, 0),
-                    LocalTime.of(1, 30, 0, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimeAddWithParameter() {
-    String sql = "SELECT TIME_ADD(@p0, INTERVAL @p1 SECOND)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", parseTimeToValue("12:13:14.123"),
-            "p1", Value.createInt64Value(1L));
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("f_time", SqlTypes.TIME).build())
-                .addValues(LocalTime.of(12, 13, 15, 123000000))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimeSub() {
-    String sql =
-        "SELECT "
-            + "TIME_SUB(TIME '15:30:00', INTERVAL 10 MICROSECOND), "
-            + "TIME_SUB(TIME '15:30:00', INTERVAL 10 MILLISECOND), "
-            + "TIME_SUB(TIME '15:30:00', INTERVAL 10 SECOND), "
-            + "TIME_SUB(TIME '15:30:00', INTERVAL 10 MINUTE), "
-            + "TIME_SUB(TIME '15:30:00', INTERVAL 10 HOUR) ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addLogicalTypeField("f_time1", SqlTypes.TIME)
-                        .addLogicalTypeField("f_time2", SqlTypes.TIME)
-                        .addLogicalTypeField("f_time3", SqlTypes.TIME)
-                        .addLogicalTypeField("f_time4", SqlTypes.TIME)
-                        .addLogicalTypeField("f_time5", SqlTypes.TIME)
-                        .build())
-                .addValues(
-                    LocalTime.of(15, 29, 59, 999990000),
-                    LocalTime.of(15, 29, 59, 990000000),
-                    LocalTime.of(15, 29, 50, 0),
-                    LocalTime.of(15, 20, 0, 0),
-                    LocalTime.of(5, 30, 0, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimeDiff() {
-    String sql = "SELECT TIME_DIFF(TIME '15:30:00', TIME '14:35:00', MINUTE)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("f_time_diff").build())
-                .addValues(55L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimeDiffNegativeResult() {
-    String sql = "SELECT TIME_DIFF(TIME '14:35:00', TIME '15:30:00', MINUTE)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("f_time_diff").build())
-                .addValues(-55L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimeTrunc() {
-    String sql = "SELECT TIME_TRUNC(TIME '15:30:35', HOUR)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("f_time_trunc", SqlTypes.TIME).build())
-                .addValues(LocalTime.of(15, 0, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testFormatTime() {
-    String sql = "SELECT FORMAT_TIME('%R', TIME '15:30:00')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("f_time_str").build())
-                .addValues("15:30")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testParseTime() {
-    String sql = "SELECT PARSE_TIME('%H', '15')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("f_time", SqlTypes.TIME).build())
-                .addValues(LocalTime.of(15, 0, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  /////////////////////////////////////////////////////////////////////////////
-  // DATETIME type tests
-  /////////////////////////////////////////////////////////////////////////////
-
-  @Test
-  public void testDateTimeLiteral() {
-    String sql = "SELECT DATETIME '2008-12-25 15:30:00.123456'";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("f_datetime", SqlTypes.DATETIME).build())
-                .addValues(LocalDateTime.of(2008, 12, 25, 15, 30, 0).withNano(123456000))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTimeColumn() {
-    String sql = "SELECT FORMAT_DATETIME('%D %T %E6S', datetime_field) FROM table_with_datetime";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("f_datetime_str").build())
-                .addValues("12/25/08 15:30:00 00.123456")
-                .build(),
-            Row.withSchema(Schema.builder().addStringField("f_datetime_str").build())
-                .addValues("10/06/12 11:45:00 00.987654")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testGroupByDateTime() {
-    String sql = "SELECT datetime_field, COUNT(*) FROM table_with_datetime GROUP BY datetime_field";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addLogicalTypeField("datetime_field", SqlTypes.DATETIME)
-            .addInt64Field("count")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(LocalDateTime.of(2008, 12, 25, 15, 30, 0).withNano(123456000), 1L)
-                .build(),
-            Row.withSchema(schema)
-                .addValues(LocalDateTime.of(2012, 10, 6, 11, 45, 0).withNano(987654000), 1L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testAggregateOnDateTime() {
-    String sql = "SELECT MAX(datetime_field) FROM table_with_datetime GROUP BY str_field";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addLogicalTypeField("datetime_field", SqlTypes.DATETIME)
-                        .build())
-                .addValues(LocalDateTime.of(2012, 10, 6, 11, 45, 0).withNano(987654000))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  // TODO[https://github.com/apache/beam/issues/19980]: Add a test for CURRENT_DATETIME function
-  // ("SELECT CURRENT_DATETIME()")
-
-  @Test
-  public void testExtractFromDateTime() {
-    String sql =
-        "SELECT "
-            + "EXTRACT(YEAR FROM DATETIME '2008-12-25 15:30:00') as year, "
-            + "EXTRACT(QUARTER FROM DATETIME '2008-12-25 15:30:00') as quarter, "
-            + "EXTRACT(MONTH FROM DATETIME '2008-12-25 15:30:00') as month, "
-            // TODO[https://github.com/apache/beam/issues/20338]: Add tests for DATETIME_TRUNC and
-            // EXTRACT with "week with weekday"
-            //  date parts once they are supported
-            // + "EXTRACT(WEEK FROM DATETIME '2008-12-25 15:30:00') as week, "
-            + "EXTRACT(DAY FROM DATETIME '2008-12-25 15:30:00') as day, "
-            + "EXTRACT(DAYOFWEEK FROM DATETIME '2008-12-25 15:30:00') as dayofweek, "
-            + "EXTRACT(DAYOFYEAR FROM DATETIME '2008-12-25 15:30:00') as dayofyear, "
-            + "EXTRACT(HOUR FROM DATETIME '2008-12-25 15:30:00.123456') as hour, "
-            + "EXTRACT(MINUTE FROM DATETIME '2008-12-25 15:30:00.123456') as minute, "
-            + "EXTRACT(SECOND FROM DATETIME '2008-12-25 15:30:00.123456') as second, "
-            + "EXTRACT(MILLISECOND FROM DATETIME '2008-12-25 15:30:00.123456') as millisecond, "
-            + "EXTRACT(MICROSECOND FROM DATETIME '2008-12-25 15:30:00.123456') as microsecond, "
-            + "EXTRACT(DATE FROM DATETIME '2008-12-25 15:30:00.123456') as date, "
-            + "EXTRACT(TIME FROM DATETIME '2008-12-25 15:30:00.123456') as time ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("year")
-            .addInt64Field("quarter")
-            .addInt64Field("month")
-            // .addInt64Field("week")
-            .addInt64Field("day")
-            .addInt64Field("dayofweek")
-            .addInt64Field("dayofyear")
-            .addInt64Field("hour")
-            .addInt64Field("minute")
-            .addInt64Field("second")
-            .addInt64Field("millisecond")
-            .addInt64Field("microsecond")
-            .addLogicalTypeField("date", SqlTypes.DATE)
-            .addLogicalTypeField("time", SqlTypes.TIME)
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    2008L,
-                    4L,
-                    12L,
-                    // 52L,
-                    25L,
-                    5L,
-                    360L,
-                    15L,
-                    30L,
-                    0L,
-                    123L,
-                    123456L,
-                    LocalDate.of(2008, 12, 25),
-                    LocalTime.of(15, 30, 0, 123456000))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTimeFromDateAndTime() {
-    String sql = "SELECT DATETIME(DATE '2008-12-25', TIME '15:30:00.123456')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("f_datetime", SqlTypes.DATETIME).build())
-                .addValues(LocalDateTime.of(2008, 12, 25, 15, 30, 0).withNano(123456000))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTimeFromDate() {
-    String sql = "SELECT DATETIME(DATE '2008-12-25')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("f_datetime", SqlTypes.DATETIME).build())
-                .addValues(LocalDateTime.of(2008, 12, 25, 0, 0, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTimeFromYearMonthDayHourMinuteSecond() {
-    String sql = "SELECT DATETIME(2008, 12, 25, 15, 30, 0)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("f_datetime", SqlTypes.DATETIME).build())
-                .addValues(LocalDateTime.of(2008, 12, 25, 15, 30, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTimeFromTimestamp() {
-    String sql = "SELECT DATETIME(TIMESTAMP '2008-12-25 15:30:00+08', 'America/Los_Angeles')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("f_datetime", SqlTypes.DATETIME).build())
-                .addValues(LocalDateTime.of(2008, 12, 24, 23, 30, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTimeAdd() {
-    String sql =
-        "SELECT "
-            + "DATETIME_ADD(DATETIME '2008-12-25 15:30:00', INTERVAL 10 MICROSECOND), "
-            + "DATETIME_ADD(DATETIME '2008-12-25 15:30:00', INTERVAL 10 MILLISECOND), "
-            + "DATETIME_ADD(DATETIME '2008-12-25 15:30:00', INTERVAL 10 SECOND), "
-            + "DATETIME_ADD(DATETIME '2008-12-25 15:30:00', INTERVAL 10 MINUTE), "
-            + "DATETIME_ADD(DATETIME '2008-12-25 15:30:00', INTERVAL 10 HOUR), "
-            + "DATETIME_ADD(DATETIME '2008-12-25 15:30:00', INTERVAL 10 DAY), "
-            + "DATETIME_ADD(DATETIME '2008-12-25 15:30:00', INTERVAL 10 MONTH), "
-            + "DATETIME_ADD(DATETIME '2008-12-25 15:30:00', INTERVAL 10 QUARTER), "
-            + "DATETIME_ADD(DATETIME '2008-12-25 15:30:00', INTERVAL 10 YEAR) ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addLogicalTypeField("f_time1", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time2", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time3", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time4", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time5", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time6", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time7", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time8", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time9", SqlTypes.DATETIME)
-                        .build())
-                .addValues(
-                    LocalDateTime.of(2008, 12, 25, 15, 30, 0).withNano(10000),
-                    LocalDateTime.of(2008, 12, 25, 15, 30, 0).withNano(10000000),
-                    LocalDateTime.of(2008, 12, 25, 15, 30, 10),
-                    LocalDateTime.of(2008, 12, 25, 15, 40, 0),
-                    LocalDateTime.of(2008, 12, 26, 1, 30, 0),
-                    LocalDateTime.of(2009, 1, 4, 15, 30, 0),
-                    LocalDateTime.of(2009, 10, 25, 15, 30, 0),
-                    LocalDateTime.of(2011, 6, 25, 15, 30, 0),
-                    LocalDateTime.of(2018, 12, 25, 15, 30, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTimeAddWithParameter() {
-    String sql = "SELECT DATETIME_ADD(@p0, INTERVAL @p1 HOUR)";
-
-    LocalDateTime datetime = LocalDateTime.of(2008, 12, 25, 15, 30, 0).withNano(123456000);
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", Value.createDatetimeValue(datetime),
-            "p1", Value.createInt64Value(3L));
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("f_datetime", SqlTypes.DATETIME).build())
-                .addValues(LocalDateTime.of(2008, 12, 25, 18, 30, 0).withNano(123456000))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTimeSub() {
-    String sql =
-        "SELECT "
-            + "DATETIME_SUB(DATETIME '2008-12-25 15:30:00', INTERVAL 10 MICROSECOND), "
-            + "DATETIME_SUB(DATETIME '2008-12-25 15:30:00', INTERVAL 10 MILLISECOND), "
-            + "DATETIME_SUB(DATETIME '2008-12-25 15:30:00', INTERVAL 10 SECOND), "
-            + "DATETIME_SUB(DATETIME '2008-12-25 15:30:00', INTERVAL 10 MINUTE), "
-            + "DATETIME_SUB(DATETIME '2008-12-25 15:30:00', INTERVAL 10 HOUR), "
-            + "DATETIME_SUB(DATETIME '2008-12-25 15:30:00', INTERVAL 10 DAY), "
-            + "DATETIME_SUB(DATETIME '2008-12-25 15:30:00', INTERVAL 10 MONTH), "
-            + "DATETIME_SUB(DATETIME '2008-12-25 15:30:00', INTERVAL 10 QUARTER), "
-            + "DATETIME_SUB(DATETIME '2008-12-25 15:30:00', INTERVAL 10 YEAR) ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addLogicalTypeField("f_time1", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time2", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time3", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time4", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time5", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time6", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time7", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time8", SqlTypes.DATETIME)
-                        .addLogicalTypeField("f_time9", SqlTypes.DATETIME)
-                        .build())
-                .addValues(
-                    LocalDateTime.of(2008, 12, 25, 15, 29, 59).withNano(999990000),
-                    LocalDateTime.of(2008, 12, 25, 15, 29, 59).withNano(990000000),
-                    LocalDateTime.of(2008, 12, 25, 15, 29, 50),
-                    LocalDateTime.of(2008, 12, 25, 15, 20, 0),
-                    LocalDateTime.of(2008, 12, 25, 5, 30, 0),
-                    LocalDateTime.of(2008, 12, 15, 15, 30, 0),
-                    LocalDateTime.of(2008, 2, 25, 15, 30, 0),
-                    LocalDateTime.of(2006, 6, 25, 15, 30, 0),
-                    LocalDateTime.of(1998, 12, 25, 15, 30, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTimeDiff() {
-    String sql =
-        "SELECT DATETIME_DIFF(DATETIME '2008-12-25 15:30:00', DATETIME '2008-10-25 15:30:00', DAY)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("f_datetime_diff").build())
-                .addValues(61L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTimeDiffNegativeResult() {
-    String sql =
-        "SELECT DATETIME_DIFF(DATETIME '2008-10-25 15:30:00', DATETIME '2008-12-25 15:30:00', DAY)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("f_datetime_diff").build())
-                .addValues(-61L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testDateTimeTrunc() {
-    String sql = "SELECT DATETIME_TRUNC(DATETIME '2008-12-25 15:30:00', HOUR)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addLogicalTypeField("f_datetime_trunc", SqlTypes.DATETIME)
-                        .build())
-                .addValues(LocalDateTime.of(2008, 12, 25, 15, 0, 0))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testFormatDateTime() {
-    String sql = "SELECT FORMAT_DATETIME('%D %T %E6S', DATETIME '2008-12-25 15:30:00.123456')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("f_datetime_str").build())
-                .addValues("12/25/08 15:30:00 00.123456")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testParseDateTime() {
-    String sql = "SELECT PARSE_DATETIME('%Y-%m-%d %H:%M:%E6S', '2008-12-25 15:30:00.123456')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("f_datetime", SqlTypes.DATETIME).build())
-                .addValues(LocalDateTime.of(2008, 12, 25, 15, 30, 0).withNano(123456000))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  /////////////////////////////////////////////////////////////////////////////
-  // TIMESTAMP type tests
-  /////////////////////////////////////////////////////////////////////////////
-
-  @Test
-  public void testTimestampMicrosecondUnsupported() {
-    String sql =
-        "WITH Timestamps AS (\n"
-            + "  SELECT TIMESTAMP '2000-01-01 00:11:22.345678+00' as timestamp\n"
-            + ")\n"
-            + "SELECT\n"
-            + "  timestamp,\n"
-            + "  EXTRACT(ISOYEAR FROM timestamp) AS isoyear,\n"
-            + "  EXTRACT(YEAR FROM timestamp) AS year,\n"
-            + "  EXTRACT(ISOWEEK FROM timestamp) AS week,\n"
-            + "  EXTRACT(MINUTE FROM timestamp) AS minute\n"
-            + "FROM Timestamps\n";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    thrown.expect(UnsupportedOperationException.class);
-    zetaSQLQueryPlanner.convertToBeamRel(sql);
-  }
-
-  @Test
-  public void testTimestampLiteralWithoutTimeZone() {
-    String sql = "SELECT TIMESTAMP '2016-12-25 05:30:00'";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDateTimeField("field1").build())
-                .addValues(parseTimestampWithUTCTimeZone("2016-12-25 05:30:00"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampLiteralWithUTCTimeZone() {
-    String sql = "SELECT TIMESTAMP '2016-12-25 05:30:00+00'";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDateTimeField("field1").build())
-                .addValues(parseTimestampWithUTCTimeZone("2016-12-25 05:30:00"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampLiteralWithNonUTCTimeZone() {
-    String sql = "SELECT TIMESTAMP '2018-12-10 10:38:59-10:00'";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDateTimeField("f_timestamp_with_time_zone").build())
-                .addValues(parseTimestampWithTimeZone("2018-12-10 10:38:59-1000"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  // TODO[https://github.com/apache/beam/issues/19980]: Add a test for CURRENT_TIMESTAMP function
-  // ("SELECT CURRENT_TIMESTAMP()")
-
-  @Test
-  public void testExtractFromTimestamp() {
-    String sql =
-        "WITH Timestamps AS (\n"
-            + "  SELECT TIMESTAMP '2007-12-31 12:34:56.789' AS timestamp UNION ALL\n"
-            + "  SELECT TIMESTAMP '2009-12-31'\n"
-            + ")\n"
-            + "SELECT\n"
-            + "  EXTRACT(ISOYEAR FROM timestamp) AS isoyear,\n"
-            + "  EXTRACT(YEAR FROM timestamp) AS year,\n"
-            + "  EXTRACT(ISOWEEK FROM timestamp) AS isoweek,\n"
-            // TODO[https://github.com/apache/beam/issues/20338]: Add tests for TIMESTAMP_TRUNC and
-            // EXTRACT with "week with weekday"
-            //  date parts once they are supported
-            // + "  EXTRACT(WEEK FROM timestamp) AS week,\n"
-            + "  EXTRACT(MONTH FROM timestamp) AS month,\n"
-            + "  EXTRACT(QUARTER FROM timestamp) AS quarter,\n"
-            + "  EXTRACT(DAY FROM timestamp) AS day,\n"
-            + "  EXTRACT(DAYOFYEAR FROM timestamp) AS dayofyear,\n"
-            + "  EXTRACT(DAYOFWEEK FROM timestamp) AS dayofweek,\n"
-            + "  EXTRACT(HOUR FROM timestamp) AS hour,\n"
-            + "  EXTRACT(MINUTE FROM timestamp) AS minute,\n"
-            + "  EXTRACT(SECOND FROM timestamp) AS second,\n"
-            + "  EXTRACT(MILLISECOND FROM timestamp) AS millisecond\n"
-            + "FROM Timestamps";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("isoyear")
-            .addInt64Field("year")
-            .addInt64Field("isoweek")
-            // .addInt64Field("week")
-            .addInt64Field("month")
-            .addInt64Field("quarter")
-            .addInt64Field("day")
-            .addInt64Field("dayofyear")
-            .addInt64Field("dayofweek")
-            .addInt64Field("hour")
-            .addInt64Field("minute")
-            .addInt64Field("second")
-            .addInt64Field("millisecond")
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(
-                    2008L, 2007L, 1L /* , 53L */, 12L, 4L, 31L, 365L, 2L, 12L, 34L, 56L, 789L)
-                .build(),
-            Row.withSchema(schema)
-                .addValues(2009L, 2009L, 53L /* , 52L */, 12L, 4L, 31L, 365L, 5L, 0L, 0L, 0L, 0L)
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testExtractDateFromTimestamp() {
-    String sql = "SELECT EXTRACT(DATE FROM TIMESTAMP '2017-05-26 12:34:56')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("date", SqlTypes.DATE).build())
-                .addValues(LocalDate.of(2017, 5, 26))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testExtractTimeFromTimestamp() {
-    String sql = "SELECT EXTRACT(TIME FROM TIMESTAMP '2017-05-26 12:34:56')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addLogicalTypeField("time", SqlTypes.TIME).build())
-                .addValues(LocalTime.of(12, 34, 56))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testExtractDateTimeFromTimestamp() {
-    String sql = "SELECT EXTRACT(DATETIME FROM TIMESTAMP '2017-05-26 12:34:56')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder().addLogicalTypeField("datetime", SqlTypes.DATETIME).build())
-                .addValues(LocalDateTime.of(2017, 5, 26, 12, 34, 56))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testExtractFromTimestampAtTimeZone() {
-    String sql =
-        "WITH Timestamps AS (\n"
-            + "  SELECT TIMESTAMP '2007-12-31 12:34:56.789' AS timestamp\n"
-            + ")\n"
-            + "SELECT\n"
-            + "  EXTRACT(DAY FROM timestamp AT TIME ZONE 'America/Vancouver') AS day,\n"
-            + "  EXTRACT(DATE FROM timestamp AT TIME ZONE 'UTC') AS date,\n"
-            + "  EXTRACT(TIME FROM timestamp AT TIME ZONE 'Asia/Shanghai') AS time\n"
-            + "FROM Timestamps";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema =
-        Schema.builder()
-            .addInt64Field("day")
-            .addLogicalTypeField("date", SqlTypes.DATE)
-            .addLogicalTypeField("time", SqlTypes.TIME)
-            .build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(31L, LocalDate.of(2007, 12, 31), LocalTime.of(20, 34, 56, 789000000))
-                .build());
-
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testStringFromTimestamp() {
-    String sql = "SELECT STRING(TIMESTAMP '2008-12-25 15:30:00', 'America/Los_Angeles')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("f_timestamp_string").build())
-                .addValues("2008-12-25 07:30:00-08")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampFromString() {
-    String sql = "SELECT TIMESTAMP('2008-12-25 15:30:00', 'America/Los_Angeles')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDateTimeField("f_timestamp").build())
-                .addValues(parseTimestampWithTimeZone("2008-12-25 15:30:00-08"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampFromDate() {
-    String sql = "SELECT TIMESTAMP(DATE '2014-01-31')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDateTimeField("f_timestamp").build())
-                .addValues(parseTimestampWithTimeZone("2014-01-31 00:00:00+00"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  // test default timezone works properly in query execution stage
-  public void testTimestampFromDateWithDefaultTimezoneSet() {
-    String sql = "SELECT TIMESTAMP(DATE '2014-01-31')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    zetaSQLQueryPlanner.setDefaultTimezone("Asia/Shanghai");
-    pipeline
-        .getOptions()
-        .as(BeamSqlPipelineOptions.class)
-        .setZetaSqlDefaultTimezone("Asia/Shanghai");
-
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDateTimeField("f_timestamp").build())
-                .addValues(parseTimestampWithTimeZone("2014-01-31 00:00:00+08"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampFromDateTime() {
-    String sql = "SELECT TIMESTAMP(DATETIME '2008-12-25 15:30:00')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDateTimeField("f_timestamp").build())
-                .addValues(parseTimestampWithTimeZone("2008-12-25 15:30:00+00"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  // test default timezone works properly in query execution stage
-  public void testTimestampFromDateTimeWithDefaultTimezoneSet() {
-    String sql = "SELECT TIMESTAMP(DATETIME '2008-12-25 15:30:00')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    zetaSQLQueryPlanner.setDefaultTimezone("Asia/Shanghai");
-    pipeline
-        .getOptions()
-        .as(BeamSqlPipelineOptions.class)
-        .setZetaSqlDefaultTimezone("Asia/Shanghai");
-
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDateTimeField("f_timestamp").build())
-                .addValues(parseTimestampWithTimeZone("2008-12-25 15:30:00+08"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampAdd() {
-    String sql =
-        "SELECT "
-            + "TIMESTAMP_ADD(TIMESTAMP '2008-12-25 15:30:00 UTC', INTERVAL 5+5 MINUTE), "
-            + "TIMESTAMP_ADD(TIMESTAMP '2008-12-25 15:30:00+07:30', INTERVAL 10 MINUTE)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDateTimeField("f_timestamp_add")
-                        .addDateTimeField("f_timestamp_with_time_zone_add")
-                        .build())
-                .addValues(
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2008-12-25 15:40:00"),
-                    parseTimestampWithTimeZone("2008-12-25 15:40:00+0730"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampAddWithParameter1() {
-    String sql = "SELECT TIMESTAMP_ADD(@p0, INTERVAL @p1 MILLISECOND)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", parseTimestampWithTZToValue("2001-01-01 00:00:00+00"),
-            "p1", Value.createInt64Value(1L));
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema = Schema.builder().addDateTimeField("field1").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(parseTimestampWithTimeZone("2001-01-01 00:00:00.001+00"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampAddWithParameter2() {
-    String sql = "SELECT TIMESTAMP_ADD(@p0, INTERVAL @p1 MINUTE)";
-    ImmutableMap<String, Value> params =
-        ImmutableMap.of(
-            "p0", parseTimestampWithTZToValue("2008-12-25 15:30:00+07:30"),
-            "p1", Value.createInt64Value(10L));
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql, params);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    final Schema schema = Schema.builder().addDateTimeField("field1").build();
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(schema)
-                .addValues(parseTimestampWithTimeZone("2008-12-25 15:40:00+07:30"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampSub() {
-    String sql =
-        "SELECT "
-            + "TIMESTAMP_SUB(TIMESTAMP '2008-12-25 15:30:00 UTC', INTERVAL 5+5 MINUTE), "
-            + "TIMESTAMP_SUB(TIMESTAMP '2008-12-25 15:30:00+07:30', INTERVAL 10 MINUTE)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDateTimeField("f_timestamp_sub")
-                        .addDateTimeField("f_timestamp_with_time_zone_sub")
-                        .build())
-                .addValues(
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2008-12-25 15:20:00"),
-                    parseTimestampWithTimeZone("2008-12-25 15:20:00+0730"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampDiff() {
-    String sql =
-        "SELECT TIMESTAMP_DIFF("
-            + "TIMESTAMP '2018-10-14 15:30:00.000 UTC', "
-            + "TIMESTAMP '2018-08-14 15:05:00.001 UTC', "
-            + "MILLISECOND)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("f_timestamp_diff").build())
-                .addValues((61L * 24 * 60 + 25) * 60 * 1000 - 1)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampDiffNegativeResult() {
-    String sql = "SELECT TIMESTAMP_DIFF(TIMESTAMP '2018-08-14', TIMESTAMP '2018-10-14', DAY)";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addInt64Field("f_timestamp_diff").build())
-                .addValues(-61L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampTrunc() {
-    String sql = "SELECT TIMESTAMP_TRUNC(TIMESTAMP '2017-11-06 00:00:00+12', ISOWEEK, 'UTC')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDateTimeField("f_timestamp_trunc").build())
-                .addValues(DateTimeUtils.parseTimestampWithUTCTimeZone("2017-10-30 00:00:00"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testFormatTimestamp() {
-    String sql = "SELECT FORMAT_TIMESTAMP('%D %T', TIMESTAMP '2018-10-14 15:30:00.123+00', 'UTC')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addStringField("f_timestamp_str").build())
-                .addValues("10/14/18 15:30:00")
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testParseTimestamp() {
-    String sql = "SELECT PARSE_TIMESTAMP('%m-%d-%y %T', '10-14-18 15:30:00', 'UTC')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(Schema.builder().addDateTimeField("f_timestamp").build())
-                .addValues(DateTimeUtils.parseTimestampWithUTCTimeZone("2018-10-14 15:30:00"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampFromInt64() {
-    String sql = "SELECT TIMESTAMP_SECONDS(1230219000), TIMESTAMP_MILLIS(1230219000123) ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDateTimeField("f_timestamp_seconds")
-                        .addDateTimeField("f_timestamp_millis")
-                        .build())
-                .addValues(
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2008-12-25 15:30:00"),
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2008-12-25 15:30:00.123"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampToUnixInt64() {
-    String sql =
-        "SELECT "
-            + "UNIX_SECONDS(TIMESTAMP '2008-12-25 15:30:00 UTC'), "
-            + "UNIX_MILLIS(TIMESTAMP '2008-12-25 15:30:00.123 UTC')";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addInt64Field("f_unix_seconds")
-                        .addInt64Field("f_unix_millis")
-                        .build())
-                .addValues(1230219000L, 1230219000123L)
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-
-  @Test
-  public void testTimestampFromUnixInt64() {
-    String sql =
-        "SELECT "
-            + "TIMESTAMP_FROM_UNIX_SECONDS(1230219000), "
-            + "TIMESTAMP_FROM_UNIX_MILLIS(1230219000123) ";
-
-    ZetaSQLQueryPlanner zetaSQLQueryPlanner = new ZetaSQLQueryPlanner(config);
-    BeamRelNode beamRelNode = zetaSQLQueryPlanner.convertToBeamRel(sql);
-    PCollection<Row> stream = BeamSqlRelUtils.toPCollection(pipeline, beamRelNode);
-
-    PAssert.that(stream)
-        .containsInAnyOrder(
-            Row.withSchema(
-                    Schema.builder()
-                        .addDateTimeField("f_timestamp_seconds")
-                        .addDateTimeField("f_timestamp_millis")
-                        .build())
-                .addValues(
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2008-12-25 15:30:00"),
-                    DateTimeUtils.parseTimestampWithUTCTimeZone("2008-12-25 15:30:00.123"))
-                .build());
-    pipeline.run().waitUntilFinish(Duration.standardMinutes(PIPELINE_EXECUTION_WAITTIME_MINUTES));
-  }
-}
diff --git a/sdks/java/harness/build.gradle b/sdks/java/harness/build.gradle
index b213a716dcf9..00a8fa8a5ac5 100644
--- a/sdks/java/harness/build.gradle
+++ b/sdks/java/harness/build.gradle
@@ -34,6 +34,7 @@ dependencies {
   provided library.java.jackson_databind
   provided library.java.joda_time
   provided library.java.slf4j_api
+  provided library.java.hamcrest
   provided library.java.vendored_grpc_1_69_0
   provided library.java.vendored_guava_32_1_2_jre
 
@@ -79,4 +80,5 @@ dependencies {
   shadowTest project(path: ":sdks:java:core", configuration: "shadowTest")
   shadowTestRuntimeClasspath library.java.slf4j_jdk14
   permitUnusedDeclared library.java.avro
+  permitUnusedDeclared library.java.hamcrest
 }
diff --git a/sdks/java/harness/jmh/src/main/java/org/apache/beam/fn/harness/jmh/control/ExecutionStateSamplerBenchmark.java b/sdks/java/harness/jmh/src/main/java/org/apache/beam/fn/harness/jmh/control/ExecutionStateSamplerBenchmark.java
index 4b5af92b492b..f0fc2b2422f3 100644
--- a/sdks/java/harness/jmh/src/main/java/org/apache/beam/fn/harness/jmh/control/ExecutionStateSamplerBenchmark.java
+++ b/sdks/java/harness/jmh/src/main/java/org/apache/beam/fn/harness/jmh/control/ExecutionStateSamplerBenchmark.java
@@ -122,7 +122,7 @@ public void tearDown() {
   public static class HarnessStateSampler {
     public final org.apache.beam.fn.harness.control.ExecutionStateSampler sampler =
         new org.apache.beam.fn.harness.control.ExecutionStateSampler(
-            PipelineOptionsFactory.create(), System::currentTimeMillis);
+            PipelineOptionsFactory.create(), System::currentTimeMillis, null);
 
     @TearDown(Level.Trial)
     public void tearDown() {
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/AssignWindowsRunner.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/AssignWindowsRunner.java
index 0b3c677bb54d..48b87c270807 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/AssignWindowsRunner.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/AssignWindowsRunner.java
@@ -21,7 +21,6 @@
 
 import com.google.auto.service.AutoService;
 import java.io.IOException;
-import java.util.Collection;
 import java.util.Map;
 import org.apache.beam.fn.harness.MapFnRunners.WindowedValueMapFnFactory;
 import org.apache.beam.model.pipeline.v1.RunnerApi.PTransform;
@@ -92,7 +91,7 @@ private AssignWindowsRunner(WindowFn<T, W> windowFn) {
   WindowedValue<T> assignWindows(WindowedValue<T> input) throws Exception {
     // TODO: https://github.com/apache/beam/issues/18870 consider allocating only once and updating
     // the current value per call.
-    WindowFn<T, W>.AssignContext ctxt =
+    WindowFn<T, W>.AssignContext assignContext =
         windowFn.new AssignContext() {
           @Override
           public T element() {
@@ -109,7 +108,7 @@ public BoundedWindow window() {
             return Iterables.getOnlyElement(input.getWindows());
           }
         };
-    Collection<W> windows = windowFn.assignWindows(ctxt);
-    return WindowedValues.of(input.getValue(), input.getTimestamp(), windows, input.getPaneInfo());
+
+    return WindowedValues.builder(input).setWindows(windowFn.assignWindows(assignContext)).build();
   }
 }
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/Caches.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/Caches.java
index d378c7b86f28..089ee3eda0fb 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/Caches.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/Caches.java
@@ -74,6 +74,9 @@ public static long weigh(Object o) {
     if (o == null) {
       return REFERENCE_SIZE;
     }
+    if (o instanceof Weighted) {
+      return ((Weighted) o).getWeight() + REFERENCE_SIZE + 8;
+    }
     try {
       return MEMORY_METER.measureDeep(o);
     } catch (RuntimeException e) {
@@ -197,17 +200,19 @@ static <K, V> Cache<K, V> forMaximumBytes(long maximumBytes) {
 
                           @Override
                           public int weigh(CompositeKey key, WeightedValue<Object> value) {
+                            // Since our weights are tracking bytes used, we need to account for the
+                            // cache internal bytes.
+                            long weight = key.getWeight() + value.getWeight() + REFERENCE_SIZE * 15;
                             // Round up to the next closest multiple of WEIGHT_RATIO
-                            long size =
-                                ((key.getWeight() + value.getWeight() - 1) >> WEIGHT_RATIO) + 1;
-                            if (size > Integer.MAX_VALUE) {
+                            weight = ((weight - 1) >> WEIGHT_RATIO) + 1;
+                            if (weight > Integer.MAX_VALUE) {
                               LOG.warn(
                                   "Entry with size {} MiBs inserted into the cache. This is larger than the maximum individual entry size of {} MiBs. The cache will under report its memory usage by the difference. This may lead to OutOfMemoryErrors.",
-                                  ((size - 1) >> 20) + 1,
+                                  ((weight - 1) >> 20) + 1,
                                   2 << (WEIGHT_RATIO + 10));
                               return Integer.MAX_VALUE;
                             }
-                            return (int) size;
+                            return (int) weight;
                           }
                         })
                     // The maximum size of an entry in the cache is maxWeight / concurrencyLevel
@@ -232,27 +237,20 @@ public int weigh(CompositeKey key, WeightedValue<Object> value) {
         weightInBytes);
   }
 
-  private static long findWeight(Object o) {
-    if (o instanceof WeightedValue) {
-      return ((WeightedValue<Object>) o).getWeight();
-    } else if (o instanceof Weighted) {
-      return ((Weighted) o).getWeight();
-    } else {
-      return weigh(o);
-    }
-  }
-
   private static WeightedValue<Object> addWeightedValue(
       CompositeKey key, Object o, LongAdder weightInBytes) {
     WeightedValue<Object> rval;
+    long additionalBytes = 0;
     if (o instanceof WeightedValue) {
       rval = (WeightedValue<Object>) o;
     } else if (o instanceof Weighted) {
       rval = WeightedValue.of(o, ((Weighted) o).getWeight());
+      additionalBytes = REFERENCE_SIZE * 2;
     } else {
       rval = WeightedValue.of(o, weigh(o));
+      additionalBytes = REFERENCE_SIZE * 2;
     }
-    weightInBytes.add(key.getWeight() + rval.getWeight());
+    weightInBytes.add(key.getWeight() + rval.getWeight() + additionalBytes);
     return rval;
   }
 
@@ -351,9 +349,9 @@ CompositeKeyPrefix subKey(Object suffix, Object... additionalSuffixes) {
       subKey[namespace.length] = suffix;
       System.arraycopy(
           additionalSuffixes, 0, subKey, namespace.length + 1, additionalSuffixes.length);
-      long subKeyWeight = weight + findWeight(suffix);
+      long subKeyWeight = weight + weigh(suffix);
       for (int i = 0; i < additionalSuffixes.length; ++i) {
-        subKeyWeight += findWeight(additionalSuffixes[i]);
+        subKeyWeight += weigh(additionalSuffixes[i]);
       }
       return new CompositeKeyPrefix(subKey, subKeyWeight);
     }
@@ -399,7 +397,7 @@ static class CompositeKey implements Weighted {
     private CompositeKey(Object[] namespace, long namespaceWeight, Object key) {
       this.namespace = namespace;
       this.key = key;
-      this.weight = namespaceWeight + findWeight(key);
+      this.weight = namespaceWeight + weigh(key);
     }
 
     @Override
@@ -426,7 +424,7 @@ public int hashCode() {
 
     @Override
     public long getWeight() {
-      return weight;
+      return weight + 24 + REFERENCE_SIZE * namespace.length;
     }
   }
 
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/FnApiDoFnRunner.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/FnApiDoFnRunner.java
index 4258681a494f..0388d3c03f00 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/FnApiDoFnRunner.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/FnApiDoFnRunner.java
@@ -90,7 +90,6 @@
 import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker;
 import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker.HasProgress;
 import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker.Progress;
-import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker.TruncateResult;
 import org.apache.beam.sdk.transforms.splittabledofn.SplitResult;
 import org.apache.beam.sdk.transforms.splittabledofn.TimestampObservingWatermarkEstimator;
 import org.apache.beam.sdk.transforms.splittabledofn.WatermarkEstimator;
@@ -104,6 +103,7 @@
 import org.apache.beam.sdk.util.construction.RehydratedComponents;
 import org.apache.beam.sdk.util.construction.Timer;
 import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.OutputBuilder;
 import org.apache.beam.sdk.values.PCollectionView;
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.sdk.values.TupleTag;
@@ -146,7 +146,6 @@ public Map<String, PTransformRunnerFactory> getPTransformRunnerFactories() {
       Factory factory = new Factory();
       return ImmutableMap.<String, PTransformRunnerFactory>builder()
           .put(PTransformTranslation.PAR_DO_TRANSFORM_URN, factory)
-          .put(PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN, factory)
           .put(
               PTransformTranslation.SPLITTABLE_PROCESS_SIZED_ELEMENTS_AND_RESTRICTIONS_URN, factory)
           .build();
@@ -251,8 +250,7 @@ public final void addRunnerForPTransform(Context context) throws IOException {
 
   /**
    * Only valid during {@link
-   * #processElementForWindowObservingSizedElementAndRestriction(WindowedValue)} and {@link
-   * #processElementForWindowObservingTruncateRestriction(WindowedValue)}.
+   * #processElementForWindowObservingSizedElementAndRestriction(WindowedValue)}.
    */
   private List<BoundedWindow> currentWindows;
 
@@ -261,8 +259,7 @@ public final void addRunnerForPTransform(Context context) throws IOException {
    * processed.
    *
    * <p>Only valid during {@link
-   * #processElementForWindowObservingSizedElementAndRestriction(WindowedValue)} and {@link
-   * #processElementForWindowObservingTruncateRestriction(WindowedValue)}.
+   * #processElementForWindowObservingSizedElementAndRestriction(WindowedValue)}.
    */
   private int windowStopIndex;
 
@@ -271,28 +268,17 @@ public final void addRunnerForPTransform(Context context) throws IOException {
    * windowStopIndex.
    *
    * <p>Only valid during {@link
-   * #processElementForWindowObservingSizedElementAndRestriction(WindowedValue)} and {@link
-   * #processElementForWindowObservingTruncateRestriction(WindowedValue)}.
+   * #processElementForWindowObservingSizedElementAndRestriction(WindowedValue)}.
    */
   private int windowCurrentIndex;
 
-  /**
-   * Only valid during {@link #processElementForPairWithRestriction}, {@link
-   * #processElementForSplitRestriction}, and {@link
-   * #processElementForWindowObservingSizedElementAndRestriction}, null otherwise.
-   */
+  /** Only valid during #processElementForWindowObservingSizedElementAndRestriction}. */
   private RestrictionT currentRestriction;
 
-  /**
-   * Only valid during {@link #processElementForSplitRestriction}, and {@link
-   * #processElementForWindowObservingSizedElementAndRestriction}, null otherwise.
-   */
+  /** Only valid during {@link #processElementForWindowObservingSizedElementAndRestriction}. */
   private WatermarkEstimatorStateT currentWatermarkEstimatorState;
 
-  /**
-   * Only valid during {@link #processElementForWindowObservingSizedElementAndRestriction} and
-   * {@link #processElementForWindowObservingTruncateRestriction}.
-   */
+  /** Only valid during {@link #processElementForWindowObservingSizedElementAndRestriction}. */
   private Instant initialWatermark;
 
   /**
@@ -361,7 +347,6 @@ public final void addRunnerForPTransform(Context context) throws IOException {
           mainOutputTag = (TupleTag) ParDoTranslation.getMainOutputTag(parDoPayload);
           break;
         case PTransformTranslation.SPLITTABLE_SPLIT_AND_SIZE_RESTRICTIONS_URN:
-        case PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN:
           mainOutputTag =
               new TupleTag(Iterables.getOnlyElement(pTransform.getOutputsMap().keySet()));
           break;
@@ -463,8 +448,6 @@ public final void addRunnerForPTransform(Context context) throws IOException {
         break;
       case PTransformTranslation.SPLITTABLE_SPLIT_AND_SIZE_RESTRICTIONS_URN:
         // startBundle should not be invoked
-      case PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN:
-        // startBundle should not be invoked
       default:
         // no-op
     }
@@ -486,79 +469,6 @@ public final void addRunnerForPTransform(Context context) throws IOException {
           this.processContext = new NonWindowObservingProcessBundleContext();
         }
         break;
-      case PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN:
-        if ((doFnSignature.truncateRestriction() != null
-                && doFnSignature.truncateRestriction().observesWindow())
-            || (doFnSignature.newTracker() != null && doFnSignature.newTracker().observesWindow())
-            || (doFnSignature.getSize() != null && doFnSignature.getSize().observesWindow())
-            || !sideInputMapping.isEmpty()) {
-          // Only forward split/progress when the only consumer is splittable.
-          if (mainOutputConsumer instanceof HandlesSplits) {
-            mainInputConsumer =
-                new SplittableFnDataReceiver() {
-                  private final HandlesSplits splitDelegate = (HandlesSplits) mainOutputConsumer;
-
-                  @Override
-                  public void accept(WindowedValue input) throws Exception {
-                    processElementForWindowObservingTruncateRestriction(input);
-                  }
-
-                  @Override
-                  public HandlesSplits.SplitResult trySplit(double fractionOfRemainder) {
-                    return trySplitForWindowObservingTruncateRestriction(
-                        fractionOfRemainder, splitDelegate);
-                  }
-
-                  @Override
-                  public double getProgress() {
-                    Progress progress =
-                        FnApiDoFnRunner.this.getProgressFromWindowObservingTruncate(
-                            splitDelegate.getProgress());
-                    if (progress != null) {
-                      double totalWork = progress.getWorkCompleted() + progress.getWorkRemaining();
-                      if (totalWork > 0) {
-                        return progress.getWorkCompleted() / totalWork;
-                      }
-                    }
-                    return 0;
-                  }
-                };
-          } else {
-            mainInputConsumer = this::processElementForWindowObservingTruncateRestriction;
-          }
-          this.processContext =
-              new SizedRestrictionWindowObservingProcessBundleContext(
-                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN);
-        } else {
-          // Only forward split/progress when the only consumer is splittable.
-          if (mainOutputConsumer instanceof HandlesSplits) {
-            mainInputConsumer =
-                new SplittableFnDataReceiver() {
-                  private final HandlesSplits splitDelegate = (HandlesSplits) mainOutputConsumer;
-
-                  @Override
-                  public void accept(WindowedValue input) throws Exception {
-                    processElementForTruncateRestriction(input);
-                  }
-
-                  @Override
-                  public HandlesSplits.SplitResult trySplit(double fractionOfRemainder) {
-                    return splitDelegate.trySplit(fractionOfRemainder);
-                  }
-
-                  @Override
-                  public double getProgress() {
-                    return splitDelegate.getProgress();
-                  }
-                };
-          } else {
-            mainInputConsumer = this::processElementForTruncateRestriction;
-          }
-          this.processContext =
-              new SizedRestrictionNonWindowObservingProcessBundleContext(
-                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN);
-        }
-        break;
       case PTransformTranslation.SPLITTABLE_PROCESS_SIZED_ELEMENTS_AND_RESTRICTIONS_URN:
         if (doFnSignature.processElement().observesWindow()
             || (doFnSignature.newTracker() != null && doFnSignature.newTracker().observesWindow())
@@ -600,8 +510,6 @@ public void accept(WindowedValue input) throws Exception {
         break;
       case PTransformTranslation.SPLITTABLE_SPLIT_AND_SIZE_RESTRICTIONS_URN:
         // finishBundle should not be invoked
-      case PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN:
-        // finishBundle should not be invoked
       default:
         // no-op
     }
@@ -736,90 +644,6 @@ private void processElementForWindowObservingParDo(WindowedValue<InputT> elem) {
     }
   }
 
-  private void processElementForTruncateRestriction(
-      WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>> elem) {
-    currentElement = elem.withValue(elem.getValue().getKey().getKey());
-    currentRestriction = elem.getValue().getKey().getValue().getKey();
-    currentWatermarkEstimatorState = elem.getValue().getKey().getValue().getValue();
-    // For truncation, we don't set currentTrackerClaimed so that we enable checkpointing even if no
-    // progress is made.
-    currentTracker =
-        RestrictionTrackers.observe(
-            doFnInvoker.invokeNewTracker(processContext),
-            new ClaimObserver<PositionT>() {
-              @Override
-              public void onClaimed(PositionT position) {}
-
-              @Override
-              public void onClaimFailed(PositionT position) {}
-            });
-    try {
-      TruncateResult<OutputT> truncatedRestriction =
-          doFnInvoker.invokeTruncateRestriction(processContext);
-      if (truncatedRestriction != null) {
-        processContext.output(truncatedRestriction.getTruncatedRestriction());
-      }
-    } finally {
-      currentTracker = null;
-      currentElement = null;
-      currentRestriction = null;
-      currentWatermarkEstimatorState = null;
-    }
-
-    this.stateAccessor.finalizeState();
-  }
-
-  private void processElementForWindowObservingTruncateRestriction(
-      WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>> elem) {
-    currentElement = elem.withValue(elem.getValue().getKey().getKey());
-    windowCurrentIndex = -1;
-    windowStopIndex = currentElement.getWindows().size();
-    currentWindows = ImmutableList.copyOf(currentElement.getWindows());
-    while (true) {
-      synchronized (splitLock) {
-        windowCurrentIndex++;
-        if (windowCurrentIndex >= windowStopIndex) {
-          // Careful to reset the split state under the same synchronized block.
-          windowCurrentIndex = -1;
-          windowStopIndex = 0;
-          currentElement = null;
-          currentWindows = null;
-          currentRestriction = null;
-          currentWatermarkEstimatorState = null;
-          currentWindow = null;
-          currentTracker = null;
-          currentWatermarkEstimator = null;
-          initialWatermark = null;
-          break;
-        }
-        currentRestriction = elem.getValue().getKey().getValue().getKey();
-        currentWatermarkEstimatorState = elem.getValue().getKey().getValue().getValue();
-        currentWindow = currentWindows.get(windowCurrentIndex);
-        // We leave currentTrackerClaimed unset as we want to split regardless of if tryClaim is
-        // called.
-        currentTracker =
-            RestrictionTrackers.observe(
-                doFnInvoker.invokeNewTracker(processContext),
-                new ClaimObserver<PositionT>() {
-                  @Override
-                  public void onClaimed(PositionT position) {}
-
-                  @Override
-                  public void onClaimFailed(PositionT position) {}
-                });
-        currentWatermarkEstimator =
-            WatermarkEstimators.threadSafe(doFnInvoker.invokeNewWatermarkEstimator(processContext));
-        initialWatermark = currentWatermarkEstimator.getWatermarkAndState().getKey();
-      }
-      TruncateResult<OutputT> truncatedRestriction =
-          doFnInvoker.invokeTruncateRestriction(processContext);
-      if (truncatedRestriction != null) {
-        processContext.output(truncatedRestriction.getTruncatedRestriction());
-      }
-    }
-    this.stateAccessor.finalizeState();
-  }
-
   private void processElementForWindowObservingSizedElementAndRestriction(
       WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>> elem) {
     currentElement = elem.withValue(elem.getValue().getKey().getKey());
@@ -922,41 +746,13 @@ public double getProgress() {
   private Progress getProgress() {
     synchronized (splitLock) {
       if (currentTracker instanceof RestrictionTracker.HasProgress && currentWindow != null) {
-        return scaleProgress(
+        return ProgressUtils.scaleProgress(
             ((HasProgress) currentTracker).getProgress(), windowCurrentIndex, windowStopIndex);
       }
     }
     return null;
   }
 
-  private Progress getProgressFromWindowObservingTruncate(double elementCompleted) {
-    synchronized (splitLock) {
-      if (currentWindow != null) {
-        return scaleProgress(
-            Progress.from(elementCompleted, 1 - elementCompleted),
-            windowCurrentIndex,
-            windowStopIndex);
-      }
-    }
-    return null;
-  }
-
-  @VisibleForTesting
-  static Progress scaleProgress(Progress progress, int currentWindowIndex, int stopWindowIndex) {
-    checkArgument(
-        currentWindowIndex < stopWindowIndex,
-        "Current window index (%s) must be less than stop window index (%s)",
-        currentWindowIndex,
-        stopWindowIndex);
-
-    double totalWorkPerWindow = progress.getWorkCompleted() + progress.getWorkRemaining();
-    double completed = totalWorkPerWindow * currentWindowIndex + progress.getWorkCompleted();
-    double remaining =
-        totalWorkPerWindow * (stopWindowIndex - currentWindowIndex - 1)
-            + progress.getWorkRemaining();
-    return Progress.from(completed, remaining);
-  }
-
   private WindowedSplitResult calculateRestrictionSize(
       WindowedSplitResult splitResult, String errorContext) {
     double fullSize =
@@ -1040,61 +836,6 @@ public Object restriction() {
                 splitResult.getResidualInUnprocessedWindowsRoot().getPaneInfo()));
   }
 
-  private HandlesSplits.SplitResult trySplitForWindowObservingTruncateRestriction(
-      double fractionOfRemainder, HandlesSplits splitDelegate) {
-    WindowedSplitResult windowedSplitResult = null;
-    HandlesSplits.SplitResult downstreamSplitResult = null;
-    synchronized (splitLock) {
-      // There is nothing to split if we are between truncate processing calls.
-      if (currentWindow == null) {
-        return null;
-      }
-      // We are requesting a checkpoint but have not yet progressed on the restriction, skip
-      // request.
-      if (fractionOfRemainder == 0
-          && currentTrackerClaimed != null
-          && !currentTrackerClaimed.get()) {
-        return null;
-      }
-
-      SplitResultsWithStopIndex splitResult =
-          computeSplitForProcessOrTruncate(
-              currentElement,
-              currentRestriction,
-              currentWindow,
-              currentWindows,
-              currentWatermarkEstimatorState,
-              fractionOfRemainder,
-              null,
-              splitDelegate,
-              null,
-              windowCurrentIndex,
-              windowStopIndex);
-      if (splitResult == null) {
-        return null;
-      }
-      windowStopIndex = splitResult.getNewWindowStopIndex();
-      windowedSplitResult =
-          calculateRestrictionSize(
-              splitResult.getWindowSplit(),
-              PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN + "/GetSize");
-      downstreamSplitResult = splitResult.getDownstreamSplit();
-    }
-    // Note that the assumption here is the fullInputCoder of the Truncate transform should be the
-    // the same as the SDF/Process transform.
-    Coder fullInputCoder = WindowedValues.getFullCoder(inputCoder, windowCoder);
-    return constructSplitResult(
-        windowedSplitResult,
-        downstreamSplitResult,
-        fullInputCoder,
-        initialWatermark,
-        null,
-        pTransformId,
-        mainInputId,
-        pTransform.getOutputsMap().keySet(),
-        null);
-  }
-
   private static <WatermarkEstimatorStateT> WindowedSplitResult computeWindowSplitResult(
       WindowedValue currentElement,
       Object currentRestriction,
@@ -1152,7 +893,7 @@ private static <WatermarkEstimatorStateT> WindowedSplitResult computeWindowSplit
   }
 
   @VisibleForTesting
-  static <WatermarkEstimatorStateT> SplitResultsWithStopIndex computeSplitForProcessOrTruncate(
+  static <WatermarkEstimatorStateT> SplitResultsWithStopIndex computeSplitForProcess(
       WindowedValue currentElement,
       Object currentRestriction,
       BoundedWindow currentWindow,
@@ -1189,7 +930,8 @@ static <WatermarkEstimatorStateT> SplitResultsWithStopIndex computeSplitForProce
         double elementCompleted = splitDelegate.getProgress();
         elementProgress = Progress.from(elementCompleted, 1 - elementCompleted);
       }
-      Progress scaledProgress = scaleProgress(elementProgress, currentWindowIndex, stopWindowIndex);
+      Progress scaledProgress =
+          ProgressUtils.scaleProgress(elementProgress, currentWindowIndex, stopWindowIndex);
       double scaledFractionOfRemainder = scaledProgress.getWorkRemaining() * fractionOfRemainder;
 
       // The fraction is out of the current window and hence we will split at the closest window
@@ -1417,7 +1159,7 @@ private HandlesSplits.SplitResult trySplitForElementAndRestriction(
       // applies to the residual.
       watermarkAndState = currentWatermarkEstimator.getWatermarkAndState();
       SplitResultsWithStopIndex splitResult =
-          computeSplitForProcessOrTruncate(
+          computeSplitForProcess(
               currentElement,
               currentRestriction,
               currentWindow,
@@ -1439,18 +1181,18 @@ private HandlesSplits.SplitResult trySplitForElementAndRestriction(
               splitResult.getWindowSplit(),
               PTransformTranslation.SPLITTABLE_PROCESS_SIZED_ELEMENTS_AND_RESTRICTIONS_URN
                   + "/GetSize");
+      Coder fullInputCoder = WindowedValues.getFullCoder(inputCoder, windowCoder);
+      return constructSplitResult(
+          windowedSplitResult,
+          null,
+          fullInputCoder,
+          initialWatermark,
+          watermarkAndState,
+          pTransformId,
+          mainInputId,
+          pTransform.getOutputsMap().keySet(),
+          resumeDelay);
     }
-    Coder fullInputCoder = WindowedValues.getFullCoder(inputCoder, windowCoder);
-    return constructSplitResult(
-        windowedSplitResult,
-        null,
-        fullInputCoder,
-        initialWatermark,
-        watermarkAndState,
-        pTransformId,
-        mainInputId,
-        pTransform.getOutputsMap().keySet(),
-        resumeDelay);
   }
 
   private <K> void processTimer(
@@ -1926,6 +1668,48 @@ public <T> void output(TupleTag<T> tag, T output, Instant timestamp, BoundedWind
         }
         outputTo(consumer, WindowedValues.of(output, timestamp, window, PaneInfo.NO_FIRING));
       }
+
+      @Override
+      public void output(
+          OutputT output,
+          Instant timestamp,
+          BoundedWindow window,
+          @Nullable String currentRecordId,
+          @Nullable Long currentRecordOffset) {
+        outputTo(
+            mainOutputConsumer,
+            WindowedValues.of(
+                output,
+                timestamp,
+                Collections.singletonList(window),
+                PaneInfo.NO_FIRING,
+                currentRecordId,
+                currentRecordOffset));
+      }
+
+      @Override
+      public <T> void output(
+          TupleTag<T> tag,
+          T output,
+          Instant timestamp,
+          BoundedWindow window,
+          @Nullable String currentRecordId,
+          @Nullable Long currentRecordOffset) {
+        FnDataReceiver<WindowedValue<T>> consumer =
+            (FnDataReceiver) localNameToConsumer.get(tag.getId());
+        if (consumer == null) {
+          throw new IllegalArgumentException(String.format("Unknown output tag %s", tag));
+        }
+        outputTo(
+            consumer,
+            WindowedValues.of(
+                output,
+                timestamp,
+                Collections.singletonList(window),
+                PaneInfo.NO_FIRING,
+                currentRecordId,
+                currentRecordOffset));
+      }
     }
 
     private final FinishBundleArgumentProvider.Context context =
@@ -1974,6 +1758,13 @@ public <T> T sideInput(PCollectionView<T> view) {
   private class WindowObservingProcessBundleContext
       extends WindowObservingProcessBundleContextBase {
 
+    @Override
+    public OutputBuilder<OutputT> builder(OutputT value) {
+      return WindowedValues.<OutputT>builder()
+          .setValue(value)
+          .setReceiver(windowedValue -> outputTo(mainOutputConsumer, windowedValue));
+    }
+
     @Override
     public void output(OutputT output) {
       // Don't need to check timestamp since we can always output using the input timestamp.
@@ -2017,6 +1808,22 @@ public void outputWindowedValue(
       outputTo(mainOutputConsumer, WindowedValues.of(output, timestamp, windows, paneInfo));
     }
 
+    @Override
+    public void outputWindowedValue(
+        OutputT output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      // TODO(https://github.com/apache/beam/issues/29637): Check that timestamp is valid once all
+      // runners can provide proper timestamps.
+      outputTo(
+          mainOutputConsumer,
+          WindowedValues.of(
+              output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
+    }
+
     @Override
     public <T> void outputWithTimestamp(TupleTag<T> tag, T output, Instant timestamp) {
       // TODO(https://github.com/apache/beam/issues/29637): Check that timestamp is valid once all
@@ -2048,6 +1855,26 @@ public <T> void outputWindowedValue(
       outputTo(consumer, WindowedValues.of(output, timestamp, windows, paneInfo));
     }
 
+    @Override
+    public <T> void outputWindowedValue(
+        TupleTag<T> tag,
+        T output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
+      FnDataReceiver<WindowedValue<T>> consumer =
+          (FnDataReceiver) localNameToConsumer.get(tag.getId());
+      if (consumer == null) {
+        throw new IllegalArgumentException(String.format("Unknown output tag %s", tag));
+      }
+      outputTo(
+          consumer,
+          WindowedValues.of(
+              output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
+    }
+
     @Override
     public State state(String stateId, boolean alwaysFetched) {
       StateDeclaration stateDeclaration = doFnSignature.stateDeclarations().get(stateId);
@@ -2101,95 +1928,45 @@ public TimerMap timerFamily(String timerFamilyId) {
     }
   }
 
-  /** This context outputs KV<KV<Element, KV<Restriction, WatemarkEstimatorState>>, Size>. */
-  private class SizedRestrictionWindowObservingProcessBundleContext
-      extends WindowObservingProcessBundleContextBase {
-    private final String errorContextPrefix;
+  /** Provides arguments for a {@link DoFnInvoker} for a non-window observing method. */
+  private class NonWindowObservingProcessBundleContext
+      extends NonWindowObservingProcessBundleContextBase {
 
-    SizedRestrictionWindowObservingProcessBundleContext(String errorContextPrefix) {
-      this.errorContextPrefix = errorContextPrefix;
+    @Override
+    public OutputBuilder<OutputT> builder(OutputT value) {
+      return WindowedValues.builder(currentElement)
+          .withValue(value)
+          .setReceiver(
+              windowedValue -> {
+                checkTimestamp(windowedValue.getTimestamp());
+                outputTo(mainOutputConsumer, windowedValue);
+              });
     }
 
     @Override
-    // OutputT == RestrictionT
     public void output(OutputT output) {
-      double size =
-          doFnInvoker.invokeGetSize(
-              new DelegatingArgumentProvider<InputT, OutputT>(
-                  this, this.errorContextPrefix + "/GetSize") {
-                @Override
-                public Object restriction() {
-                  return output;
-                }
-
-                @Override
-                public Instant timestamp(DoFn<InputT, OutputT> doFn) {
-                  return currentElement.getTimestamp();
-                }
-
-                @Override
-                public RestrictionTracker<?, ?> restrictionTracker() {
-                  return doFnInvoker.invokeNewTracker(this);
-                }
-              });
-
       // Don't need to check timestamp since we can always output using the input timestamp.
-      outputTo(
-          mainOutputConsumer,
-          (WindowedValue<OutputT>)
-              WindowedValues.of(
-                  KV.of(
-                      KV.of(
-                          currentElement.getValue(), KV.of(output, currentWatermarkEstimatorState)),
-                      size),
-                  currentElement.getTimestamp(),
-                  currentWindow,
-                  currentElement.getPaneInfo()));
+      if (currentElement == null) {
+        throw new IllegalStateException(
+            "Attempting to emit an element outside of a @ProcessElement context.");
+      }
+      outputTo(mainOutputConsumer, currentElement.withValue(output));
     }
 
     @Override
     public <T> void output(TupleTag<T> tag, T output) {
-      // Note that the OutputReceiver/RowOutputReceiver specifically will use the non-tag versions
-      // of these methods when producing output.
-      throw new UnsupportedOperationException(
-          String.format("Non-main output %s unsupported in %s", tag, errorContextPrefix));
+      FnDataReceiver<WindowedValue<T>> consumer =
+          (FnDataReceiver) localNameToConsumer.get(tag.getId());
+      if (consumer == null) {
+        throw new IllegalArgumentException(String.format("Unknown output tag %s", tag));
+      }
+      // Don't need to check timestamp since we can always output using the input timestamp.
+      outputTo(consumer, currentElement.withValue(output));
     }
 
     @Override
-    // OutputT == RestrictionT
     public void outputWithTimestamp(OutputT output, Instant timestamp) {
-      checkTimestamp(timestamp);
-      double size =
-          doFnInvoker.invokeGetSize(
-              new DelegatingArgumentProvider<InputT, OutputT>(
-                  this, this.errorContextPrefix + "/GetSize") {
-                @Override
-                public Object restriction() {
-                  return output;
-                }
-
-                @Override
-                public Instant timestamp(DoFn<InputT, OutputT> doFn) {
-                  return timestamp;
-                }
-
-                @Override
-                public RestrictionTracker<?, ?> restrictionTracker() {
-                  return doFnInvoker.invokeNewTracker(this);
-                }
-              });
-
-      outputTo(
-          mainOutputConsumer,
-          (WindowedValue<OutputT>)
-              WindowedValues.of(
-                  KV.of(
-                      KV.of(
-                          currentElement.getValue(), KV.of(output, currentWatermarkEstimatorState)),
-                      size),
-                  timestamp,
-                  currentWindow,
-                  currentElement.getPaneInfo()));
+      builder(output).setValue(output).setTimestamp(timestamp).output();
     }
 
     @Override
@@ -2198,166 +1975,7 @@ public void outputWindowedValue(
         Instant timestamp,
         Collection<? extends BoundedWindow> windows,
         PaneInfo paneInfo) {
-      checkTimestamp(timestamp);
-      double size =
-          doFnInvoker.invokeGetSize(
-              new DelegatingArgumentProvider<InputT, OutputT>(
-                  this, this.errorContextPrefix + "/GetSize") {
-                @Override
-                public Object restriction() {
-                  return output;
-                }
-
-                @Override
-                public Instant timestamp(DoFn<InputT, OutputT> doFn) {
-                  return timestamp;
-                }
-
-                @Override
-                public RestrictionTracker<?, ?> restrictionTracker() {
-                  return doFnInvoker.invokeNewTracker(this);
-                }
-              });
-
-      outputTo(
-          mainOutputConsumer,
-          (WindowedValue<OutputT>)
-              WindowedValues.of(
-                  KV.of(
-                      KV.of(
-                          currentElement.getValue(), KV.of(output, currentWatermarkEstimatorState)),
-                      size),
-                  timestamp,
-                  windows,
-                  paneInfo));
-    }
-
-    @Override
-    public <T> void outputWithTimestamp(TupleTag<T> tag, T output, Instant timestamp) {
-      // Note that the OutputReceiver/RowOutputReceiver specifically will use the non-tag versions
-      // of these methods when producing output.
-      throw new UnsupportedOperationException(
-          String.format("Non-main output %s unsupported in %s", tag, errorContextPrefix));
-    }
-
-    @Override
-    public <T> void outputWindowedValue(
-        TupleTag<T> tag,
-        T output,
-        Instant timestamp,
-        Collection<? extends BoundedWindow> windows,
-        PaneInfo paneInfo) {
-      // Note that the OutputReceiver/RowOutputReceiver specifically will use the non-tag versions
-      // of these methods when producing output.
-      throw new UnsupportedOperationException(
-          String.format("Non-main output %s unsupported in %s", tag, errorContextPrefix));
-    }
-
-    @Override
-    public State state(String stateId, boolean alwaysFetched) {
-      throw new UnsupportedOperationException(
-          String.format("State unsupported in %s", errorContextPrefix));
-    }
-
-    @Override
-    public org.apache.beam.sdk.state.Timer timer(String timerId) {
-      throw new UnsupportedOperationException(
-          String.format("Timer unsupported in %s", errorContextPrefix));
-    }
-
-    @Override
-    public TimerMap timerFamily(String tagId) {
-      throw new UnsupportedOperationException(
-          String.format("Timer unsupported in %s", errorContextPrefix));
-    }
-  }
-
-  /** This context outputs KV<KV<Element, KV<Restriction, WatermarkEstimatorState>>, Size>. */
-  private class SizedRestrictionNonWindowObservingProcessBundleContext
-      extends NonWindowObservingProcessBundleContextBase {
-    private final String errorContextPrefix;
-
-    SizedRestrictionNonWindowObservingProcessBundleContext(String errorContextPrefix) {
-      this.errorContextPrefix = errorContextPrefix;
-    }
-
-    @Override
-    // OutputT == RestrictionT
-    public void output(OutputT output) {
-      double size =
-          doFnInvoker.invokeGetSize(
-              new DelegatingArgumentProvider<InputT, OutputT>(
-                  this, errorContextPrefix + "/GetSize") {
-                @Override
-                public Object restriction() {
-                  return output;
-                }
-
-                @Override
-                public Instant timestamp(DoFn<InputT, OutputT> doFn) {
-                  return currentElement.getTimestamp();
-                }
-
-                @Override
-                public RestrictionTracker<?, ?> restrictionTracker() {
-                  return doFnInvoker.invokeNewTracker(this);
-                }
-              });
-
-      // Don't need to check timestamp since we can always output using the input timestamp.
-      outputTo(
-          mainOutputConsumer,
-          (WindowedValue<OutputT>)
-              currentElement.withValue(
-                  KV.of(
-                      KV.of(
-                          currentElement.getValue(), KV.of(output, currentWatermarkEstimatorState)),
-                      size)));
-    }
-
-    @Override
-    public <T> void output(TupleTag<T> tag, T output) {
-      // Note that the OutputReceiver/RowOutputReceiver specifically will use the non-tag versions
-      // of these methods when producing output.
-      throw new UnsupportedOperationException(
-          String.format("Non-main output %s unsupported in %s", tag, errorContextPrefix));
-    }
-
-    @Override
-    // OutputT == RestrictionT
-    public void outputWithTimestamp(OutputT output, Instant timestamp) {
-      checkTimestamp(timestamp);
-      double size =
-          doFnInvoker.invokeGetSize(
-              new DelegatingArgumentProvider<InputT, OutputT>(
-                  this, errorContextPrefix + "/GetSize") {
-                @Override
-                public Object restriction() {
-                  return output;
-                }
-
-                @Override
-                public Instant timestamp(DoFn<InputT, OutputT> doFn) {
-                  return timestamp;
-                }
-
-                @Override
-                public RestrictionTracker<?, ?> restrictionTracker() {
-                  return doFnInvoker.invokeNewTracker(this);
-                }
-              });
-
-      outputTo(
-          mainOutputConsumer,
-          (WindowedValue<OutputT>)
-              WindowedValues.of(
-                  KV.of(
-                      KV.of(
-                          currentElement.getValue(), KV.of(output, currentWatermarkEstimatorState)),
-                      size),
-                  timestamp,
-                  currentElement.getWindows(),
-                  currentElement.getPaneInfo()));
+      builder(output).setTimestamp(timestamp).setWindows(windows).setPaneInfo(paneInfo).output();
     }
 
     @Override
@@ -2365,119 +1983,44 @@ public void outputWindowedValue(
         OutputT output,
         Instant timestamp,
         Collection<? extends BoundedWindow> windows,
-        PaneInfo paneInfo) {
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
       checkTimestamp(timestamp);
-      double size =
-          doFnInvoker.invokeGetSize(
-              new DelegatingArgumentProvider<InputT, OutputT>(
-                  this, errorContextPrefix + "/GetSize") {
-                @Override
-                public Object restriction() {
-                  return output;
-                }
-
-                @Override
-                public Instant timestamp(DoFn<InputT, OutputT> doFn) {
-                  return timestamp;
-                }
-
-                @Override
-                public RestrictionTracker<?, ?> restrictionTracker() {
-                  return doFnInvoker.invokeNewTracker(this);
-                }
-              });
-
       outputTo(
           mainOutputConsumer,
-          (WindowedValue<OutputT>)
-              WindowedValues.of(
-                  KV.of(
-                      KV.of(
-                          currentElement.getValue(), KV.of(output, currentWatermarkEstimatorState)),
-                      size),
-                  timestamp,
-                  windows,
-                  paneInfo));
+          WindowedValues.of(
+              output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
     }
 
     @Override
     public <T> void outputWithTimestamp(TupleTag<T> tag, T output, Instant timestamp) {
-      // Note that the OutputReceiver/RowOutputReceiver specifically will use the non-tag versions
-      // of these methods when producing output.
-      throw new UnsupportedOperationException(
-          String.format("Non-main output %s unsupported in %s", tag, errorContextPrefix));
-    }
-
-    @Override
-    public <T> void outputWindowedValue(
-        TupleTag<T> tag,
-        T output,
-        Instant timestamp,
-        Collection<? extends BoundedWindow> windows,
-        PaneInfo paneInfo) {
-      // Note that the OutputReceiver/RowOutputReceiver specifically will use the non-tag versions
-      // of these methods when producing output.
-      throw new UnsupportedOperationException(
-          String.format("Non-main output %s unsupported in %s", tag, errorContextPrefix));
-    }
-  }
-
-  /** Provides arguments for a {@link DoFnInvoker} for a non-window observing method. */
-  private class NonWindowObservingProcessBundleContext
-      extends NonWindowObservingProcessBundleContextBase {
-
-    @Override
-    public void output(OutputT output) {
-      // Don't need to check timestamp since we can always output using the input timestamp.
-      if (currentElement == null) {
-        throw new IllegalStateException(
-            "Attempting to emit an element outside of a @ProcessElement context.");
-      }
-      outputTo(mainOutputConsumer, currentElement.withValue(output));
-    }
-
-    @Override
-    public <T> void output(TupleTag<T> tag, T output) {
+      checkTimestamp(timestamp);
       FnDataReceiver<WindowedValue<T>> consumer =
           (FnDataReceiver) localNameToConsumer.get(tag.getId());
       if (consumer == null) {
         throw new IllegalArgumentException(String.format("Unknown output tag %s", tag));
       }
-      // Don't need to check timestamp since we can always output using the input timestamp.
-      outputTo(consumer, currentElement.withValue(output));
-    }
-
-    @Override
-    public void outputWithTimestamp(OutputT output, Instant timestamp) {
-      checkTimestamp(timestamp);
       outputTo(
-          mainOutputConsumer,
+          consumer,
           WindowedValues.of(
               output, timestamp, currentElement.getWindows(), currentElement.getPaneInfo()));
     }
 
     @Override
-    public void outputWindowedValue(
-        OutputT output,
+    public <T> void outputWindowedValue(
+        TupleTag<T> tag,
+        T output,
         Instant timestamp,
         Collection<? extends BoundedWindow> windows,
         PaneInfo paneInfo) {
       checkTimestamp(timestamp);
-      outputTo(mainOutputConsumer, WindowedValues.of(output, timestamp, windows, paneInfo));
-    }
-
-    @Override
-    public <T> void outputWithTimestamp(TupleTag<T> tag, T output, Instant timestamp) {
-      checkTimestamp(timestamp);
       FnDataReceiver<WindowedValue<T>> consumer =
           (FnDataReceiver) localNameToConsumer.get(tag.getId());
       if (consumer == null) {
         throw new IllegalArgumentException(String.format("Unknown output tag %s", tag));
       }
-      outputTo(
-          consumer,
-          WindowedValues.of(
-              output, timestamp, currentElement.getWindows(), currentElement.getPaneInfo()));
+      outputTo(consumer, WindowedValues.of(output, timestamp, windows, paneInfo));
     }
 
     @Override
@@ -2486,14 +2029,19 @@ public <T> void outputWindowedValue(
         T output,
         Instant timestamp,
         Collection<? extends BoundedWindow> windows,
-        PaneInfo paneInfo) {
+        PaneInfo paneInfo,
+        @Nullable String currentRecordId,
+        @Nullable Long currentRecordOffset) {
       checkTimestamp(timestamp);
       FnDataReceiver<WindowedValue<T>> consumer =
           (FnDataReceiver) localNameToConsumer.get(tag.getId());
       if (consumer == null) {
         throw new IllegalArgumentException(String.format("Unknown output tag %s", tag));
       }
-      outputTo(consumer, WindowedValues.of(output, timestamp, windows, paneInfo));
+      outputTo(
+          consumer,
+          WindowedValues.of(
+              output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
     }
   }
 
@@ -2607,6 +2155,12 @@ public OutputReceiver<OutputT> outputReceiver(DoFn<InputT, OutputT> doFn) {
       return this;
     }
 
+    @Override
+    // OutputT == RestrictionT
+    public void output(OutputT output) {
+      OutputReceiver.super.output(output);
+    }
+
     private final OutputReceiver<Row> mainRowOutputReceiver =
         mainOutputSchemaCoder == null
             ? null
@@ -2615,24 +2169,16 @@ public OutputReceiver<OutputT> outputReceiver(DoFn<InputT, OutputT> doFn) {
                   mainOutputSchemaCoder.getFromRowFunction();
 
               @Override
-              public void output(Row output) {
-                ProcessBundleContextBase.this.output(fromRowFunction.apply(output));
-              }
-
-              @Override
-              public void outputWithTimestamp(Row output, Instant timestamp) {
-                ProcessBundleContextBase.this.outputWithTimestamp(
-                    fromRowFunction.apply(output), timestamp);
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  Row output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                ProcessBundleContextBase.this.outputWindowedValue(
-                    fromRowFunction.apply(output), timestamp, windows, paneInfo);
+              public OutputBuilder<Row> builder(Row value) {
+                return WindowedValues.builder(currentElement)
+                    .withValue(value)
+                    .setReceiver(
+                        windowedRow ->
+                            ProcessBundleContextBase.this.outputWindowedValue(
+                                fromRowFunction.apply(windowedRow.getValue()),
+                                windowedRow.getTimestamp(),
+                                windowedRow.getWindows(),
+                                windowedRow.getPaneInfo()));
               }
             };
 
@@ -2661,23 +2207,17 @@ private <T> OutputReceiver<T> createTaggedOutputReceiver(TupleTag<T> tag) {
             }
             return new OutputReceiver<T>() {
               @Override
-              public void output(T output) {
-                ProcessBundleContextBase.this.output(tag, output);
-              }
-
-              @Override
-              public void outputWithTimestamp(T output, Instant timestamp) {
-                ProcessBundleContextBase.this.outputWithTimestamp(tag, output, timestamp);
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  T output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                ProcessBundleContextBase.this.outputWindowedValue(
-                    tag, output, timestamp, windows, paneInfo);
+              public OutputBuilder<T> builder(T value) {
+                return WindowedValues.builder(currentElement)
+                    .withValue(value)
+                    .setReceiver(
+                        windowedValue ->
+                            ProcessBundleContextBase.this.outputWindowedValue(
+                                tag,
+                                windowedValue.getValue(),
+                                windowedValue.getTimestamp(),
+                                windowedValue.getWindows(),
+                                windowedValue.getPaneInfo()));
               }
             };
           }
@@ -2696,7 +2236,7 @@ private <T> OutputReceiver<Row> createTaggedRowReceiver(TupleTag<T> tag) {
             }
 
             Coder<T> outputCoder = (Coder<T>) outputCoders.get(tag);
-            checkState(outputCoder != null, "No output tag for " + tag);
+            checkState(outputCoder != null, "No output tag for %s", tag);
             checkState(
                 outputCoder instanceof SchemaCoder,
                 "Output with tag " + tag + " must have a schema in order to call getRowReceiver");
@@ -2705,24 +2245,17 @@ private <T> OutputReceiver<Row> createTaggedRowReceiver(TupleTag<T> tag) {
                   ((SchemaCoder) outputCoder).getFromRowFunction();
 
               @Override
-              public void output(Row output) {
-                ProcessBundleContextBase.this.output(tag, fromRowFunction.apply(output));
-              }
-
-              @Override
-              public void outputWithTimestamp(Row output, Instant timestamp) {
-                ProcessBundleContextBase.this.outputWithTimestamp(
-                    tag, fromRowFunction.apply(output), timestamp);
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  Row output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                ProcessBundleContextBase.this.outputWindowedValue(
-                    tag, fromRowFunction.apply(output), timestamp, windows, paneInfo);
+              public OutputBuilder<Row> builder(Row value) {
+                return WindowedValues.builder(currentElement)
+                    .withValue(value)
+                    .setReceiver(
+                        windowedRow ->
+                            ProcessBundleContextBase.this.outputWindowedValue(
+                                tag,
+                                fromRowFunction.apply(windowedRow.getValue()),
+                                windowedRow.getTimestamp(),
+                                windowedRow.getWindows(),
+                                windowedRow.getPaneInfo()));
               }
             };
           }
@@ -2785,6 +2318,16 @@ public Instant timestamp() {
       return currentElement.getTimestamp();
     }
 
+    @Override
+    public String currentRecordId() {
+      return currentElement.getRecordId();
+    }
+
+    @Override
+    public Long currentRecordOffset() {
+      return currentElement.getRecordOffset();
+    }
+
     @Override
     public PaneInfo pane() {
       return currentElement.getPaneInfo();
@@ -2808,6 +2351,7 @@ public WatermarkEstimator<?> watermarkEstimator() {
   private class OnWindowExpirationContext<K> extends BaseArgumentProvider<InputT, OutputT> {
     private class Context extends DoFn<InputT, OutputT>.OnWindowExpirationContext
         implements OutputReceiver<OutputT> {
+
       private Context() {
         doFn.super();
       }
@@ -2817,28 +2361,14 @@ public PipelineOptions getPipelineOptions() {
         return pipelineOptions;
       }
 
-      @Override
-      public BoundedWindow window() {
-        return currentWindow;
-      }
-
       @Override
       public void output(OutputT output) {
-        outputTo(
-            mainOutputConsumer,
-            WindowedValues.of(
-                output,
-                currentTimer.getHoldTimestamp(),
-                currentWindow,
-                currentTimer.getPaneInfo()));
+        OutputReceiver.super.output(output);
       }
 
       @Override
       public void outputWithTimestamp(OutputT output, Instant timestamp) {
-        checkOnWindowExpirationTimestamp(timestamp);
-        outputTo(
-            mainOutputConsumer,
-            WindowedValues.of(output, timestamp, currentWindow, currentTimer.getPaneInfo()));
+        OutputReceiver.super.outputWithTimestamp(output, timestamp);
       }
 
       @Override
@@ -2847,8 +2377,41 @@ public void outputWindowedValue(
           Instant timestamp,
           Collection<? extends BoundedWindow> windows,
           PaneInfo paneInfo) {
+        OutputReceiver.super.outputWindowedValue(output, timestamp, windows, paneInfo);
+      }
+
+      @Override
+      public BoundedWindow window() {
+        return currentWindow;
+      }
+
+      @Override
+      public OutputBuilder<OutputT> builder(OutputT value) {
+        return WindowedValues.<OutputT>builder()
+            .setValue(value)
+            .setWindow(currentWindow)
+            .setTimestamp(currentTimer.getHoldTimestamp())
+            .setPaneInfo(currentTimer.getPaneInfo())
+            .setReceiver(
+                windowedValue -> {
+                  checkOnWindowExpirationTimestamp(windowedValue.getTimestamp());
+                  outputTo(mainOutputConsumer, windowedValue);
+                });
+      }
+
+      @Override
+      public void outputWindowedValue(
+          OutputT output,
+          Instant timestamp,
+          Collection<? extends BoundedWindow> windows,
+          PaneInfo paneInfo,
+          @Nullable String currentRecordId,
+          @Nullable Long currentRecordOffset) {
         checkOnWindowExpirationTimestamp(timestamp);
-        outputTo(mainOutputConsumer, WindowedValues.of(output, timestamp, windows, paneInfo));
+        outputTo(
+            mainOutputConsumer,
+            WindowedValues.of(
+                output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
       }
 
       @Override
@@ -2887,10 +2450,25 @@ public <T> void outputWindowedValue(
           Instant timestamp,
           Collection<? extends BoundedWindow> windows,
           PaneInfo paneInfo) {
+        outputWindowedValue(tag, output, timestamp, windows, paneInfo, null, null);
+      }
+
+      @Override
+      public <T> void outputWindowedValue(
+          TupleTag<T> tag,
+          T output,
+          Instant timestamp,
+          Collection<? extends BoundedWindow> windows,
+          PaneInfo paneInfo,
+          @Nullable String currentRecordId,
+          @Nullable Long currentRecordOffset) {
         checkOnWindowExpirationTimestamp(timestamp);
         FnDataReceiver<WindowedValue<T>> consumer =
             (FnDataReceiver) localNameToConsumer.get(tag.getId());
-        outputTo(consumer, WindowedValues.of(output, timestamp, windows, paneInfo));
+        outputTo(
+            consumer,
+            WindowedValues.of(
+                output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
       }
 
       @SuppressWarnings(
@@ -2956,23 +2534,18 @@ public OutputReceiver<OutputT> outputReceiver(DoFn<InputT, OutputT> doFn) {
                   mainOutputSchemaCoder.getFromRowFunction();
 
               @Override
-              public void output(Row output) {
-                context.output(fromRowFunction.apply(output));
-              }
-
-              @Override
-              public void outputWithTimestamp(Row output, Instant timestamp) {
-                context.outputWithTimestamp(fromRowFunction.apply(output), timestamp);
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  Row output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                context.outputWindowedValue(
-                    fromRowFunction.apply(output), timestamp, windows, paneInfo);
+              public OutputBuilder<Row> builder(Row value) {
+                return WindowedValues.<Row>builder()
+                    .setValue(value)
+                    .setTimestamp(currentTimer.getHoldTimestamp())
+                    .setWindow(currentWindow)
+                    .setReceiver(
+                        windowedValue ->
+                            context.outputWindowedValue(
+                                fromRowFunction.apply(windowedValue.getValue()),
+                                windowedValue.getTimestamp(),
+                                windowedValue.getWindows(),
+                                windowedValue.getPaneInfo()));
               }
             };
 
@@ -2998,22 +2571,19 @@ private <T> OutputReceiver<T> createTaggedOutputReceiver(TupleTag<T> tag) {
             }
             return new OutputReceiver<T>() {
               @Override
-              public void output(T output) {
-                context.output(tag, output);
-              }
-
-              @Override
-              public void outputWithTimestamp(T output, Instant timestamp) {
-                context.outputWithTimestamp(tag, output, timestamp);
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  T output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                context.outputWindowedValue(tag, output, timestamp, windows, paneInfo);
+              public OutputBuilder<T> builder(T value) {
+                return WindowedValues.<T>builder()
+                    .setValue(value)
+                    .setTimestamp(currentTimer.getHoldTimestamp())
+                    .setWindow(currentWindow)
+                    .setReceiver(
+                        windowedValue ->
+                            context.outputWindowedValue(
+                                tag,
+                                windowedValue.getValue(),
+                                windowedValue.getTimestamp(),
+                                windowedValue.getWindows(),
+                                windowedValue.getPaneInfo()));
               }
             };
           }
@@ -3029,7 +2599,7 @@ private <T> OutputReceiver<Row> createTaggedRowReceiver(TupleTag<T> tag) {
             }
 
             Coder<T> outputCoder = (Coder<T>) outputCoders.get(tag);
-            checkState(outputCoder != null, "No output tag for " + tag);
+            checkState(outputCoder != null, "No output tag for %s", tag);
             checkState(
                 outputCoder instanceof SchemaCoder,
                 "Output with tag " + tag + " must have a schema in order to call getRowReceiver");
@@ -3038,23 +2608,19 @@ private <T> OutputReceiver<Row> createTaggedRowReceiver(TupleTag<T> tag) {
                   ((SchemaCoder) outputCoder).getFromRowFunction();
 
               @Override
-              public void output(Row output) {
-                context.output(tag, fromRowFunction.apply(output));
-              }
-
-              @Override
-              public void outputWithTimestamp(Row output, Instant timestamp) {
-                context.outputWithTimestamp(tag, fromRowFunction.apply(output), timestamp);
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  Row output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                context.outputWindowedValue(
-                    tag, fromRowFunction.apply(output), timestamp, windows, paneInfo);
+              public OutputBuilder<Row> builder(Row value) {
+                return WindowedValues.<Row>builder()
+                    .setValue(value)
+                    .setTimestamp(currentTimer.getHoldTimestamp())
+                    .setWindow(currentWindow)
+                    .setReceiver(
+                        windowedValue ->
+                            context.outputWindowedValue(
+                                tag,
+                                fromRowFunction.apply(windowedValue.getValue()),
+                                windowedValue.getTimestamp(),
+                                windowedValue.getWindows(),
+                                windowedValue.getPaneInfo()));
               }
             };
           }
@@ -3124,24 +2690,28 @@ public BoundedWindow window() {
         return currentWindow;
       }
 
+      @Override
+      public OutputBuilder<OutputT> builder(OutputT value) {
+        return WindowedValues.<OutputT>builder()
+            .setValue(value)
+            .setTimestamp(currentTimer.getHoldTimestamp())
+            .setWindow(currentWindow)
+            .setPaneInfo(currentTimer.getPaneInfo())
+            .setReceiver(
+                windowedValue -> {
+                  checkTimerTimestamp(windowedValue.getTimestamp());
+                  outputTo(mainOutputConsumer, windowedValue);
+                });
+      }
+
       @Override
       public void output(OutputT output) {
-        checkTimerTimestamp(currentTimer.getHoldTimestamp());
-        outputTo(
-            mainOutputConsumer,
-            WindowedValues.of(
-                output,
-                currentTimer.getHoldTimestamp(),
-                currentWindow,
-                currentTimer.getPaneInfo()));
+        OutputReceiver.super.output(output);
       }
 
       @Override
       public void outputWithTimestamp(OutputT output, Instant timestamp) {
-        checkTimerTimestamp(timestamp);
-        outputTo(
-            mainOutputConsumer,
-            WindowedValues.of(output, timestamp, currentWindow, currentTimer.getPaneInfo()));
+        OutputReceiver.super.outputWithTimestamp(output, timestamp);
       }
 
       @Override
@@ -3150,8 +2720,22 @@ public void outputWindowedValue(
           Instant timestamp,
           Collection<? extends BoundedWindow> windows,
           PaneInfo paneInfo) {
+        OutputReceiver.super.outputWindowedValue(output, timestamp, windows, paneInfo);
+      }
+
+      @Override
+      public void outputWindowedValue(
+          OutputT output,
+          Instant timestamp,
+          Collection<? extends BoundedWindow> windows,
+          PaneInfo paneInfo,
+          @Nullable String currentRecordId,
+          @Nullable Long currentRecordOffset) {
         checkTimerTimestamp(timestamp);
-        outputTo(mainOutputConsumer, WindowedValues.of(output, timestamp, windows, paneInfo));
+        outputTo(
+            mainOutputConsumer,
+            WindowedValues.of(
+                output, timestamp, windows, paneInfo, currentRecordId, currentRecordOffset));
       }
 
       @Override
@@ -3192,6 +2776,16 @@ public <T> void outputWindowedValue(
           Collection<? extends BoundedWindow> windows,
           PaneInfo paneInfo) {}
 
+      @Override
+      public <T> void outputWindowedValue(
+          TupleTag<T> tag,
+          T output,
+          Instant timestamp,
+          Collection<? extends BoundedWindow> windows,
+          PaneInfo paneInfo,
+          @Nullable String currentRecordId,
+          @Nullable Long currentRecordOffset) {}
+
       @Override
       public TimeDomain timeDomain() {
         return currentTimeDomain;
@@ -3269,24 +2863,16 @@ public OutputReceiver<OutputT> outputReceiver(DoFn<InputT, OutputT> doFn) {
                   mainOutputSchemaCoder.getFromRowFunction();
 
               @Override
-              public void output(Row output) {
-                context.outputWithTimestamp(
-                    fromRowFunction.apply(output), currentElement.getTimestamp());
-              }
-
-              @Override
-              public void outputWithTimestamp(Row output, Instant timestamp) {
-                context.outputWithTimestamp(fromRowFunction.apply(output), timestamp);
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  Row output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                context.outputWindowedValue(
-                    fromRowFunction.apply(output), timestamp, windows, paneInfo);
+              public OutputBuilder<Row> builder(Row value) {
+                return WindowedValues.builder(currentElement)
+                    .withValue(value)
+                    .setReceiver(
+                        windowedValue ->
+                            context.outputWindowedValue(
+                                fromRowFunction.apply(windowedValue.getValue()),
+                                windowedValue.getTimestamp(),
+                                windowedValue.getWindows(),
+                                windowedValue.getPaneInfo()));
               }
             };
 
@@ -3312,22 +2898,19 @@ private <T> OutputReceiver<T> createTaggedOutputReceiver(TupleTag<T> tag) {
             }
             return new OutputReceiver<T>() {
               @Override
-              public void output(T output) {
-                context.output(tag, output);
-              }
-
-              @Override
-              public void outputWithTimestamp(T output, Instant timestamp) {
-                context.outputWithTimestamp(tag, output, timestamp);
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  T output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                context.outputWindowedValue(tag, output, timestamp, windows, paneInfo);
+              public OutputBuilder<T> builder(T value) {
+                return WindowedValues.<T>builder()
+                    .setValue(value)
+                    .setTimestamp(currentTimer.getHoldTimestamp())
+                    .setWindow(currentWindow)
+                    .setPaneInfo(currentTimer.getPaneInfo())
+                    .setReceiver(
+                        windowedValue ->
+                            context.outputWindowedValue(
+                                windowedValue.getValue(),
+                                windowedValue.getTimestamp(),
+                                windowedValue.getWindows(),
+                                windowedValue.getPaneInfo()));
               }
             };
           }
@@ -3343,7 +2926,7 @@ private <T> OutputReceiver<Row> createTaggedRowReceiver(TupleTag<T> tag) {
             }
 
             Coder<T> outputCoder = (Coder<T>) outputCoders.get(tag);
-            checkState(outputCoder != null, "No output tag for " + tag);
+            checkState(outputCoder != null, "No output tag for %s", tag);
             checkState(
                 outputCoder instanceof SchemaCoder,
                 "Output with tag " + tag + " must have a schema in order to call getRowReceiver");
@@ -3352,23 +2935,19 @@ private <T> OutputReceiver<Row> createTaggedRowReceiver(TupleTag<T> tag) {
                   ((SchemaCoder) outputCoder).getFromRowFunction();
 
               @Override
-              public void output(Row output) {
-                context.output(tag, fromRowFunction.apply(output));
-              }
-
-              @Override
-              public void outputWithTimestamp(Row output, Instant timestamp) {
-                context.outputWithTimestamp(tag, fromRowFunction.apply(output), timestamp);
-              }
-
-              @Override
-              public void outputWindowedValue(
-                  Row output,
-                  Instant timestamp,
-                  Collection<? extends BoundedWindow> windows,
-                  PaneInfo paneInfo) {
-                context.outputWindowedValue(
-                    tag, fromRowFunction.apply(output), timestamp, windows, paneInfo);
+              public OutputBuilder<Row> builder(Row value) {
+                return WindowedValues.<Row>builder()
+                    .withValue(value)
+                    .setTimestamp(currentTimer.getHoldTimestamp())
+                    .setWindow(currentWindow)
+                    .setPaneInfo(currentTimer.getPaneInfo())
+                    .setReceiver(
+                        windowedValue ->
+                            context.outputWindowedValue(
+                                fromRowFunction.apply(windowedValue.getValue()),
+                                windowedValue.getTimestamp(),
+                                windowedValue.getWindows(),
+                                windowedValue.getPaneInfo()));
               }
             };
           }
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/FnHarness.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/FnHarness.java
index 831337072b06..034695237d83 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/FnHarness.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/FnHarness.java
@@ -29,7 +29,6 @@
 import java.util.Map;
 import java.util.Set;
 import java.util.concurrent.CompletableFuture;
-import java.util.concurrent.ExecutorService;
 import java.util.function.Function;
 import javax.annotation.Nullable;
 import org.apache.beam.fn.harness.control.BeamFnControlClient;
@@ -64,6 +63,7 @@
 import org.apache.beam.sdk.options.ExperimentalOptions;
 import org.apache.beam.sdk.options.PipelineOptions;
 import org.apache.beam.sdk.options.SdkHarnessOptions;
+import org.apache.beam.sdk.util.UnboundedScheduledExecutorService;
 import org.apache.beam.sdk.util.construction.CoderTranslation;
 import org.apache.beam.sdk.util.construction.PipelineOptionsTranslation;
 import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.TextFormat;
@@ -276,10 +276,17 @@ public static void main(
 
     IdGenerator idGenerator = IdGenerators.decrementingLongs();
     ShortIdMap metricsShortIds = new ShortIdMap();
-    ExecutorService executorService =
-        options.as(ExecutorOptions.class).getScheduledExecutorService();
+    UnboundedScheduledExecutorService executorService = new UnboundedScheduledExecutorService();
+    options.as(ExecutorOptions.class).setScheduledExecutorService(executorService);
+    CompletableFuture<Void> samplerTerminationFuture = new CompletableFuture<>();
     ExecutionStateSampler executionStateSampler =
-        new ExecutionStateSampler(options, System::currentTimeMillis);
+        new ExecutionStateSampler(
+            options,
+            System::currentTimeMillis,
+            message -> {
+              String errMsg = "FATAL ERROR: Timeout occurred! Exiting JVM. Details:" + message;
+              samplerTerminationFuture.completeExceptionally(new RuntimeException(errMsg));
+            });
 
     final @Nullable DataSampler dataSampler = DataSampler.create(options);
 
@@ -413,9 +420,11 @@ private BeamFnApi.ProcessBundleDescriptor loadDescriptor(String id) {
               executorService,
               handlers);
       if (options.as(SdkHarnessOptions.class).getEnableLogViaFnApi()) {
-        CompletableFuture.anyOf(control.terminationFuture(), logging.terminationFuture()).get();
+        CompletableFuture.anyOf(
+                control.terminationFuture(), logging.terminationFuture(), samplerTerminationFuture)
+            .get();
       } else {
-        control.terminationFuture().get();
+        CompletableFuture.anyOf(control.terminationFuture(), samplerTerminationFuture).get();
       }
       if (beamFnStatusClient != null) {
         beamFnStatusClient.close();
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/HandlesSplits.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/HandlesSplits.java
index 54e19837e01e..af7638d61cab 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/HandlesSplits.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/HandlesSplits.java
@@ -22,6 +22,7 @@
 import org.apache.beam.model.fnexecution.v1.BeamFnApi;
 import org.apache.beam.model.fnexecution.v1.BeamFnApi.BundleApplication;
 import org.apache.beam.sdk.fn.data.FnDataReceiver;
+import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
  * An interface that may be used to extend a {@link FnDataReceiver} signalling that the downstream
@@ -30,6 +31,7 @@
 public interface HandlesSplits {
 
   /** Returns null if the split was unsuccessful. */
+  @Nullable
   SplitResult trySplit(double fractionOfRemainder);
 
   /** Returns the current progress of the active element as a fraction between 0.0 and 1.0. */
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/ProgressUtils.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/ProgressUtils.java
new file mode 100644
index 000000000000..6895df746ad3
--- /dev/null
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/ProgressUtils.java
@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.fn.harness;
+
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
+
+import org.apache.beam.sdk.annotations.Internal;
+import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker;
+
+/** Miscellaneous methods for working with progress. */
+@Internal
+public abstract class ProgressUtils {
+  static RestrictionTracker.Progress scaleProgress(
+      RestrictionTracker.Progress progress, int currentWindowIndex, int stopWindowIndex) {
+    checkArgument(
+        currentWindowIndex < stopWindowIndex,
+        "Current window index (%s) must be less than stop window index (%s)",
+        currentWindowIndex,
+        stopWindowIndex);
+
+    double totalWorkPerWindow = progress.getWorkCompleted() + progress.getWorkRemaining();
+    double completed = totalWorkPerWindow * currentWindowIndex + progress.getWorkCompleted();
+    double remaining =
+        totalWorkPerWindow * (stopWindowIndex - currentWindowIndex - 1)
+            + progress.getWorkRemaining();
+    return RestrictionTracker.Progress.from(completed, remaining);
+  }
+}
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/SplitResultsWithStopIndex.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/SplitResultsWithStopIndex.java
index b0d6b5eee106..a686aa992864 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/SplitResultsWithStopIndex.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/SplitResultsWithStopIndex.java
@@ -20,6 +20,7 @@
 import com.google.auto.value.AutoValue;
 import org.apache.beam.sdk.annotations.Internal;
 import org.checkerframework.checker.nullness.qual.Nullable;
+import org.checkerframework.dataflow.qual.Pure;
 
 @AutoValue
 @AutoValue.CopyAnnotations
@@ -27,15 +28,18 @@
 abstract class SplitResultsWithStopIndex {
   public static SplitResultsWithStopIndex of(
       WindowedSplitResult windowSplit,
-      HandlesSplits.SplitResult downstreamSplit,
+      HandlesSplits.@Nullable SplitResult downstreamSplit,
       int newWindowStopIndex) {
     return new AutoValue_SplitResultsWithStopIndex(
         windowSplit, downstreamSplit, newWindowStopIndex);
   }
 
-  public abstract @Nullable WindowedSplitResult getWindowSplit();
+  @Pure
+  public abstract WindowedSplitResult getWindowSplit();
 
+  @Pure
   public abstract HandlesSplits.@Nullable SplitResult getDownstreamSplit();
 
+  @Pure
   public abstract int getNewWindowStopIndex();
 }
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/SplittableSplitAndSizeRestrictionsDoFnRunner.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/SplittableSplitAndSizeRestrictionsDoFnRunner.java
index b701c468abca..e42cbdaf6435 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/SplittableSplitAndSizeRestrictionsDoFnRunner.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/SplittableSplitAndSizeRestrictionsDoFnRunner.java
@@ -49,6 +49,7 @@
 import org.apache.beam.sdk.util.construction.PTransformTranslation;
 import org.apache.beam.sdk.util.construction.ParDoTranslation;
 import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.OutputBuilder;
 import org.apache.beam.sdk.values.PCollectionView;
 import org.apache.beam.sdk.values.TupleTag;
 import org.apache.beam.sdk.values.WindowedValue;
@@ -338,42 +339,48 @@ public Object sideInput(String tagId) {
     }
 
     @Override
-    public void output(RestrictionT subrestriction) {
-      // This OutputReceiver is only for being passed to SplitRestriction OutputT == RestrictionT
-      double size = getSize(subrestriction);
-
-      // Don't need to check timestamp since we can always output using the input timestamp.
-      outputTo(
-          mainOutputConsumer,
-          WindowedValues.of(
-              KV.of(
-                  KV.of(
-                      getCurrentElement().getValue(),
-                      KV.of(subrestriction, getCurrentWatermarkEstimatorState())),
-                  size),
-              getCurrentElement().getTimestamp(),
-              getCurrentWindow(),
-              getCurrentElement().getPaneInfo()));
+    public OutputBuilder<RestrictionT> builder(RestrictionT subrestriction) {
+      return WindowedValues.builder(getCurrentElement())
+          .withValue(subrestriction)
+          .setWindow(getCurrentWindow())
+          .setReceiver(
+              windowedValue -> {
+                double size = getSize(windowedValue.getValue());
+
+                outputTo(
+                    mainOutputConsumer,
+                    windowedValue.withValue(
+                        KV.of(
+                            KV.of(
+                                getCurrentElement().getValue(),
+                                KV.of(
+                                    windowedValue.getValue(), getCurrentWatermarkEstimatorState())),
+                            size)));
+              });
     }
   }
 
   /** This context outputs KV<KV<Element, KV<Restriction, WatermarkEstimatorState>>, Size>. */
   private class SizedRestrictionNonWindowObservingArgumentProvider
-      extends SplitRestrictionArgumentProvider implements OutputReceiver<RestrictionT> {
+      extends SplitRestrictionArgumentProvider {
     @Override
-    public void output(RestrictionT subrestriction) {
-      double size = getSize(subrestriction);
-
-      // Don't need to check timestamp since we can always output using the input timestamp.
-      outputTo(
-          mainOutputConsumer,
-          getCurrentElement()
-              .withValue(
-                  KV.of(
-                      KV.of(
-                          getCurrentElement().getValue(),
-                          KV.of(subrestriction, getCurrentWatermarkEstimatorState())),
-                      size)));
+    public OutputBuilder<RestrictionT> builder(RestrictionT subrestriction) {
+      return WindowedValues.builder(getCurrentElement())
+          .withValue(subrestriction)
+          .setReceiver(
+              windowedValue -> {
+                double size = getSize(windowedValue.getValue());
+
+                outputTo(
+                    mainOutputConsumer,
+                    windowedValue.withValue(
+                        KV.of(
+                            KV.of(
+                                getCurrentElement().getValue(),
+                                KV.of(
+                                    windowedValue.getValue(), getCurrentWatermarkEstimatorState())),
+                            size)));
+              });
     }
   }
 
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/SplittableTruncateSizedRestrictionsDoFnRunner.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/SplittableTruncateSizedRestrictionsDoFnRunner.java
new file mode 100644
index 000000000000..6c300295eb6d
--- /dev/null
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/SplittableTruncateSizedRestrictionsDoFnRunner.java
@@ -0,0 +1,990 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.fn.harness;
+
+import static org.apache.beam.sdk.util.Preconditions.checkStateNotNull;
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
+
+import com.google.auto.service.AutoService;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import org.apache.beam.fn.harness.state.FnApiStateAccessor;
+import org.apache.beam.model.fnexecution.v1.BeamFnApi.BundleApplication;
+import org.apache.beam.model.fnexecution.v1.BeamFnApi.DelayedBundleApplication;
+import org.apache.beam.model.pipeline.v1.RunnerApi;
+import org.apache.beam.model.pipeline.v1.RunnerApi.PTransform;
+import org.apache.beam.model.pipeline.v1.RunnerApi.ParDoPayload;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.Coder;
+import org.apache.beam.sdk.fn.data.FnDataReceiver;
+import org.apache.beam.sdk.fn.splittabledofn.RestrictionTrackers;
+import org.apache.beam.sdk.fn.splittabledofn.WatermarkEstimators;
+import org.apache.beam.sdk.options.PipelineOptions;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.DoFn.OutputReceiver;
+import org.apache.beam.sdk.transforms.DoFnSchemaInformation;
+import org.apache.beam.sdk.transforms.SerializableFunction;
+import org.apache.beam.sdk.transforms.reflect.DoFnInvoker;
+import org.apache.beam.sdk.transforms.reflect.DoFnInvoker.DelegatingArgumentProvider;
+import org.apache.beam.sdk.transforms.reflect.DoFnInvokers;
+import org.apache.beam.sdk.transforms.reflect.DoFnSignature;
+import org.apache.beam.sdk.transforms.reflect.DoFnSignatures;
+import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker;
+import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker.Progress;
+import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker.TruncateResult;
+import org.apache.beam.sdk.transforms.splittabledofn.TimestampObservingWatermarkEstimator;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.transforms.windowing.GlobalWindow;
+import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.apache.beam.sdk.util.ByteStringOutputStream;
+import org.apache.beam.sdk.util.Holder;
+import org.apache.beam.sdk.util.UserCodeException;
+import org.apache.beam.sdk.util.construction.PTransformTranslation;
+import org.apache.beam.sdk.util.construction.ParDoTranslation;
+import org.apache.beam.sdk.util.construction.RehydratedComponents;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.OutputBuilder;
+import org.apache.beam.sdk.values.PCollectionView;
+import org.apache.beam.sdk.values.TupleTag;
+import org.apache.beam.sdk.values.WindowedValue;
+import org.apache.beam.sdk.values.WindowedValues;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
+import org.checkerframework.checker.nullness.qual.NonNull;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import org.joda.time.Instant;
+
+/**
+ * A runner for the PTransform that truncates sized restrictions, for the case of draining a
+ * pipeline.
+ *
+ * <p>The input and output types for this transform are
+ * <li>{@code WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>>}
+ */
+public class SplittableTruncateSizedRestrictionsDoFnRunner<
+        InputT, RestrictionT extends @NonNull Object, PositionT, WatermarkEstimatorStateT, OutputT>
+    implements FnApiStateAccessor.MutatingStateContext<Void, BoundedWindow> {
+
+  /** A registrar which provides a factory to handle Java {@link DoFn}s. */
+  @AutoService(PTransformRunnerFactory.Registrar.class)
+  public static class Registrar implements PTransformRunnerFactory.Registrar {
+    @Override
+    public Map<String, PTransformRunnerFactory> getPTransformRunnerFactories() {
+      Factory factory = new Factory();
+      return ImmutableMap.<String, PTransformRunnerFactory>builder()
+          .put(PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN, factory)
+          .build();
+    }
+  }
+
+  static class Factory implements PTransformRunnerFactory {
+    @Override
+    public final void addRunnerForPTransform(Context context) throws IOException {
+      addRunnerForTruncateSizedRestrictions(context);
+    }
+
+    private <
+            InputT,
+            RestrictionT extends @NonNull Object,
+            PositionT,
+            WatermarkEstimatorStateT,
+            OutputT>
+        void addRunnerForTruncateSizedRestrictions(Context context) throws IOException {
+
+      FnApiStateAccessor<Void> stateAccessor =
+          FnApiStateAccessor.Factory.<Void>factoryForPTransformContext(context).create();
+
+      // Main output
+      checkArgument(
+          context.getPTransform().getOutputsMap().size() == 1,
+          "TruncateSizedRestrictions expects exact one output, but got: ",
+          context.getPTransform().getOutputsMap().size());
+      TupleTag<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>> mainOutputTag =
+          new TupleTag<>(
+              Iterables.getOnlyElement(context.getPTransform().getOutputsMap().keySet()));
+
+      FnDataReceiver<
+              WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>>>
+          mainOutputConsumer =
+              context.getPCollectionConsumer(
+                  context.getPTransform().getOutputsOrThrow(mainOutputTag.getId()));
+
+      SplittableTruncateSizedRestrictionsDoFnRunner<
+              InputT, RestrictionT, PositionT, WatermarkEstimatorStateT, OutputT>
+          runner =
+              new SplittableTruncateSizedRestrictionsDoFnRunner<>(
+                  context.getPipelineOptions(),
+                  context.getPTransformId(),
+                  context.getPTransform(),
+                  context.getComponents(),
+                  mainOutputConsumer,
+                  stateAccessor);
+
+      // Register input consumer that delegates splitting
+      FnDataReceiver<
+              WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>>>
+          mainInputConsumer;
+
+      if (mainOutputConsumer instanceof HandlesSplits) {
+        mainInputConsumer =
+            new SplitDelegatingFnDataReceiver<>(runner, (HandlesSplits) mainOutputConsumer);
+      } else {
+        mainInputConsumer = runner::processElement;
+      }
+
+      context.addPCollectionConsumer(
+          context
+              .getPTransform()
+              .getInputsOrThrow(ParDoTranslation.getMainInputName(context.getPTransform())),
+          mainInputConsumer);
+      context.addTearDownFunction(runner::tearDown);
+    }
+  }
+
+  //////////////////////////////////////////////////////////////////////////////////////////////////
+
+  private final boolean observesWindow;
+  private final PipelineOptions pipelineOptions;
+
+  private final DoFnInvoker<InputT, OutputT> doFnInvoker;
+
+  private final FnDataReceiver<
+          WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>>>
+      mainOutputConsumer;
+
+  private final FnApiStateAccessor<?> stateAccessor;
+
+  private final TruncateSizedRestrictionArgumentProvider mutableArgumentProvider;
+
+  private final String pTransformId;
+  private final RunnerApi.PTransform pTransform;
+  private final String mainInputId;
+  private final Coder<WindowedValue<?>> fullInputCoder;
+
+  /**
+   * Used to guarantee a consistent view of this {@link
+   * SplittableTruncateSizedRestrictionsDoFnRunner} while setting up for {@link
+   * DoFnInvoker#invokeProcessElement} since {@link #trySplit} may access internal {@link
+   * SplittableTruncateSizedRestrictionsDoFnRunner} state concurrently.
+   */
+  // TODO: explicitly mark guarded fields with @GuardedBy
+  private final Object splitLock = new Object();
+
+  private final DoFnSchemaInformation doFnSchemaInformation;
+  private final Map<String, PCollectionView<?>> sideInputMapping;
+
+  ///
+  // Mutating fields that change with the element and window being processed
+  //
+  private int windowCurrentIndex;
+  private @Nullable List<BoundedWindow> currentWindows;
+  private @Nullable RestrictionT currentRestriction;
+  private @Nullable Holder<WatermarkEstimatorStateT> currentWatermarkEstimatorState;
+  private @Nullable Instant initialWatermark;
+  private WatermarkEstimators.@Nullable WatermarkAndStateObserver<WatermarkEstimatorStateT>
+      currentWatermarkEstimator;
+  private @Nullable BoundedWindow currentWindow;
+  private @Nullable RestrictionTracker<RestrictionT, PositionT> currentTracker;
+  private @Nullable WindowedValue<InputT> currentElement;
+
+  /**
+   * The window index at which processing should stop. The window with this index should not be
+   * processed.
+   */
+  private int windowStopIndex;
+
+  /**
+   * The window index which is currently being processed. This should always be less than
+   * windowStopIndex.
+   */
+  SplittableTruncateSizedRestrictionsDoFnRunner(
+      PipelineOptions pipelineOptions,
+      String pTransformId,
+      PTransform pTransform,
+      RunnerApi.Components components,
+      FnDataReceiver<
+              WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>>>
+          mainOutputConsumer,
+      FnApiStateAccessor<Void> stateAccessor)
+      throws IOException {
+    this.pipelineOptions = pipelineOptions;
+    this.stateAccessor = stateAccessor;
+    this.pTransformId = pTransformId;
+    this.pTransform = pTransform;
+
+    ParDoPayload parDoPayload = ParDoPayload.parseFrom(pTransform.getSpec().getPayload());
+
+    // DoFn and metadata
+    DoFn<InputT, OutputT> doFn = (DoFn<InputT, OutputT>) ParDoTranslation.getDoFn(parDoPayload);
+    DoFnSignature doFnSignature = DoFnSignatures.signatureForDoFn(doFn);
+    this.doFnInvoker = DoFnInvokers.tryInvokeSetupFor(doFn, pipelineOptions);
+    this.doFnSchemaInformation = ParDoTranslation.getSchemaInformation(parDoPayload);
+
+    this.mainOutputConsumer = mainOutputConsumer;
+
+    // Side inputs
+    this.sideInputMapping = ParDoTranslation.getSideInputMapping(parDoPayload);
+
+    // Register processing methods
+    this.observesWindow =
+        (doFnSignature.splitRestriction() != null
+                && doFnSignature.splitRestriction().observesWindow())
+            || (doFnSignature.newTracker() != null && doFnSignature.newTracker().observesWindow())
+            || (doFnSignature.getSize() != null && doFnSignature.getSize().observesWindow())
+            || !sideInputMapping.isEmpty();
+
+    if (observesWindow) {
+      this.mutableArgumentProvider = new TruncateSizedRestrictionWindowObservingArgumentProvider();
+    } else {
+      this.mutableArgumentProvider =
+          new TruncateSizedRestrictionNonWindowObservingArgumentProvider();
+    }
+
+    // Main Input
+    this.mainInputId = ParDoTranslation.getMainInputName(pTransform);
+    RunnerApi.PCollection mainInput =
+        components.getPcollectionsOrThrow(pTransform.getInputsOrThrow(mainInputId));
+    RehydratedComponents rehydratedComponents =
+        RehydratedComponents.forComponents(components).withPipeline(Pipeline.create());
+    Coder<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>> inputCoder =
+        (Coder<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>>)
+            rehydratedComponents.getCoder(mainInput.getCoderId());
+    Coder<BoundedWindow> windowCoder =
+        (Coder<BoundedWindow>)
+            rehydratedComponents
+                .getWindowingStrategy(mainInput.getWindowingStrategyId())
+                .getWindowFn()
+                .windowCoder();
+    this.fullInputCoder =
+        (Coder<WindowedValue<?>>) (Object) WindowedValues.getFullCoder(inputCoder, windowCoder);
+  }
+
+  @Override
+  public Void getCurrentKey() {
+    return null;
+  }
+
+  @Override
+  public BoundedWindow getCurrentWindow() {
+    return checkStateNotNull(
+        currentWindow, "Attempt to access window outside windowed element processing context.");
+  }
+
+  public List<BoundedWindow> getCurrentWindows() {
+    return checkStateNotNull(
+        currentWindows,
+        "Attempt to access window collection outside windowed element processing context.");
+  }
+
+  public WindowedValue<InputT> getCurrentElement() {
+    return checkStateNotNull(
+        currentElement, "Attempt to access element outside element processing context.");
+  }
+
+  private RestrictionT getCurrentRestriction() {
+    return checkStateNotNull(
+        this.currentRestriction,
+        "Attempt to access restriction outside element processing context.");
+  }
+
+  private RestrictionTracker<RestrictionT, ?> getCurrentTracker() {
+    return checkStateNotNull(
+        this.currentTracker,
+        "Attempt to access restriction tracker state outside element processing context.");
+  }
+
+  private WatermarkEstimatorStateT getCurrentWatermarkEstimatorState() {
+    checkStateNotNull(
+        this.currentWatermarkEstimatorState,
+        "Attempt to access watermark estimator state outside element processing context.");
+    return this.currentWatermarkEstimatorState.get();
+  }
+
+  void processElement(
+      WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>> elem) {
+    if (observesWindow) {
+      processElementForWindowObservingTruncateRestriction(elem);
+    } else {
+      processElementForTruncateRestriction(elem);
+    }
+  }
+
+  HandlesSplits.@Nullable SplitResult trySplit(
+      double fractionOfRemainder, HandlesSplits splitDelegate) {
+    if (observesWindow) {
+      return trySplitForWindowObservingTruncateRestriction(fractionOfRemainder, splitDelegate);
+    } else {
+      return splitDelegate.trySplit(fractionOfRemainder);
+    }
+  }
+
+  private void processElementForTruncateRestriction(
+      WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>> elem) {
+    currentElement = elem.withValue(elem.getValue().getKey().getKey());
+    currentRestriction = elem.getValue().getKey().getValue().getKey();
+    currentWatermarkEstimatorState = Holder.of(elem.getValue().getKey().getValue().getValue());
+    currentTracker =
+        RestrictionTrackers.synchronize(doFnInvoker.invokeNewTracker(mutableArgumentProvider));
+    try {
+      TruncateResult<RestrictionT> truncatedRestriction =
+          doFnInvoker.invokeTruncateRestriction(mutableArgumentProvider);
+      if (truncatedRestriction != null) {
+        mutableArgumentProvider.output(truncatedRestriction.getTruncatedRestriction());
+      }
+    } finally {
+      currentTracker = null;
+      currentElement = null;
+      currentRestriction = null;
+      currentWatermarkEstimatorState = null;
+    }
+
+    this.stateAccessor.finalizeState();
+  }
+
+  private void processElementForWindowObservingTruncateRestriction(
+      WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>> elem) {
+    currentElement = elem.withValue(elem.getValue().getKey().getKey());
+    windowCurrentIndex = -1;
+    windowStopIndex = elem.getWindows().size();
+    currentWindows = ImmutableList.copyOf(elem.getWindows());
+    while (true) {
+      synchronized (splitLock) {
+        windowCurrentIndex++;
+        if (windowCurrentIndex >= windowStopIndex) {
+          // Careful to reset the split state under the same synchronized block.
+          windowCurrentIndex = -1;
+          windowStopIndex = 0;
+          currentElement = null;
+          currentWindows = null;
+          currentRestriction = null;
+          currentWatermarkEstimatorState = null;
+          currentWindow = null;
+          currentTracker = null;
+          currentWatermarkEstimator = null;
+          initialWatermark = null;
+          break;
+        }
+        currentRestriction = elem.getValue().getKey().getValue().getKey();
+        currentWatermarkEstimatorState = Holder.of(elem.getValue().getKey().getValue().getValue());
+        currentWindow =
+            checkStateNotNull(
+                    currentWindows,
+                    "internal error: currentWindows is null during element processing")
+                .get(windowCurrentIndex);
+        currentTracker =
+            RestrictionTrackers.synchronize(doFnInvoker.invokeNewTracker(mutableArgumentProvider));
+        currentWatermarkEstimator =
+            WatermarkEstimators.threadSafe(
+                doFnInvoker.invokeNewWatermarkEstimator(mutableArgumentProvider));
+        initialWatermark = currentWatermarkEstimator.getWatermarkAndState().getKey();
+      }
+      TruncateResult<RestrictionT> truncatedRestriction =
+          doFnInvoker.invokeTruncateRestriction(mutableArgumentProvider);
+      if (truncatedRestriction != null) {
+        mutableArgumentProvider.output(truncatedRestriction.getTruncatedRestriction());
+      }
+    }
+    this.stateAccessor.finalizeState();
+  }
+
+  private @Nullable Progress getProgressFromWindowObservingTruncate(double elementCompleted) {
+    synchronized (splitLock) {
+      if (currentWindow != null) {
+        return ProgressUtils.scaleProgress(
+            Progress.from(elementCompleted, 1 - elementCompleted),
+            windowCurrentIndex,
+            windowStopIndex);
+      }
+    }
+    return null;
+  }
+
+  private WindowedSplitResult calculateRestrictionSize(
+      WindowedSplitResult splitResult, String errorContext) {
+    double fullSize =
+        splitResult.getResidualInUnprocessedWindowsRoot() == null
+                && splitResult.getPrimaryInFullyProcessedWindowsRoot() == null
+            ? 0
+            : doFnInvoker.invokeGetSize(
+                new DelegatingArgumentProvider<InputT, OutputT>(
+                    mutableArgumentProvider, errorContext) {
+                  @Override
+                  public Object restriction() {
+                    return getCurrentRestriction();
+                  }
+
+                  @Override
+                  public RestrictionTracker<?, ?> restrictionTracker() {
+                    return doFnInvoker.invokeNewTracker(this);
+                  }
+                });
+    double primarySize =
+        splitResult.getPrimarySplitRoot() == null
+            ? 0
+            : doFnInvoker.invokeGetSize(
+                new DelegatingArgumentProvider<InputT, OutputT>(
+                    mutableArgumentProvider, errorContext) {
+                  @Override
+                  public Object restriction() {
+                    WindowedValue<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>>
+                        splitRoot =
+                            (WindowedValue<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>>)
+                                splitResult.getPrimarySplitRoot();
+
+                    return splitRoot.getValue().getValue().getKey();
+                  }
+
+                  @Override
+                  public RestrictionTracker<?, ?> restrictionTracker() {
+                    return doFnInvoker.invokeNewTracker(this);
+                  }
+                });
+    double residualSize =
+        splitResult.getResidualSplitRoot() == null
+            ? 0
+            : doFnInvoker.invokeGetSize(
+                new DelegatingArgumentProvider<InputT, OutputT>(
+                    mutableArgumentProvider, errorContext) {
+                  @Override
+                  public Object restriction() {
+                    WindowedValue<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>>
+                        splitRoot =
+                            (WindowedValue<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>>)
+                                splitResult.getResidualSplitRoot();
+
+                    return splitRoot.getValue().getValue().getKey();
+                  }
+
+                  @Override
+                  public RestrictionTracker<?, ?> restrictionTracker() {
+                    return doFnInvoker.invokeNewTracker(this);
+                  }
+                });
+    return WindowedSplitResult.forRoots(
+        splitResult.getPrimaryInFullyProcessedWindowsRoot() == null
+            ? null
+            : WindowedValues.of(
+                KV.of(splitResult.getPrimaryInFullyProcessedWindowsRoot().getValue(), fullSize),
+                splitResult.getPrimaryInFullyProcessedWindowsRoot().getTimestamp(),
+                splitResult.getPrimaryInFullyProcessedWindowsRoot().getWindows(),
+                splitResult.getPrimaryInFullyProcessedWindowsRoot().getPaneInfo()),
+        splitResult.getPrimarySplitRoot() == null
+            ? null
+            : WindowedValues.of(
+                KV.of(splitResult.getPrimarySplitRoot().getValue(), primarySize),
+                splitResult.getPrimarySplitRoot().getTimestamp(),
+                splitResult.getPrimarySplitRoot().getWindows(),
+                splitResult.getPrimarySplitRoot().getPaneInfo()),
+        splitResult.getResidualSplitRoot() == null
+            ? null
+            : WindowedValues.of(
+                KV.of(splitResult.getResidualSplitRoot().getValue(), residualSize),
+                splitResult.getResidualSplitRoot().getTimestamp(),
+                splitResult.getResidualSplitRoot().getWindows(),
+                splitResult.getResidualSplitRoot().getPaneInfo()),
+        splitResult.getResidualInUnprocessedWindowsRoot() == null
+            ? null
+            : WindowedValues.of(
+                KV.of(splitResult.getResidualInUnprocessedWindowsRoot().getValue(), fullSize),
+                splitResult.getResidualInUnprocessedWindowsRoot().getTimestamp(),
+                splitResult.getResidualInUnprocessedWindowsRoot().getWindows(),
+                splitResult.getResidualInUnprocessedWindowsRoot().getPaneInfo()));
+  }
+
+  private HandlesSplits.@Nullable SplitResult trySplitForWindowObservingTruncateRestriction(
+      double fractionOfRemainder, HandlesSplits splitDelegate) {
+    WindowedSplitResult windowedSplitResult;
+    HandlesSplits.SplitResult downstreamSplitResult;
+    synchronized (splitLock) {
+      // There is nothing to split if we are between truncate processing calls.
+      if (currentWindow == null) {
+        return null;
+      }
+
+      SplitResultsWithStopIndex splitResult =
+          computeSplitForTruncate(
+              getCurrentElement(),
+              getCurrentRestriction(),
+              getCurrentWindow(),
+              getCurrentWindows(),
+              getCurrentWatermarkEstimatorState(),
+              fractionOfRemainder,
+              splitDelegate,
+              windowCurrentIndex,
+              windowStopIndex);
+      if (splitResult == null) {
+        return null;
+      }
+      windowStopIndex = splitResult.getNewWindowStopIndex();
+      windowedSplitResult =
+          calculateRestrictionSize(
+              splitResult.getWindowSplit(),
+              PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN + "/GetSize");
+      downstreamSplitResult = splitResult.getDownstreamSplit();
+    }
+    return constructSplitResult(
+        windowedSplitResult,
+        downstreamSplitResult,
+        fullInputCoder,
+        checkStateNotNull(
+            initialWatermark, "Attempt to construct split result without initial watermark"),
+        pTransformId,
+        mainInputId,
+        pTransform.getOutputsMap().keySet());
+  }
+
+  private static <WatermarkEstimatorStateT> WindowedSplitResult computeWindowSplitResult(
+      WindowedValue<?> currentElement,
+      Object currentRestriction,
+      List<BoundedWindow> windows,
+      WatermarkEstimatorStateT currentWatermarkEstimatorState,
+      int toIndex,
+      int fromIndex,
+      int stopWindowIndex) {
+    List<BoundedWindow> primaryFullyProcessedWindows = windows.subList(0, toIndex);
+    List<BoundedWindow> residualUnprocessedWindows = windows.subList(fromIndex, stopWindowIndex);
+    WindowedSplitResult windowedSplitResult;
+
+    windowedSplitResult =
+        WindowedSplitResult.forRoots(
+            primaryFullyProcessedWindows.isEmpty()
+                ? null
+                : WindowedValues.of(
+                    KV.of(
+                        currentElement.getValue(),
+                        KV.of(currentRestriction, currentWatermarkEstimatorState)),
+                    currentElement.getTimestamp(),
+                    primaryFullyProcessedWindows,
+                    currentElement.getPaneInfo()),
+            null,
+            null,
+            residualUnprocessedWindows.isEmpty()
+                ? null
+                : WindowedValues.of(
+                    KV.of(
+                        currentElement.getValue(),
+                        KV.of(currentRestriction, currentWatermarkEstimatorState)),
+                    currentElement.getTimestamp(),
+                    residualUnprocessedWindows,
+                    currentElement.getPaneInfo()));
+    return windowedSplitResult;
+  }
+
+  @VisibleForTesting
+  static <WatermarkEstimatorStateT> @Nullable SplitResultsWithStopIndex computeSplitForTruncate(
+      WindowedValue<?> element,
+      Object restriction,
+      BoundedWindow window,
+      List<BoundedWindow> windows,
+      WatermarkEstimatorStateT watermarkEstimatorState,
+      double fractionOfRemainder,
+      HandlesSplits splitDelegate,
+      int currentWindowIndex,
+      int stopWindowIndex) {
+    checkArgument(splitDelegate != null);
+
+    WindowedSplitResult windowedSplitResult;
+    HandlesSplits.@Nullable SplitResult downstreamSplitResult = null;
+    int newWindowStopIndex;
+    // If we are not on the last window, try to compute the split which is on the current window or
+    // on a future window.
+    if (currentWindowIndex != stopWindowIndex - 1) {
+      // Compute the fraction of the remainder relative to the scaled progress.
+      Progress elementProgress;
+      double elementCompleted = splitDelegate.getProgress();
+      elementProgress = Progress.from(elementCompleted, 1 - elementCompleted);
+      Progress scaledProgress =
+          ProgressUtils.scaleProgress(elementProgress, currentWindowIndex, stopWindowIndex);
+      double scaledFractionOfRemainder = scaledProgress.getWorkRemaining() * fractionOfRemainder;
+
+      // The fraction is out of the current window and hence we will split at the closest window
+      // boundary.
+      if (scaledFractionOfRemainder >= elementProgress.getWorkRemaining()) {
+        newWindowStopIndex =
+            (int)
+                Math.min(
+                    stopWindowIndex - 1,
+                    currentWindowIndex
+                        + Math.max(
+                            1,
+                            Math.round(
+                                (elementProgress.getWorkCompleted() + scaledFractionOfRemainder)
+                                    / (elementProgress.getWorkCompleted()
+                                        + elementProgress.getWorkRemaining()))));
+        windowedSplitResult =
+            computeWindowSplitResult(
+                element,
+                restriction,
+                windows,
+                watermarkEstimatorState,
+                newWindowStopIndex,
+                newWindowStopIndex,
+                stopWindowIndex);
+      } else {
+        // Compute the element split with the scaled fraction.
+        downstreamSplitResult = splitDelegate.trySplit(scaledFractionOfRemainder);
+        newWindowStopIndex = currentWindowIndex + 1;
+        int toIndex = (downstreamSplitResult == null) ? newWindowStopIndex : currentWindowIndex;
+        windowedSplitResult =
+            computeWindowSplitResult(
+                element,
+                restriction,
+                windows,
+                watermarkEstimatorState,
+                toIndex,
+                newWindowStopIndex,
+                stopWindowIndex);
+      }
+    } else {
+      // We are on the last window then compute the element split with given fraction.
+      newWindowStopIndex = stopWindowIndex;
+      downstreamSplitResult = splitDelegate.trySplit(fractionOfRemainder);
+      if (downstreamSplitResult == null) {
+        return null;
+      }
+      windowedSplitResult =
+          computeWindowSplitResult(
+              element,
+              restriction,
+              windows,
+              watermarkEstimatorState,
+              currentWindowIndex,
+              stopWindowIndex,
+              stopWindowIndex);
+    }
+    return SplitResultsWithStopIndex.of(
+        windowedSplitResult, downstreamSplitResult, newWindowStopIndex);
+  }
+
+  @VisibleForTesting
+  static HandlesSplits.SplitResult constructSplitResult(
+      @Nullable WindowedSplitResult windowedSplitResult,
+      HandlesSplits.@Nullable SplitResult downstreamElementSplit,
+      Coder<WindowedValue<?>> fullInputCoder,
+      Instant initialWatermark,
+      String pTransformId,
+      String mainInputId,
+      Collection<String> outputIds) {
+    // The element split cannot from both windowedSplitResult and downstreamElementSplit.
+    checkArgument(
+        (windowedSplitResult == null || windowedSplitResult.getResidualSplitRoot() == null)
+            || downstreamElementSplit == null);
+    List<BundleApplication> primaryRoots = new ArrayList<>();
+    List<DelayedBundleApplication> residualRoots = new ArrayList<>();
+
+    // Encode window splits.
+    if (windowedSplitResult != null
+        && windowedSplitResult.getPrimaryInFullyProcessedWindowsRoot() != null) {
+      ByteStringOutputStream primaryInOtherWindowsBytes = new ByteStringOutputStream();
+      try {
+        fullInputCoder.encode(
+            windowedSplitResult.getPrimaryInFullyProcessedWindowsRoot(),
+            primaryInOtherWindowsBytes);
+      } catch (IOException e) {
+        throw new RuntimeException(e);
+      }
+      BundleApplication.Builder primaryApplicationInOtherWindows =
+          BundleApplication.newBuilder()
+              .setTransformId(pTransformId)
+              .setInputId(mainInputId)
+              .setElement(primaryInOtherWindowsBytes.toByteString());
+      primaryRoots.add(primaryApplicationInOtherWindows.build());
+    }
+    if (windowedSplitResult != null
+        && windowedSplitResult.getResidualInUnprocessedWindowsRoot() != null) {
+      ByteStringOutputStream bytesOut = new ByteStringOutputStream();
+      try {
+        fullInputCoder.encode(windowedSplitResult.getResidualInUnprocessedWindowsRoot(), bytesOut);
+      } catch (IOException e) {
+        throw new RuntimeException(e);
+      }
+      BundleApplication.Builder residualInUnprocessedWindowsRoot =
+          BundleApplication.newBuilder()
+              .setTransformId(pTransformId)
+              .setInputId(mainInputId)
+              .setElement(bytesOut.toByteString());
+      // We don't want to change the output watermarks or set the checkpoint resume time since
+      // that applies to the current window.
+      Map<String, org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.Timestamp>
+          outputWatermarkMapForUnprocessedWindows = new HashMap<>();
+      if (!initialWatermark.equals(GlobalWindow.TIMESTAMP_MIN_VALUE)) {
+        org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.Timestamp outputWatermark =
+            org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.Timestamp.newBuilder()
+                .setSeconds(initialWatermark.getMillis() / 1000)
+                .setNanos((int) (initialWatermark.getMillis() % 1000) * 1000000)
+                .build();
+        for (String outputId : outputIds) {
+          outputWatermarkMapForUnprocessedWindows.put(outputId, outputWatermark);
+        }
+      }
+      residualInUnprocessedWindowsRoot.putAllOutputWatermarks(
+          outputWatermarkMapForUnprocessedWindows);
+      residualRoots.add(
+          DelayedBundleApplication.newBuilder()
+              .setApplication(residualInUnprocessedWindowsRoot)
+              .build());
+    }
+
+    // Encode element split from windowedSplitResult or from downstream element split. It's possible
+    // that there is no element split.
+    if (downstreamElementSplit != null) {
+      primaryRoots.add(Iterables.getOnlyElement(downstreamElementSplit.getPrimaryRoots()));
+      residualRoots.add(Iterables.getOnlyElement(downstreamElementSplit.getResidualRoots()));
+    }
+
+    return HandlesSplits.SplitResult.of(primaryRoots, residualRoots);
+  }
+
+  /** Outputs the given element to the specified set of consumers wrapping any exceptions. */
+  private <T> void outputTo(FnDataReceiver<WindowedValue<T>> consumer, WindowedValue<T> output) {
+    if (currentWatermarkEstimator instanceof TimestampObservingWatermarkEstimator) {
+      ((TimestampObservingWatermarkEstimator) currentWatermarkEstimator)
+          .observeTimestamp(output.getTimestamp());
+    }
+    try {
+      consumer.accept(output);
+    } catch (Throwable t) {
+      throw UserCodeException.wrap(t);
+    }
+  }
+
+  private void tearDown() {
+    doFnInvoker.invokeTeardown();
+  }
+
+  /** This context outputs KV<KV<Element, KV<Restriction, WatemarkEstimatorState>>, Size>. */
+  private class TruncateSizedRestrictionWindowObservingArgumentProvider
+      extends TruncateSizedRestrictionArgumentProvider {
+
+    @Override
+    public OutputBuilder<RestrictionT> builder(RestrictionT value) {
+      return WindowedValues.builder(getCurrentElement())
+          .withValue(value)
+          .setWindow(getCurrentWindow())
+          .setReceiver(
+              windowedValue -> {
+                double size = getSize(windowedValue.getValue());
+                outputTo(
+                    mainOutputConsumer,
+                    windowedValue.withValue(
+                        KV.of(
+                            KV.of(
+                                getCurrentElement().getValue(),
+                                KV.of(
+                                    windowedValue.getValue(), getCurrentWatermarkEstimatorState())),
+                            size)));
+              });
+    }
+
+    @Override
+    public BoundedWindow window() {
+      return getCurrentWindow();
+    }
+
+    @Override
+    public Object sideInput(String tagId) {
+      PCollectionView<Object> pCollectionView =
+          (PCollectionView<Object>)
+              checkStateNotNull(sideInputMapping.get(tagId), "Side input tag not found: %s", tagId);
+
+      return stateAccessor.get(pCollectionView, getCurrentWindow());
+    }
+  }
+
+  /** This context outputs KV<KV<Element, KV<Restriction, WatermarkEstimatorState>>, Size>. */
+  private class TruncateSizedRestrictionNonWindowObservingArgumentProvider
+      extends TruncateSizedRestrictionArgumentProvider {
+
+    @Override
+    public OutputBuilder<RestrictionT> builder(RestrictionT value) {
+      return WindowedValues.builder(getCurrentElement())
+          .withValue(value)
+          .setReceiver(
+              windowedValue -> {
+                double size = getSize(windowedValue.getValue());
+                outputTo(
+                    mainOutputConsumer,
+                    getCurrentElement()
+                        .withValue(
+                            KV.of(
+                                KV.of(
+                                    getCurrentElement().getValue(),
+                                    KV.of(
+                                        windowedValue.getValue(),
+                                        getCurrentWatermarkEstimatorState())),
+                                size)));
+              });
+    }
+  }
+
+  /** Base implementation that does not override methods which need to be window aware. */
+  private abstract class TruncateSizedRestrictionArgumentProvider
+      extends DoFnInvoker.BaseArgumentProvider<InputT, OutputT>
+      implements OutputReceiver<RestrictionT> {
+
+    protected double getSize(RestrictionT subrestriction) {
+      return doFnInvoker.invokeGetSize(
+          new DelegatingArgumentProvider<InputT, OutputT>(this, getErrorContext() + "/GetSize") {
+            @Override
+            public Object restriction() {
+              return subrestriction;
+            }
+
+            @Override
+            public Instant timestamp(DoFn<InputT, OutputT> doFn) {
+              return getCurrentElement().getTimestamp();
+            }
+
+            @Override
+            public RestrictionTracker<?, ?> restrictionTracker() {
+              return doFnInvoker.invokeNewTracker(this);
+            }
+          });
+    }
+
+    @Override
+    public String getErrorContext() {
+      return "TruncateRestriction";
+    }
+
+    @Override
+    public PaneInfo paneInfo(DoFn<InputT, OutputT> doFn) {
+      return getCurrentElement().getPaneInfo();
+    }
+
+    @Override
+    public InputT element(DoFn<InputT, OutputT> doFn) {
+      return getCurrentElement().getValue();
+    }
+
+    @Override
+    public Object schemaElement(int index) {
+      SerializableFunction<InputT, Object> converter =
+          (SerializableFunction<InputT, Object>)
+              doFnSchemaInformation.getElementConverters().get(index);
+      return converter.apply(getCurrentElement().getValue());
+    }
+
+    @Override
+    public Instant timestamp(DoFn<InputT, OutputT> doFn) {
+      return getCurrentElement().getTimestamp();
+    }
+
+    @Override
+    public OutputReceiver<OutputT> outputReceiver(DoFn<InputT, OutputT> doFn) {
+      // OutputT == RestrictionT
+      return (OutputReceiver<OutputT>) this;
+    }
+
+    @Override
+    public Object restriction() {
+      return getCurrentRestriction();
+    }
+
+    @Override
+    @SuppressWarnings("nullness")
+    public Object watermarkEstimatorState() {
+      return getCurrentWatermarkEstimatorState();
+    }
+
+    @Override
+    public RestrictionTracker<?, ?> restrictionTracker() {
+      return getCurrentTracker();
+    }
+
+    @Override
+    public PipelineOptions pipelineOptions() {
+      return pipelineOptions;
+    }
+
+    @Override
+    public void outputWithTimestamp(RestrictionT output, Instant timestamp) {
+      throw new UnsupportedOperationException(
+          "Cannot outputWithTimestamp from TruncateRestriction");
+    }
+
+    @Override
+    public void outputWindowedValue(
+        RestrictionT output,
+        Instant timestamp,
+        Collection<? extends BoundedWindow> windows,
+        PaneInfo paneInfo) {
+      throw new UnsupportedOperationException(
+          "Cannot outputWindowedValue from TruncateRestriction");
+    }
+  }
+
+  /**
+   * Passes split requests downstream, by way of trySplit on the overall runner, which will split
+   * per window if needed.
+   */
+  private static class SplitDelegatingFnDataReceiver<
+          InputT, RestrictionT extends @NonNull Object, WatermarkEstimatorStateT>
+      implements FnDataReceiver<
+              WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>>>,
+          HandlesSplits {
+
+    private final HandlesSplits splitDelegate;
+    private final SplittableTruncateSizedRestrictionsDoFnRunner<
+            InputT, RestrictionT, ?, WatermarkEstimatorStateT, ?>
+        runner;
+
+    public SplitDelegatingFnDataReceiver(
+        SplittableTruncateSizedRestrictionsDoFnRunner<
+                InputT, RestrictionT, ?, WatermarkEstimatorStateT, ?>
+            runner,
+        HandlesSplits splitDelegate) {
+      this.runner = runner;
+      this.splitDelegate = splitDelegate;
+    }
+
+    @Override
+    public void accept(
+        WindowedValue<KV<KV<InputT, KV<RestrictionT, WatermarkEstimatorStateT>>, Double>> input) {
+      runner.processElement(input);
+    }
+
+    @Override
+    public @Nullable SplitResult trySplit(double fractionOfRemainder) {
+      return runner.trySplit(fractionOfRemainder, splitDelegate);
+    }
+
+    @Override
+    public double getProgress() {
+      double delegateProgress = splitDelegate.getProgress();
+      if (!runner.observesWindow) {
+        return delegateProgress;
+      } else {
+        Progress progress = runner.getProgressFromWindowObservingTruncate(delegateProgress);
+        if (progress != null) {
+          double totalWork = progress.getWorkCompleted() + progress.getWorkRemaining();
+          if (totalWork > 0) {
+            return progress.getWorkCompleted() / totalWork;
+          }
+        }
+        return 0;
+      }
+    }
+  }
+}
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/WindowedSplitResult.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/WindowedSplitResult.java
index bf9eefd03a84..82517781388d 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/WindowedSplitResult.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/WindowedSplitResult.java
@@ -21,6 +21,7 @@
 import org.apache.beam.sdk.annotations.Internal;
 import org.apache.beam.sdk.values.WindowedValue;
 import org.checkerframework.checker.nullness.qual.Nullable;
+import org.checkerframework.dataflow.qual.Pure;
 
 /** Internal class to hold the primary and residual roots when converted to an input element. */
 @AutoValue
@@ -28,10 +29,10 @@
 @Internal
 abstract class WindowedSplitResult {
   public static WindowedSplitResult forRoots(
-      WindowedValue<?> primaryInFullyProcessedWindowsRoot,
-      WindowedValue<?> primarySplitRoot,
-      WindowedValue<?> residualSplitRoot,
-      WindowedValue<?> residualInUnprocessedWindowsRoot) {
+      @Nullable WindowedValue<?> primaryInFullyProcessedWindowsRoot,
+      @Nullable WindowedValue<?> primarySplitRoot,
+      @Nullable WindowedValue<?> residualSplitRoot,
+      @Nullable WindowedValue<?> residualInUnprocessedWindowsRoot) {
     return new AutoValue_WindowedSplitResult(
         primaryInFullyProcessedWindowsRoot,
         primarySplitRoot,
@@ -39,11 +40,15 @@ public static WindowedSplitResult forRoots(
         residualInUnprocessedWindowsRoot);
   }
 
+  @Pure
   public abstract @Nullable WindowedValue<?> getPrimaryInFullyProcessedWindowsRoot();
 
+  @Pure
   public abstract @Nullable WindowedValue<?> getPrimarySplitRoot();
 
+  @Pure
   public abstract @Nullable WindowedValue<?> getResidualSplitRoot();
 
+  @Pure
   public abstract @Nullable WindowedValue<?> getResidualInUnprocessedWindowsRoot();
 }
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/control/ExecutionStateSampler.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/control/ExecutionStateSampler.java
index a0c6876ea62e..fdc273b64b3f 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/control/ExecutionStateSampler.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/control/ExecutionStateSampler.java
@@ -22,6 +22,7 @@
 import java.util.HashSet;
 import java.util.List;
 import java.util.Map;
+import java.util.Optional;
 import java.util.Set;
 import java.util.concurrent.CancellationException;
 import java.util.concurrent.ExecutionException;
@@ -30,6 +31,7 @@
 import java.util.concurrent.TimeoutException;
 import java.util.concurrent.atomic.AtomicLong;
 import java.util.concurrent.atomic.AtomicReference;
+import java.util.function.Consumer;
 import javax.annotation.concurrent.GuardedBy;
 import org.apache.beam.fn.harness.logging.BeamFnLoggingMDC;
 import org.apache.beam.model.pipeline.v1.MetricsApi.MonitoringInfo;
@@ -46,8 +48,10 @@
 import org.apache.beam.sdk.options.ExecutorOptions;
 import org.apache.beam.sdk.options.ExperimentalOptions;
 import org.apache.beam.sdk.options.PipelineOptions;
+import org.apache.beam.sdk.options.SdkHarnessOptions;
 import org.apache.beam.sdk.util.HistogramData;
 import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.ByteString;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Joiner;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.joda.time.DateTimeUtils.MillisProvider;
@@ -79,14 +83,20 @@ public class ExecutionStateSampler {
           .toFormatter();
   private final int periodMs;
   private final MillisProvider clock;
+  private final long userSpecifiedLullTimeMsForRestart;
+  private final boolean userSpecifiedTimeoutForRestart;
 
   @GuardedBy("activeStateTrackers")
   private final Set<ExecutionStateTracker> activeStateTrackers;
 
   private final Future<Void> stateSamplingThread;
+  private final @Nullable Consumer<String> onTimeoutExceededCallback;
 
   @SuppressWarnings("methodref.receiver.bound" /* Synchronization ensures proper initialization */)
-  public ExecutionStateSampler(PipelineOptions options, MillisProvider clock) {
+  public ExecutionStateSampler(
+      PipelineOptions options,
+      MillisProvider clock,
+      @Nullable Consumer<String> onTimeoutExceededCallback) {
     String samplingPeriodMills =
         ExperimentalOptions.getExperimentValue(
             options, ExperimentalOptions.STATE_SAMPLING_PERIOD_MILLIS);
@@ -96,6 +106,17 @@ public ExecutionStateSampler(PipelineOptions options, MillisProvider clock) {
             : Integer.parseInt(samplingPeriodMills);
     this.clock = clock;
     this.activeStateTrackers = new HashSet<>();
+
+    int timeoutOption = options.as(SdkHarnessOptions.class).getElementProcessingTimeoutMinutes();
+    if (timeoutOption <= 0) {
+      this.userSpecifiedTimeoutForRestart = false;
+      this.userSpecifiedLullTimeMsForRestart = 0L;
+    } else {
+      this.userSpecifiedTimeoutForRestart = true;
+      this.userSpecifiedLullTimeMsForRestart = TimeUnit.MINUTES.toMillis(timeoutOption);
+    }
+    this.onTimeoutExceededCallback = onTimeoutExceededCallback;
+
     // We specifically synchronize to ensure that this object can complete
     // being published before the state sampler thread starts.
     synchronized (this) {
@@ -148,6 +169,16 @@ public void stop() {
     }
   }
 
+  @VisibleForTesting
+  public boolean getUserSpecifiedTimeoutForRestart() {
+    return this.userSpecifiedTimeoutForRestart;
+  }
+
+  @VisibleForTesting
+  public long getUserSpecifiedLullTimeMsForRestart() {
+    return this.userSpecifiedLullTimeMsForRestart;
+  }
+
   /** Entry point for the state sampling thread. */
   private Void stateSampler() throws Exception {
     // Ensure the object finishes being published safely.
@@ -166,11 +197,17 @@ private Void stateSampler() throws Exception {
         Thread.sleep(difference);
       } else {
         long millisSinceLastSample = currentTimeMillis - lastSampleTimeMillis;
+        Optional<String> timeoutMsg = Optional.empty();
         synchronized (activeStateTrackers) {
           for (ExecutionStateTracker activeTracker : activeStateTrackers) {
-            activeTracker.takeSample(currentTimeMillis, millisSinceLastSample);
+            if (!timeoutMsg.isPresent()) {
+              timeoutMsg = activeTracker.takeSample(currentTimeMillis, millisSinceLastSample);
+            }
           }
         }
+        if (timeoutMsg.isPresent() && this.onTimeoutExceededCallback != null) {
+          this.onTimeoutExceededCallback.accept(timeoutMsg.get());
+        }
         lastSampleTimeMillis = currentTimeMillis;
         targetTimeMillis = lastSampleTimeMillis + periodMs;
       }
@@ -343,7 +380,7 @@ public ExecutionState create(
      * @param millisSinceLastSample the time since the last sample was reported. As an
      *     approximation, all of that time should be associated with this state.
      */
-    private void takeSample(long currentTimeMillis, long millisSinceLastSample) {
+    private Optional<String> takeSample(long currentTimeMillis, long millisSinceLastSample) {
       ExecutionStateImpl currentExecutionState = currentStateLazy.get();
       if (currentExecutionState != null) {
         currentExecutionState.takeSample(millisSinceLastSample);
@@ -356,6 +393,44 @@ private void takeSample(long currentTimeMillis, long millisSinceLastSample) {
         transitionsAtLastSample = transitionsAtThisSample;
       } else {
         long lullTimeMs = currentTimeMillis - lastTransitionTimeMillis.get();
+
+        if (userSpecifiedTimeoutForRestart && lullTimeMs > userSpecifiedLullTimeMsForRestart) {
+          String timeoutMessage = "";
+          Thread thread = trackedThread.get();
+          if (thread == null) {
+            timeoutMessage =
+                String.format(
+                    "Processing of an element in bundle %s has exceeded the specified timeout of %s "
+                        + "(stack trace unable to be generated). The SDK worker will be terminated.",
+                    processBundleId.get(),
+                    DURATION_FORMATTER.print(
+                        Duration.millis(userSpecifiedLullTimeMsForRestart).toPeriod()));
+          } else if (currentExecutionState == null) {
+            timeoutMessage =
+                String.format(
+                    "Processing of an element in bundle %s has exceeded the specified timeout of %s "
+                        + "without outputting or completing:%n  at %s. The SDK worker will be terminated.",
+                    processBundleId.get(),
+                    DURATION_FORMATTER.print(
+                        Duration.millis(userSpecifiedLullTimeMsForRestart).toPeriod()),
+                    Joiner.on("\n  at ").join(thread.getStackTrace()));
+          } else {
+            timeoutMessage =
+                String.format(
+                    "Processing of an element in bundle %s for PTransform{id=%s, name=%s, state=%s} "
+                        + "has exceeded the specified timeout of %s without outputting or completing:%n  at %s. "
+                        + "The SDK worker will be terminated.",
+                    processBundleId.get(),
+                    currentExecutionState.ptransformId,
+                    currentExecutionState.ptransformUniqueName,
+                    currentExecutionState.stateName,
+                    DURATION_FORMATTER.print(
+                        Duration.millis(userSpecifiedLullTimeMsForRestart).toPeriod()),
+                    Joiner.on("\n  at ").join(thread.getStackTrace()));
+          }
+          return Optional.of(timeoutMessage);
+        }
+
         if (lullTimeMs > MAX_LULL_TIME_MS) {
           if (lullTimeMs < lastLullReport // This must be a new report.
               || lullTimeMs > 1.2 * lastLullReport // Exponential backoff.
@@ -394,6 +469,7 @@ private void takeSample(long currentTimeMillis, long millisSinceLastSample) {
           }
         }
       }
+      return Optional.empty();
     }
 
     /** Returns status information related to this tracker or null if not tracking a bundle. */
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/control/ProcessBundleHandler.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/control/ProcessBundleHandler.java
index df9cf428ff1b..fe422939e535 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/control/ProcessBundleHandler.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/control/ProcessBundleHandler.java
@@ -589,10 +589,10 @@ public BeamFnApi.InstructionResponse.Builder processBundle(InstructionRequest re
       return BeamFnApi.InstructionResponse.newBuilder().setProcessBundle(response);
     } catch (Exception e) {
       LOG.debug(
-          "Error processing bundle {} with bundleProcessor for {} after exception: {}",
+          "Error processing bundle {} with bundleProcessor for {} after exception",
           request.getInstructionId(),
           request.getProcessBundle().getProcessBundleDescriptorId(),
-          e.getMessage());
+          e);
       if (bundleProcessor != null) {
         // Make sure we clean up from the active set of bundle processors.
         bundleProcessorCache.discard(bundleProcessor);
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/BagUserState.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/BagUserState.java
index 64094a9b62b2..ba56c6d656ca 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/BagUserState.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/BagUserState.java
@@ -108,7 +108,9 @@ public void clear() {
         "Bag user state is no longer usable because it is closed for %s",
         request.getStateKey());
     isCleared = true;
-    newValues = new ArrayList<>();
+    if (!newValues.isEmpty()) {
+      newValues = new ArrayList<>();
+    }
   }
 
   @SuppressWarnings("FutureReturnValueIgnored")
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/FnApiStateAccessor.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/FnApiStateAccessor.java
index 164589bc40aa..e06a82c8e25f 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/FnApiStateAccessor.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/FnApiStateAccessor.java
@@ -26,6 +26,7 @@
 import java.util.Iterator;
 import java.util.List;
 import java.util.Map;
+import java.util.Objects;
 import java.util.Set;
 import java.util.function.Function;
 import java.util.function.Supplier;
@@ -65,6 +66,7 @@
 import org.apache.beam.sdk.transforms.windowing.WindowMappingFn;
 import org.apache.beam.sdk.util.ByteStringOutputStream;
 import org.apache.beam.sdk.util.CombineFnUtil;
+import org.apache.beam.sdk.util.Weighted;
 import org.apache.beam.sdk.util.construction.BeamUrns;
 import org.apache.beam.sdk.util.construction.PCollectionViewTranslation;
 import org.apache.beam.sdk.util.construction.RehydratedComponents;
@@ -349,6 +351,8 @@ public <T> T get(PCollectionView<T> view, BoundedWindow window) {
       throw new IllegalStateException(e);
     }
     ByteString encodedWindow = encodedWindowOut.toByteString();
+    // IDEA: If this StateKey shows up on caching profiles, create a custom object that is cheap to
+    // weigh and compare similar to the other statekey types in this file.
     StateKey.Builder cacheKeyBuilder = StateKey.newBuilder();
 
     switch (sideInputSpec.getAccessPattern()) {
@@ -972,16 +976,71 @@ public WatermarkHoldState bindWatermark(
     throw new UnsupportedOperationException("WatermarkHoldState is unsupported by the Fn API.");
   }
 
+  private static class UserStateCacheTokenKey implements Weighted {
+    private final ByteString bytes;
+    private final int hash;
+
+    public UserStateCacheTokenKey(ByteString bytes) {
+      this.bytes = bytes;
+      this.hash = Objects.hash(UserStateCacheTokenKey.class, bytes);
+    }
+
+    @Override
+    public boolean equals(Object o) {
+      if (!(o instanceof UserStateCacheTokenKey)) {
+        return false;
+      }
+      UserStateCacheTokenKey other = (UserStateCacheTokenKey) o;
+      return hash == other.hash && bytes.equals(other.bytes);
+    }
+
+    @Override
+    public int hashCode() {
+      return hash;
+    }
+
+    @Override
+    public long getWeight() {
+      // 12 = 4 bytes for int + 8 for reference.
+      // This doesn't account for backing memory of bytes but reducing
+      // overhead of weighing is more important.
+      return 12L + bytes.size();
+    }
+  }
+
   private Cache<?, ?> getCacheFor(StateKey stateKey) {
     switch (stateKey.getTypeCase()) {
       case BAG_USER_STATE:
+        for (CacheToken token : cacheTokens.get()) {
+          if (!token.hasUserState()) {
+            continue;
+          }
+          return Caches.subCache(
+              processWideCache,
+              new UserStateCacheTokenKey(token.getToken()),
+              new BagUserStateCacheKey(stateKey.getBagUserState()));
+        }
+        break;
       case MULTIMAP_KEYS_USER_STATE:
+        for (CacheToken token : cacheTokens.get()) {
+          if (!token.hasUserState()) {
+            continue;
+          }
+          return Caches.subCache(
+              processWideCache,
+              new UserStateCacheTokenKey(token.getToken()),
+              new MultimapKeysUserStateCacheKey(stateKey.getMultimapKeysUserState()));
+        }
+        break;
       case ORDERED_LIST_USER_STATE:
         for (CacheToken token : cacheTokens.get()) {
           if (!token.hasUserState()) {
             continue;
           }
-          return Caches.subCache(processWideCache, token, stateKey);
+          return Caches.subCache(
+              processWideCache,
+              new UserStateCacheTokenKey(token.getToken()),
+              new OrderedListUserStateCacheKey(stateKey.getOrderedListUserState()));
         }
         break;
       case ITERABLE_SIDE_INPUT:
@@ -997,6 +1056,8 @@ public WatermarkHoldState bindWatermark(
                   .getIterableSideInput()
                   .getSideInputId()
                   .equals(token.getSideInput().getSideInputId())) {
+            // IDEA: If cachetoken shows up on profiles, create a simpler type to weigh like
+            // UserStateCacheTokenKey.
             return Caches.subCache(processWideCache, token, stateKey);
           }
         }
@@ -1014,6 +1075,8 @@ public WatermarkHoldState bindWatermark(
                   .getMultimapKeysSideInput()
                   .getSideInputId()
                   .equals(token.getSideInput().getSideInputId())) {
+            // IDEA: If cachetoken shows up on profiles, create a simpler type to weigh like
+            // UserStateCacheTokenKey.
             return Caches.subCache(processWideCache, token, stateKey);
           }
         }
@@ -1038,6 +1101,63 @@ private <T> BagUserState<T> createBagUserState(StateKey stateKey, Coder<T> value
     return rval;
   }
 
+  // Shared base for implementation of cache keys with the same
+  // fields that also uses the subclass for hashing and equality.
+  private abstract static class UserStateCacheKeyBase implements Weighted {
+    private final String ptransformId;
+    private final String stateId;
+    private final ByteString window;
+    private final ByteString key;
+    private final int hash;
+
+    protected UserStateCacheKeyBase(
+        Class subclass, String ptransformId, String stateId, ByteString window, ByteString key) {
+      this.ptransformId = ptransformId;
+      this.stateId = stateId;
+      this.window = window;
+      this.key = key;
+      this.hash = Objects.hash(subclass, ptransformId, stateId, window, key);
+    }
+
+    @Override
+    public final boolean equals(Object o) {
+      if (!(o instanceof UserStateCacheKeyBase)) {
+        return false;
+      }
+      UserStateCacheKeyBase other = (UserStateCacheKeyBase) o;
+      return hash == other.hash
+          && this.getClass().equals(o.getClass())
+          && ptransformId.equals(other.ptransformId)
+          && stateId.equals(other.stateId)
+          && window.equals(other.window)
+          && key.equals(other.key);
+    }
+
+    @Override
+    public final int hashCode() {
+      return hash;
+    }
+
+    @Override
+    public final long getWeight() {
+      // 36 = 4 bytes for int + 8 * 4 references.
+      // This doesn't account for backing memory of bytes but reducing
+      // overhead of weighing is more important.
+      return 36L + ptransformId.length() + stateId.length() + window.size() + key.size();
+    }
+  }
+
+  private static final class BagUserStateCacheKey extends UserStateCacheKeyBase {
+    public BagUserStateCacheKey(StateKey.BagUserState proto) {
+      super(
+          BagUserStateCacheKey.class,
+          proto.getTransformId(),
+          proto.getUserStateId(),
+          proto.getWindow(),
+          proto.getKey());
+    }
+  }
+
   private StateKey createBagUserStateKey(String stateId) {
     StateKey.Builder builder = StateKey.newBuilder();
     builder
@@ -1063,11 +1183,23 @@ private <KeyT, ValueT> MultimapUserState<KeyT, ValueT> createMultimapUserState(
     return rval;
   }
 
+  private static final class MultimapKeysUserStateCacheKey extends UserStateCacheKeyBase {
+    public MultimapKeysUserStateCacheKey(StateKey.MultimapKeysUserState proto) {
+      super(
+          MultimapKeysUserStateCacheKey.class,
+          proto.getTransformId(),
+          proto.getUserStateId(),
+          proto.getWindow(),
+          proto.getKey());
+    }
+  }
+
   private StateKey createMultimapKeysUserStateKey(String stateId) {
     StateKey.Builder builder = StateKey.newBuilder();
     builder
         .getMultimapKeysUserStateBuilder()
         .setWindow(encodedCurrentWindowSupplier.get())
+        .setKey(encodedCurrentKeySupplier.get())
         .setTransformId(ptransformId)
         .setUserStateId(stateId);
     return builder.build();
@@ -1086,6 +1218,17 @@ private <T> OrderedListUserState<T> createOrderedListUserState(
     return rval;
   }
 
+  private static final class OrderedListUserStateCacheKey extends UserStateCacheKeyBase {
+    public OrderedListUserStateCacheKey(StateKey.OrderedListUserState proto) {
+      super(
+          OrderedListUserStateCacheKey.class,
+          proto.getTransformId(),
+          proto.getUserStateId(),
+          proto.getWindow(),
+          proto.getKey());
+    }
+  }
+
   private StateKey createOrderedListUserStateKey(String stateId) {
     StateKey.Builder builder = StateKey.newBuilder();
     builder
diff --git a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/StateFetchingIterators.java b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/StateFetchingIterators.java
index e4144bcbb353..1e06c98f2e31 100644
--- a/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/StateFetchingIterators.java
+++ b/sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/StateFetchingIterators.java
@@ -208,6 +208,18 @@ abstract static class Blocks<T> implements Weighted {
       public abstract List<Block<T>> getBlocks();
     }
 
+    static class EmptyBlocks<T> extends Blocks<T> {
+      @Override
+      public List<Block<T>> getBlocks() {
+        return Collections.singletonList(Block.emptyBlock());
+      }
+
+      @Override
+      public long getWeight() {
+        return 8;
+      }
+    }
+
     static class MutatedBlocks<T> extends Blocks<T> {
 
       private final Block<T> wholeBlock;
@@ -223,19 +235,7 @@ public List<Block<T>> getBlocks() {
 
       @Override
       public long getWeight() {
-        return wholeBlock.getWeight();
-      }
-    }
-
-    private static <T> long sumWeight(List<Block<T>> blocks) {
-      try {
-        long sum = 0;
-        for (Block<T> block : blocks) {
-          sum = Math.addExact(sum, block.getWeight());
-        }
-        return sum;
-      } catch (ArithmeticException e) {
-        return Long.MAX_VALUE;
+        return wholeBlock.getWeight() + 8;
       }
     }
 
@@ -249,7 +249,15 @@ static class BlocksPrefix<T> extends Blocks<T> implements Shrinkable<BlocksPrefi
 
       @Override
       public long getWeight() {
-        return sumWeight(blocks);
+        try {
+          long sum = 8 + blocks.size() * 8L;
+          for (Block<T> block : blocks) {
+            sum = Math.addExact(sum, block.getWeight());
+          }
+          return sum;
+        } catch (ArithmeticException e) {
+          return Long.MAX_VALUE;
+        }
       }
 
       BlocksPrefix(List<Block<T>> blocks) {
@@ -274,24 +282,38 @@ public List<Block<T>> getBlocks() {
 
     @AutoValue
     abstract static class Block<T> implements Weighted {
+      private static final Block<Void> EMPTY =
+          fromValues(WeightedList.of(Collections.emptyList(), 0), null);
 
-      public static <T> Block<T> mutatedBlock(List<T> values, long weight) {
-        return mutatedBlock(new WeightedList<>(values, weight));
+      @SuppressWarnings("unchecked") // Based upon as Collections.emptyList()
+      public static <T> Block<T> emptyBlock() {
+        return (Block<T>) EMPTY;
       }
 
-      public static <T> Block<T> mutatedBlock(WeightedList<T> weightedList) {
-        return new AutoValue_StateFetchingIterators_CachingStateIterable_Block<>(
-            weightedList.getBacking(), null, weightedList.getWeight());
+      public static <T> Block<T> mutatedBlock(List<T> values) {
+        return fromValues(values, null);
+      }
+
+      public static <T> Block<T> mutatedBlock(WeightedList<T> values) {
+        return fromValues(values, null);
       }
 
       public static <T> Block<T> fromValues(List<T> values, @Nullable ByteString nextToken) {
-        return fromValues(new WeightedList<>(values, Caches.weigh(values)), nextToken);
+        return fromValues(WeightedList.of(values, Caches.weigh(values)), nextToken);
       }
 
       public static <T> Block<T> fromValues(
           WeightedList<T> values, @Nullable ByteString nextToken) {
+        long weight = values.getWeight() + 24;
+        if (nextToken != null) {
+          if (nextToken.isEmpty()) {
+            nextToken = ByteString.EMPTY;
+          } else {
+            weight += Caches.weigh(nextToken);
+          }
+        }
         return new AutoValue_StateFetchingIterators_CachingStateIterable_Block<>(
-            values.getBacking(), nextToken, values.getWeight() + Caches.weigh(nextToken));
+            values.getBacking(), nextToken, weight);
       }
 
       abstract List<T> getValues();
@@ -350,7 +372,7 @@ public void remove(Set<Object> toRemoveStructuralValues) {
         totalSize += tBlock.getValues().size();
       }
 
-      WeightedList<T> allValues = new WeightedList<>(new ArrayList<>(totalSize), 0L);
+      WeightedList<T> allValues = WeightedList.of(new ArrayList<>(totalSize), 0L);
       for (Block<T> block : blocks) {
         boolean valueRemovedFromBlock = false;
         List<T> blockValuesToKeep = new ArrayList<>();
@@ -383,7 +405,11 @@ public void remove(Set<Object> toRemoveStructuralValues) {
      * requesting data from the state cache.
      */
     public void clearAndAppend(List<T> values) {
-      clearAndAppend(new WeightedList<>(values, Caches.weigh(values)));
+      if (values.isEmpty()) {
+        cache.put(IterableCacheKey.INSTANCE, new EmptyBlocks<>());
+      } else {
+        cache.put(IterableCacheKey.INSTANCE, new MutatedBlocks<>(Block.mutatedBlock(values)));
+      }
     }
 
     /**
@@ -396,7 +422,11 @@ public void clearAndAppend(List<T> values) {
      * requesting data from the state cache.
      */
     public void clearAndAppend(WeightedList<T> values) {
-      cache.put(IterableCacheKey.INSTANCE, new MutatedBlocks<>(Block.mutatedBlock(values)));
+      if (values.isEmpty()) {
+        cache.put(IterableCacheKey.INSTANCE, new EmptyBlocks<>());
+      } else {
+        cache.put(IterableCacheKey.INSTANCE, new MutatedBlocks<>(Block.mutatedBlock(values)));
+      }
     }
 
     @Override
@@ -413,7 +443,7 @@ public PrefetchableIterator<T> createIterator() {
      * cache.
      */
     public void append(List<T> values) {
-      append(new WeightedList<>(values, Caches.weigh(values)));
+      appendHelper(values, -1);
     }
 
     /**
@@ -425,7 +455,15 @@ public void append(List<T> values) {
      * cache.
      */
     public void append(WeightedList<T> values) {
-      if (values.isEmpty()) {
+      appendHelper(values.getBacking(), values.getWeight());
+    }
+
+    /**
+     * Appends the newValues to the cached iterable with newWeight weight. If newWeight is negative,
+     * the weight will be calculated using Caches.weigh.
+     */
+    private void appendHelper(List<T> newValues, long newWeight) {
+      if (newValues.isEmpty()) {
         return;
       }
       Blocks<T> existing = cache.peek(IterableCacheKey.INSTANCE);
@@ -442,15 +480,23 @@ public void append(WeightedList<T> values) {
       // they were mutated, and we must evict all or none of the blocks. When consuming the blocks,
       // we must have a reference to all or none of the blocks (which forces a load).
       List<Block<T>> blocks = existing.getBlocks();
-      int totalSize = values.size();
+      int totalSize = newValues.size();
       for (Block<T> block : blocks) {
         totalSize += block.getValues().size();
       }
-      WeightedList<T> allValues = new WeightedList<>(new ArrayList<>(totalSize), 0L);
+      WeightedList<T> allValues = WeightedList.of(new ArrayList<>(totalSize), 0L);
       for (Block<T> block : blocks) {
         allValues.addAll(block.getValues(), block.getWeight());
       }
-      allValues.addAll(values);
+      if (newWeight < 0) {
+        if (newValues.size() == 1) {
+          // Optimize weighing of the common value state as single single-element bag state.
+          newWeight = Caches.weigh(newValues.get(0));
+        } else {
+          newWeight = Caches.weigh(newValues);
+        }
+      }
+      allValues.addAll(newValues, newWeight);
 
       cache.put(IterableCacheKey.INSTANCE, new MutatedBlocks<>(Block.mutatedBlock(allValues)));
     }
@@ -469,7 +515,7 @@ public CachingStateIterator() {
             new DataStreamDecoder<>(valueCoder, underlyingStateFetchingIterator);
         this.currentBlock =
             Block.fromValues(
-                new WeightedList<>(Collections.emptyList(), 0L),
+                WeightedList.of(Collections.emptyList(), 0L),
                 stateRequestForFirstChunk.getGet().getContinuationToken());
         this.currentCachedBlockValueIndex = 0;
       }
@@ -535,7 +581,7 @@ public boolean hasNext() {
           }
           // Release the block while we are loading the next one.
           currentBlock =
-              Block.fromValues(new WeightedList<>(Collections.emptyList(), 0L), ByteString.EMPTY);
+              Block.fromValues(WeightedList.of(Collections.emptyList(), 0L), ByteString.EMPTY);
 
           @Nullable Blocks<T> existing = cache.peek(IterableCacheKey.INSTANCE);
           boolean isFirstBlock = ByteString.EMPTY.equals(nextToken);
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/CachesTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/CachesTest.java
index 61b62a21083c..9843cf11da60 100644
--- a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/CachesTest.java
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/CachesTest.java
@@ -57,13 +57,13 @@ public Object shrink() {
           }
         };
 
-    Cache<Object, Object> cache = Caches.forMaximumBytes(2 * MB);
+    Cache<Object, Object> cache = Caches.forMaximumBytes(3 * MB - 1);
     cache.put(shrinkableKey, WeightedValue.of(shrinkable, MB));
     // Check that we didn't evict it yet
     assertSame(shrinkable, cache.peek(shrinkableKey));
 
     // The next insertion should cause the value to be "shrunk"
-    cache.put(WeightedValue.of("other", 1), WeightedValue.of("value", 1));
+    cache.put(WeightedValue.of("other", 1), WeightedValue.of("value", MB));
     assertEquals("wasShrunk", cache.peek(shrinkableKey));
   }
 
@@ -209,18 +209,18 @@ public void testDescribeStats() throws Exception {
     assertThat(cache.describeStats(), containsString("evictions 0"));
 
     // Test eviction, evict all the other 200 elements that were added
-    cache.put(WeightedValue.of(1000, 100 * MB), new ShrinkableString("value", 900 * MB));
-    assertThat(cache.describeStats(), containsString("used/max 1000/1000 MB"));
+    cache.put(WeightedValue.of(1000, 100 * MB), new ShrinkableString("value", 899 * MB));
+    assertThat(cache.describeStats(), containsString("used/max 999/1000 MB"));
     assertThat(cache.describeStats(), containsString("evictions 200"));
 
     // Test shrinking, 900 -> 450 + 100 + 55 + 1 = 606
     cache.put(WeightedValue.of(1001, MB), new ShrinkableString("value", 55 * MB));
-    assertThat(cache.describeStats(), containsString("used/max 606/1000 MB"));
+    assertThat(cache.describeStats(), containsString("used/max 605/1000 MB"));
     assertThat(cache.describeStats(), containsString("evictions 201"));
 
     // Test composite key namespace is weighed as well.
-    // 33 + 8 + 3 = 44 more then last used/max of 606 = 650
-    Caches.subCache(cache, WeightedValue.of("subCache", 33 * MB))
+    // 34 + 8 + 3 = 45 more then last used/max of 605 = 650
+    Caches.subCache(cache, WeightedValue.of("subCache", 34 * MB))
         .put(WeightedValue.of("subCacheKey", 8 * MB), WeightedValue.of("subCacheValue", 3 * MB));
     assertThat(cache.describeStats(), containsString("used/max 650/1000 MB"));
   }
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/FnApiDoFnRunnerTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/FnApiDoFnRunnerTest.java
index bdc21cdadfe1..ef19b7c18804 100644
--- a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/FnApiDoFnRunnerTest.java
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/FnApiDoFnRunnerTest.java
@@ -18,6 +18,7 @@
 package org.apache.beam.fn.harness;
 
 import static java.util.Arrays.asList;
+import static org.apache.beam.runners.core.WindowMatchers.isValueInGlobalWindow;
 import static org.apache.beam.sdk.options.ExperimentalOptions.addExperiment;
 import static org.apache.beam.sdk.values.WindowedValues.timestampedValueInGlobalWindow;
 import static org.apache.beam.sdk.values.WindowedValues.valueInGlobalWindow;
@@ -103,7 +104,6 @@
 import org.apache.beam.sdk.transforms.ParDo;
 import org.apache.beam.sdk.transforms.View;
 import org.apache.beam.sdk.transforms.splittabledofn.OffsetRangeTracker;
-import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker.Progress;
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.FixedWindows;
 import org.apache.beam.sdk.transforms.windowing.GlobalWindow;
@@ -1003,36 +1003,36 @@ public void testTimers() throws Exception {
               dynamicTimerInGlobalWindow(
                   "Y", "processing-timer2", new Instant(2100L), new Instant(3100L)));
 
+      assertThat(
+          mainOutputValues.get(0), isValueInGlobalWindow("key:X mainX[X0]", new Instant(1000L)));
+
       assertThat(
           mainOutputValues,
-          contains(
-              timestampedValueInGlobalWindow("key:X mainX[X0]", new Instant(1000L)),
-              timestampedValueInGlobalWindow("key:Y mainY[]", new Instant(1100L)),
-              timestampedValueInGlobalWindow("key:X mainX[X0, X1]", new Instant(1200L)),
-              timestampedValueInGlobalWindow("key:Y mainY[Y1]", new Instant(1300L)),
-              timestampedValueInGlobalWindow("key:A event[A0]", new Instant(1400L)),
-              timestampedValueInGlobalWindow("key:B event[]", new Instant(1500L)),
-              timestampedValueInGlobalWindow("key:A event[A0, event]", new Instant(1400L)),
-              timestampedValueInGlobalWindow("key:A event[A0, event, event]", new Instant(1400L)),
-              timestampedValueInGlobalWindow(
-                  "key:A event[A0, event, event, event]", new Instant(1400L)),
-              timestampedValueInGlobalWindow(
+          containsInAnyOrder(
+              isValueInGlobalWindow("key:X mainX[X0]", new Instant(1000L)),
+              isValueInGlobalWindow("key:Y mainY[]", new Instant(1100L)),
+              isValueInGlobalWindow("key:X mainX[X0, X1]", new Instant(1200L)),
+              isValueInGlobalWindow("key:Y mainY[Y1]", new Instant(1300L)),
+              isValueInGlobalWindow("key:A event[A0]", new Instant(1400L)),
+              isValueInGlobalWindow("key:B event[]", new Instant(1500L)),
+              isValueInGlobalWindow("key:A event[A0, event]", new Instant(1400L)),
+              isValueInGlobalWindow("key:A event[A0, event, event]", new Instant(1400L)),
+              isValueInGlobalWindow("key:A event[A0, event, event, event]", new Instant(1400L)),
+              isValueInGlobalWindow(
                   "key:A event[A0, event, event, event, event]", new Instant(1400L)),
-              timestampedValueInGlobalWindow(
+              isValueInGlobalWindow(
                   "key:A event[A0, event, event, event, event, event]", new Instant(1400L)),
-              timestampedValueInGlobalWindow(
+              isValueInGlobalWindow(
                   "key:A event[A0, event, event, event, event, event, event]", new Instant(1400L)),
-              timestampedValueInGlobalWindow("key:C processing[C0]", new Instant(1800L)),
-              timestampedValueInGlobalWindow("key:B processing[event]", new Instant(1500L)),
-              timestampedValueInGlobalWindow("key:B event[event, processing]", new Instant(1500)),
-              timestampedValueInGlobalWindow(
-                  "key:B event[event, processing, event]", new Instant(1500)),
-              timestampedValueInGlobalWindow(
+              isValueInGlobalWindow("key:C processing[C0]", new Instant(1800L)),
+              isValueInGlobalWindow("key:B processing[event]", new Instant(1500L)),
+              isValueInGlobalWindow("key:B event[event, processing]", new Instant(1500)),
+              isValueInGlobalWindow("key:B event[event, processing, event]", new Instant(1500)),
+              isValueInGlobalWindow(
                   "key:B event[event, processing, event, event]", new Instant(1500)),
-              timestampedValueInGlobalWindow(
+              isValueInGlobalWindow(
                   "key:B event-family[event, processing, event, event, event]", new Instant(2000L)),
-              timestampedValueInGlobalWindow(
-                  "key:Y processing-family[Y1, Y2]", new Instant(2100L))));
+              isValueInGlobalWindow("key:Y processing-family[Y1, Y2]", new Instant(2100L))));
 
       mainOutputValues.clear();
 
@@ -2603,227 +2603,64 @@ public void accept(WindowedValue input) throws Exception {
       }
     }
 
-    @Test
-    public void testProcessElementForTruncateAndSizeRestrictionForwardSplitWhenObservingWindows()
-        throws Exception {
-      Pipeline p = Pipeline.create();
-      PCollection<String> valuePCollection = p.apply(Create.of("unused"));
-      PCollectionView<String> singletonSideInputView = valuePCollection.apply(View.asSingleton());
-      WindowObservingTestSplittableDoFn doFn =
-          WindowObservingTestSplittableDoFn.forSplitAtTruncate(singletonSideInputView);
-      valuePCollection
-          .apply(Window.into(SlidingWindows.of(Duration.standardSeconds(1))))
-          .apply(TEST_TRANSFORM_ID, ParDo.of(doFn).withSideInputs(singletonSideInputView));
-
-      RunnerApi.Pipeline pProto =
-          ProtoOverrides.updateTransform(
-              PTransformTranslation.PAR_DO_TRANSFORM_URN,
-              PipelineTranslation.toProto(p, SdkComponents.create(p.getOptions()), true),
-              SplittableParDoExpander.createTruncateReplacement());
-      String expandedTransformId =
-          Iterables.find(
-                  pProto.getComponents().getTransformsMap().entrySet(),
-                  entry ->
-                      entry
-                              .getValue()
-                              .getSpec()
-                              .getUrn()
-                              .equals(
-                                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN)
-                          && entry.getValue().getUniqueName().contains(TEST_TRANSFORM_ID))
-              .getKey();
-      RunnerApi.PTransform pTransform =
-          pProto.getComponents().getTransformsOrThrow(expandedTransformId);
-      String inputPCollectionId =
-          pTransform.getInputsOrThrow(ParDoTranslation.getMainInputName(pTransform));
-      RunnerApi.PCollection inputPCollection =
-          pProto.getComponents().getPcollectionsOrThrow(inputPCollectionId);
-      RehydratedComponents rehydratedComponents =
-          RehydratedComponents.forComponents(pProto.getComponents());
-      Coder<WindowedValue> inputCoder =
-          WindowedValues.getFullCoder(
-              CoderTranslation.fromProto(
-                  pProto.getComponents().getCodersOrThrow(inputPCollection.getCoderId()),
-                  rehydratedComponents,
-                  TranslationContext.DEFAULT),
-              (Coder)
-                  CoderTranslation.fromProto(
-                      pProto
-                          .getComponents()
-                          .getCodersOrThrow(
-                              pProto
-                                  .getComponents()
-                                  .getWindowingStrategiesOrThrow(
-                                      inputPCollection.getWindowingStrategyId())
-                                  .getWindowCoderId()),
-                      rehydratedComponents,
-                      TranslationContext.DEFAULT));
+    /**
+     * A {@link DoFn} that outputs elements with timestamp equal to the input timestamp minus the
+     * input element.
+     */
+    private static class SkewingDoFn extends DoFn<String, String> {
+      private final Duration allowedSkew;
 
-      String outputPCollectionId = Iterables.getOnlyElement(pTransform.getOutputsMap().values());
+      private SkewingDoFn(Duration allowedSkew) {
+        this.allowedSkew = allowedSkew;
+      }
 
-      FakeBeamFnStateClient fakeClient =
-          new FakeBeamFnStateClient(StringUtf8Coder.of(), ImmutableMap.of());
+      @ProcessElement
+      public void processElement(ProcessContext context) {
+        Duration duration = Duration.millis(Long.valueOf(context.element()));
+        context.outputWithTimestamp(context.element(), context.timestamp().minus(duration));
+      }
 
-      PTransformRunnerFactoryTestContext context =
-          PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
-              .beamFnStateClient(fakeClient)
-              .processBundleInstructionId("57")
-              // .pCollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
-              .components(
-                  RunnerApi.Components.newBuilder()
-                      .putAllCoders(pProto.getComponents().getCodersMap())
-                      .putAllWindowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
-                      .putAllEnvironments(Collections.emptyMap())
-                      .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
-                      .build())
-              // .coders(pProto.getComponents().getCodersMap())
-              // .windowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
-              .build();
-      List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues = new ArrayList<>();
-      Coder coder =
-          KvCoder.of(
-              KvCoder.of(
-                  StringUtf8Coder.of(), KvCoder.of(OffsetRange.Coder.of(), InstantCoder.of())),
-              DoubleCoder.of());
-      context.addPCollectionConsumer(
-          outputPCollectionId, (FnDataReceiver) new SplittableFnDataReceiver(mainOutputValues));
+      @Override
+      public Duration getAllowedTimestampSkew() {
+        return allowedSkew;
+      }
+    }
 
-      new FnApiDoFnRunner.Factory<>().addRunnerForPTransform(context);
-      FnDataReceiver<WindowedValue<?>> mainInput =
-          context.getPCollectionConsumer(inputPCollectionId);
-      assertThat(mainInput, instanceOf(HandlesSplits.class));
+    private static class OutputFnDataReceiver implements FnDataReceiver<WindowedValue> {
+      OutputFnDataReceiver(List<WindowedValue<String>> mainOutputValues) {
+        this.mainOutputValues = mainOutputValues;
+      }
 
-      mainOutputValues.clear();
-      BoundedWindow window1 = new IntervalWindow(new Instant(5), new Instant(10));
-      BoundedWindow window2 = new IntervalWindow(new Instant(6), new Instant(11));
-      BoundedWindow window3 = new IntervalWindow(new Instant(7), new Instant(12));
-      // Setup and launch the trySplit thread.
-      ExecutorService executorService = Executors.newSingleThreadExecutor();
-      Future<HandlesSplits.SplitResult> trySplitFuture =
-          executorService.submit(
-              () -> {
-                try {
-                  doFn.waitForSplitElementToBeProcessed();
-                  HandlesSplits.SplitResult result = ((HandlesSplits) mainInput).trySplit(0);
-                  Assert.assertNotNull(result);
-                  return result;
-                } finally {
-                  doFn.trySplitPerformed();
-                }
-              });
-
-      WindowedValue<?> splitValue =
-          valueInWindows(
-              KV.of(
-                  KV.of("7", KV.of(new OffsetRange(0, 6), GlobalWindow.TIMESTAMP_MIN_VALUE)), 6.0),
-              window1,
-              window2,
-              window3);
-      mainInput.accept(splitValue);
-      HandlesSplits.SplitResult trySplitResult = trySplitFuture.get();
+      private final List<WindowedValue<String>> mainOutputValues;
 
-      // We expect that there are outputs from window1 and window2
-      assertThat(
-          mainOutputValues,
-          contains(
-              WindowedValues.of(
-                  KV.of(
-                      KV.of("7", KV.of(new OffsetRange(0, 3), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                      3.0),
-                  splitValue.getTimestamp(),
-                  window1,
-                  splitValue.getPaneInfo()),
-              WindowedValues.of(
-                  KV.of(
-                      KV.of("7", KV.of(new OffsetRange(0, 3), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                      3.0),
-                  splitValue.getTimestamp(),
-                  window2,
-                  splitValue.getPaneInfo())));
-
-      SplitResult expectedElementSplit = createSplitResult(0);
-      BundleApplication expectedElementSplitPrimary =
-          Iterables.getOnlyElement(expectedElementSplit.getPrimaryRoots());
-      ByteStringOutputStream primaryBytes = new ByteStringOutputStream();
-      inputCoder.encode(
-          WindowedValues.of(
-              KV.of(
-                  KV.of("7", KV.of(new OffsetRange(0, 6), GlobalWindow.TIMESTAMP_MIN_VALUE)), 6.0),
-              splitValue.getTimestamp(),
-              window1,
-              splitValue.getPaneInfo()),
-          primaryBytes);
-      BundleApplication expectedWindowedPrimary =
-          BundleApplication.newBuilder()
-              .setElement(primaryBytes.toByteString())
-              .setInputId(ParDoTranslation.getMainInputName(pTransform))
-              .setTransformId(TEST_TRANSFORM_ID)
-              .build();
-      DelayedBundleApplication expectedElementSplitResidual =
-          Iterables.getOnlyElement(expectedElementSplit.getResidualRoots());
-      ByteStringOutputStream residualBytes = new ByteStringOutputStream();
-      inputCoder.encode(
-          WindowedValues.of(
-              KV.of(
-                  KV.of("7", KV.of(new OffsetRange(0, 6), GlobalWindow.TIMESTAMP_MIN_VALUE)), 6.0),
-              splitValue.getTimestamp(),
-              window3,
-              splitValue.getPaneInfo()),
-          residualBytes);
-      DelayedBundleApplication expectedWindowedResidual =
-          DelayedBundleApplication.newBuilder()
-              .setApplication(
-                  BundleApplication.newBuilder()
-                      .setElement(residualBytes.toByteString())
-                      .setInputId(ParDoTranslation.getMainInputName(pTransform))
-                      .setTransformId(TEST_TRANSFORM_ID)
-                      .build())
-              .build();
-      assertThat(
-          trySplitResult.getPrimaryRoots(),
-          contains(expectedWindowedPrimary, expectedElementSplitPrimary));
-      assertThat(
-          trySplitResult.getResidualRoots(),
-          contains(expectedWindowedResidual, expectedElementSplitResidual));
+      @Override
+      public void accept(WindowedValue input) throws Exception {
+        mainOutputValues.add(input);
+      }
     }
 
     @Test
-    public void testProcessElementForTruncateAndSizeRestrictionForwardSplitWithoutObservingWindow()
-        throws Exception {
+    public void testDoFnSkewNotAllowed() throws Exception {
       Pipeline p = Pipeline.create();
-      PCollection<String> valuePCollection = p.apply(Create.of("unused"));
-      valuePCollection.apply(
-          TEST_TRANSFORM_ID, ParDo.of(new NonWindowObservingTestSplittableDoFn()));
+      PCollection<String> valuePCollection = p.apply(Create.of("0", "1"));
+      PCollection<String> outputPCollection =
+          valuePCollection.apply(TEST_TRANSFORM_ID, ParDo.of(new SkewingDoFn(Duration.ZERO)));
 
-      RunnerApi.Pipeline pProto =
-          ProtoOverrides.updateTransform(
-              PTransformTranslation.PAR_DO_TRANSFORM_URN,
-              PipelineTranslation.toProto(p, SdkComponents.create(p.getOptions()), true),
-              SplittableParDoExpander.createTruncateReplacement());
-      String expandedTransformId =
-          Iterables.find(
-                  pProto.getComponents().getTransformsMap().entrySet(),
-                  entry ->
-                      entry
-                              .getValue()
-                              .getSpec()
-                              .getUrn()
-                              .equals(
-                                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN)
-                          && entry.getValue().getUniqueName().contains(TEST_TRANSFORM_ID))
-              .getKey();
+      SdkComponents sdkComponents = SdkComponents.create(p.getOptions());
+      RunnerApi.Pipeline pProto = PipelineTranslation.toProto(p, sdkComponents);
+      String inputPCollectionId = sdkComponents.registerPCollection(valuePCollection);
+      String outputPCollectionId = sdkComponents.registerPCollection(outputPCollection);
       RunnerApi.PTransform pTransform =
-          pProto.getComponents().getTransformsOrThrow(expandedTransformId);
-      String inputPCollectionId =
-          pTransform.getInputsOrThrow(ParDoTranslation.getMainInputName(pTransform));
-      String outputPCollectionId = Iterables.getOnlyElement(pTransform.getOutputsMap().values());
-
-      FakeBeamFnStateClient fakeClient =
-          new FakeBeamFnStateClient(StringUtf8Coder.of(), ImmutableMap.of());
+          pProto
+              .getComponents()
+              .getTransformsOrThrow(
+                  pProto
+                      .getComponents()
+                      .getTransformsOrThrow(TEST_TRANSFORM_ID)
+                      .getSubtransforms(0));
 
       PTransformRunnerFactoryTestContext context =
           PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
-              .beamFnStateClient(fakeClient)
               .processBundleInstructionId("57")
               .components(
                   RunnerApi.Components.newBuilder()
@@ -2833,57 +2670,59 @@ public void testProcessElementForTruncateAndSizeRestrictionForwardSplitWithoutOb
                       .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
                       .build())
               .build();
-      List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues = new ArrayList<>();
-      Coder coder =
-          KvCoder.of(KvCoder.of(StringUtf8Coder.of(), OffsetRange.Coder.of()), DoubleCoder.of());
+      List<WindowedValue<String>> mainOutputValues = new ArrayList<>();
       context.addPCollectionConsumer(
-          outputPCollectionId, (FnDataReceiver) new SplittableFnDataReceiver(mainOutputValues));
+          outputPCollectionId, (FnDataReceiver) new OutputFnDataReceiver(mainOutputValues));
 
       new FnApiDoFnRunner.Factory<>().addRunnerForPTransform(context);
+
+      mainOutputValues.clear();
       FnDataReceiver<WindowedValue<?>> mainInput =
           context.getPCollectionConsumer(inputPCollectionId);
-      assertThat(mainInput, instanceOf(HandlesSplits.class));
+      mainInput.accept(valueInGlobalWindow("0"));
+
+      String message =
+          assertThrows(
+                  UserCodeException.class,
+                  () -> {
+                    mainInput.accept(timestampedValueInGlobalWindow("1", new Instant(0L)));
+                  })
+              .getMessage();
 
-      assertEquals(0.7, ((HandlesSplits) mainInput).getProgress(), 0.0);
-      assertEquals(createSplitResult(0.4), ((HandlesSplits) mainInput).trySplit(0.4));
+      assertThat(
+          message,
+          allOf(
+              containsString(
+                  String.format("timestamp %s", new Instant(0).minus(Duration.millis(1L)))),
+              containsString(
+                  String.format(
+                      "allowed skew (%s)",
+                      PeriodFormat.getDefault().print(Duration.ZERO.toPeriod())))));
     }
 
     @Test
-    public void testProcessElementForTruncateAndSizeRestriction() throws Exception {
+    public void testDoFnSkewAllowed() throws Exception {
       Pipeline p = Pipeline.create();
-      PCollection<String> valuePCollection = p.apply(Create.of("unused"));
-      valuePCollection.apply(
-          TEST_TRANSFORM_ID, ParDo.of(new NonWindowObservingTestSplittableDoFn()));
+      PCollection<String> valuePCollection = p.apply(Create.of("0", "3"));
+      PCollection<String> outputPCollection =
+          valuePCollection.apply(TEST_TRANSFORM_ID, ParDo.of(new SkewingDoFn(Duration.millis(5L))));
 
-      RunnerApi.Pipeline pProto =
-          ProtoOverrides.updateTransform(
-              PTransformTranslation.PAR_DO_TRANSFORM_URN,
-              PipelineTranslation.toProto(p, SdkComponents.create(p.getOptions()), true),
-              SplittableParDoExpander.createTruncateReplacement());
-      String expandedTransformId =
-          Iterables.find(
-                  pProto.getComponents().getTransformsMap().entrySet(),
-                  entry ->
-                      entry
-                              .getValue()
-                              .getSpec()
-                              .getUrn()
-                              .equals(
-                                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN)
-                          && entry.getValue().getUniqueName().contains(TEST_TRANSFORM_ID))
-              .getKey();
+      SdkComponents sdkComponents = SdkComponents.create(p.getOptions());
+      RunnerApi.Pipeline pProto = PipelineTranslation.toProto(p, sdkComponents);
+      String inputPCollectionId = sdkComponents.registerPCollection(valuePCollection);
+      String outputPCollectionId = sdkComponents.registerPCollection(outputPCollection);
       RunnerApi.PTransform pTransform =
-          pProto.getComponents().getTransformsOrThrow(expandedTransformId);
-      String inputPCollectionId =
-          pTransform.getInputsOrThrow(ParDoTranslation.getMainInputName(pTransform));
-      String outputPCollectionId = Iterables.getOnlyElement(pTransform.getOutputsMap().values());
-
-      FakeBeamFnStateClient fakeClient =
-          new FakeBeamFnStateClient(StringUtf8Coder.of(), ImmutableMap.of());
+          pProto
+              .getComponents()
+              .getTransformsOrThrow(
+                  pProto
+                      .getComponents()
+                      .getTransformsOrThrow(TEST_TRANSFORM_ID)
+                      .getSubtransforms(0));
 
+      List<WindowedValue<String>> mainOutputValues = new ArrayList<>();
       PTransformRunnerFactoryTestContext context =
           PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
-              .beamFnStateClient(fakeClient)
               .processBundleInstructionId("57")
               .components(
                   RunnerApi.Components.newBuilder()
@@ -2893,549 +2732,128 @@ public void testProcessElementForTruncateAndSizeRestriction() throws Exception {
                       .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
                       .build())
               .build();
-      List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues = new ArrayList<>();
-      Coder coder =
-          KvCoder.of(
-              KvCoder.of(
-                  StringUtf8Coder.of(), KvCoder.of(OffsetRange.Coder.of(), InstantCoder.of())),
-              DoubleCoder.of());
       context.addPCollectionConsumer(
-          outputPCollectionId, (FnDataReceiver) new SplittableFnDataReceiver(mainOutputValues));
+          outputPCollectionId, (FnDataReceiver) new OutputFnDataReceiver(mainOutputValues));
 
       new FnApiDoFnRunner.Factory<>().addRunnerForPTransform(context);
 
-      assertTrue(context.getStartBundleFunctions().isEmpty());
       mainOutputValues.clear();
 
-      assertThat(
-          context.getPCollectionConsumers().keySet(),
-          containsInAnyOrder(inputPCollectionId, outputPCollectionId));
-
       FnDataReceiver<WindowedValue<?>> mainInput =
           context.getPCollectionConsumer(inputPCollectionId);
-      assertThat(mainInput, instanceOf(HandlesSplits.class));
-
-      mainInput.accept(
-          valueInGlobalWindow(
-              KV.of(
-                  KV.of("5", KV.of(new OffsetRange(0, 5), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                  5.0)));
-      mainInput.accept(
-          valueInGlobalWindow(
-              KV.of(
-                  KV.of("2", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                  2.0)));
-      assertThat(
-          mainOutputValues,
-          contains(
-              valueInGlobalWindow(
-                  KV.of(
-                      KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                      2.0)),
-              valueInGlobalWindow(
-                  KV.of(
-                      KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                      1.0))));
-      mainOutputValues.clear();
-
-      assertTrue(context.getFinishBundleFunctions().isEmpty());
-      assertThat(mainOutputValues, empty());
-
-      Iterables.getOnlyElement(context.getTearDownFunctions()).run();
-      assertThat(mainOutputValues, empty());
+      mainInput.accept(valueInGlobalWindow("0"));
+      mainInput.accept(timestampedValueInGlobalWindow("3", new Instant(0L)));
     }
+  }
 
-    @Test
-    public void testProcessElementForWindowedTruncateAndSizeRestriction() throws Exception {
-      Pipeline p = Pipeline.create();
-      PCollection<String> valuePCollection = p.apply(Create.of("unused"));
-      PCollectionView<String> singletonSideInputView = valuePCollection.apply(View.asSingleton());
-      valuePCollection
-          .apply(Window.into(SlidingWindows.of(Duration.standardSeconds(1))))
-          .apply(
-              TEST_TRANSFORM_ID,
-              ParDo.of(new WindowObservingTestSplittableDoFn(singletonSideInputView))
-                  .withSideInputs(singletonSideInputView));
-
-      RunnerApi.Pipeline pProto =
-          ProtoOverrides.updateTransform(
-              PTransformTranslation.PAR_DO_TRANSFORM_URN,
-              PipelineTranslation.toProto(p, SdkComponents.create(p.getOptions()), true),
-              SplittableParDoExpander.createTruncateReplacement());
-      String expandedTransformId =
-          Iterables.find(
-                  pProto.getComponents().getTransformsMap().entrySet(),
-                  entry ->
-                      entry
-                              .getValue()
-                              .getSpec()
-                              .getUrn()
-                              .equals(
-                                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN)
-                          && entry.getValue().getUniqueName().contains(TEST_TRANSFORM_ID))
-              .getKey();
-      RunnerApi.PTransform pTransform =
-          pProto.getComponents().getTransformsOrThrow(expandedTransformId);
-      String inputPCollectionId =
-          pTransform.getInputsOrThrow(ParDoTranslation.getMainInputName(pTransform));
-      String outputPCollectionId = Iterables.getOnlyElement(pTransform.getOutputsMap().values());
-
-      FakeBeamFnStateClient fakeClient =
-          new FakeBeamFnStateClient(StringUtf8Coder.of(), ImmutableMap.of());
-
-      PTransformRunnerFactoryTestContext context =
-          PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
-              .beamFnStateClient(fakeClient)
-              .processBundleInstructionId("57")
-              .components(
-                  RunnerApi.Components.newBuilder()
-                      .putAllCoders(pProto.getComponents().getCodersMap())
-                      .putAllEnvironments(Collections.emptyMap())
-                      .putAllWindowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
-                      .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
-                      .build())
-              .build();
-      List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues = new ArrayList<>();
-      Coder coder =
-          KvCoder.of(
-              KvCoder.of(
-                  StringUtf8Coder.of(), KvCoder.of(OffsetRange.Coder.of(), InstantCoder.of())),
-              DoubleCoder.of());
-      context.addPCollectionConsumer(
-          outputPCollectionId, (FnDataReceiver) new SplittableFnDataReceiver(mainOutputValues));
-
-      new FnApiDoFnRunner.Factory<>().addRunnerForPTransform(context);
-
-      assertTrue(context.getStartBundleFunctions().isEmpty());
-      mainOutputValues.clear();
-
-      assertThat(
-          context.getPCollectionConsumers().keySet(),
-          containsInAnyOrder(inputPCollectionId, outputPCollectionId));
+  @RunWith(JUnit4.class)
+  public static class SplitTest {
+    @Rule public final ExpectedException expected = ExpectedException.none();
+    private IntervalWindow window1;
+    private IntervalWindow window2;
+    private IntervalWindow window3;
+    private WindowedValue<String> currentElement;
+    private OffsetRange currentRestriction;
+    private Instant currentWatermarkEstimatorState;
+    private Instant initialWatermark;
+    KV<Instant, Instant> watermarkAndState;
 
-      FnDataReceiver<WindowedValue<?>> mainInput =
-          context.getPCollectionConsumer(inputPCollectionId);
-      assertThat(mainInput, instanceOf(HandlesSplits.class));
+    private static final String PROCESS_TRANSFORM_ID = "processPTransformId";
+    private static final String TRUNCATE_TRANSFORM_ID = "truncatePTransformId";
+    private static final String PROCESS_INPUT_ID = "processInputId";
+    private static final String TRUNCATE_INPUT_ID = "truncateInputId";
+    private static final String PROCESS_OUTPUT_ID = "processOutputId";
 
-      IntervalWindow window1 = new IntervalWindow(new Instant(5), new Instant(10));
-      IntervalWindow window2 = new IntervalWindow(new Instant(6), new Instant(11));
-      WindowedValue<?> firstValue =
-          valueInWindows(
+    private KV<WindowedValue, WindowedValue> createSplitInWindow(
+        OffsetRange primaryRestriction, OffsetRange residualRestriction, BoundedWindow window) {
+      return KV.of(
+          WindowedValues.of(
               KV.of(
-                  KV.of("5", KV.of(new OffsetRange(0, 5), GlobalWindow.TIMESTAMP_MIN_VALUE)), 5.0),
-              window1,
-              window2);
-      WindowedValue<?> secondValue =
-          valueInWindows(
+                  currentElement.getValue(),
+                  KV.of(primaryRestriction, currentWatermarkEstimatorState)),
+              currentElement.getTimestamp(),
+              window,
+              currentElement.getPaneInfo()),
+          WindowedValues.of(
               KV.of(
-                  KV.of("2", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)), 2.0),
-              window1,
-              window2);
-      mainInput.accept(firstValue);
-      mainInput.accept(secondValue);
-      assertThat(
-          mainOutputValues,
-          contains(
-              WindowedValues.of(
-                  KV.of(
-                      KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                      2.0),
-                  firstValue.getTimestamp(),
-                  window1,
-                  firstValue.getPaneInfo()),
-              WindowedValues.of(
-                  KV.of(
-                      KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                      2.0),
-                  firstValue.getTimestamp(),
-                  window2,
-                  firstValue.getPaneInfo()),
-              WindowedValues.of(
+                  currentElement.getValue(),
+                  KV.of(residualRestriction, watermarkAndState.getValue())),
+              currentElement.getTimestamp(),
+              window,
+              currentElement.getPaneInfo()));
+    }
+
+    private KV<WindowedValue, WindowedValue> createSplitAcrossWindows(
+        List<BoundedWindow> primaryWindows, List<BoundedWindow> residualWindows) {
+      return KV.of(
+          primaryWindows.isEmpty()
+              ? null
+              : WindowedValues.of(
                   KV.of(
-                      KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                      1.0),
-                  firstValue.getTimestamp(),
-                  window1,
-                  firstValue.getPaneInfo()),
-              WindowedValues.of(
+                      currentElement.getValue(),
+                      KV.of(currentRestriction, currentWatermarkEstimatorState)),
+                  currentElement.getTimestamp(),
+                  primaryWindows,
+                  currentElement.getPaneInfo()),
+          residualWindows.isEmpty()
+              ? null
+              : WindowedValues.of(
                   KV.of(
-                      KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                      1.0),
-                  firstValue.getTimestamp(),
-                  window2,
-                  firstValue.getPaneInfo())));
-      mainOutputValues.clear();
-
-      assertTrue(context.getFinishBundleFunctions().isEmpty());
-      assertThat(mainOutputValues, empty());
-
-      Iterables.getOnlyElement(context.getTearDownFunctions()).run();
-      assertThat(mainOutputValues, empty());
+                      currentElement.getValue(),
+                      KV.of(currentRestriction, currentWatermarkEstimatorState)),
+                  currentElement.getTimestamp(),
+                  residualWindows,
+                  currentElement.getPaneInfo()));
     }
 
-    @Test
-    public void
-        testProcessElementForWindowedTruncateAndSizeRestrictionWithNonWindowObservingOptimization()
-            throws Exception {
-      Pipeline p = Pipeline.create();
-      PCollection<String> valuePCollection = p.apply(Create.of("unused"));
-      valuePCollection
-          .apply(Window.into(SlidingWindows.of(Duration.standardSeconds(1))))
-          .apply(TEST_TRANSFORM_ID, ParDo.of(new NonWindowObservingTestSplittableDoFn()));
-
-      RunnerApi.Pipeline pProto =
-          ProtoOverrides.updateTransform(
-              PTransformTranslation.PAR_DO_TRANSFORM_URN,
-              PipelineTranslation.toProto(p, SdkComponents.create(p.getOptions()), true),
-              SplittableParDoExpander.createTruncateReplacement());
-      String expandedTransformId =
-          Iterables.find(
-                  pProto.getComponents().getTransformsMap().entrySet(),
-                  entry ->
-                      entry
-                              .getValue()
-                              .getSpec()
-                              .getUrn()
-                              .equals(
-                                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN)
-                          && entry.getValue().getUniqueName().contains(TEST_TRANSFORM_ID))
-              .getKey();
-      RunnerApi.PTransform pTransform =
-          pProto.getComponents().getTransformsOrThrow(expandedTransformId);
-      String inputPCollectionId =
-          pTransform.getInputsOrThrow(ParDoTranslation.getMainInputName(pTransform));
-      String outputPCollectionId = Iterables.getOnlyElement(pTransform.getOutputsMap().values());
-
-      PTransformRunnerFactoryTestContext context =
-          PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
-              .processBundleInstructionId("57")
-              .components(
-                  RunnerApi.Components.newBuilder()
-                      .putAllCoders(pProto.getComponents().getCodersMap())
-                      .putAllEnvironments(Collections.emptyMap())
-                      .putAllWindowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
-                      .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
-                      .build())
-              .build();
-      List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues = new ArrayList<>();
-      Coder coder =
-          KvCoder.of(
-              KvCoder.of(
-                  StringUtf8Coder.of(), KvCoder.of(OffsetRange.Coder.of(), InstantCoder.of())),
-              DoubleCoder.of());
-      context.addPCollectionConsumer(
-          outputPCollectionId, (FnDataReceiver) new SplittableFnDataReceiver(mainOutputValues));
-
-      new FnApiDoFnRunner.Factory<>().addRunnerForPTransform(context);
-
-      assertTrue(context.getStartBundleFunctions().isEmpty());
-      mainOutputValues.clear();
-
-      assertThat(
-          context.getPCollectionConsumers().keySet(),
-          containsInAnyOrder(inputPCollectionId, outputPCollectionId));
-
-      FnDataReceiver<WindowedValue<?>> mainInput =
-          context.getPCollectionConsumer(inputPCollectionId);
-      assertThat(mainInput, instanceOf(HandlesSplits.class));
-
-      IntervalWindow window1 = new IntervalWindow(new Instant(5), new Instant(10));
-      IntervalWindow window2 = new IntervalWindow(new Instant(6), new Instant(11));
-      WindowedValue<?> firstValue =
-          valueInWindows(
-              KV.of(
-                  KV.of("5", KV.of(new OffsetRange(0, 5), GlobalWindow.TIMESTAMP_MIN_VALUE)), 5.0),
-              window1,
-              window2);
-      WindowedValue<?> secondValue =
-          valueInWindows(
+    private KV<WindowedValue, WindowedValue> createSplitWithSizeInWindow(
+        OffsetRange primaryRestriction, OffsetRange residualRestriction, BoundedWindow window) {
+      return KV.of(
+          WindowedValues.of(
               KV.of(
-                  KV.of("2", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)), 2.0),
-              window1,
-              window2);
-      mainInput.accept(firstValue);
-      mainInput.accept(secondValue);
-      // Ensure that each output element is in all the windows and not one per window.
-      assertThat(
-          mainOutputValues,
-          contains(
-              WindowedValues.of(
                   KV.of(
-                      KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                      2.0),
-                  firstValue.getTimestamp(),
-                  ImmutableList.of(window1, window2),
-                  firstValue.getPaneInfo()),
-              WindowedValues.of(
+                      currentElement.getValue(),
+                      KV.of(primaryRestriction, currentWatermarkEstimatorState)),
+                  (double) (primaryRestriction.getTo() - primaryRestriction.getFrom())),
+              currentElement.getTimestamp(),
+              window,
+              currentElement.getPaneInfo()),
+          WindowedValues.of(
+              KV.of(
                   KV.of(
-                      KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
-                      1.0),
-                  firstValue.getTimestamp(),
-                  ImmutableList.of(window1, window2),
-                  firstValue.getPaneInfo())));
-      mainOutputValues.clear();
-
-      assertTrue(context.getFinishBundleFunctions().isEmpty());
-      assertThat(mainOutputValues, empty());
-
-      Iterables.getOnlyElement(context.getTearDownFunctions()).run();
-      assertThat(mainOutputValues, empty());
+                      currentElement.getValue(),
+                      KV.of(residualRestriction, watermarkAndState.getValue())),
+                  (double) (residualRestriction.getTo() - residualRestriction.getFrom())),
+              currentElement.getTimestamp(),
+              window,
+              currentElement.getPaneInfo()));
     }
 
-    /**
-     * A {@link DoFn} that outputs elements with timestamp equal to the input timestamp minus the
-     * input element.
-     */
-    private static class SkewingDoFn extends DoFn<String, String> {
-      private final Duration allowedSkew;
-
-      private SkewingDoFn(Duration allowedSkew) {
-        this.allowedSkew = allowedSkew;
-      }
-
-      @ProcessElement
-      public void processElement(ProcessContext context) {
-        Duration duration = Duration.millis(Long.valueOf(context.element()));
-        context.outputWithTimestamp(context.element(), context.timestamp().minus(duration));
-      }
-
-      @Override
-      public Duration getAllowedTimestampSkew() {
-        return allowedSkew;
-      }
-    }
-
-    private static class OutputFnDataReceiver implements FnDataReceiver<WindowedValue> {
-      OutputFnDataReceiver(List<WindowedValue<String>> mainOutputValues) {
-        this.mainOutputValues = mainOutputValues;
-      }
-
-      private final List<WindowedValue<String>> mainOutputValues;
-
-      @Override
-      public void accept(WindowedValue input) throws Exception {
-        mainOutputValues.add(input);
-      }
-    }
-
-    @Test
-    public void testDoFnSkewNotAllowed() throws Exception {
-      Pipeline p = Pipeline.create();
-      PCollection<String> valuePCollection = p.apply(Create.of("0", "1"));
-      PCollection<String> outputPCollection =
-          valuePCollection.apply(TEST_TRANSFORM_ID, ParDo.of(new SkewingDoFn(Duration.ZERO)));
-
-      SdkComponents sdkComponents = SdkComponents.create(p.getOptions());
-      RunnerApi.Pipeline pProto = PipelineTranslation.toProto(p, sdkComponents);
-      String inputPCollectionId = sdkComponents.registerPCollection(valuePCollection);
-      String outputPCollectionId = sdkComponents.registerPCollection(outputPCollection);
-      RunnerApi.PTransform pTransform =
-          pProto
-              .getComponents()
-              .getTransformsOrThrow(
-                  pProto
-                      .getComponents()
-                      .getTransformsOrThrow(TEST_TRANSFORM_ID)
-                      .getSubtransforms(0));
-
-      PTransformRunnerFactoryTestContext context =
-          PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
-              .processBundleInstructionId("57")
-              .components(
-                  RunnerApi.Components.newBuilder()
-                      .putAllCoders(pProto.getComponents().getCodersMap())
-                      .putAllEnvironments(Collections.emptyMap())
-                      .putAllWindowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
-                      .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
-                      .build())
-              .build();
-      List<WindowedValue<String>> mainOutputValues = new ArrayList<>();
-      Coder coder = StringUtf8Coder.of();
-      context.addPCollectionConsumer(
-          outputPCollectionId, (FnDataReceiver) new OutputFnDataReceiver(mainOutputValues));
-
-      new FnApiDoFnRunner.Factory<>().addRunnerForPTransform(context);
-
-      mainOutputValues.clear();
-      FnDataReceiver<WindowedValue<?>> mainInput =
-          context.getPCollectionConsumer(inputPCollectionId);
-      mainInput.accept(valueInGlobalWindow("0"));
-
-      String message =
-          assertThrows(
-                  UserCodeException.class,
-                  () -> {
-                    mainInput.accept(timestampedValueInGlobalWindow("1", new Instant(0L)));
-                  })
-              .getMessage();
-
-      assertThat(
-          message,
-          allOf(
-              containsString(
-                  String.format("timestamp %s", new Instant(0).minus(Duration.millis(1L)))),
-              containsString(
-                  String.format(
-                      "allowed skew (%s)",
-                      PeriodFormat.getDefault().print(Duration.ZERO.toPeriod())))));
-    }
-
-    @Test
-    public void testDoFnSkewAllowed() throws Exception {
-      Pipeline p = Pipeline.create();
-      PCollection<String> valuePCollection = p.apply(Create.of("0", "3"));
-      PCollection<String> outputPCollection =
-          valuePCollection.apply(TEST_TRANSFORM_ID, ParDo.of(new SkewingDoFn(Duration.millis(5L))));
-
-      SdkComponents sdkComponents = SdkComponents.create(p.getOptions());
-      RunnerApi.Pipeline pProto = PipelineTranslation.toProto(p, sdkComponents);
-      String inputPCollectionId = sdkComponents.registerPCollection(valuePCollection);
-      String outputPCollectionId = sdkComponents.registerPCollection(outputPCollection);
-      RunnerApi.PTransform pTransform =
-          pProto
-              .getComponents()
-              .getTransformsOrThrow(
-                  pProto
-                      .getComponents()
-                      .getTransformsOrThrow(TEST_TRANSFORM_ID)
-                      .getSubtransforms(0));
-
-      List<WindowedValue<String>> mainOutputValues = new ArrayList<>();
-      PTransformRunnerFactoryTestContext context =
-          PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
-              .processBundleInstructionId("57")
-              .components(
-                  RunnerApi.Components.newBuilder()
-                      .putAllCoders(pProto.getComponents().getCodersMap())
-                      .putAllEnvironments(Collections.emptyMap())
-                      .putAllWindowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
-                      .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
-                      .build())
-              .build();
-      Coder coder = StringUtf8Coder.of();
-      context.addPCollectionConsumer(
-          outputPCollectionId, (FnDataReceiver) new OutputFnDataReceiver(mainOutputValues));
-
-      new FnApiDoFnRunner.Factory<>().addRunnerForPTransform(context);
-
-      mainOutputValues.clear();
-
-      FnDataReceiver<WindowedValue<?>> mainInput =
-          context.getPCollectionConsumer(inputPCollectionId);
-      mainInput.accept(valueInGlobalWindow("0"));
-      mainInput.accept(timestampedValueInGlobalWindow("3", new Instant(0L)));
-    }
-  }
-
-  @RunWith(JUnit4.class)
-  public static class SplitTest {
-    @Rule public final ExpectedException expected = ExpectedException.none();
-    private IntervalWindow window1;
-    private IntervalWindow window2;
-    private IntervalWindow window3;
-    private WindowedValue<String> currentElement;
-    private OffsetRange currentRestriction;
-    private Instant currentWatermarkEstimatorState;
-    private Instant initialWatermark;
-    KV<Instant, Instant> watermarkAndState;
-
-    private static final String PROCESS_TRANSFORM_ID = "processPTransformId";
-    private static final String TRUNCATE_TRANSFORM_ID = "truncatePTransformId";
-    private static final String PROCESS_INPUT_ID = "processInputId";
-    private static final String TRUNCATE_INPUT_ID = "truncateInputId";
-    private static final String PROCESS_OUTPUT_ID = "processOutputId";
-    private static final String TRUNCATE_OUTPUT_ID = "truncateOutputId";
-
-    private KV<WindowedValue, WindowedValue> createSplitInWindow(
-        OffsetRange primaryRestriction, OffsetRange residualRestriction, BoundedWindow window) {
-      return KV.of(
-          WindowedValues.of(
-              KV.of(
-                  currentElement.getValue(),
-                  KV.of(primaryRestriction, currentWatermarkEstimatorState)),
-              currentElement.getTimestamp(),
-              window,
-              currentElement.getPaneInfo()),
-          WindowedValues.of(
-              KV.of(
-                  currentElement.getValue(),
-                  KV.of(residualRestriction, watermarkAndState.getValue())),
-              currentElement.getTimestamp(),
-              window,
-              currentElement.getPaneInfo()));
-    }
-
-    private KV<WindowedValue, WindowedValue> createSplitAcrossWindows(
-        List<BoundedWindow> primaryWindows, List<BoundedWindow> residualWindows) {
-      return KV.of(
-          primaryWindows.isEmpty()
-              ? null
-              : WindowedValues.of(
-                  KV.of(
-                      currentElement.getValue(),
-                      KV.of(currentRestriction, currentWatermarkEstimatorState)),
-                  currentElement.getTimestamp(),
-                  primaryWindows,
-                  currentElement.getPaneInfo()),
-          residualWindows.isEmpty()
-              ? null
-              : WindowedValues.of(
-                  KV.of(
-                      currentElement.getValue(),
-                      KV.of(currentRestriction, currentWatermarkEstimatorState)),
-                  currentElement.getTimestamp(),
-                  residualWindows,
-                  currentElement.getPaneInfo()));
-    }
-
-    private KV<WindowedValue, WindowedValue> createSplitWithSizeInWindow(
-        OffsetRange primaryRestriction, OffsetRange residualRestriction, BoundedWindow window) {
-      return KV.of(
-          WindowedValues.of(
-              KV.of(
-                  KV.of(
-                      currentElement.getValue(),
-                      KV.of(primaryRestriction, currentWatermarkEstimatorState)),
-                  (double) (primaryRestriction.getTo() - primaryRestriction.getFrom())),
-              currentElement.getTimestamp(),
-              window,
-              currentElement.getPaneInfo()),
-          WindowedValues.of(
-              KV.of(
-                  KV.of(
-                      currentElement.getValue(),
-                      KV.of(residualRestriction, watermarkAndState.getValue())),
-                  (double) (residualRestriction.getTo() - residualRestriction.getFrom())),
-              currentElement.getTimestamp(),
-              window,
-              currentElement.getPaneInfo()));
-    }
-
-    private KV<WindowedValue, WindowedValue> createSplitWithSizeAcrossWindows(
-        List<BoundedWindow> primaryWindows, List<BoundedWindow> residualWindows) {
-      return KV.of(
-          primaryWindows.isEmpty()
-              ? null
-              : WindowedValues.of(
-                  KV.of(
-                      KV.of(
-                          currentElement.getValue(),
-                          KV.of(currentRestriction, currentWatermarkEstimatorState)),
-                      (double) (currentRestriction.getTo() - currentRestriction.getFrom())),
-                  currentElement.getTimestamp(),
-                  primaryWindows,
-                  currentElement.getPaneInfo()),
-          residualWindows.isEmpty()
-              ? null
-              : WindowedValues.of(
-                  KV.of(
-                      KV.of(
-                          currentElement.getValue(),
-                          KV.of(currentRestriction, currentWatermarkEstimatorState)),
-                      (double) (currentRestriction.getTo() - currentRestriction.getFrom())),
-                  currentElement.getTimestamp(),
-                  residualWindows,
-                  currentElement.getPaneInfo()));
+    private KV<WindowedValue, WindowedValue> createSplitWithSizeAcrossWindows(
+        List<BoundedWindow> primaryWindows, List<BoundedWindow> residualWindows) {
+      return KV.of(
+          primaryWindows.isEmpty()
+              ? null
+              : WindowedValues.of(
+                  KV.of(
+                      KV.of(
+                          currentElement.getValue(),
+                          KV.of(currentRestriction, currentWatermarkEstimatorState)),
+                      (double) (currentRestriction.getTo() - currentRestriction.getFrom())),
+                  currentElement.getTimestamp(),
+                  primaryWindows,
+                  currentElement.getPaneInfo()),
+          residualWindows.isEmpty()
+              ? null
+              : WindowedValues.of(
+                  KV.of(
+                      KV.of(
+                          currentElement.getValue(),
+                          KV.of(currentRestriction, currentWatermarkEstimatorState)),
+                      (double) (currentRestriction.getTo() - currentRestriction.getFrom())),
+                  currentElement.getTimestamp(),
+                  residualWindows,
+                  currentElement.getPaneInfo()));
     }
 
     @Before
@@ -3454,442 +2872,83 @@ public void setUp() {
       initialWatermark = Instant.ofEpochMilli(25);
       watermarkAndState = KV.of(Instant.ofEpochMilli(42), Instant.ofEpochMilli(42));
     }
-
-    @Test
-    public void testScaledProgress() throws Exception {
-      Progress elementProgress = Progress.from(2, 8);
-      // There is only one window.
-      Progress scaledResult = FnApiDoFnRunner.scaleProgress(elementProgress, 0, 1);
-      assertEquals(2, scaledResult.getWorkCompleted(), 0.0);
-      assertEquals(8, scaledResult.getWorkRemaining(), 0.0);
-
-      // We are at the first window of 3 in total.
-      scaledResult = FnApiDoFnRunner.scaleProgress(elementProgress, 0, 3);
-      assertEquals(2, scaledResult.getWorkCompleted(), 0.0);
-      assertEquals(28, scaledResult.getWorkRemaining(), 0.0);
-
-      // We are at the second window of 3 in total.
-      scaledResult = FnApiDoFnRunner.scaleProgress(elementProgress, 1, 3);
-      assertEquals(12, scaledResult.getWorkCompleted(), 0.0);
-      assertEquals(18, scaledResult.getWorkRemaining(), 0.0);
-
-      // We are at the last window of 3 in total.
-      scaledResult = FnApiDoFnRunner.scaleProgress(elementProgress, 2, 3);
-      assertEquals(22, scaledResult.getWorkCompleted(), 0.0);
-      assertEquals(8, scaledResult.getWorkRemaining(), 0.0);
-    }
-
-    @Test
-    public void testComputeSplitForProcessOrTruncateWithNullTrackerAndSplitDelegate()
-        throws Exception {
-      expected.expect(IllegalArgumentException.class);
-      FnApiDoFnRunner.computeSplitForProcessOrTruncate(
-          currentElement,
-          currentRestriction,
-          window1,
-          ImmutableList.copyOf(currentElement.getWindows()),
-          currentWatermarkEstimatorState,
-          0.0,
-          null,
-          null,
-          null,
-          0,
-          3);
-    }
-
-    @Test
-    public void testComputeSplitForProcessOrTruncateWithNotNullTrackerAndDelegate()
-        throws Exception {
-      expected.expect(IllegalArgumentException.class);
-      FnApiDoFnRunner.computeSplitForProcessOrTruncate(
-          currentElement,
-          currentRestriction,
-          window1,
-          ImmutableList.copyOf(currentElement.getWindows()),
-          currentWatermarkEstimatorState,
-          0.0,
-          new OffsetRangeTracker(currentRestriction),
-          createSplitDelegate(0.3, 0.0, null),
-          null,
-          0,
-          3);
-    }
-
-    @Test
-    public void testComputeSplitForProcessOrTruncateWithInvalidWatermarkAndState()
-        throws Exception {
-      expected.expect(NullPointerException.class);
-      FnApiDoFnRunner.computeSplitForProcessOrTruncate(
-          currentElement,
-          currentRestriction,
-          window1,
-          ImmutableList.copyOf(currentElement.getWindows()),
-          currentWatermarkEstimatorState,
-          0.0,
-          new OffsetRangeTracker(currentRestriction),
-          null,
-          null,
-          0,
-          3);
-    }
-
-    @Test
-    public void testTrySplitForProcessCheckpointOnFirstWindow() throws Exception {
-      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
-      tracker.tryClaim(30L);
-      SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.<Instant>computeSplitForProcessOrTruncate(
-              currentElement,
-              currentRestriction,
-              window1,
-              windows,
-              currentWatermarkEstimatorState,
-              0.0,
-              tracker,
-              null,
-              watermarkAndState,
-              0,
-              3);
-      assertEquals(1, result.getNewWindowStopIndex());
-      KV<WindowedValue, WindowedValue> expectedElementSplit =
-          createSplitInWindow(new OffsetRange(0, 31), new OffsetRange(31, 100), window1);
-      KV<WindowedValue, WindowedValue> expectedWindowSplit =
-          createSplitAcrossWindows(ImmutableList.of(), ImmutableList.of(window2, window3));
-      assertEquals(expectedElementSplit.getKey(), result.getWindowSplit().getPrimarySplitRoot());
-      assertEquals(expectedElementSplit.getValue(), result.getWindowSplit().getResidualSplitRoot());
-      assertEquals(
-          expectedWindowSplit.getKey(),
-          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
-      assertEquals(
-          expectedWindowSplit.getValue(),
-          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
-    }
-
-    @Test
-    public void testTrySplitForProcessCheckpointOnFirstWindowAfterOneSplit() throws Exception {
-      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
-      tracker.tryClaim(30L);
-      SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.<Instant>computeSplitForProcessOrTruncate(
-              currentElement,
-              currentRestriction,
-              window1,
-              windows,
-              currentWatermarkEstimatorState,
-              0.0,
-              tracker,
-              null,
-              watermarkAndState,
-              0,
-              2);
-      assertEquals(1, result.getNewWindowStopIndex());
-      KV<WindowedValue, WindowedValue> expectedElementSplit =
-          createSplitInWindow(new OffsetRange(0, 31), new OffsetRange(31, 100), window1);
-      KV<WindowedValue, WindowedValue> expectedWindowSplit =
-          createSplitAcrossWindows(ImmutableList.of(), ImmutableList.of(window2));
-      assertEquals(expectedElementSplit.getKey(), result.getWindowSplit().getPrimarySplitRoot());
-      assertEquals(expectedElementSplit.getValue(), result.getWindowSplit().getResidualSplitRoot());
-      assertEquals(
-          expectedWindowSplit.getKey(),
-          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
-      assertEquals(
-          expectedWindowSplit.getValue(),
-          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
-    }
-
-    @Test
-    public void testTrySplitForProcessSplitOnFirstWindow() throws Exception {
-      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
-      tracker.tryClaim(30L);
-      SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.<Instant>computeSplitForProcessOrTruncate(
-              currentElement,
-              currentRestriction,
-              window1,
-              windows,
-              currentWatermarkEstimatorState,
-              0.2,
-              tracker,
-              null,
-              watermarkAndState,
-              0,
-              3);
-      assertEquals(1, result.getNewWindowStopIndex());
-      KV<WindowedValue, WindowedValue> expectedElementSplit =
-          createSplitInWindow(new OffsetRange(0, 84), new OffsetRange(84, 100), window1);
-      KV<WindowedValue, WindowedValue> expectedWindowSplit =
-          createSplitAcrossWindows(ImmutableList.of(), ImmutableList.of(window2, window3));
-      assertEquals(expectedElementSplit.getKey(), result.getWindowSplit().getPrimarySplitRoot());
-      assertEquals(expectedElementSplit.getValue(), result.getWindowSplit().getResidualSplitRoot());
-      assertEquals(
-          expectedWindowSplit.getKey(),
-          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
-      assertEquals(
-          expectedWindowSplit.getValue(),
-          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
-    }
-
-    @Test
-    public void testTrySplitForProcessSplitOnMiddleWindow() throws Exception {
-      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
-      tracker.tryClaim(30L);
-      SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
-              currentElement,
-              currentRestriction,
-              window2,
-              windows,
-              currentWatermarkEstimatorState,
-              0.2,
-              tracker,
-              null,
-              watermarkAndState,
-              1,
-              3);
-      assertEquals(2, result.getNewWindowStopIndex());
-      // Java uses BigDecimal so 0.2 * 170 = 63.9...
-      // BigDecimal.longValue() will round down to 63 instead of the expected 64
-      KV<WindowedValue, WindowedValue> expectedElementSplit =
-          createSplitInWindow(new OffsetRange(0, 63), new OffsetRange(63, 100), window2);
-      KV<WindowedValue, WindowedValue> expectedWindowSplit =
-          createSplitAcrossWindows(ImmutableList.of(window1), ImmutableList.of(window3));
-      assertEquals(expectedElementSplit.getKey(), result.getWindowSplit().getPrimarySplitRoot());
-      assertEquals(expectedElementSplit.getValue(), result.getWindowSplit().getResidualSplitRoot());
-      assertEquals(
-          expectedWindowSplit.getKey(),
-          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
-      assertEquals(
-          expectedWindowSplit.getValue(),
-          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
-    }
-
-    @Test
-    public void testTrySplitForProcessSplitOnLastWindow() throws Exception {
-      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
-      tracker.tryClaim(30L);
-      SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
-              currentElement,
-              currentRestriction,
-              window3,
-              windows,
-              currentWatermarkEstimatorState,
-              0.2,
-              tracker,
-              null,
-              watermarkAndState,
-              2,
-              3);
-      assertEquals(3, result.getNewWindowStopIndex());
-      KV<WindowedValue, WindowedValue> expectedElementSplit =
-          createSplitInWindow(new OffsetRange(0, 44), new OffsetRange(44, 100), window3);
-      KV<WindowedValue, WindowedValue> expectedWindowSplit =
-          createSplitAcrossWindows(ImmutableList.of(window1, window2), ImmutableList.of());
-      assertEquals(expectedElementSplit.getKey(), result.getWindowSplit().getPrimarySplitRoot());
-      assertEquals(expectedElementSplit.getValue(), result.getWindowSplit().getResidualSplitRoot());
-      assertEquals(
-          expectedWindowSplit.getKey(),
-          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
-      assertEquals(
-          expectedWindowSplit.getValue(),
-          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
-    }
-
-    @Test
-    public void testTrySplitForProcessSplitOnFirstWindowFallback() throws Exception {
-      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
-      tracker.tryClaim(100L);
-      assertNull(tracker.trySplit(0.0));
-      SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
-              currentElement,
-              currentRestriction,
-              window3,
-              windows,
-              currentWatermarkEstimatorState,
-              0,
-              tracker,
-              null,
-              watermarkAndState,
-              0,
-              3);
-      assertEquals(1, result.getNewWindowStopIndex());
-      KV<WindowedValue, WindowedValue> expectedWindowSplit =
-          createSplitAcrossWindows(ImmutableList.of(window1), ImmutableList.of(window2, window3));
-      assertNull(result.getWindowSplit().getPrimarySplitRoot());
-      assertNull(result.getWindowSplit().getResidualSplitRoot());
-      assertEquals(
-          expectedWindowSplit.getKey(),
-          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
-      assertEquals(
-          expectedWindowSplit.getValue(),
-          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
-    }
-
-    @Test
-    public void testTrySplitForProcessSplitOnLastWindowWhenNoElementSplit() throws Exception {
-      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
-      tracker.tryClaim(100L);
-      assertNull(tracker.trySplit(0.0));
-      SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
-              currentElement,
-              currentRestriction,
-              window3,
-              windows,
-              currentWatermarkEstimatorState,
-              0,
-              tracker,
-              null,
-              watermarkAndState,
-              2,
-              3);
-      assertNull(result);
-    }
-
-    @Test
-    public void testTrySplitForProcessOnWindowBoundaryRoundUp() throws Exception {
-      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
-      tracker.tryClaim(30L);
-      SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
-              currentElement,
-              currentRestriction,
-              window2,
-              windows,
-              currentWatermarkEstimatorState,
-              0.6,
-              tracker,
-              null,
-              watermarkAndState,
-              0,
-              3);
-      assertEquals(2, result.getNewWindowStopIndex());
-      KV<WindowedValue, WindowedValue> expectedWindowSplit =
-          createSplitAcrossWindows(ImmutableList.of(window1, window2), ImmutableList.of(window3));
-      assertNull(result.getWindowSplit().getPrimarySplitRoot());
-      assertNull(result.getWindowSplit().getResidualSplitRoot());
-      assertEquals(
-          expectedWindowSplit.getKey(),
-          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
-      assertEquals(
-          expectedWindowSplit.getValue(),
-          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
-    }
-
-    @Test
-    public void testTrySplitForProcessOnWindowBoundaryRoundDown() throws Exception {
-      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
-      tracker.tryClaim(30L);
-      SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
-              currentElement,
-              currentRestriction,
-              window2,
-              windows,
-              currentWatermarkEstimatorState,
-              0.3,
-              tracker,
-              null,
-              watermarkAndState,
-              0,
-              3);
-      assertEquals(1, result.getNewWindowStopIndex());
-      KV<WindowedValue, WindowedValue> expectedWindowSplit =
-          createSplitAcrossWindows(ImmutableList.of(window1), ImmutableList.of(window2, window3));
-      assertNull(result.getWindowSplit().getPrimarySplitRoot());
-      assertNull(result.getWindowSplit().getResidualSplitRoot());
-      assertEquals(
-          expectedWindowSplit.getKey(),
-          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
-      assertEquals(
-          expectedWindowSplit.getValue(),
-          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
-    }
-
-    @Test
-    public void testTrySplitForProcessOnWindowBoundaryRoundDownOnLastWindow() throws Exception {
-      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
-      tracker.tryClaim(30L);
-      SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
-              currentElement,
-              currentRestriction,
-              window2,
-              windows,
-              currentWatermarkEstimatorState,
-              0.9,
-              tracker,
-              null,
-              watermarkAndState,
-              0,
-              3);
-      assertEquals(2, result.getNewWindowStopIndex());
-      KV<WindowedValue, WindowedValue> expectedWindowSplit =
-          createSplitAcrossWindows(ImmutableList.of(window1, window2), ImmutableList.of(window3));
-      assertNull(result.getWindowSplit().getPrimarySplitRoot());
-      assertNull(result.getWindowSplit().getResidualSplitRoot());
-      assertEquals(
-          expectedWindowSplit.getKey(),
-          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
-      assertEquals(
-          expectedWindowSplit.getValue(),
-          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
+
+    @Test
+    public void testComputeSplitForProcessWithNullTrackerAndSplitDelegate() throws Exception {
+      expected.expect(IllegalArgumentException.class);
+      FnApiDoFnRunner.computeSplitForProcess(
+          currentElement,
+          currentRestriction,
+          window1,
+          ImmutableList.copyOf(currentElement.getWindows()),
+          currentWatermarkEstimatorState,
+          0.0,
+          null,
+          null,
+          null,
+          0,
+          3);
     }
 
-    private HandlesSplits createSplitDelegate(
-        double progress, double expectedFraction, HandlesSplits.SplitResult result) {
-      return new HandlesSplits() {
-        @Override
-        public SplitResult trySplit(double fractionOfRemainder) {
-          checkArgument(fractionOfRemainder == expectedFraction);
-          return result;
-        }
+    @Test
+    public void testComputeSplitForProcessWithNotNullTrackerAndDelegate() throws Exception {
+      expected.expect(IllegalArgumentException.class);
+      FnApiDoFnRunner.computeSplitForProcess(
+          currentElement,
+          currentRestriction,
+          window1,
+          ImmutableList.copyOf(currentElement.getWindows()),
+          currentWatermarkEstimatorState,
+          0.0,
+          new OffsetRangeTracker(currentRestriction),
+          createSplitDelegate(0.3, 0.0, null),
+          null,
+          0,
+          3);
+    }
 
-        @Override
-        public double getProgress() {
-          return progress;
-        }
-      };
+    @Test
+    public void testComputeSplitForProcessWithInvalidWatermarkAndState() throws Exception {
+      expected.expect(NullPointerException.class);
+      FnApiDoFnRunner.computeSplitForProcess(
+          currentElement,
+          currentRestriction,
+          window1,
+          ImmutableList.copyOf(currentElement.getWindows()),
+          currentWatermarkEstimatorState,
+          0.0,
+          new OffsetRangeTracker(currentRestriction),
+          null,
+          null,
+          0,
+          3);
     }
 
     @Test
-    public void testTrySplitForTruncateCheckpointOnFirstWindow() throws Exception {
+    public void testTrySplitForProcessCheckpointOnFirstWindow() throws Exception {
       List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      SplitResult splitResult =
-          SplitResult.of(
-              ImmutableList.of(BundleApplication.getDefaultInstance()),
-              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
-      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.0, splitResult);
+      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
+      tracker.tryClaim(30L);
       SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
+          FnApiDoFnRunner.<Instant>computeSplitForProcess(
               currentElement,
               currentRestriction,
               window1,
               windows,
               currentWatermarkEstimatorState,
               0.0,
+              tracker,
               null,
-              splitDelegate,
-              null,
+              watermarkAndState,
               0,
               3);
       assertEquals(1, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedElementSplit =
+          createSplitInWindow(new OffsetRange(0, 31), new OffsetRange(31, 100), window1);
       KV<WindowedValue, WindowedValue> expectedWindowSplit =
           createSplitAcrossWindows(ImmutableList.of(), ImmutableList.of(window2, window3));
-      assertEquals(splitResult, result.getDownstreamSplit());
-      assertNull(result.getWindowSplit().getPrimarySplitRoot());
-      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(expectedElementSplit.getKey(), result.getWindowSplit().getPrimarySplitRoot());
+      assertEquals(expectedElementSplit.getValue(), result.getWindowSplit().getResidualSplitRoot());
       assertEquals(
           expectedWindowSplit.getKey(),
           result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
@@ -3899,32 +2958,30 @@ public void testTrySplitForTruncateCheckpointOnFirstWindow() throws Exception {
     }
 
     @Test
-    public void testTrySplitForTruncateCheckpointOnFirstWindowAfterOneSplit() throws Exception {
+    public void testTrySplitForProcessCheckpointOnFirstWindowAfterOneSplit() throws Exception {
       List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      SplitResult splitResult =
-          SplitResult.of(
-              ImmutableList.of(BundleApplication.getDefaultInstance()),
-              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
-      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.0, splitResult);
+      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
+      tracker.tryClaim(30L);
       SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
+          FnApiDoFnRunner.<Instant>computeSplitForProcess(
               currentElement,
               currentRestriction,
               window1,
               windows,
               currentWatermarkEstimatorState,
               0.0,
+              tracker,
               null,
-              splitDelegate,
-              null,
+              watermarkAndState,
               0,
               2);
       assertEquals(1, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedElementSplit =
+          createSplitInWindow(new OffsetRange(0, 31), new OffsetRange(31, 100), window1);
       KV<WindowedValue, WindowedValue> expectedWindowSplit =
           createSplitAcrossWindows(ImmutableList.of(), ImmutableList.of(window2));
-      assertEquals(splitResult, result.getDownstreamSplit());
-      assertNull(result.getWindowSplit().getPrimarySplitRoot());
-      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(expectedElementSplit.getKey(), result.getWindowSplit().getPrimarySplitRoot());
+      assertEquals(expectedElementSplit.getValue(), result.getWindowSplit().getResidualSplitRoot());
       assertEquals(
           expectedWindowSplit.getKey(),
           result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
@@ -3934,32 +2991,30 @@ public void testTrySplitForTruncateCheckpointOnFirstWindowAfterOneSplit() throws
     }
 
     @Test
-    public void testTrySplitForTruncateSplitOnFirstWindow() throws Exception {
+    public void testTrySplitForProcessSplitOnFirstWindow() throws Exception {
       List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      SplitResult splitResult =
-          SplitResult.of(
-              ImmutableList.of(BundleApplication.getDefaultInstance()),
-              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
-      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.54, splitResult);
+      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
+      tracker.tryClaim(30L);
       SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
+          FnApiDoFnRunner.<Instant>computeSplitForProcess(
               currentElement,
               currentRestriction,
               window1,
               windows,
               currentWatermarkEstimatorState,
               0.2,
+              tracker,
               null,
-              splitDelegate,
-              null,
+              watermarkAndState,
               0,
               3);
       assertEquals(1, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedElementSplit =
+          createSplitInWindow(new OffsetRange(0, 84), new OffsetRange(84, 100), window1);
       KV<WindowedValue, WindowedValue> expectedWindowSplit =
           createSplitAcrossWindows(ImmutableList.of(), ImmutableList.of(window2, window3));
-      assertEquals(splitResult, result.getDownstreamSplit());
-      assertNull(result.getWindowSplit().getPrimarySplitRoot());
-      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(expectedElementSplit.getKey(), result.getWindowSplit().getPrimarySplitRoot());
+      assertEquals(expectedElementSplit.getValue(), result.getWindowSplit().getResidualSplitRoot());
       assertEquals(
           expectedWindowSplit.getKey(),
           result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
@@ -3969,32 +3024,32 @@ public void testTrySplitForTruncateSplitOnFirstWindow() throws Exception {
     }
 
     @Test
-    public void testTrySplitForTruncateSplitOnMiddleWindow() throws Exception {
+    public void testTrySplitForProcessSplitOnMiddleWindow() throws Exception {
       List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      SplitResult splitResult =
-          SplitResult.of(
-              ImmutableList.of(BundleApplication.getDefaultInstance()),
-              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
-      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.34, splitResult);
+      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
+      tracker.tryClaim(30L);
       SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
+          FnApiDoFnRunner.computeSplitForProcess(
               currentElement,
               currentRestriction,
-              window1,
+              window2,
               windows,
               currentWatermarkEstimatorState,
               0.2,
+              tracker,
               null,
-              splitDelegate,
-              null,
+              watermarkAndState,
               1,
               3);
       assertEquals(2, result.getNewWindowStopIndex());
+      // Java uses BigDecimal so 0.2 * 170 = 63.9...
+      // BigDecimal.longValue() will round down to 63 instead of the expected 64
+      KV<WindowedValue, WindowedValue> expectedElementSplit =
+          createSplitInWindow(new OffsetRange(0, 63), new OffsetRange(63, 100), window2);
       KV<WindowedValue, WindowedValue> expectedWindowSplit =
           createSplitAcrossWindows(ImmutableList.of(window1), ImmutableList.of(window3));
-      assertEquals(splitResult, result.getDownstreamSplit());
-      assertNull(result.getWindowSplit().getPrimarySplitRoot());
-      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(expectedElementSplit.getKey(), result.getWindowSplit().getPrimarySplitRoot());
+      assertEquals(expectedElementSplit.getValue(), result.getWindowSplit().getResidualSplitRoot());
       assertEquals(
           expectedWindowSplit.getKey(),
           result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
@@ -4004,32 +3059,30 @@ public void testTrySplitForTruncateSplitOnMiddleWindow() throws Exception {
     }
 
     @Test
-    public void testTrySplitForTruncateSplitOnLastWindow() throws Exception {
+    public void testTrySplitForProcessSplitOnLastWindow() throws Exception {
       List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      SplitResult splitResult =
-          SplitResult.of(
-              ImmutableList.of(BundleApplication.getDefaultInstance()),
-              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
-      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.2, splitResult);
+      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
+      tracker.tryClaim(30L);
       SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
+          FnApiDoFnRunner.computeSplitForProcess(
               currentElement,
               currentRestriction,
-              window1,
+              window3,
               windows,
               currentWatermarkEstimatorState,
               0.2,
+              tracker,
               null,
-              splitDelegate,
-              null,
+              watermarkAndState,
               2,
               3);
       assertEquals(3, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedElementSplit =
+          createSplitInWindow(new OffsetRange(0, 44), new OffsetRange(44, 100), window3);
       KV<WindowedValue, WindowedValue> expectedWindowSplit =
           createSplitAcrossWindows(ImmutableList.of(window1, window2), ImmutableList.of());
-      assertEquals(splitResult, result.getDownstreamSplit());
-      assertNull(result.getWindowSplit().getPrimarySplitRoot());
-      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(expectedElementSplit.getKey(), result.getWindowSplit().getPrimarySplitRoot());
+      assertEquals(expectedElementSplit.getValue(), result.getWindowSplit().getResidualSplitRoot());
       assertEquals(
           expectedWindowSplit.getKey(),
           result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
@@ -4039,30 +3092,27 @@ public void testTrySplitForTruncateSplitOnLastWindow() throws Exception {
     }
 
     @Test
-    public void testTrySplitForTruncateSplitOnFirstWindowFallback() throws Exception {
+    public void testTrySplitForProcessSplitOnFirstWindowFallback() throws Exception {
       List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      SplitResult unusedSplitResult =
-          SplitResult.of(
-              ImmutableList.of(BundleApplication.getDefaultInstance()),
-              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
-      HandlesSplits splitDelegate = createSplitDelegate(1.0, 0.0, unusedSplitResult);
+      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
+      tracker.tryClaim(100L);
+      assertNull(tracker.trySplit(0.0));
       SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
+          FnApiDoFnRunner.computeSplitForProcess(
               currentElement,
               currentRestriction,
-              window1,
+              window3,
               windows,
               currentWatermarkEstimatorState,
-              0.0,
-              null,
-              splitDelegate,
+              0,
+              tracker,
               null,
+              watermarkAndState,
               0,
               3);
       assertEquals(1, result.getNewWindowStopIndex());
       KV<WindowedValue, WindowedValue> expectedWindowSplit =
           createSplitAcrossWindows(ImmutableList.of(window1), ImmutableList.of(window2, window3));
-      assertNull(result.getDownstreamSplit());
       assertNull(result.getWindowSplit().getPrimarySplitRoot());
       assertNull(result.getWindowSplit().getResidualSplitRoot());
       assertEquals(
@@ -4074,50 +3124,48 @@ public void testTrySplitForTruncateSplitOnFirstWindowFallback() throws Exception
     }
 
     @Test
-    public void testTrySplitForTruncateSplitOnLastWindowWhenNoElementSplit() throws Exception {
+    public void testTrySplitForProcessSplitOnLastWindowWhenNoElementSplit() throws Exception {
       List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      HandlesSplits splitDelegate = createSplitDelegate(1.0, 0.0, null);
+      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
+      tracker.tryClaim(100L);
+      assertNull(tracker.trySplit(0.0));
       SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
+          FnApiDoFnRunner.computeSplitForProcess(
               currentElement,
               currentRestriction,
-              window1,
+              window3,
               windows,
               currentWatermarkEstimatorState,
-              0.0,
-              null,
-              splitDelegate,
+              0,
+              tracker,
               null,
+              watermarkAndState,
               2,
               3);
       assertNull(result);
     }
 
     @Test
-    public void testTrySplitForTruncateOnWindowBoundaryRoundUp() throws Exception {
+    public void testTrySplitForProcessOnWindowBoundaryRoundUp() throws Exception {
       List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      SplitResult unusedSplitResult =
-          SplitResult.of(
-              ImmutableList.of(BundleApplication.getDefaultInstance()),
-              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
-      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.0, unusedSplitResult);
+      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
+      tracker.tryClaim(30L);
       SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
+          FnApiDoFnRunner.computeSplitForProcess(
               currentElement,
               currentRestriction,
-              window1,
+              window2,
               windows,
               currentWatermarkEstimatorState,
               0.6,
+              tracker,
               null,
-              splitDelegate,
-              null,
+              watermarkAndState,
               0,
               3);
       assertEquals(2, result.getNewWindowStopIndex());
       KV<WindowedValue, WindowedValue> expectedWindowSplit =
           createSplitAcrossWindows(ImmutableList.of(window1, window2), ImmutableList.of(window3));
-      assertNull(result.getDownstreamSplit());
       assertNull(result.getWindowSplit().getPrimarySplitRoot());
       assertNull(result.getWindowSplit().getResidualSplitRoot());
       assertEquals(
@@ -4129,30 +3177,26 @@ public void testTrySplitForTruncateOnWindowBoundaryRoundUp() throws Exception {
     }
 
     @Test
-    public void testTrySplitForTruncateOnWindowBoundaryRoundDown() throws Exception {
+    public void testTrySplitForProcessOnWindowBoundaryRoundDown() throws Exception {
       List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      SplitResult unusedSplitResult =
-          SplitResult.of(
-              ImmutableList.of(BundleApplication.getDefaultInstance()),
-              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
-      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.0, unusedSplitResult);
+      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
+      tracker.tryClaim(30L);
       SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
+          FnApiDoFnRunner.computeSplitForProcess(
               currentElement,
               currentRestriction,
-              window1,
+              window2,
               windows,
               currentWatermarkEstimatorState,
               0.3,
+              tracker,
               null,
-              splitDelegate,
-              null,
+              watermarkAndState,
               0,
               3);
       assertEquals(1, result.getNewWindowStopIndex());
       KV<WindowedValue, WindowedValue> expectedWindowSplit =
           createSplitAcrossWindows(ImmutableList.of(window1), ImmutableList.of(window2, window3));
-      assertNull(result.getDownstreamSplit());
       assertNull(result.getWindowSplit().getPrimarySplitRoot());
       assertNull(result.getWindowSplit().getResidualSplitRoot());
       assertEquals(
@@ -4164,30 +3208,26 @@ public void testTrySplitForTruncateOnWindowBoundaryRoundDown() throws Exception
     }
 
     @Test
-    public void testTrySplitForTruncateOnWindowBoundaryRoundDownOnLastWindow() throws Exception {
+    public void testTrySplitForProcessOnWindowBoundaryRoundDownOnLastWindow() throws Exception {
       List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
-      SplitResult unusedSplitResult =
-          SplitResult.of(
-              ImmutableList.of(BundleApplication.getDefaultInstance()),
-              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
-      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.0, unusedSplitResult);
+      OffsetRangeTracker tracker = new OffsetRangeTracker(currentRestriction);
+      tracker.tryClaim(30L);
       SplitResultsWithStopIndex result =
-          FnApiDoFnRunner.computeSplitForProcessOrTruncate(
+          FnApiDoFnRunner.computeSplitForProcess(
               currentElement,
               currentRestriction,
-              window1,
+              window2,
               windows,
               currentWatermarkEstimatorState,
-              0.6,
-              null,
-              splitDelegate,
+              0.9,
+              tracker,
               null,
+              watermarkAndState,
               0,
               3);
       assertEquals(2, result.getNewWindowStopIndex());
       KV<WindowedValue, WindowedValue> expectedWindowSplit =
           createSplitAcrossWindows(ImmutableList.of(window1, window2), ImmutableList.of(window3));
-      assertNull(result.getDownstreamSplit());
       assertNull(result.getWindowSplit().getPrimarySplitRoot());
       assertNull(result.getWindowSplit().getResidualSplitRoot());
       assertEquals(
@@ -4198,14 +3238,30 @@ public void testTrySplitForTruncateOnWindowBoundaryRoundDownOnLastWindow() throw
           result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
     }
 
+    private HandlesSplits createSplitDelegate(
+        double progress, double expectedFraction, HandlesSplits.SplitResult result) {
+      return new HandlesSplits() {
+        @Override
+        public SplitResult trySplit(double fractionOfRemainder) {
+          checkArgument(fractionOfRemainder == expectedFraction);
+          return result;
+        }
+
+        @Override
+        public double getProgress() {
+          return progress;
+        }
+      };
+    }
+
     @Test
     public void testConstructSplitResultWithInvalidElementSplits() throws Exception {
       expected.expect(IllegalArgumentException.class);
       FnApiDoFnRunner.constructSplitResult(
           WindowedSplitResult.forRoots(
               null,
-              WindowedValues.valueInGlobalWindow("elementPrimary"),
-              WindowedValues.valueInGlobalWindow("elementResidual"),
+              valueInGlobalWindow("elementPrimary"),
+              valueInGlobalWindow("elementResidual"),
               null),
           HandlesSplits.SplitResult.of(
               ImmutableList.of(BundleApplication.getDefaultInstance()),
@@ -4228,24 +3284,6 @@ private Coder getFullInputCoder(
       return WindowedValues.getFullCoder(inputCoder, windowCoder);
     }
 
-    private HandlesSplits.SplitResult getProcessElementSplit(String transformId, String inputId) {
-      return SplitResult.of(
-          ImmutableList.of(
-              BundleApplication.newBuilder()
-                  .setTransformId(transformId)
-                  .setInputId(inputId)
-                  .build()),
-          ImmutableList.of(
-              DelayedBundleApplication.newBuilder()
-                  .setApplication(
-                      BundleApplication.newBuilder()
-                          .setTransformId(transformId)
-                          .setInputId(inputId)
-                          .build())
-                  .setRequestedTimeDelay(Durations.fromMillis(1000L))
-                  .build()));
-    }
-
     private org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.Timestamp toTimestamp(
         Instant time) {
       return org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.Timestamp.newBuilder()
@@ -4254,31 +3292,6 @@ private org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.Timestamp toTime
           .build();
     }
 
-    @Test
-    public void testConstructSplitResultWithElementSplitFromDelegate() throws Exception {
-      Coder fullInputCoder =
-          getFullInputCoder(
-              StringUtf8Coder.of(),
-              OffsetRange.Coder.of(),
-              InstantCoder.of(),
-              IntervalWindow.getCoder());
-      HandlesSplits.SplitResult elementSplit =
-          getProcessElementSplit(PROCESS_TRANSFORM_ID, PROCESS_INPUT_ID);
-      HandlesSplits.SplitResult result =
-          FnApiDoFnRunner.constructSplitResult(
-              null,
-              elementSplit,
-              fullInputCoder,
-              null,
-              null,
-              TRUNCATE_TRANSFORM_ID,
-              TRUNCATE_INPUT_ID,
-              ImmutableList.of(TRUNCATE_OUTPUT_ID),
-              null);
-      assertEquals(elementSplit.getPrimaryRoots(), result.getPrimaryRoots());
-      assertEquals(elementSplit.getResidualRoots(), result.getResidualRoots());
-    }
-
     @Test
     public void testConstructSplitResultWithElementSplitFromTracker() throws Exception {
       Coder fullInputCoder =
@@ -4429,55 +3442,5 @@ public void testConstructSplitResultWithElementAndWindowSplitFromProcess() throw
           elementSplit.getValue(),
           fullInputCoder.decode(elementResidual.getApplication().getElement().newInput()));
     }
-
-    @Test
-    public void testConstructSplitResultWithElementAndWindowSplitFromTruncate() throws Exception {
-      Coder fullInputCoder =
-          getFullInputCoder(
-              StringUtf8Coder.of(),
-              OffsetRange.Coder.of(),
-              InstantCoder.of(),
-              IntervalWindow.getCoder());
-      KV<WindowedValue, WindowedValue> windowSplit =
-          createSplitWithSizeAcrossWindows(ImmutableList.of(window1), ImmutableList.of(window3));
-      HandlesSplits.SplitResult elementSplit =
-          getProcessElementSplit(PROCESS_TRANSFORM_ID, PROCESS_INPUT_ID);
-      HandlesSplits.SplitResult result =
-          FnApiDoFnRunner.constructSplitResult(
-              WindowedSplitResult.forRoots(
-                  windowSplit.getKey(), null, null, windowSplit.getValue()),
-              elementSplit,
-              fullInputCoder,
-              initialWatermark,
-              watermarkAndState,
-              TRUNCATE_TRANSFORM_ID,
-              TRUNCATE_INPUT_ID,
-              ImmutableList.of(TRUNCATE_OUTPUT_ID),
-              Duration.millis(100L));
-      assertEquals(2, result.getPrimaryRoots().size());
-      BundleApplication windowPrimary = result.getPrimaryRoots().get(0);
-      BundleApplication elementPrimary = result.getPrimaryRoots().get(1);
-      assertEquals(TRUNCATE_TRANSFORM_ID, windowPrimary.getTransformId());
-      assertEquals(TRUNCATE_INPUT_ID, windowPrimary.getInputId());
-      assertEquals(
-          windowSplit.getKey(), fullInputCoder.decode(windowPrimary.getElement().newInput()));
-      assertEquals(elementSplit.getPrimaryRoots().get(0), elementPrimary);
-
-      assertEquals(2, result.getResidualRoots().size());
-      DelayedBundleApplication windowResidual = result.getResidualRoots().get(0);
-      DelayedBundleApplication elementResidual = result.getResidualRoots().get(1);
-      assertEquals(
-          org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.Duration.getDefaultInstance(),
-          windowResidual.getRequestedTimeDelay());
-      assertEquals(TRUNCATE_TRANSFORM_ID, windowResidual.getApplication().getTransformId());
-      assertEquals(TRUNCATE_INPUT_ID, windowResidual.getApplication().getInputId());
-      assertEquals(
-          toTimestamp(initialWatermark),
-          windowResidual.getApplication().getOutputWatermarksMap().get(TRUNCATE_OUTPUT_ID));
-      assertEquals(
-          windowSplit.getValue(),
-          fullInputCoder.decode(windowResidual.getApplication().getElement().newInput()));
-      assertEquals(elementSplit.getResidualRoots().get(0), elementResidual);
-    }
   }
 }
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/ProgressUtilsTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/ProgressUtilsTest.java
new file mode 100644
index 000000000000..313e86a51512
--- /dev/null
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/ProgressUtilsTest.java
@@ -0,0 +1,54 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.fn.harness;
+
+import static org.junit.Assert.assertEquals;
+
+import java.io.Serializable;
+import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker.Progress;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+
+/** Tests for {@link ProgressUtils}. */
+@RunWith(JUnit4.class)
+public class ProgressUtilsTest implements Serializable {
+  @Test
+  public void testScaledProgress() throws Exception {
+    Progress elementProgress = Progress.from(2, 8);
+    // There is only one window.
+    Progress scaledResult = ProgressUtils.scaleProgress(elementProgress, 0, 1);
+    assertEquals(2, scaledResult.getWorkCompleted(), 0.0);
+    assertEquals(8, scaledResult.getWorkRemaining(), 0.0);
+
+    // We are at the first window of 3 in total.
+    scaledResult = ProgressUtils.scaleProgress(elementProgress, 0, 3);
+    assertEquals(2, scaledResult.getWorkCompleted(), 0.0);
+    assertEquals(28, scaledResult.getWorkRemaining(), 0.0);
+
+    // We are at the second window of 3 in total.
+    scaledResult = ProgressUtils.scaleProgress(elementProgress, 1, 3);
+    assertEquals(12, scaledResult.getWorkCompleted(), 0.0);
+    assertEquals(18, scaledResult.getWorkRemaining(), 0.0);
+
+    // We are at the last window of 3 in total.
+    scaledResult = ProgressUtils.scaleProgress(elementProgress, 2, 3);
+    assertEquals(22, scaledResult.getWorkCompleted(), 0.0);
+    assertEquals(8, scaledResult.getWorkRemaining(), 0.0);
+  }
+}
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/SplittableSplitAndSizeRestrictionsDoFnRunnerTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/SplittableSplitAndSizeRestrictionsDoFnRunnerTest.java
index 1336d2f4ba9f..34ef3e95b191 100644
--- a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/SplittableSplitAndSizeRestrictionsDoFnRunnerTest.java
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/SplittableSplitAndSizeRestrictionsDoFnRunnerTest.java
@@ -17,9 +17,11 @@
  */
 package org.apache.beam.fn.harness;
 
+import static org.apache.beam.runners.core.WindowMatchers.isSingleWindowedValue;
+import static org.apache.beam.runners.core.WindowMatchers.isValueInGlobalWindow;
+import static org.apache.beam.runners.core.WindowMatchers.isWindowedValue;
 import static org.apache.beam.sdk.values.WindowedValues.valueInGlobalWindow;
 import static org.hamcrest.MatcherAssert.assertThat;
-import static org.hamcrest.Matchers.contains;
 import static org.hamcrest.Matchers.containsInAnyOrder;
 import static org.hamcrest.Matchers.empty;
 import static org.junit.Assert.assertTrue;
@@ -214,20 +216,20 @@ public void testProcessElementForSplitAndSizeRestriction() throws Exception {
             KV.of("2", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE))));
     assertThat(
         mainOutputValues,
-        contains(
-            valueInGlobalWindow(
+        containsInAnyOrder(
+            isValueInGlobalWindow(
                 KV.of(
                     KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     2.0)),
-            valueInGlobalWindow(
+            isValueInGlobalWindow(
                 KV.of(
                     KV.of("5", KV.of(new OffsetRange(2, 5), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     3.0)),
-            valueInGlobalWindow(
+            isValueInGlobalWindow(
                 KV.of(
                     KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     1.0)),
-            valueInGlobalWindow(
+            isValueInGlobalWindow(
                 KV.of(
                     KV.of("2", KV.of(new OffsetRange(1, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     1.0))));
@@ -325,59 +327,60 @@ public void testProcessElementForWindowedSplitAndSizeRestriction() throws Except
     // Since the DoFn observes the window and it may affect the output, each input is processed
     // separately and each
     // output is per-window.
+
     assertThat(
         mainOutputValues,
-        contains(
-            WindowedValues.of(
+        containsInAnyOrder(
+            isSingleWindowedValue(
                 KV.of(
                     KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     2.0),
                 firstValue.getTimestamp(),
                 window1,
                 firstValue.getPaneInfo()),
-            WindowedValues.of(
+            isSingleWindowedValue(
                 KV.of(
                     KV.of("5", KV.of(new OffsetRange(2, 5), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     3.0),
                 firstValue.getTimestamp(),
                 window1,
                 firstValue.getPaneInfo()),
-            WindowedValues.of(
+            isSingleWindowedValue(
                 KV.of(
                     KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     2.0),
                 firstValue.getTimestamp(),
                 window2,
                 firstValue.getPaneInfo()),
-            WindowedValues.of(
+            isSingleWindowedValue(
                 KV.of(
                     KV.of("5", KV.of(new OffsetRange(2, 5), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     3.0),
                 firstValue.getTimestamp(),
                 window2,
                 firstValue.getPaneInfo()),
-            WindowedValues.of(
+            isSingleWindowedValue(
                 KV.of(
                     KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     1.0),
                 secondValue.getTimestamp(),
                 window1,
                 secondValue.getPaneInfo()),
-            WindowedValues.of(
+            isSingleWindowedValue(
                 KV.of(
                     KV.of("2", KV.of(new OffsetRange(1, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     1.0),
                 secondValue.getTimestamp(),
                 window1,
                 secondValue.getPaneInfo()),
-            WindowedValues.of(
+            isSingleWindowedValue(
                 KV.of(
                     KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     1.0),
                 secondValue.getTimestamp(),
                 window2,
                 secondValue.getPaneInfo()),
-            WindowedValues.of(
+            isSingleWindowedValue(
                 KV.of(
                     KV.of("2", KV.of(new OffsetRange(1, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     1.0),
@@ -470,29 +473,29 @@ public void testProcessElementForWindowedSplitAndSizeRestriction() throws Except
     // Ensure that each output element is in all the windows and not one per window.
     assertThat(
         mainOutputValues,
-        contains(
-            WindowedValues.of(
+        containsInAnyOrder(
+            isWindowedValue(
                 KV.of(
                     KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     2.0),
                 firstValue.getTimestamp(),
                 ImmutableList.of(window1, window2),
                 firstValue.getPaneInfo()),
-            WindowedValues.of(
+            isWindowedValue(
                 KV.of(
                     KV.of("5", KV.of(new OffsetRange(2, 5), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     3.0),
                 firstValue.getTimestamp(),
                 ImmutableList.of(window1, window2),
                 firstValue.getPaneInfo()),
-            WindowedValues.of(
+            isWindowedValue(
                 KV.of(
                     KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     1.0),
                 firstValue.getTimestamp(),
                 ImmutableList.of(window1, window2),
                 firstValue.getPaneInfo()),
-            WindowedValues.of(
+            isWindowedValue(
                 KV.of(
                     KV.of("2", KV.of(new OffsetRange(1, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
                     1.0),
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/SplittableTruncateSizedRestrictionsDoFnRunnerTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/SplittableTruncateSizedRestrictionsDoFnRunnerTest.java
new file mode 100644
index 000000000000..615a681095e1
--- /dev/null
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/SplittableTruncateSizedRestrictionsDoFnRunnerTest.java
@@ -0,0 +1,1191 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.fn.harness;
+
+import static org.apache.beam.sdk.values.WindowedValues.valueInGlobalWindow;
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
+import static org.hamcrest.MatcherAssert.assertThat;
+import static org.hamcrest.Matchers.contains;
+import static org.hamcrest.Matchers.containsInAnyOrder;
+import static org.hamcrest.Matchers.empty;
+import static org.hamcrest.Matchers.instanceOf;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNull;
+import static org.junit.Assert.assertTrue;
+import static org.junit.Assert.fail;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+import java.util.ServiceLoader;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+import org.apache.beam.fn.harness.HandlesSplits.SplitResult;
+import org.apache.beam.fn.harness.state.FakeBeamFnStateClient;
+import org.apache.beam.model.fnexecution.v1.BeamFnApi.BundleApplication;
+import org.apache.beam.model.fnexecution.v1.BeamFnApi.DelayedBundleApplication;
+import org.apache.beam.model.pipeline.v1.RunnerApi;
+import org.apache.beam.runners.core.metrics.MetricUpdates.MetricUpdate;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.Coder;
+import org.apache.beam.sdk.coders.DoubleCoder;
+import org.apache.beam.sdk.coders.InstantCoder;
+import org.apache.beam.sdk.coders.KvCoder;
+import org.apache.beam.sdk.coders.StringUtf8Coder;
+import org.apache.beam.sdk.fn.data.FnDataReceiver;
+import org.apache.beam.sdk.io.range.OffsetRange;
+import org.apache.beam.sdk.metrics.MetricKey;
+import org.apache.beam.sdk.metrics.MetricName;
+import org.apache.beam.sdk.testing.ResetDateTimeProvider;
+import org.apache.beam.sdk.transforms.Create;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.transforms.View;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.sdk.transforms.windowing.GlobalWindow;
+import org.apache.beam.sdk.transforms.windowing.IntervalWindow;
+import org.apache.beam.sdk.transforms.windowing.PaneInfo;
+import org.apache.beam.sdk.transforms.windowing.SlidingWindows;
+import org.apache.beam.sdk.transforms.windowing.Window;
+import org.apache.beam.sdk.util.ByteStringOutputStream;
+import org.apache.beam.sdk.util.construction.CoderTranslation;
+import org.apache.beam.sdk.util.construction.CoderTranslation.TranslationContext;
+import org.apache.beam.sdk.util.construction.PTransformTranslation;
+import org.apache.beam.sdk.util.construction.ParDoTranslation;
+import org.apache.beam.sdk.util.construction.PipelineTranslation;
+import org.apache.beam.sdk.util.construction.RehydratedComponents;
+import org.apache.beam.sdk.util.construction.SdkComponents;
+import org.apache.beam.sdk.util.construction.graph.ProtoOverrides;
+import org.apache.beam.sdk.util.construction.graph.SplittableParDoExpander;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollectionView;
+import org.apache.beam.sdk.values.WindowedValue;
+import org.apache.beam.sdk.values.WindowedValues;
+import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.util.Durations;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
+import org.hamcrest.collection.IsMapContaining;
+import org.joda.time.Duration;
+import org.joda.time.Instant;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.experimental.runners.Enclosed;
+import org.junit.rules.ExpectedException;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+
+/** Tests for {@link SplittableTruncateSizedRestrictionsDoFnRunner}. */
+@RunWith(Enclosed.class)
+@SuppressWarnings({
+  "rawtypes", // TODO(https://github.com/apache/beam/issues/20447)
+  // TODO(https://github.com/apache/beam/issues/21230): Remove when new version of
+  // errorprone is released (2.11.0)
+  "unused"
+})
+public class SplittableTruncateSizedRestrictionsDoFnRunnerTest implements Serializable {
+
+  @RunWith(JUnit4.class)
+  public static class ExecutionTest implements Serializable {
+    @Rule public transient ResetDateTimeProvider dateTimeProvider = new ResetDateTimeProvider();
+
+    public static final String TEST_TRANSFORM_ID = "pTransformId";
+
+    /** @return a test MetricUpdate for expected metrics to compare against */
+    public MetricUpdate create(String stepName, MetricName name, long value) {
+      return MetricUpdate.create(MetricKey.create(stepName, name), value);
+    }
+
+    private <T> WindowedValue<T> valueInWindows(
+        T value, BoundedWindow window, BoundedWindow... windows) {
+      return WindowedValues.of(
+          value,
+          window.maxTimestamp(),
+          ImmutableList.<BoundedWindow>builder().add(window).add(windows).build(),
+          PaneInfo.NO_FIRING);
+    }
+
+    @Test
+    public void testRegistration() {
+      for (PTransformRunnerFactory.Registrar registrar :
+          ServiceLoader.load(PTransformRunnerFactory.Registrar.class)) {
+        if (registrar instanceof SplittableTruncateSizedRestrictionsDoFnRunner.Registrar) {
+          assertThat(
+              registrar.getPTransformRunnerFactories(),
+              IsMapContaining.hasKey(
+                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN));
+          return;
+        }
+      }
+      fail("Expected registrar not found.");
+    }
+
+    private static SplitResult createSplitResult(double fractionOfRemainder) {
+      ByteStringOutputStream primaryBytes = new ByteStringOutputStream();
+      ByteStringOutputStream residualBytes = new ByteStringOutputStream();
+      try {
+        DoubleCoder.of().encode(fractionOfRemainder, primaryBytes);
+        DoubleCoder.of().encode(1 - fractionOfRemainder, residualBytes);
+      } catch (Exception e) {
+        // No-op.
+      }
+      return SplitResult.of(
+          ImmutableList.of(
+              BundleApplication.newBuilder()
+                  .setElement(primaryBytes.toByteString())
+                  .setInputId("mainInputId-process")
+                  .setTransformId("processPTransfromId")
+                  .build()),
+          ImmutableList.of(
+              DelayedBundleApplication.newBuilder()
+                  .setApplication(
+                      BundleApplication.newBuilder()
+                          .setElement(residualBytes.toByteString())
+                          .setInputId("mainInputId-process")
+                          .setTransformId("processPTransfromId")
+                          .build())
+                  .build()));
+    }
+
+    private static class SplittableFnDataReceiver
+        implements HandlesSplits, FnDataReceiver<WindowedValue> {
+      SplittableFnDataReceiver(
+          List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues) {
+        this.mainOutputValues = mainOutputValues;
+      }
+
+      private final List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues;
+
+      @Override
+      public SplitResult trySplit(double fractionOfRemainder) {
+        return createSplitResult(fractionOfRemainder);
+      }
+
+      @Override
+      public double getProgress() {
+        return 0.7;
+      }
+
+      @Override
+      public void accept(WindowedValue input) throws Exception {
+        mainOutputValues.add(input);
+      }
+    }
+
+    @Test
+    public void testProcessElementForTruncateAndSizeRestrictionForwardSplitWhenObservingWindows()
+        throws Exception {
+      Pipeline p = Pipeline.create();
+      PCollection<String> valuePCollection = p.apply(Create.of("unused"));
+      PCollectionView<String> singletonSideInputView = valuePCollection.apply(View.asSingleton());
+      WindowObservingTestSplittableDoFn doFn =
+          WindowObservingTestSplittableDoFn.forSplitAtTruncate(singletonSideInputView);
+      valuePCollection
+          .apply(Window.into(SlidingWindows.of(Duration.standardSeconds(1))))
+          .apply(TEST_TRANSFORM_ID, ParDo.of(doFn).withSideInputs(singletonSideInputView));
+
+      RunnerApi.Pipeline pProto =
+          ProtoOverrides.updateTransform(
+              PTransformTranslation.PAR_DO_TRANSFORM_URN,
+              PipelineTranslation.toProto(p, SdkComponents.create(p.getOptions()), true),
+              SplittableParDoExpander.createTruncateReplacement());
+      String expandedTransformId =
+          Iterables.find(
+                  pProto.getComponents().getTransformsMap().entrySet(),
+                  entry ->
+                      entry
+                              .getValue()
+                              .getSpec()
+                              .getUrn()
+                              .equals(
+                                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN)
+                          && entry.getValue().getUniqueName().contains(TEST_TRANSFORM_ID))
+              .getKey();
+      RunnerApi.PTransform pTransform =
+          pProto.getComponents().getTransformsOrThrow(expandedTransformId);
+      String inputPCollectionId =
+          pTransform.getInputsOrThrow(ParDoTranslation.getMainInputName(pTransform));
+      RunnerApi.PCollection inputPCollection =
+          pProto.getComponents().getPcollectionsOrThrow(inputPCollectionId);
+      RehydratedComponents rehydratedComponents =
+          RehydratedComponents.forComponents(pProto.getComponents());
+      Coder<WindowedValue> inputCoder =
+          WindowedValues.getFullCoder(
+              CoderTranslation.fromProto(
+                  pProto.getComponents().getCodersOrThrow(inputPCollection.getCoderId()),
+                  rehydratedComponents,
+                  TranslationContext.DEFAULT),
+              (Coder)
+                  CoderTranslation.fromProto(
+                      pProto
+                          .getComponents()
+                          .getCodersOrThrow(
+                              pProto
+                                  .getComponents()
+                                  .getWindowingStrategiesOrThrow(
+                                      inputPCollection.getWindowingStrategyId())
+                                  .getWindowCoderId()),
+                      rehydratedComponents,
+                      TranslationContext.DEFAULT));
+
+      String outputPCollectionId = Iterables.getOnlyElement(pTransform.getOutputsMap().values());
+
+      FakeBeamFnStateClient fakeClient =
+          new FakeBeamFnStateClient(StringUtf8Coder.of(), ImmutableMap.of());
+
+      PTransformRunnerFactoryTestContext context =
+          PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
+              .beamFnStateClient(fakeClient)
+              .processBundleInstructionId("57")
+              // .pCollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
+              .components(
+                  RunnerApi.Components.newBuilder()
+                      .putAllCoders(pProto.getComponents().getCodersMap())
+                      .putAllWindowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
+                      .putAllEnvironments(Collections.emptyMap())
+                      .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
+                      .build())
+              // .coders(pProto.getComponents().getCodersMap())
+              // .windowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
+              .build();
+      List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues = new ArrayList<>();
+      context.addPCollectionConsumer(
+          outputPCollectionId, (FnDataReceiver) new SplittableFnDataReceiver(mainOutputValues));
+
+      new SplittableTruncateSizedRestrictionsDoFnRunner.Factory().addRunnerForPTransform(context);
+      FnDataReceiver<WindowedValue<?>> mainInput =
+          context.getPCollectionConsumer(inputPCollectionId);
+      assertThat(mainInput, instanceOf(HandlesSplits.class));
+
+      mainOutputValues.clear();
+      BoundedWindow window1 = new IntervalWindow(new Instant(5), new Instant(10));
+      BoundedWindow window2 = new IntervalWindow(new Instant(6), new Instant(11));
+      BoundedWindow window3 = new IntervalWindow(new Instant(7), new Instant(12));
+      // Setup and launch the trySplit thread.
+      ExecutorService executorService = Executors.newSingleThreadExecutor();
+      Future<SplitResult> trySplitFuture =
+          executorService.submit(
+              () -> {
+                try {
+                  doFn.waitForSplitElementToBeProcessed();
+                  SplitResult result = ((HandlesSplits) mainInput).trySplit(0);
+                  Assert.assertNotNull(result);
+                  return result;
+                } finally {
+                  doFn.trySplitPerformed();
+                }
+              });
+
+      WindowedValue<?> splitValue =
+          valueInWindows(
+              KV.of(
+                  KV.of("7", KV.of(new OffsetRange(0, 6), GlobalWindow.TIMESTAMP_MIN_VALUE)), 6.0),
+              window1,
+              window2,
+              window3);
+      mainInput.accept(splitValue);
+      SplitResult trySplitResult = trySplitFuture.get();
+
+      // We expect that there are outputs from window1 and window2
+      assertThat(
+          mainOutputValues,
+          contains(
+              WindowedValues.of(
+                  KV.of(
+                      KV.of("7", KV.of(new OffsetRange(0, 3), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                      3.0),
+                  splitValue.getTimestamp(),
+                  window1,
+                  splitValue.getPaneInfo()),
+              WindowedValues.of(
+                  KV.of(
+                      KV.of("7", KV.of(new OffsetRange(0, 3), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                      3.0),
+                  splitValue.getTimestamp(),
+                  window2,
+                  splitValue.getPaneInfo())));
+
+      SplitResult expectedElementSplit = createSplitResult(0);
+      BundleApplication expectedElementSplitPrimary =
+          Iterables.getOnlyElement(expectedElementSplit.getPrimaryRoots());
+      ByteStringOutputStream primaryBytes = new ByteStringOutputStream();
+      inputCoder.encode(
+          WindowedValues.of(
+              KV.of(
+                  KV.of("7", KV.of(new OffsetRange(0, 6), GlobalWindow.TIMESTAMP_MIN_VALUE)), 6.0),
+              splitValue.getTimestamp(),
+              window1,
+              splitValue.getPaneInfo()),
+          primaryBytes);
+      BundleApplication expectedWindowedPrimary =
+          BundleApplication.newBuilder()
+              .setElement(primaryBytes.toByteString())
+              .setInputId(ParDoTranslation.getMainInputName(pTransform))
+              .setTransformId(TEST_TRANSFORM_ID)
+              .build();
+      DelayedBundleApplication expectedElementSplitResidual =
+          Iterables.getOnlyElement(expectedElementSplit.getResidualRoots());
+      ByteStringOutputStream residualBytes = new ByteStringOutputStream();
+      inputCoder.encode(
+          WindowedValues.of(
+              KV.of(
+                  KV.of("7", KV.of(new OffsetRange(0, 6), GlobalWindow.TIMESTAMP_MIN_VALUE)), 6.0),
+              splitValue.getTimestamp(),
+              window3,
+              splitValue.getPaneInfo()),
+          residualBytes);
+      DelayedBundleApplication expectedWindowedResidual =
+          DelayedBundleApplication.newBuilder()
+              .setApplication(
+                  BundleApplication.newBuilder()
+                      .setElement(residualBytes.toByteString())
+                      .setInputId(ParDoTranslation.getMainInputName(pTransform))
+                      .setTransformId(TEST_TRANSFORM_ID)
+                      .build())
+              .build();
+      assertThat(
+          trySplitResult.getPrimaryRoots(),
+          containsInAnyOrder(expectedWindowedPrimary, expectedElementSplitPrimary));
+      assertThat(
+          trySplitResult.getResidualRoots(),
+          containsInAnyOrder(expectedWindowedResidual, expectedElementSplitResidual));
+    }
+
+    @Test
+    public void testProcessElementForTruncateAndSizeRestrictionForwardSplitWithoutObservingWindow()
+        throws Exception {
+      Pipeline p = Pipeline.create();
+      PCollection<String> valuePCollection = p.apply(Create.of("unused"));
+      valuePCollection.apply(
+          TEST_TRANSFORM_ID, ParDo.of(new NonWindowObservingTestSplittableDoFn()));
+
+      RunnerApi.Pipeline pProto =
+          ProtoOverrides.updateTransform(
+              PTransformTranslation.PAR_DO_TRANSFORM_URN,
+              PipelineTranslation.toProto(p, SdkComponents.create(p.getOptions()), true),
+              SplittableParDoExpander.createTruncateReplacement());
+      String expandedTransformId =
+          Iterables.find(
+                  pProto.getComponents().getTransformsMap().entrySet(),
+                  entry ->
+                      entry
+                              .getValue()
+                              .getSpec()
+                              .getUrn()
+                              .equals(
+                                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN)
+                          && entry.getValue().getUniqueName().contains(TEST_TRANSFORM_ID))
+              .getKey();
+      RunnerApi.PTransform pTransform =
+          pProto.getComponents().getTransformsOrThrow(expandedTransformId);
+      String inputPCollectionId =
+          pTransform.getInputsOrThrow(ParDoTranslation.getMainInputName(pTransform));
+      String outputPCollectionId = Iterables.getOnlyElement(pTransform.getOutputsMap().values());
+
+      FakeBeamFnStateClient fakeClient =
+          new FakeBeamFnStateClient(StringUtf8Coder.of(), ImmutableMap.of());
+
+      PTransformRunnerFactoryTestContext context =
+          PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
+              .beamFnStateClient(fakeClient)
+              .processBundleInstructionId("57")
+              .components(
+                  RunnerApi.Components.newBuilder()
+                      .putAllCoders(pProto.getComponents().getCodersMap())
+                      .putAllEnvironments(Collections.emptyMap())
+                      .putAllWindowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
+                      .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
+                      .build())
+              .build();
+      List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues = new ArrayList<>();
+      context.addPCollectionConsumer(
+          outputPCollectionId, (FnDataReceiver) new SplittableFnDataReceiver(mainOutputValues));
+
+      new SplittableTruncateSizedRestrictionsDoFnRunner.Factory().addRunnerForPTransform(context);
+      FnDataReceiver<WindowedValue<?>> mainInput =
+          context.getPCollectionConsumer(inputPCollectionId);
+      assertThat(mainInput, instanceOf(HandlesSplits.class));
+
+      assertEquals(0.7, ((HandlesSplits) mainInput).getProgress(), 0.0);
+      assertEquals(createSplitResult(0.4), ((HandlesSplits) mainInput).trySplit(0.4));
+    }
+
+    @Test
+    public void testProcessElementForTruncateAndSizeRestriction() throws Exception {
+      Pipeline p = Pipeline.create();
+      PCollection<String> valuePCollection = p.apply(Create.of("unused"));
+      valuePCollection.apply(
+          TEST_TRANSFORM_ID, ParDo.of(new NonWindowObservingTestSplittableDoFn()));
+
+      RunnerApi.Pipeline pProto =
+          ProtoOverrides.updateTransform(
+              PTransformTranslation.PAR_DO_TRANSFORM_URN,
+              PipelineTranslation.toProto(p, SdkComponents.create(p.getOptions()), true),
+              SplittableParDoExpander.createTruncateReplacement());
+      String expandedTransformId =
+          Iterables.find(
+                  pProto.getComponents().getTransformsMap().entrySet(),
+                  entry ->
+                      entry
+                              .getValue()
+                              .getSpec()
+                              .getUrn()
+                              .equals(
+                                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN)
+                          && entry.getValue().getUniqueName().contains(TEST_TRANSFORM_ID))
+              .getKey();
+      RunnerApi.PTransform pTransform =
+          pProto.getComponents().getTransformsOrThrow(expandedTransformId);
+      String inputPCollectionId =
+          pTransform.getInputsOrThrow(ParDoTranslation.getMainInputName(pTransform));
+      String outputPCollectionId = Iterables.getOnlyElement(pTransform.getOutputsMap().values());
+
+      FakeBeamFnStateClient fakeClient =
+          new FakeBeamFnStateClient(StringUtf8Coder.of(), ImmutableMap.of());
+
+      PTransformRunnerFactoryTestContext context =
+          PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
+              .beamFnStateClient(fakeClient)
+              .processBundleInstructionId("57")
+              .components(
+                  RunnerApi.Components.newBuilder()
+                      .putAllCoders(pProto.getComponents().getCodersMap())
+                      .putAllEnvironments(Collections.emptyMap())
+                      .putAllWindowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
+                      .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
+                      .build())
+              .build();
+      List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues = new ArrayList<>();
+      context.addPCollectionConsumer(
+          outputPCollectionId, (FnDataReceiver) new SplittableFnDataReceiver(mainOutputValues));
+
+      new SplittableTruncateSizedRestrictionsDoFnRunner.Factory().addRunnerForPTransform(context);
+
+      assertTrue(context.getStartBundleFunctions().isEmpty());
+      mainOutputValues.clear();
+
+      assertThat(
+          context.getPCollectionConsumers().keySet(),
+          containsInAnyOrder(inputPCollectionId, outputPCollectionId));
+
+      FnDataReceiver<WindowedValue<?>> mainInput =
+          context.getPCollectionConsumer(inputPCollectionId);
+      assertThat(mainInput, instanceOf(HandlesSplits.class));
+
+      mainInput.accept(
+          valueInGlobalWindow(
+              KV.of(
+                  KV.of("5", KV.of(new OffsetRange(0, 5), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                  5.0)));
+      mainInput.accept(
+          valueInGlobalWindow(
+              KV.of(
+                  KV.of("2", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                  2.0)));
+      assertThat(
+          mainOutputValues,
+          contains(
+              valueInGlobalWindow(
+                  KV.of(
+                      KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                      2.0)),
+              valueInGlobalWindow(
+                  KV.of(
+                      KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                      1.0))));
+      mainOutputValues.clear();
+
+      assertTrue(context.getFinishBundleFunctions().isEmpty());
+      assertThat(mainOutputValues, empty());
+
+      Iterables.getOnlyElement(context.getTearDownFunctions()).run();
+      assertThat(mainOutputValues, empty());
+    }
+
+    @Test
+    public void testProcessElementForWindowedTruncateAndSizeRestriction() throws Exception {
+      Pipeline p = Pipeline.create();
+      PCollection<String> valuePCollection = p.apply(Create.of("unused"));
+      PCollectionView<String> singletonSideInputView = valuePCollection.apply(View.asSingleton());
+      valuePCollection
+          .apply(Window.into(SlidingWindows.of(Duration.standardSeconds(1))))
+          .apply(
+              TEST_TRANSFORM_ID,
+              ParDo.of(new WindowObservingTestSplittableDoFn(singletonSideInputView))
+                  .withSideInputs(singletonSideInputView));
+
+      RunnerApi.Pipeline pProto =
+          ProtoOverrides.updateTransform(
+              PTransformTranslation.PAR_DO_TRANSFORM_URN,
+              PipelineTranslation.toProto(p, SdkComponents.create(p.getOptions()), true),
+              SplittableParDoExpander.createTruncateReplacement());
+      String expandedTransformId =
+          Iterables.find(
+                  pProto.getComponents().getTransformsMap().entrySet(),
+                  entry ->
+                      entry
+                              .getValue()
+                              .getSpec()
+                              .getUrn()
+                              .equals(
+                                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN)
+                          && entry.getValue().getUniqueName().contains(TEST_TRANSFORM_ID))
+              .getKey();
+      RunnerApi.PTransform pTransform =
+          pProto.getComponents().getTransformsOrThrow(expandedTransformId);
+      String inputPCollectionId =
+          pTransform.getInputsOrThrow(ParDoTranslation.getMainInputName(pTransform));
+      String outputPCollectionId = Iterables.getOnlyElement(pTransform.getOutputsMap().values());
+
+      FakeBeamFnStateClient fakeClient =
+          new FakeBeamFnStateClient(StringUtf8Coder.of(), ImmutableMap.of());
+
+      PTransformRunnerFactoryTestContext context =
+          PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
+              .beamFnStateClient(fakeClient)
+              .processBundleInstructionId("57")
+              .components(
+                  RunnerApi.Components.newBuilder()
+                      .putAllCoders(pProto.getComponents().getCodersMap())
+                      .putAllEnvironments(Collections.emptyMap())
+                      .putAllWindowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
+                      .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
+                      .build())
+              .build();
+      List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues = new ArrayList<>();
+      context.addPCollectionConsumer(
+          outputPCollectionId, (FnDataReceiver) new SplittableFnDataReceiver(mainOutputValues));
+
+      new SplittableTruncateSizedRestrictionsDoFnRunner.Factory().addRunnerForPTransform(context);
+
+      assertTrue(context.getStartBundleFunctions().isEmpty());
+      mainOutputValues.clear();
+
+      assertThat(
+          context.getPCollectionConsumers().keySet(),
+          containsInAnyOrder(inputPCollectionId, outputPCollectionId));
+
+      FnDataReceiver<WindowedValue<?>> mainInput =
+          context.getPCollectionConsumer(inputPCollectionId);
+      assertThat(mainInput, instanceOf(HandlesSplits.class));
+
+      IntervalWindow window1 = new IntervalWindow(new Instant(5), new Instant(10));
+      IntervalWindow window2 = new IntervalWindow(new Instant(6), new Instant(11));
+      WindowedValue<?> firstValue =
+          valueInWindows(
+              KV.of(
+                  KV.of("5", KV.of(new OffsetRange(0, 5), GlobalWindow.TIMESTAMP_MIN_VALUE)), 5.0),
+              window1,
+              window2);
+      WindowedValue<?> secondValue =
+          valueInWindows(
+              KV.of(
+                  KV.of("2", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)), 2.0),
+              window1,
+              window2);
+      mainInput.accept(firstValue);
+      mainInput.accept(secondValue);
+      assertThat(
+          mainOutputValues,
+          contains(
+              WindowedValues.of(
+                  KV.of(
+                      KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                      2.0),
+                  firstValue.getTimestamp(),
+                  window1,
+                  firstValue.getPaneInfo()),
+              WindowedValues.of(
+                  KV.of(
+                      KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                      2.0),
+                  firstValue.getTimestamp(),
+                  window2,
+                  firstValue.getPaneInfo()),
+              WindowedValues.of(
+                  KV.of(
+                      KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                      1.0),
+                  firstValue.getTimestamp(),
+                  window1,
+                  firstValue.getPaneInfo()),
+              WindowedValues.of(
+                  KV.of(
+                      KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                      1.0),
+                  firstValue.getTimestamp(),
+                  window2,
+                  firstValue.getPaneInfo())));
+      mainOutputValues.clear();
+
+      assertTrue(context.getFinishBundleFunctions().isEmpty());
+      assertThat(mainOutputValues, empty());
+
+      Iterables.getOnlyElement(context.getTearDownFunctions()).run();
+      assertThat(mainOutputValues, empty());
+    }
+
+    @Test
+    public void
+        testProcessElementForWindowedTruncateAndSizeRestrictionWithNonWindowObservingOptimization()
+            throws Exception {
+      Pipeline p = Pipeline.create();
+      PCollection<String> valuePCollection = p.apply(Create.of("unused"));
+      valuePCollection
+          .apply(Window.into(SlidingWindows.of(Duration.standardSeconds(1))))
+          .apply(TEST_TRANSFORM_ID, ParDo.of(new NonWindowObservingTestSplittableDoFn()));
+
+      RunnerApi.Pipeline pProto =
+          ProtoOverrides.updateTransform(
+              PTransformTranslation.PAR_DO_TRANSFORM_URN,
+              PipelineTranslation.toProto(p, SdkComponents.create(p.getOptions()), true),
+              SplittableParDoExpander.createTruncateReplacement());
+      String expandedTransformId =
+          Iterables.find(
+                  pProto.getComponents().getTransformsMap().entrySet(),
+                  entry ->
+                      entry
+                              .getValue()
+                              .getSpec()
+                              .getUrn()
+                              .equals(
+                                  PTransformTranslation.SPLITTABLE_TRUNCATE_SIZED_RESTRICTION_URN)
+                          && entry.getValue().getUniqueName().contains(TEST_TRANSFORM_ID))
+              .getKey();
+      RunnerApi.PTransform pTransform =
+          pProto.getComponents().getTransformsOrThrow(expandedTransformId);
+      String inputPCollectionId =
+          pTransform.getInputsOrThrow(ParDoTranslation.getMainInputName(pTransform));
+      String outputPCollectionId = Iterables.getOnlyElement(pTransform.getOutputsMap().values());
+
+      PTransformRunnerFactoryTestContext context =
+          PTransformRunnerFactoryTestContext.builder(TEST_TRANSFORM_ID, pTransform)
+              .processBundleInstructionId("57")
+              .components(
+                  RunnerApi.Components.newBuilder()
+                      .putAllCoders(pProto.getComponents().getCodersMap())
+                      .putAllEnvironments(Collections.emptyMap())
+                      .putAllWindowingStrategies(pProto.getComponents().getWindowingStrategiesMap())
+                      .putAllPcollections(pProto.getComponentsOrBuilder().getPcollectionsMap())
+                      .build())
+              .build();
+      List<WindowedValue<KV<KV<String, OffsetRange>, Double>>> mainOutputValues = new ArrayList<>();
+      context.addPCollectionConsumer(
+          outputPCollectionId, (FnDataReceiver) new SplittableFnDataReceiver(mainOutputValues));
+
+      new SplittableTruncateSizedRestrictionsDoFnRunner.Factory().addRunnerForPTransform(context);
+
+      assertTrue(context.getStartBundleFunctions().isEmpty());
+      mainOutputValues.clear();
+
+      assertThat(
+          context.getPCollectionConsumers().keySet(),
+          containsInAnyOrder(inputPCollectionId, outputPCollectionId));
+
+      FnDataReceiver<WindowedValue<?>> mainInput =
+          context.getPCollectionConsumer(inputPCollectionId);
+      assertThat(mainInput, instanceOf(HandlesSplits.class));
+
+      IntervalWindow window1 = new IntervalWindow(new Instant(5), new Instant(10));
+      IntervalWindow window2 = new IntervalWindow(new Instant(6), new Instant(11));
+      WindowedValue<?> firstValue =
+          valueInWindows(
+              KV.of(
+                  KV.of("5", KV.of(new OffsetRange(0, 5), GlobalWindow.TIMESTAMP_MIN_VALUE)), 5.0),
+              window1,
+              window2);
+      WindowedValue<?> secondValue =
+          valueInWindows(
+              KV.of(
+                  KV.of("2", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)), 2.0),
+              window1,
+              window2);
+      mainInput.accept(firstValue);
+      mainInput.accept(secondValue);
+      // Ensure that each output element is in all the windows and not one per window.
+      assertThat(
+          mainOutputValues,
+          contains(
+              WindowedValues.of(
+                  KV.of(
+                      KV.of("5", KV.of(new OffsetRange(0, 2), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                      2.0),
+                  firstValue.getTimestamp(),
+                  ImmutableList.of(window1, window2),
+                  firstValue.getPaneInfo()),
+              WindowedValues.of(
+                  KV.of(
+                      KV.of("2", KV.of(new OffsetRange(0, 1), GlobalWindow.TIMESTAMP_MIN_VALUE)),
+                      1.0),
+                  firstValue.getTimestamp(),
+                  ImmutableList.of(window1, window2),
+                  firstValue.getPaneInfo())));
+      mainOutputValues.clear();
+
+      assertTrue(context.getFinishBundleFunctions().isEmpty());
+      assertThat(mainOutputValues, empty());
+
+      Iterables.getOnlyElement(context.getTearDownFunctions()).run();
+      assertThat(mainOutputValues, empty());
+    }
+  }
+
+  @RunWith(JUnit4.class)
+  public static class SplitTest {
+    @Rule public final ExpectedException expected = ExpectedException.none();
+    private IntervalWindow window1;
+    private IntervalWindow window2;
+    private IntervalWindow window3;
+    private WindowedValue<String> currentElement;
+    private OffsetRange currentRestriction;
+    private Instant currentWatermarkEstimatorState;
+    KV<Instant, Instant> watermarkAndState;
+
+    private KV<WindowedValue, WindowedValue> createSplitAcrossWindows(
+        List<BoundedWindow> primaryWindows, List<BoundedWindow> residualWindows) {
+      return KV.of(
+          primaryWindows.isEmpty()
+              ? null
+              : WindowedValues.of(
+                  KV.of(
+                      currentElement.getValue(),
+                      KV.of(currentRestriction, currentWatermarkEstimatorState)),
+                  currentElement.getTimestamp(),
+                  primaryWindows,
+                  currentElement.getPaneInfo()),
+          residualWindows.isEmpty()
+              ? null
+              : WindowedValues.of(
+                  KV.of(
+                      currentElement.getValue(),
+                      KV.of(currentRestriction, currentWatermarkEstimatorState)),
+                  currentElement.getTimestamp(),
+                  residualWindows,
+                  currentElement.getPaneInfo()));
+    }
+
+    @Before
+    public void setUp() {
+      window1 = new IntervalWindow(Instant.ofEpochMilli(0), Instant.ofEpochMilli(10));
+      window2 = new IntervalWindow(Instant.ofEpochMilli(10), Instant.ofEpochMilli(20));
+      window3 = new IntervalWindow(Instant.ofEpochMilli(20), Instant.ofEpochMilli(30));
+      currentElement =
+          WindowedValues.of(
+              "a",
+              Instant.ofEpochMilli(57),
+              ImmutableList.of(window1, window2, window3),
+              PaneInfo.NO_FIRING);
+      currentRestriction = new OffsetRange(0L, 100L);
+      currentWatermarkEstimatorState = Instant.ofEpochMilli(21);
+      watermarkAndState = KV.of(Instant.ofEpochMilli(42), Instant.ofEpochMilli(42));
+    }
+
+    private static final String PROCESS_TRANSFORM_ID = "processPTransformId";
+    private static final String TRUNCATE_TRANSFORM_ID = "truncatePTransformId";
+    private static final String PROCESS_INPUT_ID = "processInputId";
+    private static final String TRUNCATE_INPUT_ID = "truncateInputId";
+    private static final String TRUNCATE_OUTPUT_ID = "truncateOutputId";
+
+    private HandlesSplits.SplitResult getProcessElementSplit(String transformId, String inputId) {
+      return SplitResult.of(
+          ImmutableList.of(
+              BundleApplication.newBuilder()
+                  .setTransformId(transformId)
+                  .setInputId(inputId)
+                  .build()),
+          ImmutableList.of(
+              DelayedBundleApplication.newBuilder()
+                  .setApplication(
+                      BundleApplication.newBuilder()
+                          .setTransformId(transformId)
+                          .setInputId(inputId)
+                          .build())
+                  .setRequestedTimeDelay(Durations.fromMillis(1000L))
+                  .build()));
+    }
+
+    @Test
+    public void testConstructSplitResultWithElementSplitFromDelegate() throws Exception {
+      Coder fullInputCoder =
+          getFullInputCoder(
+              StringUtf8Coder.of(),
+              OffsetRange.Coder.of(),
+              InstantCoder.of(),
+              IntervalWindow.getCoder());
+      HandlesSplits.SplitResult elementSplit =
+          getProcessElementSplit(PROCESS_TRANSFORM_ID, PROCESS_INPUT_ID);
+      HandlesSplits.SplitResult result =
+          FnApiDoFnRunner.constructSplitResult(
+              null,
+              elementSplit,
+              fullInputCoder,
+              null,
+              null,
+              TRUNCATE_TRANSFORM_ID,
+              TRUNCATE_INPUT_ID,
+              ImmutableList.of(TRUNCATE_OUTPUT_ID),
+              null);
+      assertEquals(elementSplit.getPrimaryRoots(), result.getPrimaryRoots());
+      assertEquals(elementSplit.getResidualRoots(), result.getResidualRoots());
+    }
+
+    private HandlesSplits createSplitDelegate(
+        double progress, double expectedFraction, SplitResult result) {
+      return new HandlesSplits() {
+        @Override
+        public SplitResult trySplit(double fractionOfRemainder) {
+          checkArgument(fractionOfRemainder == expectedFraction);
+          return result;
+        }
+
+        @Override
+        public double getProgress() {
+          return progress;
+        }
+      };
+    }
+
+    @Test
+    public void testTrySplitForTruncateCheckpointOnFirstWindow() throws Exception {
+      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
+      SplitResult splitResult =
+          SplitResult.of(
+              ImmutableList.of(BundleApplication.getDefaultInstance()),
+              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
+      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.0, splitResult);
+      SplitResultsWithStopIndex result =
+          SplittableTruncateSizedRestrictionsDoFnRunner.computeSplitForTruncate(
+              currentElement,
+              currentRestriction,
+              window1,
+              windows,
+              currentWatermarkEstimatorState,
+              0.0,
+              splitDelegate,
+              0,
+              3);
+      assertEquals(1, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedWindowSplit =
+          createSplitAcrossWindows(ImmutableList.of(), ImmutableList.of(window2, window3));
+      assertEquals(splitResult, result.getDownstreamSplit());
+      assertNull(result.getWindowSplit().getPrimarySplitRoot());
+      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(
+          expectedWindowSplit.getKey(),
+          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
+      assertEquals(
+          expectedWindowSplit.getValue(),
+          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
+    }
+
+    @Test
+    public void testTrySplitForTruncateCheckpointOnFirstWindowAfterOneSplit() throws Exception {
+      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
+      SplitResult splitResult =
+          SplitResult.of(
+              ImmutableList.of(BundleApplication.getDefaultInstance()),
+              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
+      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.0, splitResult);
+      SplitResultsWithStopIndex result =
+          SplittableTruncateSizedRestrictionsDoFnRunner.computeSplitForTruncate(
+              currentElement,
+              currentRestriction,
+              window1,
+              windows,
+              currentWatermarkEstimatorState,
+              0.0,
+              splitDelegate,
+              0,
+              2);
+      assertEquals(1, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedWindowSplit =
+          createSplitAcrossWindows(ImmutableList.of(), ImmutableList.of(window2));
+      assertEquals(splitResult, result.getDownstreamSplit());
+      assertNull(result.getWindowSplit().getPrimarySplitRoot());
+      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(
+          expectedWindowSplit.getKey(),
+          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
+      assertEquals(
+          expectedWindowSplit.getValue(),
+          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
+    }
+
+    @Test
+    public void testTrySplitForTruncateSplitOnFirstWindow() throws Exception {
+      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
+      SplitResult splitResult =
+          SplitResult.of(
+              ImmutableList.of(BundleApplication.getDefaultInstance()),
+              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
+      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.54, splitResult);
+      SplitResultsWithStopIndex result =
+          SplittableTruncateSizedRestrictionsDoFnRunner.computeSplitForTruncate(
+              currentElement,
+              currentRestriction,
+              window1,
+              windows,
+              currentWatermarkEstimatorState,
+              0.2,
+              splitDelegate,
+              0,
+              3);
+      assertEquals(1, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedWindowSplit =
+          createSplitAcrossWindows(ImmutableList.of(), ImmutableList.of(window2, window3));
+      assertEquals(splitResult, result.getDownstreamSplit());
+      assertNull(result.getWindowSplit().getPrimarySplitRoot());
+      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(
+          expectedWindowSplit.getKey(),
+          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
+      assertEquals(
+          expectedWindowSplit.getValue(),
+          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
+    }
+
+    @Test
+    public void testTrySplitForTruncateSplitOnMiddleWindow() throws Exception {
+      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
+      SplitResult splitResult =
+          SplitResult.of(
+              ImmutableList.of(BundleApplication.getDefaultInstance()),
+              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
+      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.34, splitResult);
+      SplitResultsWithStopIndex result =
+          SplittableTruncateSizedRestrictionsDoFnRunner.computeSplitForTruncate(
+              currentElement,
+              currentRestriction,
+              window1,
+              windows,
+              currentWatermarkEstimatorState,
+              0.2,
+              splitDelegate,
+              1,
+              3);
+      assertEquals(2, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedWindowSplit =
+          createSplitAcrossWindows(ImmutableList.of(window1), ImmutableList.of(window3));
+      assertEquals(splitResult, result.getDownstreamSplit());
+      assertNull(result.getWindowSplit().getPrimarySplitRoot());
+      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(
+          expectedWindowSplit.getKey(),
+          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
+      assertEquals(
+          expectedWindowSplit.getValue(),
+          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
+    }
+
+    @Test
+    public void testTrySplitForTruncateSplitOnLastWindow() throws Exception {
+      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
+      SplitResult splitResult =
+          SplitResult.of(
+              ImmutableList.of(BundleApplication.getDefaultInstance()),
+              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
+      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.2, splitResult);
+      SplitResultsWithStopIndex result =
+          SplittableTruncateSizedRestrictionsDoFnRunner.computeSplitForTruncate(
+              currentElement,
+              currentRestriction,
+              window1,
+              windows,
+              currentWatermarkEstimatorState,
+              0.2,
+              splitDelegate,
+              2,
+              3);
+      assertEquals(3, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedWindowSplit =
+          createSplitAcrossWindows(ImmutableList.of(window1, window2), ImmutableList.of());
+      assertEquals(splitResult, result.getDownstreamSplit());
+      assertNull(result.getWindowSplit().getPrimarySplitRoot());
+      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(
+          expectedWindowSplit.getKey(),
+          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
+      assertEquals(
+          expectedWindowSplit.getValue(),
+          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
+    }
+
+    @Test
+    public void testTrySplitForTruncateSplitOnFirstWindowFallback() throws Exception {
+      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
+      SplitResult unusedSplitResult =
+          SplitResult.of(
+              ImmutableList.of(BundleApplication.getDefaultInstance()),
+              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
+      HandlesSplits splitDelegate = createSplitDelegate(1.0, 0.0, unusedSplitResult);
+      SplitResultsWithStopIndex result =
+          SplittableTruncateSizedRestrictionsDoFnRunner.computeSplitForTruncate(
+              currentElement,
+              currentRestriction,
+              window1,
+              windows,
+              currentWatermarkEstimatorState,
+              0.0,
+              splitDelegate,
+              0,
+              3);
+      assertEquals(1, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedWindowSplit =
+          createSplitAcrossWindows(ImmutableList.of(window1), ImmutableList.of(window2, window3));
+      assertNull(result.getDownstreamSplit());
+      assertNull(result.getWindowSplit().getPrimarySplitRoot());
+      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(
+          expectedWindowSplit.getKey(),
+          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
+      assertEquals(
+          expectedWindowSplit.getValue(),
+          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
+    }
+
+    @Test
+    public void testTrySplitForTruncateSplitOnLastWindowWhenNoElementSplit() throws Exception {
+      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
+      HandlesSplits splitDelegate = createSplitDelegate(1.0, 0.0, null);
+      SplitResultsWithStopIndex result =
+          SplittableTruncateSizedRestrictionsDoFnRunner.computeSplitForTruncate(
+              currentElement,
+              currentRestriction,
+              window1,
+              windows,
+              currentWatermarkEstimatorState,
+              0.0,
+              splitDelegate,
+              2,
+              3);
+      assertNull(result);
+    }
+
+    @Test
+    public void testTrySplitForTruncateOnWindowBoundaryRoundUp() throws Exception {
+      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
+      SplitResult unusedSplitResult =
+          SplitResult.of(
+              ImmutableList.of(BundleApplication.getDefaultInstance()),
+              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
+      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.0, unusedSplitResult);
+      SplitResultsWithStopIndex result =
+          SplittableTruncateSizedRestrictionsDoFnRunner.computeSplitForTruncate(
+              currentElement,
+              currentRestriction,
+              window1,
+              windows,
+              currentWatermarkEstimatorState,
+              0.6,
+              splitDelegate,
+              0,
+              3);
+      assertEquals(2, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedWindowSplit =
+          createSplitAcrossWindows(ImmutableList.of(window1, window2), ImmutableList.of(window3));
+      assertNull(result.getDownstreamSplit());
+      assertNull(result.getWindowSplit().getPrimarySplitRoot());
+      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(
+          expectedWindowSplit.getKey(),
+          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
+      assertEquals(
+          expectedWindowSplit.getValue(),
+          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
+    }
+
+    @Test
+    public void testTrySplitForTruncateOnWindowBoundaryRoundDown() throws Exception {
+      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
+      SplitResult unusedSplitResult =
+          SplitResult.of(
+              ImmutableList.of(BundleApplication.getDefaultInstance()),
+              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
+      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.0, unusedSplitResult);
+      SplitResultsWithStopIndex result =
+          SplittableTruncateSizedRestrictionsDoFnRunner.computeSplitForTruncate(
+              currentElement,
+              currentRestriction,
+              window1,
+              windows,
+              currentWatermarkEstimatorState,
+              0.3,
+              splitDelegate,
+              0,
+              3);
+      assertEquals(1, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedWindowSplit =
+          createSplitAcrossWindows(ImmutableList.of(window1), ImmutableList.of(window2, window3));
+      assertNull(result.getDownstreamSplit());
+      assertNull(result.getWindowSplit().getPrimarySplitRoot());
+      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(
+          expectedWindowSplit.getKey(),
+          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
+      assertEquals(
+          expectedWindowSplit.getValue(),
+          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
+    }
+
+    @Test
+    public void testTrySplitForTruncateOnWindowBoundaryRoundDownOnLastWindow() throws Exception {
+      List<BoundedWindow> windows = ImmutableList.copyOf(currentElement.getWindows());
+      SplitResult unusedSplitResult =
+          SplitResult.of(
+              ImmutableList.of(BundleApplication.getDefaultInstance()),
+              ImmutableList.of(DelayedBundleApplication.getDefaultInstance()));
+      HandlesSplits splitDelegate = createSplitDelegate(0.3, 0.0, unusedSplitResult);
+      SplitResultsWithStopIndex result =
+          SplittableTruncateSizedRestrictionsDoFnRunner.computeSplitForTruncate(
+              currentElement,
+              currentRestriction,
+              window1,
+              windows,
+              currentWatermarkEstimatorState,
+              0.6,
+              splitDelegate,
+              0,
+              3);
+      assertEquals(2, result.getNewWindowStopIndex());
+      KV<WindowedValue, WindowedValue> expectedWindowSplit =
+          createSplitAcrossWindows(ImmutableList.of(window1, window2), ImmutableList.of(window3));
+      assertNull(result.getDownstreamSplit());
+      assertNull(result.getWindowSplit().getPrimarySplitRoot());
+      assertNull(result.getWindowSplit().getResidualSplitRoot());
+      assertEquals(
+          expectedWindowSplit.getKey(),
+          result.getWindowSplit().getPrimaryInFullyProcessedWindowsRoot());
+      assertEquals(
+          expectedWindowSplit.getValue(),
+          result.getWindowSplit().getResidualInUnprocessedWindowsRoot());
+    }
+
+    private Coder getFullInputCoder(
+        Coder elementCoder, Coder restrictionCoder, Coder watermarkStateCoder, Coder windowCoder) {
+      Coder inputCoder =
+          KvCoder.of(
+              KvCoder.of(elementCoder, KvCoder.of(restrictionCoder, watermarkStateCoder)),
+              DoubleCoder.of());
+      return WindowedValues.getFullCoder(inputCoder, windowCoder);
+    }
+  }
+}
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/ExecutionStateSamplerTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/ExecutionStateSamplerTest.java
index b5a860704a8b..8b9678733f85 100644
--- a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/ExecutionStateSamplerTest.java
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/ExecutionStateSamplerTest.java
@@ -25,11 +25,18 @@
 import static org.junit.Assert.assertFalse;
 import static org.junit.Assert.assertNull;
 import static org.junit.Assert.assertTrue;
+import static org.mockito.ArgumentMatchers.anyString;
+import static org.mockito.Mockito.atLeastOnce;
 import static org.mockito.Mockito.mock;
+import static org.mockito.Mockito.verify;
+import static org.mockito.Mockito.verifyNoInteractions;
+import static org.mockito.Mockito.when;
 
 import java.util.HashMap;
 import java.util.Map;
 import java.util.concurrent.CountDownLatch;
+import java.util.concurrent.TimeUnit;
+import java.util.function.Consumer;
 import org.apache.beam.fn.harness.control.ExecutionStateSampler.ExecutionState;
 import org.apache.beam.fn.harness.control.ExecutionStateSampler.ExecutionStateTracker;
 import org.apache.beam.fn.harness.control.ExecutionStateSampler.ExecutionStateTrackerStatus;
@@ -77,6 +84,7 @@ public class ExecutionStateSamplerTest {
   private static final Histogram TEST_USER_HISTOGRAM =
       new DelegatingHistogram(
           MetricName.named("foo", "histogram"), HistogramData.LinearBuckets.of(0, 100, 1), false);
+  private final Consumer<String> mockOnTimeoutExceededCallback = mock(Consumer.class);
 
   @Rule public ExpectedLogs expectedLogs = ExpectedLogs.none(ExecutionStateSampler.class);
 
@@ -92,7 +100,8 @@ public void testSamplingProducesCorrectFinalResults() throws Exception {
         new ExecutionStateSampler(
             PipelineOptionsFactory.fromArgs("--experiments=state_sampling_period_millis=10")
                 .create(),
-            clock);
+            clock,
+            mockOnTimeoutExceededCallback);
     ExecutionStateTracker tracker1 = sampler.create();
     ExecutionState state1 =
         tracker1.create("shortId1", "ptransformId1", "ptransformIdName1", "process");
@@ -291,7 +300,8 @@ public void testSamplingDoesntReportDuplicateFinalResults() throws Exception {
         new ExecutionStateSampler(
             PipelineOptionsFactory.fromArgs("--experiments=state_sampling_period_millis=10")
                 .create(),
-            clock);
+            clock,
+            mockOnTimeoutExceededCallback);
     ExecutionStateTracker tracker1 = sampler.create();
     ExecutionState state1 =
         tracker1.create("shortId1", "ptransformId1", "ptransformIdName1", "process");
@@ -379,7 +389,8 @@ public void testCountersReturnedAreBasedUponCurrentExecutionState() throws Excep
         new ExecutionStateSampler(
             PipelineOptionsFactory.fromArgs("--experiments=state_sampling_period_millis=10")
                 .create(),
-            clock);
+            clock,
+            mockOnTimeoutExceededCallback);
     ExecutionStateTracker tracker = sampler.create();
     MetricsEnvironment.setCurrentContainer(tracker.getMetricsContainer());
     ExecutionState state = tracker.create("shortId", "ptransformId", "uniqueName", "state");
@@ -517,7 +528,8 @@ public void testTrackerReuse() throws Exception {
         new ExecutionStateSampler(
             PipelineOptionsFactory.fromArgs("--experiments=state_sampling_period_millis=10")
                 .create(),
-            clock);
+            clock,
+            mockOnTimeoutExceededCallback);
     ExecutionStateTracker tracker = sampler.create();
     MetricsEnvironment.setCurrentContainer(tracker.getMetricsContainer());
     ExecutionState state = tracker.create("shortId", "ptransformId", "ptransformIdName", "process");
@@ -617,7 +629,8 @@ public void testLullDetectionOccursInActiveBundle() throws Exception {
         new ExecutionStateSampler(
             PipelineOptionsFactory.fromArgs("--experiments=state_sampling_period_millis=10")
                 .create(),
-            clock);
+            clock,
+            mockOnTimeoutExceededCallback);
     ExecutionStateTracker tracker = sampler.create();
 
     CountDownLatch waitTillActive = new CountDownLatch(1);
@@ -660,7 +673,8 @@ public void testLullDetectionOccursInActiveState() throws Exception {
         new ExecutionStateSampler(
             PipelineOptionsFactory.fromArgs("--experiments=state_sampling_period_millis=10")
                 .create(),
-            clock);
+            clock,
+            mockOnTimeoutExceededCallback);
     ExecutionStateTracker tracker = sampler.create();
     ExecutionState state = tracker.create("shortId", "ptransformId", "ptransformIdName", "process");
 
@@ -706,7 +720,8 @@ public void testErrorState() throws Exception {
         new ExecutionStateSampler(
             PipelineOptionsFactory.fromArgs("--experiments=state_sampling_period_millis=10")
                 .create(),
-            clock);
+            clock,
+            mockOnTimeoutExceededCallback);
     ExecutionStateTracker tracker = sampler.create();
     ExecutionState state1 =
         tracker.create("shortId1", "ptransformId1", "ptransformIdName1", "process");
@@ -722,4 +737,163 @@ public void testErrorState() throws Exception {
     tracker.reset();
     assertTrue(state1.error());
   }
+
+  @Test
+  public void testDefaultElementProcessingTimeoutMinutesHasNoTimeout() throws Exception {
+    MillisProvider clock = mock(MillisProvider.class);
+    ExecutionStateSampler sampler =
+        new ExecutionStateSampler(
+            PipelineOptionsFactory.create(), clock, mockOnTimeoutExceededCallback);
+    ExecutionStateTracker tracker = sampler.create();
+    ExecutionState state = tracker.create("shortId", "ptransformId", "ptransformIdName", "process");
+
+    CountDownLatch waitTillActive = new CountDownLatch(1);
+    CountDownLatch waitForSamples = new CountDownLatch(10);
+    Thread testThread = Thread.currentThread();
+    Mockito.when(clock.getMillis())
+        .thenAnswer(
+            new Answer<Long>() {
+              private long currentTime;
+
+              @Override
+              public Long answer(InvocationOnMock invocation) throws Throwable {
+                if (Thread.currentThread().equals(testThread)) {
+                  return 0L;
+                } else {
+                  // Block the state sampling thread till the state is active
+                  // and unblock the state transition once a certain number of samples
+                  // have been taken.
+                  waitTillActive.await();
+                  waitForSamples.countDown();
+                  currentTime += Duration.standardMinutes(100000).getMillis();
+                  return currentTime;
+                }
+              }
+            });
+
+    tracker.start("bundleId");
+    state.activate();
+    waitTillActive.countDown();
+    waitForSamples.await();
+    state.deactivate();
+    tracker.reset();
+    sampler.stop();
+    verifyNoInteractions(mockOnTimeoutExceededCallback);
+  }
+
+  @Test
+  public void testUserSpecifiedElementProcessingTimeoutNotExceeded() throws Exception {
+    MillisProvider clock = mock(MillisProvider.class);
+    ExecutionStateSampler sampler =
+        new ExecutionStateSampler(
+            PipelineOptionsFactory.fromArgs("--elementProcessingTimeoutMinutes=20").create(),
+            clock,
+            mockOnTimeoutExceededCallback);
+    ExecutionStateTracker tracker = sampler.create();
+    ExecutionState state = tracker.create("shortId", "ptransformId", "ptransformIdName", "process");
+
+    CountDownLatch waitTillActive = new CountDownLatch(1);
+    CountDownLatch waitForSamples = new CountDownLatch(10);
+    Thread testThread = Thread.currentThread();
+    when(clock.getMillis())
+        .thenAnswer(
+            new Answer<Long>() {
+              private long currentTime;
+
+              @Override
+              public Long answer(InvocationOnMock invocation) throws Throwable {
+                if (Thread.currentThread().equals(testThread)) {
+                  return 0L;
+                } else {
+                  // Block the state sampling thread till the state is active
+                  // and unblock the state transition once a certain number of samples
+                  // have been taken.
+                  waitTillActive.await();
+                  // Freeze time after the desired number of samples to avoid races where
+                  // the sampling loop spins and exceeds the timeout before we deactivate.
+                  if (waitForSamples.getCount() > 0) {
+                    waitForSamples.countDown();
+                    currentTime += Duration.standardMinutes(1).getMillis();
+                  }
+                  return currentTime;
+                }
+              }
+            });
+
+    tracker.start("bundleId");
+    state.activate();
+    waitTillActive.countDown();
+    waitForSamples.await();
+    state.deactivate();
+    tracker.reset();
+    sampler.stop();
+    verifyNoInteractions(mockOnTimeoutExceededCallback);
+  }
+
+  @Test
+  public void testUserSpecifiedElementProcessingTimeoutExceeded() throws Exception {
+    MillisProvider clock = mock(MillisProvider.class);
+    ExecutionStateSampler sampler =
+        new ExecutionStateSampler(
+            PipelineOptionsFactory.fromArgs("--elementProcessingTimeoutMinutes=20").create(),
+            clock,
+            mockOnTimeoutExceededCallback);
+    ExecutionStateTracker tracker = sampler.create();
+    ExecutionState state = tracker.create("shortId", "ptransformId", "ptransformIdName", "process");
+
+    CountDownLatch waitTillActive = new CountDownLatch(1);
+    CountDownLatch waitForSamples = new CountDownLatch(10);
+    Thread testThread = Thread.currentThread();
+    when(clock.getMillis())
+        .thenAnswer(
+            new Answer<Long>() {
+              private long currentTime;
+
+              @Override
+              public Long answer(InvocationOnMock invocation) throws Throwable {
+                if (Thread.currentThread().equals(testThread)) {
+                  return 0L;
+                } else {
+                  // Block the state sampling thread till the state is active
+                  // and unblock the state transition once a certain number of samples
+                  // have been taken.
+                  waitTillActive.await();
+                  waitForSamples.countDown();
+                  currentTime += Duration.standardMinutes(100).getMillis();
+                  return currentTime;
+                }
+              }
+            });
+
+    tracker.start("bundleId");
+    state.activate();
+    waitTillActive.countDown();
+    waitForSamples.await();
+    state.deactivate();
+    tracker.reset();
+    sampler.stop();
+    verify(mockOnTimeoutExceededCallback, atLeastOnce()).accept(anyString());
+  }
+
+  @Test
+  public void testUserSpecifiedElementProcessingTimeoutMinutes() {
+    MillisProvider clock = mock(MillisProvider.class);
+    ExecutionStateSampler sampler =
+        new ExecutionStateSampler(
+            PipelineOptionsFactory.fromArgs("--elementProcessingTimeoutMinutes=2").create(),
+            clock,
+            mockOnTimeoutExceededCallback);
+    assertThat(
+        sampler.getUserSpecifiedLullTimeMsForRestart(), equalTo(TimeUnit.MINUTES.toMillis(2)));
+  }
+
+  @Test
+  public void testDefaultElementProcessingTimeoutMinutes() {
+    MillisProvider clock = mock(MillisProvider.class);
+    ExecutionStateSampler sampler =
+        new ExecutionStateSampler(
+            PipelineOptionsFactory.create(), clock, mockOnTimeoutExceededCallback);
+    assertThat(sampler.getUserSpecifiedLullTimeMsForRestart(), equalTo(0L));
+    assertThat(sampler.getUserSpecifiedTimeoutForRestart(), equalTo(false));
+  }
 }
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/HarnessMonitoringInfosInstructionHandlerTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/HarnessMonitoringInfosInstructionHandlerTest.java
index ac69ed29a565..9e69cb2ec700 100644
--- a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/HarnessMonitoringInfosInstructionHandlerTest.java
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/HarnessMonitoringInfosInstructionHandlerTest.java
@@ -30,7 +30,10 @@
 import org.apache.beam.sdk.metrics.Counter;
 import org.apache.beam.sdk.metrics.MetricsEnvironment;
 import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
 
+@RunWith(JUnit4.class)
 public class HarnessMonitoringInfosInstructionHandlerTest {
 
   @Test
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/ProcessBundleHandlerTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/ProcessBundleHandlerTest.java
index 4da0998837ba..a7a62571e38e 100644
--- a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/ProcessBundleHandlerTest.java
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/control/ProcessBundleHandlerTest.java
@@ -180,7 +180,7 @@ public void setUp() {
     MockitoAnnotations.initMocks(this);
     TestBundleProcessor.resetCnt = 0;
     executionStateSampler =
-        new ExecutionStateSampler(PipelineOptionsFactory.create(), System::currentTimeMillis);
+        new ExecutionStateSampler(PipelineOptionsFactory.create(), System::currentTimeMillis, null);
   }
 
   @After
@@ -236,6 +236,7 @@ public void finishBundle(FinishBundleContext context) {
     }
   }
 
+  @SuppressWarnings("ExtendsAutoValue")
   private static class TestBundleProcessor extends BundleProcessor {
     static int resetCnt = 0;
 
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/data/PCollectionConsumerRegistryTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/data/PCollectionConsumerRegistryTest.java
index f4207d472f84..7ba2f8921be3 100644
--- a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/data/PCollectionConsumerRegistryTest.java
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/data/PCollectionConsumerRegistryTest.java
@@ -130,7 +130,8 @@ public class PCollectionConsumerRegistryTest {
 
   @Before
   public void setUp() throws Exception {
-    sampler = new ExecutionStateSampler(PipelineOptionsFactory.create(), System::currentTimeMillis);
+    sampler =
+        new ExecutionStateSampler(PipelineOptionsFactory.create(), System::currentTimeMillis, null);
   }
 
   @After
@@ -632,7 +633,7 @@ public StreamObserver<BeamFnApi.LogEntry.List> logging(
 
     // This section is to set up the StateSampler with the expected metadata.
     ExecutionStateSampler sampler =
-        new ExecutionStateSampler(PipelineOptionsFactory.create(), System::currentTimeMillis);
+        new ExecutionStateSampler(PipelineOptionsFactory.create(), System::currentTimeMillis, null);
     ExecutionStateSampler.ExecutionStateTracker stateTracker = sampler.create();
     stateTracker.start("process-bundle");
     ExecutionStateSampler.ExecutionState state =
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/data/PTransformFunctionRegistryTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/data/PTransformFunctionRegistryTest.java
index 6e06c16c653f..238e6e75c77f 100644
--- a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/data/PTransformFunctionRegistryTest.java
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/data/PTransformFunctionRegistryTest.java
@@ -47,7 +47,8 @@ public class PTransformFunctionRegistryTest {
 
   @Before
   public void setUp() {
-    sampler = new ExecutionStateSampler(PipelineOptionsFactory.create(), System::currentTimeMillis);
+    sampler =
+        new ExecutionStateSampler(PipelineOptionsFactory.create(), System::currentTimeMillis, null);
   }
 
   @After
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/logging/BeamFnLoggingClientTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/logging/BeamFnLoggingClientTest.java
index e59f7298d801..249e720d1e42 100644
--- a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/logging/BeamFnLoggingClientTest.java
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/logging/BeamFnLoggingClientTest.java
@@ -131,7 +131,7 @@ public class BeamFnLoggingClientTest {
   @Test
   public void testLogging() throws Exception {
     ExecutionStateSampler sampler =
-        new ExecutionStateSampler(PipelineOptionsFactory.create(), null);
+        new ExecutionStateSampler(PipelineOptionsFactory.create(), null, null);
     ExecutionStateSampler.ExecutionStateTracker stateTracker = sampler.create();
     ExecutionStateSampler.ExecutionState state =
         stateTracker.create("shortId", "ptransformId", "ptransformIdName", "process");
@@ -220,8 +220,9 @@ public synchronized String formatMessage(LogRecord record) {
               }
             });
       }
-      MDC.put("testMdcKey", "testMdcValue");
-      configuredLogger.log(TEST_RECORD);
+      try (MDC.MDCCloseable ignored = MDC.putCloseable("testMdcKey", "testMdcValue")) {
+        configuredLogger.log(TEST_RECORD);
+      }
 
       client.close();
 
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/state/MultimapUserStateTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/state/MultimapUserStateTest.java
index 17550793a8b2..48c9ce43bdf0 100644
--- a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/state/MultimapUserStateTest.java
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/state/MultimapUserStateTest.java
@@ -21,6 +21,7 @@
 import static java.util.Collections.singletonList;
 import static org.hamcrest.MatcherAssert.assertThat;
 import static org.hamcrest.Matchers.emptyIterable;
+import static org.hamcrest.collection.ArrayMatching.arrayContainingInAnyOrder;
 import static org.hamcrest.core.Is.is;
 import static org.junit.Assert.assertArrayEquals;
 import static org.junit.Assert.assertEquals;
@@ -167,7 +168,9 @@ public void testKeys() throws Exception {
     userState.put(A3, "V1");
     userState.put(A1, "V3");
     assertArrayEquals(new byte[][] {A1, A2}, Iterables.toArray(initKeys, byte[].class));
-    assertArrayEquals(new byte[][] {A1, A2, A3}, Iterables.toArray(userState.keys(), byte[].class));
+    assertThat(
+        Iterables.toArray(userState.keys(), byte[].class),
+        is(arrayContainingInAnyOrder(A1, A2, A3)));
 
     userState.clear();
     assertArrayEquals(new byte[][] {A1, A2}, Iterables.toArray(initKeys, byte[].class));
@@ -822,8 +825,9 @@ public void testKeysCached() throws Exception {
 
       userState.put(A2, "V1");
       userState.put(A3, "V1");
-      assertArrayEquals(
-          new byte[][] {A1, A2, A3}, Iterables.toArray(userState.keys(), byte[].class));
+      assertThat(
+          Iterables.toArray(userState.keys(), byte[].class),
+          is(arrayContainingInAnyOrder(A1, A2, A3)));
       userState.asyncClose();
     }
 
@@ -841,8 +845,9 @@ public void testKeysCached() throws Exception {
               ByteArrayCoder.of(),
               StringUtf8Coder.of());
 
-      assertArrayEquals(
-          new byte[][] {A1, A2, A3}, Iterables.toArray(userState.keys(), byte[].class));
+      assertThat(
+          Iterables.toArray(userState.keys(), byte[].class),
+          is(arrayContainingInAnyOrder(A1, A2, A3)));
       userState.asyncClose();
     }
   }
diff --git a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/state/StateFetchingIteratorsTest.java b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/state/StateFetchingIteratorsTest.java
index 2bb591e52982..d1cacf534ee5 100644
--- a/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/state/StateFetchingIteratorsTest.java
+++ b/sdks/java/harness/src/test/java/org/apache/beam/fn/harness/state/StateFetchingIteratorsTest.java
@@ -47,6 +47,7 @@
 import org.apache.beam.model.fnexecution.v1.BeamFnApi.StateRequest;
 import org.apache.beam.model.fnexecution.v1.BeamFnApi.StateResponse;
 import org.apache.beam.sdk.coders.BigEndianIntegerCoder;
+import org.apache.beam.sdk.fn.data.WeightedList;
 import org.apache.beam.sdk.fn.stream.PrefetchableIterator;
 import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.ByteString;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
@@ -283,12 +284,13 @@ public void testBlocksPrefixShrinkage() throws Exception {
     public void testBlocksWeight() throws Exception {
       List<Block<String>> originalBlocks =
           Arrays.asList(
-              Block.mutatedBlock(Arrays.asList("A"), 10),
-              Block.mutatedBlock(Arrays.asList("B"), Long.MAX_VALUE / 2),
-              Block.mutatedBlock(Arrays.asList("C"), Long.MAX_VALUE / 2),
-              Block.mutatedBlock(Arrays.asList("D"), 5));
+              Block.mutatedBlock(WeightedList.of(Arrays.asList("A"), 10_000)),
+              Block.mutatedBlock(WeightedList.of(Arrays.asList("B"), Long.MAX_VALUE / 2)),
+              Block.mutatedBlock(WeightedList.of(Arrays.asList("C"), Long.MAX_VALUE / 2)),
+              Block.mutatedBlock(WeightedList.of(Arrays.asList("D"), 5)));
       BlocksPrefix<String> blocks = new BlocksPrefix<>(originalBlocks.subList(0, 2));
-      assertEquals(10 + Long.MAX_VALUE / 2, blocks.getWeight());
+      assertTrue(10_000 + Long.MAX_VALUE / 2 < blocks.getWeight());
+      assertTrue(blocks.getWeight() < 10_000 + Long.MAX_VALUE / 2 + 100);
 
       BlocksPrefix<String> blocksOverflow = new BlocksPrefix<>(originalBlocks);
       assertEquals(Long.MAX_VALUE, blocksOverflow.getWeight());
diff --git a/sdks/java/io/amazon-web-services2/src/main/java/org/apache/beam/sdk/io/aws2/kinesis/KinesisIO.java b/sdks/java/io/amazon-web-services2/src/main/java/org/apache/beam/sdk/io/aws2/kinesis/KinesisIO.java
index 7302a5a47600..835bde170d33 100644
--- a/sdks/java/io/amazon-web-services2/src/main/java/org/apache/beam/sdk/io/aws2/kinesis/KinesisIO.java
+++ b/sdks/java/io/amazon-web-services2/src/main/java/org/apache/beam/sdk/io/aws2/kinesis/KinesisIO.java
@@ -1244,7 +1244,7 @@ public void refreshPeriodically(
         private void refresh(
             KinesisAsyncClient client,
             Supplier<Instant> nextRefreshFn,
-            TreeSet<BigInteger> bounds,
+            NavigableSet<BigInteger> bounds,
             @Nullable String nextToken) {
           ListShardsRequest.Builder reqBuilder =
               ListShardsRequest.builder().shardFilter(f -> f.type(AT_LATEST));
diff --git a/sdks/java/io/amazon-web-services2/src/test/java/org/apache/beam/sdk/io/aws2/StaticSupplier.java b/sdks/java/io/amazon-web-services2/src/test/java/org/apache/beam/sdk/io/aws2/StaticSupplier.java
index bd56c241429c..5eab91b24f76 100644
--- a/sdks/java/io/amazon-web-services2/src/test/java/org/apache/beam/sdk/io/aws2/StaticSupplier.java
+++ b/sdks/java/io/amazon-web-services2/src/test/java/org/apache/beam/sdk/io/aws2/StaticSupplier.java
@@ -45,6 +45,7 @@ public V get() {
   }
 
   @Override
+  @SuppressWarnings("Finalize")
   protected void finalize() {
     if (cleanup) {
       objects.remove(id);
diff --git a/sdks/java/io/amazon-web-services2/src/test/java/org/apache/beam/sdk/io/aws2/common/AsyncBatchWriteHandlerTest.java b/sdks/java/io/amazon-web-services2/src/test/java/org/apache/beam/sdk/io/aws2/common/AsyncBatchWriteHandlerTest.java
index 7bf28c4f394a..cd7aca3c9f9b 100644
--- a/sdks/java/io/amazon-web-services2/src/test/java/org/apache/beam/sdk/io/aws2/common/AsyncBatchWriteHandlerTest.java
+++ b/sdks/java/io/amazon-web-services2/src/test/java/org/apache/beam/sdk/io/aws2/common/AsyncBatchWriteHandlerTest.java
@@ -219,11 +219,16 @@ public void correctlyLimitConcurrency() throws Throwable {
     assertThat(future).isNotDone();
 
     // complete responses and unblock last request
+    CompletableFuture<List<Boolean>> nextResults = new CompletableFuture<>();
     resultsByPos.complete(emptyList());
+    resultsByPos = nextResults;
 
     eventually(
         5,
         () -> verify(handler.submitFn, times(CONCURRENCY + 1)).apply("destination", emptyList()));
+
+    nextResults.complete(emptyList());
+
     handler.waitForCompletion();
     assertThat(future).isDone();
   }
diff --git a/sdks/java/io/cdap/src/main/java/org/apache/beam/sdk/io/cdap/context/FailureCollectorWrapper.java b/sdks/java/io/cdap/src/main/java/org/apache/beam/sdk/io/cdap/context/FailureCollectorWrapper.java
index 12ba03df379b..c61240c289a5 100644
--- a/sdks/java/io/cdap/src/main/java/org/apache/beam/sdk/io/cdap/context/FailureCollectorWrapper.java
+++ b/sdks/java/io/cdap/src/main/java/org/apache/beam/sdk/io/cdap/context/FailureCollectorWrapper.java
@@ -67,7 +67,7 @@ public ValidationException getOrThrowException() throws ValidationException {
   }
 
   @Override
-  public ArrayList<ValidationFailure> getValidationFailures() {
+  public List<ValidationFailure> getValidationFailures() {
     return this.failuresCollection;
   }
 }
diff --git a/sdks/java/io/cdap/src/test/java/org/apache/beam/sdk/io/cdap/context/FailureCollectorWrapperTest.java b/sdks/java/io/cdap/src/test/java/org/apache/beam/sdk/io/cdap/context/FailureCollectorWrapperTest.java
index 5031cb7e0af7..a0b5edf6a6aa 100644
--- a/sdks/java/io/cdap/src/test/java/org/apache/beam/sdk/io/cdap/context/FailureCollectorWrapperTest.java
+++ b/sdks/java/io/cdap/src/test/java/org/apache/beam/sdk/io/cdap/context/FailureCollectorWrapperTest.java
@@ -23,7 +23,7 @@
 import io.cdap.cdap.etl.api.validation.CauseAttributes;
 import io.cdap.cdap.etl.api.validation.ValidationException;
 import io.cdap.cdap.etl.api.validation.ValidationFailure;
-import java.util.ArrayList;
+import java.util.List;
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.junit.runners.JUnit4;
@@ -64,7 +64,7 @@ public void getOrThrowException() {
     assertEquals(expectedMessage, e.getMessage());
 
     // A case when return ValidationException with empty collector
-    ArrayList<ValidationFailure> exceptionCollector =
+    List<ValidationFailure> exceptionCollector =
         emptyFailureCollectorWrapper.getValidationFailures();
     assertEquals(0, exceptionCollector.size());
   }
@@ -81,9 +81,8 @@ public void getValidationFailures() {
     failureCollectorWrapper.addFailure(error.getMessage(), null);
 
     /** act */
-    ArrayList<ValidationFailure> exceptionCollector =
-        failureCollectorWrapper.getValidationFailures();
-    ArrayList<ValidationFailure> emptyExceptionCollector =
+    List<ValidationFailure> exceptionCollector = failureCollectorWrapper.getValidationFailures();
+    List<ValidationFailure> emptyExceptionCollector =
         emptyFailureCollectorWrapper.getValidationFailures();
 
     /** assert */
diff --git a/sdks/java/io/common/src/main/java/org/apache/beam/sdk/io/common/DatabaseTestHelper.java b/sdks/java/io/common/src/main/java/org/apache/beam/sdk/io/common/DatabaseTestHelper.java
index 319e932265db..bbd7c6eaa38c 100644
--- a/sdks/java/io/common/src/main/java/org/apache/beam/sdk/io/common/DatabaseTestHelper.java
+++ b/sdks/java/io/common/src/main/java/org/apache/beam/sdk/io/common/DatabaseTestHelper.java
@@ -174,7 +174,7 @@ public static void createTableWithStatement(DataSource dataSource, String stmt)
     }
   }
 
-  public static ArrayList<KV<Integer, String>> getTestDataToWrite(long rowsToAdd) {
+  public static List<KV<Integer, String>> getTestDataToWrite(long rowsToAdd) {
     ArrayList<KV<Integer, String>> data = new ArrayList<>();
     for (int i = 0; i < rowsToAdd; i++) {
       KV<Integer, String> kv = KV.of(i, TestRow.getNameForSeed(i));
diff --git a/sdks/java/io/common/src/main/java/org/apache/beam/sdk/io/common/IOITHelper.java b/sdks/java/io/common/src/main/java/org/apache/beam/sdk/io/common/IOITHelper.java
index d14eacb8230b..57dca46af0b5 100644
--- a/sdks/java/io/common/src/main/java/org/apache/beam/sdk/io/common/IOITHelper.java
+++ b/sdks/java/io/common/src/main/java/org/apache/beam/sdk/io/common/IOITHelper.java
@@ -86,7 +86,7 @@ public static void executeWithRetry(int maxAttempts, long minDelay, RetryFunctio
         function.run();
         return;
       } catch (Exception e) {
-        LOG.warn("Attempt #{} of {} failed: {}.", attempts, maxAttempts, e.getMessage());
+        LOG.warn("Attempt #{} of {} failed", attempts, maxAttempts, e);
         if (attempts == maxAttempts) {
           throw e;
         } else {
diff --git a/sdks/java/io/debezium/src/main/java/org/apache/beam/io/debezium/DebeziumReadSchemaTransformProvider.java b/sdks/java/io/debezium/src/main/java/org/apache/beam/io/debezium/DebeziumReadSchemaTransformProvider.java
index a0838174759c..d5f3f98f3b5e 100644
--- a/sdks/java/io/debezium/src/main/java/org/apache/beam/io/debezium/DebeziumReadSchemaTransformProvider.java
+++ b/sdks/java/io/debezium/src/main/java/org/apache/beam/io/debezium/DebeziumReadSchemaTransformProvider.java
@@ -97,8 +97,7 @@ public PCollectionRowTuple expand(PCollectionRowTuple input) {
                   + ". Unable to select a JDBC driver for it. Supported Databases are: "
                   + String.join(", ", connectors));
         }
-        Class<?> connectorClass =
-            Objects.requireNonNull(Connectors.valueOf(configuration.getDatabase())).getConnector();
+        Class<?> connectorClass = Connectors.valueOf(configuration.getDatabase()).getConnector();
         DebeziumIO.ConnectorConfiguration connectorConfiguration =
             DebeziumIO.ConnectorConfiguration.create()
                 .withUsername(configuration.getUsername())
diff --git a/sdks/java/io/expansion-service/build.gradle b/sdks/java/io/expansion-service/build.gradle
index c139315d925f..08c3f2b051dc 100644
--- a/sdks/java/io/expansion-service/build.gradle
+++ b/sdks/java/io/expansion-service/build.gradle
@@ -25,6 +25,8 @@ applyJavaNature(
   exportJavadoc: false,
   validateShadowJar: false,
   shadowClosure: {},
+  // iceberg requires Java11+
+  requireJavaVersion: JavaVersion.VERSION_11
 )
 
 // We don't want to use the latest version for the entire beam sdk since beam Java users can override it themselves.
@@ -33,9 +35,8 @@ applyJavaNature(
 configurations.runtimeClasspath {
   // Pin kafka-clients version due to <3.4.0 missing auth callback classes.
   resolutionStrategy.force 'org.apache.kafka:kafka-clients:3.9.0'
-  // Pin avro to 1.11.4 due to https://github.com/apache/beam/issues/34968
-  // cannot upgrade this to the latest version due to https://github.com/apache/beam/issues/34993
-  resolutionStrategy.force 'org.apache.avro:avro:1.11.4'
+  // iceberg needs avro:1.12.0
+  resolutionStrategy.force 'org.apache.avro:avro:1.12.0'
   // force parquet-avro:1.15.2 to fix CVE-2025-46762
   resolutionStrategy.force 'org.apache.parquet:parquet-avro:1.15.2'
 
@@ -66,18 +67,17 @@ dependencies {
   permitUnusedDeclared project(":sdks:java:expansion-service") // BEAM-11761
   implementation project(":sdks:java:managed")
   permitUnusedDeclared project(":sdks:java:managed") // BEAM-11761
-  implementation project(":sdks:java:io:iceberg")
-  permitUnusedDeclared project(":sdks:java:io:iceberg") // BEAM-11761
   implementation project(":sdks:java:io:kafka")
   permitUnusedDeclared project(":sdks:java:io:kafka") // BEAM-11761
   implementation project(":sdks:java:io:kafka:upgrade")
   permitUnusedDeclared project(":sdks:java:io:kafka:upgrade") // BEAM-11761
 
-  // **** IcebergIO catalogs ****
-  // HiveCatalog
-  runtimeOnly project(path: ":sdks:java:io:iceberg:hive")
-  // BigQueryMetastoreCatalog (Java 11+)
-  runtimeOnly project(path: ":sdks:java:io:iceberg:bqms", configuration: "shadow")
+  if (JavaVersion.current().compareTo(JavaVersion.VERSION_11) >= 0 && project.findProperty('testJavaVersion') != '8') {
+    // iceberg ended support for Java 8 in 1.7.0
+    runtimeOnly project(":sdks:java:io:iceberg")
+    runtimeOnly project(":sdks:java:io:iceberg:hive")
+    runtimeOnly project(path: ":sdks:java:io:iceberg:bqms", configuration: "shadow")
+  }
 
   runtimeOnly library.java.kafka_clients
   runtimeOnly library.java.slf4j_jdk14
diff --git a/sdks/java/io/file-based-io-tests/src/test/java/org/apache/beam/sdk/io/text/TextIOIT.java b/sdks/java/io/file-based-io-tests/src/test/java/org/apache/beam/sdk/io/text/TextIOIT.java
index e50a8aba4162..d0ea19ffdf85 100644
--- a/sdks/java/io/file-based-io-tests/src/test/java/org/apache/beam/sdk/io/text/TextIOIT.java
+++ b/sdks/java/io/file-based-io-tests/src/test/java/org/apache/beam/sdk/io/text/TextIOIT.java
@@ -136,7 +136,7 @@ public void writeThenReadAll() {
 
     PCollection<String> consolidatedHashcode =
         testFilenames
-            .apply("Match all files", FileIO.matchAll())
+            .apply("Match all files", FileIO.matchAll().withOutputParallelization(false))
             .apply(
                 "Read matches",
                 FileIO.readMatches().withDirectoryTreatment(DirectoryTreatment.PROHIBIT))
diff --git a/sdks/java/io/file-schema-transform/src/main/java/org/apache/beam/sdk/io/fileschematransform/FileWriteSchemaTransformFormatProviders.java b/sdks/java/io/file-schema-transform/src/main/java/org/apache/beam/sdk/io/fileschematransform/FileWriteSchemaTransformFormatProviders.java
index 85c48ea498a3..b02c1bcb7e1a 100644
--- a/sdks/java/io/file-schema-transform/src/main/java/org/apache/beam/sdk/io/fileschematransform/FileWriteSchemaTransformFormatProviders.java
+++ b/sdks/java/io/file-schema-transform/src/main/java/org/apache/beam/sdk/io/fileschematransform/FileWriteSchemaTransformFormatProviders.java
@@ -80,7 +80,7 @@ static MapElements<Row, GenericRecord> mapRowsToGenericRecords(Schema beamSchema
   //    mapFn: the mapping function for mapping from Beam row to other data types.
   //    outputTag: TupleTag for output. Used to direct output to correct output source, or in the
   //        case of error, a DLQ.
-  static class BeamRowMapperWithDlq<OutputT extends Object> extends DoFn<Row, OutputT> {
+  static class BeamRowMapperWithDlq<OutputT> extends DoFn<Row, OutputT> {
     private SerializableFunction<Row, OutputT> mapFn;
     private Counter errorCounter;
     private TupleTag<OutputT> outputTag;
diff --git a/sdks/java/io/file-schema-transform/src/main/java/org/apache/beam/sdk/io/fileschematransform/XmlRowAdapter.java b/sdks/java/io/file-schema-transform/src/main/java/org/apache/beam/sdk/io/fileschematransform/XmlRowAdapter.java
index 0b5d859dadf1..f57ebc5f8d5e 100644
--- a/sdks/java/io/file-schema-transform/src/main/java/org/apache/beam/sdk/io/fileschematransform/XmlRowAdapter.java
+++ b/sdks/java/io/file-schema-transform/src/main/java/org/apache/beam/sdk/io/fileschematransform/XmlRowAdapter.java
@@ -38,6 +38,7 @@
  * XmlRowAdapter} exposes the String key and Object value pairs of the {@link Row} to the {@link
  * javax.xml.bind.Marshaller}.
  */
+// return value used for assignment
 @XmlRootElement(name = "row")
 @XmlAccessorType(XmlAccessType.PROPERTY)
 class XmlRowAdapter implements Serializable {
diff --git a/sdks/java/io/google-ads/src/test/java/org/apache/beam/sdk/io/googleads/GoogleAdsIOTest.java b/sdks/java/io/google-ads/src/test/java/org/apache/beam/sdk/io/googleads/GoogleAdsIOTest.java
index 4804918bed6c..29ac09951801 100644
--- a/sdks/java/io/google-ads/src/test/java/org/apache/beam/sdk/io/googleads/GoogleAdsIOTest.java
+++ b/sdks/java/io/google-ads/src/test/java/org/apache/beam/sdk/io/googleads/GoogleAdsIOTest.java
@@ -290,6 +290,7 @@ public static class ExecutionTests {
     @Rule public final transient TestPipeline pipeline = TestPipeline.create();
 
     @Before
+    @SuppressWarnings("LockOnNonEnclosingClassLiteral") // valid use
     public void init() {
       GoogleAdsOptions options = pipeline.getOptions().as(GoogleAdsOptions.class);
       options.setGoogleAdsCredentialFactoryClass(NoopCredentialFactory.class);
diff --git a/sdks/java/io/google-cloud-platform/build.gradle b/sdks/java/io/google-cloud-platform/build.gradle
index b5b27003b944..0381193993f2 100644
--- a/sdks/java/io/google-cloud-platform/build.gradle
+++ b/sdks/java/io/google-cloud-platform/build.gradle
@@ -31,7 +31,17 @@ description = "Apache Beam :: SDKs :: Java :: IO :: Google Cloud Platform"
 ext.summary = "IO library to read and write Google Cloud Platform systems from Beam."
 
 dependencies {
-  implementation enforcedPlatform(library.java.google_cloud_platform_libraries_bom)
+  implementation(enforcedPlatform(library.java.google_cloud_platform_libraries_bom)) {
+    // TODO(https://github.com/apache/beam/issues/35868) remove exclude after upstream and/or tests fixed
+    exclude group: "com.google.cloud", module: "google-cloud-spanner"
+    exclude group: "com.google.api.grpc", module: "proto-google-cloud-spanner-v1"
+    exclude group: "com.google.api.grpc", module: "proto-google-cloud-spanner-admin-instance-v1"
+    exclude group: "com.google.api.grpc", module: "proto-google-cloud-spanner-admin-database-v1"
+    exclude group: "com.google.api.grpc", module: "grpc-google-cloud-spanner-v1"
+    exclude group: "com.google.api.grpc", module: "grpc-google-cloud-spanner-admin-instance-v1"
+    exclude group: "com.google.api.grpc", module: "grpc-google-cloud-spanner-admin-database-v1"
+  }
+  implementation(enforcedPlatform(library.java.google_cloud_spanner_bom))
   implementation project(path: ":model:pipeline", configuration: "shadow")
   implementation project(":runners:core-java")
   implementation project(path: ":sdks:java:core", configuration: "shadow")
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BatchLoads.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BatchLoads.java
index d0879eb76950..dd1d831f1950 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BatchLoads.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BatchLoads.java
@@ -285,7 +285,7 @@ public void validate(@Nullable PipelineOptions maybeOptions) {
     PipelineOptions options = Preconditions.checkArgumentNotNull(maybeOptions);
     // We will use a BigQuery load job -- validate the temp location.
     String tempLocation;
-    if (customGcsTempLocation == null) {
+    if (customGcsTempLocation == null || customGcsTempLocation.get() == null) {
       tempLocation = options.getTempLocation();
     } else {
       if (!customGcsTempLocation.isAccessible()) {
@@ -589,7 +589,7 @@ private PCollectionView<String> createTempFilePrefixView(
                       @ProcessElement
                       public void getTempFilePrefix(ProcessContext c) {
                         String tempLocationRoot;
-                        if (customGcsTempLocation != null) {
+                        if (customGcsTempLocation != null && customGcsTempLocation.get() != null) {
                           tempLocationRoot = customGcsTempLocation.get();
                         } else {
                           tempLocationRoot = c.getPipelineOptions().getTempLocation();
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryHelpers.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryHelpers.java
index 129c8314fc80..d468ffbea43c 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryHelpers.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryHelpers.java
@@ -25,6 +25,7 @@
 import com.google.api.client.util.Sleeper;
 import com.google.api.services.bigquery.model.Clustering;
 import com.google.api.services.bigquery.model.Dataset;
+import com.google.api.services.bigquery.model.ErrorProto;
 import com.google.api.services.bigquery.model.Job;
 import com.google.api.services.bigquery.model.JobReference;
 import com.google.api.services.bigquery.model.JobStatus;
@@ -205,6 +206,7 @@ static class PendingJob implements Serializable {
     void runJob() throws IOException {
       ++currentAttempt;
       if (!shouldRetry()) {
+        logBigQueryError(lastJobAttempted);
         throw new RuntimeException(
             String.format(
                 "Failed to create job with prefix %s, "
@@ -281,6 +283,21 @@ boolean pollJob() throws IOException {
     boolean shouldRetry() {
       return currentAttempt < maxRetries + 1;
     }
+
+    void logBigQueryError(@Nullable Job job) {
+      if (job == null || !parseStatus(job).equals(Status.FAILED)) {
+        return;
+      }
+
+      List<ErrorProto> jobErrors = job.getStatus().getErrors();
+      String finalError = job.getStatus().getErrorResult().getMessage();
+      String causativeError =
+          jobErrors != null && !jobErrors.isEmpty()
+              ? String.format(" due to: %s", jobErrors.get(jobErrors.size() - 1).getMessage())
+              : "";
+
+      LOG.error(String.format("BigQuery Error : %s %s", finalError, causativeError));
+    }
   }
 
   static class RetryJobId implements Serializable {
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryIO.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryIO.java
index f986e802f1ca..e3f9de3b7ab3 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryIO.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryIO.java
@@ -551,7 +551,8 @@
  * using {@link Write#withPrimaryKey}.
  */
 @SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20506)
+  "nullness", // TODO(https://github.com/apache/beam/issues/20506),
+  "SameNameButDifferent"
 })
 public class BigQueryIO {
 
@@ -596,8 +597,8 @@ public class BigQueryIO {
   private static final String TABLE_REGEXP = "[-_\\p{L}\\p{N}\\p{M}$@ ]{1,1024}";
 
   /**
-   * Matches table specifications in the form {@code "[project_id]:[dataset_id].[table_id]"} or
-   * {@code "[dataset_id].[table_id]"}.
+   * Matches table specifications in the form {@code "[project_id]:[dataset_id].[table_id]"}, {@code
+   * "[project_id].[dataset_id].[table_id]"}, or {@code "[dataset_id].[table_id]"}.
    */
   private static final String DATASET_TABLE_REGEXP =
       String.format(
@@ -852,8 +853,9 @@ public Read withTestServices(BigQueryServices testServices) {
     }
 
     /**
-     * Reads a BigQuery table specified as {@code "[project_id]:[dataset_id].[table_id]"} or {@code
-     * "[dataset_id].[table_id]"} for tables within the current project.
+     * Reads a BigQuery table specified as {@code "[project_id]:[dataset_id].[table_id]"}, {@code
+     * "[project_id].[dataset_id].[table_id]"}, or {@code "[dataset_id].[table_id]"} for tables
+     * within the current project.
      */
     public Read from(String tableSpec) {
       return new Read(this.inner.from(tableSpec));
@@ -3472,19 +3474,22 @@ && getStorageApiTriggeringFrequency(bqOptions) != null) {
           }
         }
       } else { // PCollection is bounded
-        String error =
-            String.format(
-                " is only applicable to an unbounded PCollection, but the input PCollection is %s.",
-                input.isBounded());
-        checkArgument(getTriggeringFrequency() == null, "Triggering frequency" + error);
-        checkArgument(!getAutoSharding(), "Auto-sharding" + error);
-        checkArgument(getNumFileShards() == 0, "Number of file shards" + error);
+        checkArgument(
+            getTriggeringFrequency() == null,
+            "Triggering frequency is only applicable to an unbounded PCollection.");
+        checkArgument(
+            !getAutoSharding(), "Auto-sharding is only applicable to an unbounded PCollection.");
+        checkArgument(
+            getNumFileShards() == 0,
+            "Number of file shards is only applicable to an unbounded PCollection.");
 
         if (getStorageApiTriggeringFrequency(bqOptions) != null) {
-          LOG.warn("Setting a triggering frequency" + error);
+          LOG.warn(
+              "Setting the triggering frequency is only applicable to an unbounded PCollection.");
         }
         if (getStorageApiNumStreams(bqOptions) != 0) {
-          LOG.warn("Setting the number of Storage API streams" + error);
+          LOG.warn(
+              "Setting the number of Storage API streams is only applicable to an unbounded PCollection.");
         }
       }
 
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryServicesImpl.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryServicesImpl.java
index 390ffa1aa991..f4303886c7ab 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryServicesImpl.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryServicesImpl.java
@@ -109,12 +109,14 @@
 import java.util.Optional;
 import java.util.Set;
 import java.util.concurrent.Callable;
+import java.util.concurrent.ConcurrentHashMap;
 import java.util.concurrent.ExecutionException;
 import java.util.concurrent.Executors;
 import java.util.concurrent.Future;
 import java.util.concurrent.Semaphore;
 import java.util.concurrent.TimeUnit;
 import java.util.concurrent.atomic.AtomicLong;
+import java.util.stream.Collectors;
 import org.apache.beam.fn.harness.logging.QuotaEvent;
 import org.apache.beam.fn.harness.logging.QuotaEvent.QuotaEventCloseable;
 import org.apache.beam.runners.core.metrics.MonitoringInfoConstants;
@@ -172,7 +174,12 @@ public class BigQueryServicesImpl implements BigQueryServices {
 
   // The approximate maximum payload of rows for an insertAll request.
   // We set it to 9MB, which leaves room for request overhead.
-  private static final Integer MAX_BQ_ROW_PAYLOAD = 9 * 1024 * 1024;
+  private static final Integer MAX_BQ_ROW_PAYLOAD_MB = 9;
+
+  private static final Integer MAX_BQ_ROW_PAYLOAD_BYTES = MAX_BQ_ROW_PAYLOAD_MB * 1024 * 1024;
+
+  private static final String MAX_BQ_ROW_PAYLOAD_DESC =
+      String.format("%sMB", MAX_BQ_ROW_PAYLOAD_MB);
 
   // The initial backoff for polling the status of a BigQuery job.
   private static final Duration INITIAL_JOB_STATUS_POLL_BACKOFF = Duration.standardSeconds(1);
@@ -596,6 +603,7 @@ public static class DatasetServiceImpl implements DatasetService {
     private final PipelineOptions options;
     private final long maxRowsPerBatch;
     private final long maxRowBatchSize;
+    private final Map<String, TableSchema> tableSchemaCache = new ConcurrentHashMap<>();
     // aggregate the total time spent in exponential backoff
     private final Counter throttlingMsecs =
         Metrics.counter(DatasetServiceImpl.class, Metrics.THROTTLE_TIME_COUNTER_NAME);
@@ -1149,22 +1157,53 @@ <T> long insertAll(
           // If this row's encoding by itself is larger than the maximum row payload, then it's
           // impossible to insert into BigQuery, and so we send it out through the dead-letter
           // queue.
-          if (nextRowSize >= MAX_BQ_ROW_PAYLOAD) {
+          if (nextRowSize >= MAX_BQ_ROW_PAYLOAD_BYTES) {
             InsertErrors error =
                 new InsertErrors()
                     .setErrors(ImmutableList.of(new ErrorProto().setReason("row-too-large")));
             // We verify whether the retryPolicy parameter expects us to retry. If it does, then
             // it will return true. Otherwise it will return false.
-            Boolean isRetry = retryPolicy.shouldRetry(new InsertRetryPolicy.Context(error));
-            if (isRetry) {
-              throw new RuntimeException(
+            if (retryPolicy.shouldRetry(new InsertRetryPolicy.Context(error))) {
+              // Obtain table schema
+              TableSchema tableSchema = null;
+              try {
+                String tableSpec = BigQueryHelpers.toTableSpec(ref);
+                if (tableSchemaCache.containsKey(tableSpec)) {
+                  tableSchema = tableSchemaCache.get(tableSpec);
+                } else {
+                  Table table = getTable(ref);
+                  if (table != null) {
+                    tableSchema =
+                        TableRowToStorageApiProto.schemaToProtoTableSchema(table.getSchema());
+                    tableSchemaCache.put(tableSpec, tableSchema);
+                  }
+                }
+              } catch (Exception e) {
+                LOG.warn("Failed to get table schema", e);
+              }
+
+              // Validate row schema
+              String rowDetails = "";
+              if (tableSchema != null) {
+                rowDetails = validateRowSchema(row, tableSchema);
+              }
+
+              // Basic log to return
+              String bqLimitLog =
                   String.format(
-                      "We have observed a row that is %s bytes in size and exceeded BigQueryIO"
-                          + " limit of 9MB. While BigQuery supports request sizes up to 10MB,"
-                          + " BigQueryIO sets the limit at 9MB to leave room for request"
-                          + " overhead. You may change your retry strategy to unblock this"
-                          + " pipeline, and the row will be output as a failed insert.",
-                      nextRowSize));
+                      "We have observed a row of size %s bytes exceeding the "
+                          + "BigQueryIO limit of %s.",
+                      nextRowSize, MAX_BQ_ROW_PAYLOAD_DESC);
+
+              // Add on row schema diff details if present
+              if (!rowDetails.isEmpty()) {
+                bqLimitLog +=
+                    String.format(
+                        " This is probably due to a schema "
+                            + "mismatch. Problematic row had extra schema fields: %s.",
+                        rowDetails);
+              }
+              throw new RuntimeException(bqLimitLog);
             } else {
               numFailedRows += 1;
               errorContainer.add(failedInserts, error, ref, rowsToPublish.get(rowIndex));
@@ -1177,7 +1216,7 @@ <T> long insertAll(
           // If adding the next row will push the request above BQ row limits, or
           // if the current batch of elements is larger than the targeted request size,
           // we immediately go and issue the data insertion.
-          if (dataSize + nextRowSize >= MAX_BQ_ROW_PAYLOAD
+          if (dataSize + nextRowSize >= MAX_BQ_ROW_PAYLOAD_BYTES
               || dataSize >= maxRowBatchSize
               || rows.size() + 1 > maxRowsPerBatch) {
             // If the row does not fit into the insert buffer, then we take the current buffer,
@@ -1317,6 +1356,48 @@ <T> long insertAll(
       }
     }
 
+    /**
+     * Validates a {@link TableRow} for logging, comparing the provided row against a BigQuery
+     * schema. The formatted string shows the field names in the row indicating any mismatches
+     * unknown entries.
+     *
+     * <p>For example, a {@link TableRow} with a "names" field, where the schema expects
+     * "name" would return "names".
+     *
+     * <pre>{@code {'name': java.lang.String}</pre>
+     *
+     * <p>If a field exists in the row but not in the schema,
+     * "Unknown fields" is prefixed to the log.</p>
+     *
+     * @param row The {@link TableRow} to validate.
+     * @param tableSchema The {@link TableSchema} to check against.
+     * @return A string representation of the row, indicating any schema mismatches.
+     */
+    private String validateRowSchema(TableRow row, TableSchema tableSchema) {
+      // Creates bqSchemaFields containing field names
+      Set<String> bqSchemaFields =
+          tableSchema.getFieldsList().stream().map(f -> f.getName()).collect(Collectors.toSet());
+
+      // Validate
+      String rowDetails =
+          row.keySet().stream()
+              .map(
+                  fieldName -> {
+                    if (!bqSchemaFields.contains(fieldName)) {
+                      return fieldName;
+                    }
+                    return "";
+                  })
+              .filter(s -> !s.isEmpty())
+              .collect(Collectors.joining(", ", "{Unknown fields: ", "}"));
+
+      // Shorten row details if too long for human readability
+      if (rowDetails.length() > 1024) {
+        rowDetails = rowDetails.substring(0, 1024) + "...}";
+      }
+      return rowDetails;
+    }
+
     @Override
     public <T> long insertAll(
         TableReference ref,
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQuerySourceBase.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQuerySourceBase.java
index b7b83dccaece..d2aed44d9f48 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQuerySourceBase.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQuerySourceBase.java
@@ -221,8 +221,7 @@ private List<ResourceId> executeExtract(
       // The error messages thrown in this case are generic and misleading, so leave this breadcrumb
       // in case it's the root cause.
       LOG.warn(
-          "Error extracting table: {} "
-              + "Note that external tables cannot be exported: "
+          "Error extracting table. Note that external tables cannot be exported: "
               + "https://cloud.google.com/bigquery/docs/external-tables#external_table_limitations",
           exn);
       throw exn;
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiFlushAndFinalizeDoFn.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiFlushAndFinalizeDoFn.java
index dec86c3360b0..fd3853d15e0f 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiFlushAndFinalizeDoFn.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiFlushAndFinalizeDoFn.java
@@ -61,6 +61,8 @@ public class StorageApiFlushAndFinalizeDoFn extends DoFn<KV<String, Operation>,
       Metrics.counter(StorageApiFlushAndFinalizeDoFn.class, "flushOperationsAlreadyExists");
   private final Counter flushOperationsInvalidArgument =
       Metrics.counter(StorageApiFlushAndFinalizeDoFn.class, "flushOperationsInvalidArgument");
+  private final Counter flushOperationsOffsetBeyondEnd =
+      Metrics.counter(StorageApiFlushAndFinalizeDoFn.class, "flushOperationsOffsetBeyondEnd");
   private final Distribution flushLatencyDistribution =
       Metrics.distribution(StorageApiFlushAndFinalizeDoFn.class, "flushOperationLatencyMs");
   private final Counter finalizeOperationsSent =
@@ -70,6 +72,52 @@ public class StorageApiFlushAndFinalizeDoFn extends DoFn<KV<String, Operation>,
   private final Counter finalizeOperationsFailed =
       Metrics.counter(StorageApiFlushAndFinalizeDoFn.class, "finalizeOperationsFailed");
 
+  /**
+   * Checks if the given throwable indicates that an offset is beyond the end of a BigQuery stream.
+   * It primarily uses {@code io.grpc.Status.fromThrowable} to determine the gRPC status code and
+   * then checks for specific message content.
+   */
+  private boolean isOffsetBeyondEndOfStreamError(Throwable t) {
+    if (t == null) {
+      return false;
+    }
+
+    // Status.fromThrowable() searches the cause chain for the most specific gRPC status.
+    io.grpc.Status grpcStatus = io.grpc.Status.fromThrowable(t);
+
+    // Check if grpcStatus is valid and the code is OUT_OF_RANGE
+    if (grpcStatus != null && grpcStatus.getCode() == io.grpc.Status.Code.OUT_OF_RANGE) {
+      // The gRPC status is OUT_OF_RANGE.
+      // Now, verify the message content for the specific "is beyond the end of the stream" text.
+      // This text might be in the grpcStatus's description, or in the message of the original
+      // throwable 't', or one of its causes.
+
+      // Check the description from the derived gRPC status first.
+      // grpcStatus is confirmed not null here.
+      String description = grpcStatus.getDescription();
+      if (description != null
+          && description.toLowerCase().contains("is beyond the end of the stream")) {
+        return true;
+      }
+
+      // If the description didn't match, iterate through the exception chain of 't'
+      // to find a message that confirms the "offset beyond end of stream" scenario.
+      Throwable currentThrowable = t;
+      while (currentThrowable != null) {
+        String message = currentThrowable.getMessage();
+        if (message != null && message.toLowerCase().contains("is beyond the end of the stream")) {
+          // If any exception in the chain has this message, and the overall gRPC status
+          // (determined by Status.fromThrowable(t)) is OUT_OF_RANGE, we consider it a match.
+          return true;
+        }
+        currentThrowable = currentThrowable.getCause();
+      }
+    }
+    // If grpcStatus was null, or the gRPC status code was not OUT_OF_RANGE,
+    // or if it was OUT_OF_RANGE but no matching message was found.
+    return false;
+  }
+
   @DefaultSchema(JavaFieldSchema.class)
   static class Operation implements Comparable<Operation>, Serializable {
     final long flushOffset;
@@ -186,6 +234,20 @@ public void process(PipelineOptions pipelineOptions, @Element KV<String, Operati
               if (statusCode.equals(Code.NOT_FOUND)) {
                 return RetryType.DONT_RETRY;
               }
+
+              // check the offset beyond the end of the stream
+              if (isOffsetBeyondEndOfStreamError(error)) {
+                flushOperationsOffsetBeyondEnd.inc();
+                LOG.warn(
+                    "Flush of stream {} to offset {} failed because the offset is beyond the end of the stream. "
+                        + "This typically means the stream was finalized or truncated by BQ. "
+                        + "The operation will not be retried on this stream. Error: {}",
+                    streamId,
+                    offset,
+                    error.toString());
+                // This specific error is not retriable on the same stream.
+                return RetryType.DONT_RETRY;
+              }
             }
             return RetryType.RETRY_ALL_OPERATIONS;
           },
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiWriteUnshardedRecords.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiWriteUnshardedRecords.java
index cbcd70753aca..0d483367f7b9 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiWriteUnshardedRecords.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiWriteUnshardedRecords.java
@@ -73,12 +73,15 @@
 import org.apache.beam.sdk.transforms.Reshuffle;
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.GlobalWindow;
+import org.apache.beam.sdk.transforms.windowing.PaneInfo;
 import org.apache.beam.sdk.util.Preconditions;
 import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.OutputBuilder;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionTuple;
 import org.apache.beam.sdk.values.TupleTag;
 import org.apache.beam.sdk.values.TupleTagList;
+import org.apache.beam.sdk.values.WindowedValues;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Predicates;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Strings;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.cache.Cache;
@@ -1175,35 +1178,59 @@ public void process(
       numPendingRecordBytes += element.getValue().getPayload().length;
     }
 
+    private OutputReceiver<TableRow> makeSuccessfulRowsreceiver(
+        FinishBundleContext context, TupleTag<TableRow> successfulRowsTag) {
+      return new OutputReceiver<TableRow>() {
+        @Override
+        public OutputBuilder<TableRow> builder(TableRow value) {
+          return WindowedValues.<TableRow>builder()
+              .setValue(value)
+              .setTimestamp(GlobalWindow.INSTANCE.maxTimestamp())
+              .setWindow(GlobalWindow.INSTANCE)
+              .setPaneInfo(PaneInfo.NO_FIRING)
+              .setReceiver(
+                  windowedValue -> {
+                    for (BoundedWindow window : windowedValue.getWindows()) {
+                      context.output(
+                          successfulRowsTag,
+                          windowedValue.getValue(),
+                          windowedValue.getTimestamp(),
+                          window);
+                    }
+                  });
+        }
+      };
+    }
+
     @FinishBundle
     public void finishBundle(FinishBundleContext context) throws Exception {
+
       OutputReceiver<BigQueryStorageApiInsertError> failedRowsReceiver =
           new OutputReceiver<BigQueryStorageApiInsertError>() {
             @Override
-            public void output(BigQueryStorageApiInsertError output) {
-              outputWithTimestamp(output, GlobalWindow.INSTANCE.maxTimestamp());
-            }
-
-            @Override
-            public void outputWithTimestamp(
-                BigQueryStorageApiInsertError output, org.joda.time.Instant timestamp) {
-              context.output(failedRowsTag, output, timestamp, GlobalWindow.INSTANCE);
+            public OutputBuilder<BigQueryStorageApiInsertError> builder(
+                BigQueryStorageApiInsertError value) {
+              return WindowedValues.<BigQueryStorageApiInsertError>builder()
+                  .setValue(value)
+                  .setTimestamp(GlobalWindow.INSTANCE.maxTimestamp())
+                  .setWindow(GlobalWindow.INSTANCE)
+                  .setPaneInfo(PaneInfo.NO_FIRING)
+                  .setReceiver(
+                      windowedValue -> {
+                        for (BoundedWindow window : windowedValue.getWindows()) {
+                          context.output(
+                              failedRowsTag,
+                              windowedValue.getValue(),
+                              windowedValue.getTimestamp(),
+                              window);
+                        }
+                      });
             }
           };
+
       @Nullable OutputReceiver<TableRow> successfulRowsReceiver = null;
       if (successfulRowsTag != null) {
-        successfulRowsReceiver =
-            new OutputReceiver<TableRow>() {
-              @Override
-              public void output(TableRow output) {
-                outputWithTimestamp(output, GlobalWindow.INSTANCE.maxTimestamp());
-              }
-
-              @Override
-              public void outputWithTimestamp(TableRow output, org.joda.time.Instant timestamp) {
-                context.output(successfulRowsTag, output, timestamp, GlobalWindow.INSTANCE);
-              }
-            };
+        successfulRowsReceiver = makeSuccessfulRowsreceiver(context, successfulRowsTag);
       }
 
       flushAll(failedRowsReceiver, successfulRowsReceiver);
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiWritesShardedRecords.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiWritesShardedRecords.java
index d905c4bf93ca..a441803cc4fa 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiWritesShardedRecords.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/StorageApiWritesShardedRecords.java
@@ -182,11 +182,11 @@ public String toString() {
     }
   };
 
-  private static final Cache<ShardedKey<?>, AppendClientInfo> APPEND_CLIENTS =
+  private static final Cache<KV<String, ShardedKey<?>>, AppendClientInfo> APPEND_CLIENTS =
       CacheBuilder.newBuilder()
           .expireAfterAccess(5, TimeUnit.MINUTES)
           .removalListener(
-              (RemovalNotification<ShardedKey<?>, AppendClientInfo> removal) -> {
+              (RemovalNotification<KV<String, ShardedKey<?>>, AppendClientInfo> removal) -> {
                 final @Nullable AppendClientInfo appendClientInfo = removal.getValue();
                 if (appendClientInfo != null) {
                   appendClientInfo.close();
@@ -580,14 +580,18 @@ public void process(
           };
 
       AtomicReference<AppendClientInfo> appendClientInfo =
-          new AtomicReference<>(APPEND_CLIENTS.get(element.getKey(), getAppendClientInfo));
+          new AtomicReference<>(
+              APPEND_CLIENTS.get(
+                  messageConverters.getAppendClientKey(element.getKey()), getAppendClientInfo));
       String currentStream = getOrCreateStream.get();
       if (!currentStream.equals(appendClientInfo.get().getStreamName())) {
         // Cached append client is inconsistent with persisted state. Throw away cached item and
         // force it to be
         // recreated.
-        APPEND_CLIENTS.invalidate(element.getKey());
-        appendClientInfo.set(APPEND_CLIENTS.get(element.getKey(), getAppendClientInfo));
+        APPEND_CLIENTS.invalidate(messageConverters.getAppendClientKey(element.getKey()));
+        appendClientInfo.set(
+            APPEND_CLIENTS.get(
+                messageConverters.getAppendClientKey(element.getKey()), getAppendClientInfo));
       }
 
       TableSchema updatedSchemaValue = updatedSchema.read();
@@ -596,8 +600,9 @@ public void process(
           appendClientInfo.set(
               AppendClientInfo.of(
                   updatedSchemaValue, appendClientInfo.get().getCloseAppendClient(), false));
-          APPEND_CLIENTS.invalidate(element.getKey());
-          APPEND_CLIENTS.put(element.getKey(), appendClientInfo.get());
+          APPEND_CLIENTS.invalidate(messageConverters.getAppendClientKey(element.getKey()));
+          APPEND_CLIENTS.put(
+              messageConverters.getAppendClientKey(element.getKey()), appendClientInfo.get());
         }
       }
 
@@ -664,9 +669,10 @@ public void process(
 
       Consumer<Iterable<AppendRowsContext>> clearClients =
           contexts -> {
-            APPEND_CLIENTS.invalidate(element.getKey());
+            APPEND_CLIENTS.invalidate(messageConverters.getAppendClientKey(element.getKey()));
             appendClientInfo.set(appendClientInfo.get().withNoAppendClient());
-            APPEND_CLIENTS.put(element.getKey(), appendClientInfo.get());
+            APPEND_CLIENTS.put(
+                messageConverters.getAppendClientKey(element.getKey()), appendClientInfo.get());
             for (AppendRowsContext context : contexts) {
               if (context.client != null) {
                 // Unpin in a different thread, as it may execute a blocking close.
@@ -960,8 +966,9 @@ public void process(
               appendClientInfo.set(
                   AppendClientInfo.of(
                       newSchema.get(), appendClientInfo.get().getCloseAppendClient(), false));
-              APPEND_CLIENTS.invalidate(element.getKey());
-              APPEND_CLIENTS.put(element.getKey(), appendClientInfo.get());
+              APPEND_CLIENTS.invalidate(messageConverters.getAppendClientKey(element.getKey()));
+              APPEND_CLIENTS.put(
+                  messageConverters.getAppendClientKey(element.getKey()), appendClientInfo.get());
               LOG.debug(
                   "Fetched updated schema for table {}:\n\t{}", tableId, updatedSchemaReturned);
               updatedSchema.write(newSchema.get());
@@ -993,7 +1000,7 @@ private void finalizeStream(
         streamName.clear();
         streamOffset.clear();
         // Make sure that the stream object is closed.
-        APPEND_CLIENTS.invalidate(key);
+        APPEND_CLIENTS.invalidate(messageConverters.getAppendClientKey(key));
       }
     }
 
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableDestination.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableDestination.java
index df96e1bc2260..6b81c2322a51 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableDestination.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableDestination.java
@@ -64,7 +64,7 @@ public TableDestination(
   public TableDestination(
       String tableSpec,
       @Nullable String tableDescription,
-      TimePartitioning timePartitioning,
+      @Nullable TimePartitioning timePartitioning,
       Clustering clustering) {
     this(
         tableSpec,
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowJsonCoder.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowJsonCoder.java
index 8cf3eeb479c0..f8e877fe98e6 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowJsonCoder.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowJsonCoder.java
@@ -75,10 +75,8 @@ public long getEncodedElementByteSize(TableRow value) throws Exception {
   private static final TypeDescriptor<TableRow> TYPE_DESCRIPTOR;
 
   static {
-    RowJsonUtils.increaseDefaultStreamReadConstraints(100 * 1024 * 1024);
-
     MAPPER =
-        new ObjectMapper()
+        new ObjectMapper(RowJsonUtils.createJsonFactory(RowJsonUtils.MAX_STRING_LENGTH))
             .registerModule(new JavaTimeModule())
             .registerModule(new JodaModule())
             .disable(SerializationFeature.WRITE_DATES_AS_TIMESTAMPS)
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProto.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProto.java
index 37b09349646e..bf9c4c28bc1b 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProto.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProto.java
@@ -21,10 +21,13 @@
 
 import com.google.api.services.bigquery.model.TableCell;
 import com.google.api.services.bigquery.model.TableRow;
+import com.google.cloud.bigquery.storage.v1.AnnotationsProto;
 import com.google.cloud.bigquery.storage.v1.BigDecimalByteStringEncoder;
+import com.google.cloud.bigquery.storage.v1.BigQuerySchemaUtil;
 import com.google.cloud.bigquery.storage.v1.TableFieldSchema;
 import com.google.cloud.bigquery.storage.v1.TableSchema;
 import com.google.protobuf.ByteString;
+import com.google.protobuf.DescriptorProtos;
 import com.google.protobuf.DescriptorProtos.DescriptorProto;
 import com.google.protobuf.DescriptorProtos.FieldDescriptorProto;
 import com.google.protobuf.DescriptorProtos.FieldDescriptorProto.Label;
@@ -487,7 +490,11 @@ public static DynamicMessage messageFromMap(
     DynamicMessage.Builder builder = DynamicMessage.newBuilder(descriptor);
     for (final Map.Entry<String, Object> entry : map.entrySet()) {
       String key = entry.getKey().toLowerCase();
-      @Nullable FieldDescriptor fieldDescriptor = descriptor.findFieldByName(key);
+      String protoFieldName =
+          BigQuerySchemaUtil.isProtoCompatible(key)
+              ? key
+              : BigQuerySchemaUtil.generatePlaceholderFieldName(key);
+      @Nullable FieldDescriptor fieldDescriptor = descriptor.findFieldByName(protoFieldName);
       if (fieldDescriptor == null) {
         if (unknownFields != null) {
           unknownFields.set(key, entry.getValue());
@@ -724,9 +731,18 @@ static TableSchema tableSchemaFromDescriptor(Descriptor descriptor) {
     return TableSchema.newBuilder().addAllFields(tableFields).build();
   }
 
+  private static String fieldNameFromProtoFieldDescriptor(FieldDescriptor fieldDescriptor) {
+    if (fieldDescriptor.getOptions().hasExtension(AnnotationsProto.columnName)) {
+      return fieldDescriptor.getOptions().getExtension(AnnotationsProto.columnName);
+    } else {
+      return fieldDescriptor.getName();
+    }
+  }
+
   static TableFieldSchema tableFieldSchemaFromDescriptorField(FieldDescriptor fieldDescriptor) {
     TableFieldSchema.Builder tableFieldSchemaBuilder = TableFieldSchema.newBuilder();
-    tableFieldSchemaBuilder = tableFieldSchemaBuilder.setName(fieldDescriptor.getName());
+    tableFieldSchemaBuilder =
+        tableFieldSchemaBuilder.setName(fieldNameFromProtoFieldDescriptor(fieldDescriptor));
 
     switch (fieldDescriptor.getType()) {
       case MESSAGE:
@@ -809,8 +825,21 @@ private static void fieldDescriptorFromTableField(
           "Reserved field name " + fieldSchema.getName() + " in user schema.");
     }
     FieldDescriptorProto.Builder fieldDescriptorBuilder = FieldDescriptorProto.newBuilder();
-    fieldDescriptorBuilder = fieldDescriptorBuilder.setName(fieldSchema.getName().toLowerCase());
+    final String fieldName = fieldSchema.getName().toLowerCase();
+    fieldDescriptorBuilder = fieldDescriptorBuilder.setName(fieldName);
     fieldDescriptorBuilder = fieldDescriptorBuilder.setNumber(fieldNumber);
+    if (!BigQuerySchemaUtil.isProtoCompatible(fieldName)) {
+      fieldDescriptorBuilder =
+          fieldDescriptorBuilder.setName(
+              BigQuerySchemaUtil.generatePlaceholderFieldName(fieldName));
+
+      Message.Builder fieldOptionBuilder = DescriptorProtos.FieldOptions.newBuilder();
+      fieldOptionBuilder =
+          fieldOptionBuilder.setField(AnnotationsProto.columnName.getDescriptor(), fieldName);
+      fieldDescriptorBuilder =
+          fieldDescriptorBuilder.setOptions(
+              (DescriptorProtos.FieldOptions) fieldOptionBuilder.build());
+    }
     switch (fieldSchema.getType()) {
       case STRUCT:
         DescriptorProto nested =
@@ -1113,12 +1142,13 @@ public static TableRow tableRowFromMessage(
     for (Map.Entry<FieldDescriptor, Object> field : message.getAllFields().entrySet()) {
       StringBuilder fullName = new StringBuilder();
       FieldDescriptor fieldDescriptor = field.getKey();
-      fullName = fullName.append(namePrefix).append(fieldDescriptor.getName());
+      String fieldName = fieldNameFromProtoFieldDescriptor(fieldDescriptor);
+      fullName = fullName.append(namePrefix).append(fieldName);
       Object fieldValue = field.getValue();
       if ((includeCdcColumns || !StorageApiCDC.COLUMNS.contains(fullName.toString()))
-          && includeField.test(fieldDescriptor.getName())) {
+          && includeField.test(fieldName)) {
         tableRow.put(
-            fieldDescriptor.getName(),
+            fieldName,
             jsonValueFromMessageValue(
                 fieldDescriptor, fieldValue, true, includeField, fullName.append(".").toString()));
       }
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TwoLevelMessageConverterCache.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TwoLevelMessageConverterCache.java
index 5f90e1dd3950..0ce7c7573c9c 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TwoLevelMessageConverterCache.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/TwoLevelMessageConverterCache.java
@@ -20,6 +20,7 @@
 import java.io.Serializable;
 import org.apache.beam.sdk.io.gcp.bigquery.BigQueryServices.DatasetService;
 import org.apache.beam.sdk.io.gcp.bigquery.StorageApiDynamicDestinations.MessageConverter;
+import org.apache.beam.sdk.util.ShardedKey;
 import org.apache.beam.sdk.values.KV;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.cache.Cache;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.cache.CacheBuilder;
@@ -71,4 +72,8 @@ public MessageConverter<ElementT> get(
                     KV.of(operationName, destination),
                     () -> dynamicDestinations.getMessageConverter(destination, datasetService)));
   }
+
+  public KV<String, ShardedKey<?>> getAppendClientKey(ShardedKey<DestinationT> shardedKey) {
+    return KV.of(operationName, shardedKey);
+  }
 }
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryStorageWriteApiSchemaTransformProvider.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryStorageWriteApiSchemaTransformProvider.java
index 1e53ad3553e0..bb8f72003429 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryStorageWriteApiSchemaTransformProvider.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryStorageWriteApiSchemaTransformProvider.java
@@ -307,7 +307,6 @@ BigQueryIO.Write<Row> createStorageWriteApiTransform(Schema schema) {
             CreateDisposition.valueOf(configuration.getCreateDisposition().toUpperCase());
         write = write.withCreateDisposition(createDisposition);
       }
-
       if (!Strings.isNullOrEmpty(configuration.getWriteDisposition())) {
         WriteDisposition writeDisposition =
             WriteDisposition.valueOf(configuration.getWriteDisposition().toUpperCase());
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryWriteConfiguration.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryWriteConfiguration.java
index 505ce7125cee..5df6e1f6afcd 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryWriteConfiguration.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryWriteConfiguration.java
@@ -194,6 +194,9 @@ public static Builder builder() {
           + "Is mutually exclusive with 'keep' and 'drop'.")
   public abstract @Nullable String getOnly();
 
+  @SchemaFieldDescription("A list of columns to cluster the BigQuery table by.")
+  public abstract @Nullable List<String> getClusteringFields();
+
   /** Builder for {@link BigQueryWriteConfiguration}. */
   @AutoValue.Builder
   public abstract static class Builder {
@@ -226,6 +229,8 @@ public abstract static class Builder {
 
     public abstract Builder setOnly(String only);
 
+    public abstract Builder setClusteringFields(List<String> clusteringFields);
+
     /** Builds a {@link BigQueryWriteConfiguration} instance. */
     public abstract BigQueryWriteConfiguration build();
   }
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/PortableBigQueryDestinations.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/PortableBigQueryDestinations.java
index 0cd2b65b0858..42eee4f3f03c 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/PortableBigQueryDestinations.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/providers/PortableBigQueryDestinations.java
@@ -21,6 +21,7 @@
 import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
 import static org.apache.beam.sdk.util.Preconditions.checkStateNotNull;
 
+import com.google.api.services.bigquery.model.Clustering;
 import com.google.api.services.bigquery.model.TableConstraints;
 import com.google.api.services.bigquery.model.TableRow;
 import com.google.api.services.bigquery.model.TableSchema;
@@ -48,8 +49,10 @@ public class PortableBigQueryDestinations extends DynamicDestinations<Row, Strin
   private @MonotonicNonNull RowStringInterpolator interpolator = null;
   private final @Nullable List<String> primaryKey;
   private final RowFilter rowFilter;
+  private final @Nullable List<String> clusteringFields;
 
   public PortableBigQueryDestinations(Schema rowSchema, BigQueryWriteConfiguration configuration) {
+    this.clusteringFields = configuration.getClusteringFields();
     // DYNAMIC_DESTINATIONS magic string is the old way of doing it for cross-language.
     // In that case, we do no interpolation
     if (!configuration.getTable().equals(DYNAMIC_DESTINATIONS)) {
@@ -79,6 +82,11 @@ public String getDestination(@Nullable ValueInSingleWindow<Row> element) {
 
   @Override
   public TableDestination getTable(String destination) {
+
+    if (clusteringFields != null && !clusteringFields.isEmpty()) {
+      Clustering clustering = new Clustering().setFields(clusteringFields);
+      return new TableDestination(destination, null, null, clustering);
+    }
     return new TableDestination(destination, null);
   }
 
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableReadSchemaTransformProvider.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableReadSchemaTransformProvider.java
index f48a23559141..2ed75d7bc7e0 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableReadSchemaTransformProvider.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableReadSchemaTransformProvider.java
@@ -24,7 +24,8 @@
 import com.google.bigtable.v2.Cell;
 import com.google.bigtable.v2.Column;
 import com.google.bigtable.v2.Family;
-import java.nio.ByteBuffer;
+import com.google.protobuf.ByteString;
+import java.io.Serializable;
 import java.util.ArrayList;
 import java.util.Collections;
 import java.util.HashMap;
@@ -37,11 +38,12 @@
 import org.apache.beam.sdk.schemas.transforms.SchemaTransform;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
 import org.apache.beam.sdk.schemas.transforms.TypedSchemaTransformProvider;
-import org.apache.beam.sdk.transforms.MapElements;
-import org.apache.beam.sdk.transforms.SimpleFunction;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.ParDo;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionRowTuple;
 import org.apache.beam.sdk.values.Row;
+import org.checkerframework.checker.nullness.qual.Nullable;
 
 /**
  * An implementation of {@link TypedSchemaTransformProvider} for Bigtable Read jobs configured via
@@ -69,6 +71,13 @@ public class BigtableReadSchemaTransformProvider
                   Schema.FieldType.STRING,
                   Schema.FieldType.array(Schema.FieldType.row(CELL_SCHEMA))))
           .build();
+  public static final Schema FLATTENED_ROW_SCHEMA =
+      Schema.builder()
+          .addByteArrayField("key")
+          .addStringField("family_name")
+          .addByteArrayField("column_qualifier")
+          .addArrayField("cells", Schema.FieldType.row(CELL_SCHEMA))
+          .build();
 
   @Override
   protected SchemaTransform from(BigtableReadSchemaTransformConfiguration configuration) {
@@ -88,7 +97,7 @@ public List<String> outputCollectionNames() {
   /** Configuration for reading from Bigtable. */
   @DefaultSchema(AutoValueSchema.class)
   @AutoValue
-  public abstract static class BigtableReadSchemaTransformConfiguration {
+  public abstract static class BigtableReadSchemaTransformConfiguration implements Serializable {
     /** Instantiates a {@link BigtableReadSchemaTransformConfiguration.Builder} instance. */
     public void validate() {
       String emptyStringMessage =
@@ -100,7 +109,8 @@ public void validate() {
 
     public static Builder builder() {
       return new AutoValue_BigtableReadSchemaTransformProvider_BigtableReadSchemaTransformConfiguration
-          .Builder();
+              .Builder()
+          .setFlatten(true);
     }
 
     public abstract String getTableId();
@@ -109,6 +119,8 @@ public static Builder builder() {
 
     public abstract String getProjectId();
 
+    public abstract @Nullable Boolean getFlatten();
+
     /** Builder for the {@link BigtableReadSchemaTransformConfiguration}. */
     @AutoValue.Builder
     public abstract static class Builder {
@@ -118,6 +130,8 @@ public abstract static class Builder {
 
       public abstract Builder setProjectId(String projectId);
 
+      public abstract Builder setFlatten(Boolean flatten);
+
       /** Builds a {@link BigtableReadSchemaTransformConfiguration} instance. */
       public abstract BigtableReadSchemaTransformConfiguration build();
     }
@@ -152,45 +166,97 @@ public PCollectionRowTuple expand(PCollectionRowTuple input) {
                       .withInstanceId(configuration.getInstanceId())
                       .withProjectId(configuration.getProjectId()));
 
+      Schema outputSchema =
+          Boolean.FALSE.equals(configuration.getFlatten()) ? ROW_SCHEMA : FLATTENED_ROW_SCHEMA;
+
       PCollection<Row> beamRows =
-          bigtableRows.apply(MapElements.via(new BigtableRowToBeamRow())).setRowSchema(ROW_SCHEMA);
+          bigtableRows
+              .apply("ConvertToBeamRows", ParDo.of(new BigtableRowConverterDoFn(configuration)))
+              .setRowSchema(outputSchema);
 
       return PCollectionRowTuple.of(OUTPUT_TAG, beamRows);
     }
   }
 
-  public static class BigtableRowToBeamRow extends SimpleFunction<com.google.bigtable.v2.Row, Row> {
-    @Override
-    public Row apply(com.google.bigtable.v2.Row bigtableRow) {
-      // The collection of families is represented as a Map of column families.
-      // Each column family is represented as a Map of columns.
-      // Each column is represented as a List of cells
-      // Each cell is represented as a Beam Row consisting of value and timestamp_micros
-      Map<String, Map<String, List<Row>>> families = new HashMap<>();
-
-      for (Family fam : bigtableRow.getFamiliesList()) {
-        // Map of column qualifier to list of cells
-        Map<String, List<Row>> columns = new HashMap<>();
-        for (Column col : fam.getColumnsList()) {
-          List<Row> cells = new ArrayList<>();
-          for (Cell cell : col.getCellsList()) {
-            Row cellRow =
-                Row.withSchema(CELL_SCHEMA)
-                    .withFieldValue("value", ByteBuffer.wrap(cell.getValue().toByteArray()))
-                    .withFieldValue("timestamp_micros", cell.getTimestampMicros())
+  /**
+   * A {@link DoFn} that converts a Bigtable {@link com.google.bigtable.v2.Row} to a Beam {@link
+   * Row}. It supports both a nested representation and a flattened representation where each column
+   * becomes a separate output element.
+   */
+  private static class BigtableRowConverterDoFn extends DoFn<com.google.bigtable.v2.Row, Row> {
+    private final BigtableReadSchemaTransformConfiguration configuration;
+
+    BigtableRowConverterDoFn(BigtableReadSchemaTransformConfiguration configuration) {
+      this.configuration = configuration;
+    }
+
+    private List<Row> convertCells(List<Cell> bigtableCells) {
+      List<Row> beamCells = new ArrayList<>();
+      for (Cell cell : bigtableCells) {
+        Row cellRow =
+            Row.withSchema(CELL_SCHEMA)
+                .withFieldValue("value", cell.getValue().toByteArray())
+                .withFieldValue("timestamp_micros", cell.getTimestampMicros())
+                .build();
+        beamCells.add(cellRow);
+      }
+      return beamCells;
+    }
+
+    @ProcessElement
+    public void processElement(
+        @Element com.google.bigtable.v2.Row bigtableRow, OutputReceiver<Row> out) {
+      // The builder defaults flatten to true. We check for an explicit false setting to disable it.
+
+      if (Boolean.FALSE.equals(configuration.getFlatten())) {
+        // Non-flattening logic (original behavior): one output row per Bigtable row.
+        Map<String, Map<String, List<Row>>> families = new HashMap<>();
+        for (Family fam : bigtableRow.getFamiliesList()) {
+          Map<String, List<Row>> columns = new HashMap<>();
+          for (Column col : fam.getColumnsList()) {
+
+            List<Cell> bigTableCells = col.getCellsList();
+
+            List<Row> cells = convertCells(bigTableCells);
+
+            columns.put(col.getQualifier().toStringUtf8(), cells);
+          }
+          families.put(fam.getName(), columns);
+        }
+        Row beamRow =
+            Row.withSchema(ROW_SCHEMA)
+                .withFieldValue("key", bigtableRow.getKey().toByteArray())
+                .withFieldValue("column_families", families)
+                .build();
+        out.output(beamRow);
+      } else {
+        // Flattening logic (new behavior): one output row per column qualifier.
+        byte[] key = bigtableRow.getKey().toByteArray();
+        for (Family fam : bigtableRow.getFamiliesList()) {
+          String familyName = fam.getName();
+          for (Column col : fam.getColumnsList()) {
+            ByteString qualifierName = col.getQualifier();
+            List<Row> cells = new ArrayList<>();
+            for (Cell cell : col.getCellsList()) {
+              Row cellRow =
+                  Row.withSchema(CELL_SCHEMA)
+                      .withFieldValue("value", cell.getValue().toByteArray())
+                      .withFieldValue("timestamp_micros", cell.getTimestampMicros())
+                      .build();
+              cells.add(cellRow);
+            }
+
+            Row flattenedRow =
+                Row.withSchema(FLATTENED_ROW_SCHEMA)
+                    .withFieldValue("key", key)
+                    .withFieldValue("family_name", familyName)
+                    .withFieldValue("column_qualifier", qualifierName.toByteArray())
+                    .withFieldValue("cells", cells)
                     .build();
-            cells.add(cellRow);
+            out.output(flattenedRow);
           }
-          columns.put(col.getQualifier().toStringUtf8(), cells);
         }
-        families.put(fam.getName(), columns);
       }
-      Row beamRow =
-          Row.withSchema(ROW_SCHEMA)
-              .withFieldValue("key", ByteBuffer.wrap(bigtableRow.getKey().toByteArray()))
-              .withFieldValue("column_families", families)
-              .build();
-      return beamRow;
     }
   }
 }
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableWriteSchemaTransformProvider.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableWriteSchemaTransformProvider.java
index cc480be6aa7e..455591543898 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableWriteSchemaTransformProvider.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableWriteSchemaTransformProvider.java
@@ -19,6 +19,7 @@
 
 import static java.util.Optional.ofNullable;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkState;
 
 import com.google.auto.service.AutoService;
 import com.google.auto.value.AutoValue;
@@ -34,16 +35,21 @@
 import java.util.Map;
 import org.apache.beam.sdk.io.gcp.bigtable.BigtableWriteSchemaTransformProvider.BigtableWriteSchemaTransformConfiguration;
 import org.apache.beam.sdk.schemas.AutoValueSchema;
+import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.schemas.annotations.DefaultSchema;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransform;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
 import org.apache.beam.sdk.schemas.transforms.TypedSchemaTransformProvider;
+import org.apache.beam.sdk.transforms.GroupByKey;
 import org.apache.beam.sdk.transforms.MapElements;
 import org.apache.beam.sdk.transforms.SimpleFunction;
+import org.apache.beam.sdk.util.Preconditions;
 import org.apache.beam.sdk.values.KV;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionRowTuple;
 import org.apache.beam.sdk.values.Row;
+import org.apache.beam.sdk.values.TypeDescriptor;
+import org.apache.beam.sdk.values.TypeDescriptors;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.primitives.Longs;
 
 /**
@@ -60,6 +66,13 @@ public class BigtableWriteSchemaTransformProvider
 
   private static final String INPUT_TAG = "input";
 
+  private static final Schema BATCHED_MUTATIONS_SCHEMA =
+      Schema.builder()
+          .addByteArrayField("key")
+          .addArrayField(
+              "mutations", Schema.FieldType.map(Schema.FieldType.STRING, Schema.FieldType.BYTES))
+          .build();
+
   @Override
   protected SchemaTransform from(BigtableWriteSchemaTransformConfiguration configuration) {
     return new BigtableWriteSchemaTransform(configuration);
@@ -135,18 +148,203 @@ public PCollectionRowTuple expand(PCollectionRowTuple input) {
           String.format(
               "Could not find expected input [%s] to %s.", INPUT_TAG, getClass().getSimpleName()));
 
-      PCollection<Row> beamRowMutations = input.get(INPUT_TAG);
-      PCollection<KV<ByteString, Iterable<Mutation>>> bigtableMutations =
-          beamRowMutations.apply(MapElements.via(new GetMutationsFromBeamRow()));
+      Schema inputSchema = input.getSinglePCollection().getSchema();
 
-      bigtableMutations.apply(
-          BigtableIO.write()
-              .withTableId(configuration.getTableId())
-              .withInstanceId(configuration.getInstanceId())
-              .withProjectId(configuration.getProjectId()));
+      PCollection<KV<ByteString, Iterable<Mutation>>> bigtableMutations = null;
+      if (inputSchema.equals(BATCHED_MUTATIONS_SCHEMA)) {
+        PCollection<Row> beamRowMutations = input.get(INPUT_TAG);
+        bigtableMutations =
+            beamRowMutations.apply(
+                // Original schema inputs gets sent out to the original transform provider mutations
+                // function
+                MapElements.via(new GetMutationsFromBeamRow()));
+      } else if (inputSchema.hasField("type")) {
+        validateField(inputSchema, "key", Schema.TypeName.BYTES);
+        validateField(inputSchema, "type", Schema.TypeName.STRING);
+        if (inputSchema.hasField("value")) {
+          validateField(inputSchema, "value", Schema.TypeName.BYTES);
+        }
+        if (inputSchema.hasField("column_qualifier")) {
+          validateField(inputSchema, "column_qualifier", Schema.TypeName.BYTES);
+        }
+        if (inputSchema.hasField("family_name")) {
+          validateField(inputSchema, "family_name", Schema.TypeName.STRING);
+        }
+        if (inputSchema.hasField("timestamp_micros")) {
+          validateField(inputSchema, "timestamp_micros", Schema.TypeName.INT64);
+        }
+        if (inputSchema.hasField("start_timestamp_micros")) {
+          validateField(inputSchema, "start_timestamp_micros", Schema.TypeName.INT64);
+        }
+        if (inputSchema.hasField("end_timestamp_micros")) {
+          validateField(inputSchema, "end_timestamp_micros", Schema.TypeName.INT64);
+        }
+        bigtableMutations = changeMutationInput(input);
+      } else {
+        throw new RuntimeException(
+            "Input Schema is invalid: "
+                + inputSchema
+                + "\n\nSchema should be formatted in one of two ways:\n "
+                + "key\": ByteString\n"
+                + "\"type\": String\n"
+                + "\"value\": ByteString\n"
+                + "\"column_qualifier\": ByteString\n"
+                + "\"family_name\": String\n"
+                + "\"timestamp_micros\": Long\n"
+                + "\"start_timestamp_micros\": Long\n"
+                + "\"end_timestamp_micros\": Long\n"
+                + "\nOR\n"
+                + "\n"
+                + "\"key\": ByteString\n"
+                + "(\"mutations\", contains map(String, ByteString) of mutations in the mutation schema format");
+      }
 
+      if (bigtableMutations != null) {
+        bigtableMutations.apply(
+            BigtableIO.write()
+                .withTableId(configuration.getTableId())
+                .withInstanceId(configuration.getInstanceId())
+                .withProjectId(configuration.getProjectId()));
+      } else {
+        throw new RuntimeException(
+            "Inputted Schema caused mutation error, check error logs and input schema format");
+      }
       return PCollectionRowTuple.empty(input.getPipeline());
     }
+
+    private void validateField(Schema inputSchema, String field, Schema.TypeName expectedType) {
+      Schema.TypeName actualType = inputSchema.getField(field).getType().getTypeName();
+      checkState(
+          actualType.equals(expectedType),
+          "Schema field '%s' should be of type %s, but was %s.",
+          field,
+          expectedType,
+          actualType);
+    }
+
+    public PCollection<KV<ByteString, Iterable<Mutation>>> changeMutationInput(
+        PCollectionRowTuple inputR) {
+      PCollection<Row> beamRowMutationsList = inputR.getSinglePCollection();
+      // convert all row inputs into KV<ByteString, Mutation>
+      PCollection<KV<ByteString, Mutation>> changedBeamRowMutationsList =
+          beamRowMutationsList.apply(
+              MapElements.into(
+                      TypeDescriptors.kvs(
+                          TypeDescriptor.of(ByteString.class), TypeDescriptor.of(Mutation.class)))
+                  .via(
+                      (Row input) -> {
+                        ByteString key =
+                            ByteString.copyFrom(
+                                Preconditions.checkStateNotNull(
+                                    input.getBytes("key"),
+                                    "Encountered row with null 'key' property."));
+
+                        Mutation bigtableMutation;
+                        String mutationType =
+                            input.getString("type"); // Direct call, can return null
+                        if (mutationType == null) {
+                          throw new IllegalArgumentException("Mutation type cannot be null.");
+                        }
+                        switch (mutationType) {
+                          case "SetCell":
+                            Mutation.SetCell.Builder setMutation =
+                                Mutation.SetCell.newBuilder()
+                                    .setValue(
+                                        ByteString.copyFrom(
+                                            Preconditions.checkStateNotNull(
+                                                input.getBytes("value"),
+                                                "Encountered SetCell mutation with null 'value' property.")))
+                                    .setColumnQualifier(
+                                        ByteString.copyFrom(
+                                            Preconditions.checkStateNotNull(
+                                                input.getBytes("column_qualifier"),
+                                                "Encountered SetCell mutation with null 'column_qualifier' property. ")))
+                                    .setFamilyName(
+                                        Preconditions.checkStateNotNull(
+                                            input.getString("family_name"),
+                                            "Encountered SetCell mutation with null 'family_name' property."));
+                            // Use timestamp if provided, else default to -1 (current
+                            // Bigtable
+                            // server time)
+                            // Timestamp (optional, assuming Long type in Row schema)
+                            Long timestampMicros = input.getInt64("timestamp_micros");
+                            setMutation.setTimestampMicros(
+                                timestampMicros != null ? timestampMicros : -1);
+
+                            bigtableMutation =
+                                Mutation.newBuilder().setSetCell(setMutation.build()).build();
+                            break;
+                          case "DeleteFromColumn":
+                            // set timestamp range if applicable
+                            Mutation.DeleteFromColumn.Builder deleteMutation =
+                                Mutation.DeleteFromColumn.newBuilder()
+                                    .setColumnQualifier(
+                                        ByteString.copyFrom(
+                                            Preconditions.checkStateNotNull(
+                                                input.getBytes("column_qualifier"),
+                                                "Encountered DeleteFromColumn mutation with null 'column_qualifier' property.")))
+                                    .setFamilyName(
+                                        Preconditions.checkStateNotNull(
+                                            input.getString("family_name"),
+                                            "Encountered DeleteFromColumn mutation with null 'family_name' property."));
+
+                            // if start or end timestamp provided
+                            // Timestamp Range (optional, assuming Long type in Row schema)
+                            Long startTimestampMicros = null;
+                            Long endTimestampMicros = null;
+
+                            if (input.getSchema().hasField("start_timestamp_micros")) {
+                              startTimestampMicros = input.getInt64("start_timestamp_micros");
+                            }
+                            if (input.getSchema().hasField("end_timestamp_micros")) {
+                              endTimestampMicros = input.getInt64("end_timestamp_micros");
+                            }
+
+                            if (startTimestampMicros != null || endTimestampMicros != null) {
+                              TimestampRange.Builder timeRange = TimestampRange.newBuilder();
+                              if (startTimestampMicros != null) {
+                                timeRange.setStartTimestampMicros(startTimestampMicros);
+                              }
+                              if (endTimestampMicros != null) {
+                                timeRange.setEndTimestampMicros(endTimestampMicros);
+                              }
+                              deleteMutation.setTimeRange(timeRange.build());
+                            }
+                            bigtableMutation =
+                                Mutation.newBuilder()
+                                    .setDeleteFromColumn(deleteMutation.build())
+                                    .build();
+                            break;
+                          case "DeleteFromFamily":
+                            bigtableMutation =
+                                Mutation.newBuilder()
+                                    .setDeleteFromFamily(
+                                        Mutation.DeleteFromFamily.newBuilder()
+                                            .setFamilyName(
+                                                Preconditions.checkStateNotNull(
+                                                    input.getString("family_name"),
+                                                    "Encountered DeleteFromFamily mutation with null 'family_name' property."))
+                                            .build())
+                                    .build();
+                            break;
+                          case "DeleteFromRow":
+                            bigtableMutation =
+                                Mutation.newBuilder()
+                                    .setDeleteFromRow(Mutation.DeleteFromRow.newBuilder().build())
+                                    .build();
+                            break;
+                          default:
+                            throw new RuntimeException(
+                                String.format(
+                                    "Unexpected mutation type [%s]: Key value is %s",
+                                    ((input.getString("type"))),
+                                    Arrays.toString(input.getBytes("key"))));
+                        }
+                        return KV.of(key, bigtableMutation);
+                      }));
+      // now we need to make the KV into a PCollection of KV<ByteString, Iterable<Mutation>>
+      return changedBeamRowMutationsList.apply(GroupByKey.create());
+    }
   }
 
   public static class GetMutationsFromBeamRow
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/datastore/DatastoreV1.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/datastore/DatastoreV1.java
index 471675a2c988..c9507475648d 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/datastore/DatastoreV1.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/datastore/DatastoreV1.java
@@ -614,7 +614,7 @@ static Query translateGqlQueryWithLimitCheck(
         // limit, so we just check for INVALID_ARGUMENT and assume that that the query might have
         // a limit already set.
         if (e.getCode() == Code.INVALID_ARGUMENT) {
-          LOG.warn("Failed to translate Gql query '{}': {}", gqlQueryWithZeroLimit, e.getMessage());
+          LOG.warn("Failed to translate Gql query '{}'", gqlQueryWithZeroLimit, e);
           LOG.warn("User query might have a limit already set, so trying without zero limit");
           // Retry without the zero limit.
           return translateGqlQuery(gql, datastore, projectId, databaseId, namespace, readTime);
@@ -2440,10 +2440,10 @@ private synchronized void flushBatch(ContextAdapter<OutT> context)
           // Only log the code and message for potentially-transient errors. The entire exception
           // will be propagated upon the last retry.
           LOG.error(
-              "Error writing batch of {} mutations to Datastore ({}): {}",
+              "Error writing batch of {} mutations to Datastore ({})",
               mutations.size(),
               exception.getCode(),
-              exception.getMessage());
+              exception);
           rpcErrors.inc();
 
           if (NON_RETRYABLE_ERRORS.contains(exception.getCode())) {
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/firestore/FirestoreV1WriteFn.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/firestore/FirestoreV1WriteFn.java
index 70c2b91ffbfd..6bbb00e76f2d 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/firestore/FirestoreV1WriteFn.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/firestore/FirestoreV1WriteFn.java
@@ -51,6 +51,7 @@
 import org.apache.beam.sdk.transforms.display.DisplayData;
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.util.BackOffUtils;
+import org.apache.beam.sdk.util.Preconditions;
 import org.apache.beam.sdk.values.KV;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
@@ -87,7 +88,6 @@ static final class BatchWriteFnWithSummary extends BaseBatchWriteFn<WriteSuccess
     @Override
     void handleWriteFailures(
         ContextAdapter<WriteSuccessSummary> context,
-        Instant timestamp,
         List<KV<WriteFailure, BoundedWindow>> writeFailures,
         Runnable logMessage) {
       throw new FailedWritesException(
@@ -125,12 +125,11 @@ static final class BatchWriteFnWithDeadLetterQueue extends BaseBatchWriteFn<Writ
     @Override
     void handleWriteFailures(
         ContextAdapter<WriteFailure> context,
-        Instant timestamp,
         List<KV<WriteFailure, BoundedWindow>> writeFailures,
         Runnable logMessage) {
       logMessage.run();
       for (KV<WriteFailure, BoundedWindow> kv : writeFailures) {
-        context.output(kv.getKey(), timestamp, kv.getValue());
+        context.output(kv.getKey(), kv.getValue().maxTimestamp(), kv.getValue());
       }
     }
 
@@ -274,7 +273,6 @@ public void processElement(ProcessContext context, BoundedWindow window) throws
                 getWriteType(write), getName(write));
         handleWriteFailures(
             contextAdapter,
-            clock.instant(),
             ImmutableList.of(
                 KV.of(
                     new WriteFailure(
@@ -466,7 +464,7 @@ private DoFlushStatus doFlush(
         if (okCount == writesCount) {
           handleWriteSummary(
               context,
-              end,
+              Preconditions.checkArgumentNotNull(okWindow).maxTimestamp(),
               KV.of(new WriteSuccessSummary(okCount, okBytes), coerceNonNull(okWindow)),
               () ->
                   LOG.debug(
@@ -481,7 +479,6 @@ private DoFlushStatus doFlush(
             int finalOkCount = okCount;
             handleWriteFailures(
                 context,
-                end,
                 ImmutableList.copyOf(nonRetryableWrites),
                 () ->
                     LOG.warn(
@@ -506,7 +503,7 @@ private DoFlushStatus doFlush(
             if (okCount > 0) {
               handleWriteSummary(
                   context,
-                  end,
+                  Preconditions.checkArgumentNotNull(okWindow).maxTimestamp(),
                   KV.of(new WriteSuccessSummary(okCount, okBytes), coerceNonNull(okWindow)),
                   logMessage);
             } else {
@@ -542,7 +539,6 @@ private enum DoFlushStatus {
 
     abstract void handleWriteFailures(
         ContextAdapter<OutT> context,
-        Instant timestamp,
         List<KV<WriteFailure, BoundedWindow>> writeFailures,
         Runnable logMessage);
 
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/healthcare/FhirIO.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/healthcare/FhirIO.java
index 0206d48b813c..0fbd2a1b45a7 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/healthcare/FhirIO.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/healthcare/FhirIO.java
@@ -1531,7 +1531,6 @@ private void parseResponse(ProcessContext context, HttpBody resp)
               SUCCESSFUL_BUNDLES, FhirBundleResponse.of(context.element(), bundle.toString()));
         }
         EXECUTE_BUNDLE_SUCCESS.inc();
-        return;
       }
 
       // parseBundleStatus parses out the status code from a Bundle.entry.response.status string,
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PreparePubsubWriteDoFn.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PreparePubsubWriteDoFn.java
index fb096e382994..9171bdf28494 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PreparePubsubWriteDoFn.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PreparePubsubWriteDoFn.java
@@ -59,7 +59,7 @@ public class PreparePubsubWriteDoFn<InputT> extends DoFn<InputT, PubsubMessage>
 
   private final TupleTag<PubsubMessage> outputTag;
 
-  static int validatePubsubMessageSize(PubsubMessage message, int maxPublishBatchSize)
+  static int validatePubsubMessage(PubsubMessage message, int maxPublishBatchSize)
       throws SizeLimitExceededException {
     int payloadSize = message.getPayload().length;
     if (payloadSize > PUBSUB_MESSAGE_DATA_MAX_BYTES) {
@@ -86,7 +86,12 @@ static int validatePubsubMessageSize(PubsubMessage message, int maxPublishBatchS
       totalSize += orderingKeySize;
     }
 
-    @Nullable Map<String, String> attributes = message.getAttributeMap();
+    final @Nullable Map<String, String> attributes = message.getAttributeMap();
+    if (payloadSize == 0 && (attributes == null || attributes.isEmpty())) {
+      throw new IllegalArgumentException(
+          "Pubsub message must contain a non-empty payload or at least one attribute.");
+    }
+
     if (attributes != null) {
       if (attributes.size() > PUBSUB_MESSAGE_MAX_ATTRIBUTES) {
         throw new SizeLimitExceededException(
@@ -212,7 +217,7 @@ public void process(
       message = message.withOrderingKey(null);
     }
     try {
-      validatePubsubMessageSize(message, maxPublishBatchSize);
+      validatePubsubMessage(message, maxPublishBatchSize);
     } catch (SizeLimitExceededException e) {
       badRecordRouter.route(
           o,
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubIO.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubIO.java
index 3c08bcbf2819..d62d294ed2a7 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubIO.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubIO.java
@@ -1559,9 +1559,8 @@ public Write<T> withPubsubRootUrl(String pubsubRootUrl) {
 
     /**
      * Writes any serialization failures out to the Error Handler. See {@link ErrorHandler} for
-     * details on how to configure an Error Handler. Error Handlers are not well supported when
-     * writing to topics with schemas, and it is not recommended to configure an error handler if
-     * the target topic has a schema.
+     * details on how to configure an Error Handler. Schema errors are not handled by Error
+     * Handlers, and will be handled using the default behavior of the runner.
      */
     public Write<T> withErrorHandler(ErrorHandler<BadRecord, ?> badRecordErrorHandler) {
       return toBuilder()
@@ -1738,7 +1737,7 @@ public void processElement(@Element PubsubMessage message, @Timestamp Instant ti
         // TODO(sjvanrossum): https://github.com/apache/beam/issues/31800
         // - Size validation makes no distinction between JSON and Protobuf encoding
         // - Accounting for HTTP to gRPC transcoding is non-trivial
-        PreparePubsubWriteDoFn.validatePubsubMessageSize(message, maxPublishBatchByteSize);
+        PreparePubsubWriteDoFn.validatePubsubMessage(message, maxPublishBatchByteSize);
         // NOTE: The record id is always null since it will be assigned by Pub/Sub.
         final OutgoingMessage msg =
             OutgoingMessage.of(message, timestamp.getMillis(), null, message.getTopic());
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubTestClient.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubTestClient.java
index 7bf342eee8c6..22fcaae20cad 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubTestClient.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubTestClient.java
@@ -535,7 +535,7 @@ public List<IncomingMessage> pull(
             incomingMessageWithRequestTime.ackId(), incomingMessageWithRequestTime);
         STATE.ackDeadline.put(
             incomingMessageWithRequestTime.ackId(),
-            requestTimeMsSinceEpoch + STATE.ackTimeoutSec * 1000);
+            requestTimeMsSinceEpoch + STATE.ackTimeoutSec * 1000L);
         if (incomingMessages.size() >= batchSize) {
           break;
         }
@@ -588,7 +588,7 @@ public void modifyAckDeadline(
               STATE.pendingAckIncomingMessages.containsKey(ackId),
               "No message with ACK id %s is waiting for an ACK",
               ackId);
-          STATE.ackDeadline.put(ackId, STATE.clock.currentTimeMillis() + deadlineSeconds * 1000);
+          STATE.ackDeadline.put(ackId, STATE.clock.currentTimeMillis() + deadlineSeconds * 1000L);
         } else {
           checkState(
               STATE.ackDeadline.remove(ackId) != null,
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/CloserReference.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/CloserReference.java
index 089f0f2242f1..9853e24e8ff3 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/CloserReference.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/CloserReference.java
@@ -60,7 +60,7 @@ public void run() {
     }
   }
 
-  @SuppressWarnings("deprecation")
+  @SuppressWarnings({"deprecation", "Finalize"})
   @Override
   protected void finalize() {
     SystemExecutors.getFuturesExecutor().execute(new Closer(object));
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/MemoryLimiterImpl.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/MemoryLimiterImpl.java
index 3f86e880f8de..753c45e0c1e3 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/MemoryLimiterImpl.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/MemoryLimiterImpl.java
@@ -82,6 +82,7 @@ public void close() {
     }
 
     @Override
+    @SuppressWarnings("Finalize")
     public void finalize() {
       if (!released) {
         LOG.error("Failed to release memory block- likely SDF implementation error.");
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/MutationUtils.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/MutationUtils.java
index 5a106a34b0c6..dcdbdb44c00c 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/MutationUtils.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/MutationUtils.java
@@ -219,7 +219,7 @@ private static void setBeamValueToMutation(
         @Nullable BigDecimal decimal = row.getDecimal(columnName);
         // BigDecimal is not nullable
         if (decimal == null) {
-          checkNotNull(decimal, "Null decimal at column " + columnName);
+          checkNotNull(decimal, "Null decimal at column %s", columnName);
         } else {
           mutationBuilder.set(columnName).to(decimal);
         }
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerAccessor.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerAccessor.java
index 1100578dacac..96ce735cad4a 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerAccessor.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerAccessor.java
@@ -43,6 +43,7 @@
 import org.apache.beam.sdk.options.ValueProvider;
 import org.apache.beam.sdk.util.ReleaseInfo;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Strings;
 import org.joda.time.Duration;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -60,6 +61,9 @@ public class SpannerAccessor implements AutoCloseable {
    */
   private static final String USER_AGENT_PREFIX = "Apache_Beam_Java";
 
+  /** Instance ID to use when connecting to an experimental host. */
+  public static final String EXPERIMENTAL_HOST_INSTANCE_ID = "default";
+
   // Only create one SpannerAccessor for each different SpannerConfig.
   private static final ConcurrentHashMap<SpannerConfig, SpannerAccessor> spannerAccessors =
       new ConcurrentHashMap<>();
@@ -219,6 +223,24 @@ static SpannerOptions buildSpannerOptions(SpannerConfig spannerConfig) {
       builder.setServiceFactory(serviceFactory);
     }
     builder.setHost(spannerConfig.getHostValue());
+
+    ValueProvider<String> experimentalHost = spannerConfig.getExperimentalHost();
+    if (experimentalHost != null && !Strings.isNullOrEmpty(experimentalHost.get())) {
+      builder.setExperimentalHost(experimentalHost.get());
+      ValueProvider<Boolean> plainText = spannerConfig.getPlainText();
+      ValueProvider<String> instanceId = spannerConfig.getInstanceId();
+      if (Strings.isNullOrEmpty(instanceId.get())
+          || !instanceId.get().equals(EXPERIMENTAL_HOST_INSTANCE_ID)) {
+        throw new IllegalArgumentException(
+            "Experimental host can only be used with instance id: "
+                + EXPERIMENTAL_HOST_INSTANCE_ID);
+      }
+      if (plainText != null && Boolean.TRUE.equals(plainText.get())) {
+        builder.setChannelConfigurator(b -> b.usePlaintext());
+        builder.setCredentials(NoCredentials.getInstance());
+      }
+    }
+
     ValueProvider<String> emulatorHost = spannerConfig.getEmulatorHost();
     if (emulatorHost != null) {
       builder.setEmulatorHost(emulatorHost.get());
@@ -229,6 +251,10 @@ static SpannerOptions buildSpannerOptions(SpannerConfig spannerConfig) {
       builder.setCredentials(NoCredentials.getInstance());
     }
     String userAgentString = USER_AGENT_PREFIX + "/" + ReleaseInfo.getReleaseInfo().getVersion();
+    SpannerIOMetadata spannerIOMetadata = SpannerIOMetadata.create();
+    if (!Strings.isNullOrEmpty(spannerIOMetadata.getBeamJobId())) {
+      userAgentString = userAgentString + "/" + spannerIOMetadata.getBeamJobId();
+    }
     builder.setHeaderProvider(FixedHeaderProvider.create("user-agent", userAgentString));
     ValueProvider<String> databaseRole = spannerConfig.getDatabaseRole();
     if (databaseRole != null && databaseRole.get() != null && !databaseRole.get().isEmpty()) {
@@ -264,7 +290,7 @@ private static SpannerAccessor createAndConnect(SpannerConfig spannerConfig) {
       // fetch instanceConfigId is fail-free.
       // Do not emit warning when serviceFactory is overridden (e.g. in tests).
       if (spannerConfig.getServiceFactory() == null) {
-        LOG.warn("unable to get Spanner instanceConfigId for {}: {}", instanceId, e.getMessage());
+        LOG.warn("unable to get Spanner instanceConfigId for {}", instanceId, e);
       }
     }
 
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerConfig.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerConfig.java
index 5141251f6d94..f52b8378cb6a 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerConfig.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerConfig.java
@@ -48,6 +48,9 @@ public abstract class SpannerConfig implements Serializable {
   private static final Duration DEFAULT_COMMIT_DEADLINE = Duration.standardSeconds(15);
   // Total allowable backoff time.
   private static final Duration DEFAULT_MAX_CUMULATIVE_BACKOFF = Duration.standardMinutes(15);
+  // Instance id of experimental hosts
+  private static final ValueProvider<String> EXPERIMENTAL_HOST_INSTANCE_ID =
+      ValueProvider.StaticValueProvider.of("default");
   // A default priority for batch traffic.
   static final RpcPriority DEFAULT_RPC_PRIORITY = RpcPriority.MEDIUM;
 
@@ -68,6 +71,8 @@ public String getHostValue() {
 
   public abstract @Nullable ValueProvider<String> getEmulatorHost();
 
+  public abstract @Nullable ValueProvider<String> getExperimentalHost();
+
   public abstract @Nullable ValueProvider<Boolean> getIsLocalChannelProvider();
 
   public abstract @Nullable ValueProvider<Duration> getCommitDeadline();
@@ -90,6 +95,8 @@ public String getHostValue() {
 
   public abstract @Nullable ValueProvider<Duration> getPartitionReadTimeout();
 
+  public abstract @Nullable ValueProvider<Boolean> getPlainText();
+
   @VisibleForTesting
   abstract @Nullable ServiceFactory<Spanner, SpannerOptions> getServiceFactory();
 
@@ -149,6 +156,8 @@ public abstract static class Builder {
 
     abstract Builder setEmulatorHost(ValueProvider<String> emulatorHost);
 
+    abstract Builder setExperimentalHost(ValueProvider<String> experimentalHost);
+
     abstract Builder setIsLocalChannelProvider(ValueProvider<Boolean> isLocalChannelProvider);
 
     abstract Builder setCommitDeadline(ValueProvider<Duration> commitDeadline);
@@ -178,6 +187,8 @@ abstract Builder setExecuteStreamingSqlRetrySettings(
 
     abstract Builder setCredentials(ValueProvider<Credentials> credentials);
 
+    abstract Builder setPlainText(ValueProvider<Boolean> plainText);
+
     public abstract SpannerConfig build();
   }
 
@@ -345,4 +356,37 @@ public SpannerConfig withCredentials(Credentials credentials) {
   public SpannerConfig withCredentials(ValueProvider<Credentials> credentials) {
     return toBuilder().setCredentials(credentials).build();
   }
+
+  /** Specifies the experimental host to set on SpannerOptions (setExperimentalHost). */
+  public SpannerConfig withExperimentalHost(ValueProvider<String> experimentalHost) {
+    return toBuilder()
+        .setInstanceId(EXPERIMENTAL_HOST_INSTANCE_ID)
+        .setExperimentalHost(experimentalHost)
+        .build();
+  }
+
+  /** Specifies the experimental host to set on SpannerOptions (setExperimentalHost). */
+  public SpannerConfig withExperimentalHost(String experimentalHost) {
+    return withExperimentalHost(ValueProvider.StaticValueProvider.of(experimentalHost));
+  }
+
+  /**
+   * Specifies whether to use plaintext channel.
+   *
+   * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+   * withExperimentalHost}).
+   */
+  public SpannerConfig withUsingPlainTextChannel(ValueProvider<Boolean> plainText) {
+    return toBuilder().setPlainText(plainText).build();
+  }
+
+  /**
+   * Specifies whether to use plaintext channel.
+   *
+   * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+   * withExperimentalHost}).
+   */
+  public SpannerConfig withUsingPlainTextChannel(boolean plainText) {
+    return withUsingPlainTextChannel(ValueProvider.StaticValueProvider.of(plainText));
+  }
 }
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIO.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIO.java
index d3b2632bad0e..e060766cbd22 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIO.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIO.java
@@ -52,6 +52,8 @@
 import com.google.cloud.spanner.Statement;
 import com.google.cloud.spanner.Struct;
 import com.google.cloud.spanner.TimestampBound;
+import com.google.gson.Gson;
+import com.google.gson.GsonBuilder;
 import java.io.ByteArrayInputStream;
 import java.io.ByteArrayOutputStream;
 import java.io.IOException;
@@ -111,13 +113,17 @@
 import org.apache.beam.sdk.transforms.Wait;
 import org.apache.beam.sdk.transforms.WithTimestamps;
 import org.apache.beam.sdk.transforms.display.DisplayData;
+import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
 import org.apache.beam.sdk.transforms.windowing.DefaultTrigger;
 import org.apache.beam.sdk.transforms.windowing.GlobalWindow;
 import org.apache.beam.sdk.transforms.windowing.GlobalWindows;
+import org.apache.beam.sdk.transforms.windowing.PaneInfo;
 import org.apache.beam.sdk.transforms.windowing.Window;
 import org.apache.beam.sdk.util.BackOff;
 import org.apache.beam.sdk.util.FluentBackoff;
+import org.apache.beam.sdk.util.OutputBuilderSupplier;
 import org.apache.beam.sdk.util.Sleeper;
+import org.apache.beam.sdk.values.OutputBuilder;
 import org.apache.beam.sdk.values.PBegin;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollection.IsBounded;
@@ -130,6 +136,7 @@
 import org.apache.beam.sdk.values.TupleTag;
 import org.apache.beam.sdk.values.TupleTagList;
 import org.apache.beam.sdk.values.TypeDescriptor;
+import org.apache.beam.sdk.values.WindowedValues;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.MoreObjects;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Stopwatch;
@@ -610,6 +617,37 @@ public ReadAll withEmulatorHost(String emulatorHost) {
       return withEmulatorHost(ValueProvider.StaticValueProvider.of(emulatorHost));
     }
 
+    /** Specifies the SpannerOptions experimental host (setExperimentalHost). */
+    public ReadAll withExperimentalHost(ValueProvider<String> experimentalHost) {
+      SpannerConfig config = getSpannerConfig();
+      return withSpannerConfig(config.withExperimentalHost(experimentalHost));
+    }
+
+    public ReadAll withExperimentalHost(String experimentalHost) {
+      return withExperimentalHost(ValueProvider.StaticValueProvider.of(experimentalHost));
+    }
+
+    /**
+     * Specifies whether to use plaintext channel.
+     *
+     * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+     * withExperimentalHost}).
+     */
+    public ReadAll withUsingPlainTextChannel(ValueProvider<Boolean> plainText) {
+      SpannerConfig config = getSpannerConfig();
+      return withSpannerConfig(config.withUsingPlainTextChannel(plainText));
+    }
+
+    /**
+     * Specifies whether to use plaintext channel.
+     *
+     * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+     * withExperimentalHost}).
+     */
+    public ReadAll withUsingPlainTextChannel(boolean plainText) {
+      return withUsingPlainTextChannel(ValueProvider.StaticValueProvider.of(plainText));
+    }
+
     /** Specifies the Cloud Spanner database. */
     public ReadAll withDatabaseId(ValueProvider<String> databaseId) {
       SpannerConfig config = getSpannerConfig();
@@ -839,6 +877,37 @@ public Read withEmulatorHost(String emulatorHost) {
       return withEmulatorHost(ValueProvider.StaticValueProvider.of(emulatorHost));
     }
 
+    /** Specifies the SpannerOptions experimental host (setExperimentalHost). */
+    public Read withExperimentalHost(ValueProvider<String> experimentalHost) {
+      SpannerConfig config = getSpannerConfig();
+      return withSpannerConfig(config.withExperimentalHost(experimentalHost));
+    }
+
+    public Read withExperimentalHost(String experimentalHost) {
+      return withExperimentalHost(ValueProvider.StaticValueProvider.of(experimentalHost));
+    }
+
+    /**
+     * Specifies whether to use plaintext channel.
+     *
+     * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+     * withExperimentalHost}).
+     */
+    public Read withUsingPlainTextChannel(ValueProvider<Boolean> plainText) {
+      SpannerConfig config = getSpannerConfig();
+      return withSpannerConfig(config.withUsingPlainTextChannel(plainText));
+    }
+
+    /**
+     * Specifies whether to use plaintext channel.
+     *
+     * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+     * withExperimentalHost}).
+     */
+    public Read withUsingPlainTextChannel(boolean plainText) {
+      return withUsingPlainTextChannel(ValueProvider.StaticValueProvider.of(plainText));
+    }
+
     /** If true the uses Cloud Spanner batch API. */
     public Read withBatching(boolean batching) {
       return toBuilder().setBatching(batching).build();
@@ -1015,6 +1084,32 @@ public PCollection<Row> expand(PBegin input) {
     }
   }
 
+  static class ChangeStreamRead extends PTransform<PBegin, PCollection<String>> {
+
+    ReadChangeStream readChangeStream;
+
+    public ChangeStreamRead(ReadChangeStream readChangeStream) {
+      this.readChangeStream = readChangeStream;
+    }
+
+    @Override
+    public PCollection<String> expand(PBegin input) {
+      return input
+          .apply(readChangeStream)
+          .apply("DataChangeRecordToStringJSON", ParDo.of(new DataChangeRecordToJsonFn()));
+    }
+  }
+
+  private static class DataChangeRecordToJsonFn extends DoFn<DataChangeRecord, String> {
+    private static Gson gson = new GsonBuilder().disableHtmlEscaping().create();
+
+    @ProcessElement
+    public void process(@Element DataChangeRecord input, OutputReceiver<String> receiver) {
+      String modJsonString = gson.toJson(input, DataChangeRecord.class);
+      receiver.output(modJsonString);
+    }
+  }
+
   /**
    * A {@link PTransform} that create a transaction. If applied to a {@link PCollection}, it will
    * create a transaction after the {@link PCollection} is closed.
@@ -1109,6 +1204,37 @@ public CreateTransaction withEmulatorHost(String emulatorHost) {
       return withEmulatorHost(ValueProvider.StaticValueProvider.of(emulatorHost));
     }
 
+    /** Specifies the SpannerOptions experimental host (setExperimentalHost). */
+    public CreateTransaction withExperimentalHost(ValueProvider<String> experimentalHost) {
+      SpannerConfig config = getSpannerConfig();
+      return withSpannerConfig(config.withExperimentalHost(experimentalHost));
+    }
+
+    public CreateTransaction withExperimentalHost(String experimentalHost) {
+      return withExperimentalHost(ValueProvider.StaticValueProvider.of(experimentalHost));
+    }
+
+    /**
+     * Specifies whether to use plaintext channel.
+     *
+     * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+     * withExperimentalHost}).
+     */
+    public CreateTransaction withUsingPlainTextChannel(ValueProvider<Boolean> plainText) {
+      SpannerConfig config = getSpannerConfig();
+      return withSpannerConfig(config.withUsingPlainTextChannel(plainText));
+    }
+
+    /**
+     * Specifies whether to use plaintext channel.
+     *
+     * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+     * withExperimentalHost}).
+     */
+    public CreateTransaction withUsingPlainTextChannel(boolean plainText) {
+      return withUsingPlainTextChannel(ValueProvider.StaticValueProvider.of(plainText));
+    }
+
     @VisibleForTesting
     CreateTransaction withServiceFactory(ServiceFactory<Spanner, SpannerOptions> serviceFactory) {
       SpannerConfig config = getSpannerConfig();
@@ -1246,6 +1372,37 @@ public Write withEmulatorHost(String emulatorHost) {
       return withEmulatorHost(ValueProvider.StaticValueProvider.of(emulatorHost));
     }
 
+    /** Specifies the SpannerOptions experimental host (setExperimentalHost). */
+    public Write withExperimentalHost(ValueProvider<String> experimentalHost) {
+      SpannerConfig config = getSpannerConfig();
+      return withSpannerConfig(config.withExperimentalHost(experimentalHost));
+    }
+
+    public Write withExperimentalHost(String experimentalHost) {
+      return withExperimentalHost(ValueProvider.StaticValueProvider.of(experimentalHost));
+    }
+
+    /**
+     * Specifies whether to use plaintext channel.
+     *
+     * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+     * withExperimentalHost}).
+     */
+    public Write withUsingPlainTextChannel(ValueProvider<Boolean> plainText) {
+      SpannerConfig config = getSpannerConfig();
+      return withSpannerConfig(config.withUsingPlainTextChannel(plainText));
+    }
+
+    /**
+     * Specifies whether to use plaintext channel.
+     *
+     * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+     * withExperimentalHost}).
+     */
+    public Write withUsingPlainTextChannel(boolean plainText) {
+      return withUsingPlainTextChannel(ValueProvider.StaticValueProvider.of(plainText));
+    }
+
     public Write withDialectView(PCollectionView<Dialect> dialect) {
       return toBuilder().setDialectView(dialect).build();
     }
@@ -1598,6 +1755,10 @@ public abstract static class ReadChangeStream
 
     abstract @Nullable Duration getWatermarkRefreshRate();
 
+    abstract @Nullable ValueProvider<String> getExperimentalHost();
+
+    abstract @Nullable ValueProvider<Boolean> getPlainText();
+
     abstract Builder toBuilder();
 
     @AutoValue.Builder
@@ -1623,6 +1784,10 @@ abstract static class Builder {
 
       abstract Builder setWatermarkRefreshRate(Duration refreshRate);
 
+      abstract Builder setExperimentalHost(ValueProvider<String> experimentalHost);
+
+      abstract Builder setPlainText(ValueProvider<Boolean> plainText);
+
       abstract ReadChangeStream build();
     }
 
@@ -1713,6 +1878,38 @@ public ReadChangeStream withWatermarkRefreshRate(Duration refreshRate) {
       return toBuilder().setWatermarkRefreshRate(refreshRate).build();
     }
 
+    /** Specifies the experimental host to set on SpannerOptions (setExperimentalHost). */
+    public ReadChangeStream withExperimentalHost(ValueProvider<String> experimentalHost) {
+      SpannerConfig config = getSpannerConfig();
+      return withSpannerConfig(config.withExperimentalHost(experimentalHost));
+    }
+
+    /** Specifies the experimental host to set on SpannerOptions (setExperimentalHost). */
+    public ReadChangeStream withExperimentalHost(String experimentalHost) {
+      return withExperimentalHost(ValueProvider.StaticValueProvider.of(experimentalHost));
+    }
+
+    /**
+     * Specifies whether to use plaintext channel.
+     *
+     * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+     * withExperimentalHost}).
+     */
+    public ReadChangeStream withUsingPlainTextChannel(ValueProvider<Boolean> plainText) {
+      SpannerConfig config = getSpannerConfig();
+      return withSpannerConfig(config.withUsingPlainTextChannel(plainText));
+    }
+
+    /**
+     * Specifies whether to use plaintext channel.
+     *
+     * <p>Note: This parameter is only valid when using an experimental host (set via {@code
+     * withExperimentalHost}).
+     */
+    public ReadChangeStream withUsingPlainTextChannel(boolean plainText) {
+      return withUsingPlainTextChannel(ValueProvider.StaticValueProvider.of(plainText));
+    }
+
     @Override
     public PCollection<DataChangeRecord> expand(PBegin input) {
       checkArgument(
@@ -2116,20 +2313,34 @@ public int compareTo(MutationGroupContainer o) {
     private static class OutputReceiverForFinishBundle
         implements OutputReceiver<Iterable<MutationGroup>> {
 
-      private final FinishBundleContext c;
-
-      OutputReceiverForFinishBundle(FinishBundleContext c) {
-        this.c = c;
-      }
-
-      @Override
-      public void output(Iterable<MutationGroup> output) {
-        outputWithTimestamp(output, Instant.now());
+      private final OutputBuilderSupplier outputBuilderSupplier;
+      private final DoFn<MutationGroup, Iterable<MutationGroup>>.FinishBundleContext context;
+
+      OutputReceiverForFinishBundle(FinishBundleContext context) {
+        this.context = context;
+        this.outputBuilderSupplier =
+            new OutputBuilderSupplier() {
+              @Override
+              public <OutputT> WindowedValues.Builder<OutputT> builder(OutputT value) {
+                return WindowedValues.<OutputT>builder()
+                    .setValue(value)
+                    .setTimestamp(Instant.now())
+                    .setPaneInfo(PaneInfo.NO_FIRING)
+                    .setWindow(GlobalWindow.INSTANCE);
+              }
+            };
       }
 
       @Override
-      public void outputWithTimestamp(Iterable<MutationGroup> output, Instant timestamp) {
-        c.output(output, timestamp, GlobalWindow.INSTANCE);
+      public OutputBuilder<Iterable<MutationGroup>> builder(Iterable<MutationGroup> value) {
+        return outputBuilderSupplier
+            .builder(value)
+            .setReceiver(
+                wv -> {
+                  for (BoundedWindow window : wv.getWindows()) {
+                    context.output(wv.getValue(), wv.getTimestamp(), window);
+                  }
+                });
       }
     }
   }
@@ -2138,7 +2349,7 @@ public void outputWithTimestamp(Iterable<MutationGroup> output, Instant timestam
    * Filters MutationGroups larger than the batch size to the output tagged with {@code
    * UNBATCHABLE_MUTATIONS_TAG}.
    *
-   * <p>Testing notes: As batching does not occur during full pipline testing, this DoFn must be
+   * <p>Testing notes: As batching does not occur during full pipeline testing, this DoFn must be
    * tested in isolation.
    */
   @VisibleForTesting
@@ -2433,11 +2644,10 @@ private void writeMutations(Iterable<Mutation> mutationIterable)
             }
             LOG.info(
                 "DEADLINE_EXCEEDED writing batch of {} mutations to Cloud Spanner, "
-                    + "retrying after backoff of {}ms\n"
-                    + "({})",
+                    + "retrying after backoff of {}ms",
                 mutations.size(),
                 sleepTimeMsecs,
-                exception.getMessage());
+                exception);
             spannerWriteRetries.inc();
             try {
               sleeper.sleep(sleepTimeMsecs);
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIOMetadata.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIOMetadata.java
new file mode 100644
index 000000000000..7d135a128bb2
--- /dev/null
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIOMetadata.java
@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.gcp.spanner;
+
+import java.util.concurrent.TimeUnit;
+import org.apache.beam.sdk.extensions.gcp.util.GceMetadataUtil;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Strings;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Supplier;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Suppliers;
+import org.checkerframework.checker.nullness.qual.Nullable;
+
+/** Metadata class for SpannerIO. */
+final class SpannerIOMetadata {
+
+  private final @Nullable String beamJobId;
+
+  static final Supplier<SpannerIOMetadata> INSTANCE =
+      Suppliers.memoizeWithExpiration(() -> refreshInstance(), 5, TimeUnit.MINUTES);
+
+  private SpannerIOMetadata(@Nullable String beamJobId) {
+    this.beamJobId = beamJobId;
+  }
+
+  /**
+   * Creates a SpannerIOMetadata. This will request metadata properly based on which runner is being
+   * used.
+   */
+  public static SpannerIOMetadata create() {
+    return INSTANCE.get();
+  }
+
+  private static SpannerIOMetadata refreshInstance() {
+    String dataflowJobId = GceMetadataUtil.fetchDataflowJobId();
+    if (Strings.isNullOrEmpty(dataflowJobId)) {
+      return new SpannerIOMetadata(null);
+    }
+
+    return new SpannerIOMetadata(dataflowJobId);
+  }
+
+  /*
+   * Returns the beam job id. Can be null if it is not running on Dataflow.
+   */
+  public @Nullable String getBeamJobId() {
+    return this.beamJobId;
+  }
+}
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerTransformRegistrar.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerTransformRegistrar.java
index 72b51beadb57..70908f982721 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerTransformRegistrar.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/SpannerTransformRegistrar.java
@@ -23,6 +23,7 @@
 import com.google.auto.service.AutoService;
 import com.google.cloud.Timestamp;
 import com.google.cloud.spanner.Mutation;
+import com.google.cloud.spanner.Options.RpcPriority;
 import com.google.cloud.spanner.TimestampBound;
 import java.util.Map;
 import java.util.concurrent.TimeUnit;
@@ -43,8 +44,8 @@
 import org.joda.time.Duration;
 
 /**
- * Exposes {@link SpannerIO.WriteRows} and {@link SpannerIO.ReadRows} as an external transform for
- * cross-language usage.
+ * Exposes {@link SpannerIO.WriteRows}, {@link SpannerIO.ReadRows} and {@link
+ * SpannerIO.ChangeStreamRead} as an external transform for cross-language usage.
  */
 @AutoService(ExternalTransformRegistrar.class)
 public class SpannerTransformRegistrar implements ExternalTransformRegistrar {
@@ -55,6 +56,8 @@ public class SpannerTransformRegistrar implements ExternalTransformRegistrar {
       "beam:transform:org.apache.beam:spanner_insert_or_update:v1";
   public static final String DELETE_URN = "beam:transform:org.apache.beam:spanner_delete:v1";
   public static final String READ_URN = "beam:transform:org.apache.beam:spanner_read:v1";
+  public static final String READ_CHANGE_STREAM_URN =
+      "beam:transform:org.apache.beam:spanner_change_stream_reader:v1";
 
   @Override
   @NonNull
@@ -66,6 +69,7 @@ public class SpannerTransformRegistrar implements ExternalTransformRegistrar {
         .put(INSERT_OR_UPDATE_URN, new InsertOrUpdateBuilder())
         .put(DELETE_URN, new DeleteBuilder())
         .put(READ_URN, new ReadBuilder())
+        .put(READ_CHANGE_STREAM_URN, new ChangeStreamReaderBuilder())
         .build();
   }
 
@@ -75,6 +79,8 @@ public abstract static class CrossLanguageConfiguration {
     String projectId = "";
     @Nullable String host;
     @Nullable String emulatorHost;
+    @Nullable String experimentalHost;
+    @Nullable Boolean plainText;
 
     public void setInstanceId(String instanceId) {
       this.instanceId = instanceId;
@@ -96,6 +102,14 @@ public void setEmulatorHost(@Nullable String emulatorHost) {
       this.emulatorHost = emulatorHost;
     }
 
+    public void setExperimentalHost(@Nullable String experimentalHost) {
+      this.experimentalHost = experimentalHost;
+    }
+
+    public void setPlainText(@Nullable Boolean plainText) {
+      this.plainText = plainText;
+    }
+
     void checkMandatoryFields() {
       if (projectId.isEmpty()) {
         throw new IllegalArgumentException("projectId can't be empty");
@@ -229,6 +243,12 @@ public PTransform<PBegin, PCollection<Row>> buildExternal(
       if (configuration.emulatorHost != null) {
         readTransform = readTransform.withEmulatorHost(configuration.emulatorHost);
       }
+      if (configuration.experimentalHost != null) {
+        readTransform = readTransform.withExperimentalHost(configuration.experimentalHost);
+      }
+      if (configuration.plainText != null) {
+        readTransform = readTransform.withUsingPlainTextChannel(configuration.plainText);
+      }
       @Nullable TimestampBound timestampBound = configuration.getTimestampBound();
       if (timestampBound != null) {
         readTransform = readTransform.withTimestampBound(timestampBound);
@@ -367,6 +387,12 @@ public PTransform<PCollection<Row>, PDone> buildExternal(
       if (configuration.emulatorHost != null) {
         writeTransform = writeTransform.withEmulatorHost(configuration.emulatorHost);
       }
+      if (configuration.experimentalHost != null) {
+        writeTransform = writeTransform.withExperimentalHost(configuration.experimentalHost);
+      }
+      if (configuration.plainText != null) {
+        writeTransform = writeTransform.withUsingPlainTextChannel(configuration.plainText);
+      }
       if (configuration.commitDeadline != null) {
         writeTransform = writeTransform.withCommitDeadline(configuration.commitDeadline);
       }
@@ -382,4 +408,113 @@ public PTransform<PCollection<Row>, PDone> buildExternal(
       return SpannerIO.WriteRows.of(writeTransform, operation, configuration.table);
     }
   }
+
+  public static class ChangeStreamReaderBuilder
+      implements ExternalTransformBuilder<
+          ChangeStreamReaderBuilder.Configuration, PBegin, PCollection<String>> {
+
+    public static class Configuration extends CrossLanguageConfiguration {
+      private String changeStreamName = "";
+      private String metadataDatabase = "";
+      private String metadataInstance = "";
+      private @Nullable Timestamp inclusiveStartAt;
+      private @Nullable Timestamp inclusiveEndAt;
+      private @Nullable String metadataTable;
+      private @Nullable RpcPriority rpcPriority;
+      private @Nullable Duration watermarkRefreshRate;
+
+      public void setChangeStreamName(String changeStreamName) {
+        this.changeStreamName = changeStreamName;
+      }
+
+      public void setInclusiveStartAt(@Nullable String inclusiveStartAtString) {
+        if (inclusiveStartAtString != null) {
+          this.inclusiveStartAt = Timestamp.parseTimestamp(inclusiveStartAtString);
+        }
+      }
+
+      public void setInclusiveEndAt(@Nullable String inclusiveEndAtString) {
+        if (inclusiveEndAtString != null) {
+          this.inclusiveEndAt = Timestamp.parseTimestamp(inclusiveEndAtString);
+        }
+      }
+
+      public void setMetadataDatabase(String metadataDatabase) {
+        this.metadataDatabase = metadataDatabase;
+      }
+
+      public void setMetadataInstance(String metadataInstance) {
+        this.metadataInstance = metadataInstance;
+      }
+
+      public void setMetadataTable(@Nullable String metadataTable) {
+        this.metadataTable = metadataTable;
+      }
+
+      public void setRpcPriority(@Nullable String rpcPriorityString) {
+        if (rpcPriorityString != null) {
+          this.rpcPriority = RpcPriority.valueOf(rpcPriorityString);
+        }
+      }
+
+      public void setWatermarkRefreshRate(@Nullable String watermarkRefreshRateString) {
+        if (watermarkRefreshRateString != null) {
+          this.watermarkRefreshRate = Duration.parse(watermarkRefreshRateString);
+        }
+      }
+    }
+
+    @Override
+    @NonNull
+    public PTransform<PBegin, PCollection<String>> buildExternal(
+        ChangeStreamReaderBuilder.Configuration configuration) {
+
+      configuration.checkMandatoryFields();
+
+      if (configuration.changeStreamName.isEmpty()) {
+        throw new IllegalArgumentException("ChangeStreamName can't be empty");
+      }
+
+      if (configuration.metadataInstance.isEmpty()) {
+        throw new IllegalArgumentException("MetadataInstance can't be empty");
+      }
+
+      if (configuration.metadataDatabase.isEmpty()) {
+        throw new IllegalArgumentException("MetadataDatabase can't be empty");
+      }
+
+      SpannerIO.ReadChangeStream readChangeStream =
+          SpannerIO.readChangeStream()
+              .withProjectId(configuration.projectId)
+              .withInstanceId(configuration.instanceId)
+              .withDatabaseId(configuration.databaseId)
+              .withChangeStreamName(configuration.changeStreamName)
+              .withMetadataInstance(configuration.metadataInstance)
+              .withMetadataDatabase(configuration.metadataDatabase);
+
+      if (configuration.inclusiveStartAt != null) {
+        readChangeStream = readChangeStream.withInclusiveStartAt(configuration.inclusiveStartAt);
+      }
+
+      if (configuration.inclusiveEndAt != null) {
+        readChangeStream = readChangeStream.withInclusiveEndAt(configuration.inclusiveEndAt);
+      }
+
+      if (configuration.metadataTable != null) {
+        readChangeStream = readChangeStream.withMetadataTable(configuration.metadataTable);
+      }
+
+      if (configuration.rpcPriority != null) {
+
+        readChangeStream = readChangeStream.withRpcPriority(configuration.rpcPriority);
+      }
+
+      if (configuration.watermarkRefreshRate != null) {
+        readChangeStream =
+            readChangeStream.withWatermarkRefreshRate(configuration.watermarkRefreshRate);
+      }
+
+      return new SpannerIO.ChangeStreamRead(readChangeStream);
+    }
+  }
 }
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/StructUtils.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/StructUtils.java
index 6183ac9768f7..51eda7d16eb9 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/StructUtils.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/StructUtils.java
@@ -171,7 +171,7 @@ public static Struct beamRowToStruct(Row row) {
               @Nullable BigDecimal decimal = row.getDecimal(column);
               // BigDecimal is not nullable
               if (decimal == null) {
-                checkNotNull(decimal, "Null decimal at column " + column);
+                checkNotNull(decimal, "Null decimal at column %s", column);
               } else {
                 structBuilder.set(column).to(decimal);
               }
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/MetadataSpannerConfigFactory.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/MetadataSpannerConfigFactory.java
index 7132d4deb030..959582e9c35f 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/MetadataSpannerConfigFactory.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/MetadataSpannerConfigFactory.java
@@ -77,6 +77,16 @@ public static SpannerConfig create(
       config = config.withEmulatorHost(StaticValueProvider.of(emulatorHost.get()));
     }
 
+    ValueProvider<String> experimentalHost = primaryConfig.getExperimentalHost();
+    if (experimentalHost != null && experimentalHost.get() != null) {
+      config = config.withExperimentalHost(experimentalHost.get());
+    }
+
+    ValueProvider<Boolean> plainText = primaryConfig.getPlainText();
+    if (plainText != null && plainText.get() != null) {
+      config = config.withUsingPlainTextChannel(plainText.get());
+    }
+
     ValueProvider<Boolean> isLocalChannelProvider = primaryConfig.getIsLocalChannelProvider();
     if (isLocalChannelProvider != null) {
       config =
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/action/DetectNewPartitionsAction.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/action/DetectNewPartitionsAction.java
index 40160de7b958..c889d41279ff 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/action/DetectNewPartitionsAction.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/action/DetectNewPartitionsAction.java
@@ -149,7 +149,7 @@ private ProcessContinuation schedulePartitions(
       RestrictionTracker<TimestampRange, Timestamp> tracker,
       OutputReceiver<PartitionMetadata> receiver,
       Timestamp minWatermark,
-      TreeMap<Timestamp, List<PartitionMetadata>> batches) {
+      Map<Timestamp, List<PartitionMetadata>> batches) {
     List<PartitionMetadata> batchPartitionsDifferentCreatedAt = new ArrayList<>();
     int numTimestampsHandledSofar = 0;
     for (Map.Entry<Timestamp, List<PartitionMetadata>> batch : batches.entrySet()) {
@@ -190,11 +190,13 @@ private void outputBatch(
           partition.toBuilder().setScheduledAt(scheduledAt).build();
 
       LOG.info(
-          "[{}] Outputting partition at {} with start time {} and end time {}",
+          "[{}] Outputting partition at {} with start time {}, end time {}, creation time {} and output timestamp {}",
           updatedPartition.getPartitionToken(),
           updatedPartition.getScheduledAt(),
           updatedPartition.getStartTimestamp(),
-          updatedPartition.getEndTimestamp());
+          updatedPartition.getEndTimestamp(),
+          createdAt,
+          minWatermark);
 
       receiver.outputWithTimestamp(partition, new Instant(minWatermark.toSqlTimestamp()));
 
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/action/QueryChangeStreamAction.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/action/QueryChangeStreamAction.java
index 344300b9322d..3176abd9f247 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/action/QueryChangeStreamAction.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/action/QueryChangeStreamAction.java
@@ -325,7 +325,7 @@ private BundleFinalizer.Callback updateWatermarkCallback(
         if (e.getErrorCode() == ErrorCode.NOT_FOUND) {
           LOG.debug("[{}] Unable to update the current watermark, partition NOT FOUND", token);
         } else {
-          LOG.error("[{}] Error updating the current watermark: {}", token, e.getMessage(), e);
+          LOG.error("[{}] Error updating the current watermark", token, e);
         }
       }
     };
diff --git a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/testing/FakeDatasetService.java b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/testing/FakeDatasetService.java
index 3d100413cb2d..77fc7cab0245 100644
--- a/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/testing/FakeDatasetService.java
+++ b/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/testing/FakeDatasetService.java
@@ -84,7 +84,8 @@
 /** A fake dataset service that can be serialized, for use in testReadFromTable. */
 @Internal
 @SuppressWarnings({
-  "nullness" // TODO(https://github.com/apache/beam/issues/20497)
+  "nullness", // TODO(https://github.com/apache/beam/issues/20497)
+  "LockOnNonEnclosingClassLiteral"
 })
 public class FakeDatasetService implements DatasetService, WriteStreamService, Serializable {
   // Table information must be static, as each ParDo will get a separate instance of
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryIOWriteTest.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryIOWriteTest.java
index 88d7d2e73262..89059634631f 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryIOWriteTest.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryIOWriteTest.java
@@ -1452,9 +1452,19 @@ private void storageWrite(boolean autoSharding) throws Exception {
       assumeTrue(!useStorageApiApproximate);
       assumeTrue(useStreaming);
     }
+    TableSchema tableSchema =
+        new TableSchema()
+            .setFields(
+                ImmutableList.of(
+                    new TableFieldSchema().setName("number").setType("INTEGER"),
+                    // Make sure to exercise the name override by specifying an illegal proto field
+                    // name.
+                    new TableFieldSchema().setName("123_number").setType("INTEGER")));
+
     List<TableRow> elements = Lists.newArrayList();
     for (int i = 0; i < 30; ++i) {
-      elements.add(new TableRow().set("number", String.valueOf(i)));
+      String value = String.valueOf(i);
+      elements.add(new TableRow().set("number", value).set("123_number", value));
     }
 
     TestStream<TableRow> testStream =
@@ -1473,11 +1483,7 @@ private void storageWrite(boolean autoSharding) throws Exception {
         BigQueryIO.writeTableRows()
             .to("project-id:dataset-id.table-id")
             .withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED)
-            .withSchema(
-                new TableSchema()
-                    .setFields(
-                        ImmutableList.of(
-                            new TableFieldSchema().setName("number").setType("INTEGER"))))
+            .withSchema(tableSchema)
             .withTestServices(fakeBqServices)
             .withoutValidation();
 
@@ -2398,7 +2404,8 @@ public void updateTableSchemaTest(boolean useSet) throws Exception {
                     new TableFieldSchema().setName("name").setType("STRING"),
                     new TableFieldSchema().setName("number").setType("INTEGER"),
                     new TableFieldSchema().setName("req").setType("STRING"),
-                    new TableFieldSchema().setName("double_number").setType("INTEGER")));
+                    new TableFieldSchema().setName("double_number").setType("INTEGER"),
+                    new TableFieldSchema().setName("12_special_name").setType("STRING")));
     fakeDatasetService.createTable(new Table().setTableReference(tableRef).setSchema(tableSchema));
 
     LongFunction<TableRow> getRowSet =
@@ -2408,7 +2415,8 @@ public void updateTableSchemaTest(boolean useSet) throws Exception {
                   new TableRow()
                       .set("name", "name" + i)
                       .set("number", Long.toString(i))
-                      .set("double_number", Long.toString(i * 2));
+                      .set("double_number", Long.toString(i * 2))
+                      .set("12_special_name", "name" + i);
               if (i <= 5) {
                 row = row.set("req", "foo");
               }
@@ -2424,7 +2432,8 @@ public void updateTableSchemaTest(boolean useSet) throws Exception {
                             new TableCell().setV(Long.toString(i)),
                             new TableCell().setV("name" + i),
                             new TableCell().setV(i > 5 ? null : "foo"),
-                            new TableCell().setV(Long.toString(i * 2))));
+                            new TableCell().setV(Long.toString(i * 2)),
+                            new TableCell().setV("name" + i)));
 
     LongFunction<TableRow> getRow = useSet ? getRowSet : getRowSetF;
 
@@ -2708,8 +2717,7 @@ public void testWriteValidateFailsWithBatchAutoSharding() {
     p.enableAbandonedNodeEnforcement(false);
 
     thrown.expect(IllegalArgumentException.class);
-    thrown.expectMessage(
-        "Auto-sharding is only applicable to an unbounded PCollection, but the input PCollection is BOUNDED.");
+    thrown.expectMessage("Auto-sharding is only applicable to an unbounded PCollection.");
     p.apply(Create.empty(INPUT_RECORD_CODER))
         .apply(
             BigQueryIO.<InputRecord>write()
@@ -3137,7 +3145,7 @@ public void testRemoveTemporaryTables() throws Exception {
 
     for (TableReference ref : tableRefs) {
       loggedWriteRename.verifyDebug("Deleting table " + toJsonString(ref));
-      checkState(datasetService.getTable(ref) == null, "Table " + ref + " was not deleted!");
+      checkState(datasetService.getTable(ref) == null, "Table %s was not deleted!", ref);
     }
   }
 
@@ -4433,4 +4441,29 @@ public void testUpsertAndDeleteBeamRows() throws Exception {
         fakeDatasetService.getAllRows("project-id", "dataset-id", "table-id"),
         containsInAnyOrder(Iterables.toArray(expected, TableRow.class)));
   }
+
+  @Test
+  public void testCustomGcsTempLocationNull() throws Exception {
+    BigQueryIO.Write<TableRow> write =
+        BigQueryIO.writeTableRows()
+            .to("dataset-id.table-id")
+            .withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED)
+            .withSchema(
+                new TableSchema()
+                    .setFields(
+                        ImmutableList.of(new TableFieldSchema().setName("name").setType("STRING"))))
+            .withMethod(Method.FILE_LOADS)
+            .withoutValidation()
+            .withTestServices(fakeBqServices)
+            .withCustomGcsTempLocation(ValueProvider.StaticValueProvider.of(null));
+
+    p.apply(
+            Create.of(new TableRow().set("name", "a"), new TableRow().set("name", "b"))
+                .withCoder(TableRowJsonCoder.of()))
+        .apply("WriteToBQ", write);
+    p.run();
+    assertThat(
+        fakeDatasetService.getAllRows("project-id", "dataset-id", "table-id"),
+        containsInAnyOrder(new TableRow().set("name", "a"), new TableRow().set("name", "b")));
+  }
 }
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryServicesImplTest.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryServicesImplTest.java
index 01454a50b251..3902fb1fca33 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryServicesImplTest.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryServicesImplTest.java
@@ -1119,6 +1119,11 @@ public void testInsertWithinRowCountLimits() throws Exception {
   public void testInsertWithinRequestByteSizeLimitsErrorsOut() throws Exception {
     TableReference ref =
         new TableReference().setProjectId("project").setDatasetId("dataset").setTableId("tablersl");
+    TableSchema schema =
+        new TableSchema()
+            .setFields(ImmutableList.of(new TableFieldSchema().setName("rows").setType("STRING")));
+    Table testTable = new Table().setTableReference(ref).setSchema(schema);
+
     List<FailsafeValueInSingleWindow<TableRow, TableRow>> rows =
         ImmutableList.of(
             wrapValue(new TableRow().set("row", Strings.repeat("abcdefghi", 1024 * 1025))),
@@ -1133,11 +1138,81 @@ public void testInsertWithinRequestByteSizeLimitsErrorsOut() throws Exception {
           when(response.getContentType()).thenReturn(Json.MEDIA_TYPE);
           when(response.getStatusCode()).thenReturn(200);
           when(response.getContent()).thenReturn(toStream(allRowsSucceeded));
+          when(response.getContent()).thenReturn(toStream(testTable));
         },
         response -> {
           when(response.getContentType()).thenReturn(Json.MEDIA_TYPE);
           when(response.getStatusCode()).thenReturn(200);
           when(response.getContent()).thenReturn(toStream(allRowsSucceeded));
+          when(response.getContent()).thenReturn(toStream(testTable));
+        });
+
+    DatasetServiceImpl dataService =
+        new DatasetServiceImpl(
+            bigquery, PipelineOptionsFactory.fromArgs("--maxStreamingBatchSize=15").create());
+    List<ValueInSingleWindow<TableRow>> failedInserts = Lists.newArrayList();
+    List<ValueInSingleWindow<TableRow>> successfulRows = Lists.newArrayList();
+    RuntimeException e =
+        assertThrows(
+            RuntimeException.class,
+            () ->
+                dataService.<TableRow>insertAll(
+                    ref,
+                    rows,
+                    insertIds,
+                    BackOffAdapter.toGcpBackOff(TEST_BACKOFF.backoff()),
+                    TEST_BACKOFF,
+                    new MockSleeper(),
+                    InsertRetryPolicy.alwaysRetry(),
+                    failedInserts,
+                    ErrorContainer.TABLE_ROW_ERROR_CONTAINER,
+                    false,
+                    false,
+                    false,
+                    successfulRows));
+
+    assertThat(e.getMessage(), containsString("exceeding the BigQueryIO limit"));
+  }
+
+  /**
+   * Tests that {@link DatasetServiceImpl#insertAll} does not go over limit of rows per request and
+   * schema difference check.
+   */
+  @SuppressWarnings("InlineMeInliner") // inline `Strings.repeat()` - Java 11+ API only
+  @Test
+  public void testInsertWithinRequestByteSizeLimitsWithBadSchemaErrorsOut() throws Exception {
+    TableReference ref =
+        new TableReference().setProjectId("project").setDatasetId("dataset").setTableId("tablersl");
+
+    TableSchema schema =
+        new TableSchema()
+            .setFields(ImmutableList.of(new TableFieldSchema().setName("row").setType("STRING")));
+    Table testTable = new Table().setTableReference(ref).setSchema(schema);
+
+    List<FailsafeValueInSingleWindow<TableRow, TableRow>> rows =
+        ImmutableList.of(
+            wrapValue(
+                new TableRow()
+                    .set("row", Strings.repeat("abcdefghi", 1024 * 1025))
+                    .set("badField", "goodValue")),
+            wrapValue(new TableRow().set("row", "a")),
+            wrapValue(new TableRow().set("row", "b")));
+    List<String> insertIds = ImmutableList.of("a", "b", "c");
+
+    final TableDataInsertAllResponse allRowsSucceeded = new TableDataInsertAllResponse();
+
+    setupMockResponses(
+        response -> {
+          when(response.getContentType()).thenReturn(Json.MEDIA_TYPE);
+          when(response.getStatusCode()).thenReturn(200);
+          when(response.getContent()).thenReturn(toStream(allRowsSucceeded));
+          when(response.getContent()).thenReturn(toStream(testTable));
+        },
+        response -> {
+          when(response.getContentType()).thenReturn(Json.MEDIA_TYPE);
+          when(response.getStatusCode()).thenReturn(200);
+          when(response.getContent()).thenReturn(toStream(allRowsSucceeded));
+          when(response.getContent()).thenReturn(toStream(testTable));
         });
 
     DatasetServiceImpl dataService =
@@ -1164,7 +1239,10 @@ public void testInsertWithinRequestByteSizeLimitsErrorsOut() throws Exception {
                     false,
                     successfulRows));
 
-    assertThat(e.getMessage(), containsString("exceeded BigQueryIO limit of 9MB."));
+    assertThat(e.getMessage(), containsString("exceeding the BigQueryIO limit"));
+    assertThat(
+        e.getMessage(),
+        containsString("Problematic row had extra schema fields: {Unknown fields: badField}"));
   }
 
   @SuppressWarnings("InlineMeInliner") // inline `Strings.repeat()` - Java 11+ API only
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProtoIT.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProtoIT.java
index f28ae588a5ec..1ae691cb7e99 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProtoIT.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProtoIT.java
@@ -88,6 +88,8 @@ public class TableRowToStorageApiProtoIT {
                           .setType("BYTES")
                           .setMode("REPEATED")
                           .setName("arrayValue"))
+                  .add(
+                      new TableFieldSchema().setType("STRING").setName("123_IllegalProtoFieldName"))
                   .build());
 
   private static final List<Object> REPEATED_BYTES =
@@ -113,7 +115,8 @@ public class TableRowToStorageApiProtoIT {
           .set("dateValue", "2019-08-16")
           .set("numericValue", "23.4")
           .set("bigNumericValue", "23334.4")
-          .set("arrayValue", REPEATED_BYTES);
+          .set("arrayValue", REPEATED_BYTES)
+          .set("123_IllegalProtoFieldName", "string");
 
   private static final TableRow BASE_TABLE_ROW_JODA_TIME =
       new TableRow()
@@ -131,7 +134,8 @@ public class TableRowToStorageApiProtoIT {
           .set("dateValue", org.joda.time.LocalDate.parse("2019-08-16"))
           .set("numericValue", new BigDecimal("23.4"))
           .set("bigNumericValue", "23334.4")
-          .set("arrayValue", REPEATED_BYTES);
+          .set("arrayValue", REPEATED_BYTES)
+          .set("123_IllegalProtoFieldName", "string");
 
   private static final TableRow BASE_TABLE_ROW_JAVA_TIME =
       new TableRow()
@@ -149,7 +153,8 @@ public class TableRowToStorageApiProtoIT {
           .set("dateValue", LocalDate.parse("2019-08-16"))
           .set("numericValue", new BigDecimal("23.4"))
           .set("bigNumericValue", "23334.4")
-          .set("arrayValue", REPEATED_BYTES);
+          .set("arrayValue", REPEATED_BYTES)
+          .set("123_IllegalProtoFieldName", "string");
 
   private static final TableRow BASE_TABLE_ROW_NUM_TIME =
       new TableRow()
@@ -167,7 +172,8 @@ public class TableRowToStorageApiProtoIT {
           .set("dateValue", 18124)
           .set("numericValue", new BigDecimal("23.4"))
           .set("bigNumericValue", "23334.4")
-          .set("arrayValue", REPEATED_BYTES);
+          .set("arrayValue", REPEATED_BYTES)
+          .set("123_IllegalProtoFieldName", "string");
 
   @SuppressWarnings({
     "FloatingPointLiteralPrecision" // https://github.com/apache/beam/issues/23666
@@ -188,7 +194,8 @@ public class TableRowToStorageApiProtoIT {
           .set("dateValue", 18124)
           .set("numericValue", 23.4)
           .set("bigNumericValue", "23334.4")
-          .set("arrayValue", REPEATED_BYTES);
+          .set("arrayValue", REPEATED_BYTES)
+          .set("123_IllegalProtoFieldName", "string");
 
   private static final TableRow BASE_TABLE_ROW_NULL =
       new TableRow()
@@ -230,7 +237,8 @@ public class TableRowToStorageApiProtoIT {
           .set("dateValue", "2019-08-16")
           .set("numericValue", "23.4")
           .set("bigNumericValue", "23334.4")
-          .set("arrayValue", REPEATED_BYTES_EXPECTED);
+          .set("arrayValue", REPEATED_BYTES_EXPECTED)
+          .set("123_IllegalProtoFieldName", "string");
 
   // joda is up to millisecond precision, expect truncation
   private static final TableRow BASE_TABLE_ROW_JODA_EXPECTED =
@@ -250,7 +258,8 @@ public class TableRowToStorageApiProtoIT {
           .set("dateValue", "2019-08-16")
           .set("numericValue", "23.4")
           .set("bigNumericValue", "23334.4")
-          .set("arrayValue", REPEATED_BYTES_EXPECTED);
+          .set("arrayValue", REPEATED_BYTES_EXPECTED)
+          .set("123_IllegalProtoFieldName", "string");
 
   private static final TableRow BASE_TABLE_ROW_NUM_EXPECTED =
       new TableRow()
@@ -269,7 +278,8 @@ public class TableRowToStorageApiProtoIT {
           .set("dateValue", "2019-08-16")
           .set("numericValue", "23.4")
           .set("bigNumericValue", "23334.4")
-          .set("arrayValue", REPEATED_BYTES_EXPECTED);
+          .set("arrayValue", REPEATED_BYTES_EXPECTED)
+          .set("123_IllegalProtoFieldName", "string");
 
   private static final TableRow BASE_TABLE_ROW_FLOATS_EXPECTED =
       new TableRow()
@@ -288,7 +298,8 @@ public class TableRowToStorageApiProtoIT {
           .set("dateValue", "2019-08-16")
           .set("numericValue", "23.4")
           .set("bigNumericValue", "23334.4")
-          .set("arrayValue", REPEATED_BYTES_EXPECTED);
+          .set("arrayValue", REPEATED_BYTES_EXPECTED)
+          .set("123_IllegalProtoFieldName", "string");
 
   // only nonnull values are returned, null in arrayValue should be converted to empty list
   private static final TableRow BASE_TABLE_ROW_NULL_EXPECTED =
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProtoTest.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProtoTest.java
index 6e080d82de4d..1a6b83c5ebd6 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProtoTest.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/TableRowToStorageApiProtoTest.java
@@ -27,8 +27,11 @@
 import com.google.api.services.bigquery.model.TableFieldSchema;
 import com.google.api.services.bigquery.model.TableRow;
 import com.google.api.services.bigquery.model.TableSchema;
+import com.google.cloud.bigquery.storage.v1.AnnotationsProto;
 import com.google.cloud.bigquery.storage.v1.BigDecimalByteStringEncoder;
+import com.google.cloud.bigquery.storage.v1.BigQuerySchemaUtil;
 import com.google.protobuf.ByteString;
+import com.google.protobuf.DescriptorProtos;
 import com.google.protobuf.DescriptorProtos.DescriptorProto;
 import com.google.protobuf.DescriptorProtos.FieldDescriptorProto;
 import com.google.protobuf.DescriptorProtos.FieldDescriptorProto.Label;
@@ -119,6 +122,8 @@ public class TableRowToStorageApiProtoTest {
                           .setName("timestampValueSpaceTrailingZero"))
                   .add(new TableFieldSchema().setType("DATETIME").setName("datetimeValueSpace"))
                   .add(new TableFieldSchema().setType("TIMESTAMP").setName("timestampValueMaximum"))
+                  .add(
+                      new TableFieldSchema().setType("STRING").setName("123_IllegalProtoFieldName"))
                   .build());
 
   private static final TableSchema BASE_TABLE_SCHEMA_NO_F =
@@ -169,6 +174,8 @@ public class TableRowToStorageApiProtoTest {
                           .setName("timestampValueSpaceTrailingZero"))
                   .add(new TableFieldSchema().setType("DATETIME").setName("datetimeValueSpace"))
                   .add(new TableFieldSchema().setType("TIMESTAMP").setName("timestampValueMaximum"))
+                  .add(
+                      new TableFieldSchema().setType("STRING").setName("123_IllegalProtoFieldName"))
                   .build());
 
   private static final DescriptorProto BASE_TABLE_SCHEMA_PROTO_DESCRIPTOR =
@@ -369,6 +376,19 @@ public class TableRowToStorageApiProtoTest {
                   .setType(Type.TYPE_INT64)
                   .setLabel(Label.LABEL_OPTIONAL)
                   .build())
+          .addField(
+              FieldDescriptorProto.newBuilder()
+                  .setName(
+                      BigQuerySchemaUtil.generatePlaceholderFieldName("123_illegalprotofieldname"))
+                  .setNumber(29)
+                  .setType(Type.TYPE_STRING)
+                  .setLabel(Label.LABEL_OPTIONAL)
+                  .setOptions(
+                      DescriptorProtos.FieldOptions.newBuilder()
+                          .setField(
+                              AnnotationsProto.columnName.getDescriptor(),
+                              "123_illegalprotofieldname"))
+                  .build())
           .build();
 
   private static final com.google.cloud.bigquery.storage.v1.TableSchema BASE_TABLE_PROTO_SCHEMA =
@@ -513,6 +533,11 @@ public class TableRowToStorageApiProtoTest {
                   .setName("timestampvaluemaximum")
                   .setType(com.google.cloud.bigquery.storage.v1.TableFieldSchema.Type.INT64)
                   .build())
+          .addFields(
+              com.google.cloud.bigquery.storage.v1.TableFieldSchema.newBuilder()
+                  .setName("123_illegalprotofieldname")
+                  .setType(com.google.cloud.bigquery.storage.v1.TableFieldSchema.Type.STRING)
+                  .build())
           .build();
 
   private static final DescriptorProto BASE_TABLE_SCHEMA_NO_F_PROTO =
@@ -706,6 +731,19 @@ public class TableRowToStorageApiProtoTest {
                   .setType(Type.TYPE_INT64)
                   .setLabel(Label.LABEL_OPTIONAL)
                   .build())
+          .addField(
+              FieldDescriptorProto.newBuilder()
+                  .setName(
+                      BigQuerySchemaUtil.generatePlaceholderFieldName("123_illegalprotofieldname"))
+                  .setNumber(28)
+                  .setType(Type.TYPE_STRING)
+                  .setLabel(Label.LABEL_OPTIONAL)
+                  .setOptions(
+                      DescriptorProtos.FieldOptions.newBuilder()
+                          .setField(
+                              AnnotationsProto.columnName.getDescriptor(),
+                              "123_illegalprotofieldname"))
+                  .build())
           .build();
 
   private static final com.google.cloud.bigquery.storage.v1.TableSchema
@@ -846,6 +884,11 @@ public class TableRowToStorageApiProtoTest {
                       .setName("timestampvaluemaximum")
                       .setType(com.google.cloud.bigquery.storage.v1.TableFieldSchema.Type.INT64)
                       .build())
+              .addFields(
+                  com.google.cloud.bigquery.storage.v1.TableFieldSchema.newBuilder()
+                      .setName("123_illegalprotofieldname")
+                      .setType(com.google.cloud.bigquery.storage.v1.TableFieldSchema.Type.STRING)
+                      .build())
               .build();
   private static final TableSchema NESTED_TABLE_SCHEMA =
       new TableSchema()
@@ -1108,7 +1151,8 @@ public void testNestedFromTableSchema() throws Exception {
                   new TableCell().setV("1970-01-01 00:00:00.123"),
                   new TableCell().setV("1970-01-01 00:00:00.1230"),
                   new TableCell().setV("2019-08-16 00:52:07.123456"),
-                  new TableCell().setV("9999-12-31 23:59:59.999999Z")));
+                  new TableCell().setV("9999-12-31 23:59:59.999999Z"),
+                  new TableCell().setV("madeit")));
 
   private static final TableRow BASE_TABLE_ROW_NO_F =
       new TableRow()
@@ -1141,7 +1185,8 @@ public void testNestedFromTableSchema() throws Exception {
           .set("timestampValueSpaceMilli", "1970-01-01 00:00:00.123")
           .set("timestampValueSpaceTrailingZero", "1970-01-01 00:00:00.1230")
           .set("datetimeValueSpace", "2019-08-16 00:52:07.123456")
-          .set("timestampValueMaximum", "9999-12-31 23:59:59.999999Z");
+          .set("timestampValueMaximum", "9999-12-31 23:59:59.999999Z")
+          .set("123_illegalprotofieldname", "madeit");
 
   private static final Map<String, Object> BASE_ROW_EXPECTED_PROTO_VALUES =
       ImmutableMap.<String, Object>builder()
@@ -1182,8 +1227,16 @@ public void testNestedFromTableSchema() throws Exception {
           .put("timestampvaluespacetrailingzero", 123000L)
           .put("datetimevaluespace", 142111881387172416L)
           .put("timestampvaluemaximum", 253402300799999999L)
+          .put(
+              BigQuerySchemaUtil.generatePlaceholderFieldName("123_illegalprotofieldname"),
+              "madeit")
           .build();
 
+  private static final Map<String, String> BASE_ROW_EXPECTED_NAME_OVERRIDES =
+      ImmutableMap.of(
+          BigQuerySchemaUtil.generatePlaceholderFieldName("123_illegalprotofieldname"),
+          "123_illegalprotofieldname");
+
   private static final Map<String, Object> BASE_ROW_NO_F_EXPECTED_PROTO_VALUES =
       ImmutableMap.<String, Object>builder()
           .put("stringvalue", "string")
@@ -1222,15 +1275,36 @@ public void testNestedFromTableSchema() throws Exception {
           .put("timestampvaluespacetrailingzero", 123000L)
           .put("datetimevaluespace", 142111881387172416L)
           .put("timestampvaluemaximum", 253402300799999999L)
+          .put(
+              BigQuerySchemaUtil.generatePlaceholderFieldName("123_illegalprotofieldname"),
+              "madeit")
           .build();
 
+  private static final Map<String, String> BASE_ROW_NO_F_EXPECTED_NAME_OVERRIDES =
+      ImmutableMap.of(
+          BigQuerySchemaUtil.generatePlaceholderFieldName("123_illegalprotofieldname"),
+          "123_illegalprotofieldname");
+
   private void assertBaseRecord(DynamicMessage msg, boolean withF) {
     Map<String, Object> recordFields =
         msg.getAllFields().entrySet().stream()
             .collect(
                 Collectors.toMap(entry -> entry.getKey().getName(), entry -> entry.getValue()));
+
+    Map<String, String> overriddenNames =
+        msg.getAllFields().entrySet().stream()
+            .filter(entry -> entry.getKey().getOptions().hasExtension(AnnotationsProto.columnName))
+            .collect(
+                Collectors.toMap(
+                    entry -> entry.getKey().getName(),
+                    entry ->
+                        entry.getKey().getOptions().getExtension(AnnotationsProto.columnName)));
+
     assertEquals(
         withF ? BASE_ROW_EXPECTED_PROTO_VALUES : BASE_ROW_NO_F_EXPECTED_PROTO_VALUES, recordFields);
+    assertEquals(
+        withF ? BASE_ROW_EXPECTED_NAME_OVERRIDES : BASE_ROW_NO_F_EXPECTED_NAME_OVERRIDES,
+        overriddenNames);
   }
 
   @Test
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryManagedIT.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryManagedIT.java
index 6a422f1832d8..4c164e6a38db 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryManagedIT.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigquery/providers/BigQueryManagedIT.java
@@ -20,6 +20,8 @@
 import static org.hamcrest.MatcherAssert.assertThat;
 import static org.hamcrest.Matchers.containsInAnyOrder;
 
+import com.google.api.services.bigquery.model.Clustering;
+import com.google.api.services.bigquery.model.Table;
 import java.io.IOException;
 import java.util.Arrays;
 import java.util.Collections;
@@ -50,6 +52,7 @@
 import org.joda.time.Duration;
 import org.joda.time.Instant;
 import org.junit.AfterClass;
+import org.junit.Assert;
 import org.junit.BeforeClass;
 import org.junit.Rule;
 import org.junit.Test;
@@ -82,6 +85,8 @@ public class BigQueryManagedIT {
       TestPipeline.testingPipelineOptions().as(GcpOptions.class).getProject();
   private static final String BIG_QUERY_DATASET_ID = "bigquery_managed_" + System.nanoTime();
 
+  private static final Clustering CLUSTERING = new Clustering().setFields(Arrays.asList("str"));
+
   @BeforeClass
   public static void setUpTestEnvironment() throws IOException, InterruptedException {
     // Create one BQ dataset for all test cases.
@@ -94,10 +99,11 @@ public static void cleanup() {
   }
 
   @Test
-  public void testBatchFileLoadsWriteRead() {
+  public void testBatchFileLoadsWriteRead() throws IOException, InterruptedException {
     String table =
         String.format("%s.%s.%s", PROJECT, BIG_QUERY_DATASET_ID, testName.getMethodName());
-    Map<String, Object> writeConfig = ImmutableMap.of("table", table);
+    Map<String, Object> writeConfig =
+        ImmutableMap.of("table", table, "clustering_fields", Collections.singletonList("str"));
 
     // file loads requires a GCS temp location
     String tempLocation = writePipeline.getOptions().as(TestPipelineOptions.class).getTempRoot();
@@ -117,6 +123,11 @@ public void testBatchFileLoadsWriteRead() {
             .getSinglePCollection();
     PAssert.that(outputRows).containsInAnyOrder(ROWS);
     readPipeline.run().waitUntilFinish();
+
+    // Asserting clustering
+    Table tableMetadata =
+        BQ_CLIENT.getTableResource(PROJECT, BIG_QUERY_DATASET_ID, testName.getMethodName());
+    Assert.assertEquals(CLUSTERING, tableMetadata.getClustering());
   }
 
   @Test
@@ -148,7 +159,7 @@ public void testStreamingStorageWriteRead() {
 
   public void testDynamicDestinations(boolean streaming) throws IOException, InterruptedException {
     String baseTableName =
-        String.format("%s:%s.dynamic_" + System.nanoTime(), PROJECT, BIG_QUERY_DATASET_ID);
+        String.format("%s.%s.dynamic_" + System.nanoTime(), PROJECT, BIG_QUERY_DATASET_ID);
     String destinationTemplate = baseTableName + "_{dest}";
     Map<String, Object> config =
         ImmutableMap.of("table", destinationTemplate, "drop", Collections.singletonList("dest"));
@@ -173,8 +184,7 @@ public void testDynamicDestinations(boolean streaming) throws IOException, Inter
       long mod = i;
       String dest = destinations.get(i);
       List<Row> writtenRows =
-          BQ_CLIENT
-              .queryUnflattened(String.format("SELECT * FROM [%s]", dest), PROJECT, true, false)
+          BQ_CLIENT.queryUnflattened(String.format("SELECT * FROM `%s`", dest), PROJECT, true, true)
               .stream()
               .map(tableRow -> BigQueryUtils.toBeamRow(rowFilter.outputSchema(), tableRow))
               .collect(Collectors.toList());
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableReadSchemaTransformProviderIT.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableReadSchemaTransformProviderIT.java
index 81d3103f38bf..99db2641fa4f 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableReadSchemaTransformProviderIT.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableReadSchemaTransformProviderIT.java
@@ -18,6 +18,7 @@
 package org.apache.beam.sdk.io.gcp.bigtable;
 
 import static org.apache.beam.sdk.io.gcp.bigtable.BigtableReadSchemaTransformProvider.CELL_SCHEMA;
+import static org.apache.beam.sdk.io.gcp.bigtable.BigtableReadSchemaTransformProvider.FLATTENED_ROW_SCHEMA;
 import static org.apache.beam.sdk.io.gcp.bigtable.BigtableReadSchemaTransformProvider.ROW_SCHEMA;
 import static org.junit.Assert.assertThrows;
 
@@ -28,7 +29,6 @@
 import com.google.cloud.bigtable.data.v2.BigtableDataClient;
 import com.google.cloud.bigtable.data.v2.BigtableDataSettings;
 import com.google.cloud.bigtable.data.v2.models.RowMutation;
-import java.nio.ByteBuffer;
 import java.nio.charset.StandardCharsets;
 import java.util.ArrayList;
 import java.util.Arrays;
@@ -130,101 +130,200 @@ public void tearDown() {
       tableAdminClient.deleteTable(tableId);
       LOG.info("Table {} deleted successfully.", tableId);
     } catch (NotFoundException e) {
-      LOG.warn("Failed to delete a non-existent table [{}]: \n{}", tableId, e.getMessage());
+      LOG.warn("Failed to delete a non-existent table [{}]", tableId, e);
     }
     dataClient.close();
     tableAdminClient.close();
   }
 
-  public List<Row> writeToTable(int numRows) {
+  @Test
+  public void testRead() {
+    int numRows = 20;
     List<Row> expectedRows = new ArrayList<>();
+    for (int i = 1; i <= numRows; i++) {
+      String key = "key" + i;
+      byte[] keyBytes = key.getBytes(StandardCharsets.UTF_8);
+      String valueA = "value a" + i;
+      byte[] valueABytes = valueA.getBytes(StandardCharsets.UTF_8);
+      String valueB = "value b" + i;
+      byte[] valueBBytes = valueB.getBytes(StandardCharsets.UTF_8);
+      String valueC = "value c" + i;
+      byte[] valueCBytes = valueC.getBytes(StandardCharsets.UTF_8);
+      String valueD = "value d" + i;
+      byte[] valueDBytes = valueD.getBytes(StandardCharsets.UTF_8);
+      long timestamp = 1000L * i;
 
-    try {
-      for (int i = 1; i <= numRows; i++) {
-        String key = "key" + i;
-        String valueA = "value a" + i;
-        String valueB = "value b" + i;
-        String valueC = "value c" + i;
-        String valueD = "value d" + i;
-        long timestamp = 1000L * i;
-
-        RowMutation rowMutation =
-            RowMutation.create(tableId, key)
-                .setCell(COLUMN_FAMILY_NAME_1, "a", timestamp, valueA)
-                .setCell(COLUMN_FAMILY_NAME_1, "b", timestamp, valueB)
-                .setCell(COLUMN_FAMILY_NAME_2, "c", timestamp, valueC)
-                .setCell(COLUMN_FAMILY_NAME_2, "d", timestamp, valueD);
-        dataClient.mutateRow(rowMutation);
-
-        // Set up expected Beam Row
-        Map<String, List<Row>> columns1 = new HashMap<>();
-        columns1.put(
-            "a",
-            Arrays.asList(
-                Row.withSchema(CELL_SCHEMA)
-                    .withFieldValue(
-                        "value", ByteBuffer.wrap(valueA.getBytes(StandardCharsets.UTF_8)))
-                    .withFieldValue("timestamp_micros", timestamp)
-                    .build()));
-        columns1.put(
-            "b",
-            Arrays.asList(
-                Row.withSchema(CELL_SCHEMA)
-                    .withFieldValue(
-                        "value", ByteBuffer.wrap(valueB.getBytes(StandardCharsets.UTF_8)))
-                    .withFieldValue("timestamp_micros", timestamp)
-                    .build()));
-
-        Map<String, List<Row>> columns2 = new HashMap<>();
-        columns2.put(
-            "c",
-            Arrays.asList(
-                Row.withSchema(CELL_SCHEMA)
-                    .withFieldValue(
-                        "value", ByteBuffer.wrap(valueC.getBytes(StandardCharsets.UTF_8)))
-                    .withFieldValue("timestamp_micros", timestamp)
-                    .build()));
-        columns2.put(
-            "d",
-            Arrays.asList(
-                Row.withSchema(CELL_SCHEMA)
-                    .withFieldValue(
-                        "value", ByteBuffer.wrap(valueD.getBytes(StandardCharsets.UTF_8)))
-                    .withFieldValue("timestamp_micros", timestamp)
-                    .build()));
-
-        Map<String, Map<String, List<Row>>> families = new HashMap<>();
-        families.put(COLUMN_FAMILY_NAME_1, columns1);
-        families.put(COLUMN_FAMILY_NAME_2, columns2);
-
-        Row expectedRow =
-            Row.withSchema(ROW_SCHEMA)
-                .withFieldValue("key", ByteBuffer.wrap(key.getBytes(StandardCharsets.UTF_8)))
-                .withFieldValue("column_families", families)
-                .build();
-
-        expectedRows.add(expectedRow);
-      }
-      LOG.info("Finished writing {} rows to table {}", numRows, tableId);
-    } catch (NotFoundException e) {
-      throw new RuntimeException("Failed to write to table", e);
+      RowMutation rowMutation =
+          RowMutation.create(tableId, key)
+              .setCell(COLUMN_FAMILY_NAME_1, "a", timestamp, valueA)
+              .setCell(COLUMN_FAMILY_NAME_1, "b", timestamp, valueB)
+              .setCell(COLUMN_FAMILY_NAME_2, "c", timestamp, valueC)
+              .setCell(COLUMN_FAMILY_NAME_2, "d", timestamp, valueD);
+      dataClient.mutateRow(rowMutation);
+
+      // Set up expected Beam Row
+      Map<String, List<Row>> columns1 = new HashMap<>();
+      columns1.put(
+          "a",
+          Arrays.asList(
+              Row.withSchema(CELL_SCHEMA)
+                  .withFieldValue("value", valueABytes)
+                  .withFieldValue("timestamp_micros", timestamp)
+                  .build()));
+      columns1.put(
+          "b",
+          Arrays.asList(
+              Row.withSchema(CELL_SCHEMA)
+                  .withFieldValue("value", valueBBytes)
+                  .withFieldValue("timestamp_micros", timestamp)
+                  .build()));
+
+      Map<String, List<Row>> columns2 = new HashMap<>();
+      columns2.put(
+          "c",
+          Arrays.asList(
+              Row.withSchema(CELL_SCHEMA)
+                  .withFieldValue("value", valueCBytes)
+                  .withFieldValue("timestamp_micros", timestamp)
+                  .build()));
+      columns2.put(
+          "d",
+          Arrays.asList(
+              Row.withSchema(CELL_SCHEMA)
+                  .withFieldValue("value", valueDBytes)
+                  .withFieldValue("timestamp_micros", timestamp)
+                  .build()));
+
+      Map<String, Map<String, List<Row>>> families = new HashMap<>();
+      families.put(COLUMN_FAMILY_NAME_1, columns1);
+      families.put(COLUMN_FAMILY_NAME_2, columns2);
+
+      Row expectedRow =
+          Row.withSchema(ROW_SCHEMA)
+              .withFieldValue("key", keyBytes)
+              .withFieldValue("column_families", families)
+              .build();
+
+      expectedRows.add(expectedRow);
     }
-    return expectedRows;
+    LOG.info("Finished writing {} rows to table {}", numRows, tableId);
+
+    BigtableReadSchemaTransformConfiguration config =
+        BigtableReadSchemaTransformConfiguration.builder()
+            .setTableId(tableId)
+            .setInstanceId(instanceId)
+            .setProjectId(projectId)
+            .setFlatten(false)
+            .build();
+
+    SchemaTransform transform = new BigtableReadSchemaTransformProvider().from(config);
+
+    PCollection<Row> rows = PCollectionRowTuple.empty(p).apply(transform).get("output");
+
+    PAssert.that(rows).containsInAnyOrder(expectedRows);
+    p.run().waitUntilFinish();
   }
 
   @Test
-  public void testRead() {
-    List<Row> expectedRows = writeToTable(20);
+  public void testReadFlatten() {
+    int numRows = 20;
+    List<Row> expectedRows = new ArrayList<>();
+    for (int i = 1; i <= numRows; i++) {
+      String key = "key" + i;
+      byte[] keyBytes = key.getBytes(StandardCharsets.UTF_8);
+      String valueA = "value a" + i;
+      byte[] valueABytes = valueA.getBytes(StandardCharsets.UTF_8);
+      String valueB = "value b" + i;
+      byte[] valueBBytes = valueB.getBytes(StandardCharsets.UTF_8);
+      String valueC = "value c" + i;
+      byte[] valueCBytes = valueC.getBytes(StandardCharsets.UTF_8);
+      String valueD = "value d" + i;
+      byte[] valueDBytes = valueD.getBytes(StandardCharsets.UTF_8);
+      long timestamp = 1000L * i;
 
+      // Write a row with four distinct columns to Bigtable
+      RowMutation rowMutation =
+          RowMutation.create(tableId, key)
+              .setCell(COLUMN_FAMILY_NAME_1, "a", timestamp, valueA)
+              .setCell(COLUMN_FAMILY_NAME_1, "b", timestamp, valueB)
+              .setCell(COLUMN_FAMILY_NAME_2, "c", timestamp, valueC)
+              .setCell(COLUMN_FAMILY_NAME_2, "d", timestamp, valueD);
+      dataClient.mutateRow(rowMutation);
+
+      // For each Bigtable row, we expect four flattened Beam Rows as output.
+      // Each Row corresponds to one column.
+      expectedRows.add(
+          Row.withSchema(FLATTENED_ROW_SCHEMA)
+              .withFieldValue("key", keyBytes)
+              .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+              .withFieldValue("column_qualifier", "a".getBytes(StandardCharsets.UTF_8))
+              .withFieldValue(
+                  "cells",
+                  Arrays.asList(
+                      Row.withSchema(CELL_SCHEMA)
+                          .withFieldValue("value", valueABytes)
+                          .withFieldValue("timestamp_micros", timestamp)
+                          .build()))
+              .build());
+
+      expectedRows.add(
+          Row.withSchema(FLATTENED_ROW_SCHEMA)
+              .withFieldValue("key", keyBytes)
+              .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+              .withFieldValue("column_qualifier", "b".getBytes(StandardCharsets.UTF_8))
+              .withFieldValue(
+                  "cells",
+                  Arrays.asList(
+                      Row.withSchema(CELL_SCHEMA)
+                          .withFieldValue("value", valueBBytes)
+                          .withFieldValue("timestamp_micros", timestamp)
+                          .build()))
+              .build());
+
+      expectedRows.add(
+          Row.withSchema(FLATTENED_ROW_SCHEMA)
+              .withFieldValue("key", keyBytes)
+              .withFieldValue("family_name", COLUMN_FAMILY_NAME_2)
+              .withFieldValue("column_qualifier", "c".getBytes(StandardCharsets.UTF_8))
+              .withFieldValue(
+                  "cells",
+                  Arrays.asList(
+                      Row.withSchema(CELL_SCHEMA)
+                          .withFieldValue("value", valueCBytes)
+                          .withFieldValue("timestamp_micros", timestamp)
+                          .build()))
+              .build());
+
+      expectedRows.add(
+          Row.withSchema(FLATTENED_ROW_SCHEMA)
+              .withFieldValue("key", keyBytes)
+              .withFieldValue("family_name", COLUMN_FAMILY_NAME_2)
+              .withFieldValue("column_qualifier", "d".getBytes(StandardCharsets.UTF_8))
+              .withFieldValue(
+                  "cells",
+                  Arrays.asList(
+                      Row.withSchema(CELL_SCHEMA)
+                          .withFieldValue("value", valueDBytes)
+                          .withFieldValue("timestamp_micros", timestamp)
+                          .build()))
+              .build());
+    }
+    LOG.info("Finished writing {} rows to table {} with Flatten state true", numRows, tableId);
+
+    // Configure the transform to use flatten mode (the default).
     BigtableReadSchemaTransformConfiguration config =
         BigtableReadSchemaTransformConfiguration.builder()
             .setTableId(tableId)
             .setInstanceId(instanceId)
             .setProjectId(projectId)
+            .setFlatten(true)
             .build();
+
     SchemaTransform transform = new BigtableReadSchemaTransformProvider().from(config);
 
     PCollection<Row> rows = PCollectionRowTuple.empty(p).apply(transform).get("output");
+
+    // Assert that the actual rows match the expected flattened rows.
     PAssert.that(rows).containsInAnyOrder(expectedRows);
     p.run().waitUntilFinish();
   }
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableSimpleWriteSchemaTransformProviderIT.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableSimpleWriteSchemaTransformProviderIT.java
new file mode 100644
index 000000000000..eceb1ddff4be
--- /dev/null
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableSimpleWriteSchemaTransformProviderIT.java
@@ -0,0 +1,655 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.gcp.bigtable;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertThrows;
+import static org.junit.Assert.assertTrue;
+
+import com.google.api.gax.rpc.NotFoundException;
+import com.google.cloud.bigtable.admin.v2.BigtableTableAdminClient;
+import com.google.cloud.bigtable.admin.v2.BigtableTableAdminSettings;
+import com.google.cloud.bigtable.admin.v2.models.CreateTableRequest;
+import com.google.cloud.bigtable.data.v2.BigtableDataClient;
+import com.google.cloud.bigtable.data.v2.BigtableDataSettings;
+import com.google.cloud.bigtable.data.v2.models.Query;
+import com.google.cloud.bigtable.data.v2.models.RowCell;
+import com.google.cloud.bigtable.data.v2.models.RowMutation;
+import java.nio.charset.StandardCharsets;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Date;
+import java.util.List;
+import java.util.stream.Collectors;
+import org.apache.beam.sdk.extensions.gcp.options.GcpOptions;
+import org.apache.beam.sdk.io.gcp.bigtable.BigtableWriteSchemaTransformProvider.BigtableWriteSchemaTransformConfiguration;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.schemas.Schema.FieldType; // Import FieldType
+import org.apache.beam.sdk.testing.TestPipeline;
+import org.apache.beam.sdk.transforms.Create;
+import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollectionRowTuple;
+import org.apache.beam.sdk.values.Row;
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+
+@RunWith(JUnit4.class)
+public class BigtableSimpleWriteSchemaTransformProviderIT {
+  @Rule public final transient TestPipeline p = TestPipeline.create();
+
+  private static final String COLUMN_FAMILY_NAME_1 = "test_cf_1";
+  private static final String COLUMN_FAMILY_NAME_2 = "test_cf_2";
+  private BigtableTableAdminClient tableAdminClient;
+  private BigtableDataClient dataClient;
+  private String tableId = String.format("BigtableWriteIT-%tF-%<tH-%<tM-%<tS-%<tL", new Date());
+  private String projectId;
+  private String instanceId;
+  private PTransform<PCollectionRowTuple, PCollectionRowTuple> writeTransform;
+
+  @Test
+  public void testInvalidConfigs() {
+    // Properties cannot be empty (project, instance, and table)
+    List<BigtableWriteSchemaTransformConfiguration.Builder> invalidConfigs =
+        Arrays.asList(
+            BigtableWriteSchemaTransformConfiguration.builder()
+                .setProjectId("project")
+                .setInstanceId("instance")
+                .setTableId(""),
+            BigtableWriteSchemaTransformConfiguration.builder()
+                .setProjectId("")
+                .setInstanceId("instance")
+                .setTableId("table"),
+            BigtableWriteSchemaTransformConfiguration.builder()
+                .setProjectId("project")
+                .setInstanceId("")
+                .setTableId("table"));
+
+    for (BigtableWriteSchemaTransformConfiguration.Builder config : invalidConfigs) {
+      assertThrows(
+          IllegalArgumentException.class,
+          () -> {
+            config.build().validate();
+          });
+    }
+  }
+
+  @Before
+  public void setup() throws Exception {
+    BigtableTestOptions options =
+        TestPipeline.testingPipelineOptions().as(BigtableTestOptions.class);
+    projectId = options.as(GcpOptions.class).getProject();
+    instanceId = options.getInstanceId();
+
+    BigtableDataSettings settings =
+        BigtableDataSettings.newBuilder().setProjectId(projectId).setInstanceId(instanceId).build();
+    // Creates a bigtable data client.
+    dataClient = BigtableDataClient.create(settings);
+
+    BigtableTableAdminSettings adminSettings =
+        BigtableTableAdminSettings.newBuilder()
+            .setProjectId(projectId)
+            .setInstanceId(instanceId)
+            .build();
+    tableAdminClient = BigtableTableAdminClient.create(adminSettings);
+
+    // set up the table with some pre-written rows to test our mutations on.
+    // each test is independent of the others
+    if (!tableAdminClient.exists(tableId)) {
+      CreateTableRequest createTableRequest =
+          CreateTableRequest.of(tableId)
+              .addFamily(COLUMN_FAMILY_NAME_1)
+              .addFamily(COLUMN_FAMILY_NAME_2);
+      tableAdminClient.createTable(createTableRequest);
+    }
+
+    BigtableWriteSchemaTransformConfiguration config =
+        BigtableWriteSchemaTransformConfiguration.builder()
+            .setProjectId(projectId)
+            .setInstanceId(instanceId)
+            .setTableId(tableId)
+            .build();
+    writeTransform = new BigtableWriteSchemaTransformProvider().from(config);
+  }
+
+  @After
+  public void tearDown() {
+    try {
+      tableAdminClient.deleteTable(tableId);
+      System.out.printf("Table %s deleted successfully%n", tableId);
+    } catch (NotFoundException e) {
+      System.err.println("Failed to delete a non-existent table: " + e.getMessage());
+    }
+    dataClient.close();
+    tableAdminClient.close();
+  }
+
+  @Test
+  public void testSetMutationsExistingColumn() {
+    RowMutation rowMutation =
+        RowMutation.create(tableId, "key-1")
+            .setCell(COLUMN_FAMILY_NAME_1, "col_a", 1000, "val-1-a")
+            .setCell(COLUMN_FAMILY_NAME_2, "col_c", 1000, "val-1-c");
+    dataClient.mutateRow(rowMutation);
+    Schema testSchema =
+        Schema.builder()
+            .addByteArrayField("key")
+            .addStringField("type")
+            .addByteArrayField("value")
+            .addByteArrayField("column_qualifier")
+            .addStringField("family_name")
+            .addField("timestamp_micros", FieldType.INT64) // Changed to INT64
+            .build();
+
+    Row mutationRow1 =
+        Row.withSchema(testSchema)
+            .withFieldValue("key", "key-1".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "SetCell")
+            .withFieldValue("value", "new-val-1-a".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("column_qualifier", "col_a".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+            .withFieldValue("timestamp_micros", 2000L)
+            .build();
+    Row mutationRow2 =
+        Row.withSchema(testSchema)
+            .withFieldValue("key", "key-1".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "SetCell")
+            .withFieldValue("value", "new-val-1-c".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("column_qualifier", "col_c".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("family_name", COLUMN_FAMILY_NAME_2)
+            .withFieldValue("timestamp_micros", 2000L)
+            .build();
+
+    PCollection<Row> inputPCollection =
+        p.apply(Create.of(Arrays.asList(mutationRow1, mutationRow2)));
+    inputPCollection.setRowSchema(testSchema);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
+        .apply(writeTransform);
+    p.run().waitUntilFinish();
+
+    // get rows from table
+    List<com.google.cloud.bigtable.data.v2.models.Row> rows =
+        dataClient.readRows(Query.create(tableId)).stream().collect(Collectors.toList());
+    // we should still have only one row with the same key
+    assertEquals(1, rows.size());
+    assertEquals("key-1", rows.get(0).getKey().toStringUtf8());
+
+    // check that we now have two cells in each column we added to and that
+    // the last cell in each column has the updated value
+    com.google.cloud.bigtable.data.v2.models.Row row = rows.get(0);
+    List<RowCell> cellsColA =
+        row.getCells(COLUMN_FAMILY_NAME_1, "col_a").stream()
+            .sorted(RowCell.compareByNative())
+            .collect(Collectors.toList());
+    List<RowCell> cellsColC =
+        row.getCells(COLUMN_FAMILY_NAME_2, "col_c").stream()
+            .sorted(RowCell.compareByNative())
+            .collect(Collectors.toList());
+    assertEquals(2, cellsColA.size());
+    assertEquals(2, cellsColC.size());
+    // Bigtable keeps cell history ordered by descending timestamp
+    assertEquals("new-val-1-a", cellsColA.get(0).getValue().toStringUtf8());
+    assertEquals("new-val-1-c", cellsColC.get(0).getValue().toStringUtf8());
+    assertEquals("val-1-a", cellsColA.get(1).getValue().toStringUtf8());
+    assertEquals("val-1-c", cellsColC.get(1).getValue().toStringUtf8());
+  }
+
+  @Test
+  public void testSetMutationNewColumn() {
+    RowMutation rowMutation =
+        RowMutation.create(tableId, "key-1").setCell(COLUMN_FAMILY_NAME_1, "col_a", "val-1-a");
+    dataClient.mutateRow(rowMutation);
+    Schema testSchema =
+        Schema.builder()
+            .addByteArrayField("key")
+            .addStringField("type")
+            .addByteArrayField("value")
+            .addByteArrayField("column_qualifier")
+            .addStringField("family_name")
+            .addField("timestamp_micros", FieldType.INT64)
+            .build();
+    Row mutationRow =
+        Row.withSchema(testSchema)
+            .withFieldValue("key", "key-1".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "SetCell")
+            .withFieldValue("value", "new-val-1".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("column_qualifier", "new_col".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+            .withFieldValue("timestamp_micros", 999_000L)
+            .build();
+
+    PCollection<Row> inputPCollection = p.apply(Create.of(Arrays.asList(mutationRow)));
+    inputPCollection.setRowSchema(testSchema);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
+        .apply(writeTransform);
+    p.run().waitUntilFinish();
+
+    // get rows from table
+    List<com.google.cloud.bigtable.data.v2.models.Row> rows =
+        dataClient.readRows(Query.create(tableId)).stream().collect(Collectors.toList());
+
+    // we should still have only one row with the same key
+    assertEquals(1, rows.size());
+    assertEquals("key-1", rows.get(0).getKey().toStringUtf8());
+    // check the new column exists with only one cell.
+    // also check cell value is correct
+    com.google.cloud.bigtable.data.v2.models.Row row = rows.get(0);
+    List<RowCell> cellsNewCol = row.getCells(COLUMN_FAMILY_NAME_1, "new_col");
+    assertEquals(1, cellsNewCol.size());
+    assertEquals("new-val-1", cellsNewCol.get(0).getValue().toStringUtf8());
+  }
+
+  @Test
+  public void testDeleteCellsFromColumn() {
+    RowMutation rowMutation =
+        RowMutation.create(tableId, "key-1")
+            .setCell(COLUMN_FAMILY_NAME_1, "col_a", "val-1-a")
+            .setCell(COLUMN_FAMILY_NAME_1, "col_b", "val-1-b");
+    dataClient.mutateRow(rowMutation);
+    // write two cells in col_a. both should get deleted
+    rowMutation =
+        RowMutation.create(tableId, "key-1").setCell(COLUMN_FAMILY_NAME_1, "col_a", "new-val-1-a");
+    dataClient.mutateRow(rowMutation);
+    Schema testSchema =
+        Schema.builder()
+            .addByteArrayField("key")
+            .addStringField("type")
+            .addByteArrayField("column_qualifier")
+            .addStringField("family_name")
+            .build();
+    Row mutationRow =
+        Row.withSchema(testSchema)
+            .withFieldValue("key", "key-1".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "DeleteFromColumn")
+            .withFieldValue("column_qualifier", "col_a".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+            .build();
+
+    PCollection<Row> inputPCollection = p.apply(Create.of(Arrays.asList(mutationRow)));
+    inputPCollection.setRowSchema(testSchema);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
+        .apply(writeTransform);
+    p.run().waitUntilFinish();
+
+    // get rows from table
+    List<com.google.cloud.bigtable.data.v2.models.Row> rows =
+        dataClient.readRows(Query.create(tableId)).stream().collect(Collectors.toList());
+
+    // we should still have one row with the same key
+    assertEquals(1, rows.size());
+    assertEquals("key-1", rows.get(0).getKey().toStringUtf8());
+    // get cells from this column family. we started with three cells and deleted two from one
+    // column.
+    // we should end up with one cell in the column we didn't touch.
+    com.google.cloud.bigtable.data.v2.models.Row row = rows.get(0);
+    List<RowCell> cells = row.getCells(COLUMN_FAMILY_NAME_1);
+    assertEquals(1, cells.size());
+    assertEquals("col_b", cells.get(0).getQualifier().toStringUtf8());
+  }
+
+  @Test
+  public void testDeleteCellsFromColumnWithTimestampRange() {
+    // write two cells in one column with different timestamps.
+    RowMutation rowMutation =
+        RowMutation.create(tableId, "key-1")
+            .setCell(COLUMN_FAMILY_NAME_1, "col", 100_000_000, "val");
+    dataClient.mutateRow(rowMutation);
+    rowMutation =
+        RowMutation.create(tableId, "key-1")
+            .setCell(COLUMN_FAMILY_NAME_1, "col", 200_000_000, "new-val");
+    dataClient.mutateRow(rowMutation);
+    Schema testSchema =
+        Schema.builder()
+            .addByteArrayField("key")
+            .addStringField("type")
+            .addByteArrayField("column_qualifier")
+            .addStringField("family_name")
+            .addField("start_timestamp_micros", FieldType.INT64)
+            .addField("end_timestamp_micros", FieldType.INT64)
+            .build();
+    Row mutationRow =
+        Row.withSchema(testSchema)
+            .withFieldValue("key", "key-1".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "DeleteFromColumn")
+            .withFieldValue("column_qualifier", "col".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+            .withFieldValue("start_timestamp_micros", 99_990_000L)
+            .withFieldValue("end_timestamp_micros", 100_000_000L)
+            .build();
+
+    PCollection<Row> inputPCollection = p.apply(Create.of(Arrays.asList(mutationRow)));
+    inputPCollection.setRowSchema(testSchema);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
+        .apply(writeTransform);
+    p.run().waitUntilFinish();
+
+    // get rows from table
+    List<com.google.cloud.bigtable.data.v2.models.Row> rows =
+        dataClient.readRows(Query.create(tableId)).stream().collect(Collectors.toList());
+
+    // we should still have one row with the same key
+    assertEquals(1, rows.size());
+    assertEquals("key-1", rows.get(0).getKey().toStringUtf8());
+    // we had two cells in col_a and deleted the older one. we should be left with the newer cell.
+    // check cell has correct value and timestamp
+    com.google.cloud.bigtable.data.v2.models.Row row = rows.get(0);
+    List<RowCell> cells = row.getCells(COLUMN_FAMILY_NAME_1, "col");
+    assertEquals(2, cells.size());
+    assertEquals("new-val", cells.get(0).getValue().toStringUtf8());
+    assertEquals(200_000_000, cells.get(0).getTimestamp());
+  }
+
+  @Test
+  public void testDeleteColumnFamily() {
+    RowMutation rowMutation =
+        RowMutation.create(tableId, "key-1")
+            .setCell(COLUMN_FAMILY_NAME_1, "col_a", "val")
+            .setCell(COLUMN_FAMILY_NAME_2, "col_b", "val");
+    dataClient.mutateRow(rowMutation);
+    Schema testSchema =
+        Schema.builder()
+            .addByteArrayField("key")
+            .addStringField("type")
+            .addStringField("family_name")
+            .build();
+    Row mutationRow =
+        Row.withSchema(testSchema)
+            .withFieldValue("key", "key-1".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "DeleteFromFamily")
+            .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+            .build();
+
+    PCollection<Row> inputPCollection = p.apply(Create.of(Arrays.asList(mutationRow)));
+    inputPCollection.setRowSchema(testSchema);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
+        .apply(writeTransform);
+    p.run().waitUntilFinish();
+
+    // get rows from table
+    List<com.google.cloud.bigtable.data.v2.models.Row> rows =
+        dataClient.readRows(Query.create(tableId)).stream().collect(Collectors.toList());
+
+    // we should still have one row with the same key
+    assertEquals(1, rows.size());
+    assertEquals("key-1", rows.get(0).getKey().toStringUtf8());
+    // we had one cell in each of two column families. we deleted a column family, so should end up
+    // with
+    // one cell in the column family we didn't touch.
+    com.google.cloud.bigtable.data.v2.models.Row row = rows.get(0);
+    List<RowCell> cells = row.getCells();
+    assertEquals(1, cells.size());
+    assertEquals(COLUMN_FAMILY_NAME_2, cells.get(0).getFamily());
+  }
+
+  @Test
+  public void testDeleteRow() {
+    RowMutation rowMutation =
+        RowMutation.create(tableId, "key-1").setCell(COLUMN_FAMILY_NAME_1, "col", "val-1");
+    dataClient.mutateRow(rowMutation);
+    rowMutation =
+        RowMutation.create(tableId, "key-2").setCell(COLUMN_FAMILY_NAME_1, "col", "val-2");
+    dataClient.mutateRow(rowMutation);
+    Schema testSchema = Schema.builder().addByteArrayField("key").addStringField("type").build();
+    Row mutationRow =
+        Row.withSchema(testSchema)
+            .withFieldValue("key", "key-1".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "DeleteFromRow")
+            .build();
+
+    PCollection<Row> inputPCollection = p.apply(Create.of(Arrays.asList(mutationRow)));
+    inputPCollection.setRowSchema(testSchema);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
+        .apply(writeTransform);
+    p.run().waitUntilFinish();
+
+    // get rows from table
+    List<com.google.cloud.bigtable.data.v2.models.Row> rows =
+        dataClient.readRows(Query.create(tableId)).stream().collect(Collectors.toList());
+
+    // we created two rows then deleted one, so should end up with the row we didn't touch
+    assertEquals(1, rows.size());
+    assertEquals("key-2", rows.get(0).getKey().toStringUtf8());
+  }
+
+  @Test
+  public void testAllMutations() {
+
+    // --- Initial Setup: Populate the table with diverse data ---
+    dataClient.mutateRow(
+        RowMutation.create(tableId, "row-setcell")
+            .setCell(COLUMN_FAMILY_NAME_1, "col_initial_1", "initial_val_1")
+            .setCell(COLUMN_FAMILY_NAME_2, "col_initial_2", "initial_val_2"));
+
+    dataClient.mutateRow(
+        RowMutation.create(tableId, "row-delete-col")
+            .setCell(COLUMN_FAMILY_NAME_1, "col_to_delete_A", 1000, "val_to_delete_A_old")
+            .setCell(COLUMN_FAMILY_NAME_1, "col_to_delete_A", 2000, "val_to_delete_A_new")
+            .setCell(COLUMN_FAMILY_NAME_1, "col_to_keep_B", "val_to_keep_B"));
+
+    dataClient.mutateRow(
+        RowMutation.create(tableId, "row-delete-col-ts")
+            .setCell(COLUMN_FAMILY_NAME_1, "ts_col", 1000, "ts_val_old")
+            .setCell(COLUMN_FAMILY_NAME_1, "ts_col", 2000, "ts_val_new")
+            .setCell(COLUMN_FAMILY_NAME_2, "ts_col_other_cf", "ts_val_other_cf"));
+
+    dataClient.mutateRow(
+        RowMutation.create(tableId, "row-delete-family")
+            .setCell(COLUMN_FAMILY_NAME_1, "col_to_delete_family", "val_delete_family")
+            .setCell(COLUMN_FAMILY_NAME_2, "col_to_keep_family", "val_keep_family"));
+
+    dataClient.mutateRow(
+        RowMutation.create(tableId, "row-delete-row")
+            .setCell(COLUMN_FAMILY_NAME_1, "col", "val_delete_row"));
+
+    dataClient.mutateRow(
+        RowMutation.create(tableId, "row-final-check")
+            .setCell(COLUMN_FAMILY_NAME_1, "col_final_1", "val_final_1"));
+
+    // --- Define Schema for various mutation types ---
+
+    Schema uberSchema =
+        Schema.builder()
+            .addByteArrayField("key") // Key is always present and non-null
+            .addStringField(
+                "type") // Type is always present and non-null (e.g., "SetCell", "DeleteFromRow")
+            // All other fields are conditional based on the 'type' of mutation, so they must be
+            // nullable.
+            .addNullableField("value", FieldType.BYTES) // Used by SetCell
+            .addNullableField(
+                "column_qualifier", FieldType.BYTES) // Used by SetCell, DeleteFromColumn
+            .addNullableField(
+                "family_name",
+                FieldType.STRING) // Used by SetCell, DeleteFromColumn, DeleteFromFamily
+            .addNullableField("timestamp_micros", FieldType.INT64) // Optional for SetCell
+            .addNullableField(
+                "start_timestamp_micros", FieldType.INT64) // Used by DeleteFromColumn with range
+            .addNullableField(
+                "end_timestamp_micros", FieldType.INT64) // Used by DeleteFromColumn with range
+            .build();
+
+    // --- Create a list of mutation Rows ---
+    List<Row> mutations = new ArrayList<>();
+
+    // 1. SetCell (Update an existing cell, add a new cell)
+    // Update "row-setcell", col_initial_1
+    mutations.add(
+        Row.withSchema(uberSchema)
+            .withFieldValue("key", "row-setcell".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "SetCell")
+            .withFieldValue("value", "updated_val_1".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("column_qualifier", "col_initial_1".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+            .withFieldValue("timestamp_micros", 3000L)
+            .build());
+    // Add new cell to "row-setcell"
+    mutations.add(
+        Row.withSchema(uberSchema)
+            .withFieldValue("key", "row-setcell".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "SetCell")
+            .withFieldValue("value", "new_col_val".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("column_qualifier", "new_col_A".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+            .withFieldValue("timestamp_micros", 4000L)
+            .build());
+
+    // 2. DeleteFromColumn
+    // Delete "col_to_delete_A" from "row-delete-col"
+    mutations.add(
+        Row.withSchema(uberSchema)
+            .withFieldValue("key", "row-delete-col".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "DeleteFromColumn")
+            .withFieldValue("column_qualifier", "col_to_delete_A".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+            .build());
+
+    // 3. DeleteFromColumn with Timestamp Range
+    // Delete "ts_col" with timestamp 1000 from "row-delete-col-ts"
+    mutations.add(
+        Row.withSchema(uberSchema)
+            .withFieldValue("key", "row-delete-col-ts".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "DeleteFromColumn")
+            .withFieldValue("column_qualifier", "ts_col".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+            .withFieldValue("start_timestamp_micros", 999L) // Inclusive
+            .withFieldValue("end_timestamp_micros", 1001L) // Exclusive
+            .build());
+
+    // 4. DeleteFromFamily
+    // Delete COLUMN_FAMILY_NAME_1 from "row-delete-family"
+    mutations.add(
+        Row.withSchema(uberSchema)
+            .withFieldValue("key", "row-delete-family".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "DeleteFromFamily")
+            .withFieldValue("family_name", COLUMN_FAMILY_NAME_1)
+            .build());
+
+    // 5. DeleteFromRow
+    // Delete "row-delete-row"
+    mutations.add(
+        Row.withSchema(uberSchema)
+            .withFieldValue("key", "row-delete-row".getBytes(StandardCharsets.UTF_8))
+            .withFieldValue("type", "DeleteFromRow")
+            .build());
+
+    // --- Apply the mutations --
+    PCollection<Row> inputPCollection = p.apply(Create.of(mutations));
+    inputPCollection.setRowSchema(uberSchema); // Set the comprehensive schema for the PCollection
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
+        .apply(writeTransform);
+    p.run().waitUntilFinish();
+
+    // --- Assertions: Verify the final state of the table ---
+
+    List<com.google.cloud.bigtable.data.v2.models.Row> rows =
+        dataClient.readRows(Query.create(tableId)).stream().collect(Collectors.toList());
+
+    assertEquals(5, rows.size()); // Expecting 'row-setcell', 'row-delete-col', 'row-delete-col-ts',
+    // 'row-final-check'
+
+    // Verify "row-setcell"
+    com.google.cloud.bigtable.data.v2.models.Row rowSetCell =
+        rows.stream()
+            .filter(r -> r.getKey().toStringUtf8().equals("row-setcell"))
+            .findFirst()
+            .orElse(null);
+    assertEquals("row-setcell", rowSetCell.getKey().toStringUtf8());
+    List<RowCell> cellsSetCellCol1 =
+        rowSetCell.getCells(COLUMN_FAMILY_NAME_1, "col_initial_1").stream()
+            .sorted(RowCell.compareByNative())
+            .collect(Collectors.toList());
+    assertEquals(2, cellsSetCellCol1.size()); // Original + updated
+    assertEquals(
+        "initial_val_1", cellsSetCellCol1.get(0).getValue().toStringUtf8()); // Newest value
+    assertEquals(
+        "updated_val_1", cellsSetCellCol1.get(1).getValue().toStringUtf8()); // Oldest value
+    List<RowCell> cellsSetCellNewCol = rowSetCell.getCells(COLUMN_FAMILY_NAME_1, "new_col_A");
+    assertEquals(1, cellsSetCellNewCol.size());
+    assertEquals("new_col_val", cellsSetCellNewCol.get(0).getValue().toStringUtf8());
+    List<RowCell> cellsSetCellCol2 = rowSetCell.getCells(COLUMN_FAMILY_NAME_2, "col_initial_2");
+    assertEquals(1, cellsSetCellCol2.size());
+    assertEquals("initial_val_2", cellsSetCellCol2.get(0).getValue().toStringUtf8());
+
+    // Verify "row-delete-col"
+    com.google.cloud.bigtable.data.v2.models.Row rowDeleteCol =
+        rows.stream()
+            .filter(r -> r.getKey().toStringUtf8().equals("row-delete-col"))
+            .findFirst()
+            .orElse(null);
+    assertEquals("row-delete-col", rowDeleteCol.getKey().toStringUtf8());
+    List<RowCell> cellsColToDeleteA =
+        rowDeleteCol.getCells(COLUMN_FAMILY_NAME_1, "col_to_delete_A");
+    assertTrue(cellsColToDeleteA.isEmpty()); // Should be deleted
+    List<RowCell> cellsColToKeepB = rowDeleteCol.getCells(COLUMN_FAMILY_NAME_1, "col_to_keep_B");
+    assertEquals(1, cellsColToKeepB.size());
+    assertEquals("val_to_keep_B", cellsColToKeepB.get(0).getValue().toStringUtf8());
+
+    // Verify "row-delete-col-ts"
+    com.google.cloud.bigtable.data.v2.models.Row rowDeleteColTs =
+        rows.stream()
+            .filter(r -> r.getKey().toStringUtf8().equals("row-delete-col-ts"))
+            .findFirst()
+            .orElse(null);
+    assertEquals("row-delete-col-ts", rowDeleteColTs.getKey().toStringUtf8());
+    List<RowCell> cellsTsCol = rowDeleteColTs.getCells(COLUMN_FAMILY_NAME_1, "ts_col");
+    assertEquals(1, cellsTsCol.size()); // Only the 2000 timestamp cell should remain
+    assertEquals("ts_val_new", cellsTsCol.get(0).getValue().toStringUtf8());
+    assertEquals(2000, cellsTsCol.get(0).getTimestamp());
+    List<RowCell> cellsTsColOtherCf =
+        rowDeleteColTs.getCells(COLUMN_FAMILY_NAME_2, "ts_col_other_cf");
+    assertEquals(1, cellsTsColOtherCf.size());
+    assertEquals("ts_val_other_cf", cellsTsColOtherCf.get(0).getValue().toStringUtf8());
+
+    // Verify "row-delete-family"
+    com.google.cloud.bigtable.data.v2.models.Row rowDeleteFamily =
+        rows.stream()
+            .filter(r -> r.getKey().toStringUtf8().equals("row-delete-family"))
+            .findFirst()
+            .orElse(null);
+    assertEquals("row-delete-family", rowDeleteFamily.getKey().toStringUtf8());
+    List<RowCell> cellsCf1 = rowDeleteFamily.getCells(COLUMN_FAMILY_NAME_1);
+    assertTrue(cellsCf1.isEmpty()); // COLUMN_FAMILY_NAME_1 should be empty
+    List<RowCell> cellsCf2 = rowDeleteFamily.getCells(COLUMN_FAMILY_NAME_2);
+    assertEquals(1, cellsCf2.size());
+    assertEquals("val_keep_family", cellsCf2.get(0).getValue().toStringUtf8());
+
+    // Verify "row-delete-row" is gone
+    assertTrue(rows.stream().noneMatch(r -> r.getKey().toStringUtf8().equals("row-delete-row")));
+
+    // Verify "row-final-check" still exists
+    com.google.cloud.bigtable.data.v2.models.Row rowFinalCheck =
+        rows.stream()
+            .filter(r -> r.getKey().toStringUtf8().equals("row-final-check"))
+            .findFirst()
+            .orElse(null);
+    assertEquals("row-final-check", rowFinalCheck.getKey().toStringUtf8());
+    List<RowCell> cellsFinalCheck = rowFinalCheck.getCells(COLUMN_FAMILY_NAME_1, "col_final_1");
+    assertEquals(1, cellsFinalCheck.size());
+    assertEquals("val_final_1", cellsFinalCheck.get(0).getValue().toStringUtf8());
+  }
+}
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableWriteSchemaTransformProviderIT.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableWriteSchemaTransformProviderIT.java
index 1a60fe661b52..22159d5fb724 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableWriteSchemaTransformProviderIT.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/bigtable/BigtableWriteSchemaTransformProviderIT.java
@@ -42,6 +42,7 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.transforms.Create;
 import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionRowTuple;
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
@@ -180,7 +181,10 @@ public void testSetMutationsExistingColumn() {
             .withFieldValue("mutations", mutations)
             .build();
 
-    PCollectionRowTuple.of("input", p.apply(Create.of(Arrays.asList(mutationRow))))
+    PCollection<Row> inputPCollection = p.apply(Create.of(Arrays.asList(mutationRow)));
+    inputPCollection.setRowSchema(SCHEMA);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
         .apply(writeTransform);
     p.run().waitUntilFinish();
 
@@ -231,7 +235,10 @@ public void testSetMutationNewColumn() {
             .withFieldValue("mutations", mutations)
             .build();
 
-    PCollectionRowTuple.of("input", p.apply(Create.of(Arrays.asList(mutationRow))))
+    PCollection<Row> inputPCollection = p.apply(Create.of(Arrays.asList(mutationRow)));
+    inputPCollection.setRowSchema(SCHEMA);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
         .apply(writeTransform);
     p.run().waitUntilFinish();
 
@@ -275,7 +282,10 @@ public void testDeleteCellsFromColumn() {
             .withFieldValue("mutations", mutations)
             .build();
 
-    PCollectionRowTuple.of("input", p.apply(Create.of(Arrays.asList(mutationRow))))
+    PCollection<Row> inputPCollection = p.apply(Create.of(Arrays.asList(mutationRow)));
+    inputPCollection.setRowSchema(SCHEMA);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
         .apply(writeTransform);
     p.run().waitUntilFinish();
 
@@ -323,7 +333,10 @@ public void testDeleteCellsFromColumnWithTimestampRange() {
             .withFieldValue("mutations", mutations)
             .build();
 
-    PCollectionRowTuple.of("input", p.apply(Create.of(Arrays.asList(mutationRow))))
+    PCollection<Row> inputPCollection = p.apply(Create.of(Arrays.asList(mutationRow)));
+    inputPCollection.setRowSchema(SCHEMA);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
         .apply(writeTransform);
     p.run().waitUntilFinish();
 
@@ -363,7 +376,10 @@ public void testDeleteColumnFamily() {
             .withFieldValue("mutations", mutations)
             .build();
 
-    PCollectionRowTuple.of("input", p.apply(Create.of(Arrays.asList(mutationRow))))
+    PCollection<Row> inputPCollection = p.apply(Create.of(Arrays.asList(mutationRow)));
+    inputPCollection.setRowSchema(SCHEMA);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
         .apply(writeTransform);
     p.run().waitUntilFinish();
 
@@ -401,7 +417,10 @@ public void testDeleteRow() {
             .withFieldValue("mutations", mutations)
             .build();
 
-    PCollectionRowTuple.of("input", p.apply(Create.of(Arrays.asList(mutationRow))))
+    PCollection<Row> inputPCollection = p.apply(Create.of(Arrays.asList(mutationRow)));
+    inputPCollection.setRowSchema(SCHEMA);
+
+    PCollectionRowTuple.of("input", inputPCollection) // Use the schema-set PCollection
         .apply(writeTransform);
     p.run().waitUntilFinish();
 
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/datastore/V1TestUtil.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/datastore/V1TestUtil.java
index dbe689c05759..70bf061e9412 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/datastore/V1TestUtil.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/datastore/V1TestUtil.java
@@ -316,10 +316,7 @@ private void flushBatch() throws DatastoreException, InterruptedException {
           // Break if the commit threw no exception.
           break;
         } catch (DatastoreException exception) {
-          LOG.error(
-              "Error writing to the Datastore ({}): {}",
-              exception.getCode(),
-              exception.getMessage());
+          LOG.error("Error writing to the Datastore ({})", exception.getCode(), exception);
           if (!BackOffUtils.next(sleeper, backoff)) {
             LOG.error("Aborting after {} retries.", MAX_RETRIES);
             throw exception;
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsub/PreparePubsubWriteDoFnTest.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsub/PreparePubsubWriteDoFnTest.java
index a125a7b67e69..caae41aaab65 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsub/PreparePubsubWriteDoFnTest.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsub/PreparePubsubWriteDoFnTest.java
@@ -34,32 +34,30 @@
 @RunWith(JUnit4.class)
 public class PreparePubsubWriteDoFnTest implements Serializable {
   @Test
-  public void testValidatePubsubMessageSizeOnlyPayload() throws SizeLimitExceededException {
+  public void testValidatePubsubMessageOnlyPayload() throws SizeLimitExceededException {
     byte[] data = new byte[1024];
     PubsubMessage message = new PubsubMessage(data, null);
 
     int messageSize =
-        PreparePubsubWriteDoFn.validatePubsubMessageSize(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE);
+        PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE);
 
     assertEquals(data.length, messageSize);
   }
 
   @Test
-  public void testValidatePubsubMessageSizePayloadAndOrderingKey()
-      throws SizeLimitExceededException {
+  public void testValidatePubsubMessagePayloadAndOrderingKey() throws SizeLimitExceededException {
     byte[] data = new byte[1024];
     String orderingKey = "key";
     PubsubMessage message = new PubsubMessage(data, null, null, orderingKey);
 
     int messageSize =
-        PreparePubsubWriteDoFn.validatePubsubMessageSize(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE);
+        PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE);
 
     assertEquals(data.length + orderingKey.getBytes(StandardCharsets.UTF_8).length, messageSize);
   }
 
   @Test
-  public void testValidatePubsubMessageSizePayloadAndAttributes()
-      throws SizeLimitExceededException {
+  public void testValidatePubsubMessagePayloadAndAttributes() throws SizeLimitExceededException {
     byte[] data = new byte[1024];
     String attributeKey = "key";
     String attributeValue = "value";
@@ -67,7 +65,7 @@ public void testValidatePubsubMessageSizePayloadAndAttributes()
     PubsubMessage message = new PubsubMessage(data, attributes);
 
     int messageSize =
-        PreparePubsubWriteDoFn.validatePubsubMessageSize(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE);
+        PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE);
 
     assertEquals(
         data.length
@@ -78,32 +76,28 @@ public void testValidatePubsubMessageSizePayloadAndAttributes()
   }
 
   @Test
-  public void testValidatePubsubMessageSizePayloadTooLarge() {
+  public void testValidatePubsubMessagePayloadTooLarge() {
     byte[] data = new byte[(10 << 20) + 1];
     PubsubMessage message = new PubsubMessage(data, null);
 
     assertThrows(
         SizeLimitExceededException.class,
-        () ->
-            PreparePubsubWriteDoFn.validatePubsubMessageSize(
-                message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
+        () -> PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
   }
 
   @Test
-  public void testValidatePubsubMessageSizePayloadPlusOrderingKeyTooLarge() {
+  public void testValidatePubsubMessagePayloadPlusOrderingKeyTooLarge() {
     byte[] data = new byte[(10 << 20)];
     String orderingKey = "key";
     PubsubMessage message = new PubsubMessage(data, null, null, orderingKey);
 
     assertThrows(
         SizeLimitExceededException.class,
-        () ->
-            PreparePubsubWriteDoFn.validatePubsubMessageSize(
-                message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
+        () -> PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
   }
 
   @Test
-  public void testValidatePubsubMessageSizePayloadPlusAttributesTooLarge() {
+  public void testValidatePubsubMessagePayloadPlusAttributesTooLarge() {
     byte[] data = new byte[(10 << 20)];
     String attributeKey = "key";
     String attributeValue = "value";
@@ -112,13 +106,11 @@ public void testValidatePubsubMessageSizePayloadPlusAttributesTooLarge() {
 
     assertThrows(
         SizeLimitExceededException.class,
-        () ->
-            PreparePubsubWriteDoFn.validatePubsubMessageSize(
-                message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
+        () -> PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
   }
 
   @Test
-  public void testValidatePubsubMessageSizeAttributeKeyTooLarge() {
+  public void testValidatePubsubMessageAttributeKeyTooLarge() {
     byte[] data = new byte[1024];
     String attributeKey = RandomStringUtils.randomAscii(257);
     String attributeValue = "value";
@@ -127,13 +119,11 @@ public void testValidatePubsubMessageSizeAttributeKeyTooLarge() {
 
     assertThrows(
         SizeLimitExceededException.class,
-        () ->
-            PreparePubsubWriteDoFn.validatePubsubMessageSize(
-                message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
+        () -> PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
   }
 
   @Test
-  public void testValidatePubsubMessageSizeAttributeValueTooLarge() {
+  public void testValidatePubsubMessageAttributeValueTooLarge() {
     byte[] data = new byte[1024];
     String attributeKey = "key";
     String attributeValue = RandomStringUtils.randomAscii(1025);
@@ -142,33 +132,45 @@ public void testValidatePubsubMessageSizeAttributeValueTooLarge() {
 
     assertThrows(
         SizeLimitExceededException.class,
-        () ->
-            PreparePubsubWriteDoFn.validatePubsubMessageSize(
-                message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
+        () -> PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
   }
 
   @Test
-  public void testValidatePubsubMessageSizeOrderingKeyTooLarge() {
+  public void testValidatePubsubMessageOrderingKeyTooLarge() {
     byte[] data = new byte[1024];
     String orderingKey = RandomStringUtils.randomAscii(1025);
     PubsubMessage message = new PubsubMessage(data, null, null, orderingKey);
 
     assertThrows(
         SizeLimitExceededException.class,
-        () ->
-            PreparePubsubWriteDoFn.validatePubsubMessageSize(
-                message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
+        () -> PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
   }
 
   @Test
-  public void testValidatePubsubMessagePayloadTooLarge() {
-    byte[] data = new byte[(10 << 20) + 1];
+  public void testValidatePubsubMessageEmptyMessageRejectedNullMap() {
+    byte[] data = new byte[0];
     PubsubMessage message = new PubsubMessage(data, null);
+    assertThrows(
+        "non-empty payload or at least one attribute",
+        IllegalArgumentException.class,
+        () -> PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
+  }
 
+  @Test
+  public void testValidatePubsubMessageEmptyMessageRejectedEmptyMap() {
+    byte[] data = new byte[0];
+    PubsubMessage message = new PubsubMessage(data, ImmutableMap.of());
     assertThrows(
-        SizeLimitExceededException.class,
-        () ->
-            PreparePubsubWriteDoFn.validatePubsubMessageSize(
-                message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
+        "non-empty payload or at least one attribute",
+        IllegalArgumentException.class,
+        () -> PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE));
+  }
+
+  @Test
+  public void testValidatePubsubMessageEmptyDataButAttributesAllowed()
+      throws SizeLimitExceededException {
+    byte[] data = new byte[0];
+    PubsubMessage message = new PubsubMessage(data, ImmutableMap.of("key", "value"));
+    PreparePubsubWriteDoFn.validatePubsubMessage(message, PUBSUB_MESSAGE_MAX_TOTAL_SIZE);
   }
 }
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubIOTest.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubIOTest.java
index bec157ae83cc..3d9c65aa1376 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubIOTest.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsub/PubsubIOTest.java
@@ -125,15 +125,10 @@ public void testTopicValidationSuccess() throws Exception {
     PubsubIO.readStrings().fromTopic("projects/my-project/topics/AbC-1234-_.~%+-_.~%+-_.~%+-abc");
     PubsubIO.readStrings()
         .fromTopic(
-            new StringBuilder()
-                .append("projects/my-project/topics/A-really-long-one-")
-                .append(
-                    "111111111111111111111111111111111111111111111111111111111111111111111111111111111")
-                .append(
-                    "111111111111111111111111111111111111111111111111111111111111111111111111111111111")
-                .append(
-                    "11111111111111111111111111111111111111111111111111111111111111111111111111")
-                .toString());
+            "projects/my-project/topics/A-really-long-one-"
+                + "111111111111111111111111111111111111111111111111111111111111111111111111111111111"
+                + "111111111111111111111111111111111111111111111111111111111111111111111111111111111"
+                + "11111111111111111111111111111111111111111111111111111111111111111111111111");
   }
 
   @Test
@@ -147,15 +142,10 @@ public void testTopicValidationTooLong() throws Exception {
     thrown.expect(IllegalArgumentException.class);
     PubsubIO.readStrings()
         .fromTopic(
-            new StringBuilder()
-                .append("projects/my-project/topics/A-really-long-one-")
-                .append(
-                    "111111111111111111111111111111111111111111111111111111111111111111111111111111111")
-                .append(
-                    "111111111111111111111111111111111111111111111111111111111111111111111111111111111")
-                .append(
-                    "1111111111111111111111111111111111111111111111111111111111111111111111111111")
-                .toString());
+            "projects/my-project/topics/A-really-long-one-"
+                + "111111111111111111111111111111111111111111111111111111111111111111111111111111111"
+                + "111111111111111111111111111111111111111111111111111111111111111111111111111111111"
+                + "1111111111111111111111111111111111111111111111111111111111111111111111111111");
   }
 
   @Test
@@ -1008,10 +998,8 @@ public void testWriteTopicValidationSuccess() throws Exception {
     PubsubIO.writeStrings().to("projects/my-project/topics/AbC-1234-_.~%+-_.~%+-_.~%+-abc");
     PubsubIO.writeStrings()
         .to(
-            new StringBuilder()
-                .append("projects/my-project/topics/A-really-long-one-")
-                .append(RandomStringUtils.randomAlphanumeric(100))
-                .toString());
+            "projects/my-project/topics/A-really-long-one-"
+                + RandomStringUtils.randomAlphanumeric(100));
   }
 
   @Test
@@ -1025,10 +1013,8 @@ public void testWriteValidationTooLong() throws Exception {
     thrown.expect(IllegalArgumentException.class);
     PubsubIO.writeStrings()
         .to(
-            new StringBuilder()
-                .append("projects/my-project/topics/A-really-long-one-")
-                .append(RandomStringUtils.randomAlphanumeric(1000))
-                .toString());
+            "projects/my-project/topics/A-really-long-one-"
+                + RandomStringUtils.randomAlphanumeric(1000));
   }
 
   @Test
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/PubsubLiteDlqTest.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/PubsubLiteDlqTest.java
index 4acf0a1149e1..3d0ba336eeea 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/PubsubLiteDlqTest.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/pubsublite/internal/PubsubLiteDlqTest.java
@@ -282,8 +282,8 @@ public class PubsubLiteDlqTest {
               "address",
               Schema.FieldType.row(
                   Schema.builder()
-                      .addField("city", Schema.FieldType.STRING)
                       .addField("street", Schema.FieldType.STRING)
+                      .addField("city", Schema.FieldType.STRING)
                       .addField("state", Schema.FieldType.STRING)
                       .addField("zip_code", Schema.FieldType.STRING)
                       .build()))
@@ -554,6 +554,8 @@ public void testPubSubLiteErrorFnReadProto() {
             ParDo.of(new ErrorFn("Read-Error-Counter", protoValueMapper, errorSchema, Boolean.TRUE))
                 .withOutputTags(OUTPUT_TAG, TupleTagList.of(ERROR_TAG)));
 
+    // Unexpected behaviors occur if the PCollection schem differs from the schema generated in the
+    // conversion from Proto to Row.
     output.get(OUTPUT_TAG).setRowSchema(beamAttributeSchema);
     output.get(ERROR_TAG).setRowSchema(errorSchema);
 
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIOReadTest.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIOReadTest.java
index d82c50fd79a3..7abc6f9573a7 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIOReadTest.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerIOReadTest.java
@@ -827,7 +827,7 @@ private void checkMessage(String substring, @Nullable String message) {
     }
   }
 
-  private long getRequestMetricCount(HashMap<String, String> baseLabels) {
+  private long getRequestMetricCount(Map<String, String> baseLabels) {
     MonitoringInfoMetricName name =
         MonitoringInfoMetricName.named(MonitoringInfoConstants.Urns.API_REQUEST_COUNT, baseLabels);
     MetricsContainerImpl container =
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerReadIT.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerReadIT.java
index 38fc1887a887..d0164717e158 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerReadIT.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerReadIT.java
@@ -20,7 +20,6 @@
 import static org.junit.Assert.assertEquals;
 
 import com.google.api.gax.longrunning.OperationFuture;
-import com.google.cloud.spanner.BatchClient;
 import com.google.cloud.spanner.Database;
 import com.google.cloud.spanner.DatabaseAdminClient;
 import com.google.cloud.spanner.DatabaseClient;
@@ -45,12 +44,8 @@
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.testing.TestPipelineOptions;
 import org.apache.beam.sdk.transforms.Count;
-import org.apache.beam.sdk.transforms.Create;
 import org.apache.beam.sdk.transforms.MapElements;
-import org.apache.beam.sdk.transforms.ParDo;
 import org.apache.beam.sdk.transforms.SerializableFunction;
-import org.apache.beam.sdk.transforms.SimpleFunction;
-import org.apache.beam.sdk.transforms.View;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PCollectionView;
 import org.apache.beam.sdk.values.TypeDescriptor;
@@ -70,7 +65,6 @@
 public class SpannerReadIT {
 
   private static final int MAX_DB_NAME_LENGTH = 30;
-  private static final int CLEANUP_PROPAGATION_DELAY_MS = 5000;
 
   @Rule public final transient TestPipeline p = TestPipeline.create();
   @Rule public transient ExpectedException thrown = ExpectedException.none();
@@ -275,55 +269,6 @@ public void testReadFailsBadTable() throws Exception {
     p.run().waitUntilFinish();
   }
 
-  private static class CloseTransactionFn extends SimpleFunction<Transaction, Transaction> {
-    private final SpannerConfig spannerConfig;
-
-    private CloseTransactionFn(SpannerConfig spannerConfig) {
-      this.spannerConfig = spannerConfig;
-    }
-
-    @Override
-    public Transaction apply(Transaction tx) {
-      BatchClient batchClient = SpannerAccessor.getOrCreate(spannerConfig).getBatchClient();
-      batchClient.batchReadOnlyTransaction(tx.transactionId()).cleanup();
-      try {
-        // Wait for cleanup to propagate.
-        Thread.sleep(CLEANUP_PROPAGATION_DELAY_MS);
-      } catch (InterruptedException e) {
-        Thread.currentThread().interrupt();
-      }
-      return tx;
-    }
-  }
-
-  @Test
-  public void testReadFailsBadSession() throws Exception {
-
-    thrown.expect(new SpannerWriteIT.StackTraceContainsString("SpannerException"));
-    thrown.expect(new SpannerWriteIT.StackTraceContainsString("NOT_FOUND: Session not found"));
-
-    SpannerConfig spannerConfig = createSpannerConfig();
-
-    // This creates a transaction then closes the session.
-    // The (closed) transaction is then passed to SpannerIO.read() and should
-    // raise SessionNotFound errors.
-    PCollectionView<Transaction> tx =
-        p.apply("Transaction seed", Create.of(1))
-            .apply(
-                "Create transaction",
-                ParDo.of(new CreateTransactionFn(spannerConfig, TimestampBound.strong())))
-            .apply("Close Transaction", MapElements.via(new CloseTransactionFn(spannerConfig)))
-            .apply("As PCollectionView", View.asSingleton());
-    p.apply(
-        "read db",
-        SpannerIO.read()
-            .withSpannerConfig(spannerConfig)
-            .withTable(options.getTable())
-            .withColumns("Key", "Value")
-            .withTransaction(tx));
-    p.run().waitUntilFinish();
-  }
-
   @Test
   public void testQuery() throws Exception {
     SpannerConfig spannerConfig = createSpannerConfig();
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerTransformRegistrarTest.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerTransformRegistrarTest.java
index 3b38e7e528a3..666cda91f731 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerTransformRegistrarTest.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/SpannerTransformRegistrarTest.java
@@ -26,6 +26,7 @@
 import java.util.Map;
 import java.util.concurrent.TimeUnit;
 import java.util.stream.Stream;
+import org.apache.beam.sdk.io.gcp.spanner.SpannerTransformRegistrar.ChangeStreamReaderBuilder;
 import org.apache.beam.sdk.io.gcp.spanner.SpannerTransformRegistrar.InsertBuilder;
 import org.apache.beam.sdk.io.gcp.spanner.SpannerTransformRegistrar.ReadBuilder;
 import org.apache.beam.sdk.schemas.Schema;
@@ -48,22 +49,29 @@ public class SpannerTransformRegistrarTest {
   public static final String SPANNER_PROJECT = "spanner-project";
   public static final String SPANNER_TABLE = "spanner-table";
   public static final String SPANNER_SQL_QUERY = "SELECT * from spanner_table;";
+  public static final String SPANNER_CHANGE_STREAM_NAME = "spanner-change-stream-name";
+  public static final String SPANNER_CHANGE_STREAM_METADATA_INSTANCE =
+      "spanner-change-stream-instance";
+  public static final String SPANNER_CHANGE_STREAM_METADATA_DATABASE =
+      "spanner-change-stream-database";
   private SpannerTransformRegistrar spannerTransformRegistrar;
   private ReadBuilder readBuilder;
   private InsertBuilder writeBuilder;
+  private ChangeStreamReaderBuilder changeStreamReaderBuilder;
 
   @Before
   public void setup() {
     spannerTransformRegistrar = new SpannerTransformRegistrar();
     readBuilder = new ReadBuilder();
     writeBuilder = new InsertBuilder();
+    changeStreamReaderBuilder = new ChangeStreamReaderBuilder();
   }
 
   @Test
   public void testKnownBuilderInstances() {
     Map<String, ExternalTransformBuilder<?, ?, ?>> builderInstancesMap =
         spannerTransformRegistrar.knownBuilderInstances();
-    assertEquals(6, builderInstancesMap.size());
+    assertEquals(7, builderInstancesMap.size());
     assertThat(builderInstancesMap, IsMapContaining.hasKey(SpannerTransformRegistrar.INSERT_URN));
     assertThat(builderInstancesMap, IsMapContaining.hasKey(SpannerTransformRegistrar.UPDATE_URN));
     assertThat(builderInstancesMap, IsMapContaining.hasKey(SpannerTransformRegistrar.REPLACE_URN));
@@ -72,6 +80,9 @@ public void testKnownBuilderInstances() {
         IsMapContaining.hasKey(SpannerTransformRegistrar.INSERT_OR_UPDATE_URN));
     assertThat(builderInstancesMap, IsMapContaining.hasKey(SpannerTransformRegistrar.DELETE_URN));
     assertThat(builderInstancesMap, IsMapContaining.hasKey(SpannerTransformRegistrar.READ_URN));
+    assertThat(
+        builderInstancesMap,
+        IsMapContaining.hasKey(SpannerTransformRegistrar.READ_CHANGE_STREAM_URN));
   }
 
   @Test(expected = IllegalArgumentException.class)
@@ -207,4 +218,136 @@ private InsertBuilder.Configuration getBasicWriteConfiguration() {
     configuration.setMaxCumulativeBackoff(100L);
     return configuration;
   }
+
+  @Test(expected = IllegalArgumentException.class)
+  public void testChangeStreamReaderBuilderBuildExternalWithMissingMandatoryFields() {
+    changeStreamReaderBuilder.buildExternal(new ChangeStreamReaderBuilder.Configuration());
+  }
+
+  @Test(expected = IllegalArgumentException.class)
+  public void testChangeStreamReaderBuilderBuildExternalWithMissingDatabaseId() {
+    ChangeStreamReaderBuilder.Configuration configuration =
+        new ChangeStreamReaderBuilder.Configuration();
+    configuration.setProjectId(SPANNER_PROJECT);
+    configuration.setInstanceId(SPANNER_INSTANCE);
+    configuration.setChangeStreamName(SPANNER_CHANGE_STREAM_NAME);
+    configuration.setMetadataInstance(SPANNER_CHANGE_STREAM_METADATA_INSTANCE);
+    configuration.setMetadataDatabase(SPANNER_CHANGE_STREAM_METADATA_DATABASE);
+    changeStreamReaderBuilder.buildExternal(configuration);
+  }
+
+  @Test(expected = IllegalArgumentException.class)
+  public void testChangeStreamReaderBuilderBuildExternalWithMissingInstanceId() {
+    ChangeStreamReaderBuilder.Configuration configuration =
+        new ChangeStreamReaderBuilder.Configuration();
+    configuration.setProjectId(SPANNER_PROJECT);
+    configuration.setDatabaseId(SPANNER_DATABASE);
+    configuration.setChangeStreamName(SPANNER_CHANGE_STREAM_NAME);
+    configuration.setMetadataInstance(SPANNER_CHANGE_STREAM_METADATA_INSTANCE);
+    configuration.setMetadataDatabase(SPANNER_CHANGE_STREAM_METADATA_DATABASE);
+    changeStreamReaderBuilder.buildExternal(configuration);
+  }
+
+  @Test(expected = IllegalArgumentException.class)
+  public void testChangeStreamReaderBuilderBuildExternalWithMissingChangeStreamName() {
+    ChangeStreamReaderBuilder.Configuration configuration =
+        new ChangeStreamReaderBuilder.Configuration();
+    configuration.setProjectId(SPANNER_PROJECT);
+    configuration.setDatabaseId(SPANNER_DATABASE);
+    configuration.setInstanceId(SPANNER_INSTANCE);
+    configuration.setMetadataInstance(SPANNER_CHANGE_STREAM_METADATA_INSTANCE);
+    configuration.setMetadataDatabase(SPANNER_CHANGE_STREAM_METADATA_DATABASE);
+    changeStreamReaderBuilder.buildExternal(configuration);
+  }
+
+  @Test(expected = IllegalArgumentException.class)
+  public void testChangeStreamReaderBuilderBuildExternalWithMissingMetadataInstance() {
+    ChangeStreamReaderBuilder.Configuration configuration =
+        new ChangeStreamReaderBuilder.Configuration();
+    configuration.setProjectId(SPANNER_PROJECT);
+    configuration.setDatabaseId(SPANNER_DATABASE);
+    configuration.setInstanceId(SPANNER_INSTANCE);
+    configuration.setChangeStreamName(SPANNER_CHANGE_STREAM_NAME);
+    configuration.setMetadataDatabase(SPANNER_CHANGE_STREAM_METADATA_DATABASE);
+    changeStreamReaderBuilder.buildExternal(configuration);
+  }
+
+  @Test(expected = IllegalArgumentException.class)
+  public void testChangeStreamReaderBuilderBuildExternalWithMissingMetadataDatabase() {
+    ChangeStreamReaderBuilder.Configuration configuration =
+        new ChangeStreamReaderBuilder.Configuration();
+    configuration.setProjectId(SPANNER_PROJECT);
+    configuration.setDatabaseId(SPANNER_DATABASE);
+    configuration.setInstanceId(SPANNER_INSTANCE);
+    configuration.setChangeStreamName(SPANNER_CHANGE_STREAM_NAME);
+    configuration.setMetadataInstance(SPANNER_CHANGE_STREAM_METADATA_INSTANCE);
+    changeStreamReaderBuilder.buildExternal(configuration);
+  }
+
+  @Test
+  public void testChangeStreamReaderBuilderBuildExternalWithRequiredFields() {
+    ChangeStreamReaderBuilder.Configuration configuration =
+        new ChangeStreamReaderBuilder.Configuration();
+
+    configuration.setProjectId(SPANNER_PROJECT);
+    configuration.setDatabaseId(SPANNER_DATABASE);
+    configuration.setInstanceId(SPANNER_INSTANCE);
+    configuration.setChangeStreamName(SPANNER_CHANGE_STREAM_NAME);
+    configuration.setMetadataInstance(SPANNER_CHANGE_STREAM_METADATA_INSTANCE);
+    configuration.setMetadataDatabase(SPANNER_CHANGE_STREAM_METADATA_DATABASE);
+
+    PTransform<PBegin, PCollection<String>> changeStreamReaderTransform =
+        changeStreamReaderBuilder.buildExternal(configuration);
+    assertNotNull(changeStreamReaderTransform);
+  }
+
+  @Test
+  public void testChangeStreamReaderBuilderBuildExternalWithAllFields() {
+    String startAt = "2023-01-01T00:00:00Z";
+    String endAt = "2023-01-02T00:00:00Z";
+    String metadataTable = "meta-table";
+    String rpcPriority = "HIGH";
+    String refreshRate = "PT30S";
+
+    ChangeStreamReaderBuilder.Configuration configuration =
+        new ChangeStreamReaderBuilder.Configuration();
+
+    configuration.setProjectId(SPANNER_PROJECT);
+    configuration.setDatabaseId(SPANNER_DATABASE);
+    configuration.setInstanceId(SPANNER_INSTANCE);
+    configuration.setChangeStreamName(SPANNER_CHANGE_STREAM_NAME);
+    configuration.setMetadataInstance(SPANNER_CHANGE_STREAM_METADATA_INSTANCE);
+    configuration.setMetadataDatabase(SPANNER_CHANGE_STREAM_METADATA_DATABASE);
+    configuration.setInclusiveStartAt(startAt);
+    configuration.setInclusiveEndAt(endAt);
+    configuration.setMetadataTable(metadataTable);
+    configuration.setRpcPriority(rpcPriority);
+    configuration.setWatermarkRefreshRate(refreshRate);
+
+    PTransform<PBegin, PCollection<String>> changeStreamReaderTransform =
+        changeStreamReaderBuilder.buildExternal(configuration);
+    assertNotNull(changeStreamReaderTransform);
+  }
+
+  @Test
+  public void testChangeStreamReaderBuilderBuildExternalWithNullOptionalValues() {
+    ChangeStreamReaderBuilder.Configuration configuration =
+        new ChangeStreamReaderBuilder.Configuration();
+
+    configuration.setProjectId(SPANNER_PROJECT);
+    configuration.setDatabaseId(SPANNER_DATABASE);
+    configuration.setInstanceId(SPANNER_INSTANCE);
+    configuration.setChangeStreamName(SPANNER_CHANGE_STREAM_NAME);
+    configuration.setMetadataInstance(SPANNER_CHANGE_STREAM_METADATA_INSTANCE);
+    configuration.setMetadataDatabase(SPANNER_CHANGE_STREAM_METADATA_DATABASE);
+    configuration.setInclusiveStartAt(null);
+    configuration.setInclusiveEndAt(null);
+    configuration.setMetadataTable(null);
+    configuration.setRpcPriority(null);
+    configuration.setWatermarkRefreshRate(null);
+
+    PTransform<PBegin, PCollection<String>> changeStreamReaderTransform =
+        changeStreamReaderBuilder.buildExternal(configuration);
+    assertNotNull(changeStreamReaderTransform);
+  }
 }
diff --git a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/it/IntegrationTestEnv.java b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/it/IntegrationTestEnv.java
index 6d3f12ac8e53..dc10e68c1e9d 100644
--- a/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/it/IntegrationTestEnv.java
+++ b/sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/changestreams/it/IntegrationTestEnv.java
@@ -281,7 +281,6 @@ void createRoleAndGrantPrivileges(String table, String changeStream)
                     + DATABASE_ROLE),
             null)
         .get(TIMEOUT_MINUTES, TimeUnit.MINUTES);
-    return;
   }
 
   String getProjectId() {
diff --git a/sdks/java/io/hadoop-format/src/main/java/org/apache/beam/sdk/io/hadoop/format/HadoopFormatIO.java b/sdks/java/io/hadoop-format/src/main/java/org/apache/beam/sdk/io/hadoop/format/HadoopFormatIO.java
index e7ad13c97c0c..155bf2d4a77f 100644
--- a/sdks/java/io/hadoop-format/src/main/java/org/apache/beam/sdk/io/hadoop/format/HadoopFormatIO.java
+++ b/sdks/java/io/hadoop-format/src/main/java/org/apache/beam/sdk/io/hadoop/format/HadoopFormatIO.java
@@ -555,9 +555,8 @@ private void validateConfiguration(Configuration configuration) {
       if (configuration.get("mapreduce.job.inputformat.class").endsWith("DBInputFormat")) {
         checkArgument(
             configuration.get(DBConfiguration.INPUT_ORDER_BY_PROPERTY) != null,
-            "Configuration must contain \""
-                + DBConfiguration.INPUT_ORDER_BY_PROPERTY
-                + "\" when using DBInputFormat");
+            "Configuration must contain \"%s\" when using DBInputFormat",
+            DBConfiguration.INPUT_ORDER_BY_PROPERTY);
       }
     }
 
@@ -1061,8 +1060,7 @@ public static class SerializableSplit implements Serializable {
     public SerializableSplit() {}
 
     public SerializableSplit(InputSplit split) {
-      checkArgument(
-          split instanceof Writable, String.format("Split is not of type Writable: %s", split));
+      checkArgument(split instanceof Writable, "Split is not of type Writable: %s", split);
       this.inputSplit = split;
     }
 
@@ -1684,14 +1682,17 @@ private void validateConfiguration(Configuration conf) {
       checkArgument(conf != null, "Configuration can not be null");
       checkArgument(
           conf.get(OUTPUT_FORMAT_CLASS_ATTR) != null,
-          "Configuration must contain \"" + OUTPUT_FORMAT_CLASS_ATTR + "\"");
+          "Configuration must contain \"%s\"",
+          OUTPUT_FORMAT_CLASS_ATTR);
       checkArgument(
           conf.get(OUTPUT_KEY_CLASS) != null,
-          "Configuration must contain \"" + OUTPUT_KEY_CLASS + "\"");
+          "Configuration must contain \"%s\"",
+          OUTPUT_KEY_CLASS);
       checkArgument(
           conf.get(OUTPUT_VALUE_CLASS) != null,
-          "Configuration must contain \"" + OUTPUT_VALUE_CLASS + "\"");
-      checkArgument(conf.get(JOB_ID) != null, "Configuration must contain \"" + JOB_ID + "\"");
+          "Configuration must contain \"%s\"",
+          OUTPUT_VALUE_CLASS);
+      checkArgument(conf.get(JOB_ID) != null, "Configuration must contain \"%s\"", JOB_ID);
     }
 
     /**
diff --git a/sdks/java/io/hbase/build.gradle b/sdks/java/io/hbase/build.gradle
index 07014f2d5e3b..a361a593b4fe 100644
--- a/sdks/java/io/hbase/build.gradle
+++ b/sdks/java/io/hbase/build.gradle
@@ -34,7 +34,7 @@ test {
   jvmArgs "-Dtest.build.data.basedirectory=build/test-data"
 }
 
-def hbase_version = "2.6.1-hadoop3"
+def hbase_version = "2.6.3-hadoop3"
 
 dependencies {
   implementation library.java.vendored_guava_32_1_2_jre
diff --git a/sdks/java/io/hbase/src/main/java/org/apache/beam/sdk/io/hbase/HBaseRowMutationsCoder.java b/sdks/java/io/hbase/src/main/java/org/apache/beam/sdk/io/hbase/HBaseRowMutationsCoder.java
index c7ecad045a96..6d66cee21109 100644
--- a/sdks/java/io/hbase/src/main/java/org/apache/beam/sdk/io/hbase/HBaseRowMutationsCoder.java
+++ b/sdks/java/io/hbase/src/main/java/org/apache/beam/sdk/io/hbase/HBaseRowMutationsCoder.java
@@ -95,9 +95,7 @@ public List<? extends Coder<?>> getCoderArguments() {
    * @throws @UnknownKeyFor@NonNull@Initialized NonDeterministicException
    */
   @Override
-  public void verifyDeterministic() {
-    return;
-  }
+  public void verifyDeterministic() {}
 
   private static MutationType getType(Mutation mutation) {
     if (mutation instanceof Put) {
diff --git a/sdks/java/io/hcatalog/src/main/java/org/apache/beam/sdk/io/hcatalog/HCatalogIO.java b/sdks/java/io/hcatalog/src/main/java/org/apache/beam/sdk/io/hcatalog/HCatalogIO.java
index ba2674653f6b..98b13134e3b0 100644
--- a/sdks/java/io/hcatalog/src/main/java/org/apache/beam/sdk/io/hcatalog/HCatalogIO.java
+++ b/sdks/java/io/hcatalog/src/main/java/org/apache/beam/sdk/io/hcatalog/HCatalogIO.java
@@ -258,7 +258,7 @@ public Read withTerminationCondition(TerminationCondition<Read, ?> terminationCo
     }
 
     Read withSplitId(int splitId) {
-      checkArgument(splitId >= 0, "Invalid split id-" + splitId);
+      checkArgument(splitId >= 0, "Invalid split id-%s", splitId);
       return toBuilder().setSplitId(splitId).build();
     }
 
diff --git a/sdks/java/io/iceberg/build.gradle b/sdks/java/io/iceberg/build.gradle
index e4e7e2f1095b..42a624a4c5fb 100644
--- a/sdks/java/io/iceberg/build.gradle
+++ b/sdks/java/io/iceberg/build.gradle
@@ -23,26 +23,23 @@ import java.util.stream.Collectors
 plugins { id 'org.apache.beam.module' }
 applyJavaNature(
         automaticModuleName: 'org.apache.beam.sdk.io.iceberg',
+        // iceberg ended support for Java 8 in 1.7.0
+        requireJavaVersion: JavaVersion.VERSION_11,
 )
 
 description = "Apache Beam :: SDKs :: Java :: IO :: Iceberg"
 ext.summary = "Integration with Iceberg data warehouses."
 
 def hadoopVersions = [
-    "2102": "2.10.2",
-    "324": "3.2.4",
     "336": "3.3.6",
     "341": "3.4.1",
 ]
 
 hadoopVersions.each {kv -> configurations.create("hadoopVersion$kv.key")}
 
-// we cannot upgrade this since the newer iceberg requires Java 11
-// many other modules like examples/expansion use Java 8 and have the iceberg dependency
-// def iceberg_version = "1.9.0"
-def iceberg_version = "1.6.1"
-def parquet_version = "1.15.2"
-def orc_version = "1.9.2"
+def iceberg_version = "1.10.0"
+def parquet_version = "1.16.0"
+def orc_version = "1.9.6"
 def hive_version = "3.1.3"
 
 dependencies {
@@ -64,7 +61,7 @@ dependencies {
     // TODO(https://github.com/apache/beam/issues/21156): Determine how to build without this dependency
     provided "org.immutables:value:2.8.8"
     permitUnusedDeclared "org.immutables:value:2.8.8"
-    implementation library.java.vendored_calcite_1_28_0
+    implementation library.java.vendored_calcite_1_40_0
     runtimeOnly "org.apache.iceberg:iceberg-gcp:$iceberg_version"
     runtimeOnly library.java.bigdataoss_gcs_connector
     runtimeOnly library.java.hadoop_client
@@ -107,6 +104,11 @@ dependencies {
     }
 }
 
+configurations.all {
+  // iceberg-core needs avro:1.12.0
+  resolutionStrategy.force 'org.apache.avro:avro:1.12.0'
+}
+
 hadoopVersions.each {kv ->
     configurations."hadoopVersion$kv.key" {
         resolutionStrategy {
diff --git a/sdks/java/io/iceberg/hive/build.gradle b/sdks/java/io/iceberg/hive/build.gradle
index 480707b128b1..723036fb1183 100644
--- a/sdks/java/io/iceberg/hive/build.gradle
+++ b/sdks/java/io/iceberg/hive/build.gradle
@@ -26,7 +26,7 @@ description = "Apache Beam :: SDKs :: Java :: IO :: Iceberg :: Hive"
 ext.summary = "Runtime dependencies needed for Hive catalog integration."
 
 def hive_version = "3.1.3"
-def hbase_version = "2.6.1-hadoop3"
+def hbase_version = "2.6.3-hadoop3"
 def hadoop_version = "3.4.1"
 def iceberg_version = "1.6.1"
 def avatica_version = "1.25.0"
diff --git a/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/FilterUtils.java b/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/FilterUtils.java
index 614c45fcf624..fd008701c548 100644
--- a/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/FilterUtils.java
+++ b/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/FilterUtils.java
@@ -17,6 +17,8 @@
  */
 package org.apache.beam.sdk.io.iceberg;
 
+import static java.time.format.DateTimeFormatter.ISO_LOCAL_DATE;
+import static java.time.format.DateTimeFormatter.ISO_LOCAL_TIME;
 import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;
 
@@ -24,6 +26,10 @@
 import java.time.LocalDate;
 import java.time.LocalDateTime;
 import java.time.LocalTime;
+import java.time.format.DateTimeFormatter;
+import java.time.format.DateTimeFormatterBuilder;
+import java.time.format.DateTimeParseException;
+import java.util.Arrays;
 import java.util.HashSet;
 import java.util.List;
 import java.util.Map;
@@ -32,15 +38,19 @@
 import java.util.function.BiFunction;
 import java.util.stream.Collectors;
 import org.apache.beam.sdk.annotations.Internal;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlBasicCall;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlKind;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlLiteral;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNodeList;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlOperator;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParseException;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParser;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlBasicCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNodeList;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlOperator;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParseException;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParser;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.DateString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.TimeString;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.TimestampString;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 import org.apache.iceberg.Schema;
@@ -143,7 +153,17 @@ static Expression convert(@Nullable String filter, Schema schema) {
   }
 
   private static Expression convert(SqlNode expression, Schema schema) throws SqlParseException {
-    checkArgument(expression instanceof SqlBasicCall);
+    if (expression instanceof SqlIdentifier) {
+      String fieldName = ((SqlIdentifier) expression).getSimple();
+      Types.NestedField field = schema.caseInsensitiveFindField(fieldName);
+      if (field.type().equals(Types.BooleanType.get())) {
+        return Expressions.equal(field.name(), true);
+      }
+    }
+    checkArgument(
+        expression instanceof SqlBasicCall,
+        String.format(
+            "Expected SqlBasicCall, got %s: %s", expression.getClass().getName(), expression));
     SqlBasicCall call = (SqlBasicCall) expression;
 
     SqlOperator op = call.getOperator();
@@ -312,6 +332,7 @@ private static Expression convertFieldAndLiteral(
   }
 
   private static Object convertLiteral(SqlLiteral literal, String field, TypeID type) {
+    SqlTypeName typeName = literal.getTypeName();
     switch (type) {
       case BOOLEAN:
         return literal.getValueAs(Boolean.class);
@@ -328,17 +349,69 @@ private static Object convertLiteral(SqlLiteral literal, String field, TypeID ty
       case STRING:
         return literal.getValueAs(String.class);
       case DATE:
-        LocalDate date = LocalDate.parse(literal.getValueAs(String.class));
+        LocalDate date;
+        if (SqlTypeName.STRING_TYPES.contains(typeName) || SqlTypeName.UNKNOWN.equals(typeName)) {
+          date = LocalDate.parse(literal.getValueAs(String.class));
+        } else if (SqlTypeName.DATE.equals(typeName)) {
+          DateString dateValue = literal.getValueAs(DateString.class);
+          date = LocalDate.parse(dateValue.toString());
+        } else {
+          throw new IllegalArgumentException("Unexpected date type: " + literal.getTypeName());
+        }
         return DateTimeUtil.daysFromDate(date);
       case TIME:
-        LocalTime time = LocalTime.parse(literal.getValueAs(String.class));
+        LocalTime time;
+        if (SqlTypeName.STRING_TYPES.contains(typeName) || SqlTypeName.UNKNOWN.equals(typeName)) {
+          time = LocalTime.parse(literal.getValueAs(String.class));
+        } else if (SqlTypeName.TIME.equals(typeName)) {
+          TimeString timeString = literal.getValueAs(TimeString.class);
+          time = LocalTime.parse(timeString.toString());
+        } else {
+          throw new IllegalArgumentException("Unexpected date type: " + literal.getTypeName());
+        }
         return DateTimeUtil.microsFromTime(time);
       case TIMESTAMP:
-        LocalDateTime dateTime = LocalDateTime.parse(literal.getValueAs(String.class));
-        return DateTimeUtil.microsFromTimestamp(dateTime);
+        LocalDateTime datetime;
+        if (SqlTypeName.STRING_TYPES.contains(typeName) || SqlTypeName.UNKNOWN.equals(typeName)) {
+          String value = literal.getValueAs(String.class);
+          datetime = getLocalDateTime(value);
+        } else if (SqlTypeName.DATE.equals(typeName)) {
+          DateString dateString = literal.getValueAs(DateString.class);
+          datetime = LocalDateTime.of(LocalDate.parse(dateString.toString()), LocalTime.MIN);
+        } else if (SqlTypeName.TIMESTAMP.equals(typeName)) {
+          TimestampString timestampString = literal.getValueAs(TimestampString.class);
+          datetime = getLocalDateTime(timestampString.toString());
+        } else {
+          throw new IllegalArgumentException("Unexpected timestamp type: " + literal.getTypeName());
+        }
+        return DateTimeUtil.microsFromTimestamp(datetime);
       default:
         throw new IllegalArgumentException(
             String.format("Unsupported filter type in field '%s': %s", field, type));
     }
   }
+
+  private static LocalDateTime getLocalDateTime(String value) {
+    LocalDateTime datetime;
+    for (DateTimeFormatter formatter : DATE_TIME_FORMATTERS) {
+      try {
+        datetime = LocalDateTime.parse(value, formatter);
+        return datetime;
+      } catch (DateTimeParseException ignored) {
+      }
+    }
+    return LocalDateTime.of(LocalDate.parse(value), LocalTime.MIN);
+  }
+
+  private static final List<DateTimeFormatter> DATE_TIME_FORMATTERS =
+      Arrays.asList(
+          DateTimeFormatter.ISO_LOCAL_DATE_TIME, // e.g., 2023-10-26T10:30:00[.SSSSSSSSS]
+          new DateTimeFormatterBuilder()
+              .parseCaseInsensitive()
+              .append(ISO_LOCAL_DATE)
+              .appendLiteral(' ')
+              .append(ISO_LOCAL_TIME)
+              .toFormatter(), // e.g. 2023-10-26 10:30:00[.SSSSSSSSS]
+          ISO_LOCAL_DATE // For cases where you only have a date, then combine with a default time
+          );
 }
diff --git a/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/IcebergCatalogConfig.java b/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/IcebergCatalogConfig.java
index 7929d028bcdc..96357b44e54b 100644
--- a/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/IcebergCatalogConfig.java
+++ b/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/IcebergCatalogConfig.java
@@ -21,13 +21,20 @@
 import java.io.Serializable;
 import java.util.List;
 import java.util.Map;
+import java.util.Set;
+import java.util.stream.Collectors;
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.util.ReleaseInfo;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Splitter;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Maps;
 import org.apache.hadoop.conf.Configuration;
 import org.apache.iceberg.CatalogUtil;
 import org.apache.iceberg.PartitionSpec;
 import org.apache.iceberg.catalog.Catalog;
+import org.apache.iceberg.catalog.Namespace;
+import org.apache.iceberg.catalog.SupportsNamespaces;
 import org.apache.iceberg.catalog.TableIdentifier;
 import org.apache.iceberg.exceptions.AlreadyExistsException;
 import org.checkerframework.checker.nullness.qual.MonotonicNonNull;
@@ -83,6 +90,51 @@ public org.apache.iceberg.catalog.Catalog catalog() {
     return cachedCatalog;
   }
 
+  private void checkSupportsNamespaces() {
+    Preconditions.checkState(
+        catalog() instanceof SupportsNamespaces,
+        "Catalog '%s' does not support handling namespaces.",
+        catalog().name());
+  }
+
+  public boolean createNamespace(String namespace) {
+    checkSupportsNamespaces();
+    String[] components = Iterables.toArray(Splitter.on('.').split(namespace), String.class);
+
+    try {
+      ((SupportsNamespaces) catalog()).createNamespace(Namespace.of(components));
+      return true;
+    } catch (AlreadyExistsException e) {
+      return false;
+    }
+  }
+
+  public Set<String> listNamespaces() {
+    checkSupportsNamespaces();
+
+    return ((SupportsNamespaces) catalog())
+        .listNamespaces().stream().map(Namespace::toString).collect(Collectors.toSet());
+  }
+
+  public boolean dropNamespace(String namespace, boolean cascade) {
+    checkSupportsNamespaces();
+
+    String[] components = Iterables.toArray(Splitter.on('.').split(namespace), String.class);
+    Namespace ns = Namespace.of(components);
+
+    if (!((SupportsNamespaces) catalog()).namespaceExists(ns)) {
+      return false;
+    }
+
+    // Cascade will delete all contained tables first
+    if (cascade) {
+      catalog().listTables(ns).forEach(catalog()::dropTable);
+    }
+
+    // Drop the namespace
+    return ((SupportsNamespaces) catalog()).dropNamespace(Namespace.of(components));
+  }
+
   public void createTable(
       String tableIdentifier, Schema tableSchema, @Nullable List<String> partitionFields) {
     TableIdentifier icebergIdentifier = TableIdentifier.parse(tableIdentifier);
@@ -105,6 +157,12 @@ public boolean dropTable(String tableIdentifier) {
     return catalog().dropTable(icebergIdentifier);
   }
 
+  public Set<String> listTables(String namespace) {
+    return catalog().listTables(Namespace.of(namespace)).stream()
+        .map(TableIdentifier::name)
+        .collect(Collectors.toSet());
+  }
+
   @AutoValue.Builder
   public abstract static class Builder {
     public abstract Builder setCatalogName(@Nullable String catalogName);
diff --git a/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/ReadUtils.java b/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/ReadUtils.java
index 7790ad941fc3..4b127fcdef22 100644
--- a/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/ReadUtils.java
+++ b/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/ReadUtils.java
@@ -39,6 +39,7 @@
 import org.apache.iceberg.Table;
 import org.apache.iceberg.TableProperties;
 import org.apache.iceberg.data.IdentityPartitionConverters;
+import org.apache.iceberg.data.InternalRecordWrapper;
 import org.apache.iceberg.data.Record;
 import org.apache.iceberg.data.parquet.GenericParquetReaders;
 import org.apache.iceberg.encryption.EncryptedFiles;
@@ -198,10 +199,12 @@ static List<SnapshotInfo> snapshotsBetween(
 
   public static CloseableIterable<Record> maybeApplyFilter(
       CloseableIterable<Record> iterable, IcebergScanConfig scanConfig) {
+    InternalRecordWrapper wrapper =
+        new InternalRecordWrapper(scanConfig.getRequiredSchema().asStruct());
     Expression filter = scanConfig.getFilter();
     Evaluator evaluator = scanConfig.getEvaluator();
     if (filter != null && evaluator != null && filter.op() != Expression.Operation.TRUE) {
-      return CloseableIterable.filter(iterable, evaluator::eval);
+      return CloseableIterable.filter(iterable, record -> evaluator.eval(wrapper.wrap(record)));
     }
     return iterable;
   }
diff --git a/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/RecordWriter.java b/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/RecordWriter.java
index 0b32274d2495..a2425171ce91 100644
--- a/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/RecordWriter.java
+++ b/sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/RecordWriter.java
@@ -97,7 +97,7 @@ class RecordWriter {
       case PARQUET:
         icebergDataWriter =
             Parquet.writeData(outputFile)
-                .createWriterFunc(GenericParquetWriter::buildWriter)
+                .createWriterFunc(GenericParquetWriter::create)
                 .schema(table.schema())
                 .withSpec(table.spec())
                 .withPartition(partitionKey)
diff --git a/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/FilterUtilsTest.java b/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/FilterUtilsTest.java
index ff6383d4d046..893e24b61559 100644
--- a/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/FilterUtilsTest.java
+++ b/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/FilterUtilsTest.java
@@ -47,7 +47,7 @@
 import java.util.Set;
 import java.util.stream.Collectors;
 import java.util.stream.IntStream;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.commons.lang3.tuple.Pair;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.commons.lang3.tuple.Pair;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Splitter;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
@@ -114,25 +114,59 @@ public void testLessThan() {
         .withFieldType(Types.StringType.get())
         .validate();
 
-    // date
+    // date string
     TestCase.expecting(lessThan("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
         .fromFilter("\"field_1\" < '2025-05-03'")
         .withFieldType(Types.DateType.get())
         .validate();
 
-    // time
+    // date
+    TestCase.expecting(lessThan("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
+        .fromFilter("\"field_1\" < DATE '2025-05-03'")
+        .withFieldType(Types.DateType.get())
+        .validate();
+
+    // time string
     TestCase.expecting(lessThan("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
         .fromFilter("\"field_1\" < '10:30:05.123'")
         .withFieldType(Types.TimeType.get())
         .validate();
 
-    // datetime
+    // time
+    TestCase.expecting(lessThan("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
+        .fromFilter("\"field_1\" < TIME '10:30:05.123'")
+        .withFieldType(Types.TimeType.get())
+        .validate();
+
+    // datetime - timestamp string
     TestCase.expecting(
             lessThan(
                 "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
         .fromFilter("\"field_1\" < '2025-05-03T10:30:05.123'")
         .withFieldType(Types.TimestampType.withoutZone())
         .validate();
+
+    // datetime - timestamp
+    TestCase.expecting(
+            lessThan(
+                "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
+        .fromFilter("\"field_1\" < TIMESTAMP '2025-05-03 10:30:05.123'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date string
+    TestCase.expecting(
+            lessThan("field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" < '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date
+    TestCase.expecting(
+            lessThan("field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" < DATE '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
   }
 
   @Test
@@ -155,25 +189,61 @@ public void testLessThanOrEqual() {
         .withFieldType(Types.StringType.get())
         .validate();
 
-    // date
+    // date string
     TestCase.expecting(lessThanOrEqual("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
         .fromFilter("\"field_1\" <= '2025-05-03'")
         .withFieldType(Types.DateType.get())
         .validate();
 
-    // time
+    // date
+    TestCase.expecting(lessThanOrEqual("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
+        .fromFilter("\"field_1\" <= DATE '2025-05-03'")
+        .withFieldType(Types.DateType.get())
+        .validate();
+
+    // time string
     TestCase.expecting(lessThanOrEqual("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
         .fromFilter("\"field_1\" <= '10:30:05.123'")
         .withFieldType(Types.TimeType.get())
         .validate();
 
-    // datetime
+    // time
+    TestCase.expecting(lessThanOrEqual("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
+        .fromFilter("\"field_1\" <= TIME '10:30:05.123'")
+        .withFieldType(Types.TimeType.get())
+        .validate();
+
+    // datetime - timestamp string
     TestCase.expecting(
             lessThanOrEqual(
                 "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
         .fromFilter("\"field_1\" <= '2025-05-03T10:30:05.123'")
         .withFieldType(Types.TimestampType.withoutZone())
         .validate();
+
+    // datetime - timestamp
+    TestCase.expecting(
+            lessThanOrEqual(
+                "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
+        .fromFilter("\"field_1\" <= TIMESTAMP '2025-05-03 10:30:05.123'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date string
+    TestCase.expecting(
+            lessThanOrEqual(
+                "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" <= '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date
+    TestCase.expecting(
+            lessThanOrEqual(
+                "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" <= DATE '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
   }
 
   @Test
@@ -196,25 +266,59 @@ public void testGreaterThan() {
         .withFieldType(Types.StringType.get())
         .validate();
 
-    // date
+    // date string
     TestCase.expecting(greaterThan("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
         .fromFilter("\"field_1\" > '2025-05-03'")
         .withFieldType(Types.DateType.get())
         .validate();
 
-    // time
+    // date
+    TestCase.expecting(greaterThan("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
+        .fromFilter("\"field_1\" > DATE '2025-05-03'")
+        .withFieldType(Types.DateType.get())
+        .validate();
+
+    // time string
     TestCase.expecting(greaterThan("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
         .fromFilter("\"field_1\" > '10:30:05.123'")
         .withFieldType(Types.TimeType.get())
         .validate();
 
-    // datetime
+    // time
+    TestCase.expecting(greaterThan("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
+        .fromFilter("\"field_1\" > TIME '10:30:05.123'")
+        .withFieldType(Types.TimeType.get())
+        .validate();
+
+    // datetime - timestamp string
     TestCase.expecting(
             greaterThan(
                 "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
         .fromFilter("\"field_1\" > '2025-05-03T10:30:05.123'")
         .withFieldType(Types.TimestampType.withoutZone())
         .validate();
+
+    // datetime - timestamp
+    TestCase.expecting(
+            greaterThan(
+                "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
+        .fromFilter("\"field_1\" > TIMESTAMP '2025-05-03 10:30:05.123'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date string
+    TestCase.expecting(
+            greaterThan("field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" > '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date
+    TestCase.expecting(
+            greaterThan("field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" > DATE '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
   }
 
   @Test
@@ -237,26 +341,63 @@ public void testGreaterThanOrEqual() {
         .withFieldType(Types.StringType.get())
         .validate();
 
-    // date
+    // date string
     TestCase.expecting(greaterThanOrEqual("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
         .fromFilter("\"field_1\" >= '2025-05-03'")
         .withFieldType(Types.DateType.get())
         .validate();
 
-    // time
+    // date
+    TestCase.expecting(greaterThanOrEqual("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
+        .fromFilter("\"field_1\" >= DATE '2025-05-03'")
+        .withFieldType(Types.DateType.get())
+        .validate();
+
+    // time string
     TestCase.expecting(
             greaterThanOrEqual("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
         .fromFilter("\"field_1\" >= '10:30:05.123'")
         .withFieldType(Types.TimeType.get())
         .validate();
 
-    // datetime
+    // time
+    TestCase.expecting(
+            greaterThanOrEqual("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
+        .fromFilter("\"field_1\" >= TIME '10:30:05.123'")
+        .withFieldType(Types.TimeType.get())
+        .validate();
+
+    // datetime - timestamp string
     TestCase.expecting(
             greaterThanOrEqual(
                 "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
         .fromFilter("\"field_1\" >= '2025-05-03T10:30:05.123'")
         .withFieldType(Types.TimestampType.withoutZone())
         .validate();
+
+    // datetime - timestamp
+    TestCase.expecting(
+            greaterThanOrEqual(
+                "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
+        .fromFilter("\"field_1\" >= TIMESTAMP '2025-05-03 10:30:05.123'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date string
+    TestCase.expecting(
+            greaterThanOrEqual(
+                "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" >= '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date
+    TestCase.expecting(
+            greaterThanOrEqual(
+                "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" >= DATE '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
   }
 
   @Test
@@ -279,24 +420,57 @@ public void testEquals() {
         .withFieldType(Types.StringType.get())
         .validate();
 
-    // date
+    // date string
     TestCase.expecting(equal("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
         .fromFilter("\"field_1\" = '2025-05-03'")
         .withFieldType(Types.DateType.get())
         .validate();
 
-    // time
+    // date
+    TestCase.expecting(equal("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
+        .fromFilter("\"field_1\" = DATE '2025-05-03'")
+        .withFieldType(Types.DateType.get())
+        .validate();
+
+    // time string
     TestCase.expecting(equal("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
         .fromFilter("\"field_1\" = '10:30:05.123'")
         .withFieldType(Types.TimeType.get())
         .validate();
 
-    // datetime
+    // time
+    TestCase.expecting(equal("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
+        .fromFilter("\"field_1\" = TIME '10:30:05.123'")
+        .withFieldType(Types.TimeType.get())
+        .validate();
+
+    // datetime - timestamp string
     TestCase.expecting(
             equal("field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
         .fromFilter("\"field_1\" = '2025-05-03T10:30:05.123'")
         .withFieldType(Types.TimestampType.withoutZone())
         .validate();
+
+    // datetime - timestamp
+    TestCase.expecting(
+            equal("field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
+        .fromFilter("\"field_1\" = TIMESTAMP '2025-05-03 10:30:05.123'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date string
+    TestCase.expecting(
+            equal("field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" = '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date
+    TestCase.expecting(
+            equal("field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" = DATE '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
   }
 
   @Test
@@ -319,25 +493,59 @@ public void testNotEquals() {
         .withFieldType(Types.StringType.get())
         .validate();
 
-    // date
+    // date string
     TestCase.expecting(notEqual("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
         .fromFilter("\"field_1\" <> '2025-05-03'")
         .withFieldType(Types.DateType.get())
         .validate();
 
-    // time
+    // date
+    TestCase.expecting(notEqual("field_1", daysFromDate(LocalDate.parse("2025-05-03"))))
+        .fromFilter("\"field_1\" <> DATE '2025-05-03'")
+        .withFieldType(Types.DateType.get())
+        .validate();
+
+    // time string
     TestCase.expecting(notEqual("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
         .fromFilter("\"field_1\" <> '10:30:05.123'")
         .withFieldType(Types.TimeType.get())
         .validate();
 
-    // datetime
+    // time
+    TestCase.expecting(notEqual("field_1", microsFromTime(LocalTime.parse("10:30:05.123"))))
+        .fromFilter("\"field_1\" <> TIME '10:30:05.123'")
+        .withFieldType(Types.TimeType.get())
+        .validate();
+
+    // datetime - timestamp string
     TestCase.expecting(
             notEqual(
                 "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
         .fromFilter("\"field_1\" <> '2025-05-03T10:30:05.123'")
         .withFieldType(Types.TimestampType.withoutZone())
         .validate();
+
+    // datetime - timestamp
+    TestCase.expecting(
+            notEqual(
+                "field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T10:30:05.123"))))
+        .fromFilter("\"field_1\" <> TIMESTAMP '2025-05-03 10:30:05.123'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date string
+    TestCase.expecting(
+            notEqual("field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" <> '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
+
+    // datetime - date
+    TestCase.expecting(
+            notEqual("field_1", microsFromTimestamp(LocalDateTime.parse("2025-05-03T00:00:00"))))
+        .fromFilter("\"field_1\" <> DATE '2025-05-03'")
+        .withFieldType(Types.TimestampType.withoutZone())
+        .validate();
   }
 
   @Test
diff --git a/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/IcebergIOWriteTest.java b/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/IcebergIOWriteTest.java
index be1125b21734..a7349bffdfa0 100644
--- a/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/IcebergIOWriteTest.java
+++ b/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/IcebergIOWriteTest.java
@@ -328,7 +328,7 @@ public void testIdempotentCommit() throws Exception {
     OutputFile outputFile = table.io().newOutputFile(TEMPORARY_FOLDER.newFile().toString());
     DataWriter<Record> icebergDataWriter =
         Parquet.writeData(outputFile)
-            .createWriterFunc(GenericParquetWriter::buildWriter)
+            .createWriterFunc(GenericParquetWriter::create)
             .schema(table.schema())
             .withSpec(table.spec())
             .overwrite()
diff --git a/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/RecordWriterManagerTest.java b/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/RecordWriterManagerTest.java
index b240442deb6d..36b74967f0b2 100644
--- a/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/RecordWriterManagerTest.java
+++ b/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/RecordWriterManagerTest.java
@@ -34,8 +34,8 @@
 import java.nio.ByteBuffer;
 import java.time.LocalDate;
 import java.time.LocalDateTime;
+import java.time.LocalTime;
 import java.util.ArrayList;
-import java.util.Arrays;
 import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
@@ -59,11 +59,15 @@
 import org.apache.iceberg.catalog.Namespace;
 import org.apache.iceberg.catalog.TableIdentifier;
 import org.apache.iceberg.hadoop.HadoopCatalog;
+import org.apache.iceberg.transforms.Transform;
 import org.apache.iceberg.types.Conversions;
+import org.apache.iceberg.types.Type;
 import org.apache.iceberg.types.Types;
+import org.apache.iceberg.util.DateTimeUtil;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.joda.time.DateTime;
 import org.joda.time.DateTimeZone;
+import org.joda.time.ReadableDateTime;
 import org.junit.Before;
 import org.junit.ClassRule;
 import org.junit.Rule;
@@ -532,13 +536,35 @@ public void testIdentityPartitioning() throws IOException {
     assertEquals(1, dataFile.getRecordCount());
     // build this string: bool=true/int=1/long=1/float=1.0/double=1.0/str=str
     List<String> expectedPartitions = new ArrayList<>();
-    List<String> dateTypes = Arrays.asList("date", "time", "datetime", "datetime_tz");
-    for (Schema.Field field : primitiveTypeSchema.getFields()) {
-      Object val = checkStateNotNull(row.getValue(field.getName()));
-      if (dateTypes.contains(field.getName())) {
-        val = URLEncoder.encode(val.toString(), UTF_8.toString());
+
+    for (PartitionField field : spec.fields()) {
+      String name = field.name();
+      Type type = spec.schema().findType(name);
+      Transform<Object, Object> transform = (Transform<Object, Object>) field.transform();
+      String val;
+      switch (name) {
+        case "date":
+          LocalDate localDate = checkStateNotNull(row.getValue(name));
+          Integer day = Integer.parseInt(String.valueOf(localDate.toEpochDay()));
+          val = transform.toHumanString(type, day);
+          break;
+        case "time":
+          LocalTime localTime = checkStateNotNull(row.getValue(name));
+          val = transform.toHumanString(type, localTime.toNanoOfDay() / 1000);
+          break;
+        case "datetime":
+          LocalDateTime ldt = checkStateNotNull(row.getValue(name));
+          val = transform.toHumanString(type, DateTimeUtil.microsFromTimestamp(ldt));
+          break;
+        case "datetime_tz":
+          ReadableDateTime dt = checkStateNotNull(row.getDateTime(name));
+          val = transform.toHumanString(type, dt.getMillis() * 1000);
+          break;
+        default:
+          val = transform.toHumanString(type, checkStateNotNull(row.getValue(name)));
+          break;
       }
-      expectedPartitions.add(field.getName() + "=" + val);
+      expectedPartitions.add(name + "=" + URLEncoder.encode(val, UTF_8.toString()));
     }
     String expectedPartitionPath = String.join("/", expectedPartitions);
     assertEquals(expectedPartitionPath, dataFile.getPartitionPath());
diff --git a/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/TestDataWarehouse.java b/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/TestDataWarehouse.java
index 61eba3f6ff88..dcb2d804d2e6 100644
--- a/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/TestDataWarehouse.java
+++ b/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/TestDataWarehouse.java
@@ -136,7 +136,7 @@ public DataFile writeRecords(
       case PARQUET:
         appender =
             Parquet.write(fromPath(path, hadoopConf))
-                .createWriterFunc(GenericParquetWriter::buildWriter)
+                .createWriterFunc(GenericParquetWriter::create)
                 .schema(schema)
                 .overwrite()
                 .build();
diff --git a/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/catalog/IcebergCatalogBaseIT.java b/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/catalog/IcebergCatalogBaseIT.java
index f242938b34c7..9e6aa5913cc5 100644
--- a/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/catalog/IcebergCatalogBaseIT.java
+++ b/sdks/java/io/iceberg/src/test/java/org/apache/beam/sdk/io/iceberg/catalog/IcebergCatalogBaseIT.java
@@ -33,6 +33,7 @@
 import com.google.api.services.storage.model.StorageObject;
 import java.io.IOException;
 import java.io.Serializable;
+import java.time.LocalDateTime;
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.Collections;
@@ -140,6 +141,7 @@
  */
 public abstract class IcebergCatalogBaseIT implements Serializable {
   private static final long SETUP_TEARDOWN_SLEEP_MS = 5000;
+  private static final long AFTER_UPDATE_SLEEP_MS = 2000;
 
   public abstract Catalog createCatalog();
 
@@ -291,7 +293,7 @@ public Row apply(Long num) {
                   .addValue(Float.valueOf(strNum + "." + strNum))
                   .build();
 
-          long timestampMillis = offset2025Millis + TimeUnit.MICROSECONDS.toHours(num);
+          long timestampMillis = offset2025Millis + TimeUnit.HOURS.toMillis(num);
           return Row.withSchema(BEAM_SCHEMA)
               .addValue("value_" + strNum)
               .addValue(String.valueOf((char) (97 + num % 5)))
@@ -302,8 +304,7 @@ public Row apply(Long num) {
               .addValue(LongStream.range(0, num % 10).boxed().collect(Collectors.toList()))
               .addValue(num % 2 == 0 ? null : nestedRow)
               .addValue(num)
-              .addValue(
-                  new DateTime(timestampMillis).withZone(DateTimeZone.forOffsetHoursMinutes(3, 25)))
+              .addValue(new DateTime(timestampMillis).withZone(DateTimeZone.forOffsetHours(4)))
               .addValue(DateTimeUtil.timestampFromMicros(timestampMillis * 1000))
               .addValue(DateTimeUtil.dateFromDays(Integer.parseInt(strNum)))
               .addValue(DateTimeUtil.timeFromMicros(num))
@@ -346,7 +347,7 @@ private List<Row> populateTable(Table table, @Nullable String charOverride) thro
       DataWriter<Record> writer =
           Parquet.writeData(file)
               .schema(ICEBERG_SCHEMA)
-              .createWriterFunc(GenericParquetWriter::buildWriter)
+              .createWriterFunc(GenericParquetWriter::create)
               .overwrite()
               .withSpec(table.spec())
               .build();
@@ -460,20 +461,23 @@ public void testReadWithColumnPruning_keep() throws Exception {
   public void testReadWithFilterAndColumnPruning_keep() throws Exception {
     Table table = catalog.createTable(TableIdentifier.parse(tableId()), ICEBERG_SCHEMA);
 
-    List<String> keepFields = Arrays.asList("bool_field", "modulo_5", "str");
+    List<String> keepFields = Arrays.asList("datetime_tz", "modulo_5", "str");
     RowFilter rowFilter = new RowFilter(BEAM_SCHEMA).keep(keepFields);
 
     List<Row> expectedRows =
         populateTable(table).stream()
             .filter(
                 row ->
-                    row.getBoolean("bool_field")
+                    row.getLogicalTypeValue("datetime", LocalDateTime.class)
+                            .isAfter(LocalDateTime.parse("2025-01-01T09:00:00"))
                         && (row.getInt32("int_field") < 500 || row.getInt32("modulo_5") == 3))
             .map(rowFilter::filter)
             .collect(Collectors.toList());
 
     Map<String, Object> config = new HashMap<>(managedIcebergConfig(tableId()));
-    config.put("filter", "\"bool_field\" = TRUE AND (\"int_field\" < 500 OR \"modulo_5\" = 3)");
+    config.put(
+        "filter",
+        "\"datetime\" > '2025-01-01 09:00' AND (\"int_field\" < 500 OR \"modulo_5\" = 3)");
     config.put("keep", keepFields);
 
     PCollection<Row> rows =
@@ -953,11 +957,15 @@ public void runReadBetween(boolean useSnapshotBoundary, boolean streaming) throw
     Table table = catalog.createTable(TableIdentifier.parse(tableId()), ICEBERG_SCHEMA);
 
     populateTable(table, "a"); // first snapshot
+    Thread.sleep(AFTER_UPDATE_SLEEP_MS);
     List<Row> expectedRows = populateTable(table, "b"); // second snapshot
     Snapshot from = table.currentSnapshot();
+    Thread.sleep(AFTER_UPDATE_SLEEP_MS);
     expectedRows.addAll(populateTable(table, "c")); // third snapshot
     Snapshot to = table.currentSnapshot();
+    Thread.sleep(AFTER_UPDATE_SLEEP_MS);
     populateTable(table, "d"); // fourth snapshot
+    Thread.sleep(AFTER_UPDATE_SLEEP_MS);
 
     Map<String, Object> config = new HashMap<>(managedIcebergConfig(tableId()));
     if (useSnapshotBoundary) {
diff --git a/sdks/java/io/jdbc/build.gradle b/sdks/java/io/jdbc/build.gradle
index 8c5fa685fdad..87a231a5a42b 100644
--- a/sdks/java/io/jdbc/build.gradle
+++ b/sdks/java/io/jdbc/build.gradle
@@ -29,6 +29,7 @@ ext.summary = "IO to read and write on JDBC datasource."
 dependencies {
   implementation library.java.vendored_guava_32_1_2_jre
   implementation project(path: ":sdks:java:core", configuration: "shadow")
+  implementation project(path: ":model:pipeline", configuration: "shadow")
   implementation library.java.dbcp2
   implementation library.java.joda_time
   implementation "org.apache.commons:commons-pool2:2.11.1"
@@ -39,8 +40,10 @@ dependencies {
   testImplementation project(path: ":sdks:java:core", configuration: "shadowTest")
   testImplementation project(path: ":sdks:java:extensions:avro", configuration: "testRuntimeMigration")
   testImplementation project(path: ":sdks:java:io:common")
+  testImplementation project(path: ":sdks:java:managed")
   testImplementation project(path: ":sdks:java:testing:test-utils")
   testImplementation library.java.junit
+  testImplementation library.java.mockito_inline
   testImplementation library.java.slf4j_api
   testImplementation library.java.postgres
 
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcIO.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcIO.java
index 2c0ad23c5638..e6db4d82712b 100644
--- a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcIO.java
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcIO.java
@@ -355,6 +355,10 @@ public static ReadRows readRows() {
    * Like {@link #read}, but executes multiple instances of the query substituting each element of a
    * {@link PCollection} as query parameters.
    *
+   * <p>The substitution is configured via {@link ReadAll#withParameterSetter}. Substitutions
+   * allowed by the JDBC API's {@link PreparedStatement} are supported. In particular, this does not
+   * support parameterizing the table name to read from a different table for each input element.
+   *
    * @param <ParameterT> Type of the data representing query parameters.
    * @param <OutputT> Type of the data to be read.
    */
@@ -1175,6 +1179,18 @@ public ReadAll<ParameterT, OutputT> withQuery(ValueProvider<String> query) {
       return toBuilder().setQuery(query).build();
     }
 
+    /**
+     * Sets the {@link PreparedStatementSetter} to set the parameters of the query for each input
+     * element.
+     *
+     * <p>For example,
+     *
+     * <pre>{@code
+     * JdbcIO.<String, Row>readAll()
+     *     .withQuery("select * from table where field = ?")
+     *     .withParameterSetter((element, preparedStatement) -> preparedStatement.setString(1, element))
+     * }</pre>
+     */
     public ReadAll<ParameterT, OutputT> withParameterSetter(
         PreparedStatementSetter<ParameterT> parameterSetter) {
       checkArgumentNotNull(
@@ -1248,7 +1264,7 @@ public ReadAll<ParameterT, OutputT> withDisableAutoCommit(boolean disableAutoCom
         try {
           return schemaRegistry.getSchemaCoder(outputType);
         } catch (NoSuchSchemaException e) {
-          LOG.warn(
+          LOG.info(
               "Unable to infer a schema for type {}. Attempting to infer a coder without a schema.",
               outputType);
         }
@@ -1709,6 +1725,15 @@ private Connection getConnection() throws SQLException {
         try {
           connection = validSource.getConnection();
           this.connection = connection;
+
+          // PostgreSQL requires autocommit to be disabled to enable cursor streaming
+          // see https://jdbc.postgresql.org/documentation/head/query.html#query-with-cursor
+          // This option is configurable as Informix will error
+          // if calling setAutoCommit on a non-logged database
+          if (disableAutoCommit) {
+            LOG.info("Autocommit has been disabled");
+            connection.setAutoCommit(false);
+          }
         } finally {
           connectionLock.unlock();
         }
@@ -1739,14 +1764,6 @@ private Connection getConnection() throws SQLException {
     public void processElement(ProcessContext context) throws Exception {
       // Only acquire the connection if we need to perform a read.
       Connection connection = getConnection();
-      // PostgreSQL requires autocommit to be disabled to enable cursor streaming
-      // see https://jdbc.postgresql.org/documentation/head/query.html#query-with-cursor
-      // This option is configurable as Informix will error
-      // if calling setAutoCommit on a non-logged database
-      if (disableAutoCommit) {
-        LOG.info("Autocommit has been disabled");
-        connection.setAutoCommit(false);
-      }
       try (PreparedStatement statement =
           connection.prepareStatement(
               query.get(), ResultSet.TYPE_FORWARD_ONLY, ResultSet.CONCUR_READ_ONLY)) {
@@ -1829,8 +1846,8 @@ public static RetryConfiguration create(
   }
 
   /**
-   * An interface used by the JdbcIO Write to set the parameters of the {@link PreparedStatement}
-   * used to setParameters into the database.
+   * An interface used by the JdbcIO {@link ReadAll} and {@link Write} to set the parameters of the
+   * {@link PreparedStatement} used to setParameters into the database.
    */
   @FunctionalInterface
   public interface PreparedStatementSetter<T> extends Serializable {
@@ -1949,6 +1966,8 @@ public <V extends JdbcWriteResult> WriteWithResults<T, V> withWriteResults(
           .setStatement(inner.getStatement())
           .setTable(inner.getTable())
           .setAutoSharding(inner.getAutoSharding())
+          .setBatchSize(inner.getBatchSize())
+          .setMaxBatchBufferingDuration(inner.getMaxBatchBufferingDuration())
           .build();
     }
 
@@ -2055,6 +2074,10 @@ public abstract static class WriteWithResults<T, V extends JdbcWriteResult>
 
     abstract @Nullable RowMapper<V> getRowMapper();
 
+    abstract @Nullable Long getBatchSize();
+
+    abstract @Nullable Long getMaxBatchBufferingDuration();
+
     abstract Builder<T, V> toBuilder();
 
     @AutoValue.Builder
@@ -2064,6 +2087,10 @@ abstract Builder<T, V> setDataSourceProviderFn(
 
       abstract Builder<T, V> setAutoSharding(@Nullable Boolean autoSharding);
 
+      abstract Builder<T, V> setBatchSize(@Nullable Long batchSize);
+
+      abstract Builder<T, V> setMaxBatchBufferingDuration(@Nullable Long maxBatchBufferingDuration);
+
       abstract Builder<T, V> setStatement(@Nullable ValueProvider<String> statement);
 
       abstract Builder<T, V> setPreparedStatementSetter(
@@ -2080,6 +2107,19 @@ abstract Builder<T, V> setPreparedStatementSetter(
       abstract WriteWithResults<T, V> build();
     }
 
+    public WriteWithResults<T, V> withBatchSize(long batchSize) {
+      checkArgument(batchSize > 0, "batchSize must be > 0, but was %s", batchSize);
+      return toBuilder().setBatchSize(batchSize).build();
+    }
+
+    public WriteWithResults<T, V> withMaxBatchBufferingDuration(long maxBatchBufferingDuration) {
+      checkArgument(
+          maxBatchBufferingDuration > 0,
+          "maxBatchBufferingDuration must be > 0, but was %s",
+          maxBatchBufferingDuration);
+      return toBuilder().setMaxBatchBufferingDuration(maxBatchBufferingDuration).build();
+    }
+
     public WriteWithResults<T, V> withDataSourceConfiguration(DataSourceConfiguration config) {
       return withDataSourceProviderFn(new DataSourceProviderFromDataSourceConfiguration(config));
     }
@@ -2173,9 +2213,16 @@ public PCollection<V> expand(PCollection<T> input) {
           autoSharding == null || (autoSharding && input.isBounded() != IsBounded.UNBOUNDED),
           "Autosharding is only supported for streaming pipelines.");
 
+      Long batchSizeAsLong = getBatchSize();
+      long batchSize = batchSizeAsLong == null ? DEFAULT_BATCH_SIZE : batchSizeAsLong;
+      Long maxBufferingDurationAsLong = getMaxBatchBufferingDuration();
+      long maxBufferingDuration =
+          maxBufferingDurationAsLong == null
+              ? DEFAULT_MAX_BATCH_BUFFERING_DURATION
+              : maxBufferingDurationAsLong;
+
       PCollection<Iterable<T>> iterables =
-          JdbcIO.<T>batchElements(
-              input, autoSharding, DEFAULT_BATCH_SIZE, DEFAULT_MAX_BATCH_BUFFERING_DURATION);
+          JdbcIO.<T>batchElements(input, autoSharding, batchSize, maxBufferingDuration);
       return iterables.apply(
           ParDo.of(
               new WriteFn<T, V>(
@@ -2187,8 +2234,8 @@ public PCollection<V> expand(PCollection<T> input) {
                       .setStatement(getStatement())
                       .setRetryConfiguration(getRetryConfiguration())
                       .setReturnResults(true)
-                      .setBatchSize(1L)
-                      .setMaxBatchBufferingDuration(DEFAULT_MAX_BATCH_BUFFERING_DURATION)
+                      .setBatchSize(1L) // We are writing iterables 1 at a time.
+                      .setMaxBatchBufferingDuration(maxBufferingDuration)
                       .build())));
     }
   }
@@ -2842,6 +2889,8 @@ private void cleanUpStatementAndConnection() throws Exception {
       }
     }
 
+    @SuppressWarnings(
+        "Slf4jDoNotLogMessageOfExceptionExplicitly") // for tests checking error message
     private void executeBatch(ProcessContext context, Iterable<T> records)
         throws SQLException, InterruptedException {
       Long startTimeNs = System.nanoTime();
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcReadSchemaTransformProvider.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcReadSchemaTransformProvider.java
index 6777be50ab50..da75c9baaa45 100644
--- a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcReadSchemaTransformProvider.java
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcReadSchemaTransformProvider.java
@@ -27,6 +27,8 @@
 import java.util.Objects;
 import javax.annotation.Nullable;
 import org.apache.beam.sdk.schemas.AutoValueSchema;
+import org.apache.beam.sdk.schemas.NoSuchSchemaException;
+import org.apache.beam.sdk.schemas.SchemaRegistry;
 import org.apache.beam.sdk.schemas.annotations.DefaultSchema;
 import org.apache.beam.sdk.schemas.annotations.SchemaFieldDescription;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransform;
@@ -265,6 +267,20 @@ public PCollectionRowTuple expand(PCollectionRowTuple input) {
       }
       return PCollectionRowTuple.of("output", input.getPipeline().apply(readRows));
     }
+
+    public Row getConfigurationRow() {
+      try {
+        // To stay consistent with our SchemaTransform configuration naming conventions,
+        // we sort lexicographically
+        return SchemaRegistry.createDefault()
+            .getToRowFunction(JdbcReadSchemaTransformConfiguration.class)
+            .apply(config)
+            .sorted()
+            .toSnakeCase();
+      } catch (NoSuchSchemaException e) {
+        throw new RuntimeException(e);
+      }
+    }
   }
 
   @Override
@@ -401,6 +417,8 @@ public static Builder builder() {
           .Builder();
     }
 
+    public abstract Builder toBuilder();
+
     @AutoValue.Builder
     public abstract static class Builder {
       public abstract Builder setDriverClassName(String value);
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcWriteSchemaTransformProvider.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcWriteSchemaTransformProvider.java
index 6f10df56aab5..4dbb9b396f09 100644
--- a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcWriteSchemaTransformProvider.java
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcWriteSchemaTransformProvider.java
@@ -27,7 +27,9 @@
 import java.util.Objects;
 import javax.annotation.Nullable;
 import org.apache.beam.sdk.schemas.AutoValueSchema;
+import org.apache.beam.sdk.schemas.NoSuchSchemaException;
 import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.schemas.SchemaRegistry;
 import org.apache.beam.sdk.schemas.annotations.DefaultSchema;
 import org.apache.beam.sdk.schemas.annotations.SchemaFieldDescription;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransform;
@@ -265,6 +267,20 @@ public PCollectionRowTuple expand(PCollectionRowTuple input) {
               .setRowSchema(Schema.of());
       return PCollectionRowTuple.of("post_write", postWrite);
     }
+
+    public Row getConfigurationRow() {
+      try {
+        // To stay consistent with our SchemaTransform configuration naming conventions,
+        // we sort lexicographically
+        return SchemaRegistry.createDefault()
+            .getToRowFunction(JdbcWriteSchemaTransformConfiguration.class)
+            .apply(config)
+            .sorted()
+            .toSnakeCase();
+      } catch (NoSuchSchemaException e) {
+        throw new RuntimeException(e);
+      }
+    }
   }
 
   @Override
@@ -382,6 +398,8 @@ public static Builder builder() {
           .Builder();
     }
 
+    public abstract Builder toBuilder();
+
     @AutoValue.Builder
     public abstract static class Builder {
       public abstract Builder setDriverClassName(String value);
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/MySqlSchemaTransformTranslation.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/MySqlSchemaTransformTranslation.java
new file mode 100644
index 000000000000..3367248b7198
--- /dev/null
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/MySqlSchemaTransformTranslation.java
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.jdbc.providers;
+
+import static org.apache.beam.sdk.io.jdbc.providers.ReadFromMySqlSchemaTransformProvider.MySqlReadSchemaTransform;
+import static org.apache.beam.sdk.io.jdbc.providers.WriteToMySqlSchemaTransformProvider.MySqlWriteSchemaTransform;
+import static org.apache.beam.sdk.schemas.transforms.SchemaTransformTranslation.SchemaTransformPayloadTranslator;
+
+import com.google.auto.service.AutoService;
+import java.util.Map;
+import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
+import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.util.construction.PTransformTranslation;
+import org.apache.beam.sdk.util.construction.TransformPayloadTranslatorRegistrar;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+
+public class MySqlSchemaTransformTranslation {
+  static class MySqlReadSchemaTransformTranslator
+      extends SchemaTransformPayloadTranslator<MySqlReadSchemaTransform> {
+    @Override
+    public SchemaTransformProvider provider() {
+      return new ReadFromMySqlSchemaTransformProvider();
+    }
+
+    @Override
+    public Row toConfigRow(MySqlReadSchemaTransform transform) {
+      return transform.getConfigurationRow();
+    }
+  }
+
+  @AutoService(TransformPayloadTranslatorRegistrar.class)
+  public static class ReadRegistrar implements TransformPayloadTranslatorRegistrar {
+    @Override
+    @SuppressWarnings({
+      "rawtypes",
+    })
+    public Map<
+            ? extends Class<? extends PTransform>,
+            ? extends PTransformTranslation.TransformPayloadTranslator>
+        getTransformPayloadTranslators() {
+      return ImmutableMap
+          .<Class<? extends PTransform>, PTransformTranslation.TransformPayloadTranslator>builder()
+          .put(MySqlReadSchemaTransform.class, new MySqlReadSchemaTransformTranslator())
+          .build();
+    }
+  }
+
+  static class MySqlWriteSchemaTransformTranslator
+      extends SchemaTransformPayloadTranslator<MySqlWriteSchemaTransform> {
+    @Override
+    public SchemaTransformProvider provider() {
+      return new WriteToMySqlSchemaTransformProvider();
+    }
+
+    @Override
+    public Row toConfigRow(MySqlWriteSchemaTransform transform) {
+      return transform.getConfigurationRow();
+    }
+  }
+
+  @AutoService(TransformPayloadTranslatorRegistrar.class)
+  public static class WriteRegistrar implements TransformPayloadTranslatorRegistrar {
+    @Override
+    @SuppressWarnings({
+      "rawtypes",
+    })
+    public Map<
+            ? extends Class<? extends PTransform>,
+            ? extends PTransformTranslation.TransformPayloadTranslator>
+        getTransformPayloadTranslators() {
+      return ImmutableMap
+          .<Class<? extends PTransform>, PTransformTranslation.TransformPayloadTranslator>builder()
+          .put(MySqlWriteSchemaTransform.class, new MySqlWriteSchemaTransformTranslator())
+          .build();
+    }
+  }
+}
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/PostgresSchemaTransformTranslation.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/PostgresSchemaTransformTranslation.java
new file mode 100644
index 000000000000..288b29642c5a
--- /dev/null
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/PostgresSchemaTransformTranslation.java
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.jdbc.providers;
+
+import static org.apache.beam.sdk.io.jdbc.providers.ReadFromPostgresSchemaTransformProvider.PostgresReadSchemaTransform;
+import static org.apache.beam.sdk.io.jdbc.providers.WriteToPostgresSchemaTransformProvider.PostgresWriteSchemaTransform;
+import static org.apache.beam.sdk.schemas.transforms.SchemaTransformTranslation.SchemaTransformPayloadTranslator;
+
+import com.google.auto.service.AutoService;
+import java.util.Map;
+import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
+import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.util.construction.PTransformTranslation;
+import org.apache.beam.sdk.util.construction.TransformPayloadTranslatorRegistrar;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+
+public class PostgresSchemaTransformTranslation {
+  static class PostgresReadSchemaTransformTranslator
+      extends SchemaTransformPayloadTranslator<PostgresReadSchemaTransform> {
+    @Override
+    public SchemaTransformProvider provider() {
+      return new ReadFromPostgresSchemaTransformProvider();
+    }
+
+    @Override
+    public Row toConfigRow(PostgresReadSchemaTransform transform) {
+      return transform.getConfigurationRow();
+    }
+  }
+
+  @AutoService(TransformPayloadTranslatorRegistrar.class)
+  public static class ReadRegistrar implements TransformPayloadTranslatorRegistrar {
+    @Override
+    @SuppressWarnings({
+      "rawtypes",
+    })
+    public Map<
+            ? extends Class<? extends PTransform>,
+            ? extends PTransformTranslation.TransformPayloadTranslator>
+        getTransformPayloadTranslators() {
+      return ImmutableMap
+          .<Class<? extends PTransform>, PTransformTranslation.TransformPayloadTranslator>builder()
+          .put(PostgresReadSchemaTransform.class, new PostgresReadSchemaTransformTranslator())
+          .build();
+    }
+  }
+
+  static class PostgresWriteSchemaTransformTranslator
+      extends SchemaTransformPayloadTranslator<PostgresWriteSchemaTransform> {
+    @Override
+    public SchemaTransformProvider provider() {
+      return new WriteToPostgresSchemaTransformProvider();
+    }
+
+    @Override
+    public Row toConfigRow(PostgresWriteSchemaTransform transform) {
+      return transform.getConfigurationRow();
+    }
+  }
+
+  @AutoService(TransformPayloadTranslatorRegistrar.class)
+  public static class WriteRegistrar implements TransformPayloadTranslatorRegistrar {
+    @Override
+    @SuppressWarnings({
+      "rawtypes",
+    })
+    public Map<
+            ? extends Class<? extends PTransform>,
+            ? extends PTransformTranslation.TransformPayloadTranslator>
+        getTransformPayloadTranslators() {
+      return ImmutableMap
+          .<Class<? extends PTransform>, PTransformTranslation.TransformPayloadTranslator>builder()
+          .put(PostgresWriteSchemaTransform.class, new PostgresWriteSchemaTransformTranslator())
+          .build();
+    }
+  }
+}
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromMySqlSchemaTransformProvider.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromMySqlSchemaTransformProvider.java
index 3d0135ef8ecd..b51ee7236415 100644
--- a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromMySqlSchemaTransformProvider.java
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromMySqlSchemaTransformProvider.java
@@ -18,20 +18,28 @@
 package org.apache.beam.sdk.io.jdbc.providers;
 
 import static org.apache.beam.sdk.io.jdbc.JdbcUtil.MYSQL;
+import static org.apache.beam.sdk.util.construction.BeamUrns.getUrn;
 
 import com.google.auto.service.AutoService;
+import org.apache.beam.model.pipeline.v1.ExternalTransforms;
 import org.apache.beam.sdk.io.jdbc.JdbcReadSchemaTransformProvider;
+import org.apache.beam.sdk.schemas.transforms.SchemaTransform;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
 import org.checkerframework.checker.initialization.qual.Initialized;
 import org.checkerframework.checker.nullness.qual.NonNull;
 import org.checkerframework.checker.nullness.qual.UnknownKeyFor;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
 @AutoService(SchemaTransformProvider.class)
 public class ReadFromMySqlSchemaTransformProvider extends JdbcReadSchemaTransformProvider {
 
+  private static final Logger LOG =
+      LoggerFactory.getLogger(ReadFromMySqlSchemaTransformProvider.class);
+
   @Override
   public @UnknownKeyFor @NonNull @Initialized String identifier() {
-    return "beam:schematransform:org.apache.beam:mysql_read:v1";
+    return getUrn(ExternalTransforms.ManagedTransforms.Urns.MYSQL_READ);
   }
 
   @Override
@@ -43,4 +51,35 @@ public String description() {
   protected String jdbcType() {
     return MYSQL;
   }
+
+  @Override
+  public @UnknownKeyFor @NonNull @Initialized SchemaTransform from(
+      JdbcReadSchemaTransformConfiguration configuration) {
+    String jdbcType = configuration.getJdbcType();
+    if (jdbcType != null && !jdbcType.isEmpty() && !jdbcType.equals(jdbcType())) {
+      LOG.warn(
+          "Wrong JDBC type. Expected '{}' but got '{}'. Overriding with '{}'.",
+          jdbcType(),
+          jdbcType,
+          jdbcType());
+      configuration = configuration.toBuilder().setJdbcType(jdbcType()).build();
+    }
+
+    Integer fetchSize = configuration.getFetchSize();
+    if (fetchSize != null
+        && fetchSize > 0
+        && configuration.getJdbcUrl() != null
+        && !configuration.getJdbcUrl().contains("useCursorFetch=true")) {
+      throw new IllegalArgumentException(
+          "It is required to set useCursorFetch=true"
+              + " in the JDBC URL when using fetchSize for MySQL");
+    }
+    return new MySqlReadSchemaTransform(configuration);
+  }
+
+  public static class MySqlReadSchemaTransform extends JdbcReadSchemaTransform {
+    public MySqlReadSchemaTransform(JdbcReadSchemaTransformConfiguration config) {
+      super(config, MYSQL);
+    }
+  }
 }
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromPostgresSchemaTransformProvider.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromPostgresSchemaTransformProvider.java
index 62ff14c23e0a..834e7a0a4927 100644
--- a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromPostgresSchemaTransformProvider.java
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromPostgresSchemaTransformProvider.java
@@ -18,20 +18,30 @@
 package org.apache.beam.sdk.io.jdbc.providers;
 
 import static org.apache.beam.sdk.io.jdbc.JdbcUtil.POSTGRES;
+import static org.apache.beam.sdk.util.construction.BeamUrns.getUrn;
 
 import com.google.auto.service.AutoService;
+import java.util.Collections;
+import java.util.List;
+import org.apache.beam.model.pipeline.v1.ExternalTransforms;
 import org.apache.beam.sdk.io.jdbc.JdbcReadSchemaTransformProvider;
+import org.apache.beam.sdk.schemas.transforms.SchemaTransform;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
 import org.checkerframework.checker.initialization.qual.Initialized;
 import org.checkerframework.checker.nullness.qual.NonNull;
 import org.checkerframework.checker.nullness.qual.UnknownKeyFor;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
 @AutoService(SchemaTransformProvider.class)
 public class ReadFromPostgresSchemaTransformProvider extends JdbcReadSchemaTransformProvider {
 
+  private static final Logger LOG =
+      LoggerFactory.getLogger(ReadFromPostgresSchemaTransformProvider.class);
+
   @Override
   public @UnknownKeyFor @NonNull @Initialized String identifier() {
-    return "beam:schematransform:org.apache.beam:postgres_read:v1";
+    return getUrn(ExternalTransforms.ManagedTransforms.Urns.POSTGRES_READ);
   }
 
   @Override
@@ -43,4 +53,40 @@ public String description() {
   protected String jdbcType() {
     return POSTGRES;
   }
+
+  @Override
+  public @UnknownKeyFor @NonNull @Initialized SchemaTransform from(
+      JdbcReadSchemaTransformConfiguration configuration) {
+    String jdbcType = configuration.getJdbcType();
+    if (jdbcType != null && !jdbcType.isEmpty() && !jdbcType.equals(jdbcType())) {
+      throw new IllegalArgumentException(
+          String.format("Wrong JDBC type. Expected '%s' but got '%s'", jdbcType(), jdbcType));
+    }
+
+    List<@org.checkerframework.checker.nullness.qual.Nullable String> connectionInitSql =
+        configuration.getConnectionInitSql();
+    if (connectionInitSql != null && !connectionInitSql.isEmpty()) {
+      LOG.warn("Postgres does not support connectionInitSql, ignoring.");
+    }
+
+    Boolean disableAutoCommit = configuration.getDisableAutoCommit();
+    if (disableAutoCommit != null && !disableAutoCommit) {
+      LOG.warn("Postgres reads require disableAutoCommit to be true, overriding to true.");
+    }
+
+    // Override "connectionInitSql" and "disableAutoCommit" for postgres
+    configuration =
+        configuration
+            .toBuilder()
+            .setConnectionInitSql(Collections.emptyList())
+            .setDisableAutoCommit(true)
+            .build();
+    return new PostgresReadSchemaTransform(configuration);
+  }
+
+  public static class PostgresReadSchemaTransform extends JdbcReadSchemaTransform {
+    public PostgresReadSchemaTransform(JdbcReadSchemaTransformConfiguration config) {
+      super(config, POSTGRES);
+    }
+  }
 }
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromSqlServerSchemaTransformProvider.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromSqlServerSchemaTransformProvider.java
index e4767177bb2f..eec6660aa88b 100644
--- a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromSqlServerSchemaTransformProvider.java
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/ReadFromSqlServerSchemaTransformProvider.java
@@ -18,20 +18,30 @@
 package org.apache.beam.sdk.io.jdbc.providers;
 
 import static org.apache.beam.sdk.io.jdbc.JdbcUtil.MSSQL;
+import static org.apache.beam.sdk.util.construction.BeamUrns.getUrn;
 
 import com.google.auto.service.AutoService;
+import java.util.Collections;
+import java.util.List;
+import org.apache.beam.model.pipeline.v1.ExternalTransforms;
 import org.apache.beam.sdk.io.jdbc.JdbcReadSchemaTransformProvider;
+import org.apache.beam.sdk.schemas.transforms.SchemaTransform;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
 import org.checkerframework.checker.initialization.qual.Initialized;
 import org.checkerframework.checker.nullness.qual.NonNull;
 import org.checkerframework.checker.nullness.qual.UnknownKeyFor;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
 @AutoService(SchemaTransformProvider.class)
 public class ReadFromSqlServerSchemaTransformProvider extends JdbcReadSchemaTransformProvider {
 
+  private static final Logger LOG =
+      LoggerFactory.getLogger(ReadFromSqlServerSchemaTransformProvider.class);
+
   @Override
   public @UnknownKeyFor @NonNull @Initialized String identifier() {
-    return "beam:schematransform:org.apache.beam:sql_server_read:v1";
+    return getUrn(ExternalTransforms.ManagedTransforms.Urns.SQL_SERVER_READ);
   }
 
   @Override
@@ -43,4 +53,35 @@ public String description() {
   protected String jdbcType() {
     return MSSQL;
   }
+
+  @Override
+  public @UnknownKeyFor @NonNull @Initialized SchemaTransform from(
+      JdbcReadSchemaTransformConfiguration configuration) {
+    String jdbcType = configuration.getJdbcType();
+    if (jdbcType != null && !jdbcType.isEmpty() && !jdbcType.equals(jdbcType())) {
+      LOG.warn(
+          "Wrong JDBC type. Expected '{}' but got '{}'. Overriding with '{}'.",
+          jdbcType(),
+          jdbcType,
+          jdbcType());
+      configuration = configuration.toBuilder().setJdbcType(jdbcType()).build();
+    }
+
+    List<@org.checkerframework.checker.nullness.qual.Nullable String> connectionInitSql =
+        configuration.getConnectionInitSql();
+    if (connectionInitSql != null && !connectionInitSql.isEmpty()) {
+      throw new IllegalArgumentException("SQL Server does not support connectionInitSql.");
+    }
+
+    // Override "connectionInitSql" for sqlserver
+    configuration = configuration.toBuilder().setConnectionInitSql(Collections.emptyList()).build();
+    return new SqlServerReadSchemaTransform(configuration);
+  }
+
+  public static class SqlServerReadSchemaTransform extends JdbcReadSchemaTransform {
+    public SqlServerReadSchemaTransform(JdbcReadSchemaTransformConfiguration config) {
+      super(config, MSSQL);
+      config.validate(MSSQL);
+    }
+  }
 }
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/SqlServerSchemaTransformTranslation.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/SqlServerSchemaTransformTranslation.java
new file mode 100644
index 000000000000..cea52f8d9620
--- /dev/null
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/SqlServerSchemaTransformTranslation.java
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.jdbc.providers;
+
+import static org.apache.beam.sdk.io.jdbc.providers.ReadFromSqlServerSchemaTransformProvider.SqlServerReadSchemaTransform;
+import static org.apache.beam.sdk.io.jdbc.providers.WriteToSqlServerSchemaTransformProvider.SqlServerWriteSchemaTransform;
+import static org.apache.beam.sdk.schemas.transforms.SchemaTransformTranslation.SchemaTransformPayloadTranslator;
+
+import com.google.auto.service.AutoService;
+import java.util.Map;
+import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
+import org.apache.beam.sdk.transforms.PTransform;
+import org.apache.beam.sdk.util.construction.PTransformTranslation;
+import org.apache.beam.sdk.util.construction.TransformPayloadTranslatorRegistrar;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+
+public class SqlServerSchemaTransformTranslation {
+  static class SqlServerReadSchemaTransformTranslator
+      extends SchemaTransformPayloadTranslator<SqlServerReadSchemaTransform> {
+    @Override
+    public SchemaTransformProvider provider() {
+      return new ReadFromSqlServerSchemaTransformProvider();
+    }
+
+    @Override
+    public Row toConfigRow(SqlServerReadSchemaTransform transform) {
+      return transform.getConfigurationRow();
+    }
+  }
+
+  @AutoService(TransformPayloadTranslatorRegistrar.class)
+  public static class ReadRegistrar implements TransformPayloadTranslatorRegistrar {
+    @Override
+    @SuppressWarnings({
+      "rawtypes",
+    })
+    public Map<
+            ? extends Class<? extends PTransform>,
+            ? extends PTransformTranslation.TransformPayloadTranslator>
+        getTransformPayloadTranslators() {
+      return ImmutableMap
+          .<Class<? extends PTransform>, PTransformTranslation.TransformPayloadTranslator>builder()
+          .put(SqlServerReadSchemaTransform.class, new SqlServerReadSchemaTransformTranslator())
+          .build();
+    }
+  }
+
+  static class SqlServerWriteSchemaTransformTranslator
+      extends SchemaTransformPayloadTranslator<SqlServerWriteSchemaTransform> {
+    @Override
+    public SchemaTransformProvider provider() {
+      return new WriteToSqlServerSchemaTransformProvider();
+    }
+
+    @Override
+    public Row toConfigRow(SqlServerWriteSchemaTransform transform) {
+      return transform.getConfigurationRow();
+    }
+  }
+
+  @AutoService(TransformPayloadTranslatorRegistrar.class)
+  public static class WriteRegistrar implements TransformPayloadTranslatorRegistrar {
+    @Override
+    @SuppressWarnings({
+      "rawtypes",
+    })
+    public Map<
+            ? extends Class<? extends PTransform>,
+            ? extends PTransformTranslation.TransformPayloadTranslator>
+        getTransformPayloadTranslators() {
+      return ImmutableMap
+          .<Class<? extends PTransform>, PTransformTranslation.TransformPayloadTranslator>builder()
+          .put(SqlServerWriteSchemaTransform.class, new SqlServerWriteSchemaTransformTranslator())
+          .build();
+    }
+  }
+}
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToMySqlSchemaTransformProvider.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToMySqlSchemaTransformProvider.java
index 57f085220162..9f38fccf65ba 100644
--- a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToMySqlSchemaTransformProvider.java
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToMySqlSchemaTransformProvider.java
@@ -18,20 +18,28 @@
 package org.apache.beam.sdk.io.jdbc.providers;
 
 import static org.apache.beam.sdk.io.jdbc.JdbcUtil.MYSQL;
+import static org.apache.beam.sdk.util.construction.BeamUrns.getUrn;
 
 import com.google.auto.service.AutoService;
+import org.apache.beam.model.pipeline.v1.ExternalTransforms;
 import org.apache.beam.sdk.io.jdbc.JdbcWriteSchemaTransformProvider;
+import org.apache.beam.sdk.schemas.transforms.SchemaTransform;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
 import org.checkerframework.checker.initialization.qual.Initialized;
 import org.checkerframework.checker.nullness.qual.NonNull;
 import org.checkerframework.checker.nullness.qual.UnknownKeyFor;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
 @AutoService(SchemaTransformProvider.class)
 public class WriteToMySqlSchemaTransformProvider extends JdbcWriteSchemaTransformProvider {
 
+  private static final Logger LOG =
+      LoggerFactory.getLogger(WriteToMySqlSchemaTransformProvider.class);
+
   @Override
   public @UnknownKeyFor @NonNull @Initialized String identifier() {
-    return "beam:schematransform:org.apache.beam:mysql_write:v1";
+    return getUrn(ExternalTransforms.ManagedTransforms.Urns.MYSQL_WRITE);
   }
 
   @Override
@@ -43,4 +51,25 @@ public String description() {
   protected String jdbcType() {
     return MYSQL;
   }
+
+  @Override
+  public @UnknownKeyFor @NonNull @Initialized SchemaTransform from(
+      JdbcWriteSchemaTransformConfiguration configuration) {
+    String jdbcType = configuration.getJdbcType();
+    if (jdbcType != null && !jdbcType.isEmpty() && !jdbcType.equals(jdbcType())) {
+      LOG.warn(
+          "Wrong JDBC type. Expected '{}' but got '{}'. Overriding with '{}'.",
+          jdbcType(),
+          jdbcType,
+          jdbcType());
+      configuration = configuration.toBuilder().setJdbcType(jdbcType()).build();
+    }
+    return new MySqlWriteSchemaTransform(configuration);
+  }
+
+  public static class MySqlWriteSchemaTransform extends JdbcWriteSchemaTransform {
+    public MySqlWriteSchemaTransform(JdbcWriteSchemaTransformConfiguration config) {
+      super(config, MYSQL);
+    }
+  }
 }
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToPostgresSchemaTransformProvider.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToPostgresSchemaTransformProvider.java
index c50b84311630..97074742dbed 100644
--- a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToPostgresSchemaTransformProvider.java
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToPostgresSchemaTransformProvider.java
@@ -18,20 +18,30 @@
 package org.apache.beam.sdk.io.jdbc.providers;
 
 import static org.apache.beam.sdk.io.jdbc.JdbcUtil.POSTGRES;
+import static org.apache.beam.sdk.util.construction.BeamUrns.getUrn;
 
 import com.google.auto.service.AutoService;
+import java.util.Collections;
+import java.util.List;
+import org.apache.beam.model.pipeline.v1.ExternalTransforms;
 import org.apache.beam.sdk.io.jdbc.JdbcWriteSchemaTransformProvider;
+import org.apache.beam.sdk.schemas.transforms.SchemaTransform;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
 import org.checkerframework.checker.initialization.qual.Initialized;
 import org.checkerframework.checker.nullness.qual.NonNull;
 import org.checkerframework.checker.nullness.qual.UnknownKeyFor;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
 @AutoService(SchemaTransformProvider.class)
 public class WriteToPostgresSchemaTransformProvider extends JdbcWriteSchemaTransformProvider {
 
+  private static final Logger LOG =
+      LoggerFactory.getLogger(WriteToPostgresSchemaTransformProvider.class);
+
   @Override
   public @UnknownKeyFor @NonNull @Initialized String identifier() {
-    return "beam:schematransform:org.apache.beam:postgres_write:v1";
+    return getUrn(ExternalTransforms.ManagedTransforms.Urns.POSTGRES_WRITE);
   }
 
   @Override
@@ -43,4 +53,30 @@ public String description() {
   protected String jdbcType() {
     return POSTGRES;
   }
+
+  @Override
+  public @UnknownKeyFor @NonNull @Initialized SchemaTransform from(
+      JdbcWriteSchemaTransformConfiguration configuration) {
+    String jdbcType = configuration.getJdbcType();
+    if (jdbcType != null && !jdbcType.isEmpty() && !jdbcType.equals(jdbcType())) {
+      throw new IllegalArgumentException(
+          String.format("Wrong JDBC type. Expected '%s' but got '%s'", jdbcType(), jdbcType));
+    }
+
+    List<@org.checkerframework.checker.nullness.qual.Nullable String> connectionInitSql =
+        configuration.getConnectionInitSql();
+    if (connectionInitSql != null && !connectionInitSql.isEmpty()) {
+      LOG.warn("Postgres does not support connectionInitSql, ignoring.");
+    }
+
+    // Override "connectionInitSql" for postgres
+    configuration = configuration.toBuilder().setConnectionInitSql(Collections.emptyList()).build();
+    return new PostgresWriteSchemaTransform(configuration);
+  }
+
+  public static class PostgresWriteSchemaTransform extends JdbcWriteSchemaTransform {
+    public PostgresWriteSchemaTransform(JdbcWriteSchemaTransformConfiguration config) {
+      super(config, POSTGRES);
+    }
+  }
 }
diff --git a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToSqlServerSchemaTransformProvider.java b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToSqlServerSchemaTransformProvider.java
index 9e849f4e49e2..dc26c240958b 100644
--- a/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToSqlServerSchemaTransformProvider.java
+++ b/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/providers/WriteToSqlServerSchemaTransformProvider.java
@@ -18,20 +18,30 @@
 package org.apache.beam.sdk.io.jdbc.providers;
 
 import static org.apache.beam.sdk.io.jdbc.JdbcUtil.MSSQL;
+import static org.apache.beam.sdk.util.construction.BeamUrns.getUrn;
 
 import com.google.auto.service.AutoService;
+import java.util.Collections;
+import java.util.List;
+import org.apache.beam.model.pipeline.v1.ExternalTransforms;
 import org.apache.beam.sdk.io.jdbc.JdbcWriteSchemaTransformProvider;
+import org.apache.beam.sdk.schemas.transforms.SchemaTransform;
 import org.apache.beam.sdk.schemas.transforms.SchemaTransformProvider;
 import org.checkerframework.checker.initialization.qual.Initialized;
 import org.checkerframework.checker.nullness.qual.NonNull;
 import org.checkerframework.checker.nullness.qual.UnknownKeyFor;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
 @AutoService(SchemaTransformProvider.class)
 public class WriteToSqlServerSchemaTransformProvider extends JdbcWriteSchemaTransformProvider {
 
+  private static final Logger LOG =
+      LoggerFactory.getLogger(WriteToSqlServerSchemaTransformProvider.class);
+
   @Override
   public @UnknownKeyFor @NonNull @Initialized String identifier() {
-    return "beam:schematransform:org.apache.beam:sql_server_write:v1";
+    return getUrn(ExternalTransforms.ManagedTransforms.Urns.SQL_SERVER_WRITE);
   }
 
   @Override
@@ -43,4 +53,35 @@ public String description() {
   protected String jdbcType() {
     return MSSQL;
   }
+
+  @Override
+  public @UnknownKeyFor @NonNull @Initialized SchemaTransform from(
+      JdbcWriteSchemaTransformConfiguration configuration) {
+    String jdbcType = configuration.getJdbcType();
+    if (jdbcType != null && !jdbcType.isEmpty() && !jdbcType.equals(jdbcType())) {
+      LOG.warn(
+          "Wrong JDBC type. Expected '{}' but got '{}'. Overriding with '{}'.",
+          jdbcType(),
+          jdbcType,
+          jdbcType());
+      configuration = configuration.toBuilder().setJdbcType(jdbcType()).build();
+    }
+
+    List<@org.checkerframework.checker.nullness.qual.Nullable String> connectionInitSql =
+        configuration.getConnectionInitSql();
+    if (connectionInitSql != null && !connectionInitSql.isEmpty()) {
+      throw new IllegalArgumentException("SQL Server does not support connectionInitSql.");
+    }
+
+    // Override "connectionInitSql" for sqlserver
+    configuration = configuration.toBuilder().setConnectionInitSql(Collections.emptyList()).build();
+    return new SqlServerWriteSchemaTransform(configuration);
+  }
+
+  public static class SqlServerWriteSchemaTransform extends JdbcWriteSchemaTransform {
+    public SqlServerWriteSchemaTransform(JdbcWriteSchemaTransformConfiguration config) {
+      super(config, MSSQL);
+      config.validate(MSSQL);
+    }
+  }
 }
diff --git a/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/JdbcIOPostgresIT.java b/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/JdbcIOPostgresIT.java
new file mode 100644
index 000000000000..d58783096929
--- /dev/null
+++ b/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/JdbcIOPostgresIT.java
@@ -0,0 +1,178 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.jdbc;
+
+import static org.apache.beam.sdk.io.common.IOITHelper.readIOTestPipelineOptions;
+
+import java.sql.SQLException;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
+import org.apache.beam.sdk.io.common.DatabaseTestHelper;
+import org.apache.beam.sdk.io.common.PostgresIOTestPipelineOptions;
+import org.apache.beam.sdk.io.jdbc.providers.ReadFromPostgresSchemaTransformProvider;
+import org.apache.beam.sdk.io.jdbc.providers.WriteToPostgresSchemaTransformProvider;
+import org.apache.beam.sdk.managed.Managed;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.testing.PAssert;
+import org.apache.beam.sdk.testing.TestPipeline;
+import org.apache.beam.sdk.transforms.Create;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollectionRowTuple;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+import org.junit.BeforeClass;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+import org.postgresql.ds.PGSimpleDataSource;
+
+/**
+ * A test of {@link org.apache.beam.sdk.io.jdbc.JdbcIO} on an independent Postgres instance.
+ *
+ * <p>Similar to JdbcIOIT, this test requires a running instance of Postgres. Pass in connection
+ * information using PipelineOptions:
+ *
+ * <pre>
+ *  ./gradlew integrationTest -p sdks/java/io/jdbc -DintegrationTestPipelineOptions='[
+ *  "--postgresServerName=1.2.3.4",
+ *  "--postgresUsername=postgres",
+ *  "--postgresDatabaseName=myfancydb",
+ *  "--postgresPassword=mypass",
+ *  "--postgresSsl=false" ]'
+ *  --tests org.apache.beam.sdk.io.jdbc.JdbcIOPostgresIT
+ *  -DintegrationTestRunner=direct
+ * </pre>
+ */
+@RunWith(JUnit4.class)
+public class JdbcIOPostgresIT {
+  private static final Schema INPUT_SCHEMA =
+      Schema.of(
+          Schema.Field.of("id", Schema.FieldType.INT32),
+          Schema.Field.of("name", Schema.FieldType.STRING));
+
+  private static final List<Row> ROWS =
+      Arrays.asList(
+          Row.withSchema(INPUT_SCHEMA)
+              .withFieldValue("id", 1)
+              .withFieldValue("name", "foo")
+              .build(),
+          Row.withSchema(INPUT_SCHEMA)
+              .withFieldValue("id", 2)
+              .withFieldValue("name", "bar")
+              .build(),
+          Row.withSchema(INPUT_SCHEMA)
+              .withFieldValue("id", 3)
+              .withFieldValue("name", "baz")
+              .build());
+
+  private static PGSimpleDataSource dataSource;
+  private static String jdbcUrl;
+
+  @Rule public TestPipeline writePipeline = TestPipeline.create();
+  @Rule public TestPipeline readPipeline = TestPipeline.create();
+
+  @BeforeClass
+  public static void setup() {
+    PostgresIOTestPipelineOptions options;
+    try {
+      options = readIOTestPipelineOptions(PostgresIOTestPipelineOptions.class);
+    } catch (IllegalArgumentException e) {
+      options = null;
+    }
+    org.junit.Assume.assumeNotNull(options);
+    dataSource = DatabaseTestHelper.getPostgresDataSource(options);
+    jdbcUrl = DatabaseTestHelper.getPostgresDBUrl(options);
+  }
+
+  @Test
+  public void testWriteThenRead() throws SQLException {
+    String tableName = DatabaseTestHelper.getTestTableName("JdbcIOPostgresIT");
+    DatabaseTestHelper.createTable(dataSource, tableName);
+
+    JdbcWriteSchemaTransformProvider.JdbcWriteSchemaTransformConfiguration writeConfig =
+        JdbcWriteSchemaTransformProvider.JdbcWriteSchemaTransformConfiguration.builder()
+            .setJdbcUrl(jdbcUrl)
+            .setUsername(dataSource.getUser())
+            .setPassword(dataSource.getPassword())
+            .setLocation(tableName)
+            .build();
+
+    JdbcReadSchemaTransformProvider.JdbcReadSchemaTransformConfiguration readConfig =
+        JdbcReadSchemaTransformProvider.JdbcReadSchemaTransformConfiguration.builder()
+            .setJdbcUrl(jdbcUrl)
+            .setUsername(dataSource.getUser())
+            .setPassword(dataSource.getPassword())
+            .setLocation(tableName)
+            .build();
+
+    try {
+      PCollection<Row> input = writePipeline.apply(Create.of(ROWS)).setRowSchema(INPUT_SCHEMA);
+      PCollectionRowTuple inputTuple = PCollectionRowTuple.of("input", input);
+      inputTuple.apply(
+          new WriteToPostgresSchemaTransformProvider.PostgresWriteSchemaTransform(writeConfig));
+      writePipeline.run().waitUntilFinish();
+
+      PCollectionRowTuple pbeginTuple = PCollectionRowTuple.empty(readPipeline);
+      PCollectionRowTuple outputTuple =
+          pbeginTuple.apply(
+              new ReadFromPostgresSchemaTransformProvider.PostgresReadSchemaTransform(readConfig));
+      PCollection<Row> output = outputTuple.get("output");
+      PAssert.that(output).containsInAnyOrder(ROWS);
+      readPipeline.run().waitUntilFinish();
+    } finally {
+      DatabaseTestHelper.deleteTable(dataSource, tableName);
+    }
+  }
+
+  @Test
+  public void testManagedWriteThenManagedRead() throws SQLException {
+    String tableName = DatabaseTestHelper.getTestTableName("ManagedJdbcIOPostgresIT");
+    DatabaseTestHelper.createTable(dataSource, tableName);
+
+    Map<String, Object> writeConfig =
+        ImmutableMap.<String, Object>builder()
+            .put("jdbc_url", jdbcUrl)
+            .put("username", dataSource.getUser())
+            .put("password", dataSource.getPassword())
+            .put("location", tableName)
+            .build();
+
+    Map<String, Object> readConfig =
+        ImmutableMap.<String, Object>builder()
+            .put("jdbc_url", jdbcUrl)
+            .put("username", dataSource.getUser())
+            .put("password", dataSource.getPassword())
+            .put("location", tableName)
+            .build();
+
+    try {
+      PCollection<Row> input = writePipeline.apply(Create.of(ROWS)).setRowSchema(INPUT_SCHEMA);
+      input.apply(Managed.write(Managed.POSTGRES).withConfig(writeConfig));
+      writePipeline.run().waitUntilFinish();
+
+      PCollectionRowTuple output =
+          readPipeline.apply(Managed.read(Managed.POSTGRES).withConfig(readConfig));
+      PAssert.that(output.get("output")).containsInAnyOrder(ROWS);
+      readPipeline.run().waitUntilFinish();
+    } finally {
+      DatabaseTestHelper.deleteTable(dataSource, tableName);
+    }
+  }
+}
diff --git a/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/providers/MysqlSchemaTransformTranslationTest.java b/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/providers/MysqlSchemaTransformTranslationTest.java
new file mode 100644
index 000000000000..cfc48b6a8a0b
--- /dev/null
+++ b/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/providers/MysqlSchemaTransformTranslationTest.java
@@ -0,0 +1,231 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.jdbc.providers;
+
+import static org.apache.beam.model.pipeline.v1.ExternalTransforms.ExpansionMethods.Enum.SCHEMA_TRANSFORM;
+import static org.apache.beam.sdk.io.jdbc.providers.MySqlSchemaTransformTranslation.MySqlReadSchemaTransformTranslator;
+import static org.apache.beam.sdk.io.jdbc.providers.MySqlSchemaTransformTranslation.MySqlWriteSchemaTransformTranslator;
+import static org.apache.beam.sdk.io.jdbc.providers.ReadFromMySqlSchemaTransformProvider.MySqlReadSchemaTransform;
+import static org.apache.beam.sdk.io.jdbc.providers.WriteToMySqlSchemaTransformProvider.MySqlWriteSchemaTransform;
+import static org.junit.Assert.assertEquals;
+
+import java.io.IOException;
+import java.util.Collections;
+import java.util.List;
+import java.util.stream.Collectors;
+import org.apache.beam.model.pipeline.v1.ExternalTransforms.SchemaTransformPayload;
+import org.apache.beam.model.pipeline.v1.RunnerApi;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.RowCoder;
+import org.apache.beam.sdk.io.jdbc.JdbcIO;
+import org.apache.beam.sdk.options.PipelineOptionsFactory;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.schemas.SchemaTranslation;
+import org.apache.beam.sdk.transforms.Create;
+import org.apache.beam.sdk.util.construction.BeamUrns;
+import org.apache.beam.sdk.util.construction.PipelineTranslation;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollectionRowTuple;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.InvalidProtocolBufferException;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.junit.ClassRule;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+import org.junit.rules.TemporaryFolder;
+import org.mockito.MockedStatic;
+import org.mockito.Mockito;
+
+public class MysqlSchemaTransformTranslationTest {
+  @ClassRule public static final TemporaryFolder TEMPORARY_FOLDER = new TemporaryFolder();
+
+  @Rule public transient ExpectedException thrown = ExpectedException.none();
+
+  static final WriteToMySqlSchemaTransformProvider WRITE_PROVIDER =
+      new WriteToMySqlSchemaTransformProvider();
+  static final ReadFromMySqlSchemaTransformProvider READ_PROVIDER =
+      new ReadFromMySqlSchemaTransformProvider();
+
+  static final Row READ_CONFIG =
+      Row.withSchema(READ_PROVIDER.configurationSchema())
+          .withFieldValue("jdbc_url", "jdbc:mysql://host:port/database")
+          .withFieldValue("location", "test_table")
+          .withFieldValue("connection_properties", "some_property")
+          .withFieldValue("connection_init_sql", ImmutableList.<String>builder().build())
+          .withFieldValue("driver_class_name", null)
+          .withFieldValue("driver_jars", null)
+          .withFieldValue("disable_auto_commit", true)
+          .withFieldValue("fetch_size", null)
+          .withFieldValue("num_partitions", 5)
+          .withFieldValue("output_parallelization", true)
+          .withFieldValue("partition_column", "col")
+          .withFieldValue("read_query", null)
+          .withFieldValue("username", "my_user")
+          .withFieldValue("password", "my_pass")
+          .build();
+
+  static final Row WRITE_CONFIG =
+      Row.withSchema(WRITE_PROVIDER.configurationSchema())
+          .withFieldValue("jdbc_url", "jdbc:mysql://host:port/database")
+          .withFieldValue("location", "test_table")
+          .withFieldValue("autosharding", true)
+          .withFieldValue("connection_init_sql", ImmutableList.<String>builder().build())
+          .withFieldValue("connection_properties", "some_property")
+          .withFieldValue("driver_class_name", null)
+          .withFieldValue("driver_jars", null)
+          .withFieldValue("batch_size", 100L)
+          .withFieldValue("username", "my_user")
+          .withFieldValue("password", "my_pass")
+          .withFieldValue("write_statement", null)
+          .build();
+
+  @Test
+  public void testRecreateWriteTransformFromRow() {
+    MySqlWriteSchemaTransform writeTransform =
+        (MySqlWriteSchemaTransform) WRITE_PROVIDER.from(WRITE_CONFIG);
+
+    MySqlWriteSchemaTransformTranslator translator = new MySqlWriteSchemaTransformTranslator();
+    Row translatedRow = translator.toConfigRow(writeTransform);
+
+    MySqlWriteSchemaTransform writeTransformFromRow =
+        translator.fromConfigRow(translatedRow, PipelineOptionsFactory.create());
+
+    assertEquals(WRITE_CONFIG, writeTransformFromRow.getConfigurationRow());
+  }
+
+  @Test
+  public void testWriteTransformProtoTranslation()
+      throws InvalidProtocolBufferException, IOException {
+    // First build a pipeline
+    Pipeline p = Pipeline.create();
+    Schema inputSchema = Schema.builder().addStringField("name").build();
+    PCollection<Row> input =
+        p.apply(
+                Create.of(
+                    Collections.singletonList(
+                        Row.withSchema(inputSchema).addValue("test").build())))
+            .setRowSchema(inputSchema);
+
+    MySqlWriteSchemaTransform writeTransform =
+        (MySqlWriteSchemaTransform) WRITE_PROVIDER.from(WRITE_CONFIG);
+    PCollectionRowTuple.of("input", input).apply(writeTransform);
+
+    // Then translate the pipeline to a proto and extract MySqlWriteSchemaTransform proto
+    RunnerApi.Pipeline pipelineProto = PipelineTranslation.toProto(p);
+    List<RunnerApi.PTransform> writeTransformProto =
+        pipelineProto.getComponents().getTransformsMap().values().stream()
+            .filter(
+                tr -> {
+                  RunnerApi.FunctionSpec spec = tr.getSpec();
+                  try {
+                    return spec.getUrn().equals(BeamUrns.getUrn(SCHEMA_TRANSFORM))
+                        && SchemaTransformPayload.parseFrom(spec.getPayload())
+                            .getIdentifier()
+                            .equals(WRITE_PROVIDER.identifier());
+                  } catch (InvalidProtocolBufferException e) {
+                    throw new RuntimeException(e);
+                  }
+                })
+            .collect(Collectors.toList());
+    assertEquals(1, writeTransformProto.size());
+    RunnerApi.FunctionSpec spec = writeTransformProto.get(0).getSpec();
+
+    // Check that the proto contains correct values
+    SchemaTransformPayload payload = SchemaTransformPayload.parseFrom(spec.getPayload());
+    Schema schemaFromSpec = SchemaTranslation.schemaFromProto(payload.getConfigurationSchema());
+    assertEquals(WRITE_PROVIDER.configurationSchema(), schemaFromSpec);
+    Row rowFromSpec = RowCoder.of(schemaFromSpec).decode(payload.getConfigurationRow().newInput());
+
+    assertEquals(WRITE_CONFIG, rowFromSpec);
+
+    // Use the information in the proto to recreate the MySqlWriteSchemaTransform
+    MySqlWriteSchemaTransformTranslator translator = new MySqlWriteSchemaTransformTranslator();
+    MySqlWriteSchemaTransform writeTransformFromSpec =
+        translator.fromConfigRow(rowFromSpec, PipelineOptionsFactory.create());
+
+    assertEquals(WRITE_CONFIG, writeTransformFromSpec.getConfigurationRow());
+  }
+
+  @Test
+  public void testReCreateReadTransformFromRow() {
+    // setting a subset of fields here.
+    MySqlReadSchemaTransform readTransform =
+        (MySqlReadSchemaTransform) READ_PROVIDER.from(READ_CONFIG);
+
+    MySqlReadSchemaTransformTranslator translator = new MySqlReadSchemaTransformTranslator();
+    Row row = translator.toConfigRow(readTransform);
+
+    MySqlReadSchemaTransform readTransformFromRow =
+        translator.fromConfigRow(row, PipelineOptionsFactory.create());
+
+    assertEquals(READ_CONFIG, readTransformFromRow.getConfigurationRow());
+  }
+
+  @Test
+  public void testReadTransformProtoTranslation()
+      throws InvalidProtocolBufferException, IOException {
+    // First build a pipeline
+    Pipeline p = Pipeline.create();
+
+    MySqlReadSchemaTransform readTransform =
+        (MySqlReadSchemaTransform) READ_PROVIDER.from(READ_CONFIG);
+
+    // Mock inferBeamSchema since it requires database connection.
+    Schema expectedSchema = Schema.builder().addStringField("name").build();
+    try (MockedStatic<JdbcIO.ReadRows> mock = Mockito.mockStatic(JdbcIO.ReadRows.class)) {
+      mock.when(() -> JdbcIO.ReadRows.inferBeamSchema(Mockito.any(), Mockito.any()))
+          .thenReturn(expectedSchema);
+      PCollectionRowTuple.empty(p).apply(readTransform);
+    }
+
+    // Then translate the pipeline to a proto and extract MySqlReadSchemaTransform proto
+    RunnerApi.Pipeline pipelineProto = PipelineTranslation.toProto(p);
+    List<RunnerApi.PTransform> readTransformProto =
+        pipelineProto.getComponents().getTransformsMap().values().stream()
+            .filter(
+                tr -> {
+                  RunnerApi.FunctionSpec spec = tr.getSpec();
+                  try {
+                    return spec.getUrn().equals(BeamUrns.getUrn(SCHEMA_TRANSFORM))
+                        && SchemaTransformPayload.parseFrom(spec.getPayload())
+                            .getIdentifier()
+                            .equals(READ_PROVIDER.identifier());
+                  } catch (InvalidProtocolBufferException e) {
+                    throw new RuntimeException(e);
+                  }
+                })
+            .collect(Collectors.toList());
+    assertEquals(1, readTransformProto.size());
+    RunnerApi.FunctionSpec spec = readTransformProto.get(0).getSpec();
+
+    // Check that the proto contains correct values
+    SchemaTransformPayload payload = SchemaTransformPayload.parseFrom(spec.getPayload());
+    Schema schemaFromSpec = SchemaTranslation.schemaFromProto(payload.getConfigurationSchema());
+    assertEquals(READ_PROVIDER.configurationSchema(), schemaFromSpec);
+    Row rowFromSpec = RowCoder.of(schemaFromSpec).decode(payload.getConfigurationRow().newInput());
+    assertEquals(READ_CONFIG, rowFromSpec);
+
+    // Use the information in the proto to recreate the MySqlReadSchemaTransform
+    MySqlReadSchemaTransformTranslator translator = new MySqlReadSchemaTransformTranslator();
+    MySqlReadSchemaTransform readTransformFromSpec =
+        translator.fromConfigRow(rowFromSpec, PipelineOptionsFactory.create());
+
+    assertEquals(READ_CONFIG, readTransformFromSpec.getConfigurationRow());
+  }
+}
diff --git a/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/providers/PostgresSchemaTransformTranslationTest.java b/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/providers/PostgresSchemaTransformTranslationTest.java
new file mode 100644
index 000000000000..503baaefc334
--- /dev/null
+++ b/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/providers/PostgresSchemaTransformTranslationTest.java
@@ -0,0 +1,233 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.jdbc.providers;
+
+import static org.apache.beam.model.pipeline.v1.ExternalTransforms.ExpansionMethods.Enum.SCHEMA_TRANSFORM;
+import static org.apache.beam.sdk.io.jdbc.providers.PostgresSchemaTransformTranslation.PostgresReadSchemaTransformTranslator;
+import static org.apache.beam.sdk.io.jdbc.providers.PostgresSchemaTransformTranslation.PostgresWriteSchemaTransformTranslator;
+import static org.apache.beam.sdk.io.jdbc.providers.ReadFromPostgresSchemaTransformProvider.PostgresReadSchemaTransform;
+import static org.apache.beam.sdk.io.jdbc.providers.WriteToPostgresSchemaTransformProvider.PostgresWriteSchemaTransform;
+import static org.junit.Assert.assertEquals;
+
+import java.io.IOException;
+import java.util.Collections;
+import java.util.List;
+import java.util.stream.Collectors;
+import org.apache.beam.model.pipeline.v1.ExternalTransforms.SchemaTransformPayload;
+import org.apache.beam.model.pipeline.v1.RunnerApi;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.RowCoder;
+import org.apache.beam.sdk.io.jdbc.JdbcIO;
+import org.apache.beam.sdk.options.PipelineOptionsFactory;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.schemas.SchemaTranslation;
+import org.apache.beam.sdk.transforms.Create;
+import org.apache.beam.sdk.util.construction.BeamUrns;
+import org.apache.beam.sdk.util.construction.PipelineTranslation;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollectionRowTuple;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.InvalidProtocolBufferException;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.junit.ClassRule;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+import org.junit.rules.TemporaryFolder;
+import org.mockito.MockedStatic;
+import org.mockito.Mockito;
+
+public class PostgresSchemaTransformTranslationTest {
+  @ClassRule public static final TemporaryFolder TEMPORARY_FOLDER = new TemporaryFolder();
+
+  @Rule public transient ExpectedException thrown = ExpectedException.none();
+
+  static final WriteToPostgresSchemaTransformProvider WRITE_PROVIDER =
+      new WriteToPostgresSchemaTransformProvider();
+  static final ReadFromPostgresSchemaTransformProvider READ_PROVIDER =
+      new ReadFromPostgresSchemaTransformProvider();
+
+  static final Row READ_CONFIG =
+      Row.withSchema(READ_PROVIDER.configurationSchema())
+          .withFieldValue("jdbc_url", "jdbc:postgresql://host:port/database")
+          .withFieldValue("location", "test_table")
+          .withFieldValue("connection_properties", "some_property")
+          .withFieldValue("connection_init_sql", ImmutableList.<String>builder().build())
+          .withFieldValue("driver_class_name", null)
+          .withFieldValue("driver_jars", null)
+          .withFieldValue("disable_auto_commit", true)
+          .withFieldValue("fetch_size", 10)
+          .withFieldValue("num_partitions", 5)
+          .withFieldValue("output_parallelization", true)
+          .withFieldValue("partition_column", "col")
+          .withFieldValue("read_query", null)
+          .withFieldValue("username", "my_user")
+          .withFieldValue("password", "my_pass")
+          .build();
+
+  static final Row WRITE_CONFIG =
+      Row.withSchema(WRITE_PROVIDER.configurationSchema())
+          .withFieldValue("jdbc_url", "jdbc:postgresql://host:port/database")
+          .withFieldValue("location", "test_table")
+          .withFieldValue("autosharding", true)
+          .withFieldValue("connection_init_sql", ImmutableList.<String>builder().build())
+          .withFieldValue("connection_properties", "some_property")
+          .withFieldValue("driver_class_name", null)
+          .withFieldValue("driver_jars", null)
+          .withFieldValue("batch_size", 100L)
+          .withFieldValue("username", "my_user")
+          .withFieldValue("password", "my_pass")
+          .withFieldValue("write_statement", null)
+          .build();
+
+  @Test
+  public void testRecreateWriteTransformFromRow() {
+    PostgresWriteSchemaTransform writeTransform =
+        (PostgresWriteSchemaTransform) WRITE_PROVIDER.from(WRITE_CONFIG);
+
+    PostgresWriteSchemaTransformTranslator translator =
+        new PostgresWriteSchemaTransformTranslator();
+    Row translatedRow = translator.toConfigRow(writeTransform);
+
+    PostgresWriteSchemaTransform writeTransformFromRow =
+        translator.fromConfigRow(translatedRow, PipelineOptionsFactory.create());
+
+    assertEquals(WRITE_CONFIG, writeTransformFromRow.getConfigurationRow());
+  }
+
+  @Test
+  public void testWriteTransformProtoTranslation()
+      throws InvalidProtocolBufferException, IOException {
+    // First build a pipeline
+    Pipeline p = Pipeline.create();
+    Schema inputSchema = Schema.builder().addStringField("name").build();
+    PCollection<Row> input =
+        p.apply(
+                Create.of(
+                    Collections.singletonList(
+                        Row.withSchema(inputSchema).addValue("test").build())))
+            .setRowSchema(inputSchema);
+
+    PostgresWriteSchemaTransform writeTransform =
+        (PostgresWriteSchemaTransform) WRITE_PROVIDER.from(WRITE_CONFIG);
+    PCollectionRowTuple.of("input", input).apply(writeTransform);
+
+    // Then translate the pipeline to a proto and extract PostgresWriteSchemaTransform proto
+    RunnerApi.Pipeline pipelineProto = PipelineTranslation.toProto(p);
+    List<RunnerApi.PTransform> writeTransformProto =
+        pipelineProto.getComponents().getTransformsMap().values().stream()
+            .filter(
+                tr -> {
+                  RunnerApi.FunctionSpec spec = tr.getSpec();
+                  try {
+                    return spec.getUrn().equals(BeamUrns.getUrn(SCHEMA_TRANSFORM))
+                        && SchemaTransformPayload.parseFrom(spec.getPayload())
+                            .getIdentifier()
+                            .equals(WRITE_PROVIDER.identifier());
+                  } catch (InvalidProtocolBufferException e) {
+                    throw new RuntimeException(e);
+                  }
+                })
+            .collect(Collectors.toList());
+    assertEquals(1, writeTransformProto.size());
+    RunnerApi.FunctionSpec spec = writeTransformProto.get(0).getSpec();
+
+    // Check that the proto contains correct values
+    SchemaTransformPayload payload = SchemaTransformPayload.parseFrom(spec.getPayload());
+    Schema schemaFromSpec = SchemaTranslation.schemaFromProto(payload.getConfigurationSchema());
+    assertEquals(WRITE_PROVIDER.configurationSchema(), schemaFromSpec);
+    Row rowFromSpec = RowCoder.of(schemaFromSpec).decode(payload.getConfigurationRow().newInput());
+
+    assertEquals(WRITE_CONFIG, rowFromSpec);
+
+    // Use the information in the proto to recreate the PostgresWriteSchemaTransform
+    PostgresWriteSchemaTransformTranslator translator =
+        new PostgresWriteSchemaTransformTranslator();
+    PostgresWriteSchemaTransform writeTransformFromSpec =
+        translator.fromConfigRow(rowFromSpec, PipelineOptionsFactory.create());
+
+    assertEquals(WRITE_CONFIG, writeTransformFromSpec.getConfigurationRow());
+  }
+
+  @Test
+  public void testReCreateReadTransformFromRow() {
+    // setting a subset of fields here.
+    PostgresReadSchemaTransform readTransform =
+        (PostgresReadSchemaTransform) READ_PROVIDER.from(READ_CONFIG);
+
+    PostgresReadSchemaTransformTranslator translator = new PostgresReadSchemaTransformTranslator();
+    Row row = translator.toConfigRow(readTransform);
+
+    PostgresReadSchemaTransform readTransformFromRow =
+        translator.fromConfigRow(row, PipelineOptionsFactory.create());
+
+    assertEquals(READ_CONFIG, readTransformFromRow.getConfigurationRow());
+  }
+
+  @Test
+  public void testReadTransformProtoTranslation()
+      throws InvalidProtocolBufferException, IOException {
+    // First build a pipeline
+    Pipeline p = Pipeline.create();
+
+    PostgresReadSchemaTransform readTransform =
+        (PostgresReadSchemaTransform) READ_PROVIDER.from(READ_CONFIG);
+
+    // Mock inferBeamSchema since it requires database connection.
+    Schema expectedSchema = Schema.builder().addStringField("name").build();
+    try (MockedStatic<JdbcIO.ReadRows> mock = Mockito.mockStatic(JdbcIO.ReadRows.class)) {
+      mock.when(() -> JdbcIO.ReadRows.inferBeamSchema(Mockito.any(), Mockito.any()))
+          .thenReturn(expectedSchema);
+      PCollectionRowTuple.empty(p).apply(readTransform);
+    }
+
+    // Then translate the pipeline to a proto and extract PostgresReadSchemaTransform proto
+    RunnerApi.Pipeline pipelineProto = PipelineTranslation.toProto(p);
+    List<RunnerApi.PTransform> readTransformProto =
+        pipelineProto.getComponents().getTransformsMap().values().stream()
+            .filter(
+                tr -> {
+                  RunnerApi.FunctionSpec spec = tr.getSpec();
+                  try {
+                    return spec.getUrn().equals(BeamUrns.getUrn(SCHEMA_TRANSFORM))
+                        && SchemaTransformPayload.parseFrom(spec.getPayload())
+                            .getIdentifier()
+                            .equals(READ_PROVIDER.identifier());
+                  } catch (InvalidProtocolBufferException e) {
+                    throw new RuntimeException(e);
+                  }
+                })
+            .collect(Collectors.toList());
+    assertEquals(1, readTransformProto.size());
+    RunnerApi.FunctionSpec spec = readTransformProto.get(0).getSpec();
+
+    // Check that the proto contains correct values
+    SchemaTransformPayload payload = SchemaTransformPayload.parseFrom(spec.getPayload());
+    Schema schemaFromSpec = SchemaTranslation.schemaFromProto(payload.getConfigurationSchema());
+    assertEquals(READ_PROVIDER.configurationSchema(), schemaFromSpec);
+    Row rowFromSpec = RowCoder.of(schemaFromSpec).decode(payload.getConfigurationRow().newInput());
+    assertEquals(READ_CONFIG, rowFromSpec);
+
+    // Use the information in the proto to recreate the PostgresReadSchemaTransform
+    PostgresReadSchemaTransformTranslator translator = new PostgresReadSchemaTransformTranslator();
+    PostgresReadSchemaTransform readTransformFromSpec =
+        translator.fromConfigRow(rowFromSpec, PipelineOptionsFactory.create());
+
+    assertEquals(READ_CONFIG, readTransformFromSpec.getConfigurationRow());
+  }
+}
diff --git a/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/providers/SqlServerSchemaTransformTranslationTest.java b/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/providers/SqlServerSchemaTransformTranslationTest.java
new file mode 100644
index 000000000000..d8890987fbf2
--- /dev/null
+++ b/sdks/java/io/jdbc/src/test/java/org/apache/beam/sdk/io/jdbc/providers/SqlServerSchemaTransformTranslationTest.java
@@ -0,0 +1,235 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.jdbc.providers;
+
+import static org.apache.beam.model.pipeline.v1.ExternalTransforms.ExpansionMethods.Enum.SCHEMA_TRANSFORM;
+import static org.apache.beam.sdk.io.jdbc.providers.ReadFromSqlServerSchemaTransformProvider.SqlServerReadSchemaTransform;
+import static org.apache.beam.sdk.io.jdbc.providers.SqlServerSchemaTransformTranslation.SqlServerReadSchemaTransformTranslator;
+import static org.apache.beam.sdk.io.jdbc.providers.SqlServerSchemaTransformTranslation.SqlServerWriteSchemaTransformTranslator;
+import static org.apache.beam.sdk.io.jdbc.providers.WriteToSqlServerSchemaTransformProvider.SqlServerWriteSchemaTransform;
+import static org.junit.Assert.assertEquals;
+
+import java.io.IOException;
+import java.util.Collections;
+import java.util.List;
+import java.util.stream.Collectors;
+import org.apache.beam.model.pipeline.v1.ExternalTransforms.SchemaTransformPayload;
+import org.apache.beam.model.pipeline.v1.RunnerApi;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.RowCoder;
+import org.apache.beam.sdk.io.jdbc.JdbcIO;
+import org.apache.beam.sdk.options.PipelineOptionsFactory;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.schemas.SchemaTranslation;
+import org.apache.beam.sdk.transforms.Create;
+import org.apache.beam.sdk.util.construction.BeamUrns;
+import org.apache.beam.sdk.util.construction.PipelineTranslation;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.PCollectionRowTuple;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.grpc.v1p69p0.com.google.protobuf.InvalidProtocolBufferException;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.junit.ClassRule;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+import org.junit.rules.TemporaryFolder;
+import org.mockito.MockedStatic;
+import org.mockito.Mockito;
+
+public class SqlServerSchemaTransformTranslationTest {
+  @ClassRule public static final TemporaryFolder TEMPORARY_FOLDER = new TemporaryFolder();
+
+  @Rule public transient ExpectedException thrown = ExpectedException.none();
+
+  static final WriteToSqlServerSchemaTransformProvider WRITE_PROVIDER =
+      new WriteToSqlServerSchemaTransformProvider();
+  static final ReadFromSqlServerSchemaTransformProvider READ_PROVIDER =
+      new ReadFromSqlServerSchemaTransformProvider();
+
+  static final Row READ_CONFIG =
+      Row.withSchema(READ_PROVIDER.configurationSchema())
+          .withFieldValue("jdbc_url", "jdbc:sqlserver://host:port;databaseName=database")
+          .withFieldValue("location", "test_table")
+          .withFieldValue("connection_properties", "some_property")
+          .withFieldValue("connection_init_sql", ImmutableList.<String>builder().build())
+          .withFieldValue("driver_class_name", null)
+          .withFieldValue("driver_jars", null)
+          .withFieldValue("disable_auto_commit", true)
+          .withFieldValue("fetch_size", 10)
+          .withFieldValue("num_partitions", 5)
+          .withFieldValue("output_parallelization", true)
+          .withFieldValue("partition_column", "col")
+          .withFieldValue("read_query", null)
+          .withFieldValue("username", "my_user")
+          .withFieldValue("password", "my_pass")
+          .build();
+
+  static final Row WRITE_CONFIG =
+      Row.withSchema(WRITE_PROVIDER.configurationSchema())
+          .withFieldValue("jdbc_url", "jdbc:sqlserver://host:port;databaseName=database")
+          .withFieldValue("location", "test_table")
+          .withFieldValue("autosharding", true)
+          .withFieldValue("connection_init_sql", ImmutableList.<String>builder().build())
+          .withFieldValue("connection_properties", "some_property")
+          .withFieldValue("driver_class_name", null)
+          .withFieldValue("driver_jars", null)
+          .withFieldValue("batch_size", 100L)
+          .withFieldValue("username", "my_user")
+          .withFieldValue("password", "my_pass")
+          .withFieldValue("write_statement", null)
+          .build();
+
+  @Test
+  public void testRecreateWriteTransformFromRow() {
+    SqlServerWriteSchemaTransform writeTransform =
+        (SqlServerWriteSchemaTransform) WRITE_PROVIDER.from(WRITE_CONFIG);
+
+    SqlServerWriteSchemaTransformTranslator translator =
+        new SqlServerWriteSchemaTransformTranslator();
+    Row translatedRow = translator.toConfigRow(writeTransform);
+
+    SqlServerWriteSchemaTransform writeTransformFromRow =
+        translator.fromConfigRow(translatedRow, PipelineOptionsFactory.create());
+
+    assertEquals(WRITE_CONFIG, writeTransformFromRow.getConfigurationRow());
+  }
+
+  @Test
+  public void testWriteTransformProtoTranslation()
+      throws InvalidProtocolBufferException, IOException {
+    // First build a pipeline
+    Pipeline p = Pipeline.create();
+    Schema inputSchema = Schema.builder().addStringField("name").build();
+    PCollection<Row> input =
+        p.apply(
+                Create.of(
+                    Collections.singletonList(
+                        Row.withSchema(inputSchema).addValue("test").build())))
+            .setRowSchema(inputSchema);
+
+    SqlServerWriteSchemaTransform writeTransform =
+        (SqlServerWriteSchemaTransform) WRITE_PROVIDER.from(WRITE_CONFIG);
+    PCollectionRowTuple.of("input", input).apply(writeTransform);
+
+    // Then translate the pipeline to a proto and extract SqlServerWriteSchemaTransform proto
+    RunnerApi.Pipeline pipelineProto = PipelineTranslation.toProto(p);
+    List<RunnerApi.PTransform> writeTransformProto =
+        pipelineProto.getComponents().getTransformsMap().values().stream()
+            .filter(
+                tr -> {
+                  RunnerApi.FunctionSpec spec = tr.getSpec();
+                  try {
+                    return spec.getUrn().equals(BeamUrns.getUrn(SCHEMA_TRANSFORM))
+                        && SchemaTransformPayload.parseFrom(spec.getPayload())
+                            .getIdentifier()
+                            .equals(WRITE_PROVIDER.identifier());
+                  } catch (InvalidProtocolBufferException e) {
+                    throw new RuntimeException(e);
+                  }
+                })
+            .collect(Collectors.toList());
+    assertEquals(1, writeTransformProto.size());
+    RunnerApi.FunctionSpec spec = writeTransformProto.get(0).getSpec();
+
+    // Check that the proto contains correct values
+    SchemaTransformPayload payload = SchemaTransformPayload.parseFrom(spec.getPayload());
+    Schema schemaFromSpec = SchemaTranslation.schemaFromProto(payload.getConfigurationSchema());
+    assertEquals(WRITE_PROVIDER.configurationSchema(), schemaFromSpec);
+    Row rowFromSpec = RowCoder.of(schemaFromSpec).decode(payload.getConfigurationRow().newInput());
+
+    assertEquals(WRITE_CONFIG, rowFromSpec);
+
+    // Use the information in the proto to recreate the SqlServerWriteSchemaTransform
+    SqlServerWriteSchemaTransformTranslator translator =
+        new SqlServerWriteSchemaTransformTranslator();
+    SqlServerWriteSchemaTransform writeTransformFromSpec =
+        translator.fromConfigRow(rowFromSpec, PipelineOptionsFactory.create());
+
+    assertEquals(WRITE_CONFIG, writeTransformFromSpec.getConfigurationRow());
+  }
+
+  @Test
+  public void testReCreateReadTransformFromRow() {
+    // setting a subset of fields here.
+    SqlServerReadSchemaTransform readTransform =
+        (SqlServerReadSchemaTransform) READ_PROVIDER.from(READ_CONFIG);
+
+    SqlServerReadSchemaTransformTranslator translator =
+        new SqlServerReadSchemaTransformTranslator();
+    Row row = translator.toConfigRow(readTransform);
+
+    SqlServerReadSchemaTransform readTransformFromRow =
+        translator.fromConfigRow(row, PipelineOptionsFactory.create());
+
+    assertEquals(READ_CONFIG, readTransformFromRow.getConfigurationRow());
+  }
+
+  @Test
+  public void testReadTransformProtoTranslation()
+      throws InvalidProtocolBufferException, IOException {
+    // First build a pipeline
+    Pipeline p = Pipeline.create();
+
+    SqlServerReadSchemaTransform readTransform =
+        (SqlServerReadSchemaTransform) READ_PROVIDER.from(READ_CONFIG);
+
+    // Mock inferBeamSchema since it requires database connection.
+    Schema expectedSchema = Schema.builder().addStringField("name").build();
+    try (MockedStatic<JdbcIO.ReadRows> mock = Mockito.mockStatic(JdbcIO.ReadRows.class)) {
+      mock.when(() -> JdbcIO.ReadRows.inferBeamSchema(Mockito.any(), Mockito.any()))
+          .thenReturn(expectedSchema);
+      PCollectionRowTuple.empty(p).apply(readTransform);
+    }
+
+    // Then translate the pipeline to a proto and extract SqlServerReadSchemaTransform proto
+    RunnerApi.Pipeline pipelineProto = PipelineTranslation.toProto(p);
+    List<RunnerApi.PTransform> readTransformProto =
+        pipelineProto.getComponents().getTransformsMap().values().stream()
+            .filter(
+                tr -> {
+                  RunnerApi.FunctionSpec spec = tr.getSpec();
+                  try {
+                    return spec.getUrn().equals(BeamUrns.getUrn(SCHEMA_TRANSFORM))
+                        && SchemaTransformPayload.parseFrom(spec.getPayload())
+                            .getIdentifier()
+                            .equals(READ_PROVIDER.identifier());
+                  } catch (InvalidProtocolBufferException e) {
+                    throw new RuntimeException(e);
+                  }
+                })
+            .collect(Collectors.toList());
+    assertEquals(1, readTransformProto.size());
+    RunnerApi.FunctionSpec spec = readTransformProto.get(0).getSpec();
+
+    // Check that the proto contains correct values
+    SchemaTransformPayload payload = SchemaTransformPayload.parseFrom(spec.getPayload());
+    Schema schemaFromSpec = SchemaTranslation.schemaFromProto(payload.getConfigurationSchema());
+    assertEquals(READ_PROVIDER.configurationSchema(), schemaFromSpec);
+    Row rowFromSpec = RowCoder.of(schemaFromSpec).decode(payload.getConfigurationRow().newInput());
+    assertEquals(READ_CONFIG, rowFromSpec);
+
+    // Use the information in the proto to recreate the SqlServerReadSchemaTransform
+    SqlServerReadSchemaTransformTranslator translator =
+        new SqlServerReadSchemaTransformTranslator();
+    SqlServerReadSchemaTransform readTransformFromSpec =
+        translator.fromConfigRow(rowFromSpec, PipelineOptionsFactory.create());
+
+    assertEquals(READ_CONFIG, readTransformFromSpec.getConfigurationRow());
+  }
+}
diff --git a/sdks/java/io/jms/src/main/java/org/apache/beam/sdk/io/jms/JmsIO.java b/sdks/java/io/jms/src/main/java/org/apache/beam/sdk/io/jms/JmsIO.java
index 77b2e3f617c4..2a7cd62d33d2 100644
--- a/sdks/java/io/jms/src/main/java/org/apache/beam/sdk/io/jms/JmsIO.java
+++ b/sdks/java/io/jms/src/main/java/org/apache/beam/sdk/io/jms/JmsIO.java
@@ -847,6 +847,7 @@ private void closeAutoscaler() {
     }
 
     @Override
+    @SuppressWarnings("Finalize")
     protected void finalize() {
       doClose();
     }
diff --git a/sdks/java/io/kafka/build.gradle b/sdks/java/io/kafka/build.gradle
index 6e9b5aec0932..ba25078b64e3 100644
--- a/sdks/java/io/kafka/build.gradle
+++ b/sdks/java/io/kafka/build.gradle
@@ -74,6 +74,9 @@ dependencies {
   implementation (group: 'com.google.cloud.hosted.kafka', name: 'managed-kafka-auth-login-handler', version: '1.0.5') {
     // "kafka-clients" has to be provided since user can use its own version.
     exclude group: 'org.apache.kafka', module: 'kafka-clients'
+    // "kafka-schema-registry-client must be excluded per the Google Cloud documentation:
+    // https://cloud.google.com/managed-service-for-apache-kafka/docs/quickstart-avro#configure_and_run_the_producer
+    exclude group: "io.confluent", module: "kafka-schema-registry-client"
   }
   implementation ("io.confluent:kafka-avro-serializer:${confluentVersion}") {
     // zookeeper depends on "spotbugs-annotations:3.1.9" which clashes with current
diff --git a/sdks/java/io/kafka/jmh/src/main/java/org/apache/beam/sdk/io/kafka/jmh/KafkaIOUtilsBenchmark.java b/sdks/java/io/kafka/jmh/src/main/java/org/apache/beam/sdk/io/kafka/jmh/KafkaIOUtilsBenchmark.java
index 8523e2094895..36fb389053f7 100644
--- a/sdks/java/io/kafka/jmh/src/main/java/org/apache/beam/sdk/io/kafka/jmh/KafkaIOUtilsBenchmark.java
+++ b/sdks/java/io/kafka/jmh/src/main/java/org/apache/beam/sdk/io/kafka/jmh/KafkaIOUtilsBenchmark.java
@@ -33,6 +33,7 @@
 import org.openjdk.jmh.infra.IterationParams;
 import org.openjdk.jmh.infra.ThreadParams;
 
+@SuppressWarnings("SameNameButDifferent") // for MovingArg
 @BenchmarkMode(Mode.AverageTime)
 @OutputTimeUnit(TimeUnit.NANOSECONDS)
 @Threads(Threads.MAX)
diff --git a/sdks/java/io/kafka/kafka-integration-test.gradle b/sdks/java/io/kafka/kafka-integration-test.gradle
index 3bbab72ff77c..14d90349dedd 100644
--- a/sdks/java/io/kafka/kafka-integration-test.gradle
+++ b/sdks/java/io/kafka/kafka-integration-test.gradle
@@ -33,6 +33,7 @@ dependencies {
     // instead, rely on io/kafka/build.gradle's custom configurations with forced kafka-client resolutionStrategy
     testImplementation 'org.junit.jupiter:junit-jupiter-api:5.8.1'
     testRuntimeOnly 'org.junit.jupiter:junit-jupiter-engine:5.8.1'
+    testImplementation library.java.avro
 }
 
 configurations.create("kafkaVersion$undelimited")
diff --git a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaCommitOffset.java b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaCommitOffset.java
index fa692d3aaf42..ac6650c354d4 100644
--- a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaCommitOffset.java
+++ b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaCommitOffset.java
@@ -71,6 +71,8 @@ static class CommitOffsetDoFn extends DoFn<KV<KafkaSourceDescriptor, Long>, Void
       consumerFactoryFn = readSourceDescriptors.getConsumerFactoryFn();
     }
 
+    @SuppressWarnings(
+        "Slf4jDoNotLogMessageOfExceptionExplicitly") // for tests checking error message
     @RequiresStableInput
     @ProcessElement
     public void processElement(@Element KV<KafkaSourceDescriptor, Long> element) {
diff --git a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaIO.java b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaIO.java
index e632a39a8470..045a74a8507e 100644
--- a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaIO.java
+++ b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaIO.java
@@ -92,6 +92,7 @@
 import org.apache.beam.sdk.transforms.splittabledofn.WatermarkEstimators.Manual;
 import org.apache.beam.sdk.transforms.splittabledofn.WatermarkEstimators.MonotonicallyIncreasing;
 import org.apache.beam.sdk.transforms.splittabledofn.WatermarkEstimators.WallTime;
+import org.apache.beam.sdk.transforms.windowing.GlobalWindow;
 import org.apache.beam.sdk.util.Preconditions;
 import org.apache.beam.sdk.util.construction.PTransformMatchers;
 import org.apache.beam.sdk.util.construction.ReplacementOutputs;
@@ -109,6 +110,7 @@
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Joiner;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
 import org.apache.kafka.clients.CommonClientConfigs;
 import org.apache.kafka.clients.consumer.Consumer;
 import org.apache.kafka.clients.consumer.ConsumerConfig;
@@ -653,6 +655,14 @@ public static <K, V> WriteRecords<K, V> writeRecords() {
 
   ///////////////////////// Read Support \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
 
+  /**
+   * Default number of keys to redistribute Kafka inputs into.
+   *
+   * <p>This value is used when {@link Read#withRedistribute()} is used without {@link
+   * Read#withRedistributeNumKeys(int redistributeNumKeys)}.
+   */
+  private static final int DEFAULT_REDISTRIBUTE_NUM_KEYS = 32768;
+
   /**
    * A {@link PTransform} to read from Kafka topics. See {@link KafkaIO} for more information on
    * usage and configuration.
@@ -1093,19 +1103,60 @@ public Read<K, V> withTopicPartitions(List<TopicPartition> topicPartitions) {
 
     /**
      * Sets redistribute transform that hints to the runner to try to redistribute the work evenly.
+     *
+     * @return an updated {@link Read} transform.
      */
     public Read<K, V> withRedistribute() {
-      return toBuilder().setRedistributed(true).build();
+      Builder<K, V> builder = toBuilder().setRedistributed(true);
+      if (getRedistributeNumKeys() == 0) {
+        builder = builder.setRedistributeNumKeys(DEFAULT_REDISTRIBUTE_NUM_KEYS);
+      }
+      return builder.build();
     }
 
+    /**
+     * Hints to the runner that it can relax exactly-once processing guarantees, allowing duplicates
+     * in at-least-once processing mode of Kafka inputs.
+     *
+     * <p>Must be used with {@link KafkaIO#withRedistribute()}.
+     *
+     * <p>Not compatible with {@link KafkaIO#withOffsetDeduplication()}.
+     *
+     * @param allowDuplicates specifies whether to allow duplicates.
+     * @return an updated {@link Read} transform.
+     */
     public Read<K, V> withAllowDuplicates(Boolean allowDuplicates) {
       return toBuilder().setAllowDuplicates(allowDuplicates).build();
     }
 
+    /**
+     * Redistributes Kafka messages into a distinct number of keys for processing in subsequent
+     * steps.
+     *
+     * <p>If unset, defaults to {@link KafkaIO#DEFAULT_REDISTRIBUTE_NUM_KEYS}.
+     *
+     * <p>Use zero to disable bucketing into a distinct number of keys.
+     *
+     * <p>Must be used with {@link Read#withRedistribute()}.
+     *
+     * @param redistributeNumKeys specifies the total number of keys for redistributing inputs.
+     * @return an updated {@link Read} transform.
+     */
     public Read<K, V> withRedistributeNumKeys(int redistributeNumKeys) {
       return toBuilder().setRedistributeNumKeys(redistributeNumKeys).build();
     }
 
+    /**
+     * Hints to the runner to optimize the redistribute by minimizing the amount of data required
+     * for persistence as part of the redistribute operation.
+     *
+     * <p>Must be used with {@link KafkaIO#withRedistribute()}.
+     *
+     * <p>Not compatible with {@link KafkaIO#withAllowDuplicates()}.
+     *
+     * @param offsetDeduplication specifies whether to enable offset-based deduplication.
+     * @return an updated {@link Read} transform.
+     */
     public Read<K, V> withOffsetDeduplication(Boolean offsetDeduplication) {
       return toBuilder().setOffsetDeduplication(offsetDeduplication).build();
     }
@@ -1583,6 +1634,8 @@ public PCollection<KafkaRecord<K, V>> expand(PBegin input) {
       final KafkaIOReadImplementationCompatibilityResult compatibility =
           KafkaIOReadImplementationCompatibility.getCompatibility(this);
 
+      Read<K, V> kafkaRead = deduplicateTopics(this);
+
       // For a number of cases, we prefer using the UnboundedSource Kafka over the new SDF-based
       // Kafka source, for example,
       // * Experiments 'beam_fn_api_use_deprecated_read' and use_deprecated_read will result in
@@ -1599,9 +1652,9 @@ public PCollection<KafkaRecord<K, V>> expand(PBegin input) {
           || compatibility.supportsOnly(KafkaIOReadImplementation.LEGACY)
           || (compatibility.supports(KafkaIOReadImplementation.LEGACY)
               && runnerPrefersLegacyRead(input.getPipeline().getOptions()))) {
-        return input.apply(new ReadFromKafkaViaUnbounded<>(this, keyCoder, valueCoder));
+        return input.apply(new ReadFromKafkaViaUnbounded<>(kafkaRead, keyCoder, valueCoder));
       }
-      return input.apply(new ReadFromKafkaViaSDF<>(this, keyCoder, valueCoder));
+      return input.apply(new ReadFromKafkaViaSDF<>(kafkaRead, keyCoder, valueCoder));
     }
 
     private void checkRedistributeConfiguration() {
@@ -1617,10 +1670,14 @@ private void checkRedistributeConfiguration() {
             isRedistributed(),
             "withRedistributeNumKeys is ignored if withRedistribute() is not enabled on the transform.");
       }
-      if (getOffsetDeduplication() != null && getOffsetDeduplication()) {
+      if (getOffsetDeduplication() != null && getOffsetDeduplication() && isRedistributed()) {
         checkState(
-            isRedistributed() && !isAllowDuplicates(),
-            "withOffsetDeduplication should only be used with withRedistribute and withAllowDuplicates(false).");
+            !isAllowDuplicates(),
+            "withOffsetDeduplication and withRedistribute can only be used when withAllowDuplicates is set to false.");
+      }
+      if (getOffsetDeduplication() != null && getOffsetDeduplication() && !isRedistributed()) {
+        LOG.warn(
+            "Offsets used for deduplication are available in WindowedValue's metadata. Combining, aggregating, mutating them may risk with data loss.");
       }
     }
 
@@ -1648,6 +1705,29 @@ private void warnAboutUnsafeConfigurations(PBegin input) {
       }
     }
 
+    private Read<K, V> deduplicateTopics(Read<K, V> kafkaRead) {
+      final List<String> topics = getTopics();
+      if (topics != null && !topics.isEmpty()) {
+        final List<String> distinctTopics = topics.stream().distinct().collect(Collectors.toList());
+        if (topics.size() == distinctTopics.size()) {
+          return kafkaRead;
+        }
+        return kafkaRead.toBuilder().setTopics(distinctTopics).build();
+      }
+
+      final List<TopicPartition> topicPartitions = getTopicPartitions();
+      if (topicPartitions != null && !topicPartitions.isEmpty()) {
+        final List<TopicPartition> distinctTopicPartitions =
+            topicPartitions.stream().distinct().collect(Collectors.toList());
+        if (topicPartitions.size() == distinctTopicPartitions.size()) {
+          return kafkaRead;
+        }
+        return kafkaRead.toBuilder().setTopicPartitions(distinctTopicPartitions).build();
+      }
+
+      return kafkaRead;
+    }
+
     // This class is designed to mimic the Flink pipeline options, so we can check for the
     // checkpointingInterval property, but without needing to depend on the Flink runner
     // Do not use this
@@ -1765,13 +1845,18 @@ public PCollection<KafkaRecord<K, V>> expand(PBegin input) {
                   .withMaxReadTime(kafkaRead.getMaxReadTime())
                   .withMaxNumRecords(kafkaRead.getMaxNumRecords());
         }
-
+        PCollection<KafkaRecord<K, V>> output = input.getPipeline().apply(transform);
+        if (kafkaRead.getOffsetDeduplication() != null && kafkaRead.getOffsetDeduplication()) {
+          output =
+              output.apply(
+                  "Insert Offset for offset deduplication",
+                  ParDo.of(new OffsetDeduplicationIdExtractor<>()));
+        }
         if (kafkaRead.isRedistributed()) {
           if (kafkaRead.isCommitOffsetsInFinalizeEnabled() && kafkaRead.isAllowDuplicates()) {
             LOG.warn(
                 "Offsets committed due to usage of commitOffsetsInFinalize() and may not capture all work processed due to use of withRedistribute() with duplicates enabled");
           }
-          PCollection<KafkaRecord<K, V>> output = input.getPipeline().apply(transform);
 
           if (kafkaRead.getRedistributeNumKeys() == 0) {
             return output.apply(
@@ -1786,7 +1871,7 @@ public PCollection<KafkaRecord<K, V>> expand(PBegin input) {
                     .withNumBuckets((int) kafkaRead.getRedistributeNumKeys()));
           }
         }
-        return input.getPipeline().apply(transform);
+        return output;
       }
     }
 
@@ -1859,6 +1944,7 @@ public PCollection<KafkaRecord<K, V>> expand(PBegin input) {
                   .as(StreamingOptions.class)
                   .getUpdateCompatibilityVersion();
           if (requestedVersionString != null
+              && !requestedVersionString.isEmpty()
               && TransformUpgrader.compareVersions(requestedVersionString, "2.66.0") < 0) {
             // Use discouraged Impulse for backwards compatibility with previous released versions.
             output =
@@ -1894,6 +1980,29 @@ public PCollection<KafkaRecord<K, V>> expand(PBegin input) {
       }
     }
 
+    static class OffsetDeduplicationIdExtractor<K, V>
+        extends DoFn<KafkaRecord<K, V>, KafkaRecord<K, V>> {
+
+      @ProcessElement
+      public void processElement(ProcessContext pc) {
+        KafkaRecord<K, V> element = pc.element();
+        Long offset = null;
+        String uniqueId = null;
+        if (element != null) {
+          offset = element.getOffset();
+          uniqueId =
+              (String.format("%s-%d-%d", element.getTopic(), element.getPartition(), offset));
+        }
+        pc.outputWindowedValue(
+            element,
+            pc.timestamp(),
+            Lists.newArrayList(GlobalWindow.INSTANCE),
+            pc.pane(),
+            uniqueId,
+            offset);
+      }
+    }
+
     /**
      * A DoFn which generates {@link KafkaSourceDescriptor} based on the configuration of {@link
      * Read}.
@@ -2571,13 +2680,30 @@ public ReadSourceDescriptors<K, V> withProcessingTime() {
 
     /** Enable Redistribute. */
     public ReadSourceDescriptors<K, V> withRedistribute() {
-      return toBuilder().setRedistribute(true).build();
+      Builder<K, V> builder = toBuilder().setRedistribute(true);
+      if (getRedistributeNumKeys() == 0) {
+        builder = builder.setRedistributeNumKeys(DEFAULT_REDISTRIBUTE_NUM_KEYS);
+      }
+      return builder.build();
     }
 
     public ReadSourceDescriptors<K, V> withAllowDuplicates() {
       return toBuilder().setAllowDuplicates(true).build();
     }
 
+    /**
+     * Redistributes Kafka messages into a distinct number of keys for processing in subsequent
+     * steps.
+     *
+     * <p>If unset, defaults to {@link KafkaIO#DEFAULT_REDISTRIBUTE_NUM_KEYS}.
+     *
+     * <p>Use zero to disable bucketing into a distinct number of keys.
+     *
+     * <p>Must be used with {@link ReadSourceDescriptors#withRedistribute()}.
+     *
+     * @param redistributeNumKeys specifies the total number of keys for redistributing inputs.
+     * @return an updated {@link Read} transform.
+     */
     public ReadSourceDescriptors<K, V> withRedistributeNumKeys(int redistributeNumKeys) {
       return toBuilder().setRedistributeNumKeys(redistributeNumKeys).build();
     }
@@ -2831,6 +2957,7 @@ public PCollection<KafkaRecord<K, V>> expand(PCollection<KafkaSourceDescriptor>
                 .as(StreamingOptions.class)
                 .getUpdateCompatibilityVersion();
         if (requestedVersionString != null
+            && !requestedVersionString.isEmpty()
             && TransformUpgrader.compareVersions(requestedVersionString, "2.60.0") < 0) {
           // Redistribute is not allowed with commits prior to 2.59.0, since there is a Reshuffle
           // prior to the redistribute. The reshuffle will occur before commits are offsetted and
diff --git a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaIOUtils.java b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaIOUtils.java
index 1352d6bd864b..91aa85577959 100644
--- a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaIOUtils.java
+++ b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaIOUtils.java
@@ -168,18 +168,23 @@ private void setAvg(final double value) {
       AVG.lazySet(this, Double.doubleToRawLongBits(value));
     }
 
-    private long incrementAndGetNumUpdates() {
-      final long nextNumUpdates = Math.min(MOVING_AVG_WINDOW, numUpdates + 1);
-      numUpdates = nextNumUpdates;
-      return nextNumUpdates;
+    public void update(final double quantity) {
+      final double prevAvg = getAvg(); // volatile load (acquire)
+
+      final long nextNumUpdates = numUpdates + 1; // normal load
+      final double nextAvg = prevAvg + (quantity - prevAvg) / nextNumUpdates;
+
+      numUpdates = Math.min(MOVING_AVG_WINDOW, nextNumUpdates); // normal store
+      setAvg(nextAvg); // ordered store (release)
     }
 
-    public void update(final double quantity) {
+    public void update(final double sum, final long count) {
       final double prevAvg = getAvg(); // volatile load (acquire)
 
-      final long nextNumUpdates = incrementAndGetNumUpdates(); // normal load/store
-      final double nextAvg = prevAvg + (quantity - prevAvg) / nextNumUpdates; // normal load/store
+      final long nextNumUpdates = numUpdates + count; // normal load
+      final double nextAvg = prevAvg + (sum / count - prevAvg) * ((double) count / nextNumUpdates);
 
+      numUpdates = Math.min(MOVING_AVG_WINDOW, nextNumUpdates); // normal store
       setAvg(nextAvg); // ordered store (release)
     }
 
diff --git a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaReadSchemaTransformProvider.java b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaReadSchemaTransformProvider.java
index 22e183797482..57fac43640ab 100644
--- a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaReadSchemaTransformProvider.java
+++ b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaReadSchemaTransformProvider.java
@@ -21,6 +21,7 @@
 import static org.apache.beam.sdk.util.construction.BeamUrns.getUrn;
 
 import com.google.auto.service.AutoService;
+import io.confluent.kafka.serializers.KafkaAvroDeserializerConfig;
 import java.io.FileOutputStream;
 import java.io.IOException;
 import java.nio.ByteBuffer;
@@ -132,6 +133,13 @@ public List<String> outputCollectionNames() {
 
   static class KafkaReadSchemaTransform extends SchemaTransform {
     private final KafkaReadSchemaTransformConfiguration configuration;
+    private static final String googleManagedSchemaRegistryPrefix =
+        "https://managedkafka.googleapis.com/";
+
+    enum SchemaRegistryProvider {
+      UNSPECIFIED,
+      GOOGLE_MANAGED
+    }
 
     KafkaReadSchemaTransform(KafkaReadSchemaTransformConfiguration configuration) {
       this.configuration = configuration;
@@ -151,6 +159,13 @@ Row getConfigurationRow() {
       }
     }
 
+    private SchemaRegistryProvider getSchemaRegistryProvider(String confluentSchemaRegUrl) {
+      if (confluentSchemaRegUrl.contains(googleManagedSchemaRegistryPrefix)) {
+        return SchemaRegistryProvider.GOOGLE_MANAGED;
+      }
+      return SchemaRegistryProvider.UNSPECIFIED;
+    }
+
     @Override
     public PCollectionRowTuple expand(PCollectionRowTuple input) {
       configuration.validate();
@@ -178,16 +193,41 @@ public PCollectionRowTuple expand(PCollectionRowTuple input) {
       if (confluentSchemaRegUrl != null) {
         final String confluentSchemaRegSubject =
             checkArgumentNotNull(configuration.getConfluentSchemaRegistrySubject());
-        KafkaIO.Read<byte[], GenericRecord> kafkaRead =
+        KafkaIO.Read<byte[], GenericRecord> kafkaRead;
+
+        kafkaRead =
             KafkaIO.<byte[], GenericRecord>read()
                 .withTopic(configuration.getTopic())
                 .withConsumerFactoryFn(new ConsumerFactoryWithGcsTrustStores())
                 .withBootstrapServers(configuration.getBootstrapServers())
                 .withConsumerConfigUpdates(consumerConfigs)
-                .withKeyDeserializer(ByteArrayDeserializer.class)
-                .withValueDeserializer(
+                .withKeyDeserializer(ByteArrayDeserializer.class);
+
+        SchemaRegistryProvider provider = getSchemaRegistryProvider(confluentSchemaRegUrl);
+        switch (provider) {
+          case GOOGLE_MANAGED:
+            // Custom configs to authenticate with Google's Managed Schema Registry
+            Map<String, Object> configs = new HashMap<>();
+            configs.put(
+                KafkaAvroDeserializerConfig.SCHEMA_REGISTRY_URL_CONFIG, confluentSchemaRegUrl);
+            configs.put(KafkaAvroDeserializerConfig.BEARER_AUTH_CREDENTIALS_SOURCE, "CUSTOM");
+            configs.put(
+                "bearer.auth.custom.provider.class",
+                "com.google.cloud.hosted.kafka.auth.GcpBearerAuthCredentialProvider");
+
+            LOG.info("Constructing read transform with Google Managed Schema Registry URL.");
+            kafkaRead =
+                kafkaRead.withValueDeserializer(
+                    ConfluentSchemaRegistryDeserializerProvider.of(
+                        confluentSchemaRegUrl, confluentSchemaRegSubject, null, configs));
+            break;
+          case UNSPECIFIED:
+            kafkaRead =
+                kafkaRead.withValueDeserializer(
                     ConfluentSchemaRegistryDeserializerProvider.of(
                         confluentSchemaRegUrl, confluentSchemaRegSubject));
+        }
+
         Integer maxReadTimeSeconds = configuration.getMaxReadTimeSeconds();
         if (maxReadTimeSeconds != null) {
           kafkaRead = kafkaRead.withMaxReadTime(Duration.standardSeconds(maxReadTimeSeconds));
diff --git a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaWriteSchemaTransformProvider.java b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaWriteSchemaTransformProvider.java
index d6f46b11cb7d..e2a4f394ccdb 100644
--- a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaWriteSchemaTransformProvider.java
+++ b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaWriteSchemaTransformProvider.java
@@ -21,6 +21,7 @@
 
 import com.google.auto.service.AutoService;
 import com.google.auto.value.AutoValue;
+import io.confluent.kafka.serializers.KafkaAvroSerializer;
 import java.io.Serializable;
 import java.util.Collections;
 import java.util.HashMap;
@@ -28,7 +29,11 @@
 import java.util.Map;
 import java.util.Set;
 import javax.annotation.Nullable;
+import org.apache.avro.generic.GenericRecord;
 import org.apache.beam.model.pipeline.v1.ExternalTransforms;
+import org.apache.beam.sdk.coders.ByteArrayCoder;
+import org.apache.beam.sdk.coders.KvCoder;
+import org.apache.beam.sdk.extensions.avro.coders.AvroCoder;
 import org.apache.beam.sdk.extensions.avro.schemas.utils.AvroUtils;
 import org.apache.beam.sdk.extensions.protobuf.ProtoByteUtils;
 import org.apache.beam.sdk.metrics.Counter;
@@ -74,6 +79,8 @@ public class KafkaWriteSchemaTransformProvider
   public static final TupleTag<Row> ERROR_TAG = new TupleTag<Row>() {};
   public static final TupleTag<KV<byte[], byte[]>> OUTPUT_TAG =
       new TupleTag<KV<byte[], byte[]>>() {};
+  public static final TupleTag<KV<byte[], GenericRecord>> RECORD_OUTPUT_TAG =
+      new TupleTag<KV<byte[], GenericRecord>>() {};
   private static final Logger LOG =
       LoggerFactory.getLogger(KafkaWriteSchemaTransformProvider.class);
 
@@ -118,29 +125,32 @@ Row getConfigurationRow() {
       }
     }
 
-    public static class ErrorCounterFn extends DoFn<Row, KV<byte[], byte[]>> {
-      private final SerializableFunction<Row, byte[]> toBytesFn;
+    public abstract static class BaseKafkaWriterFn<T> extends DoFn<Row, KV<byte[], T>> {
+      private final SerializableFunction<Row, T> conversionFn;
       private final Counter errorCounter;
       private Long errorsInBundle = 0L;
       private final boolean handleErrors;
       private final Schema errorSchema;
+      private final TupleTag<KV<byte[], T>> successTag;
 
-      public ErrorCounterFn(
+      public BaseKafkaWriterFn(
           String name,
-          SerializableFunction<Row, byte[]> toBytesFn,
+          SerializableFunction<Row, T> conversionFn,
           Schema errorSchema,
-          boolean handleErrors) {
-        this.toBytesFn = toBytesFn;
+          boolean handleErrors,
+          TupleTag<KV<byte[], T>> successTag) {
+        this.conversionFn = conversionFn;
         this.errorCounter = Metrics.counter(KafkaWriteSchemaTransformProvider.class, name);
         this.handleErrors = handleErrors;
         this.errorSchema = errorSchema;
+        this.successTag = successTag;
       }
 
       @ProcessElement
       public void process(@DoFn.Element Row row, MultiOutputReceiver receiver) {
-        KV<byte[], byte[]> output = null;
+        KV<byte[], T> output = null;
         try {
-          output = KV.of(new byte[1], toBytesFn.apply(row));
+          output = KV.of(new byte[1], conversionFn.apply(row));
         } catch (Exception e) {
           if (!handleErrors) {
             throw new RuntimeException(e);
@@ -150,7 +160,7 @@ public void process(@DoFn.Element Row row, MultiOutputReceiver receiver) {
           receiver.get(ERROR_TAG).output(ErrorHandling.errorRecord(errorSchema, row, e));
         }
         if (output != null) {
-          receiver.get(OUTPUT_TAG).output(output);
+          receiver.get(successTag).output(output);
         }
       }
 
@@ -161,13 +171,35 @@ public void finish() {
       }
     }
 
+    public static class ErrorCounterFn extends BaseKafkaWriterFn<byte[]> {
+      public ErrorCounterFn(
+          String name,
+          SerializableFunction<Row, byte[]> toBytesFn,
+          Schema errorSchema,
+          boolean handleErrors) {
+        super(name, toBytesFn, errorSchema, handleErrors, OUTPUT_TAG);
+      }
+    }
+
+    public static class GenericRecordErrorCounterFn extends BaseKafkaWriterFn<GenericRecord> {
+      public GenericRecordErrorCounterFn(
+          String name,
+          SerializableFunction<Row, GenericRecord> toGenericRecordsFn,
+          Schema errorSchema,
+          boolean handleErrors) {
+        super(name, toGenericRecordsFn, errorSchema, handleErrors, RECORD_OUTPUT_TAG);
+      }
+    }
+
     @SuppressWarnings({
       "nullness" // TODO(https://github.com/apache/beam/issues/20497)
     })
     @Override
     public PCollectionRowTuple expand(PCollectionRowTuple input) {
       Schema inputSchema = input.get("input").getSchema();
+      org.apache.avro.Schema avroSchema = AvroUtils.toAvroSchema(inputSchema);
       final SerializableFunction<Row, byte[]> toBytesFn;
+      SerializableFunction<Row, GenericRecord> toGenericRecordsFn = null;
       if (configuration.getFormat().equals("RAW")) {
         int numFields = inputSchema.getFields().size();
         if (numFields != 1) {
@@ -198,36 +230,70 @@ public PCollectionRowTuple expand(PCollectionRowTuple input) {
           throw new IllegalArgumentException(
               "At least a descriptorPath or a proto Schema is required.");
         }
-
       } else {
-        toBytesFn = AvroUtils.getRowToAvroBytesFunction(inputSchema);
+        if (configuration.getProducerConfigUpdates() != null
+            && configuration.getProducerConfigUpdates().containsKey("schema.registry.url")) {
+          toGenericRecordsFn = AvroUtils.getRowToGenericRecordFunction(avroSchema);
+          toBytesFn = null;
+        } else {
+          toBytesFn = AvroUtils.getRowToAvroBytesFunction(inputSchema);
+        }
       }
 
       boolean handleErrors = ErrorHandling.hasOutput(configuration.getErrorHandling());
       final Map<String, String> configOverrides = configuration.getProducerConfigUpdates();
       Schema errorSchema = ErrorHandling.errorSchema(inputSchema);
-      PCollectionTuple outputTuple =
-          input
-              .get("input")
-              .apply(
-                  "Map rows to Kafka messages",
-                  ParDo.of(
-                          new ErrorCounterFn(
-                              "Kafka-write-error-counter", toBytesFn, errorSchema, handleErrors))
-                      .withOutputTags(OUTPUT_TAG, TupleTagList.of(ERROR_TAG)));
-
-      outputTuple
-          .get(OUTPUT_TAG)
-          .apply(
-              KafkaIO.<byte[], byte[]>write()
-                  .withTopic(configuration.getTopic())
-                  .withBootstrapServers(configuration.getBootstrapServers())
-                  .withProducerConfigUpdates(
-                      configOverrides == null
-                          ? new HashMap<>()
-                          : new HashMap<String, Object>(configOverrides))
-                  .withKeySerializer(ByteArraySerializer.class)
-                  .withValueSerializer(ByteArraySerializer.class));
+      PCollectionTuple outputTuple;
+      if (toGenericRecordsFn != null) {
+        LOG.info("Convert to GenericRecord with schema {}", avroSchema);
+        outputTuple =
+            input
+                .get("input")
+                .apply(
+                    "Map rows to Kafka messages",
+                    ParDo.of(
+                            new GenericRecordErrorCounterFn(
+                                "Kafka-write-error-counter",
+                                toGenericRecordsFn,
+                                errorSchema,
+                                handleErrors))
+                        .withOutputTags(RECORD_OUTPUT_TAG, TupleTagList.of(ERROR_TAG)));
+        HashMap<String, Object> producerConfig = new HashMap<>(configOverrides);
+        outputTuple
+            .get(RECORD_OUTPUT_TAG)
+            .setCoder(KvCoder.of(ByteArrayCoder.of(), AvroCoder.of(avroSchema)))
+            .apply(
+                "Map Rows to GenericRecords",
+                KafkaIO.<byte[], GenericRecord>write()
+                    .withTopic(configuration.getTopic())
+                    .withBootstrapServers(configuration.getBootstrapServers())
+                    .withProducerConfigUpdates(producerConfig)
+                    .withKeySerializer(ByteArraySerializer.class)
+                    .withValueSerializer((Class) KafkaAvroSerializer.class));
+      } else {
+        outputTuple =
+            input
+                .get("input")
+                .apply(
+                    "Map rows to Kafka messages",
+                    ParDo.of(
+                            new ErrorCounterFn(
+                                "Kafka-write-error-counter", toBytesFn, errorSchema, handleErrors))
+                        .withOutputTags(OUTPUT_TAG, TupleTagList.of(ERROR_TAG)));
+
+        outputTuple
+            .get(OUTPUT_TAG)
+            .apply(
+                KafkaIO.<byte[], byte[]>write()
+                    .withTopic(configuration.getTopic())
+                    .withBootstrapServers(configuration.getBootstrapServers())
+                    .withProducerConfigUpdates(
+                        configOverrides == null
+                            ? new HashMap<>()
+                            : new HashMap<String, Object>(configOverrides))
+                    .withKeySerializer(ByteArraySerializer.class)
+                    .withValueSerializer(ByteArraySerializer.class));
+      }
 
       // TODO: include output from KafkaIO Write once updated from PDone
       PCollection<Row> errorOutput =
diff --git a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaWriter.java b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaWriter.java
index f483c69d33bf..cad0f8a68d8c 100644
--- a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaWriter.java
+++ b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaWriter.java
@@ -202,7 +202,7 @@ public void onCompletion(RecordMetadata metadata, Exception exception) {
         }
         numSendFailures++;
         // don't log exception stacktrace here, exception will be propagated up.
-        LOG.warn("send failed : '{}'", exception.getMessage());
+        LOG.warn("send failed", exception);
       }
     }
   }
diff --git a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/ReadFromKafkaDoFn.java b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/ReadFromKafkaDoFn.java
index 70015847e19d..eab5ae083187 100644
--- a/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/ReadFromKafkaDoFn.java
+++ b/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/ReadFromKafkaDoFn.java
@@ -78,7 +78,10 @@
 import org.apache.kafka.common.config.ConfigDef;
 import org.apache.kafka.common.errors.SerializationException;
 import org.apache.kafka.common.serialization.Deserializer;
+import org.checkerframework.checker.nullness.qual.EnsuresNonNull;
+import org.checkerframework.checker.nullness.qual.MonotonicNonNull;
 import org.checkerframework.checker.nullness.qual.Nullable;
+import org.checkerframework.checker.nullness.qual.RequiresNonNull;
 import org.joda.time.Instant;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -316,6 +319,16 @@ public Consumer<byte[], byte[]> load(
   private final SerializableSupplier<LoadingCache<KafkaSourceDescriptor, Consumer<byte[], byte[]>>>
       pollConsumerCacheSupplier;
 
+  private transient @MonotonicNonNull LoadingCache<KafkaSourceDescriptor, MovingAvg>
+      avgRecordSizeCache;
+
+  private transient @MonotonicNonNull LoadingCache<
+          KafkaSourceDescriptor, KafkaLatestOffsetEstimator>
+      latestOffsetEstimatorCache;
+
+  private transient @MonotonicNonNull LoadingCache<KafkaSourceDescriptor, Consumer<byte[], byte[]>>
+      pollConsumerCache;
+
   // Valid between bundle start and bundle finish.
   private transient @Nullable Deserializer<K> keyDeserializerInstance = null;
   private transient @Nullable Deserializer<V> valueDeserializerInstance = null;
@@ -433,9 +446,12 @@ private void refresh() {
   }
 
   @GetInitialRestriction
+  @RequiresNonNull({"pollConsumerCache"})
   public OffsetRange initialRestriction(@Element KafkaSourceDescriptor kafkaSourceDescriptor) {
-    final Consumer<byte[], byte[]> consumer =
-        pollConsumerCacheSupplier.get().getUnchecked(kafkaSourceDescriptor);
+    final LoadingCache<KafkaSourceDescriptor, Consumer<byte[], byte[]>> pollConsumerCache =
+        this.pollConsumerCache;
+
+    final Consumer<byte[], byte[]> consumer = pollConsumerCache.getUnchecked(kafkaSourceDescriptor);
 
     final long startOffset;
     final long stopOffset;
@@ -513,12 +529,16 @@ public WatermarkEstimator<Instant> newWatermarkEstimator(
   }
 
   @GetSize
+  @RequiresNonNull({"avgRecordSizeCache", "latestOffsetEstimatorCache"})
   public double getSize(
       @Element KafkaSourceDescriptor kafkaSourceDescriptor, @Restriction OffsetRange offsetRange) {
+    final LoadingCache<KafkaSourceDescriptor, MovingAvg> avgRecordSizeCache =
+        this.avgRecordSizeCache;
+
     // If present, estimates the record size to offset gap ratio. Compacted topics may hold less
     // records than the estimated offset range due to record deletion within a partition.
     final @Nullable MovingAvg avgRecordSize =
-        avgRecordSizeCacheSupplier.get().getIfPresent(kafkaSourceDescriptor);
+        avgRecordSizeCache.getIfPresent(kafkaSourceDescriptor);
     // The tracker estimates the offset range by subtracting the last claimed position from the
     // currently observed end offset for the partition belonging to this split.
     final double estimatedOffsetRange =
@@ -533,8 +553,12 @@ public double getSize(
   }
 
   @NewTracker
+  @RequiresNonNull({"latestOffsetEstimatorCache"})
   public OffsetRangeTracker restrictionTracker(
       @Element KafkaSourceDescriptor kafkaSourceDescriptor, @Restriction OffsetRange restriction) {
+    final LoadingCache<KafkaSourceDescriptor, KafkaLatestOffsetEstimator>
+        latestOffsetEstimatorCache = this.latestOffsetEstimatorCache;
+
     if (restriction.getTo() < Long.MAX_VALUE) {
       return new OffsetRangeTracker(restriction);
     }
@@ -543,22 +567,28 @@ public OffsetRangeTracker restrictionTracker(
     // so we want to minimize the amount of connections that we start and track with Kafka. Another
     // point is that it has a memoized backlog, and this should make that more reusable estimations.
     return new GrowableOffsetRangeTracker(
-        restriction.getFrom(),
-        latestOffsetEstimatorCacheSupplier.get().getUnchecked(kafkaSourceDescriptor));
+        restriction.getFrom(), latestOffsetEstimatorCache.getUnchecked(kafkaSourceDescriptor));
   }
 
   @ProcessElement
+  @RequiresNonNull({"avgRecordSizeCache", "latestOffsetEstimatorCache", "pollConsumerCache"})
   public ProcessContinuation processElement(
       @Element KafkaSourceDescriptor kafkaSourceDescriptor,
       RestrictionTracker<OffsetRange, Long> tracker,
       WatermarkEstimator<Instant> watermarkEstimator,
       MultiOutputReceiver receiver)
       throws Exception {
-    final MovingAvg avgRecordSize = avgRecordSizeCacheSupplier.get().get(kafkaSourceDescriptor);
+    final LoadingCache<KafkaSourceDescriptor, MovingAvg> avgRecordSizeCache =
+        this.avgRecordSizeCache;
+    final LoadingCache<KafkaSourceDescriptor, KafkaLatestOffsetEstimator>
+        latestOffsetEstimatorCache = this.latestOffsetEstimatorCache;
+    final LoadingCache<KafkaSourceDescriptor, Consumer<byte[], byte[]>> pollConsumerCache =
+        this.pollConsumerCache;
+
+    final MovingAvg avgRecordSize = avgRecordSizeCache.get(kafkaSourceDescriptor);
     final KafkaLatestOffsetEstimator latestOffsetEstimator =
-        latestOffsetEstimatorCacheSupplier.get().get(kafkaSourceDescriptor);
-    final Consumer<byte[], byte[]> consumer =
-        pollConsumerCacheSupplier.get().get(kafkaSourceDescriptor);
+        latestOffsetEstimatorCache.get(kafkaSourceDescriptor);
+    final Consumer<byte[], byte[]> consumer = pollConsumerCache.get(kafkaSourceDescriptor);
     final Deserializer<K> keyDeserializerInstance =
         Preconditions.checkStateNotNull(this.keyDeserializerInstance);
     final Deserializer<V> valueDeserializerInstance =
@@ -588,6 +618,7 @@ public ProcessContinuation processElement(
               topicPartition, Optional.ofNullable(watermarkEstimator.currentWatermark()));
     }
 
+    Duration remainingTimeout = this.consumerPollingTimeout;
     long expectedOffset = tracker.currentRestriction().getFrom();
     consumer.resume(Collections.singleton(topicPartition));
     consumer.seek(topicPartition, expectedOffset);
@@ -595,16 +626,21 @@ public ProcessContinuation processElement(
 
     final KafkaMetrics kafkaMetrics = KafkaSinkMetrics.kafkaMetrics();
     try {
-      while (true) {
+      while (Duration.ZERO.compareTo(remainingTimeout) < 0) {
         // TODO: Remove this timer and use the existing fetch-latency-avg	metric.
         // A consumer will often have prefetches waiting to be returned immediately in which case
         // this timer may contribute more latency than it measures.
         // See https://shipilev.net/blog/2014/nanotrusting-nanotime/ for more information.
         pollTimer.reset().start();
         // Fetch the next records.
-        final ConsumerRecords<byte[], byte[]> rawRecords =
-            consumer.poll(this.consumerPollingTimeout);
-        kafkaMetrics.updateSuccessfulRpcMetrics(topicPartition.topic(), pollTimer.elapsed());
+        final ConsumerRecords<byte[], byte[]> rawRecords = consumer.poll(remainingTimeout);
+        final Duration elapsed = pollTimer.elapsed();
+        try {
+          remainingTimeout = remainingTimeout.minus(elapsed);
+        } catch (ArithmeticException e) {
+          remainingTimeout = Duration.ZERO;
+        }
+        kafkaMetrics.updateSuccessfulRpcMetrics(topicPartition.topic(), elapsed);
 
         // No progress when the polling timeout expired.
         // Self-checkpoint and move to process the next element.
@@ -624,57 +660,70 @@ public ProcessContinuation processElement(
 
         // Visible progress within the consumer polling timeout.
         // Partially or fully claim and process records in this batch.
-        for (ConsumerRecord<byte[], byte[]> rawRecord : rawRecords) {
-          if (!tracker.tryClaim(rawRecord.offset())) {
-            consumer.seek(topicPartition, rawRecord.offset());
-            consumer.pause(Collections.singleton(topicPartition));
+        long rawSizesSum = 0L;
+        long rawSizesCount = 0L;
+        long rawSizesMin = Long.MAX_VALUE;
+        long rawSizesMax = Long.MIN_VALUE;
+        try {
+          for (ConsumerRecord<byte[], byte[]> rawRecord : rawRecords) {
+            if (!tracker.tryClaim(rawRecord.offset())) {
+              consumer.seek(topicPartition, rawRecord.offset());
+              consumer.pause(Collections.singleton(topicPartition));
 
-            return ProcessContinuation.stop();
-          }
-          expectedOffset = rawRecord.offset() + 1;
-          try {
-            KafkaRecord<K, V> kafkaRecord =
-                new KafkaRecord<>(
-                    rawRecord.topic(),
-                    rawRecord.partition(),
-                    rawRecord.offset(),
-                    ConsumerSpEL.getRecordTimestamp(rawRecord),
-                    ConsumerSpEL.getRecordTimestampType(rawRecord),
-                    ConsumerSpEL.hasHeaders() ? rawRecord.headers() : null,
-                    ConsumerSpEL.deserializeKey(keyDeserializerInstance, rawRecord),
-                    ConsumerSpEL.deserializeValue(valueDeserializerInstance, rawRecord));
-            int recordSize =
-                (rawRecord.key() == null ? 0 : rawRecord.key().length)
-                    + (rawRecord.value() == null ? 0 : rawRecord.value().length);
-            avgRecordSize.update(recordSize);
-            rawSizes.update(recordSize);
-            Instant outputTimestamp;
-            // The outputTimestamp and watermark will be computed by timestampPolicy, where the
-            // WatermarkEstimator should be a manual one.
-            if (timestampPolicy != null) {
-              TimestampPolicyContext context =
-                  updateWatermarkManually(timestampPolicy, watermarkEstimator, tracker);
-              outputTimestamp = timestampPolicy.getTimestampForRecord(context, kafkaRecord);
-            } else {
-              Preconditions.checkStateNotNull(this.extractOutputTimestampFn);
-              outputTimestamp = extractOutputTimestampFn.apply(kafkaRecord);
+              return ProcessContinuation.stop();
             }
-            receiver
-                .get(recordTag)
-                .outputWithTimestamp(KV.of(kafkaSourceDescriptor, kafkaRecord), outputTimestamp);
-          } catch (SerializationException e) {
-            // This exception should only occur during the key and value deserialization when
-            // creating the Kafka Record
-            badRecordRouter.route(
-                receiver,
-                rawRecord,
-                null,
-                e,
-                "Failure deserializing Key or Value of Kakfa record reading from Kafka");
-            if (timestampPolicy != null) {
-              updateWatermarkManually(timestampPolicy, watermarkEstimator, tracker);
+            expectedOffset = rawRecord.offset() + 1;
+            try {
+              KafkaRecord<K, V> kafkaRecord =
+                  new KafkaRecord<>(
+                      rawRecord.topic(),
+                      rawRecord.partition(),
+                      rawRecord.offset(),
+                      ConsumerSpEL.getRecordTimestamp(rawRecord),
+                      ConsumerSpEL.getRecordTimestampType(rawRecord),
+                      ConsumerSpEL.hasHeaders() ? rawRecord.headers() : null,
+                      ConsumerSpEL.deserializeKey(keyDeserializerInstance, rawRecord),
+                      ConsumerSpEL.deserializeValue(valueDeserializerInstance, rawRecord));
+              int recordSize =
+                  (rawRecord.key() == null ? 0 : rawRecord.key().length)
+                      + (rawRecord.value() == null ? 0 : rawRecord.value().length);
+              rawSizesSum = rawSizesSum + recordSize;
+              rawSizesCount = rawSizesCount + 1L;
+              rawSizesMin = Math.min(rawSizesMin, recordSize);
+              rawSizesMax = Math.max(rawSizesMax, recordSize);
+              Instant outputTimestamp;
+              // The outputTimestamp and watermark will be computed by timestampPolicy, where the
+              // WatermarkEstimator should be a manual one.
+              if (timestampPolicy != null) {
+                TimestampPolicyContext context =
+                    updateWatermarkManually(timestampPolicy, watermarkEstimator, tracker);
+                outputTimestamp = timestampPolicy.getTimestampForRecord(context, kafkaRecord);
+              } else {
+                Preconditions.checkStateNotNull(this.extractOutputTimestampFn);
+                outputTimestamp = extractOutputTimestampFn.apply(kafkaRecord);
+              }
+              receiver
+                  .get(recordTag)
+                  .outputWithTimestamp(KV.of(kafkaSourceDescriptor, kafkaRecord), outputTimestamp);
+            } catch (SerializationException e) {
+              // This exception should only occur during the key and value deserialization when
+              // creating the Kafka Record
+              badRecordRouter.route(
+                  receiver,
+                  rawRecord,
+                  null,
+                  e,
+                  "Failure deserializing Key or Value of Kakfa record reading from Kafka");
+              if (timestampPolicy != null) {
+                updateWatermarkManually(timestampPolicy, watermarkEstimator, tracker);
+              }
             }
           }
+        } finally {
+          if (rawSizesCount > 0L) {
+            avgRecordSize.update(rawSizesSum, rawSizesCount);
+            rawSizes.update(rawSizesSum, rawSizesCount, rawSizesMin, rawSizesMax);
+          }
         }
 
         // Non-visible progress within the consumer polling timeout.
@@ -703,6 +752,12 @@ public ProcessContinuation processElement(
             kafkaSourceDescriptor.getPartition(),
             estimatedBacklogBytes);
       }
+
+      if (timestampPolicy != null) {
+        updateWatermarkManually(timestampPolicy, watermarkEstimator, tracker);
+      }
+
+      return ProcessContinuation.resume();
     } finally {
       kafkaMetrics.flushBufferedMetrics();
     }
@@ -734,7 +789,12 @@ public Coder<OffsetRange> restrictionCoder() {
   }
 
   @Setup
+  @EnsuresNonNull({"avgRecordSizeCache", "latestOffsetEstimatorCache", "pollConsumerCache"})
   public void setup() throws Exception {
+    avgRecordSizeCache = avgRecordSizeCacheSupplier.get();
+    latestOffsetEstimatorCache = latestOffsetEstimatorCacheSupplier.get();
+    pollConsumerCache = pollConsumerCacheSupplier.get();
+
     keyDeserializerInstance = keyDeserializerProvider.getDeserializer(consumerConfig, true);
     valueDeserializerInstance = valueDeserializerProvider.getDeserializer(consumerConfig, false);
     if (checkStopReadingFn != null) {
@@ -743,7 +803,15 @@ public void setup() throws Exception {
   }
 
   @Teardown
+  @RequiresNonNull({"avgRecordSizeCache", "latestOffsetEstimatorCache", "pollConsumerCache"})
   public void teardown() throws Exception {
+    final LoadingCache<KafkaSourceDescriptor, MovingAvg> avgRecordSizeCache =
+        this.avgRecordSizeCache;
+    final LoadingCache<KafkaSourceDescriptor, KafkaLatestOffsetEstimator>
+        latestOffsetEstimatorCache = this.latestOffsetEstimatorCache;
+    final LoadingCache<KafkaSourceDescriptor, Consumer<byte[], byte[]>> pollConsumerCache =
+        this.pollConsumerCache;
+
     try {
       if (valueDeserializerInstance != null) {
         Closeables.close(valueDeserializerInstance, true);
@@ -761,9 +829,9 @@ public void teardown() throws Exception {
     }
 
     // Allow the cache to perform clean up tasks when this instance is about to be deleted.
-    avgRecordSizeCacheSupplier.get().cleanUp();
-    latestOffsetEstimatorCacheSupplier.get().cleanUp();
-    pollConsumerCacheSupplier.get().cleanUp();
+    avgRecordSizeCache.cleanUp();
+    latestOffsetEstimatorCache.cleanUp();
+    pollConsumerCache.cleanUp();
   }
 
   private static Instant ensureTimestampWithinBounds(Instant timestamp) {
diff --git a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOIT.java b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOIT.java
index 0633887122ba..0e8cbd2183ca 100644
--- a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOIT.java
+++ b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOIT.java
@@ -879,9 +879,8 @@ public void runReadWriteKafkaViaManagedSchemaTransforms(
                             numb ->
                                 Row.withSchema(beamSchema)
                                     .withFieldValue("name", numb.toString())
-                                    .withFieldValue(
-                                        "userId", Long.valueOf(numb.hashCode())) // User ID
-                                    .withFieldValue("age", Long.valueOf(numb.intValue())) // Age
+                                    .withFieldValue("userId", (long) numb.hashCode()) // User ID
+                                    .withFieldValue("age", (long) numb.intValue()) // Age
                                     .withFieldValue("ageIsEven", numb % 2 == 0) // ageIsEven
                                     .withFieldValue("temperature", new Random(numb).nextDouble())
                                     .withFieldValue(
diff --git a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOReadImplementationCompatibilityTest.java b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOReadImplementationCompatibilityTest.java
index 8eda52bcec9e..26682946afca 100644
--- a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOReadImplementationCompatibilityTest.java
+++ b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOReadImplementationCompatibilityTest.java
@@ -116,7 +116,8 @@ private PipelineResult testReadTransformCreationWithImplementationBoundPropertie
                 false, /*redistribute*/
                 false, /*allowDuplicates*/
                 0, /*numKeys*/
-                null /*offsetDeduplication*/)));
+                null, /*offsetDeduplication*/
+                null /*topics*/)));
     return p.run();
   }
 
diff --git a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOTest.java b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOTest.java
index 6caa1868c995..83c2e1b38826 100644
--- a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOTest.java
+++ b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaIOTest.java
@@ -30,6 +30,7 @@
 import static org.hamcrest.Matchers.matchesPattern;
 import static org.hamcrest.Matchers.not;
 import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
 import static org.junit.Assert.assertNotNull;
 import static org.junit.Assert.assertNull;
 import static org.junit.Assert.assertTrue;
@@ -168,6 +169,7 @@
  * Tests of {@link KafkaIO}. Run with 'mvn test -Dkafka.clients.version=0.10.1.1', to test with a
  * specific Kafka version.
  */
+@SuppressWarnings("UnnecessaryLongToIntConversion") // for assert
 @RunWith(JUnit4.class)
 public class KafkaIOTest {
 
@@ -392,7 +394,8 @@ static KafkaIO.Read<Integer, Long> mkKafkaReadTransform(
         false, /*redistribute*/
         false, /*allowDuplicates*/
         0, /*numKeys*/
-        null /*offsetDeduplication*/);
+        null, /*offsetDeduplication*/
+        null /*topics*/);
   }
 
   static KafkaIO.Read<Integer, Long> mkKafkaReadTransformWithOffsetDedup(
@@ -404,7 +407,23 @@ static KafkaIO.Read<Integer, Long> mkKafkaReadTransformWithOffsetDedup(
         true, /*redistribute*/
         false, /*allowDuplicates*/
         100, /*numKeys*/
-        true /*offsetDeduplication*/);
+        true, /*offsetDeduplication*/
+        null /*topics*/);
+  }
+
+  static KafkaIO.Read<Integer, Long> mkKafkaReadTransformWithTopics(
+      int numElements,
+      @Nullable SerializableFunction<KV<Integer, Long>, Instant> timestampFn,
+      List<String> topics) {
+    return mkKafkaReadTransform(
+        numElements,
+        numElements,
+        timestampFn,
+        false, /*redistribute*/
+        false, /*allowDuplicates*/
+        0, /*numKeys*/
+        null, /*offsetDeduplication*/
+        topics /*topics*/);
   }
 
   /**
@@ -418,15 +437,21 @@ static KafkaIO.Read<Integer, Long> mkKafkaReadTransform(
       @Nullable Boolean redistribute,
       @Nullable Boolean withAllowDuplicates,
       @Nullable Integer numKeys,
-      @Nullable Boolean offsetDeduplication) {
+      @Nullable Boolean offsetDeduplication,
+      @Nullable List<String> topics) {
 
     KafkaIO.Read<Integer, Long> reader =
         KafkaIO.<Integer, Long>read()
             .withBootstrapServers(mkKafkaServers)
-            .withTopics(mkKafkaTopics)
+            .withTopics(topics != null ? topics : mkKafkaTopics)
             .withConsumerFactoryFn(
                 new ConsumerFactoryFn(
-                    mkKafkaTopics, 10, numElements, OffsetResetStrategy.EARLIEST)) // 20 partitions
+                    topics != null
+                        ? topics.stream().distinct().collect(Collectors.toList())
+                        : mkKafkaTopics,
+                    10,
+                    numElements,
+                    OffsetResetStrategy.EARLIEST)) // 20 partitions
             .withKeyDeserializer(IntegerDeserializer.class)
             .withValueDeserializer(LongDeserializer.class);
     if (maxNumRecords != null) {
@@ -610,7 +635,7 @@ public Long deserialize(String topic, Headers headers, byte[] data) {
   }
 
   @Test
-  public void testDeserializationWithHeaders() throws Exception {
+  public void testDeserializationWithHeaders() {
     // To assert that we continue to prefer the Deserializer API with headers in Kafka API 2.1.0
     // onwards
     int numElements = 1000;
@@ -648,6 +673,21 @@ public void testUnboundedSource() {
     p.run();
   }
 
+  @Test
+  public void testUnboundedSourceWithDuplicateTopics() {
+    int numElements = 1000;
+    List<String> topics = ImmutableList.of("topic_a", "topic_b", "topic_a");
+
+    PCollection<Long> input =
+        p.apply(
+                mkKafkaReadTransformWithTopics(numElements, new ValueAsTimestampFn(), topics)
+                    .withoutMetadata())
+            .apply(Values.create());
+
+    addCountingAsserts(input, numElements);
+    p.run();
+  }
+
   @Test
   public void testRiskyConfigurationWarnsProperly() {
     int numElements = 1000;
@@ -682,7 +722,8 @@ public void warningsWithAllowDuplicatesEnabledAndCommitOffsets() {
                         true, /*redistribute*/
                         true, /*allowDuplicates*/
                         0, /*numKeys*/
-                        null /*offsetDeduplication*/)
+                        null, /*offsetDeduplication*/
+                        null /*topics*/)
                     .commitOffsetsInFinalize()
                     .withConsumerConfigUpdates(
                         ImmutableMap.of(ConsumerConfig.GROUP_ID_CONFIG, "group_id"))
@@ -709,7 +750,8 @@ public void noWarningsWithNoAllowDuplicatesAndCommitOffsets() {
                         true, /*redistribute*/
                         false, /*allowDuplicates*/
                         0, /*numKeys*/
-                        null /*offsetDeduplication*/)
+                        null, /*offsetDeduplication*/
+                        null /*topics*/)
                     .commitOffsetsInFinalize()
                     .withConsumerConfigUpdates(
                         ImmutableMap.of(ConsumerConfig.GROUP_ID_CONFIG, "group_id"))
@@ -737,7 +779,8 @@ public void testNumKeysIgnoredWithRedistributeNotEnabled() {
                         false, /*redistribute*/
                         false, /*allowDuplicates*/
                         0, /*numKeys*/
-                        null /*offsetDeduplication*/)
+                        null, /*offsetDeduplication*/
+                        null /*topics*/)
                     .withRedistributeNumKeys(100)
                     .commitOffsetsInFinalize()
                     .withConsumerConfigUpdates(
@@ -750,6 +793,53 @@ public void testNumKeysIgnoredWithRedistributeNotEnabled() {
     p.run();
   }
 
+  @Test
+  public void testDefaultRedistributeNumKeys() {
+    int numElements = 1000;
+    // Redistribute is not used and does not modify the read transform further.
+    KafkaIO.Read<Integer, Long> read =
+        mkKafkaReadTransform(
+            numElements,
+            numElements,
+            new ValueAsTimestampFn(),
+            false, /*redistribute*/
+            false, /*allowDuplicates*/
+            null, /*numKeys*/
+            null, /*offsetDeduplication*/
+            null /*topics*/);
+    assertFalse(read.isRedistributed());
+    assertEquals(0, read.getRedistributeNumKeys());
+
+    // Redistribute is used and defaulted the number of keys due to no user setting.
+    read =
+        mkKafkaReadTransform(
+            numElements,
+            numElements,
+            new ValueAsTimestampFn(),
+            true, /*redistribute*/
+            false, /*allowDuplicates*/
+            null, /*numKeys*/
+            null, /*offsetDeduplication*/
+            null /*topics*/);
+    assertTrue(read.isRedistributed());
+    // Default is defined by DEFAULT_REDISTRIBUTE_NUM_KEYS in KafkaIO.
+    assertEquals(32768, read.getRedistributeNumKeys());
+
+    // Redistribute is set with user-specified the number of keys.
+    read =
+        mkKafkaReadTransform(
+            numElements,
+            numElements,
+            new ValueAsTimestampFn(),
+            true, /*redistribute*/
+            false, /*allowDuplicates*/
+            10, /*numKeys*/
+            null, /*offsetDeduplication*/
+            null /*topics*/);
+    assertTrue(read.isRedistributed());
+    assertEquals(10, read.getRedistributeNumKeys());
+  }
+
   @Test
   public void testDisableRedistributeKafkaOffsetLegacy() {
     thrown.expect(Exception.class);
@@ -1021,7 +1111,7 @@ public void testUnboundedSourceWithWrongTopic() {
 
   private static class ElementValueDiff extends DoFn<Long, Long> {
     @ProcessElement
-    public void processElement(ProcessContext c) throws Exception {
+    public void processElement(ProcessContext c) {
       c.output(c.element() - c.timestamp().getMillis());
     }
   }
@@ -1563,7 +1653,7 @@ public void testUnboundedReaderLogsCommitFailure() throws Exception {
   }
 
   @Test
-  public void testSink() throws Exception {
+  public void testSink() {
     // Simply read from kafka source and write to kafka sink. Then verify the records
     // are correctly published to mock kafka producer.
 
@@ -1619,7 +1709,7 @@ public void close() {
   }
 
   @Test
-  public void testSinkWithSerializationErrors() throws Exception {
+  public void testSinkWithSerializationErrors() {
     // Attempt to write 10 elements to Kafka, but they will all fail to serialize, and be sent to
     // the DLQ
 
@@ -1660,7 +1750,7 @@ public void testSinkWithSerializationErrors() throws Exception {
   }
 
   @Test
-  public void testValuesSink() throws Exception {
+  public void testValuesSink() {
     // similar to testSink(), but use values()' interface.
 
     int numElements = 1000;
@@ -1691,7 +1781,7 @@ public void testValuesSink() throws Exception {
   }
 
   @Test
-  public void testRecordsSink() throws Exception {
+  public void testRecordsSink() {
     // Simply read from kafka source and write to kafka sink using ProducerRecord transform. Then
     // verify the records are correctly published to mock kafka producer.
 
@@ -1725,7 +1815,7 @@ public void testRecordsSink() throws Exception {
   }
 
   @Test
-  public void testSinkToMultipleTopics() throws Exception {
+  public void testSinkToMultipleTopics() {
     // Set different output topic names
     int numElements = 1000;
 
@@ -1770,7 +1860,7 @@ public void testSinkToMultipleTopics() throws Exception {
   }
 
   @Test
-  public void testKafkaWriteHeaders() throws Exception {
+  public void testKafkaWriteHeaders() {
     // Set different output topic names
     int numElements = 1;
     SimpleEntry<String, String> header = new SimpleEntry<>("header_key", "header_value");
@@ -1814,7 +1904,7 @@ public void testKafkaWriteHeaders() throws Exception {
   }
 
   @Test
-  public void testSinkProducerRecordsWithCustomTS() throws Exception {
+  public void testSinkProducerRecordsWithCustomTS() {
     int numElements = 1000;
 
     try (MockProducerWrapper producerWrapper = new MockProducerWrapper(new LongSerializer())) {
@@ -1853,7 +1943,7 @@ public void testSinkProducerRecordsWithCustomTS() throws Exception {
   }
 
   @Test
-  public void testSinkProducerRecordsWithCustomPartition() throws Exception {
+  public void testSinkProducerRecordsWithCustomPartition() {
     int numElements = 1000;
 
     try (MockProducerWrapper producerWrapper = new MockProducerWrapper(new LongSerializer())) {
@@ -2109,7 +2199,8 @@ public void testUnboundedSourceStartReadTime() {
                         false, /*redistribute*/
                         false, /*allowDuplicates*/
                         0, /*numKeys*/
-                        null /*offsetDeduplication*/)
+                        null, /*offsetDeduplication*/
+                        null /*topics*/)
                     .withStartReadTime(new Instant(startTime))
                     .withoutMetadata())
             .apply(Values.create());
@@ -2154,7 +2245,8 @@ public void testUnboundedSourceStartReadTimeException() {
                     false, /*redistribute*/
                     false, /*allowDuplicates*/
                     0, /*numKeys*/
-                    null /*offsetDeduplication*/)
+                    null, /*offsetDeduplication*/
+                    null /*topics*/)
                 .withStartReadTime(new Instant(startTime))
                 .withoutMetadata())
         .apply(Values.create());
@@ -2299,7 +2391,7 @@ public void testSinkDisplayData() {
   }
 
   @Test
-  public void testSinkMetrics() throws Exception {
+  public void testSinkMetrics() {
     // Simply read from kafka source and write to kafka sink. Then verify the metrics are reported.
 
     int numElements = 1000;
diff --git a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaReadSchemaTransformProviderTest.java b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaReadSchemaTransformProviderTest.java
index e21f11518ff2..dc97dadf6e92 100644
--- a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaReadSchemaTransformProviderTest.java
+++ b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaReadSchemaTransformProviderTest.java
@@ -155,6 +155,26 @@ public void testBuildTransformWithAvroSchema() {
             .build());
   }
 
+  @Test
+  public void testBuildTransformWithAvroSchemaRegistry() {
+    ServiceLoader<SchemaTransformProvider> serviceLoader =
+        ServiceLoader.load(SchemaTransformProvider.class);
+    List<SchemaTransformProvider> providers =
+        StreamSupport.stream(serviceLoader.spliterator(), false)
+            .filter(provider -> provider.getClass() == KafkaReadSchemaTransformProvider.class)
+            .collect(Collectors.toList());
+    KafkaReadSchemaTransformProvider kafkaProvider =
+        (KafkaReadSchemaTransformProvider) providers.get(0);
+    kafkaProvider.from(
+        KafkaReadSchemaTransformConfiguration.builder()
+            .setFormat("AVRO")
+            .setTopic("anytopic")
+            .setBootstrapServers("anybootstrap")
+            .setConfluentSchemaRegistryUrl("anyschemaregistryurl")
+            .setConfluentSchemaRegistrySubject("anysubject")
+            .build());
+  }
+
   @Test
   public void testBuildTransformWithJsonSchema() throws IOException {
     ServiceLoader<SchemaTransformProvider> serviceLoader =
diff --git a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaWriteSchemaTransformProviderTest.java b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaWriteSchemaTransformProviderTest.java
index dffa6ece9d1b..b63a9334239c 100644
--- a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaWriteSchemaTransformProviderTest.java
+++ b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/KafkaWriteSchemaTransformProviderTest.java
@@ -24,9 +24,16 @@
 import java.util.Collections;
 import java.util.List;
 import java.util.Objects;
+import org.apache.avro.generic.GenericData;
+import org.apache.avro.generic.GenericRecord;
 import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.ByteArrayCoder;
+import org.apache.beam.sdk.coders.KvCoder;
+import org.apache.beam.sdk.extensions.avro.coders.AvroCoder;
+import org.apache.beam.sdk.extensions.avro.schemas.utils.AvroUtils;
 import org.apache.beam.sdk.extensions.protobuf.ProtoByteUtils;
 import org.apache.beam.sdk.io.kafka.KafkaWriteSchemaTransformProvider.KafkaWriteSchemaTransform.ErrorCounterFn;
+import org.apache.beam.sdk.io.kafka.KafkaWriteSchemaTransformProvider.KafkaWriteSchemaTransform.GenericRecordErrorCounterFn;
 import org.apache.beam.sdk.managed.Managed;
 import org.apache.beam.sdk.schemas.Schema;
 import org.apache.beam.sdk.schemas.transforms.providers.ErrorHandling;
@@ -53,6 +60,8 @@ public class KafkaWriteSchemaTransformProviderTest {
 
   private static final TupleTag<KV<byte[], byte[]>> OUTPUT_TAG =
       KafkaWriteSchemaTransformProvider.OUTPUT_TAG;
+  private static final TupleTag<KV<byte[], GenericRecord>> RECORD_OUTPUT_TAG =
+      KafkaWriteSchemaTransformProvider.RECORD_OUTPUT_TAG;
   private static final TupleTag<Row> ERROR_TAG = KafkaWriteSchemaTransformProvider.ERROR_TAG;
 
   private static final Schema BEAMSCHEMA =
@@ -126,7 +135,8 @@ public class KafkaWriteSchemaTransformProviderTest {
                   getClass().getResource("/proto_byte/file_descriptor/proto_byte_utils.pb"))
               .getPath(),
           "MyMessage");
-
+  final SerializableFunction<Row, GenericRecord> recordValueMapper =
+      AvroUtils.getRowToGenericRecordFunction(AvroUtils.toAvroSchema(BEAMSCHEMA));
   @Rule public transient TestPipeline p = TestPipeline.create();
 
   @Test
@@ -198,6 +208,38 @@ public void testKafkaErrorFnProtoSuccess() {
           + "  bool active = 3;\n"
           + "}";
 
+  @Test
+  public void testKafkaRecordErrorFnSuccess() throws Exception {
+    org.apache.avro.Schema avroSchema = AvroUtils.toAvroSchema(BEAMSCHEMA);
+
+    GenericRecord record1 = new GenericData.Record(avroSchema);
+    GenericRecord record2 = new GenericData.Record(avroSchema);
+    GenericRecord record3 = new GenericData.Record(avroSchema);
+    record1.put("name", "a");
+    record2.put("name", "b");
+    record3.put("name", "c");
+
+    List<KV<byte[], GenericRecord>> msg =
+        Arrays.asList(
+            KV.of(new byte[1], record1), KV.of(new byte[1], record2), KV.of(new byte[1], record3));
+
+    PCollection<Row> input = p.apply(Create.of(ROWS));
+    Schema errorSchema = ErrorHandling.errorSchema(BEAMSCHEMA);
+    PCollectionTuple output =
+        input.apply(
+            ParDo.of(
+                    new GenericRecordErrorCounterFn(
+                        "Kafka-write-error-counter", recordValueMapper, errorSchema, true))
+                .withOutputTags(RECORD_OUTPUT_TAG, TupleTagList.of(ERROR_TAG)));
+
+    output.get(ERROR_TAG).setRowSchema(errorSchema);
+    output
+        .get(RECORD_OUTPUT_TAG)
+        .setCoder(KvCoder.of(ByteArrayCoder.of(), AvroCoder.of(avroSchema)));
+    PAssert.that(output.get(RECORD_OUTPUT_TAG)).containsInAnyOrder(msg);
+    p.run().waitUntilFinish();
+  }
+
   @Test
   public void testBuildTransformWithManaged() {
     List<String> configs =
diff --git a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/ReadFromKafkaDoFnTest.java b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/ReadFromKafkaDoFnTest.java
index 4d22b1d6ea96..5e3e08a60664 100644
--- a/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/ReadFromKafkaDoFnTest.java
+++ b/sdks/java/io/kafka/src/test/java/org/apache/beam/sdk/io/kafka/ReadFromKafkaDoFnTest.java
@@ -46,6 +46,7 @@
 import org.apache.beam.sdk.options.PipelineOptions;
 import org.apache.beam.sdk.options.PipelineOptionsFactory;
 import org.apache.beam.sdk.runners.TransformHierarchy.Node;
+import org.apache.beam.sdk.testing.TestOutputReceiver;
 import org.apache.beam.sdk.testing.TestPipeline;
 import org.apache.beam.sdk.transforms.Create;
 import org.apache.beam.sdk.transforms.DoFn.MultiOutputReceiver;
@@ -337,10 +338,10 @@ public synchronized void seek(TopicPartition partition, long offset) {}
 
   private static class MockMultiOutputReceiver implements MultiOutputReceiver {
 
-    MockOutputReceiver<KV<KafkaSourceDescriptor, KafkaRecord<String, String>>> mockOutputReceiver =
-        new MockOutputReceiver<>();
+    TestOutputReceiver<KV<KafkaSourceDescriptor, KafkaRecord<String, String>>> mockOutputReceiver =
+        new TestOutputReceiver<>();
 
-    MockOutputReceiver<BadRecord> badOutputReceiver = new MockOutputReceiver<>();
+    TestOutputReceiver<BadRecord> badOutputReceiver = new TestOutputReceiver<>();
 
     @Override
     public @UnknownKeyFor @NonNull @Initialized <T> OutputReceiver<T> get(
@@ -370,26 +371,6 @@ public List<BadRecord> getBadRecords() {
     }
   }
 
-  private static class MockOutputReceiver<T> implements OutputReceiver<T> {
-
-    private final List<T> records = new ArrayList<>();
-
-    @Override
-    public void output(T output) {
-      records.add(output);
-    }
-
-    @Override
-    public void outputWithTimestamp(
-        T output, @UnknownKeyFor @NonNull @Initialized Instant timestamp) {
-      records.add(output);
-    }
-
-    public List<T> getOutputs() {
-      return this.records;
-    }
-  }
-
   private List<KV<KafkaSourceDescriptor, KafkaRecord<String, String>>> createExpectedRecords(
       KafkaSourceDescriptor descriptor,
       long startOffset,
diff --git a/sdks/java/io/mongodb/build.gradle b/sdks/java/io/mongodb/build.gradle
index b9e90082f0dc..56d29750dead 100644
--- a/sdks/java/io/mongodb/build.gradle
+++ b/sdks/java/io/mongodb/build.gradle
@@ -28,13 +28,14 @@ dependencies {
   implementation project(path: ":sdks:java:core", configuration: "shadow")
   implementation library.java.joda_time
   implementation library.java.mongo_java_driver
+  implementation library.java.mongo_bson
+  implementation library.java.mongodb_driver_core
   implementation library.java.slf4j_api
   implementation library.java.vendored_guava_32_1_2_jre
   testImplementation library.java.junit
   testImplementation project(path: ":sdks:java:io:common")
   testImplementation project(path: ":sdks:java:testing:test-utils")
-  testImplementation "de.flapdoodle.embed:de.flapdoodle.embed.mongo:3.0.0"
-  testImplementation "de.flapdoodle.embed:de.flapdoodle.embed.process:3.0.0"
+  testImplementation "de.flapdoodle.embed:de.flapdoodle.embed.mongo:3.5.4"
   testRuntimeOnly library.java.slf4j_jdk14
   testRuntimeOnly project(path: ":runners:direct-java", configuration: "shadow")
 }
diff --git a/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/FindQuery.java b/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/FindQuery.java
index 2131656d458a..d89db9dea54b 100644
--- a/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/FindQuery.java
+++ b/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/FindQuery.java
@@ -21,7 +21,7 @@
 
 import com.google.auto.value.AutoValue;
 import com.mongodb.BasicDBObject;
-import com.mongodb.MongoClient;
+import com.mongodb.MongoClientSettings;
 import com.mongodb.client.MongoCollection;
 import com.mongodb.client.MongoCursor;
 import com.mongodb.client.model.Projections;
@@ -79,7 +79,8 @@ private FindQuery withFilters(BsonDocument filters) {
 
   /** Convert the Bson filters into a BsonDocument via default encoding. */
   static BsonDocument bson2BsonDocument(Bson filters) {
-    return filters.toBsonDocument(BasicDBObject.class, MongoClient.getDefaultCodecRegistry());
+    return filters.toBsonDocument(
+        BasicDBObject.class, MongoClientSettings.getDefaultCodecRegistry());
   }
 
   /** Sets the filters to find. */
diff --git a/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/MongoDbGridFSIO.java b/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/MongoDbGridFSIO.java
index 07cc238c7e6b..71f8b291e0d5 100644
--- a/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/MongoDbGridFSIO.java
+++ b/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/MongoDbGridFSIO.java
@@ -21,15 +21,18 @@
 import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkNotNull;
 
 import com.google.auto.value.AutoValue;
-import com.mongodb.DB;
-import com.mongodb.DBCursor;
-import com.mongodb.DBObject;
-import com.mongodb.MongoClient;
-import com.mongodb.MongoClientURI;
-import com.mongodb.gridfs.GridFS;
-import com.mongodb.gridfs.GridFSDBFile;
-import com.mongodb.gridfs.GridFSInputFile;
-import com.mongodb.util.JSON;
+import com.mongodb.ConnectionString;
+import com.mongodb.MongoClientSettings;
+import com.mongodb.client.MongoClient;
+import com.mongodb.client.MongoClients;
+import com.mongodb.client.MongoCursor;
+import com.mongodb.client.MongoDatabase;
+import com.mongodb.client.gridfs.GridFSBucket;
+import com.mongodb.client.gridfs.GridFSBuckets;
+import com.mongodb.client.gridfs.GridFSDownloadStream;
+import com.mongodb.client.gridfs.GridFSUploadStream;
+import com.mongodb.client.gridfs.model.GridFSFile;
+import com.mongodb.client.gridfs.model.GridFSUploadOptions;
 import java.io.BufferedReader;
 import java.io.IOException;
 import java.io.InputStreamReader;
@@ -53,6 +56,7 @@
 import org.apache.beam.sdk.values.PBegin;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PDone;
+import org.bson.Document;
 import org.bson.types.ObjectId;
 import org.checkerframework.checker.nullness.qual.Nullable;
 import org.checkerframework.dataflow.qual.Pure;
@@ -117,16 +121,18 @@ public class MongoDbGridFSIO {
 
   /** Callback for the parser to use to submit data. */
   public interface ParserCallback<T> extends Serializable {
-    /** Output the object. The default timestamp will be the GridFSDBFile creation timestamp. */
+    /** Output the object. The default timestamp will be the GridFSFile creation timestamp. */
     void output(T output);
 
     /** Output the object using the specified timestamp. */
     void output(T output, Instant timestamp);
   }
 
-  /** Interface for the parser that is used to parse the GridFSDBFile into the appropriate types. */
+  /** Interface for the parser that is used to parse the GridFSFile into the appropriate types. */
   public interface Parser<T> extends Serializable {
-    void parse(GridFSDBFile input, ParserCallback<T> callback) throws IOException;
+    void parse(
+        GridFSFile gridFSFile, GridFSDownloadStream downloadStream, ParserCallback<T> callback)
+        throws IOException;
   }
 
   /**
@@ -134,11 +140,10 @@ public interface Parser<T> extends Serializable {
    * file into Strings. It uses the timestamp of the file for the event timestamp.
    */
   private static final Parser<String> TEXT_PARSER =
-      (input, callback) -> {
-        final Instant time = new Instant(input.getUploadDate().getTime());
+      (gridFSFile, downloadStream, callback) -> {
+        final Instant time = new Instant(gridFSFile.getUploadDate().getTime());
         try (BufferedReader reader =
-            new BufferedReader(
-                new InputStreamReader(input.getInputStream(), StandardCharsets.UTF_8))) {
+            new BufferedReader(new InputStreamReader(downloadStream, StandardCharsets.UTF_8))) {
           for (String line = reader.readLine(); line != null; line = reader.readLine()) {
             callback.output(line, time);
           }
@@ -197,12 +202,20 @@ static ConnectionConfiguration create(
     }
 
     MongoClient setupMongo() {
-      return uri() == null ? new MongoClient() : new MongoClient(new MongoClientURI(uri()));
+      if (uri() == null) {
+        return MongoClients.create();
+      }
+      MongoClientSettings settings =
+          MongoClientSettings.builder()
+              .applyConnectionString(new ConnectionString(Preconditions.checkStateNotNull(uri())))
+              .build();
+      return MongoClients.create(settings);
     }
 
-    GridFS setupGridFS(MongoClient mongo) {
-      DB db = database() == null ? mongo.getDB("gridfs") : mongo.getDB(database());
-      return bucket() == null ? new GridFS(db) : new GridFS(db, bucket());
+    GridFSBucket setupGridFS(MongoClient mongo) {
+      MongoDatabase db =
+          database() == null ? mongo.getDatabase("gridfs") : mongo.getDatabase(database());
+      return bucket() == null ? GridFSBuckets.create(db) : GridFSBuckets.create(db, bucket());
     }
   }
 
@@ -313,12 +326,12 @@ public PCollection<T> expand(PBegin input) {
                   ParDo.of(
                       new DoFn<ObjectId, T>() {
                         @Nullable MongoClient mongo;
-                        @Nullable GridFS gridfs;
+                        @Nullable GridFSBucket gridFSBucket;
 
                         @Setup
                         public void setup() {
                           mongo = source.spec.connectionConfiguration().setupMongo();
-                          gridfs = source.spec.connectionConfiguration().setupGridFS(mongo);
+                          gridFSBucket = source.spec.connectionConfiguration().setupGridFS(mongo);
                         }
 
                         @Teardown
@@ -331,12 +344,18 @@ public void teardown() {
 
                         @ProcessElement
                         public void processElement(final ProcessContext c) throws IOException {
-                          Preconditions.checkStateNotNull(gridfs);
+                          GridFSBucket bucket = Preconditions.checkStateNotNull(gridFSBucket);
                           ObjectId oid = c.element();
-                          GridFSDBFile file = gridfs.find(oid);
+                          GridFSDownloadStream downloadStream = bucket.openDownloadStream(oid);
+                          GridFSFile gridFSFile =
+                              bucket.find(com.mongodb.client.model.Filters.eq("_id", oid)).first();
+                          if (gridFSFile == null) {
+                            return; // Skip if file not found
+                          }
                           Parser<T> parser = Preconditions.checkStateNotNull(parser());
                           parser.parse(
-                              file,
+                              gridFSFile,
+                              downloadStream,
                               new ParserCallback<T>() {
                                 @Override
                                 public void output(T output, Instant timestamp) {
@@ -378,12 +397,12 @@ protected static class BoundedGridFSSource extends BoundedSource<ObjectId> {
         this.objectIds = objectIds;
       }
 
-      private DBCursor createCursor(GridFS gridfs) {
+      private MongoCursor<GridFSFile> createCursor(GridFSBucket gridFSBucket) {
         if (spec.filter() != null) {
-          DBObject query = (DBObject) JSON.parse(spec.filter());
-          return gridfs.getFileList(query);
+          Document query = Document.parse(spec.filter());
+          return gridFSBucket.find(query).iterator();
         }
-        return gridfs.getFileList();
+        return gridFSBucket.find().iterator();
       }
 
       @Override
@@ -391,20 +410,20 @@ public List<? extends BoundedSource<ObjectId>> split(
           long desiredBundleSizeBytes, PipelineOptions options) throws Exception {
         MongoClient mongo = spec.connectionConfiguration().setupMongo();
         try {
-          GridFS gridfs = spec.connectionConfiguration().setupGridFS(mongo);
-          DBCursor cursor = createCursor(gridfs);
+          GridFSBucket gridFSBucket = spec.connectionConfiguration().setupGridFS(mongo);
+          MongoCursor<GridFSFile> cursor = createCursor(gridFSBucket);
           long size = 0;
           List<BoundedGridFSSource> list = new ArrayList<>();
           List<ObjectId> objects = new ArrayList<>();
           while (cursor.hasNext()) {
-            GridFSDBFile file = (GridFSDBFile) cursor.next();
+            GridFSFile file = cursor.next();
             long len = file.getLength();
             if ((size + len) > desiredBundleSizeBytes && !objects.isEmpty()) {
               list.add(new BoundedGridFSSource(spec, objects));
               size = 0;
               objects = new ArrayList<>();
             }
-            objects.add((ObjectId) file.getId());
+            objects.add(file.getObjectId());
             size += len;
           }
           if (!objects.isEmpty() || list.isEmpty()) {
@@ -419,10 +438,11 @@ public List<? extends BoundedSource<ObjectId>> split(
       @Override
       public long getEstimatedSizeBytes(PipelineOptions options) throws Exception {
         try (MongoClient mongo = spec.connectionConfiguration().setupMongo();
-            DBCursor cursor = createCursor(spec.connectionConfiguration().setupGridFS(mongo))) {
+            MongoCursor<GridFSFile> cursor =
+                createCursor(spec.connectionConfiguration().setupGridFS(mongo))) {
           long size = 0;
           while (cursor.hasNext()) {
-            GridFSDBFile file = (GridFSDBFile) cursor.next();
+            GridFSFile file = cursor.next();
             size += file.getLength();
           }
           return size;
@@ -456,7 +476,7 @@ static class GridFSReader extends BoundedSource.BoundedReader<ObjectId> {
         final @Nullable List<ObjectId> objects;
 
         @Nullable MongoClient mongo;
-        @Nullable DBCursor cursor;
+        @Nullable MongoCursor<GridFSFile> cursor;
         @Nullable Iterator<ObjectId> iterator;
         @Nullable ObjectId current;
 
@@ -474,8 +494,8 @@ public BoundedSource<ObjectId> getCurrentSource() {
         public boolean start() throws IOException {
           if (objects == null) {
             mongo = source.spec.connectionConfiguration().setupMongo();
-            GridFS gridfs = source.spec.connectionConfiguration().setupGridFS(mongo);
-            cursor = source.createCursor(gridfs);
+            GridFSBucket gridFSBucket = source.spec.connectionConfiguration().setupGridFS(mongo);
+            cursor = source.createCursor(gridFSBucket);
           } else {
             iterator = objects.iterator();
           }
@@ -488,8 +508,8 @@ public boolean advance() throws IOException {
             current = iterator.next();
             return true;
           } else if (cursor != null && cursor.hasNext()) {
-            GridFSDBFile file = (GridFSDBFile) cursor.next();
-            current = (ObjectId) file.getId();
+            GridFSFile file = cursor.next();
+            current = file.getObjectId();
             return true;
           }
           current = null;
@@ -628,9 +648,9 @@ private static class GridFsWriteFn<T> extends DoFn<T, Void> {
     private final Write<T> spec;
 
     private transient @Nullable MongoClient mongo;
-    private transient @Nullable GridFS gridfs;
+    private transient @Nullable GridFSBucket gridFSBucket;
 
-    private transient @Nullable GridFSInputFile gridFsFile;
+    private transient @Nullable GridFSUploadStream gridFsUploadStream;
     private transient @Nullable OutputStream outputStream;
 
     public GridFsWriteFn(Write<T> spec) {
@@ -640,20 +660,22 @@ public GridFsWriteFn(Write<T> spec) {
     @Setup
     public void setup() throws Exception {
       mongo = spec.connectionConfiguration().setupMongo();
-      gridfs = spec.connectionConfiguration().setupGridFS(mongo);
+      gridFSBucket = spec.connectionConfiguration().setupGridFS(mongo);
     }
 
     @StartBundle
     public void startBundle() {
-      GridFS gridfs = Preconditions.checkStateNotNull(this.gridfs);
+      GridFSBucket gridFSBucket = Preconditions.checkStateNotNull(this.gridFSBucket);
       String filename = Preconditions.checkStateNotNull(spec.filename());
-      GridFSInputFile gridFsFile = gridfs.createFile(filename);
+
       if (spec.chunkSize() != null) {
-        gridFsFile.setChunkSize(spec.chunkSize());
+        gridFsUploadStream =
+            gridFSBucket.openUploadStream(
+                filename, new GridFSUploadOptions().chunkSizeBytes(spec.chunkSize().intValue()));
+      } else {
+        gridFsUploadStream = gridFSBucket.openUploadStream(filename);
       }
-      outputStream = gridFsFile.getOutputStream();
-
-      this.gridFsFile = gridFsFile;
+      outputStream = gridFsUploadStream;
     }
 
     @ProcessElement
@@ -665,35 +687,20 @@ public void processElement(ProcessContext context) throws Exception {
 
     @FinishBundle
     public void finishBundle() throws Exception {
-      if (outputStream != null) {
-        OutputStream outputStream = this.outputStream;
-        outputStream.flush();
-        outputStream.close();
-        this.outputStream = null;
-      }
-      if (gridFsFile != null) {
-        gridFsFile = null;
+      GridFSUploadStream uploadStream = gridFsUploadStream;
+      if (uploadStream != null) {
+        uploadStream.flush();
+        uploadStream.close();
+        gridFsUploadStream = null;
+        outputStream = null;
       }
     }
 
     @Teardown
     public void teardown() throws Exception {
-      try {
-        if (outputStream != null) {
-          OutputStream outputStream = this.outputStream;
-          outputStream.flush();
-          outputStream.close();
-          this.outputStream = null;
-        }
-        if (gridFsFile != null) {
-          gridFsFile = null;
-        }
-      } finally {
-        if (mongo != null) {
-          mongo.close();
-          mongo = null;
-          gridfs = null;
-        }
+      if (mongo != null) {
+        mongo.close();
+        mongo = null;
       }
     }
   }
diff --git a/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/MongoDbIO.java b/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/MongoDbIO.java
index 905c7418e26c..1283e873f2b6 100644
--- a/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/MongoDbIO.java
+++ b/sdks/java/io/mongodb/src/main/java/org/apache/beam/sdk/io/mongodb/MongoDbIO.java
@@ -22,12 +22,14 @@
 
 import com.google.auto.value.AutoValue;
 import com.mongodb.BasicDBObject;
+import com.mongodb.ConnectionString;
 import com.mongodb.MongoBulkWriteException;
-import com.mongodb.MongoClient;
-import com.mongodb.MongoClientOptions;
-import com.mongodb.MongoClientURI;
+import com.mongodb.MongoClientSettings;
+import com.mongodb.MongoClientSettings.Builder;
 import com.mongodb.MongoCommandException;
 import com.mongodb.client.AggregateIterable;
+import com.mongodb.client.MongoClient;
+import com.mongodb.client.MongoClients;
 import com.mongodb.client.MongoCollection;
 import com.mongodb.client.MongoCursor;
 import com.mongodb.client.MongoDatabase;
@@ -46,6 +48,7 @@
 import java.util.Map;
 import java.util.NoSuchElementException;
 import java.util.Optional;
+import java.util.concurrent.TimeUnit;
 import java.util.stream.Collectors;
 import javax.net.ssl.SSLContext;
 import org.apache.beam.sdk.coders.Coder;
@@ -64,6 +67,7 @@
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
 import org.bson.BsonDocument;
 import org.bson.BsonInt32;
+import org.bson.BsonObjectId;
 import org.bson.BsonString;
 import org.bson.Document;
 import org.bson.conversions.Bson;
@@ -362,22 +366,25 @@ public void populateDisplayData(DisplayData.Builder builder) {
     }
   }
 
-  private static MongoClientOptions.Builder getOptions(
+  private static MongoClientSettings.Builder getOptions(
       int maxConnectionIdleTime,
       boolean sslEnabled,
       boolean sslInvalidHostNameAllowed,
       boolean ignoreSSLCertificate) {
-    MongoClientOptions.Builder optionsBuilder = new MongoClientOptions.Builder();
-    optionsBuilder.maxConnectionIdleTime(maxConnectionIdleTime);
+    MongoClientSettings.Builder settingsBuilder = MongoClientSettings.builder();
+    settingsBuilder.applyToConnectionPoolSettings(
+        builder -> builder.maxConnectionIdleTime(maxConnectionIdleTime, TimeUnit.MILLISECONDS));
     if (sslEnabled) {
-      optionsBuilder.sslEnabled(sslEnabled).sslInvalidHostNameAllowed(sslInvalidHostNameAllowed);
-      if (ignoreSSLCertificate) {
-        SSLContext sslContext = SSLUtils.ignoreSSLCertificate();
-        optionsBuilder.sslContext(sslContext);
-        optionsBuilder.socketFactory(sslContext.getSocketFactory());
-      }
+      settingsBuilder.applyToSslSettings(
+          builder -> {
+            builder.enabled(sslEnabled).invalidHostNameAllowed(sslInvalidHostNameAllowed);
+            if (ignoreSSLCertificate) {
+              SSLContext sslContext = SSLUtils.ignoreSSLCertificate();
+              builder.context(sslContext);
+            }
+          });
     }
-    return optionsBuilder;
+    return settingsBuilder;
   }
 
   /** A MongoDB {@link BoundedSource} reading {@link Document} from a given instance. */
@@ -414,15 +421,15 @@ long getDocumentCount() {
       String uri = Preconditions.checkStateNotNull(spec.uri());
       String database = Preconditions.checkStateNotNull(spec.database());
       String collection = Preconditions.checkStateNotNull(spec.collection());
-      try (MongoClient mongoClient =
-          new MongoClient(
-              new MongoClientURI(
-                  uri,
-                  getOptions(
-                      spec.maxConnectionIdleTime(),
-                      spec.sslEnabled(),
-                      spec.sslInvalidHostNameAllowed(),
-                      spec.ignoreSSLCertificate())))) {
+      MongoClientSettings settings =
+          getOptions(
+                  spec.maxConnectionIdleTime(),
+                  spec.sslEnabled(),
+                  spec.sslInvalidHostNameAllowed(),
+                  spec.ignoreSSLCertificate())
+              .applyConnectionString(new ConnectionString(uri))
+              .build();
+      try (MongoClient mongoClient = MongoClients.create(settings)) {
         return getDocumentCount(mongoClient, database, collection);
       } catch (Exception e) {
         return -1;
@@ -446,15 +453,15 @@ public long getEstimatedSizeBytes(PipelineOptions pipelineOptions) {
       String uri = Preconditions.checkStateNotNull(spec.uri());
       String database = Preconditions.checkStateNotNull(spec.database());
       String collection = Preconditions.checkStateNotNull(spec.collection());
-      try (MongoClient mongoClient =
-          new MongoClient(
-              new MongoClientURI(
-                  uri,
-                  getOptions(
-                      spec.maxConnectionIdleTime(),
-                      spec.sslEnabled(),
-                      spec.sslInvalidHostNameAllowed(),
-                      spec.ignoreSSLCertificate())))) {
+      MongoClientSettings settings =
+          getOptions(
+                  spec.maxConnectionIdleTime(),
+                  spec.sslEnabled(),
+                  spec.sslInvalidHostNameAllowed(),
+                  spec.ignoreSSLCertificate())
+              .applyConnectionString(new ConnectionString(uri))
+              .build();
+      try (MongoClient mongoClient = MongoClients.create(settings)) {
         try {
           return getEstimatedSizeBytes(mongoClient, database, collection);
         } catch (MongoCommandException exception) {
@@ -483,15 +490,15 @@ public List<BoundedSource<Document>> split(
       String uri = Preconditions.checkStateNotNull(spec.uri());
       String database = Preconditions.checkStateNotNull(spec.database());
       String collection = Preconditions.checkStateNotNull(spec.collection());
-      try (MongoClient mongoClient =
-          new MongoClient(
-              new MongoClientURI(
-                  uri,
-                  getOptions(
-                      spec.maxConnectionIdleTime(),
-                      spec.sslEnabled(),
-                      spec.sslInvalidHostNameAllowed(),
-                      spec.ignoreSSLCertificate())))) {
+      MongoClientSettings settings =
+          getOptions(
+                  spec.maxConnectionIdleTime(),
+                  spec.sslEnabled(),
+                  spec.sslInvalidHostNameAllowed(),
+                  spec.ignoreSSLCertificate())
+              .applyConnectionString(new ConnectionString(uri))
+              .build();
+      try (MongoClient mongoClient = MongoClients.create(settings)) {
         MongoDatabase mongoDatabase = mongoClient.getDatabase(database);
 
         List<Document> splitKeys;
@@ -671,26 +678,39 @@ static List<BsonDocument> splitKeysToMatch(List<Document> splitKeys) {
         if (i == 0) {
           aggregates.add(Aggregates.match(Filters.lte("_id", splitKey)));
           if (splitKeys.size() == 1) {
-            aggregates.add(Aggregates.match(Filters.and(Filters.gt("_id", splitKey))));
+            aggregates.add(Aggregates.match(Filters.gt("_id", splitKey)));
           }
         } else if (i == splitKeys.size() - 1) {
           // this is the last split in the list, the filters define
           // the range from the previous split to the current split and also
           // the current split to the end
-          aggregates.add(
-              Aggregates.match(
-                  Filters.and(Filters.gt("_id", lowestBound), Filters.lte("_id", splitKey))));
-          aggregates.add(Aggregates.match(Filters.and(Filters.gt("_id", splitKey))));
+          // Create a custom BSON document with multiple conditions on the same field
+          BsonDocument rangeFilter =
+              new BsonDocument(
+                  "_id",
+                  new BsonDocument(
+                          "$gt", new BsonObjectId(Preconditions.checkStateNotNull(lowestBound)))
+                      .append("$lte", new BsonObjectId(splitKey)));
+          aggregates.add(Aggregates.match(rangeFilter));
+          aggregates.add(Aggregates.match(Filters.gt("_id", splitKey)));
         } else {
-          aggregates.add(
-              Aggregates.match(
-                  Filters.and(Filters.gt("_id", lowestBound), Filters.lte("_id", splitKey))));
+          // Create a custom BSON document with multiple conditions on the same field
+          BsonDocument rangeFilter =
+              new BsonDocument(
+                  "_id",
+                  new BsonDocument(
+                          "$gt", new BsonObjectId(Preconditions.checkStateNotNull(lowestBound)))
+                      .append("$lte", new BsonObjectId(splitKey)));
+          aggregates.add(Aggregates.match(rangeFilter));
         }
 
         lowestBound = splitKey;
       }
       return aggregates.stream()
-          .map(s -> s.toBsonDocument(BasicDBObject.class, MongoClient.getDefaultCodecRegistry()))
+          .map(
+              s ->
+                  s.toBsonDocument(
+                      BasicDBObject.class, MongoClientSettings.getDefaultCodecRegistry()))
           .collect(Collectors.toList());
     }
 
@@ -786,14 +806,15 @@ public void close() {
 
     private MongoClient createClient(Read spec) {
       String uri = Preconditions.checkStateNotNull(spec.uri(), "withUri() is required");
-      return new MongoClient(
-          new MongoClientURI(
-              uri,
-              getOptions(
+      MongoClientSettings settings =
+          getOptions(
                   spec.maxConnectionIdleTime(),
                   spec.sslEnabled(),
                   spec.sslInvalidHostNameAllowed(),
-                  spec.ignoreSSLCertificate())));
+                  spec.ignoreSSLCertificate())
+              .applyConnectionString(new ConnectionString(uri))
+              .build();
+      return MongoClients.create(settings);
     }
   }
 
@@ -985,15 +1006,15 @@ static class WriteFn extends DoFn<Document, Void> {
       @Setup
       public void createMongoClient() {
         String uri = Preconditions.checkStateNotNull(spec.uri());
-        client =
-            new MongoClient(
-                new MongoClientURI(
-                    uri,
-                    getOptions(
-                        spec.maxConnectionIdleTime(),
-                        spec.sslEnabled(),
-                        spec.sslInvalidHostNameAllowed(),
-                        spec.ignoreSSLCertificate())));
+        MongoClientSettings settings =
+            getOptions(
+                    spec.maxConnectionIdleTime(),
+                    spec.sslEnabled(),
+                    spec.sslInvalidHostNameAllowed(),
+                    spec.ignoreSSLCertificate())
+                .applyConnectionString(new ConnectionString(uri))
+                .build();
+        client = MongoClients.create(settings);
       }
 
       @StartBundle
diff --git a/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/FindQueryTest.java b/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/FindQueryTest.java
index df66179f3904..da90f92dc190 100644
--- a/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/FindQueryTest.java
+++ b/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/FindQueryTest.java
@@ -21,7 +21,7 @@
 
 import com.google.auto.value.AutoValue;
 import com.mongodb.BasicDBObject;
-import com.mongodb.MongoClient;
+import com.mongodb.MongoClientSettings;
 import com.mongodb.client.MongoCollection;
 import com.mongodb.client.MongoCursor;
 import com.mongodb.client.model.Projections;
@@ -79,7 +79,8 @@ private FindQueryTest withFilters(BsonDocument filters) {
 
   /** Convert the Bson filters into a BsonDocument via default encoding. */
   static BsonDocument bson2BsonDocument(Bson filters) {
-    return filters.toBsonDocument(BasicDBObject.class, MongoClient.getDefaultCodecRegistry());
+    return filters.toBsonDocument(
+        BasicDBObject.class, MongoClientSettings.getDefaultCodecRegistry());
   }
 
   /** Sets the filters to find. */
diff --git a/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/MongoDBGridFSIOTest.java b/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/MongoDBGridFSIOTest.java
index 09343606f228..d13185a08fb6 100644
--- a/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/MongoDBGridFSIOTest.java
+++ b/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/MongoDBGridFSIOTest.java
@@ -20,11 +20,13 @@
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
 
-import com.mongodb.DB;
-import com.mongodb.MongoClient;
-import com.mongodb.gridfs.GridFS;
-import com.mongodb.gridfs.GridFSDBFile;
-import com.mongodb.gridfs.GridFSInputFile;
+import com.mongodb.client.MongoClient;
+import com.mongodb.client.MongoClients;
+import com.mongodb.client.MongoDatabase;
+import com.mongodb.client.gridfs.GridFSBucket;
+import com.mongodb.client.gridfs.GridFSBuckets;
+import com.mongodb.client.gridfs.GridFSUploadStream;
+import com.mongodb.client.gridfs.model.GridFSFile;
 import de.flapdoodle.embed.mongo.MongodExecutable;
 import de.flapdoodle.embed.mongo.MongodProcess;
 import de.flapdoodle.embed.mongo.MongodStarter;
@@ -35,12 +37,10 @@
 import de.flapdoodle.embed.mongo.distribution.Version;
 import de.flapdoodle.embed.process.runtime.Network;
 import java.io.BufferedReader;
-import java.io.ByteArrayInputStream;
 import java.io.ByteArrayOutputStream;
 import java.io.DataInputStream;
 import java.io.InputStream;
 import java.io.InputStreamReader;
-import java.io.OutputStream;
 import java.io.OutputStreamWriter;
 import java.nio.charset.StandardCharsets;
 import java.util.ArrayList;
@@ -117,9 +117,9 @@ public static void start() throws Exception {
 
     LOG.info("Insert test data");
 
-    MongoClient client = new MongoClient("localhost", port);
-    DB database = client.getDB(DATABASE);
-    GridFS gridfs = new GridFS(database);
+    MongoClient client = MongoClients.create("mongodb://localhost:" + port);
+    MongoDatabase database = client.getDatabase(DATABASE);
+    GridFSBucket gridfs = GridFSBuckets.create(database);
 
     ByteArrayOutputStream out = new ByteArrayOutputStream();
     for (int x = 0; x < 100; x++) {
@@ -129,10 +129,12 @@ public static void start() throws Exception {
               .getBytes(StandardCharsets.UTF_8));
     }
     for (int x = 0; x < 5; x++) {
-      gridfs.createFile(new ByteArrayInputStream(out.toByteArray()), "file" + x).save();
+      try (GridFSUploadStream uploadStream = gridfs.openUploadStream("file" + x)) {
+        uploadStream.write(out.toByteArray());
+      }
     }
 
-    gridfs = new GridFS(database, "mapBucket");
+    GridFSBucket mapBucketGridfs = GridFSBuckets.create(database, "mapBucket");
     long now = System.currentTimeMillis();
     Random random = new Random();
     String[] scientists = {
@@ -148,26 +150,25 @@ public static void start() throws Exception {
       "Maxwell"
     };
     for (int x = 0; x < 10; x++) {
-      GridFSInputFile file = gridfs.createFile("file_" + x);
-      OutputStream outf = file.getOutputStream();
-      OutputStreamWriter writer = new OutputStreamWriter(outf, StandardCharsets.UTF_8);
-      for (int y = 0; y < 5000; y++) {
-        long time = now - random.nextInt(3600000);
-        String name = scientists[y % scientists.length];
-        writer.write(time + "\t");
-        writer.write(name + "\t");
-        writer.write(Integer.toString(random.nextInt(100)));
-        writer.write("\n");
-      }
-      for (int y = 0; y < scientists.length; y++) {
-        String name = scientists[y % scientists.length];
-        writer.write(now + "\t");
-        writer.write(name + "\t");
-        writer.write("101");
-        writer.write("\n");
+      try (GridFSUploadStream uploadStream = mapBucketGridfs.openUploadStream("file_" + x)) {
+        OutputStreamWriter writer = new OutputStreamWriter(uploadStream, StandardCharsets.UTF_8);
+        for (int y = 0; y < 5000; y++) {
+          long time = now - random.nextInt(3600000);
+          String name = scientists[y % scientists.length];
+          writer.write(time + "\t");
+          writer.write(name + "\t");
+          writer.write(Integer.toString(random.nextInt(100)));
+          writer.write("\n");
+        }
+        for (int y = 0; y < scientists.length; y++) {
+          String name = scientists[y % scientists.length];
+          writer.write(now + "\t");
+          writer.write(name + "\t");
+          writer.write("101");
+          writer.write("\n");
+        }
+        writer.flush();
       }
-      writer.flush();
-      writer.close();
     }
     client.close();
   }
@@ -208,11 +209,10 @@ public void testReadWithParser() {
                 .withDatabase(DATABASE)
                 .withBucket("mapBucket")
                 .<KV<String, Integer>>withParser(
-                    (input, callback) -> {
+                    (gridFSFile, downloadStream, callback) -> {
                       try (final BufferedReader reader =
                           new BufferedReader(
-                              new InputStreamReader(
-                                  input.getInputStream(), StandardCharsets.UTF_8))) {
+                              new InputStreamReader(downloadStream, StandardCharsets.UTF_8))) {
                         String line = reader.readLine();
                         while (line != null) {
                           try (Scanner scanner = new Scanner(line.trim())) {
@@ -311,19 +311,20 @@ public void testWriteMessage() throws Exception {
     MongoClient client = null;
     try {
       StringBuilder results = new StringBuilder();
-      client = new MongoClient("localhost", port);
-      DB database = client.getDB(DATABASE);
-      GridFS gridfs = new GridFS(database, "WriteTest");
-      List<GridFSDBFile> files = gridfs.find("WriteTestData");
-      assertTrue(files.size() > 0);
-      for (GridFSDBFile file : files) {
-        assertEquals(100, file.getChunkSize());
-        int l = (int) file.getLength();
-        try (InputStream ins = file.getInputStream()) {
-          DataInputStream dis = new DataInputStream(ins);
-          byte[] b = new byte[l];
-          dis.readFully(b);
-          results.append(new String(b, StandardCharsets.UTF_8));
+      client = MongoClients.create("mongodb://localhost:" + port);
+      MongoDatabase database = client.getDatabase(DATABASE);
+      GridFSBucket gridfs = GridFSBuckets.create(database, "WriteTest");
+
+      for (GridFSFile file : gridfs.find()) {
+        if (file.getFilename().equals("WriteTestData")) {
+          assertEquals(100, file.getChunkSize());
+          int l = (int) file.getLength();
+          try (InputStream ins = gridfs.openDownloadStream(file.getObjectId())) {
+            DataInputStream dis = new DataInputStream(ins);
+            byte[] b = new byte[l];
+            dis.readFully(b);
+            results.append(new String(b, StandardCharsets.UTF_8));
+          }
         }
       }
       String dataString = results.toString();
@@ -331,16 +332,17 @@ public void testWriteMessage() throws Exception {
         assertTrue(dataString.contains("Message " + x));
       }
 
-      files = gridfs.find("WriteTestIntData");
       boolean[] intResults = new boolean[100];
-      for (GridFSDBFile file : files) {
-        int l = (int) file.getLength();
-        try (InputStream ins = file.getInputStream()) {
-          DataInputStream dis = new DataInputStream(ins);
-          byte[] b = new byte[l];
-          dis.readFully(b);
-          for (byte aB : b) {
-            intResults[aB] = true;
+      for (GridFSFile file : gridfs.find()) {
+        if (file.getFilename().equals("WriteTestIntData")) {
+          int l = (int) file.getLength();
+          try (InputStream ins = gridfs.openDownloadStream(file.getObjectId())) {
+            DataInputStream dis = new DataInputStream(ins);
+            byte[] b = new byte[l];
+            dis.readFully(b);
+            for (byte aB : b) {
+              intResults[aB] = true;
+            }
           }
         }
       }
diff --git a/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/MongoDbIOTest.java b/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/MongoDbIOTest.java
index 4dda988e355c..cc85db937975 100644
--- a/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/MongoDbIOTest.java
+++ b/sdks/java/io/mongodb/src/test/java/org/apache/beam/sdk/io/mongodb/MongoDbIOTest.java
@@ -21,7 +21,8 @@
 import static org.hamcrest.Matchers.greaterThan;
 import static org.junit.Assert.assertEquals;
 
-import com.mongodb.MongoClient;
+import com.mongodb.client.MongoClient;
+import com.mongodb.client.MongoClients;
 import com.mongodb.client.MongoCollection;
 import com.mongodb.client.MongoDatabase;
 import com.mongodb.client.model.Filters;
@@ -107,7 +108,7 @@ public static void beforeClass() throws Exception {
             .build();
     mongodExecutable = mongodStarter.prepare(mongodConfig);
     mongodProcess = mongodExecutable.start();
-    client = new MongoClient("localhost", port);
+    client = MongoClients.create("mongodb://localhost:" + port);
     database = client.getDatabase(DATABASE_NAME);
 
     LOG.info("Insert test data");
diff --git a/sdks/java/io/mqtt/src/main/java/org/apache/beam/sdk/io/mqtt/MqttIO.java b/sdks/java/io/mqtt/src/main/java/org/apache/beam/sdk/io/mqtt/MqttIO.java
index efc51362d06a..78876eb6534d 100644
--- a/sdks/java/io/mqtt/src/main/java/org/apache/beam/sdk/io/mqtt/MqttIO.java
+++ b/sdks/java/io/mqtt/src/main/java/org/apache/beam/sdk/io/mqtt/MqttIO.java
@@ -422,20 +422,16 @@ public void populateDisplayData(DisplayData.Builder builder) {
   static class MqttCheckpointMark implements UnboundedSource.CheckpointMark, Serializable {
 
     @VisibleForTesting String clientId;
-    @VisibleForTesting Instant oldestMessageTimestamp = Instant.now();
     @VisibleForTesting transient List<Message> messages = new ArrayList<>();
 
-    public MqttCheckpointMark() {}
-
-    public MqttCheckpointMark(String id) {
-      clientId = id;
+    public MqttCheckpointMark(String id, List<Message> messages) {
+      this.clientId = id;
+      this.messages = messages;
     }
 
-    public void add(Message message, Instant timestamp) {
-      if (timestamp.isBefore(oldestMessageTimestamp)) {
-        oldestMessageTimestamp = timestamp;
-      }
-      messages.add(message);
+    @VisibleForTesting
+    MqttCheckpointMark(String id) {
+      this.clientId = id;
     }
 
     @Override
@@ -448,7 +444,6 @@ public void finalizeCheckpoint() {
           LOG.warn("Can't ack message for client ID {}", clientId, e);
         }
       }
-      oldestMessageTimestamp = Instant.now();
       messages.clear();
     }
 
@@ -464,7 +459,6 @@ public boolean equals(@Nullable Object other) {
       if (other instanceof MqttCheckpointMark) {
         MqttCheckpointMark that = (MqttCheckpointMark) other;
         return Objects.equals(this.clientId, that.clientId)
-            && Objects.equals(this.oldestMessageTimestamp, that.oldestMessageTimestamp)
             && Objects.deepEquals(this.messages, that.messages);
       } else {
         return false;
@@ -473,7 +467,38 @@ public boolean equals(@Nullable Object other) {
 
     @Override
     public int hashCode() {
-      return Objects.hash(clientId, oldestMessageTimestamp, messages);
+      return Objects.hash(clientId, messages);
+    }
+
+    static class Preparer {
+      @VisibleForTesting String clientId;
+      @VisibleForTesting Instant oldestMessageTimestamp = Instant.now();
+      @VisibleForTesting transient List<Message> messages = new ArrayList<>();
+
+      public Preparer(MqttCheckpointMark checkpointMark) {
+        clientId = checkpointMark.clientId;
+        messages = checkpointMark.messages;
+      }
+
+      public Preparer(String id) {
+        clientId = id;
+      }
+
+      public Preparer() {}
+
+      public void add(Message message, Instant timestamp) {
+        if (timestamp.isBefore(oldestMessageTimestamp)) {
+          oldestMessageTimestamp = timestamp;
+        }
+        messages.add(message);
+      }
+
+      MqttCheckpointMark newCheckpoint() {
+        List<Message> currentMessages = messages;
+        messages = new ArrayList<>();
+        oldestMessageTimestamp = Instant.now();
+        return new MqttCheckpointMark(clientId, currentMessages);
+      }
     }
   }
 
@@ -489,16 +514,20 @@ public UnboundedMqttSource(Read<T> spec) {
     @Override
     @SuppressWarnings("unchecked")
     public UnboundedReader<T> createReader(
-        PipelineOptions options, MqttCheckpointMark checkpointMark) {
+        PipelineOptions options, @Nullable MqttCheckpointMark checkpointMark) {
       final UnboundedMqttReader<T> unboundedMqttReader;
+      MqttCheckpointMark.Preparer preparer =
+          checkpointMark == null
+              ? new MqttCheckpointMark.Preparer()
+              : new MqttCheckpointMark.Preparer(checkpointMark);
       if (spec.withMetadata()) {
         unboundedMqttReader =
             new UnboundedMqttReader<>(
                 this,
-                checkpointMark,
+                preparer,
                 message -> (T) MqttRecord.of(message.getTopic(), message.getPayload()));
       } else {
-        unboundedMqttReader = new UnboundedMqttReader<>(this, checkpointMark);
+        unboundedMqttReader = new UnboundedMqttReader<>(this, preparer);
       }
 
       return unboundedMqttReader;
@@ -538,25 +567,26 @@ static class UnboundedMqttReader<T> extends UnboundedSource.UnboundedReader<T> {
     private BlockingConnection connection;
     private T current;
     private Instant currentTimestamp;
-    private MqttCheckpointMark checkpointMark;
+    private final MqttCheckpointMark.Preparer checkpointPreparer;
     private SerializableFunction<Message, T> extractFn;
 
-    public UnboundedMqttReader(UnboundedMqttSource<T> source, MqttCheckpointMark checkpointMark) {
+    public UnboundedMqttReader(
+        UnboundedMqttSource<T> source, MqttCheckpointMark.Preparer checkpointPreparer) {
       this.source = source;
       this.current = null;
-      if (checkpointMark != null) {
-        this.checkpointMark = checkpointMark;
+      if (checkpointPreparer != null) {
+        this.checkpointPreparer = checkpointPreparer;
       } else {
-        this.checkpointMark = new MqttCheckpointMark();
+        this.checkpointPreparer = new MqttCheckpointMark.Preparer();
       }
       this.extractFn = message -> (T) message.getPayload();
     }
 
     public UnboundedMqttReader(
         UnboundedMqttSource<T> source,
-        MqttCheckpointMark checkpointMark,
+        MqttCheckpointMark.Preparer checkpointPreparer,
         SerializableFunction<Message, T> extractFn) {
-      this(source, checkpointMark);
+      this(source, checkpointPreparer);
       this.extractFn = extractFn;
     }
 
@@ -567,7 +597,7 @@ public boolean start() throws IOException {
       try {
         client = spec.connectionConfiguration().createClient();
         LOG.debug("Reader client ID is {}", client.getClientId());
-        checkpointMark.clientId = client.getClientId().toString();
+        checkpointPreparer.clientId = client.getClientId().toString();
         connection = createConnection(client);
         connection.subscribe(
             new Topic[] {new Topic(spec.connectionConfiguration().getTopic(), QoS.AT_LEAST_ONCE)});
@@ -587,7 +617,7 @@ public boolean advance() throws IOException {
         }
         current = this.extractFn.apply(message);
         currentTimestamp = Instant.now();
-        checkpointMark.add(message, currentTimestamp);
+        checkpointPreparer.add(message, currentTimestamp);
       } catch (Exception e) {
         throw new IOException(e);
       }
@@ -608,12 +638,12 @@ public void close() throws IOException {
 
     @Override
     public Instant getWatermark() {
-      return checkpointMark.oldestMessageTimestamp;
+      return checkpointPreparer.oldestMessageTimestamp;
     }
 
     @Override
     public UnboundedSource.CheckpointMark getCheckpointMark() {
-      return checkpointMark;
+      return checkpointPreparer.newCheckpoint();
     }
 
     @Override
diff --git a/sdks/java/io/mqtt/src/test/java/org/apache/beam/sdk/io/mqtt/MqttIOTest.java b/sdks/java/io/mqtt/src/test/java/org/apache/beam/sdk/io/mqtt/MqttIOTest.java
index f0b4fab39535..754c88f0c6a4 100644
--- a/sdks/java/io/mqtt/src/test/java/org/apache/beam/sdk/io/mqtt/MqttIOTest.java
+++ b/sdks/java/io/mqtt/src/test/java/org/apache/beam/sdk/io/mqtt/MqttIOTest.java
@@ -27,6 +27,7 @@
 import java.io.ObjectOutputStream;
 import java.nio.charset.StandardCharsets;
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.Collection;
 import java.util.List;
 import java.util.Map;
@@ -50,11 +51,13 @@
 import org.apache.beam.sdk.values.PCollection;
 import org.fusesource.hawtbuf.Buffer;
 import org.fusesource.mqtt.client.BlockingConnection;
+import org.fusesource.mqtt.client.Callback;
 import org.fusesource.mqtt.client.MQTT;
 import org.fusesource.mqtt.client.Message;
 import org.fusesource.mqtt.client.QoS;
 import org.fusesource.mqtt.client.Topic;
 import org.joda.time.Duration;
+import org.joda.time.Instant;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Ignore;
@@ -286,6 +289,61 @@ public void testReceiveWithTimeoutAndNoData() throws Exception {
     pipeline.run();
   }
 
+  private static class FakeMessage extends Message {
+
+    private int ackCount;
+
+    public FakeMessage() {
+      super(null, null, null, null);
+      this.ackCount = 0;
+    }
+
+    @Override
+    public void ack() {
+      ++ackCount;
+    }
+
+    @Override
+    public void ack(final Callback<Void> unused) {
+      ++ackCount;
+    }
+
+    public int getAckCount() {
+      return ackCount;
+    }
+  }
+
+  @Test
+  public void testReadCheckpoint() {
+    MqttIO.MqttCheckpointMark.Preparer preparer = new MqttIO.MqttCheckpointMark.Preparer("id");
+    ArrayList<Message> messages = new ArrayList<>();
+    for (int i = 0; i < 5; ++i) {
+      messages.add(new FakeMessage());
+    }
+    preparer.add(messages.get(0), Instant.ofEpochMilli(20));
+    preparer.add(messages.get(1), Instant.ofEpochMilli(10));
+    preparer.add(messages.get(2), Instant.ofEpochMilli(30));
+    assertEquals(Instant.ofEpochMilli(10), preparer.oldestMessageTimestamp);
+    MqttIO.MqttCheckpointMark checkpointA = preparer.newCheckpoint();
+    preparer.add(messages.get(3), Instant.ofEpochMilli(40));
+    preparer.add(messages.get(4), Instant.ofEpochMilli(50));
+    MqttIO.MqttCheckpointMark checkpointB = preparer.newCheckpoint();
+    assertTrue(
+        Arrays.stream(messages.toArray()).allMatch((m -> ((FakeMessage) m).getAckCount() == 0)));
+    checkpointA.finalizeCheckpoint();
+    // only messages in finalized checkpoint acked
+    assertTrue(
+        Arrays.stream(messages.subList(0, 3).toArray())
+            .allMatch((m -> ((FakeMessage) m).getAckCount() == 1)));
+    assertTrue(
+        Arrays.stream(messages.subList(3, 5).toArray())
+            .allMatch((m -> ((FakeMessage) m).getAckCount() == 0)));
+    checkpointB.finalizeCheckpoint();
+    // all messaged acked once
+    assertTrue(
+        Arrays.stream(messages.toArray()).allMatch((m -> ((FakeMessage) m).getAckCount() == 1)));
+  }
+
   @Test
   public void testWrite() throws Exception {
     final int numberOfTestMessages = 200;
@@ -560,7 +618,6 @@ public void testReadObject() throws Exception {
     // the number of messages of the decoded checkpoint should be zero
     assertEquals(0, cp2.messages.size());
     assertEquals(cp1.clientId, cp2.clientId);
-    assertEquals(cp1.oldestMessageTimestamp, cp2.oldestMessageTimestamp);
   }
 
   /**
diff --git a/sdks/java/io/parquet/build.gradle b/sdks/java/io/parquet/build.gradle
index 73ba843e6b97..5d7bdd8f6628 100644
--- a/sdks/java/io/parquet/build.gradle
+++ b/sdks/java/io/parquet/build.gradle
@@ -48,9 +48,12 @@ dependencies {
   implementation "org.apache.parquet:parquet-common:$parquet_version"
   implementation "org.apache.parquet:parquet-hadoop:$parquet_version"
   implementation library.java.avro
-  provided library.java.hadoop_client
-  permitUnusedDeclared library.java.hadoop_client
-  provided library.java.hadoop_common
+  implementation library.java.vendored_calcite_1_40_0
+  // TODO(https://github.com/apache/beam/issues/21156): Determine how to build without this dependency
+  provided "org.immutables:value:2.8.8"
+  permitUnusedDeclared "org.immutables:value:2.8.8"
+  implementation library.java.hadoop_client
+  implementation library.java.hadoop_common
   testImplementation library.java.hadoop_client
   testImplementation project(path: ":sdks:java:core", configuration: "shadowTest")
   testImplementation project(path: ":sdks:java:extensions:avro")
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/package-info.java b/sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetFilter.java
similarity index 71%
rename from sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/package-info.java
rename to sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetFilter.java
index 31ce9c11cdc4..ccd326b97dd8 100644
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/translation/impl/package-info.java
+++ b/sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetFilter.java
@@ -15,10 +15,13 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+package org.apache.beam.sdk.io.parquet;
+
+import java.io.Serializable;
 
 /**
- * Java implementation of ZetaSQL functions.
- *
- * <p>Used only by {@link org.apache.beam.sdk.extensions.sql.impl.rel.BeamCalcRel}.
+ * A generic, serializable representation of a filter that can be pushed down into {@link
+ * ParquetIO}. This serves as an abstraction layer to hide the underlying Parquet-specific filter
+ * implementation.
  */
-package org.apache.beam.sdk.extensions.sql.zetasql.translation.impl;
+public interface ParquetFilter extends Serializable {}
diff --git a/sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetFilterFactory.java b/sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetFilterFactory.java
new file mode 100644
index 000000000000..bad304b4a132
--- /dev/null
+++ b/sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetFilterFactory.java
@@ -0,0 +1,565 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.parquet;
+
+import java.math.BigDecimal;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Set;
+import javax.annotation.Nullable;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexCall;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexInputRef;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexLiteral;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.rex.RexNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlKind;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.type.SqlTypeName;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableSet;
+import org.apache.parquet.filter2.predicate.FilterApi;
+import org.apache.parquet.filter2.predicate.FilterPredicate;
+import org.apache.parquet.filter2.predicate.Operators.BinaryColumn;
+import org.apache.parquet.io.api.Binary;
+
+/**
+ * Factory class for creating Parquet filter predicates from Calcite RexNode expressions.
+ *
+ * <p>This class converts SQL filter expressions (represented as Calcite RexNodes) into Parquet
+ * filter predicates that can be used for predicate pushdown during Parquet file reading. This
+ * enables significant performance improvements by filtering data at the storage level rather than
+ * after reading all data into memory.
+ *
+ * <p>Supported operations include:
+ *
+ * <ul>
+ *   <li>Comparison operators: =, !=, &lt;, &lt;=, &gt;, &gt;=
+ *   <li>Logical operators: AND, OR, NOT
+ *   <li>Set operations: IN, NOT IN
+ *   <li>Null checks: IS NULL, IS NOT NULL
+ * </ul>
+ *
+ * <p>Supported data types include:
+ *
+ * <ul>
+ *   <li>Integer types: TINYINT, SMALLINT, INTEGER, BIGINT
+ *   <li>Floating point: FLOAT, DOUBLE
+ *   <li>Boolean: BOOLEAN
+ *   <li>String types: CHAR, VARCHAR
+ *   <li>Binary types: BINARY, VARBINARY
+ *   <li>Decimal: DECIMAL
+ * </ul>
+ *
+ * <p>Example usage:
+ *
+ * <pre>{@code
+ * // Create a filter from SQL expressions
+ * List<RexNode> expressions = ...; // from SQL WHERE clause
+ * Schema beamSchema = ...; // Beam schema for the table
+ * ParquetFilter filter = ParquetFilterFactory.create(expressions, beamSchema);
+ *
+ * // Use with ParquetIO
+ * ParquetIO.read(schema)
+ *   .from(filePattern)
+ *   .withFilter(filter)
+ *   .withBeamSchemas(true);
+ * }</pre>
+ */
+public class ParquetFilterFactory {
+  private static final ImmutableMap<SqlKind, FilterCombiner> COMBINERS =
+      ImmutableMap.of(SqlKind.AND, FilterApi::and, SqlKind.OR, FilterApi::or);
+
+  private static final Set<SqlKind> COMPARISON_KINDS =
+      ImmutableSet.of(
+          SqlKind.EQUALS,
+          SqlKind.NOT_EQUALS,
+          SqlKind.GREATER_THAN,
+          SqlKind.GREATER_THAN_OR_EQUAL,
+          SqlKind.LESS_THAN,
+          SqlKind.LESS_THAN_OR_EQUAL);
+
+  /**
+   * Creates a ParquetFilter from a list of Calcite RexNode expressions.
+   *
+   * <p>This method converts SQL filter expressions into Parquet filter predicates. Only supported
+   * expressions will be converted; unsupported expressions are silently ignored, allowing for
+   * partial filter pushdown.
+   *
+   * @param expressions List of RexNode expressions representing SQL filter conditions. Can be null
+   *     or empty, in which case no filtering is applied.
+   * @param beamSchema The Beam schema for the table being filtered. Used to map column indices to
+   *     field names and validate column references.
+   * @return A ParquetFilter that can be used with ParquetIO for predicate pushdown.
+   * @throws IllegalArgumentException if beamSchema is null
+   */
+  public static ParquetFilter create(List<RexNode> expressions, Schema beamSchema) {
+    if (beamSchema == null) {
+      throw new IllegalArgumentException("Beam schema cannot be null");
+    }
+    FilterPredicate internalPredicate = toFilterPredicate(expressions, beamSchema);
+    return new ParquetFilterImpl(internalPredicate);
+  }
+
+  /**
+   * Creates a ParquetFilter from an existing Parquet FilterPredicate.
+   *
+   * <p>This method is useful when you already have a Parquet FilterPredicate and want to wrap it in
+   * the Beam ParquetFilter interface.
+   *
+   * @param predicate The Parquet FilterPredicate to wrap. Can be null.
+   * @return A ParquetFilter wrapping the provided predicate.
+   */
+  public static ParquetFilter fromPredicate(FilterPredicate predicate) {
+    return new ParquetFilterImpl(predicate);
+  }
+
+  /** Private implementation of our filter interface that holds the real Parquet object. */
+  static class ParquetFilterImpl implements ParquetFilter {
+    private final @Nullable FilterPredicate predicate;
+
+    ParquetFilterImpl(@Nullable FilterPredicate predicate) {
+      this.predicate = predicate;
+    }
+
+    // This method allows ParquetIO to "unwrap" the filter.
+    @Nullable
+    FilterPredicate getPredicate() {
+      return predicate;
+    }
+  }
+
+  @Nullable
+  private static FilterPredicate toFilterPredicate(List<RexNode> expressions, Schema beamSchema) {
+    if (expressions == null || expressions.isEmpty()) {
+      return null;
+    }
+
+    // Pre-allocate list with expected size for better performance
+    List<FilterPredicate> predicates = new ArrayList<>(expressions.size());
+    for (RexNode expr : expressions) {
+      if (expr == null) {
+        continue; // Skip null expressions
+      }
+      try {
+        FilterPredicate p = convert(expr, beamSchema);
+        if (p != null) {
+          predicates.add(p);
+        }
+      } catch (Exception e) {
+        // Log the error but continue processing other expressions
+        // This allows partial filter pushdown when some expressions fail
+        System.err.println(
+            "Failed to convert expression to Parquet filter: "
+                + expr
+                + ", error: "
+                + e.getMessage());
+      }
+    }
+
+    if (predicates.isEmpty()) {
+      return null;
+    }
+
+    // Optimize: if only one predicate, return it directly
+    if (predicates.size() == 1) {
+      return predicates.get(0);
+    }
+
+    return predicates.stream().reduce(FilterApi::and).orElse(null);
+  }
+
+  @Nullable
+  private static FilterPredicate convert(RexNode e, Schema beamSchema) {
+    SqlKind kind = e.getKind();
+    if (COMBINERS.containsKey(kind)) {
+      return combine((RexCall) e, beamSchema);
+    }
+
+    switch (kind) {
+      case IN:
+        return toInPredicate((RexCall) e, beamSchema, false);
+      case NOT_IN:
+        return toInPredicate((RexCall) e, beamSchema, true);
+      case NOT:
+        FilterPredicate inner = convert(((RexCall) e).getOperands().get(0), beamSchema);
+        return (inner == null) ? null : FilterApi.not(inner);
+      case IS_NULL:
+      case IS_NOT_NULL:
+        return toUnaryPredicate((RexCall) e, beamSchema);
+      default:
+        if (COMPARISON_KINDS.contains(kind)) {
+          return toBinaryPredicate((RexCall) e, beamSchema);
+        }
+        return null;
+    }
+  }
+
+  @Nullable
+  private static FilterPredicate combine(RexCall call, Schema beamSchema) {
+    FilterCombiner combiner = COMBINERS.get(call.getKind());
+    if (combiner == null) {
+      return null;
+    }
+
+    // Pre-allocate list with expected size for better performance
+    List<FilterPredicate> predicates = new ArrayList<>(call.getOperands().size());
+    for (RexNode op : call.getOperands()) {
+      FilterPredicate p = convert(op, beamSchema);
+      if (p != null) {
+        predicates.add(p);
+      }
+    }
+
+    if (predicates.isEmpty()) {
+      return null;
+    }
+
+    // Optimize: if only one predicate, return it directly
+    if (predicates.size() == 1) {
+      return predicates.get(0);
+    }
+
+    return predicates.stream().reduce(combiner::combine).orElse(null);
+  }
+
+  @Nullable
+  private static FilterPredicate toInPredicate(RexCall call, Schema beamSchema, boolean isNotIn) {
+    RexInputRef columnRef = (RexInputRef) call.getOperands().get(0);
+    List<RexNode> valueNodes = call.getOperands().subList(1, call.getOperands().size());
+    SqlKind comparison = isNotIn ? SqlKind.NOT_EQUALS : SqlKind.EQUALS;
+
+    // CHANGE: Use an explicit loop
+    List<FilterPredicate> predicates = new ArrayList<>();
+    for (RexNode valueNode : valueNodes) {
+      FilterPredicate p =
+          createSingleComparison(comparison, columnRef, (RexLiteral) valueNode, beamSchema);
+      if (p != null) {
+        predicates.add(p);
+      }
+    }
+
+    if (predicates.isEmpty()) {
+      return null;
+    }
+    return predicates.stream().reduce(isNotIn ? FilterApi::and : FilterApi::or).orElse(null);
+  }
+
+  @Nullable
+  private static FilterPredicate toUnaryPredicate(RexCall call, Schema beamSchema) {
+    RexNode operand = call.getOperands().get(0);
+    if (!(operand instanceof RexInputRef)) {
+      return null;
+    }
+    RexInputRef columnRef = (RexInputRef) operand;
+    String columnName = getColumnName(columnRef, beamSchema);
+    SqlTypeName type = columnRef.getType().getSqlTypeName();
+    boolean isNull = call.getKind() == SqlKind.IS_NULL;
+    switch (type) {
+      case INTEGER:
+        return isNull
+            ? FilterApi.eq(FilterApi.intColumn(columnName), null)
+            : FilterApi.notEq(FilterApi.intColumn(columnName), null);
+      case BIGINT:
+        return isNull
+            ? FilterApi.eq(FilterApi.longColumn(columnName), null)
+            : FilterApi.notEq(FilterApi.longColumn(columnName), null);
+      case FLOAT:
+        return isNull
+            ? FilterApi.eq(FilterApi.floatColumn(columnName), null)
+            : FilterApi.notEq(FilterApi.floatColumn(columnName), null);
+      case DOUBLE:
+        return isNull
+            ? FilterApi.eq(FilterApi.doubleColumn(columnName), null)
+            : FilterApi.notEq(FilterApi.doubleColumn(columnName), null);
+      case BOOLEAN:
+        return isNull
+            ? FilterApi.eq(FilterApi.booleanColumn(columnName), null)
+            : FilterApi.notEq(FilterApi.booleanColumn(columnName), null);
+      case VARCHAR:
+      case CHAR:
+      case DECIMAL:
+        return isNull
+            ? FilterApi.eq(FilterApi.binaryColumn(columnName), null)
+            : FilterApi.notEq(FilterApi.binaryColumn(columnName), null);
+      default:
+        return null;
+    }
+  }
+
+  @Nullable
+  private static FilterPredicate toBinaryPredicate(RexCall call, Schema beamSchema) {
+    if (call.getOperands().size() != 2) {
+      return null; // Binary predicates must have exactly 2 operands
+    }
+
+    RexNode left = call.getOperands().get(0);
+    RexNode right = call.getOperands().get(1);
+
+    // Handle CAST operations
+    if (left.getKind() == SqlKind.CAST) {
+      left = ((RexCall) left).getOperands().get(0);
+    }
+    if (right.getKind() == SqlKind.CAST) {
+      right = ((RexCall) right).getOperands().get(0);
+    }
+
+    // Only support column = literal comparisons
+    if (!(left instanceof RexInputRef) || !(right instanceof RexLiteral)) {
+      return null;
+    }
+
+    RexInputRef columnRef = (RexInputRef) left;
+    RexLiteral literal = (RexLiteral) right;
+
+    // Validate column index is within schema bounds
+    if (columnRef.getIndex() < 0 || columnRef.getIndex() >= beamSchema.getFieldCount()) {
+      return null;
+    }
+
+    return createSingleComparison(call.getKind(), columnRef, literal, beamSchema);
+  }
+
+  @Nullable
+  private static FilterPredicate createSingleComparison(
+      SqlKind kind, RexInputRef columnRef, RexLiteral literal, Schema beamSchema) {
+
+    String columnName = getColumnName(columnRef, beamSchema);
+    SqlTypeName columnType = columnRef.getType().getSqlTypeName();
+
+    Comparable<?> value = literal.getValueAs(Comparable.class);
+    if (value == null) {
+      return null;
+    }
+
+    switch (columnType) {
+      case TINYINT:
+      case SMALLINT:
+      case INTEGER:
+        return createIntPredicate(kind, columnName, ((Number) value).intValue());
+      case BIGINT:
+        return createLongPredicate(kind, columnName, ((Number) value).longValue());
+      case FLOAT:
+        return createFloatPredicate(kind, columnName, ((Number) value).floatValue());
+      case DOUBLE:
+        return createDoublePredicate(kind, columnName, ((Number) value).doubleValue());
+      case BOOLEAN:
+        return createBooleanPredicate(kind, columnName, (Boolean) value);
+      case CHAR:
+      case VARCHAR:
+        return createStringPredicate(kind, columnName, value.toString());
+      case BINARY:
+      case VARBINARY:
+        org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.BitString bitString =
+            (org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.util.BitString) value;
+        return createBinaryPredicate(
+            kind, columnName, Binary.fromConstantByteArray(bitString.getAsByteArray()));
+      case DECIMAL:
+        BigDecimal bigDecimalValue = (BigDecimal) value;
+        return createBinaryPredicate(
+            kind,
+            columnName,
+            Binary.fromConstantByteArray(bigDecimalValue.unscaledValue().toByteArray()));
+      case DATE:
+      case TIME:
+      case TIMESTAMP:
+      case ARRAY:
+      case MAP:
+      case ROW:
+      default:
+        return null;
+    }
+  }
+
+  private static String getColumnName(RexInputRef columnRef, Schema beamSchema) {
+    int fieldIndex = columnRef.getIndex();
+    if (fieldIndex < 0 || fieldIndex >= beamSchema.getFieldCount()) {
+      throw new IllegalArgumentException(
+          "Column index "
+              + fieldIndex
+              + " is out of bounds for schema with "
+              + beamSchema.getFieldCount()
+              + " fields");
+    }
+    return beamSchema.getField(fieldIndex).getName();
+  }
+
+  /** Creates a filter predicate for integer values. */
+  private static FilterPredicate createIntPredicate(SqlKind kind, String name, Integer value) {
+    return createIntComparison(kind, FilterApi.intColumn(name), value);
+  }
+
+  /** Creates a filter predicate for long values. */
+  private static FilterPredicate createLongPredicate(SqlKind kind, String name, Long value) {
+    return createLongComparison(kind, FilterApi.longColumn(name), value);
+  }
+
+  /** Creates a filter predicate for float values. */
+  private static FilterPredicate createFloatPredicate(SqlKind kind, String name, Float value) {
+    return createFloatComparison(kind, FilterApi.floatColumn(name), value);
+  }
+
+  /** Creates a filter predicate for double values. */
+  private static FilterPredicate createDoublePredicate(SqlKind kind, String name, Double value) {
+    return createDoubleComparison(kind, FilterApi.doubleColumn(name), value);
+  }
+
+  /** Creates a filter predicate for boolean values. */
+  private static FilterPredicate createBooleanPredicate(SqlKind kind, String name, Boolean value) {
+    return createBooleanComparison(kind, FilterApi.booleanColumn(name), value);
+  }
+
+  /** Creates a filter predicate for string values. */
+  private static FilterPredicate createStringPredicate(SqlKind kind, String name, String value) {
+    return createBinaryPredicate(kind, name, Binary.fromString(value));
+  }
+
+  /** Creates a filter predicate for binary values. */
+  private static FilterPredicate createBinaryPredicate(SqlKind kind, String name, Binary value) {
+    return createBinaryComparison(kind, FilterApi.binaryColumn(name), value);
+  }
+
+  /** Helper method to create comparison predicates for integer columns. */
+  private static FilterPredicate createIntComparison(
+      SqlKind kind,
+      org.apache.parquet.filter2.predicate.Operators.IntColumn column,
+      Integer value) {
+    switch (kind) {
+      case EQUALS:
+        return FilterApi.eq(column, value);
+      case NOT_EQUALS:
+        return FilterApi.notEq(column, value);
+      case GREATER_THAN:
+        return FilterApi.gt(column, value);
+      case GREATER_THAN_OR_EQUAL:
+        return FilterApi.gtEq(column, value);
+      case LESS_THAN:
+        return FilterApi.lt(column, value);
+      case LESS_THAN_OR_EQUAL:
+        return FilterApi.ltEq(column, value);
+      default:
+        throw new UnsupportedOperationException("Unsupported operator for INT: " + kind);
+    }
+  }
+
+  /** Helper method to create comparison predicates for long columns. */
+  private static FilterPredicate createLongComparison(
+      SqlKind kind, org.apache.parquet.filter2.predicate.Operators.LongColumn column, Long value) {
+    switch (kind) {
+      case EQUALS:
+        return FilterApi.eq(column, value);
+      case NOT_EQUALS:
+        return FilterApi.notEq(column, value);
+      case GREATER_THAN:
+        return FilterApi.gt(column, value);
+      case GREATER_THAN_OR_EQUAL:
+        return FilterApi.gtEq(column, value);
+      case LESS_THAN:
+        return FilterApi.lt(column, value);
+      case LESS_THAN_OR_EQUAL:
+        return FilterApi.ltEq(column, value);
+      default:
+        throw new UnsupportedOperationException("Unsupported operator for LONG: " + kind);
+    }
+  }
+
+  /** Helper method to create comparison predicates for float columns. */
+  private static FilterPredicate createFloatComparison(
+      SqlKind kind,
+      org.apache.parquet.filter2.predicate.Operators.FloatColumn column,
+      Float value) {
+    switch (kind) {
+      case EQUALS:
+        return FilterApi.eq(column, value);
+      case NOT_EQUALS:
+        return FilterApi.notEq(column, value);
+      case GREATER_THAN:
+        return FilterApi.gt(column, value);
+      case GREATER_THAN_OR_EQUAL:
+        return FilterApi.gtEq(column, value);
+      case LESS_THAN:
+        return FilterApi.lt(column, value);
+      case LESS_THAN_OR_EQUAL:
+        return FilterApi.ltEq(column, value);
+      default:
+        throw new UnsupportedOperationException("Unsupported operator for FLOAT: " + kind);
+    }
+  }
+
+  /** Helper method to create comparison predicates for double columns. */
+  private static FilterPredicate createDoubleComparison(
+      SqlKind kind,
+      org.apache.parquet.filter2.predicate.Operators.DoubleColumn column,
+      Double value) {
+    switch (kind) {
+      case EQUALS:
+        return FilterApi.eq(column, value);
+      case NOT_EQUALS:
+        return FilterApi.notEq(column, value);
+      case GREATER_THAN:
+        return FilterApi.gt(column, value);
+      case GREATER_THAN_OR_EQUAL:
+        return FilterApi.gtEq(column, value);
+      case LESS_THAN:
+        return FilterApi.lt(column, value);
+      case LESS_THAN_OR_EQUAL:
+        return FilterApi.ltEq(column, value);
+      default:
+        throw new UnsupportedOperationException("Unsupported operator for DOUBLE: " + kind);
+    }
+  }
+
+  /** Helper method to create comparison predicates for boolean columns. */
+  private static FilterPredicate createBooleanComparison(
+      SqlKind kind,
+      org.apache.parquet.filter2.predicate.Operators.BooleanColumn column,
+      Boolean value) {
+    switch (kind) {
+      case EQUALS:
+        return FilterApi.eq(column, value);
+      case NOT_EQUALS:
+        return FilterApi.notEq(column, value);
+      default:
+        throw new UnsupportedOperationException("Unsupported operator for BOOLEAN: " + kind);
+    }
+  }
+
+  /** Helper method to create comparison predicates for binary columns. */
+  private static FilterPredicate createBinaryComparison(
+      SqlKind kind, BinaryColumn column, Binary value) {
+    switch (kind) {
+      case EQUALS:
+        return FilterApi.eq(column, value);
+      case NOT_EQUALS:
+        return FilterApi.notEq(column, value);
+      case GREATER_THAN:
+        return FilterApi.gt(column, value);
+      case GREATER_THAN_OR_EQUAL:
+        return FilterApi.gtEq(column, value);
+      case LESS_THAN:
+        return FilterApi.lt(column, value);
+      case LESS_THAN_OR_EQUAL:
+        return FilterApi.ltEq(column, value);
+      default:
+        throw new UnsupportedOperationException("Unsupported operator for BINARY: " + kind);
+    }
+  }
+
+  @FunctionalInterface
+  private interface FilterCombiner {
+    FilterPredicate combine(FilterPredicate a, FilterPredicate b);
+  }
+}
diff --git a/sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetIO.java b/sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetIO.java
index 24c18f382817..5b1095efbb10 100644
--- a/sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetIO.java
+++ b/sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetIO.java
@@ -72,6 +72,7 @@
 import org.apache.parquet.column.page.PageReadStore;
 import org.apache.parquet.filter2.compat.FilterCompat;
 import org.apache.parquet.filter2.compat.FilterCompat.Filter;
+import org.apache.parquet.filter2.predicate.FilterPredicate;
 import org.apache.parquet.hadoop.ParquetFileReader;
 import org.apache.parquet.hadoop.ParquetWriter;
 import org.apache.parquet.hadoop.api.InitContext;
@@ -301,6 +302,8 @@ public abstract static class Read extends PTransform<PBegin, PCollection<Generic
 
     abstract boolean getInferBeamSchema();
 
+    abstract @Nullable ParquetFilter getPredicate();
+
     abstract Builder toBuilder();
 
     @AutoValue.Builder
@@ -320,6 +323,8 @@ abstract static class Builder {
 
       abstract Builder setConfiguration(SerializableConfiguration configuration);
 
+      abstract Builder setPredicate(ParquetFilter predicate);
+
       abstract Read build();
     }
 
@@ -340,6 +345,12 @@ public Read withProjection(Schema projectionSchema, Schema encoderSchema) {
           .build();
     }
 
+    /** Specifies a filter predicate to use for filtering records. */
+    public Read withFilter(ParquetFilter predicate) {
+      checkArgument(predicate != null, "predicate can not be null");
+      return toBuilder().setPredicate(predicate).build();
+    }
+
     /** Specify Hadoop configuration for ParquetReader. */
     public Read withConfiguration(Map<String, String> configuration) {
       checkArgument(configuration != null, "configuration can not be null");
@@ -377,7 +388,8 @@ public PCollection<GenericRecord> expand(PBegin input) {
           readFiles(getSchema())
               .withBeamSchemas(getInferBeamSchema())
               .withAvroDataModel(getAvroDataModel())
-              .withProjection(getProjectionSchema(), getEncoderSchema());
+              .withProjection(getProjectionSchema(), getEncoderSchema())
+              .withFilter(getPredicate());
       if (getConfiguration() != null) {
         readFiles = readFiles.withConfiguration(getConfiguration().get());
       }
@@ -541,7 +553,7 @@ public PCollection<T> expand(PCollection<ReadableFile> input) {
       checkArgument(!isGenericRecordOutput(), "Parse can't be used for reading as GenericRecord.");
 
       return input
-          .apply(ParDo.of(new SplitReadFn<>(null, null, getParseFn(), getConfiguration())))
+          .apply(ParDo.of(new SplitReadFn<>(null, null, getParseFn(), getConfiguration(), null)))
           .setCoder(inferCoder(input.getPipeline().getCoderRegistry()));
     }
 
@@ -614,6 +626,8 @@ public abstract static class ReadFiles
 
     abstract boolean getInferBeamSchema();
 
+    abstract @Nullable ParquetFilter getPredicate();
+
     abstract Builder toBuilder();
 
     @AutoValue.Builder
@@ -630,6 +644,8 @@ abstract static class Builder {
 
       abstract Builder setInferBeamSchema(boolean inferBeamSchema);
 
+      abstract Builder setPredicate(ParquetFilter predicate);
+
       abstract ReadFiles build();
     }
 
@@ -647,6 +663,14 @@ public ReadFiles withProjection(Schema projectionSchema, Schema encoderSchema) {
           .build();
     }
 
+    /** Specifies a filter predicate to use for filtering records. */
+    public ReadFiles withFilter(ParquetFilter predicate) {
+      if (predicate == null) {
+        return this;
+      }
+      return toBuilder().setPredicate(predicate).build();
+    }
+
     /** Specify Hadoop configuration for ParquetReader. */
     public ReadFiles withConfiguration(Map<String, String> configuration) {
       checkArgument(configuration != null, "configuration can not be null");
@@ -673,7 +697,8 @@ public PCollection<GenericRecord> expand(PCollection<ReadableFile> input) {
                       getAvroDataModel(),
                       getProjectionSchema(),
                       GenericRecordPassthroughFn.create(),
-                      getConfiguration())))
+                      getConfiguration(),
+                      getPredicate())))
           .setCoder(getCollectionCoder());
     }
 
@@ -718,20 +743,33 @@ static class SplitReadFn<T> extends DoFn<ReadableFile, T> {
 
       private final SerializableFunction<GenericRecord, T> parseFn;
 
+      private final ParquetFilter parquetFilter;
+
       SplitReadFn(
           GenericData model,
           Schema requestSchema,
           SerializableFunction<GenericRecord, T> parseFn,
-          @Nullable SerializableConfiguration configuration) {
+          @Nullable SerializableConfiguration configuration,
+          @Nullable ParquetFilter predicate) {
 
         this.modelClass = model != null ? model.getClass() : null;
         this.requestSchemaString = requestSchema != null ? requestSchema.toString() : null;
         this.parseFn = checkNotNull(parseFn, "GenericRecord parse function can't be null");
         this.configuration = configuration;
+        this.parquetFilter = predicate;
       }
 
       private ParquetFileReader getParquetFileReader(ReadableFile file) throws Exception {
-        ParquetReadOptions options = HadoopReadOptions.builder(getConfWithModelClass()).build();
+        HadoopReadOptions.Builder optionsBuilder =
+            HadoopReadOptions.builder(getConfWithModelClass());
+        if (parquetFilter != null) {
+          FilterPredicate predicate =
+              ((ParquetFilterFactory.ParquetFilterImpl) parquetFilter).getPredicate();
+          if (predicate != null) {
+            optionsBuilder.withRecordFilter(FilterCompat.get(predicate));
+          }
+        }
+        ParquetReadOptions options = optionsBuilder.build();
         return ParquetFileReader.open(new BeamParquetInputFile(file.openSeekable()), options);
       }
 
@@ -755,7 +793,15 @@ public void processElement(
           AvroReadSupport.setRequestedProjection(
               conf, new Schema.Parser().parse(requestSchemaString));
         }
-        ParquetReadOptions options = HadoopReadOptions.builder(conf).build();
+        HadoopReadOptions.Builder optionsBuilder = HadoopReadOptions.builder(conf);
+        if (parquetFilter != null) {
+          FilterPredicate predicate =
+              ((ParquetFilterFactory.ParquetFilterImpl) parquetFilter).getPredicate();
+          if (predicate != null) {
+            optionsBuilder.withRecordFilter(FilterCompat.get(predicate));
+          }
+        }
+        ParquetReadOptions options = optionsBuilder.build();
         try (ParquetFileReader reader =
             ParquetFileReader.open(new BeamParquetInputFile(file.openSeekable()), options)) {
           Filter filter = checkNotNull(options.getRecordFilter(), "filter");
diff --git a/sdks/java/io/parquet/src/test/java/org/apache/beam/sdk/io/parquet/ParquetFilterFactoryTest.java b/sdks/java/io/parquet/src/test/java/org/apache/beam/sdk/io/parquet/ParquetFilterFactoryTest.java
new file mode 100644
index 000000000000..74537b29917c
--- /dev/null
+++ b/sdks/java/io/parquet/src/test/java/org/apache/beam/sdk/io/parquet/ParquetFilterFactoryTest.java
@@ -0,0 +1,100 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.parquet;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertNull;
+import static org.junit.Assert.assertTrue;
+
+import java.util.Collections;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.parquet.filter2.predicate.FilterPredicate;
+import org.apache.parquet.filter2.predicate.Operators.IntColumn;
+import org.junit.Before;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+
+/** Comprehensive unit tests for {@link ParquetFilterFactory}. */
+@RunWith(JUnit4.class)
+public class ParquetFilterFactoryTest {
+
+  private Schema testSchema;
+
+  @Before
+  public void setUp() {
+    testSchema =
+        Schema.builder()
+            .addInt32Field("id")
+            .addStringField("name")
+            .addBooleanField("active")
+            .addDoubleField("price")
+            .addFloatField("rating")
+            .addInt64Field("timestamp")
+            .addByteArrayField("data")
+            .build();
+  }
+
+  @Test
+  public void testCreateWithNullExpressions() {
+    ParquetFilter filter = ParquetFilterFactory.create(null, testSchema);
+    assertNotNull(filter);
+    assertNull(((ParquetFilterFactory.ParquetFilterImpl) filter).getPredicate());
+  }
+
+  @Test
+  public void testCreateWithEmptyExpressions() {
+    ParquetFilter filter = ParquetFilterFactory.create(Collections.emptyList(), testSchema);
+    assertNotNull(filter);
+    assertNull(((ParquetFilterFactory.ParquetFilterImpl) filter).getPredicate());
+  }
+
+  @Test
+  public void testCreateWithValidSchema() {
+    // Test that the factory can be created with a valid schema
+    ParquetFilter filter = ParquetFilterFactory.create(Collections.emptyList(), testSchema);
+    assertNotNull(filter);
+    assertNull(((ParquetFilterFactory.ParquetFilterImpl) filter).getPredicate());
+  }
+
+  @Test
+  public void testCreateWithInvalidSchema() {
+    // Test that the factory throws an exception with null schema
+    try {
+      ParquetFilterFactory.create(Collections.emptyList(), null);
+      assertTrue("Expected IllegalArgumentException", false);
+    } catch (IllegalArgumentException e) {
+      assertTrue(e.getMessage().contains("Beam schema cannot be null"));
+    }
+  }
+
+  @Test
+  public void testFromPredicate() {
+    IntColumn intColumn = org.apache.parquet.filter2.predicate.FilterApi.intColumn("test");
+    FilterPredicate parquetPredicate =
+        org.apache.parquet.filter2.predicate.FilterApi.eq(intColumn, 42);
+
+    ParquetFilter filter = ParquetFilterFactory.fromPredicate(parquetPredicate);
+
+    assertNotNull(filter);
+    FilterPredicate retrievedPredicate =
+        ((ParquetFilterFactory.ParquetFilterImpl) filter).getPredicate();
+    assertEquals(parquetPredicate, retrievedPredicate);
+  }
+}
diff --git a/sdks/java/io/parquet/src/test/java/org/apache/beam/sdk/io/parquet/ParquetIOTest.java b/sdks/java/io/parquet/src/test/java/org/apache/beam/sdk/io/parquet/ParquetIOTest.java
index 7ee3ec5050fd..4f5ac3542203 100644
--- a/sdks/java/io/parquet/src/test/java/org/apache/beam/sdk/io/parquet/ParquetIOTest.java
+++ b/sdks/java/io/parquet/src/test/java/org/apache/beam/sdk/io/parquet/ParquetIOTest.java
@@ -40,9 +40,11 @@
 import org.apache.avro.io.EncoderFactory;
 import org.apache.avro.io.JsonEncoder;
 import org.apache.avro.reflect.ReflectData;
+import org.apache.beam.sdk.PipelineResult;
 import org.apache.beam.sdk.extensions.avro.coders.AvroCoder;
 import org.apache.beam.sdk.extensions.avro.schemas.utils.AvroUtils;
 import org.apache.beam.sdk.io.FileIO;
+import org.apache.beam.sdk.io.parquet.ParquetFilterFactory.ParquetFilterImpl;
 import org.apache.beam.sdk.io.parquet.ParquetIO.GenericRecordPassthroughFn;
 import org.apache.beam.sdk.io.range.OffsetRange;
 import org.apache.beam.sdk.schemas.SchemaCoder;
@@ -153,7 +155,7 @@ public void testBlockTracker() {
   public void testSplitBlockWithLimit() {
     ParquetIO.ReadFiles.SplitReadFn<GenericRecord> testFn =
         new ParquetIO.ReadFiles.SplitReadFn<>(
-            null, null, ParquetIO.GenericRecordPassthroughFn.create(), null);
+            null, null, ParquetIO.GenericRecordPassthroughFn.create(), null, null);
     ArrayList<BlockMetaData> blockList = new ArrayList<>();
     ArrayList<OffsetRange> rangeList;
     BlockMetaData testBlock = mock(BlockMetaData.class);
@@ -518,6 +520,121 @@ public void testWriteAndReadFilesAsJsonForUnknownSchemaWithConfiguration() {
     readPipeline.run().waitUntilFinish();
   }
 
+  @Test
+  public void testWriteAndReadWithFilter() {
+    List<GenericRecord> allRecords = generateGenericRecords(10);
+
+    List<GenericRecord> expectedRecords = new ArrayList<>();
+    expectedRecords.add(allRecords.get(5));
+
+    mainPipeline
+        .apply(Create.of(allRecords).withCoder(AvroCoder.of(SCHEMA)))
+        .apply(
+            FileIO.<GenericRecord>write()
+                .via(ParquetIO.sink(SCHEMA))
+                .to(temporaryFolder.getRoot().getAbsolutePath()));
+    mainPipeline.run().waitUntilFinish();
+
+    FilterPredicate filterPredicate =
+        FilterApi.eq(FilterApi.binaryColumn("id"), Binary.fromString("5"));
+
+    PCollection<GenericRecord> readBack =
+        readPipeline.apply(
+            ParquetIO.read(SCHEMA)
+                .from(temporaryFolder.getRoot().getAbsolutePath() + "/*")
+                .withFilter(new ParquetFilterImpl(filterPredicate)));
+
+    PAssert.that(readBack).containsInAnyOrder(expectedRecords);
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testReadFilesWithFilter() throws IOException {
+    List<GenericRecord> allRecords = generateGenericRecords(100);
+    List<GenericRecord> expectedRecords = new ArrayList<>();
+    // id = "25" OR id = "75"
+    expectedRecords.add(allRecords.get(25));
+    expectedRecords.add(allRecords.get(75));
+
+    // Write data to a file
+    mainPipeline
+        .apply(Create.of(allRecords).withCoder(AvroCoder.of(SCHEMA)))
+        .apply(
+            FileIO.<GenericRecord>write()
+                .via(ParquetIO.sink(SCHEMA))
+                .to(temporaryFolder.getRoot().getAbsolutePath()));
+    mainPipeline.run().waitUntilFinish();
+
+    // Create a more complex filter
+    FilterPredicate filter =
+        FilterApi.or(
+            FilterApi.eq(FilterApi.binaryColumn("id"), Binary.fromString("25")),
+            FilterApi.eq(FilterApi.binaryColumn("id"), Binary.fromString("75")));
+
+    // Read back using readFiles and apply the filter
+    PCollection<GenericRecord> readBack =
+        readPipeline
+            .apply(FileIO.match().filepattern(temporaryFolder.getRoot().getAbsolutePath() + "/*"))
+            .apply(FileIO.readMatches())
+            .apply(ParquetIO.readFiles(SCHEMA).withFilter(new ParquetFilterImpl(filter)));
+
+    PAssert.that(readBack).containsInAnyOrder(expectedRecords);
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testFilterWithNoResults() {
+    List<GenericRecord> allRecords = generateGenericRecords(50);
+
+    mainPipeline
+        .apply(Create.of(allRecords).withCoder(AvroCoder.of(SCHEMA)))
+        .apply(
+            FileIO.<GenericRecord>write()
+                .via(ParquetIO.sink(SCHEMA))
+                .to(temporaryFolder.getRoot().getAbsolutePath()));
+    mainPipeline.run().waitUntilFinish();
+
+    // Create a filter that will not match any records
+    FilterPredicate filterPredicate =
+        FilterApi.eq(FilterApi.binaryColumn("id"), Binary.fromString("non_existent_id"));
+
+    PCollection<GenericRecord> readBack =
+        readPipeline.apply(
+            ParquetIO.read(SCHEMA)
+                .from(temporaryFolder.getRoot().getAbsolutePath() + "/*")
+                .withFilter(new ParquetFilterImpl(filterPredicate)));
+
+    // Assert that the resulting PCollection is empty
+    PAssert.that(readBack).empty();
+    readPipeline.run().waitUntilFinish();
+  }
+
+  @Test
+  public void testWriteWithConfiguration() throws IOException {
+    List<GenericRecord> records = generateGenericRecords(100);
+
+    // Example of a writer-side Hadoop configuration property
+    Configuration conf = new Configuration();
+    conf.set("parquet.writer.version", "v2");
+
+    mainPipeline
+        .apply(Create.of(records).withCoder(AvroCoder.of(SCHEMA)))
+        .apply(
+            FileIO.<GenericRecord>write()
+                .via(ParquetIO.sink(SCHEMA).withConfiguration(conf))
+                .to(temporaryFolder.getRoot().getAbsolutePath()));
+
+    PipelineResult.State state = mainPipeline.run().waitUntilFinish();
+    assertEquals(PipelineResult.State.DONE, state);
+
+    // The read side just confirms the data was written correctly.
+    PCollection<GenericRecord> readBack =
+        readPipeline.apply(
+            ParquetIO.read(SCHEMA).from(temporaryFolder.getRoot().getAbsolutePath() + "/*"));
+    PAssert.that(readBack).containsInAnyOrder(records);
+    readPipeline.run().waitUntilFinish();
+  }
+
   /** Returns list of JSON representation of GenericRecords. */
   private static List<String> convertRecordsToJson(List<GenericRecord> records) {
     return records.stream().map(ParseGenericRecordAsJsonFn.create()::apply).collect(toList());
diff --git a/sdks/java/io/pulsar/build.gradle b/sdks/java/io/pulsar/build.gradle
index 7ffe3f22cca4..a6428e75c89d 100644
--- a/sdks/java/io/pulsar/build.gradle
+++ b/sdks/java/io/pulsar/build.gradle
@@ -18,11 +18,12 @@
 
 plugins { id 'org.apache.beam.module' }
 applyJavaNature(automaticModuleName: 'org.apache.beam.sdk.io.pulsar')
+enableJavaPerformanceTesting()
 
 description = "Apache Beam :: SDKs :: Java :: IO :: Pulsar"
 ext.summary = "IO to read and write to Pulsar"
 
-def pulsar_version = '2.8.2'
+def pulsar_version = '2.11.4'
 
 
 dependencies {
@@ -30,19 +31,19 @@ dependencies {
     implementation library.java.slf4j_api
     implementation library.java.joda_time
 
-    implementation "org.apache.pulsar:pulsar-client:$pulsar_version"
-    implementation "org.apache.pulsar:pulsar-client-admin:$pulsar_version"
-    permitUnusedDeclared "org.apache.pulsar:pulsar-client:$pulsar_version"
-    permitUnusedDeclared "org.apache.pulsar:pulsar-client-admin:$pulsar_version"
-    permitUsedUndeclared "org.apache.pulsar:pulsar-client-api:$pulsar_version"
-    permitUsedUndeclared "org.apache.pulsar:pulsar-client-admin-api:$pulsar_version"
+    implementation "org.apache.pulsar:pulsar-client-api:$pulsar_version"
+    implementation "org.apache.pulsar:pulsar-client-admin-api:$pulsar_version"
+    runtimeOnly "org.apache.pulsar:pulsar-client:$pulsar_version"
+    runtimeOnly("org.apache.pulsar:pulsar-client-admin:$pulsar_version") {
+      // To prevent a StackOverflow within Pulsar admin client because JUL -> SLF4J -> JUL
+      exclude group: "org.slf4j", module: "jul-to-slf4j"
+    }
 
     implementation project(path: ":sdks:java:core", configuration: "shadow")
 
-    testImplementation library.java.jupiter_api
-    testRuntimeOnly library.java.jupiter_engine
+    testImplementation library.java.junit
+    testRuntimeOnly library.java.slf4j_jdk14
     testRuntimeOnly project(path: ":runners:direct-java", configuration: "shadow")
     testImplementation "org.testcontainers:pulsar:1.15.3"
     testImplementation "org.assertj:assertj-core:2.9.1"
-
 }
diff --git a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/ReadFromPulsarDoFn.java b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/NaiveReadFromPulsarDoFn.java
similarity index 51%
rename from sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/ReadFromPulsarDoFn.java
rename to sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/NaiveReadFromPulsarDoFn.java
index 3d255ac9baee..a80f02590827 100644
--- a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/ReadFromPulsarDoFn.java
+++ b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/NaiveReadFromPulsarDoFn.java
@@ -17,11 +17,13 @@
  */
 package org.apache.beam.sdk.io.pulsar;
 
-import edu.umd.cs.findbugs.annotations.SuppressFBWarnings;
 import java.io.IOException;
+import java.time.Duration;
+import java.util.ArrayList;
 import java.util.concurrent.TimeUnit;
 import org.apache.beam.sdk.coders.Coder;
 import org.apache.beam.sdk.io.range.OffsetRange;
+import org.apache.beam.sdk.options.PipelineOptions;
 import org.apache.beam.sdk.transforms.DoFn;
 import org.apache.beam.sdk.transforms.SerializableFunction;
 import org.apache.beam.sdk.transforms.splittabledofn.GrowableOffsetRangeTracker;
@@ -30,6 +32,9 @@
 import org.apache.beam.sdk.transforms.splittabledofn.WatermarkEstimator;
 import org.apache.beam.sdk.transforms.splittabledofn.WatermarkEstimators;
 import org.apache.beam.sdk.transforms.windowing.BoundedWindow;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.MoreObjects;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Stopwatch;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Strings;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Supplier;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Suppliers;
 import org.apache.pulsar.client.admin.PulsarAdmin;
@@ -40,68 +45,73 @@
 import org.apache.pulsar.client.api.PulsarClientException;
 import org.apache.pulsar.client.api.Reader;
 import org.apache.pulsar.client.api.ReaderBuilder;
+import org.checkerframework.checker.nullness.qual.MonotonicNonNull;
+import org.checkerframework.checker.nullness.qual.Nullable;
 import org.joda.time.Instant;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 /**
- * Transform for reading from Apache Pulsar. Support is currently incomplete, and there may be bugs;
- * see https://github.com/apache/beam/issues/31078 for more info, and comment in that issue if you
- * run into issues with this IO.
+ * DoFn for reading from Apache Pulsar based on Pulsar {@link Reader} from the start message id. It
+ * does not support split or acknowledge message get read.
  */
 @DoFn.UnboundedPerElement
-@SuppressWarnings({"rawtypes", "nullness"})
-@SuppressFBWarnings(value = "CT_CONSTRUCTOR_THROW", justification = "Initialization is safe.")
-public class ReadFromPulsarDoFn extends DoFn<PulsarSourceDescriptor, PulsarMessage> {
+@SuppressWarnings("nullness")
+public class NaiveReadFromPulsarDoFn<T> extends DoFn<PulsarSourceDescriptor, T> {
 
-  private static final Logger LOG = LoggerFactory.getLogger(ReadFromPulsarDoFn.class);
-  private SerializableFunction<String, PulsarClient> pulsarClientSerializableFunction;
-  private PulsarClient client;
-  private PulsarAdmin admin;
-  private String clientUrl;
-  private String adminUrl;
+  private static final Logger LOG = LoggerFactory.getLogger(NaiveReadFromPulsarDoFn.class);
+  private final SerializableFunction<String, PulsarClient> clientFn;
+  private final SerializableFunction<String, PulsarAdmin> adminFn;
+  private final SerializableFunction<Message<?>, T> outputFn;
+  private final java.time.Duration pollingTimeout;
+  private transient @MonotonicNonNull PulsarClient client;
+  private transient @MonotonicNonNull PulsarAdmin admin;
+  private @MonotonicNonNull String clientUrl;
+  private @Nullable final String adminUrl;
 
   private final SerializableFunction<Message<byte[]>, Instant> extractOutputTimestampFn;
 
-  public ReadFromPulsarDoFn(PulsarIO.Read transform) {
-    this.extractOutputTimestampFn = transform.getExtractOutputTimestampFn();
+  public NaiveReadFromPulsarDoFn(PulsarIO.Read<T> transform) {
+    this.extractOutputTimestampFn =
+        transform.getTimestampType() == PulsarIO.ReadTimestampType.PUBLISH_TIME
+            ? record -> new Instant(record.getPublishTime())
+            : ignored -> Instant.now();
+    this.pollingTimeout = Duration.ofSeconds(transform.getConsumerPollingTimeout());
+    this.outputFn = transform.getOutputFn();
     this.clientUrl = transform.getClientUrl();
     this.adminUrl = transform.getAdminUrl();
-    this.pulsarClientSerializableFunction = transform.getPulsarClient();
+    this.clientFn =
+        MoreObjects.firstNonNull(
+            transform.getPulsarClient(), PulsarIOUtils.PULSAR_CLIENT_SERIALIZABLE_FUNCTION);
+    this.adminFn =
+        MoreObjects.firstNonNull(
+            transform.getPulsarAdmin(), PulsarIOUtils.PULSAR_ADMIN_SERIALIZABLE_FUNCTION);
+    admin = null;
   }
 
-  // Open connection to Pulsar clients
+  /** Open connection to Pulsar clients. */
   @Setup
   public void initPulsarClients() throws Exception {
-    if (this.clientUrl == null) {
-      this.clientUrl = PulsarIOUtils.SERVICE_URL;
-    }
-    if (this.adminUrl == null) {
-      this.adminUrl = PulsarIOUtils.SERVICE_HTTP_URL;
-    }
-
-    if (this.client == null) {
-      this.client = pulsarClientSerializableFunction.apply(this.clientUrl);
-      if (this.client == null) {
-        this.client = PulsarClient.builder().serviceUrl(clientUrl).build();
+    if (client == null) {
+      if (clientUrl == null) {
+        clientUrl = PulsarIOUtils.LOCAL_SERVICE_URL;
       }
+      client = clientFn.apply(clientUrl);
     }
 
-    if (this.admin == null) {
-      this.admin =
-          PulsarAdmin.builder()
-              .serviceHttpUrl(adminUrl)
-              .tlsTrustCertsFilePath(null)
-              .allowTlsInsecureConnection(false)
-              .build();
+    // admin is optional
+    if (this.admin == null && !Strings.isNullOrEmpty(adminUrl)) {
+      admin = adminFn.apply(adminUrl);
     }
   }
 
-  // Close connection to Pulsar clients
+  /** Close connection to Pulsar clients. */
   @Teardown
   public void teardown() throws Exception {
     this.client.close();
-    this.admin.close();
+    if (this.admin != null) {
+      this.admin.close();
+    }
   }
 
   @GetInitialRestriction
@@ -152,31 +162,60 @@ public Coder<OffsetRange> getRestrictionCoder() {
   public ProcessContinuation processElement(
       @Element PulsarSourceDescriptor pulsarSourceDescriptor,
       RestrictionTracker<OffsetRange, Long> tracker,
-      WatermarkEstimator watermarkEstimator,
-      OutputReceiver<PulsarMessage> output)
+      WatermarkEstimator<Instant> watermarkEstimator,
+      OutputReceiver<T> output)
       throws IOException {
     long startTimestamp = tracker.currentRestriction().getFrom();
     String topicDescriptor = pulsarSourceDescriptor.getTopic();
     try (Reader<byte[]> reader = newReader(this.client, topicDescriptor)) {
       if (startTimestamp > 0) {
+        //  reader.seek moves the cursor at the first occurrence of the message published after the
+        // assigned timestamp.
+        // i.e. all messages should be captured within the rangeTracker is after cursor
         reader.seek(startTimestamp);
       }
-      while (true) {
-        if (reader.hasReachedEndOfTopic()) {
-          reader.close();
-          return ProcessContinuation.stop();
+      if (reader.hasReachedEndOfTopic()) {
+        // topic has terminated
+        tracker.tryClaim(Long.MAX_VALUE);
+        reader.close();
+        return ProcessContinuation.stop();
+      }
+      boolean claimed = false;
+      ArrayList<Message<byte[]>> maybeLateMessages = new ArrayList<>();
+      final Stopwatch pollTimer = Stopwatch.createUnstarted();
+      Duration remainingTimeout = pollingTimeout;
+      while (Duration.ZERO.compareTo(remainingTimeout) < 0) {
+        pollTimer.reset().start();
+        Message<byte[]> message =
+            reader.readNext((int) remainingTimeout.toMillis(), TimeUnit.MILLISECONDS);
+        final Duration elapsed = pollTimer.elapsed();
+        try {
+          remainingTimeout = remainingTimeout.minus(elapsed);
+        } catch (ArithmeticException e) {
+          remainingTimeout = Duration.ZERO;
         }
-        Message<byte[]> message = reader.readNext();
+        // No progress when the polling timeout expired.
+        // Self-checkpoint and move to process the next element.
         if (message == null) {
           return ProcessContinuation.resume();
-        }
-        Long currentTimestamp = message.getPublishTime();
-        // if tracker.tryclaim() return true, sdf must execute work otherwise
-        // doFn must exit processElement() without doing any work associated
-        // or claiming more work
-        if (!tracker.tryClaim(currentTimestamp)) {
+        } // Trying to claim offset -1 before start of the range [0, 9223372036854775807)
+        long currentTimestamp = message.getPublishTime();
+        if (currentTimestamp < startTimestamp) {
+          // This should not happen per pulsar spec (see comments around read.seek). If it
+          // does happen, this prevents tryClaim crash (IllegalArgumentException: Trying to
+          // claim offset before start of the range)
+          LOG.warn(
+              "Received late message of publish time {} before startTimestamp {}",
+              currentTimestamp,
+              startTimestamp);
+        } else if (!tracker.tryClaim(currentTimestamp)) {
+          // if tracker.tryclaim() return true, sdf must execute work otherwise
+          // doFn must exit processElement() without doing any work associated
+          // or claiming more work
           reader.close();
           return ProcessContinuation.stop();
+        } else {
+          claimed = true;
         }
         if (pulsarSourceDescriptor.getEndMessageId() != null) {
           MessageId currentMsgId = message.getMessageId();
@@ -186,12 +225,35 @@ public ProcessContinuation processElement(
             return ProcessContinuation.stop();
           }
         }
-        PulsarMessage pulsarMessage =
-            new PulsarMessage(message.getTopicName(), message.getPublishTime(), message);
-        Instant outputTimestamp = extractOutputTimestampFn.apply(message);
-        output.outputWithTimestamp(pulsarMessage, outputTimestamp);
+        if (claimed) {
+          if (!maybeLateMessages.isEmpty()) {
+            for (Message<byte[]> lateMessage : maybeLateMessages) {
+              publishMessage(lateMessage, output);
+            }
+            maybeLateMessages.clear();
+          }
+          publishMessage(message, output);
+        } else {
+          maybeLateMessages.add(message);
+        }
       }
     }
+    return ProcessContinuation.resume();
+  }
+
+  private void publishMessage(Message<byte[]> message, OutputReceiver<T> output) {
+    T messageT = outputFn.apply(message);
+    Instant outputTimestamp = extractOutputTimestampFn.apply(message);
+    output.outputWithTimestamp(messageT, outputTimestamp);
+  }
+
+  @SplitRestriction
+  public void splitRestriction(
+      @Restriction OffsetRange restriction,
+      OutputReceiver<OffsetRange> receiver,
+      PipelineOptions unused) {
+    // read based on Reader does not support split
+    receiver.output(restriction);
   }
 
   @GetInitialWatermarkEstimatorState
@@ -221,28 +283,34 @@ public OffsetRangeTracker restrictionTracker(
   private static class PulsarLatestOffsetEstimator
       implements GrowableOffsetRangeTracker.RangeEndEstimator {
 
-    private final Supplier<Message> memoizedBacklog;
+    private final @Nullable Supplier<Message<byte[]>> memoizedBacklog;
 
-    private PulsarLatestOffsetEstimator(PulsarAdmin admin, String topic) {
-      this.memoizedBacklog =
-          Suppliers.memoizeWithExpiration(
-              () -> {
-                try {
-                  Message<byte[]> lastMsg = admin.topics().examineMessage(topic, "latest", 1);
-                  return lastMsg;
-                } catch (PulsarAdminException e) {
-                  LOG.error(e.getMessage());
-                  throw new RuntimeException(e);
-                }
-              },
-              1,
-              TimeUnit.SECONDS);
+    private PulsarLatestOffsetEstimator(@Nullable PulsarAdmin admin, String topic) {
+      if (admin != null) {
+        this.memoizedBacklog =
+            Suppliers.memoizeWithExpiration(
+                () -> {
+                  try {
+                    return admin.topics().examineMessage(topic, "latest", 1);
+                  } catch (PulsarAdminException e) {
+                    throw new RuntimeException(e);
+                  }
+                },
+                1,
+                TimeUnit.SECONDS);
+      } else {
+        memoizedBacklog = null;
+      }
     }
 
     @Override
     public long estimate() {
-      Message<byte[]> msg = memoizedBacklog.get();
-      return msg.getPublishTime();
+      if (memoizedBacklog != null) {
+        Message<byte[]> msg = memoizedBacklog.get();
+        return msg.getPublishTime();
+      } else {
+        return Long.MIN_VALUE;
+      }
     }
   }
 
diff --git a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarIO.java b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarIO.java
index aaff08a96d36..34535e7cb44f 100644
--- a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarIO.java
+++ b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarIO.java
@@ -17,6 +17,8 @@
  */
 package org.apache.beam.sdk.io.pulsar;
 
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkState;
+
 import com.google.auto.value.AutoValue;
 import org.apache.beam.sdk.transforms.Create;
 import org.apache.beam.sdk.transforms.PTransform;
@@ -25,16 +27,17 @@
 import org.apache.beam.sdk.values.PBegin;
 import org.apache.beam.sdk.values.PCollection;
 import org.apache.beam.sdk.values.PDone;
+import org.apache.beam.sdk.values.TypeDescriptor;
+import org.apache.pulsar.client.admin.PulsarAdmin;
 import org.apache.pulsar.client.api.Message;
 import org.apache.pulsar.client.api.MessageId;
 import org.apache.pulsar.client.api.PulsarClient;
 import org.checkerframework.checker.nullness.qual.Nullable;
-import org.joda.time.Instant;
 
 /**
- * Class for reading and writing from Apache Pulsar. Support is currently incomplete, and there may
- * be bugs; see https://github.com/apache/beam/issues/31078 for more info, and comment in that issue
- * if you run into issues with this IO.
+ * IO connector for reading and writing from Apache Pulsar. Support is currently experimental, and
+ * there may be bugs or performance issues; see https://github.com/apache/beam/issues/31078 for more
+ * info, and comment in that issue if you run into issues with this IO.
  */
 @SuppressWarnings({"rawtypes", "nullness"})
 public class PulsarIO {
@@ -43,19 +46,41 @@ public class PulsarIO {
   private PulsarIO() {}
 
   /**
-   * Read from Apache Pulsar. Support is currently incomplete, and there may be bugs; see
+   * Read from Apache Pulsar.
+   *
+   * <p>Support is currently experimental, and there may be bugs or performance issues; see
    * https://github.com/apache/beam/issues/31078 for more info, and comment in that issue if you run
    * into issues with this IO.
+   *
+   * @param fn a mapping function converting {@link Message} that returned by Pulsar client to a
+   *     custom type understood by Beam.
    */
-  public static Read read() {
+  public static <T> Read<T> read(SerializableFunction<Message, T> fn) {
     return new AutoValue_PulsarIO_Read.Builder()
-        .setPulsarClient(PulsarIOUtils.PULSAR_CLIENT_SERIALIZABLE_FUNCTION)
+        .setOutputFn(fn)
+        .setConsumerPollingTimeout(PulsarIOUtils.DEFAULT_CONSUMER_POLLING_TIMEOUT)
+        .setTimestampType(ReadTimestampType.PUBLISH_TIME)
         .build();
   }
 
+  /**
+   * The same as {@link PulsarIO#read(SerializableFunction)}, but returns {@link
+   * PCollection<PulsarMessage>}.
+   */
+  public static Read<PulsarMessage> read() {
+    return new AutoValue_PulsarIO_Read.Builder()
+        .setOutputFn(PULSAR_MESSAGE_SERIALIZABLE_FUNCTION)
+        .setConsumerPollingTimeout(PulsarIOUtils.DEFAULT_CONSUMER_POLLING_TIMEOUT)
+        .setTimestampType(ReadTimestampType.PUBLISH_TIME)
+        .build();
+  }
+
+  private static final SerializableFunction<Message<byte[]>, PulsarMessage>
+      PULSAR_MESSAGE_SERIALIZABLE_FUNCTION = PulsarMessage::create;
+
   @AutoValue
   @SuppressWarnings({"rawtypes"})
-  public abstract static class Read extends PTransform<PBegin, PCollection<PulsarMessage>> {
+  public abstract static class Read<T> extends PTransform<PBegin, PCollection<T>> {
 
     abstract @Nullable String getClientUrl();
 
@@ -69,107 +94,152 @@ public abstract static class Read extends PTransform<PBegin, PCollection<PulsarM
 
     abstract @Nullable MessageId getEndMessageId();
 
-    abstract @Nullable SerializableFunction<Message<byte[]>, Instant> getExtractOutputTimestampFn();
+    abstract ReadTimestampType getTimestampType();
 
-    abstract SerializableFunction<String, PulsarClient> getPulsarClient();
+    abstract long getConsumerPollingTimeout();
 
-    abstract Builder builder();
+    abstract @Nullable SerializableFunction<String, PulsarClient> getPulsarClient();
+
+    abstract @Nullable SerializableFunction<String, PulsarAdmin> getPulsarAdmin();
+
+    abstract SerializableFunction<Message<?>, T> getOutputFn();
+
+    abstract Builder<T> builder();
 
     @AutoValue.Builder
-    abstract static class Builder {
-      abstract Builder setClientUrl(String url);
+    abstract static class Builder<T> {
+      abstract Builder<T> setClientUrl(String url);
 
-      abstract Builder setAdminUrl(String url);
+      abstract Builder<T> setAdminUrl(String url);
 
-      abstract Builder setTopic(String topic);
+      abstract Builder<T> setTopic(String topic);
 
-      abstract Builder setStartTimestamp(Long timestamp);
+      abstract Builder<T> setStartTimestamp(Long timestamp);
 
-      abstract Builder setEndTimestamp(Long timestamp);
+      abstract Builder<T> setEndTimestamp(Long timestamp);
 
-      abstract Builder setEndMessageId(MessageId msgId);
+      abstract Builder<T> setEndMessageId(MessageId msgId);
 
-      abstract Builder setExtractOutputTimestampFn(
-          SerializableFunction<Message<byte[]>, Instant> fn);
+      abstract Builder<T> setTimestampType(ReadTimestampType timestampType);
 
-      abstract Builder setPulsarClient(SerializableFunction<String, PulsarClient> fn);
+      abstract Builder<T> setConsumerPollingTimeout(long timeOutMs);
+
+      abstract Builder<T> setPulsarClient(SerializableFunction<String, PulsarClient> fn);
+
+      abstract Builder<T> setPulsarAdmin(SerializableFunction<String, PulsarAdmin> fn);
 
-      abstract Read build();
+      @SuppressWarnings("getvsset") // outputFn determines generic type
+      abstract Builder<T> setOutputFn(SerializableFunction<Message<?>, T> fn);
+
+      abstract Read<T> build();
     }
 
-    public Read withAdminUrl(String url) {
+    /**
+     * Configure Pulsar admin url.
+     *
+     * <p>Admin client is used to approximate backlogs. This setting is optional.
+     *
+     * @param url admin url. For example, {@code "http://localhost:8080"}.
+     */
+    public Read<T> withAdminUrl(String url) {
       return builder().setAdminUrl(url).build();
     }
 
-    public Read withClientUrl(String url) {
+    /**
+     * Configure Pulsar client url. {@code "pulsar://localhost:6650"}.
+     *
+     * @param url client url. For example,
+     */
+    public Read<T> withClientUrl(String url) {
       return builder().setClientUrl(url).build();
     }
 
-    public Read withTopic(String topic) {
+    public Read<T> withTopic(String topic) {
       return builder().setTopic(topic).build();
     }
 
-    public Read withStartTimestamp(Long timestamp) {
+    public Read<T> withStartTimestamp(Long timestamp) {
       return builder().setStartTimestamp(timestamp).build();
     }
 
-    public Read withEndTimestamp(Long timestamp) {
+    public Read<T> withEndTimestamp(Long timestamp) {
       return builder().setEndTimestamp(timestamp).build();
     }
 
-    public Read withEndMessageId(MessageId msgId) {
+    public Read<T> withEndMessageId(MessageId msgId) {
       return builder().setEndMessageId(msgId).build();
     }
 
-    public Read withExtractOutputTimestampFn(SerializableFunction<Message<byte[]>, Instant> fn) {
-      return builder().setExtractOutputTimestampFn(fn).build();
+    /** Set elements timestamped by {@link Message#getPublishTime()}. It is the default. */
+    public Read<T> withPublishTime() {
+      return builder().setTimestampType(ReadTimestampType.PUBLISH_TIME).build();
     }
 
-    public Read withPublishTime() {
-      return withExtractOutputTimestampFn(ExtractOutputTimestampFn.usePublishTime());
+    /** Set elements timestamped to the moment it get processed. */
+    public Read<T> withProcessingTime() {
+      return builder().setTimestampType(ReadTimestampType.PROCESSING_TIME).build();
     }
 
-    public Read withProcessingTime() {
-      return withExtractOutputTimestampFn(ExtractOutputTimestampFn.useProcessingTime());
+    /**
+     * Sets the timeout time in seconds for Pulsar consumer polling request. A lower timeout
+     * optimizes for latency. Increase the timeout if the consumer is not fetching any records. The
+     * default is 2 seconds.
+     */
+    public Read<T> withConsumerPollingTimeout(long duration) {
+      checkState(duration > 0, "Consumer polling timeout must be greater than 0.");
+      return builder().setConsumerPollingTimeout(duration).build();
     }
 
-    public Read withPulsarClient(SerializableFunction<String, PulsarClient> pulsarClientFn) {
+    public Read<T> withPulsarClient(SerializableFunction<String, PulsarClient> pulsarClientFn) {
       return builder().setPulsarClient(pulsarClientFn).build();
     }
 
+    public Read<T> withPulsarAdmin(SerializableFunction<String, PulsarAdmin> pulsarAdminFn) {
+      return builder().setPulsarAdmin(pulsarAdminFn).build();
+    }
+
+    @SuppressWarnings("unchecked") // for PulsarMessage
     @Override
-    public PCollection<PulsarMessage> expand(PBegin input) {
-      return input
-          .apply(
-              Create.of(
-                  PulsarSourceDescriptor.of(
-                      getTopic(),
-                      getStartTimestamp(),
-                      getEndTimestamp(),
-                      getEndMessageId(),
-                      getClientUrl(),
-                      getAdminUrl())))
-          .apply(ParDo.of(new ReadFromPulsarDoFn(this)))
-          .setCoder(PulsarMessageCoder.of());
+    public PCollection<T> expand(PBegin input) {
+      PCollection<T> pcoll =
+          input
+              .apply(
+                  Create.of(
+                      PulsarSourceDescriptor.of(
+                          getTopic(), getStartTimestamp(), getEndTimestamp(), getEndMessageId())))
+              .apply(ParDo.of(new NaiveReadFromPulsarDoFn<>(this)));
+      if (getOutputFn().equals(PULSAR_MESSAGE_SERIALIZABLE_FUNCTION)) {
+        // register coder for default implementation of read
+        return pcoll.setTypeDescriptor((TypeDescriptor<T>) TypeDescriptor.of(PulsarMessage.class));
+      }
+      return pcoll;
     }
   }
 
+  enum ReadTimestampType {
+    PROCESSING_TIME,
+    PUBLISH_TIME,
+  }
+
   /**
-   * Write to Apache Pulsar. Support is currently incomplete, and there may be bugs; see
-   * https://github.com/apache/beam/issues/31078 for more info, and comment in that issue if you run
-   * into issues with this IO.
+   * Write to Apache Pulsar. Support is currently experimental, and there may be bugs or performance
+   * issues; see https://github.com/apache/beam/issues/31078 for more info, and comment in that
+   * issue if you run into issues with this IO.
    */
   public static Write write() {
-    return new AutoValue_PulsarIO_Write.Builder().build();
+    return new AutoValue_PulsarIO_Write.Builder()
+        .setPulsarClient(PulsarIOUtils.PULSAR_CLIENT_SERIALIZABLE_FUNCTION)
+        .build();
   }
 
   @AutoValue
-  @SuppressWarnings({"rawtypes"})
   public abstract static class Write extends PTransform<PCollection<byte[]>, PDone> {
 
     abstract @Nullable String getTopic();
 
-    abstract String getClientUrl();
+    abstract @Nullable String getClientUrl();
+
+    abstract SerializableFunction<String, PulsarClient> getPulsarClient();
 
     abstract Builder builder();
 
@@ -179,6 +249,8 @@ abstract static class Builder {
 
       abstract Builder setClientUrl(String clientUrl);
 
+      abstract Builder setPulsarClient(SerializableFunction<String, PulsarClient> fn);
+
       abstract Write build();
     }
 
@@ -190,20 +262,14 @@ public Write withClientUrl(String clientUrl) {
       return builder().setClientUrl(clientUrl).build();
     }
 
+    public Write withPulsarClient(SerializableFunction<String, PulsarClient> pulsarClientFn) {
+      return builder().setPulsarClient(pulsarClientFn).build();
+    }
+
     @Override
     public PDone expand(PCollection<byte[]> input) {
       input.apply(ParDo.of(new WriteToPulsarDoFn(this)));
       return PDone.in(input.getPipeline());
     }
   }
-
-  static class ExtractOutputTimestampFn {
-    public static SerializableFunction<Message<byte[]>, Instant> useProcessingTime() {
-      return record -> Instant.now();
-    }
-
-    public static SerializableFunction<Message<byte[]>, Instant> usePublishTime() {
-      return record -> new Instant(record.getPublishTime());
-    }
-  }
 }
diff --git a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarIOUtils.java b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarIOUtils.java
index 53bc8e448768..8c4a3af282e1 100644
--- a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarIOUtils.java
+++ b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarIOUtils.java
@@ -18,6 +18,7 @@
 package org.apache.beam.sdk.io.pulsar;
 
 import org.apache.beam.sdk.transforms.SerializableFunction;
+import org.apache.pulsar.client.admin.PulsarAdmin;
 import org.apache.pulsar.client.api.PulsarClient;
 import org.apache.pulsar.client.api.PulsarClientException;
 import org.slf4j.Logger;
@@ -26,19 +27,27 @@
 final class PulsarIOUtils {
 
   private static final Logger LOG = LoggerFactory.getLogger(PulsarIOUtils.class);
-  public static final String SERVICE_HTTP_URL = "http://localhost:8080";
-  public static final String SERVICE_URL = "pulsar://localhost:6650";
+  static final String LOCAL_SERVICE_URL = "pulsar://localhost:6650";
+  static final long DEFAULT_CONSUMER_POLLING_TIMEOUT = 2L;
 
   static final SerializableFunction<String, PulsarClient> PULSAR_CLIENT_SERIALIZABLE_FUNCTION =
-      new SerializableFunction<String, PulsarClient>() {
-        @Override
-        public PulsarClient apply(String input) {
-          try {
-            return PulsarClient.builder().serviceUrl(input).build();
-          } catch (PulsarClientException e) {
-            LOG.error(e.getMessage());
-            throw new RuntimeException(e);
-          }
+      input -> {
+        try {
+          return PulsarClient.builder().serviceUrl(input).build();
+        } catch (PulsarClientException e) {
+          throw new RuntimeException(e);
+        }
+      };
+
+  static final SerializableFunction<String, PulsarAdmin> PULSAR_ADMIN_SERIALIZABLE_FUNCTION =
+      input -> {
+        try {
+          return PulsarAdmin.builder()
+              .serviceHttpUrl(input)
+              .allowTlsInsecureConnection(false)
+              .build();
+        } catch (PulsarClientException e) {
+          throw new RuntimeException(e);
         }
       };
 }
diff --git a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarMessage.java b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarMessage.java
index 34fa989177eb..739d34c98604 100644
--- a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarMessage.java
+++ b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarMessage.java
@@ -17,40 +17,52 @@
  */
 package org.apache.beam.sdk.io.pulsar;
 
+import com.google.auto.value.AutoValue;
+import java.util.Map;
+import org.apache.beam.sdk.schemas.AutoValueSchema;
+import org.apache.beam.sdk.schemas.annotations.DefaultSchema;
+import org.apache.pulsar.client.api.Message;
+import org.checkerframework.checker.nullness.qual.Nullable;
+
 /**
  * Class representing a Pulsar Message record. Each PulsarMessage contains a single message basic
  * message data and Message record to access directly.
  */
-@SuppressWarnings("initialization.fields.uninitialized")
-public class PulsarMessage {
-  private String topic;
-  private Long publishTimestamp;
-  private Object messageRecord;
-
-  public PulsarMessage(String topic, Long publishTimestamp, Object messageRecord) {
-    this.topic = topic;
-    this.publishTimestamp = publishTimestamp;
-    this.messageRecord = messageRecord;
-  }
+@DefaultSchema(AutoValueSchema.class)
+@AutoValue
+public abstract class PulsarMessage {
+  abstract @Nullable String getTopic();
 
-  public PulsarMessage(String topic, Long publishTimestamp) {
-    this.topic = topic;
-    this.publishTimestamp = publishTimestamp;
-  }
+  abstract long getPublishTimestamp();
 
-  public String getTopic() {
-    return topic;
-  }
+  abstract @Nullable String getKey();
 
-  public Long getPublishTimestamp() {
-    return publishTimestamp;
-  }
+  @SuppressWarnings("mutable")
+  abstract byte[] getValue();
+
+  abstract @Nullable Map<String, String> getProperties();
+
+  @SuppressWarnings("mutable")
+  abstract byte[] getMessageId();
 
-  public void setMessageRecord(Object messageRecord) {
-    this.messageRecord = messageRecord;
+  public static PulsarMessage create(
+      @Nullable String topicName,
+      long publishTimestamp,
+      @Nullable String key,
+      byte[] value,
+      @Nullable Map<String, String> properties,
+      byte[] messageId) {
+    return new AutoValue_PulsarMessage(
+        topicName, publishTimestamp, key, value, properties, messageId);
   }
 
-  public Object getMessageRecord() {
-    return messageRecord;
+  public static PulsarMessage create(Message<byte[]> message) {
+    return create(
+        message.getTopicName(),
+        message.getPublishTime(),
+        message.getKey(),
+        message.getValue(),
+        message.getProperties(),
+        message.getMessageId().toByteArray());
   }
 }
diff --git a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarMessageCoder.java b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarMessageCoder.java
deleted file mode 100644
index 2f3bed5fa085..000000000000
--- a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarMessageCoder.java
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.beam.sdk.io.pulsar;
-
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.OutputStream;
-import org.apache.beam.sdk.coders.CoderException;
-import org.apache.beam.sdk.coders.CustomCoder;
-import org.apache.beam.sdk.coders.StringUtf8Coder;
-import org.apache.beam.sdk.coders.VarLongCoder;
-
-public class PulsarMessageCoder extends CustomCoder<PulsarMessage> {
-
-  private static final StringUtf8Coder stringCoder = StringUtf8Coder.of();
-  private static final VarLongCoder longCoder = VarLongCoder.of();
-
-  public static PulsarMessageCoder of() {
-    return new PulsarMessageCoder();
-  }
-
-  public PulsarMessageCoder() {}
-
-  @Override
-  public void encode(PulsarMessage value, OutputStream outStream)
-      throws CoderException, IOException {
-    stringCoder.encode(value.getTopic(), outStream);
-    longCoder.encode(value.getPublishTimestamp(), outStream);
-  }
-
-  @Override
-  public PulsarMessage decode(InputStream inStream) throws CoderException, IOException {
-    return new PulsarMessage(stringCoder.decode(inStream), longCoder.decode(inStream));
-  }
-}
diff --git a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarSourceDescriptor.java b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarSourceDescriptor.java
index 427d37d1d72a..66617f9863aa 100644
--- a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarSourceDescriptor.java
+++ b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/PulsarSourceDescriptor.java
@@ -44,20 +44,9 @@ public abstract class PulsarSourceDescriptor implements Serializable {
   @Nullable
   abstract MessageId getEndMessageId();
 
-  @SchemaFieldName("client_url")
-  abstract String getClientUrl();
-
-  @SchemaFieldName("admin_url")
-  abstract String getAdminUrl();
-
   public static PulsarSourceDescriptor of(
-      String topic,
-      Long startOffsetTimestamp,
-      Long endOffsetTimestamp,
-      MessageId endMessageId,
-      String clientUrl,
-      String adminUrl) {
+      String topic, Long startOffsetTimestamp, Long endOffsetTimestamp, MessageId endMessageId) {
     return new AutoValue_PulsarSourceDescriptor(
-        topic, startOffsetTimestamp, endOffsetTimestamp, endMessageId, clientUrl, adminUrl);
+        topic, startOffsetTimestamp, endOffsetTimestamp, endMessageId);
   }
 }
diff --git a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/WriteToPulsarDoFn.java b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/WriteToPulsarDoFn.java
index 375e8ce92a3a..7d64b6e49b19 100644
--- a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/WriteToPulsarDoFn.java
+++ b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/WriteToPulsarDoFn.java
@@ -18,33 +18,39 @@
 package org.apache.beam.sdk.io.pulsar;
 
 import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.SerializableFunction;
 import org.apache.pulsar.client.api.CompressionType;
 import org.apache.pulsar.client.api.Producer;
 import org.apache.pulsar.client.api.PulsarClient;
 import org.apache.pulsar.client.api.PulsarClientException;
 
-/**
- * Transform for writing to Apache Pulsar. Support is currently incomplete, and there may be bugs;
- * see https://github.com/apache/beam/issues/31078 for more info, and comment in that issue if you
- * run into issues with this IO.
- */
-@DoFn.UnboundedPerElement
-@SuppressWarnings({"rawtypes", "nullness"})
+/** DoFn for writing to Apache Pulsar. */
+@SuppressWarnings({"nullness"})
 public class WriteToPulsarDoFn extends DoFn<byte[], Void> {
-
-  private Producer<byte[]> producer;
-  private PulsarClient client;
+  private final SerializableFunction<String, PulsarClient> clientFn;
+  private transient Producer<byte[]> producer;
+  private transient PulsarClient client;
   private String clientUrl;
   private String topic;
 
   WriteToPulsarDoFn(PulsarIO.Write transform) {
     this.clientUrl = transform.getClientUrl();
     this.topic = transform.getTopic();
+    this.clientFn = transform.getPulsarClient();
   }
 
   @Setup
-  public void setup() throws PulsarClientException {
-    client = PulsarClient.builder().serviceUrl(clientUrl).build();
+  public void setup() {
+    if (client == null) {
+      if (clientUrl == null) {
+        clientUrl = PulsarIOUtils.LOCAL_SERVICE_URL;
+      }
+      client = clientFn.apply(clientUrl);
+    }
+  }
+
+  @StartBundle
+  public void startBundle() throws PulsarClientException {
     producer = client.newProducer().topic(topic).compressionType(CompressionType.LZ4).create();
   }
 
@@ -53,9 +59,13 @@ public void processElement(@Element byte[] messageToSend) throws Exception {
     producer.send(messageToSend);
   }
 
+  @FinishBundle
+  public void finishBundle() throws PulsarClientException {
+    producer.close();
+  }
+
   @Teardown
   public void teardown() throws PulsarClientException {
-    producer.close();
     client.close();
   }
 }
diff --git a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/package-info.java b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/package-info.java
index ffa15257fe5a..3ec49fa1f73e 100644
--- a/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/package-info.java
+++ b/sdks/java/io/pulsar/src/main/java/org/apache/beam/sdk/io/pulsar/package-info.java
@@ -16,8 +16,8 @@
  * limitations under the License.
  */
 /**
- * Transforms for reading and writing from Apache Pulsar. Support is currently incomplete, and there
- * may be bugs; see https://github.com/apache/beam/issues/31078 for more info, and comment in that
- * issue if you run into issues with this IO.
+ * Transforms for reading and writing from Apache Pulsar. Support is currently experimental, and
+ * there may be bugs and performance issues; see https://github.com/apache/beam/issues/31078 for
+ * more info, and comment in that issue if you run into issues with this IO.
  */
 package org.apache.beam.sdk.io.pulsar;
diff --git a/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakeMessage.java b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakeMessage.java
index 9cdc4af37435..b02ef98a2f85 100644
--- a/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakeMessage.java
+++ b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakeMessage.java
@@ -68,12 +68,13 @@ public int size() {
 
   @Override
   public byte[] getValue() {
-    return null;
+    return new byte[0];
   }
 
   @Override
   public MessageId getMessageId() {
-    return DefaultImplementation.newMessageId(this.ledgerId, this.entryId, this.partitionIndex);
+    return DefaultImplementation.getDefaultImplementation()
+        .newMessageId(this.ledgerId, this.entryId, this.partitionIndex);
   }
 
   @Override
@@ -158,4 +159,24 @@ public String getReplicatedFrom() {
 
   @Override
   public void release() {}
+
+  @Override
+  public boolean hasBrokerPublishTime() {
+    return false;
+  }
+
+  @Override
+  public Optional<Long> getBrokerPublishTime() {
+    return Optional.empty();
+  }
+
+  @Override
+  public boolean hasIndex() {
+    return false;
+  }
+
+  @Override
+  public Optional<Long> getIndex() {
+    return Optional.empty();
+  }
 }
diff --git a/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakePulsarClient.java b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakePulsarClient.java
index 4639d8420be9..debded32494b 100644
--- a/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakePulsarClient.java
+++ b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakePulsarClient.java
@@ -17,6 +17,7 @@
  */
 package org.apache.beam.sdk.io.pulsar;
 
+import java.time.Instant;
 import java.util.List;
 import java.util.Map;
 import java.util.concurrent.CompletableFuture;
@@ -31,11 +32,13 @@
 import org.apache.pulsar.client.api.Range;
 import org.apache.pulsar.client.api.Reader;
 import org.apache.pulsar.client.api.ReaderBuilder;
+import org.apache.pulsar.client.api.ReaderInterceptor;
 import org.apache.pulsar.client.api.ReaderListener;
 import org.apache.pulsar.client.api.Schema;
+import org.apache.pulsar.client.api.TableViewBuilder;
 import org.apache.pulsar.client.api.transaction.TransactionBuilder;
 
-@SuppressWarnings({"rawtypes"})
+@SuppressWarnings("rawtypes")
 public class FakePulsarClient implements PulsarClient {
 
   private MockReaderBuilder readerBuilder;
@@ -86,6 +89,11 @@ public <T> ReaderBuilder<T> newReader(Schema<T> schema) {
     return null;
   }
 
+  @Override
+  public <T> TableViewBuilder<T> newTableViewBuilder(Schema<T> schema) {
+    return null;
+  }
+
   @Override
   public void updateServiceUrl(String serviceUrl) throws PulsarClientException {}
 
@@ -134,7 +142,8 @@ public Reader<byte[]> create() throws PulsarClientException {
       if (this.reader != null) {
         return this.reader;
       }
-      this.reader = new FakePulsarReader(this.topic, this.numberOfMessages);
+      this.reader =
+          new FakePulsarReader(this.topic, this.numberOfMessages, Instant.now().toEpochMilli());
       return this.reader;
     }
 
@@ -145,7 +154,7 @@ public CompletableFuture<Reader<byte[]>> createAsync() {
 
     @Override
     public ReaderBuilder<byte[]> clone() {
-      return null;
+      return this;
     }
 
     @Override
@@ -162,77 +171,114 @@ public ReaderBuilder<byte[]> startMessageId(MessageId startMessageId) {
     @Override
     public ReaderBuilder<byte[]> startMessageFromRollbackDuration(
         long rollbackDuration, TimeUnit timeunit) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> startMessageIdInclusive() {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> readerListener(ReaderListener readerListener) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> cryptoKeyReader(CryptoKeyReader cryptoKeyReader) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> defaultCryptoKeyReader(String privateKey) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> cryptoFailureAction(ConsumerCryptoFailureAction action) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> receiverQueueSize(int receiverQueueSize) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> readerName(String readerName) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> subscriptionRolePrefix(String subscriptionRolePrefix) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> subscriptionName(String subscriptionName) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> readCompacted(boolean readCompacted) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> keyHashRange(Range... ranges) {
-      return null;
+      return this;
+    }
+
+    @Override
+    public ReaderBuilder<byte[]> poolMessages(boolean poolMessages) {
+      return this;
+    }
+
+    @Override
+    public ReaderBuilder<byte[]> autoUpdatePartitions(boolean autoUpdate) {
+      return this;
+    }
+
+    @Override
+    public ReaderBuilder<byte[]> autoUpdatePartitionsInterval(int interval, TimeUnit unit) {
+      return this;
+    }
+
+    @Override
+    public ReaderBuilder<byte[]> intercept(ReaderInterceptor<byte[]>... interceptors) {
+      return this;
+    }
+
+    @Override
+    public ReaderBuilder<byte[]> maxPendingChunkedMessage(int maxPendingChunkedMessage) {
+      return this;
+    }
+
+    @Override
+    public ReaderBuilder<byte[]> autoAckOldestChunkedMessageOnQueueFull(
+        boolean autoAckOldestChunkedMessageOnQueueFull) {
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> defaultCryptoKeyReader(Map privateKeys) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> topics(List topicNames) {
-      return null;
+      return this;
     }
 
     @Override
     public ReaderBuilder<byte[]> loadConf(Map config) {
-      return null;
+      return this;
+    }
+
+    @Override
+    public ReaderBuilder<byte[]> expireTimeOfIncompleteChunkedMessage(
+        long duration, TimeUnit unit) {
+      return this;
     }
   }
 }
diff --git a/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakePulsarReader.java b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakePulsarReader.java
index 834fd0427532..6d937e77ce12 100644
--- a/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakePulsarReader.java
+++ b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/FakePulsarReader.java
@@ -18,6 +18,7 @@
 package org.apache.beam.sdk.io.pulsar;
 
 import java.io.IOException;
+import java.io.Serializable;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.concurrent.CompletableFuture;
@@ -30,17 +31,18 @@
 import org.joda.time.Duration;
 import org.joda.time.Instant;
 
-public class FakePulsarReader implements Reader<byte[]> {
+public class FakePulsarReader implements Reader<byte[]>, Serializable {
 
   private String topic;
   private List<FakeMessage> fakeMessages = new ArrayList<>();
   private int currentMsg;
-  private long startTimestamp;
+  private final long startTimestamp;
   private long endTimestamp;
   private boolean reachedEndOfTopic;
   private int numberOfMessages;
 
-  public FakePulsarReader(String topic, int numberOfMessages) {
+  public FakePulsarReader(String topic, int numberOfMessages, long startTimestamp) {
+    this.startTimestamp = startTimestamp;
     this.numberOfMessages = numberOfMessages;
     this.setMock(topic, numberOfMessages);
   }
@@ -52,10 +54,9 @@ public void setReachedEndOfTopic(boolean hasReachedEnd) {
   public void setMock(String topic, int numberOfMessages) {
     this.topic = topic;
     for (int i = 0; i < numberOfMessages; i++) {
-      long timestamp = Instant.now().plus(Duration.standardSeconds(i)).getMillis();
-      if (i == 0) {
-        startTimestamp = timestamp;
-      } else if (i == 99) {
+      long timestamp =
+          Instant.ofEpochMilli(startTimestamp).plus(Duration.standardSeconds(i)).getMillis();
+      if (i == numberOfMessages - 1) {
         endTimestamp = timestamp;
       }
       fakeMessages.add(new FakeMessage(topic, timestamp, Long.valueOf(i), Long.valueOf(i), i));
@@ -89,20 +90,23 @@ public String getTopic() {
 
   @Override
   public Message<byte[]> readNext() throws PulsarClientException {
-    if (currentMsg == 0 && fakeMessages.isEmpty()) {
+    if (fakeMessages.isEmpty()) {
       return null;
     }
 
-    Message<byte[]> msg = fakeMessages.get(currentMsg);
-    if (currentMsg <= fakeMessages.size() - 1) {
+    if (currentMsg < fakeMessages.size()) {
+      Message<byte[]> msg = fakeMessages.get(currentMsg);
       currentMsg++;
+      return msg;
+    } else {
+      reachedEndOfTopic = true;
+      return null;
     }
-    return msg;
   }
 
   @Override
   public Message<byte[]> readNext(int timeout, TimeUnit unit) throws PulsarClientException {
-    return null;
+    return readNext();
   }
 
   @Override
@@ -141,11 +145,12 @@ public void seek(MessageId messageId) throws PulsarClientException {}
   @Override
   public void seek(long timestamp) throws PulsarClientException {
     for (int i = 0; i < fakeMessages.size(); i++) {
-      if (timestamp == fakeMessages.get(i).getPublishTime()) {
+      if (timestamp <= fakeMessages.get(i).getPublishTime()) {
         currentMsg = i;
-        break;
+        return;
       }
     }
+    currentMsg = fakeMessages.size();
   }
 
   @Override
diff --git a/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/PulsarIOIT.java b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/PulsarIOIT.java
new file mode 100644
index 000000000000..d3b8cea7d899
--- /dev/null
+++ b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/PulsarIOIT.java
@@ -0,0 +1,227 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.io.pulsar;
+
+import static org.junit.Assert.assertEquals;
+
+import java.nio.charset.StandardCharsets;
+import java.util.ArrayList;
+import java.util.Comparator;
+import java.util.List;
+import java.util.concurrent.TimeUnit;
+import org.apache.beam.sdk.PipelineResult;
+import org.apache.beam.sdk.metrics.Counter;
+import org.apache.beam.sdk.metrics.MetricNameFilter;
+import org.apache.beam.sdk.metrics.MetricQueryResults;
+import org.apache.beam.sdk.metrics.MetricResult;
+import org.apache.beam.sdk.metrics.Metrics;
+import org.apache.beam.sdk.metrics.MetricsFilter;
+import org.apache.beam.sdk.testing.TestPipeline;
+import org.apache.beam.sdk.transforms.Create;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.commons.lang3.RandomStringUtils;
+import org.apache.pulsar.client.api.Consumer;
+import org.apache.pulsar.client.api.Message;
+import org.apache.pulsar.client.api.MessageId;
+import org.apache.pulsar.client.api.Producer;
+import org.apache.pulsar.client.api.PulsarClient;
+import org.apache.pulsar.client.api.PulsarClientException;
+import org.junit.AfterClass;
+import org.junit.BeforeClass;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.Timeout;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+import org.testcontainers.containers.PulsarContainer;
+import org.testcontainers.utility.DockerImageName;
+
+@RunWith(JUnit4.class)
+public class PulsarIOIT {
+  @Rule public Timeout globalTimeout = Timeout.seconds(60);
+  protected static PulsarContainer pulsarContainer;
+  protected static PulsarClient client;
+
+  private long endExpectedTime = 0;
+  private long startTime = 0;
+
+  private static final Logger LOG = LoggerFactory.getLogger(PulsarIOIT.class);
+
+  @Rule public final transient TestPipeline testPipeline = TestPipeline.create();
+
+  public List<Message<byte[]>> receiveMessages(String topic) throws PulsarClientException {
+    if (client == null) {
+      initClient();
+    }
+    List<Message<byte[]>> messages = new ArrayList<>();
+    try (Consumer<byte[]> consumer =
+        client.newConsumer().topic(topic).subscriptionName("receiveMockMessageFn").subscribe()) {
+      consumer.seek(MessageId.earliest);
+      LOG.warn("started receiveMessages");
+      while (!consumer.hasReachedEndOfTopic()) {
+        Message<byte[]> msg = consumer.receive(5, TimeUnit.SECONDS);
+        if (msg == null) {
+          LOG.warn("null message");
+          break;
+        }
+        messages.add(msg);
+        consumer.acknowledge(msg);
+      }
+    }
+    messages.sort(Comparator.comparing(s -> new String(s.getValue(), StandardCharsets.UTF_8)));
+    return messages;
+  }
+
+  public List<PulsarMessage> produceMessages(String topic) throws PulsarClientException {
+    client = initClient();
+    Producer<byte[]> producer = client.newProducer().topic(topic).create();
+    Consumer<byte[]> consumer =
+        client.newConsumer().topic(topic).subscriptionName("produceMockMessageFn").subscribe();
+    int numElements = 101;
+    List<PulsarMessage> inputs = new ArrayList<>();
+    for (int i = 0; i < numElements; i++) {
+      String msg = ("PULSAR_TEST_READFROMSIMPLETOPIC_" + i);
+      producer.send(msg.getBytes(StandardCharsets.UTF_8));
+      Message<byte[]> message = consumer.receive(5, TimeUnit.SECONDS);
+      if (i == 100) {
+        endExpectedTime = message.getPublishTime();
+      } else {
+        inputs.add(PulsarMessage.create(message));
+        if (i == 0) {
+          startTime = message.getPublishTime();
+        }
+      }
+    }
+    consumer.close();
+    producer.close();
+    client.close();
+    return inputs;
+  }
+
+  private static PulsarClient initClient() throws PulsarClientException {
+    return PulsarClient.builder().serviceUrl(pulsarContainer.getPulsarBrokerUrl()).build();
+  }
+
+  private static void setupPulsarContainer() {
+    pulsarContainer = new PulsarContainer(DockerImageName.parse("apachepulsar/pulsar:2.11.4"));
+    pulsarContainer.withCommand("bin/pulsar", "standalone");
+    try {
+      pulsarContainer.start();
+    } catch (IllegalStateException unused) {
+      pulsarContainer = new PulsarContainerLocalProxy();
+    }
+  }
+
+  static class PulsarContainerLocalProxy extends PulsarContainer {
+    @Override
+    public String getPulsarBrokerUrl() {
+      return "pulsar://localhost:6650";
+    }
+
+    @Override
+    public String getHttpServiceUrl() {
+      return "http://localhost:8080";
+    }
+  }
+
+  @BeforeClass
+  public static void setup() throws PulsarClientException {
+    setupPulsarContainer();
+    client = initClient();
+  }
+
+  @AfterClass
+  public static void afterClass() {
+    if (pulsarContainer != null && pulsarContainer.isRunning()) {
+      pulsarContainer.stop();
+    }
+  }
+
+  @Test
+  public void testReadFromSimpleTopic() throws PulsarClientException {
+    String topic = "PULSARIOIT_READ" + RandomStringUtils.randomAlphanumeric(4);
+    List<PulsarMessage> inputsMock = produceMessages(topic);
+    PulsarIO.Read<PulsarMessage> reader =
+        PulsarIO.read()
+            .withClientUrl(pulsarContainer.getPulsarBrokerUrl())
+            .withAdminUrl(pulsarContainer.getHttpServiceUrl())
+            .withTopic(topic)
+            .withStartTimestamp(startTime)
+            .withEndTimestamp(endExpectedTime)
+            .withPublishTime();
+    testPipeline.apply(reader).apply(ParDo.of(new PulsarRecordsMetric()));
+
+    PipelineResult pipelineResult = testPipeline.run();
+    MetricQueryResults metrics =
+        pipelineResult
+            .metrics()
+            .queryMetrics(
+                MetricsFilter.builder()
+                    .addNameFilter(
+                        MetricNameFilter.named(PulsarIOIT.class.getName(), "PulsarRecordsCounter"))
+                    .build());
+    long recordsCount = 0;
+    for (MetricResult<Long> metric : metrics.getCounters()) {
+      if (metric
+          .getName()
+          .toString()
+          .equals("org.apache.beam.sdk.io.pulsar.PulsarIOIT:PulsarRecordsCounter")) {
+        recordsCount = metric.getAttempted();
+        break;
+      }
+    }
+    assertEquals(inputsMock.size(), (int) recordsCount);
+  }
+
+  @Test
+  public void testWriteToTopic() throws PulsarClientException {
+    String topic = "PULSARIOIT_WRITE_" + RandomStringUtils.randomAlphanumeric(4);
+    PulsarIO.Write writer =
+        PulsarIO.write().withClientUrl(pulsarContainer.getPulsarBrokerUrl()).withTopic(topic);
+    int numberOfMessages = 10;
+    List<byte[]> messages = new ArrayList<>();
+    for (int i = 0; i < numberOfMessages; i++) {
+      messages.add(("PULSAR_WRITER_TEST_" + i).getBytes(StandardCharsets.UTF_8));
+    }
+    testPipeline.apply(Create.of(messages)).apply(writer);
+
+    testPipeline.run();
+
+    List<Message<byte[]>> receiveMsgs = receiveMessages(topic);
+    assertEquals(numberOfMessages, receiveMsgs.size());
+    for (int i = 0; i < numberOfMessages; i++) {
+      assertEquals(
+          new String(receiveMsgs.get(i).getValue(), StandardCharsets.UTF_8),
+          "PULSAR_WRITER_TEST_" + i);
+    }
+  }
+
+  public static class PulsarRecordsMetric extends DoFn<PulsarMessage, PulsarMessage> {
+    private final Counter counter =
+        Metrics.counter(PulsarIOIT.class.getName(), "PulsarRecordsCounter");
+
+    @ProcessElement
+    public void processElement(ProcessContext context) {
+      counter.inc();
+      context.output(context.element());
+    }
+  }
+}
diff --git a/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/PulsarIOTest.java b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/PulsarIOTest.java
index eeb6a5d7652c..52ee3044d60c 100644
--- a/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/PulsarIOTest.java
+++ b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/PulsarIOTest.java
@@ -17,225 +17,74 @@
  */
 package org.apache.beam.sdk.io.pulsar;
 
-import static org.junit.jupiter.api.Assertions.assertEquals;
-import static org.junit.jupiter.api.Assertions.assertTrue;
-
-import java.nio.charset.StandardCharsets;
+import java.io.Serializable;
+import java.time.Instant;
 import java.util.ArrayList;
 import java.util.List;
-import java.util.concurrent.CompletableFuture;
-import java.util.concurrent.ExecutionException;
-import java.util.concurrent.TimeUnit;
-import java.util.concurrent.TimeoutException;
-import org.apache.beam.sdk.PipelineResult;
-import org.apache.beam.sdk.metrics.Counter;
-import org.apache.beam.sdk.metrics.MetricNameFilter;
-import org.apache.beam.sdk.metrics.MetricQueryResults;
-import org.apache.beam.sdk.metrics.MetricResult;
-import org.apache.beam.sdk.metrics.Metrics;
-import org.apache.beam.sdk.metrics.MetricsFilter;
+import org.apache.beam.sdk.testing.PAssert;
 import org.apache.beam.sdk.testing.TestPipeline;
-import org.apache.beam.sdk.transforms.Create;
-import org.apache.beam.sdk.transforms.DoFn;
-import org.apache.beam.sdk.transforms.ParDo;
-import org.apache.pulsar.client.api.Consumer;
-import org.apache.pulsar.client.api.Message;
-import org.apache.pulsar.client.api.Producer;
+import org.apache.beam.sdk.transforms.MapElements;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.TypeDescriptor;
 import org.apache.pulsar.client.api.PulsarClient;
-import org.apache.pulsar.client.api.PulsarClientException;
-import org.junit.AfterClass;
-import org.junit.BeforeClass;
+import org.junit.Assert;
 import org.junit.Rule;
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.junit.runners.JUnit4;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
-import org.testcontainers.containers.PulsarContainer;
-import org.testcontainers.utility.DockerImageName;
 
+// TODO(https://github.com/apache/beam/issues/31078) exceptions are currently suppressed
+@SuppressWarnings("Slf4jDoNotLogMessageOfExceptionExplicitly")
 @RunWith(JUnit4.class)
-public class PulsarIOTest {
-
-  private static final String TOPIC = "PULSAR_IO_TEST";
-  protected static PulsarContainer pulsarContainer;
-  protected static PulsarClient client;
-
-  private long endExpectedTime = 0;
-  private long startTime = 0;
-
+public class PulsarIOTest implements Serializable {
+  @Rule public final transient TestPipeline pipeline = TestPipeline.create();
   private static final Logger LOG = LoggerFactory.getLogger(PulsarIOTest.class);
 
-  @Rule public final transient TestPipeline testPipeline = TestPipeline.create();
-
-  public List<Message<byte[]>> receiveMessages() throws PulsarClientException {
-    if (client == null) {
-      initClient();
-    }
-    List<Message<byte[]>> messages = new ArrayList<>();
-    Consumer<byte[]> consumer =
-        client.newConsumer().topic(TOPIC).subscriptionName("receiveMockMessageFn").subscribe();
-    while (consumer.hasReachedEndOfTopic()) {
-      Message<byte[]> msg = consumer.receive();
-      messages.add(msg);
-      try {
-        consumer.acknowledge(msg);
-      } catch (Exception e) {
-        consumer.negativeAcknowledge(msg);
-      }
-    }
-    return messages;
-  }
-
-  public List<PulsarMessage> produceMessages() throws PulsarClientException {
-    client = initClient();
-    Producer<byte[]> producer = client.newProducer().topic(TOPIC).create();
-    Consumer<byte[]> consumer =
-        client.newConsumer().topic(TOPIC).subscriptionName("produceMockMessageFn").subscribe();
-    int numElements = 101;
-    List<PulsarMessage> inputs = new ArrayList<>();
-    for (int i = 0; i < numElements; i++) {
-      String msg = ("PULSAR_TEST_READFROMSIMPLETOPIC_" + i);
-      producer.send(msg.getBytes(StandardCharsets.UTF_8));
-      CompletableFuture<Message<byte[]>> future = consumer.receiveAsync();
-      Message<byte[]> message = null;
-      try {
-        message = future.get(5, TimeUnit.SECONDS);
-        if (i >= 100) {
-          endExpectedTime = message.getPublishTime();
-        } else {
-          inputs.add(new PulsarMessage(message.getTopicName(), message.getPublishTime(), message));
-          if (i == 0) {
-            startTime = message.getPublishTime();
-          }
-        }
-      } catch (InterruptedException e) {
-        LOG.error(e.getMessage());
-      } catch (ExecutionException e) {
-        LOG.error(e.getMessage());
-      } catch (TimeoutException e) {
-        LOG.error(e.getMessage());
-      }
-    }
-    consumer.close();
-    producer.close();
-    client.close();
-    return inputs;
-  }
-
-  private static PulsarClient initClient() throws PulsarClientException {
-    return PulsarClient.builder().serviceUrl(pulsarContainer.getPulsarBrokerUrl()).build();
-  }
-
-  private static void setupPulsarContainer() {
-    pulsarContainer = new PulsarContainer(DockerImageName.parse("apachepulsar/pulsar:2.9.0"));
-    pulsarContainer.withCommand("bin/pulsar", "standalone");
-    pulsarContainer.start();
-  }
-
-  @BeforeClass
-  public static void setup() throws PulsarClientException {
-    setupPulsarContainer();
-    client = initClient();
-  }
-
-  @AfterClass
-  public static void afterClass() {
-    if (pulsarContainer != null) {
-      pulsarContainer.stop();
-    }
-  }
+  private static final String TEST_TOPIC = "TEST_TOPIC";
+  // In order to pin fake readers having same set of messages
+  private static final long START_TIMESTAMP = Instant.now().toEpochMilli();
 
-  @Test
-  @SuppressWarnings({"rawtypes"})
-  public void testPulsarFunctionality() throws Exception {
-    try (Consumer consumer =
-            client.newConsumer().topic(TOPIC).subscriptionName("PulsarIO_IT").subscribe();
-        Producer<byte[]> producer = client.newProducer().topic(TOPIC).create(); ) {
-      String messageTxt = "testing pulsar functionality";
-      producer.send(messageTxt.getBytes(StandardCharsets.UTF_8));
-      CompletableFuture<Message> future = consumer.receiveAsync();
-      Message message = future.get(5, TimeUnit.SECONDS);
-      assertEquals(messageTxt, new String(message.getData(), StandardCharsets.UTF_8));
-      client.close();
-    }
+  /** Create a fake client. */
+  static PulsarClient newFakeClient() {
+    return new FakePulsarClient(new FakePulsarReader(TEST_TOPIC, 10, START_TIMESTAMP));
   }
 
   @Test
-  public void testReadFromSimpleTopic() {
-    try {
-      List<PulsarMessage> inputsMock = produceMessages();
-      PulsarIO.Read reader =
-          PulsarIO.read()
-              .withClientUrl(pulsarContainer.getPulsarBrokerUrl())
-              .withAdminUrl(pulsarContainer.getHttpServiceUrl())
-              .withTopic(TOPIC)
-              .withStartTimestamp(startTime)
-              .withEndTimestamp(endExpectedTime)
-              .withPublishTime();
-      testPipeline.apply(reader).apply(ParDo.of(new PulsarRecordsMetric()));
-
-      PipelineResult pipelineResult = testPipeline.run();
-      MetricQueryResults metrics =
-          pipelineResult
-              .metrics()
-              .queryMetrics(
-                  MetricsFilter.builder()
-                      .addNameFilter(
-                          MetricNameFilter.named(
-                              PulsarIOTest.class.getName(), "PulsarRecordsCounter"))
-                      .build());
-      long recordsCount = 0;
-      for (MetricResult<Long> metric : metrics.getCounters()) {
-        if (metric
-            .getName()
-            .toString()
-            .equals("org.apache.beam.sdk.io.pulsar.PulsarIOTest:PulsarRecordsCounter")) {
-          recordsCount = metric.getAttempted();
-          break;
-        }
-      }
-      assertEquals(inputsMock.size(), (int) recordsCount);
-
-    } catch (PulsarClientException e) {
-      LOG.error(e.getMessage());
-    }
+  public void testRead() {
+
+    PCollection<Integer> pcoll =
+        pipeline
+            .apply(
+                PulsarIO.read()
+                    .withTopic(TEST_TOPIC)
+                    .withPulsarClient((ignored -> newFakeClient())))
+            .apply(
+                MapElements.into(TypeDescriptor.of(Integer.class))
+                    .via(m -> (int) m.getMessageId()[1]));
+    PAssert.that(pcoll)
+        .satisfies(
+            iterable -> {
+              List<Integer> result = new ArrayList<Integer>();
+              iterable.forEach(result::add);
+              Assert.assertArrayEquals(
+                  result.toArray(), new Integer[] {0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
+              return null;
+            });
+    pipeline.run();
   }
 
   @Test
-  public void testWriteFromTopic() {
-    try {
-      PulsarIO.Write writer =
-          PulsarIO.write().withClientUrl(pulsarContainer.getPulsarBrokerUrl()).withTopic(TOPIC);
-      int numberOfMessages = 100;
-      List<byte[]> messages = new ArrayList<>();
-      for (int i = 0; i < numberOfMessages; i++) {
-        messages.add(("PULSAR_WRITER_TEST_" + i).getBytes(StandardCharsets.UTF_8));
-      }
-      testPipeline.apply(Create.of(messages)).apply(writer);
-
-      testPipeline.run();
-
-      List<Message<byte[]>> receiveMsgs = receiveMessages();
-      assertEquals(numberOfMessages, receiveMessages().size());
-      for (int i = 0; i < numberOfMessages; i++) {
-        assertTrue(
-            new String(receiveMsgs.get(i).getValue(), StandardCharsets.UTF_8)
-                .equals("PULSAR_WRITER_TEST_" + i));
-      }
-    } catch (Exception e) {
-      LOG.error(e.getMessage());
-    }
-  }
-
-  public static class PulsarRecordsMetric extends DoFn<PulsarMessage, PulsarMessage> {
-    private final Counter counter =
-        Metrics.counter(PulsarIOTest.class.getName(), "PulsarRecordsCounter");
-
-    @ProcessElement
-    public void processElement(ProcessContext context) {
-      counter.inc();
-      context.output(context.element());
-    }
+  public void testExpandReadFailUnserializableType() {
+    pipeline.apply(
+        PulsarIO.read(t -> t).withTopic(TEST_TOPIC).withPulsarClient((ignored -> newFakeClient())));
+    IllegalStateException exception =
+        Assert.assertThrows(IllegalStateException.class, pipeline::run);
+    String errorMsg = exception.getMessage();
+    Assert.assertTrue(
+        "Actual message: " + errorMsg,
+        exception.getMessage().contains("Unable to return a default Coder for PulsarIO.Read"));
+    pipeline.enableAbandonedNodeEnforcement(false);
   }
 }
diff --git a/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/ReadFromPulsarDoFnTest.java b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/ReadFromPulsarDoFnTest.java
index 273a1915d2bb..5b58c9511170 100644
--- a/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/ReadFromPulsarDoFnTest.java
+++ b/sdks/java/io/pulsar/src/test/java/org/apache/beam/sdk/io/pulsar/ReadFromPulsarDoFnTest.java
@@ -20,18 +20,14 @@
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
 
-import java.util.ArrayList;
-import java.util.List;
 import org.apache.beam.sdk.io.range.OffsetRange;
+import org.apache.beam.sdk.testing.TestOutputReceiver;
 import org.apache.beam.sdk.transforms.DoFn;
 import org.apache.beam.sdk.transforms.SerializableFunction;
 import org.apache.beam.sdk.transforms.splittabledofn.OffsetRangeTracker;
 import org.apache.pulsar.client.api.MessageId;
 import org.apache.pulsar.client.api.PulsarClient;
 import org.apache.pulsar.client.internal.DefaultImplementation;
-import org.checkerframework.checker.initialization.qual.Initialized;
-import org.checkerframework.checker.nullness.qual.NonNull;
-import org.checkerframework.checker.nullness.qual.UnknownKeyFor;
 import org.joda.time.Instant;
 import org.junit.Before;
 import org.junit.Test;
@@ -46,23 +42,19 @@ public class ReadFromPulsarDoFnTest {
   public static final String TOPIC = "PULSARIO_READFROMPULSAR_TEST";
   public static final int NUMBEROFMESSAGES = 100;
 
-  private final ReadFromPulsarDoFn dofnInstance = new ReadFromPulsarDoFn(readSourceDescriptor());
-  public FakePulsarReader fakePulsarReader = new FakePulsarReader(TOPIC, NUMBEROFMESSAGES);
+  private final NaiveReadFromPulsarDoFn<PulsarMessage> dofnInstance =
+      new NaiveReadFromPulsarDoFn<>(readSourceDescriptor());
+  public FakePulsarReader fakePulsarReader =
+      new FakePulsarReader(TOPIC, NUMBEROFMESSAGES, Instant.now().getMillis());
   private FakePulsarClient fakePulsarClient = new FakePulsarClient(fakePulsarReader);
 
-  private PulsarIO.Read readSourceDescriptor() {
+  private PulsarIO.Read<PulsarMessage> readSourceDescriptor() {
     return PulsarIO.read()
         .withClientUrl(SERVICE_URL)
         .withTopic(TOPIC)
         .withAdminUrl(ADMIN_URL)
         .withPublishTime()
-        .withPulsarClient(
-            new SerializableFunction<String, PulsarClient>() {
-              @Override
-              public PulsarClient apply(String input) {
-                return fakePulsarClient;
-              }
-            });
+        .withPulsarClient((SerializableFunction<String, PulsarClient>) ignored -> fakePulsarClient);
   }
 
   @Before
@@ -76,8 +68,7 @@ public void testInitialRestrictionWhenHasStartOffset() throws Exception {
     long expectedStartOffset = 0;
     OffsetRange result =
         dofnInstance.getInitialRestriction(
-            PulsarSourceDescriptor.of(
-                TOPIC, expectedStartOffset, null, null, SERVICE_URL, ADMIN_URL));
+            PulsarSourceDescriptor.of(TOPIC, expectedStartOffset, null, null));
     assertEquals(new OffsetRange(expectedStartOffset, Long.MAX_VALUE), result);
   }
 
@@ -86,8 +77,7 @@ public void testInitialRestrictionWithConsumerPosition() throws Exception {
     long expectedStartOffset = Instant.now().getMillis();
     OffsetRange result =
         dofnInstance.getInitialRestriction(
-            PulsarSourceDescriptor.of(
-                TOPIC, expectedStartOffset, null, null, SERVICE_URL, ADMIN_URL));
+            PulsarSourceDescriptor.of(TOPIC, expectedStartOffset, null, null));
     assertEquals(new OffsetRange(expectedStartOffset, Long.MAX_VALUE), result);
   }
 
@@ -97,20 +87,20 @@ public void testInitialRestrictionWithConsumerEndPosition() throws Exception {
     long endOffset = fakePulsarReader.getEndTimestamp();
     OffsetRange result =
         dofnInstance.getInitialRestriction(
-            PulsarSourceDescriptor.of(TOPIC, startOffset, endOffset, null, SERVICE_URL, ADMIN_URL));
+            PulsarSourceDescriptor.of(TOPIC, startOffset, endOffset, null));
     assertEquals(new OffsetRange(startOffset, endOffset), result);
   }
 
   @Test
   public void testProcessElement() throws Exception {
-    MockOutputReceiver receiver = new MockOutputReceiver();
+    TestOutputReceiver<PulsarMessage> receiver = new TestOutputReceiver<>();
     long startOffset = fakePulsarReader.getStartTimestamp();
     long endOffset = fakePulsarReader.getEndTimestamp();
     OffsetRangeTracker tracker = new OffsetRangeTracker(new OffsetRange(startOffset, endOffset));
     PulsarSourceDescriptor descriptor =
-        PulsarSourceDescriptor.of(TOPIC, startOffset, endOffset, null, SERVICE_URL, ADMIN_URL);
+        PulsarSourceDescriptor.of(TOPIC, startOffset, endOffset, null);
     DoFn.ProcessContinuation result =
-        dofnInstance.processElement(descriptor, tracker, null, (DoFn.OutputReceiver) receiver);
+        dofnInstance.processElement(descriptor, tracker, null, receiver);
     int expectedResultWithoutCountingLastOffset = NUMBEROFMESSAGES - 1;
     assertEquals(DoFn.ProcessContinuation.stop(), result);
     assertEquals(expectedResultWithoutCountingLastOffset, receiver.getOutputs().size());
@@ -118,63 +108,37 @@ public void testProcessElement() throws Exception {
 
   @Test
   public void testProcessElementWhenEndMessageIdIsDefined() throws Exception {
-    MockOutputReceiver receiver = new MockOutputReceiver();
+    TestOutputReceiver<PulsarMessage> receiver = new TestOutputReceiver<>();
     OffsetRangeTracker tracker = new OffsetRangeTracker(new OffsetRange(0L, Long.MAX_VALUE));
-    MessageId endMessageId = DefaultImplementation.newMessageId(50L, 50L, 50);
+    MessageId endMessageId =
+        DefaultImplementation.getDefaultImplementation().newMessageId(50L, 50L, 50);
     DoFn.ProcessContinuation result =
         dofnInstance.processElement(
-            PulsarSourceDescriptor.of(TOPIC, null, null, endMessageId, SERVICE_URL, ADMIN_URL),
-            tracker,
-            null,
-            (DoFn.OutputReceiver) receiver);
+            PulsarSourceDescriptor.of(TOPIC, null, null, endMessageId), tracker, null, receiver);
     assertEquals(DoFn.ProcessContinuation.stop(), result);
     assertEquals(50, receiver.getOutputs().size());
   }
 
   @Test
   public void testProcessElementWithEmptyRecords() throws Exception {
-    MockOutputReceiver receiver = new MockOutputReceiver();
+    TestOutputReceiver<PulsarMessage> receiver = new TestOutputReceiver<>();
     fakePulsarReader.emptyMockRecords();
     OffsetRangeTracker tracker = new OffsetRangeTracker(new OffsetRange(0L, Long.MAX_VALUE));
     DoFn.ProcessContinuation result =
         dofnInstance.processElement(
-            PulsarSourceDescriptor.of(TOPIC, null, null, null, SERVICE_URL, ADMIN_URL),
-            tracker,
-            null,
-            (DoFn.OutputReceiver) receiver);
+            PulsarSourceDescriptor.of(TOPIC, null, null, null), tracker, null, receiver);
     assertEquals(DoFn.ProcessContinuation.resume(), result);
     assertTrue(receiver.getOutputs().isEmpty());
   }
 
   @Test
   public void testProcessElementWhenHasReachedEndTopic() throws Exception {
-    MockOutputReceiver receiver = new MockOutputReceiver();
+    TestOutputReceiver<PulsarMessage> receiver = new TestOutputReceiver<>();
     fakePulsarReader.setReachedEndOfTopic(true);
     OffsetRangeTracker tracker = new OffsetRangeTracker(new OffsetRange(0L, Long.MAX_VALUE));
     DoFn.ProcessContinuation result =
         dofnInstance.processElement(
-            PulsarSourceDescriptor.of(TOPIC, null, null, null, SERVICE_URL, ADMIN_URL),
-            tracker,
-            null,
-            (DoFn.OutputReceiver) receiver);
+            PulsarSourceDescriptor.of(TOPIC, null, null, null), tracker, null, receiver);
     assertEquals(DoFn.ProcessContinuation.stop(), result);
   }
-
-  private static class MockOutputReceiver implements DoFn.OutputReceiver<PulsarMessage> {
-
-    private final List<PulsarMessage> records = new ArrayList<>();
-
-    @Override
-    public void output(PulsarMessage output) {}
-
-    @Override
-    public void outputWithTimestamp(
-        PulsarMessage output, @UnknownKeyFor @NonNull @Initialized Instant timestamp) {
-      records.add(output);
-    }
-
-    public List<PulsarMessage> getOutputs() {
-      return records;
-    }
-  }
 }
diff --git a/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/RabbitMqReceiverWithOffset.java b/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/RabbitMqReceiverWithOffset.java
index da8f1dde841e..730001ffe459 100644
--- a/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/RabbitMqReceiverWithOffset.java
+++ b/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/RabbitMqReceiverWithOffset.java
@@ -177,7 +177,7 @@ public void handleDelivery(
           messageConsumer.accept(sMessage);
         }
       } catch (Exception e) {
-        LOG.error("Can't read from RabbitMQ: {}", e.getMessage());
+        LOG.error("Can't read from RabbitMQ.", e);
       }
     }
   }
diff --git a/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/ReadFromSparkReceiverWithOffsetDoFnTest.java b/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/ReadFromSparkReceiverWithOffsetDoFnTest.java
index 33827164c6b7..6ab5d8393def 100644
--- a/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/ReadFromSparkReceiverWithOffsetDoFnTest.java
+++ b/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/ReadFromSparkReceiverWithOffsetDoFnTest.java
@@ -24,13 +24,11 @@
 import java.util.ArrayList;
 import java.util.List;
 import org.apache.beam.sdk.io.range.OffsetRange;
+import org.apache.beam.sdk.testing.TestOutputReceiver;
 import org.apache.beam.sdk.transforms.DoFn;
 import org.apache.beam.sdk.transforms.splittabledofn.ManualWatermarkEstimator;
 import org.apache.beam.sdk.transforms.splittabledofn.OffsetRangeTracker;
 import org.apache.beam.sdk.transforms.splittabledofn.SplitResult;
-import org.checkerframework.checker.initialization.qual.Initialized;
-import org.checkerframework.checker.nullness.qual.NonNull;
-import org.checkerframework.checker.nullness.qual.UnknownKeyFor;
 import org.joda.time.Instant;
 import org.junit.Test;
 
@@ -51,24 +49,6 @@ private SparkReceiverIO.Read<String> makeReadTransform() {
         .withTimestampFn(Instant::parse);
   }
 
-  private static class MockOutputReceiver implements DoFn.OutputReceiver<String> {
-
-    private final List<String> records = new ArrayList<>();
-
-    @Override
-    public void output(String output) {}
-
-    @Override
-    public void outputWithTimestamp(
-        String output, @UnknownKeyFor @NonNull @Initialized Instant timestamp) {
-      records.add(output);
-    }
-
-    public List<String> getOutputs() {
-      return this.records;
-    }
-  }
-
   private final ManualWatermarkEstimator<Instant> mockWatermarkEstimator =
       new ManualWatermarkEstimator<Instant>() {
 
@@ -131,7 +111,7 @@ public void testRestrictionTrackerSplit() {
 
   @Test
   public void testProcessElement() {
-    MockOutputReceiver receiver = new MockOutputReceiver();
+    TestOutputReceiver<String> receiver = new TestOutputReceiver<>();
     DoFn.ProcessContinuation result =
         dofnInstance.processElement(
             TEST_ELEMENT,
diff --git a/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/SparkReceiverIOIT.java b/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/SparkReceiverIOIT.java
index b7af2054236e..32258934d0d9 100644
--- a/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/SparkReceiverIOIT.java
+++ b/sdks/java/io/sparkreceiver/3/src/test/java/org/apache/beam/sdk/io/sparkreceiver/SparkReceiverIOIT.java
@@ -315,7 +315,7 @@ public void testSparkReceiverIOReadsInStreamingWithOffset() throws IOException {
     try {
       writeToRabbitMq(messages);
     } catch (Exception e) {
-      LOG.error("Can not write to rabbit {}", e.getMessage());
+      LOG.error("Can not write to rabbit.", e);
       fail();
     }
     LOG.info(sourceOptions.numRecords + " records were successfully written to RabbitMQ");
diff --git a/sdks/java/io/splunk/src/main/java/org/apache/beam/sdk/io/splunk/SplunkEventWriter.java b/sdks/java/io/splunk/src/main/java/org/apache/beam/sdk/io/splunk/SplunkEventWriter.java
index 615d4e932f4d..a86fdff608d2 100644
--- a/sdks/java/io/splunk/src/main/java/org/apache/beam/sdk/io/splunk/SplunkEventWriter.java
+++ b/sdks/java/io/splunk/src/main/java/org/apache/beam/sdk/io/splunk/SplunkEventWriter.java
@@ -219,7 +219,7 @@ public void setup() {
         | KeyManagementException
         | IOException
         | CertificateException e) {
-      LOG.error("Error creating HttpEventPublisher: {}", e.getMessage());
+      LOG.error("Error creating HttpEventPublisher.", e);
       throw new RuntimeException(e);
     }
   }
@@ -273,7 +273,7 @@ public void tearDown() {
         LOG.info("Successfully closed HttpEventPublisher");
 
       } catch (IOException e) {
-        LOG.warn("Received exception while closing HttpEventPublisher: {}", e.getMessage());
+        LOG.warn("Received exception while closing HttpEventPublisher.", e);
       }
     }
   }
@@ -347,7 +347,7 @@ private void flush(
         flushWriteFailures(events, e.getStatusMessage(), e.getStatusCode(), receiver);
 
       } catch (IOException ioe) {
-        LOG.error("Error writing to Splunk: {}", ioe.getMessage());
+        LOG.error("Error writing to Splunk.", ioe);
         UNSUCCESSFUL_WRITE_LATENCY_MS.update(nanosToMillis(System.nanoTime() - startTime));
         FAILED_WRITES.inc(countState.read());
         INVALID_REQUESTS.inc();
diff --git a/sdks/java/io/synthetic/src/test/java/org/apache/beam/sdk/io/synthetic/BundleSplitterTest.java b/sdks/java/io/synthetic/src/test/java/org/apache/beam/sdk/io/synthetic/BundleSplitterTest.java
index f37ac4614d83..15f3ff4f5dd9 100644
--- a/sdks/java/io/synthetic/src/test/java/org/apache/beam/sdk/io/synthetic/BundleSplitterTest.java
+++ b/sdks/java/io/synthetic/src/test/java/org/apache/beam/sdk/io/synthetic/BundleSplitterTest.java
@@ -69,7 +69,7 @@ public void bundlesShouldBeEvenForConstDistribution() {
 
     bundleSizes.stream()
         .map(range -> range.getTo() - range.getFrom())
-        .forEach(size -> assertEquals(expectedBundleSize, size.intValue()));
+        .forEach(size -> assertEquals(expectedBundleSize, size.longValue()));
   }
 
   @Test
@@ -83,7 +83,7 @@ public void bundleSizesShouldBeProportionalToTheOneSuggestedInBundleSizeDistribu
 
     bundleSizes.stream()
         .map(range -> range.getTo() - range.getFrom())
-        .forEach(size -> assertEquals(expectedBundleSize, size.intValue()));
+        .forEach(size -> assertEquals(expectedBundleSize, size.longValue()));
   }
 
   @Test
diff --git a/sdks/java/io/thrift/src/main/java/org/apache/beam/sdk/io/thrift/ThriftSchema.java b/sdks/java/io/thrift/src/main/java/org/apache/beam/sdk/io/thrift/ThriftSchema.java
index 3094ea47d6ad..e4e698faffa4 100644
--- a/sdks/java/io/thrift/src/main/java/org/apache/beam/sdk/io/thrift/ThriftSchema.java
+++ b/sdks/java/io/thrift/src/main/java/org/apache/beam/sdk/io/thrift/ThriftSchema.java
@@ -170,7 +170,10 @@ private Schema schemaFor(Class<?> targetClass) {
     final Stream<Schema.Field> fields =
         thriftFieldDescriptors(targetClass).values().stream().map(this::beamField);
     if (TUnion.class.isAssignableFrom(targetClass)) {
-      return OneOfType.create(fields.collect(Collectors.toList())).getOneOfSchema();
+      // Beam OneOf is just a record of fields where exactly one must be non-null, so it doesn't
+      // allow the types of the cases to be nullable
+      return OneOfType.create(fields.map(f -> f.withNullable(false)).collect(Collectors.toList()))
+          .getOneOfSchema();
     } else {
       return fields
           .reduce(Schema.builder(), Schema.Builder::addField, ThriftSchema::throwingCombiner)
diff --git a/sdks/java/javadoc/overview.html b/sdks/java/javadoc/overview.html
index 66d4ab613781..8c0d15de121e 100644
--- a/sdks/java/javadoc/overview.html
+++ b/sdks/java/javadoc/overview.html
@@ -37,9 +37,5 @@
       <li>minor version for new functionality added in a backward-compatible manner</li>
       <li>incremental version for forward-compatible bug fixes</li>
     </ul>
-
-    <p>Please note that APIs marked
-    {@link org.apache.beam.sdk.annotations.Experimental @Experimental}
-    may change at any point and are not guaranteed to remain compatible across versions.</p>
   </body>
 </html>
diff --git a/sdks/java/managed/src/main/java/org/apache/beam/sdk/managed/Managed.java b/sdks/java/managed/src/main/java/org/apache/beam/sdk/managed/Managed.java
index 06aed06c71c4..a5e7d879b441 100644
--- a/sdks/java/managed/src/main/java/org/apache/beam/sdk/managed/Managed.java
+++ b/sdks/java/managed/src/main/java/org/apache/beam/sdk/managed/Managed.java
@@ -96,6 +96,9 @@ public class Managed {
   public static final String ICEBERG_CDC = "iceberg_cdc";
   public static final String KAFKA = "kafka";
   public static final String BIGQUERY = "bigquery";
+  public static final String POSTGRES = "postgres";
+  public static final String MYSQL = "mysql";
+  public static final String SQL_SERVER = "sqlserver";
 
   // Supported SchemaTransforms
   public static final Map<String, String> READ_TRANSFORMS =
@@ -104,12 +107,18 @@ public class Managed {
           .put(ICEBERG_CDC, getUrn(ExternalTransforms.ManagedTransforms.Urns.ICEBERG_CDC_READ))
           .put(KAFKA, getUrn(ExternalTransforms.ManagedTransforms.Urns.KAFKA_READ))
           .put(BIGQUERY, getUrn(ExternalTransforms.ManagedTransforms.Urns.BIGQUERY_READ))
+          .put(POSTGRES, getUrn(ExternalTransforms.ManagedTransforms.Urns.POSTGRES_READ))
+          .put(MYSQL, getUrn(ExternalTransforms.ManagedTransforms.Urns.MYSQL_READ))
+          .put(SQL_SERVER, getUrn(ExternalTransforms.ManagedTransforms.Urns.SQL_SERVER_READ))
           .build();
   public static final Map<String, String> WRITE_TRANSFORMS =
       ImmutableMap.<String, String>builder()
           .put(ICEBERG, getUrn(ExternalTransforms.ManagedTransforms.Urns.ICEBERG_WRITE))
           .put(KAFKA, getUrn(ExternalTransforms.ManagedTransforms.Urns.KAFKA_WRITE))
           .put(BIGQUERY, getUrn(ExternalTransforms.ManagedTransforms.Urns.BIGQUERY_WRITE))
+          .put(POSTGRES, getUrn(ExternalTransforms.ManagedTransforms.Urns.POSTGRES_WRITE))
+          .put(MYSQL, getUrn(ExternalTransforms.ManagedTransforms.Urns.MYSQL_WRITE))
+          .put(SQL_SERVER, getUrn(ExternalTransforms.ManagedTransforms.Urns.SQL_SERVER_WRITE))
           .build();
 
   /**
diff --git a/sdks/java/testing/junit/build.gradle b/sdks/java/testing/junit/build.gradle
new file mode 100644
index 000000000000..755d491674d3
--- /dev/null
+++ b/sdks/java/testing/junit/build.gradle
@@ -0,0 +1,49 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+plugins { id 'org.apache.beam.module' }
+
+applyJavaNature(
+    automaticModuleName: 'org.apache.beam.sdk.testing.junit',
+    archivesBaseName: 'beam-sdks-java-testing-junit'
+)
+
+description = "Apache Beam :: SDKs :: Java :: Testing :: JUnit"
+
+dependencies {
+  implementation enforcedPlatform(library.java.google_cloud_platform_libraries_bom)
+  implementation project(path: ":sdks:java:core", configuration: "shadow")
+  implementation library.java.vendored_guava_32_1_2_jre
+  // Needed to resolve TestPipeline's JUnit 4 TestRule type and @Category at compile time,
+  // but should not leak to consumers at runtime.
+  provided library.java.junit
+  permitUnusedDeclared(library.java.junit)
+
+  // JUnit 5 API needed to compile the extension; not packaged for consumers of core.
+  provided library.java.jupiter_api
+
+  testImplementation library.java.jupiter_api
+  testImplementation library.java.junit
+  testRuntimeOnly library.java.jupiter_engine
+  testRuntimeOnly project(path: ":runners:direct-java", configuration: "shadow")
+}
+
+// This module runs JUnit 5 tests using the JUnit Platform.
+test {
+  useJUnitPlatform()
+}
diff --git a/sdks/java/testing/junit/src/main/java/org/apache/beam/sdk/testing/TestPipelineExtension.java b/sdks/java/testing/junit/src/main/java/org/apache/beam/sdk/testing/TestPipelineExtension.java
new file mode 100644
index 000000000000..ef95dcd611bb
--- /dev/null
+++ b/sdks/java/testing/junit/src/main/java/org/apache/beam/sdk/testing/TestPipelineExtension.java
@@ -0,0 +1,154 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.testing;
+
+import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkNotNull;
+
+import java.lang.annotation.Annotation;
+import java.lang.reflect.Method;
+import java.util.Collection;
+import org.apache.beam.sdk.options.ApplicationNameOptions;
+import org.apache.beam.sdk.options.PipelineOptions;
+import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
+import org.checkerframework.checker.nullness.qual.Nullable;
+import org.junit.jupiter.api.extension.AfterEachCallback;
+import org.junit.jupiter.api.extension.BeforeEachCallback;
+import org.junit.jupiter.api.extension.ExtensionContext;
+import org.junit.jupiter.api.extension.ParameterContext;
+import org.junit.jupiter.api.extension.ParameterResolver;
+
+/**
+ * JUnit 5 extension for {@link TestPipeline} that provides the same functionality as the JUnit 4
+ * {@link org.junit.rules.TestRule} implementation.
+ *
+ * <p>Use this extension to test pipelines in JUnit 5:
+ *
+ * <pre><code>
+ * {@literal @}ExtendWith(TestPipelineExtension.class)
+ * class MyPipelineTest {
+ *   {@literal @}Test
+ *   {@literal @}Category(NeedsRunner.class)
+ *   void myPipelineTest(TestPipeline pipeline) {
+ *     final PCollection&lt;String&gt; pCollection = pipeline.apply(...)
+ *     PAssert.that(pCollection).containsInAnyOrder(...);
+ *     pipeline.run();
+ *   }
+ * }
+ * </code></pre>
+ *
+ * <p>You can also create the extension yourself for more control:
+ *
+ * <pre><code>
+ * class MyPipelineTest {
+ *   {@literal @}RegisterExtension
+ *   final TestPipelineExtension pipeline = TestPipelineExtension.create();
+ *
+ *   {@literal @}Test
+ *   void testUsingPipeline() {
+ *     pipeline.apply(...);
+ *     pipeline.run();
+ *   }
+ * }
+ * </code></pre>
+ */
+public class TestPipelineExtension
+    implements BeforeEachCallback, AfterEachCallback, ParameterResolver {
+
+  private static final ExtensionContext.Namespace NAMESPACE =
+      ExtensionContext.Namespace.create(TestPipelineExtension.class);
+  private static final String PIPELINE_KEY = "testPipeline";
+  private static final String ENFORCEMENT_KEY = "enforcement";
+
+  /** Creates a new TestPipelineExtension with default options. */
+  public static TestPipelineExtension create() {
+    return new TestPipelineExtension();
+  }
+
+  /** Creates a new TestPipelineExtension with custom options. */
+  public static TestPipelineExtension fromOptions(PipelineOptions options) {
+    return new TestPipelineExtension(options);
+  }
+
+  private @Nullable PipelineOptions options;
+
+  /** Creates a TestPipelineExtension with default options. */
+  public TestPipelineExtension() {
+    this.options = null;
+  }
+
+  /** Creates a TestPipelineExtension with custom options. */
+  public TestPipelineExtension(PipelineOptions options) {
+    this.options = options;
+  }
+
+  @Override
+  public boolean supportsParameter(
+      ParameterContext parameterContext, ExtensionContext extensionContext) {
+    return parameterContext.getParameter().getType() == TestPipeline.class;
+  }
+
+  @Override
+  public Object resolveParameter(
+      ParameterContext parameterContext, ExtensionContext extensionContext) {
+    return getOrCreateTestPipeline(extensionContext);
+  }
+
+  @Override
+  public void beforeEach(ExtensionContext context) {
+    TestPipeline pipeline = getOrCreateTestPipeline(context);
+
+    // Set application name based on test method
+    String appName = getAppName(context);
+    pipeline.getOptions().as(ApplicationNameOptions.class).setAppName(appName);
+
+    // Set up enforcement based on annotations
+    pipeline.setDeducedEnforcementLevel(getAnnotations(context));
+  }
+
+  @Override
+  public void afterEach(ExtensionContext context) {
+    TestPipeline pipeline = getRequiredTestPipeline(context);
+    pipeline.afterUserCodeFinished();
+  }
+
+  private TestPipeline getOrCreateTestPipeline(ExtensionContext context) {
+    return context
+        .getStore(NAMESPACE)
+        .getOrComputeIfAbsent(
+            PIPELINE_KEY,
+            key -> options == null ? TestPipeline.create() : TestPipeline.fromOptions(options),
+            TestPipeline.class);
+  }
+
+  private TestPipeline getRequiredTestPipeline(ExtensionContext context) {
+    return checkNotNull(context.getStore(NAMESPACE).get(PIPELINE_KEY, TestPipeline.class));
+  }
+
+  private String getAppName(ExtensionContext context) {
+    String className = context.getTestClass().map(Class::getSimpleName).orElse("UnknownClass");
+    String methodName = context.getTestMethod().map(Method::getName).orElse("unknownMethod");
+    return className + "-" + methodName;
+  }
+
+  private static Collection<Annotation> getAnnotations(ExtensionContext context) {
+    ImmutableList.Builder<Annotation> builder = ImmutableList.builder();
+    context.getTestMethod().ifPresent(testMethod -> builder.add(testMethod.getAnnotations()));
+    context.getTestClass().ifPresent(testClass -> builder.add(testClass.getAnnotations()));
+    return builder.build();
+  }
+}
diff --git a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/package-info.java b/sdks/java/testing/junit/src/main/java/org/apache/beam/sdk/testing/package-info.java
similarity index 84%
rename from sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/package-info.java
rename to sdks/java/testing/junit/src/main/java/org/apache/beam/sdk/testing/package-info.java
index 8c741c29a36d..2909111bfec8 100644
--- a/sdks/java/extensions/sql/zetasql/src/main/java/org/apache/beam/sdk/extensions/sql/zetasql/unnest/package-info.java
+++ b/sdks/java/testing/junit/src/main/java/org/apache/beam/sdk/testing/package-info.java
@@ -15,6 +15,5 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
-/** Temporary solution to support ZetaSQL UNNEST. To be removed after Calcite upgrade. */
-package org.apache.beam.sdk.extensions.sql.zetasql.unnest;
+/** JUnit 5 testing support for Apache Beam Java SDK. */
+package org.apache.beam.sdk.testing;
diff --git a/sdks/java/testing/junit/src/test/java/org/apache/beam/sdk/testing/TestPipelineExtensionAdvancedTest.java b/sdks/java/testing/junit/src/test/java/org/apache/beam/sdk/testing/TestPipelineExtensionAdvancedTest.java
new file mode 100644
index 000000000000..fc5e015afcd3
--- /dev/null
+++ b/sdks/java/testing/junit/src/test/java/org/apache/beam/sdk/testing/TestPipelineExtensionAdvancedTest.java
@@ -0,0 +1,89 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.testing;
+
+import static org.junit.jupiter.api.Assertions.assertNotNull;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.io.Serializable;
+import org.apache.beam.sdk.options.ApplicationNameOptions;
+import org.apache.beam.sdk.transforms.Create;
+import org.apache.beam.sdk.transforms.DoFn;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.values.PCollection;
+import org.junit.experimental.categories.Category;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.extension.ExtendWith;
+
+/** Advanced tests for {@link TestPipelineExtension} demonstrating comprehensive functionality. */
+@ExtendWith(TestPipelineExtension.class)
+public class TestPipelineExtensionAdvancedTest implements Serializable {
+
+  @Test
+  public void testApplicationNameIsSet(TestPipeline pipeline) {
+    String appName = pipeline.getOptions().as(ApplicationNameOptions.class).getAppName();
+    assertNotNull(appName);
+    assertTrue(appName.contains("TestPipelineExtensionAdvancedTest"));
+    assertTrue(appName.contains("testApplicationNameIsSet"));
+  }
+
+  @Test
+  public void testMultipleTransforms(TestPipeline pipeline) {
+    PCollection<String> input = pipeline.apply("Create", Create.of("a", "b", "c"));
+
+    PCollection<String> output =
+        input.apply(
+            "Transform",
+            ParDo.of(
+                new DoFn<String, String>() {
+                  @ProcessElement
+                  public void processElement(ProcessContext c) {
+                    c.output(c.element().toUpperCase());
+                  }
+                }));
+
+    PAssert.that(output).containsInAnyOrder("A", "B", "C");
+    pipeline.run();
+  }
+
+  @Test
+  @Category(ValidatesRunner.class)
+  public void testWithValidatesRunnerCategory(TestPipeline pipeline) {
+    // This test demonstrates that @Category annotations work with JUnit 5
+    PCollection<Integer> numbers = pipeline.apply("Create", Create.of(1, 2, 3, 4, 5));
+    PAssert.that(numbers).containsInAnyOrder(1, 2, 3, 4, 5);
+    pipeline.run();
+  }
+
+  @Test
+  public void testPipelineInstancesAreIsolated(TestPipeline pipeline1) {
+    // Each test method gets its own pipeline instance
+    pipeline1.enableAutoRunIfMissing(true);
+    pipeline1.apply("Create", Create.of("test"));
+    // Don't run the pipeline - test should still pass due to auto-run functionality
+  }
+
+  @Test
+  public void testAnotherPipelineInstance(TestPipeline pipeline2) {
+    // This should be a different instance from the previous test
+    assertNotNull(pipeline2);
+    PCollection<String> data = pipeline2.apply("Create", Create.of("different", "data"));
+    PAssert.that(data).containsInAnyOrder("different", "data");
+    pipeline2.run();
+  }
+}
diff --git a/sdks/java/testing/junit/src/test/java/org/apache/beam/sdk/testing/TestPipelineExtensionTest.java b/sdks/java/testing/junit/src/test/java/org/apache/beam/sdk/testing/TestPipelineExtensionTest.java
new file mode 100644
index 000000000000..38cc59737790
--- /dev/null
+++ b/sdks/java/testing/junit/src/test/java/org/apache/beam/sdk/testing/TestPipelineExtensionTest.java
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.sdk.testing;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertNotNull;
+
+import org.apache.beam.sdk.options.ApplicationNameOptions;
+import org.apache.beam.sdk.transforms.Create;
+import org.apache.beam.sdk.values.PCollection;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.extension.ExtendWith;
+
+/** Tests for {@link TestPipelineExtension} to demonstrate JUnit 5 integration. */
+@ExtendWith(TestPipelineExtension.class)
+public class TestPipelineExtensionTest {
+
+  @Test
+  public void testPipelineInjection(TestPipeline pipeline) {
+    // Verify that the pipeline is injected and not null
+    assertNotNull(pipeline);
+    assertNotNull(pipeline.getOptions());
+    assertEquals(
+        "TestPipelineExtensionTest-testPipelineInjection",
+        pipeline.getOptions().as(ApplicationNameOptions.class).getAppName());
+  }
+
+  @Test
+  public void testBasicPipelineExecution(TestPipeline pipeline) {
+    // Create a simple pipeline
+    PCollection<String> input = pipeline.apply("Create", Create.of("hello", "world"));
+
+    // Use PAssert to verify the output
+    PAssert.that(input).containsInAnyOrder("hello", "world");
+
+    // Run the pipeline
+    pipeline.run();
+  }
+
+  @Test
+  public void testEmptyPipeline(TestPipeline pipeline) {
+    // Test that an empty pipeline doesn't cause issues
+    assertNotNull(pipeline);
+    // The extension should handle empty pipelines gracefully
+  }
+}
diff --git a/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlBoundedSideInputJoin.java b/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlBoundedSideInputJoin.java
index 9806a265df4e..171bf9c69fed 100644
--- a/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlBoundedSideInputJoin.java
+++ b/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlBoundedSideInputJoin.java
@@ -68,13 +68,13 @@ public static SqlBoundedSideInputJoin calciteSqlBoundedSideInputJoin(
     return new SqlBoundedSideInputJoin(
         configuration,
         CalciteQueryPlanner.class,
-        "WITH bid_with_side (auction, bidder, price, dateTime, extra, side_id) AS (%n"
+        "WITH bid_with_side (auction, bidder, price, `dateTime`, extra, side_id) AS (%n"
             + "  SELECT *, CAST(MOD(bidder, %d) AS BIGINT) side_id FROM bid%n"
             + ")%n"
             + " SELECT bid_with_side.auction%n"
             + ", bid_with_side.bidder%n"
             + ", bid_with_side.price%n"
-            + ", bid_with_side.dateTime%n"
+            + ", bid_with_side.`dateTime`%n"
             + ", side.extra%n"
             + " FROM bid_with_side, side%n"
             + " WHERE bid_with_side.side_id = side.id");
diff --git a/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlQuery1.java b/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlQuery1.java
index 80501c1bb9ee..6d0ca9f726ea 100644
--- a/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlQuery1.java
+++ b/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlQuery1.java
@@ -35,7 +35,7 @@
  * Query 1, 'Currency Conversion'. Convert each bid value from dollars to euros. In CQL syntax:
  *
  * <pre>
- * SELECT Istream(auction, DOLTOEUR(price), bidder, datetime)
+ * SELECT Istream(auction, DOLTOEUR(price), bidder, `datetime`)
  * FROM bid [ROWS UNBOUNDED];
  * </pre>
  *
@@ -46,7 +46,7 @@ public class SqlQuery1 extends NexmarkQueryTransform<Bid> {
 
   private static final PTransform<PInput, PCollection<Row>> QUERY =
       SqlTransform.query(
-              "SELECT auction, bidder, DolToEur(price) as price, dateTime, extra FROM PCOLLECTION")
+              "SELECT auction, bidder, DolToEur(price) as price, `dateTime`, extra FROM PCOLLECTION")
           .registerUdf("DolToEur", new DolToEur());
 
   /** Dollar to Euro conversion. */
diff --git a/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlQuery7.java b/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlQuery7.java
index 00f256fa1d70..4d3f2120f6d3 100644
--- a/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlQuery7.java
+++ b/sdks/java/testing/nexmark/src/main/java/org/apache/beam/sdk/nexmark/queries/sql/SqlQuery7.java
@@ -55,17 +55,16 @@ public SqlQuery7(NexmarkConfiguration configuration) {
 
     String queryString =
         String.format(
-            ""
-                + " SELECT B.auction, B.price, B.bidder, B.dateTime, B.extra "
-                + "    FROM (SELECT B.auction, B.price, B.bidder, B.dateTime, B.extra, "
-                + "       TUMBLE_START(B.dateTime, INTERVAL '%1$d' SECOND) AS starttime "
+            " SELECT B.auction, B.price, B.bidder, B.`dateTime`, B.extra "
+                + "    FROM (SELECT B.auction, B.price, B.bidder, B.`dateTime`, B.extra, "
+                + "       TUMBLE_START(B.`dateTime`, INTERVAL '%1$d' SECOND) AS starttime "
                 + "    FROM Bid B "
-                + "    GROUP BY B.auction, B.price, B.bidder, B.dateTime, B.extra, "
-                + "       TUMBLE(B.dateTime, INTERVAL '%1$d' SECOND)) B "
+                + "    GROUP BY B.auction, B.price, B.bidder, B.`dateTime`, B.extra, "
+                + "       TUMBLE(B.`dateTime`, INTERVAL '%1$d' SECOND)) B "
                 + " JOIN (SELECT MAX(B1.price) AS maxprice, "
-                + "       TUMBLE_START(B1.dateTime, INTERVAL '%1$d' SECOND) AS starttime "
+                + "       TUMBLE_START(B1.`dateTime`, INTERVAL '%1$d' SECOND) AS starttime "
                 + "    FROM Bid B1 "
-                + "    GROUP BY TUMBLE(B1.dateTime, INTERVAL '%1$d' SECOND)) B1 "
+                + "    GROUP BY TUMBLE(B1.`dateTime`, INTERVAL '%1$d' SECOND)) B1 "
                 + " ON B.starttime = B1.starttime AND B.price = B1.maxprice ",
             configuration.windowSizeSec);
     query = SqlTransform.query(queryString);
diff --git a/sdks/java/testing/test-utils/src/main/java/org/apache/beam/sdk/testutils/publishing/InfluxDBPublisher.java b/sdks/java/testing/test-utils/src/main/java/org/apache/beam/sdk/testutils/publishing/InfluxDBPublisher.java
index 30e72fd53dad..d7034620ed45 100644
--- a/sdks/java/testing/test-utils/src/main/java/org/apache/beam/sdk/testutils/publishing/InfluxDBPublisher.java
+++ b/sdks/java/testing/test-utils/src/main/java/org/apache/beam/sdk/testutils/publishing/InfluxDBPublisher.java
@@ -158,7 +158,7 @@ private static void publishWithCheck(final InfluxDBSettings settings, final Stri
         postRequest.setEntity(new GzipCompressingEntity(new ByteArrayEntity(data.getBytes(UTF_8))));
         executeWithVerification(postRequest, builder);
       } catch (Exception exception) {
-        LOG.warn("Unable to publish metrics due to error: {}", exception.getMessage());
+        LOG.warn("Unable to publish metrics due to error", exception);
       }
     } else {
       LOG.warn("Missing setting InfluxDB database. Metrics won't be published.");
diff --git a/sdks/java/testing/tpcds/build.gradle b/sdks/java/testing/tpcds/build.gradle
index d59895421777..1714e6e61a80 100644
--- a/sdks/java/testing/tpcds/build.gradle
+++ b/sdks/java/testing/tpcds/build.gradle
@@ -62,10 +62,9 @@ dependencies {
     implementation library.java.avro
     implementation library.java.joda_time
     implementation library.java.vendored_guava_32_1_2_jre
-    implementation library.java.vendored_calcite_1_28_0
+    implementation library.java.vendored_calcite_1_40_0
     implementation library.java.commons_csv
     implementation library.java.slf4j_api
-    implementation "com.googlecode.json-simple:json-simple:1.1.1"
     implementation library.java.jackson_databind
     implementation project(":sdks:java:extensions:sql")
     implementation project(":sdks:java:io:parquet")
@@ -79,6 +78,7 @@ dependencies {
     testRuntimeOnly library.java.slf4j_jdk14
     testImplementation project(path: ":sdks:java:io:google-cloud-platform")
     testImplementation project(path: ":sdks:java:testing:test-utils")
+    testImplementation library.java.junit
     gradleRun project(project.path)
     gradleRun project(path: tpcdsRunnerDependency, configuration: runnerConfiguration)
 }
diff --git a/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/QueryReader.java b/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/QueryReader.java
index 8071bad84d73..f6bb0d500920 100644
--- a/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/QueryReader.java
+++ b/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/QueryReader.java
@@ -19,9 +19,9 @@
 
 import java.nio.charset.StandardCharsets;
 import java.util.Set;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlNode;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParseException;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.parser.SqlParser;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlNode;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParseException;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.parser.SqlParser;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.io.Resources;
 
 /**
diff --git a/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/SqlTransformRunner.java b/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/SqlTransformRunner.java
index 6efb7e7e0659..a8f479557005 100644
--- a/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/SqlTransformRunner.java
+++ b/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/SqlTransformRunner.java
@@ -55,8 +55,8 @@
 import org.apache.beam.sdk.values.Row;
 import org.apache.beam.sdk.values.TupleTag;
 import org.apache.beam.sdk.values.TypeDescriptors;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.SqlIdentifier;
-import org.apache.beam.vendor.calcite.v1_28_0.org.apache.calcite.sql.util.SqlBasicVisitor;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.SqlIdentifier;
+import org.apache.beam.vendor.calcite.v1_40_0.org.apache.calcite.sql.util.SqlBasicVisitor;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableMap;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.io.Resources;
 import org.apache.commons.csv.CSVFormat;
diff --git a/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/TableSchemaJSONLoader.java b/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/TableSchemaJSONLoader.java
index 97116e14cdcd..0d683c6b66da 100644
--- a/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/TableSchemaJSONLoader.java
+++ b/sdks/java/testing/tpcds/src/main/java/org/apache/beam/sdk/tpcds/TableSchemaJSONLoader.java
@@ -19,6 +19,10 @@
 
 import static org.apache.beam.sdk.util.Preconditions.checkArgumentNotNull;
 
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.fasterxml.jackson.databind.node.ArrayNode;
+import com.fasterxml.jackson.databind.node.ObjectNode;
+import com.fasterxml.jackson.databind.node.TextNode;
 import java.io.IOException;
 import java.nio.charset.StandardCharsets;
 import java.util.ArrayList;
@@ -27,9 +31,6 @@
 import java.util.Map;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.io.Resources;
 import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.reflect.ClassPath;
-import org.json.simple.JSONArray;
-import org.json.simple.JSONObject;
-import org.json.simple.parser.JSONParser;
 
 /**
  * TableSchemaJSONLoader can get all table's names from resource/schemas directory and parse a
@@ -51,8 +52,9 @@ public static String parseTableSchema(String tableName) throws Exception {
     String path = "schemas/" + tableName + ".json";
     String schema = Resources.toString(Resources.getResource(path), StandardCharsets.UTF_8);
 
-    JSONObject jsonObject = (JSONObject) new JSONParser().parse(schema);
-    JSONArray jsonArray = (JSONArray) jsonObject.get("schema");
+    ObjectMapper mapper = new ObjectMapper();
+
+    ArrayNode jsonArray = (ArrayNode) mapper.readTree(schema).get("schema");
     if (jsonArray == null) {
       throw new RuntimeException("Can't get Json array for \"schema\" key.");
     }
@@ -62,19 +64,20 @@ public static String parseTableSchema(String tableName) throws Exception {
 
     Iterator jsonArrIterator = jsonArray.iterator();
     while (jsonArrIterator.hasNext()) {
-      Map jsonMap = checkArgumentNotNull((Map) jsonArrIterator.next());
-      Iterator recordIterator = jsonMap.entrySet().iterator();
+      ObjectNode jsonMap = checkArgumentNotNull((ObjectNode) jsonArrIterator.next());
+      Iterator recordIterator = jsonMap.properties().iterator();
       while (recordIterator.hasNext()) {
         Map.Entry pair = checkArgumentNotNull((Map.Entry) recordIterator.next());
         Object key = checkArgumentNotNull(pair.getKey());
+        TextNode valueNode = (TextNode) checkArgumentNotNull(pair.getValue());
+        String value = valueNode.textValue();
         if (key.equals("type")) {
           // If the key of the pair is "type", make some modification before appending it to the
           // schemaStringBuilder, then append a comma.
-          String typeName = checkArgumentNotNull((String) pair.getValue());
-          if (typeName.equalsIgnoreCase("identifier") || typeName.equalsIgnoreCase("integer")) {
+          if (value.equalsIgnoreCase("identifier") || value.equalsIgnoreCase("integer")) {
             // Use long type to represent int, prevent overflow
             schemaStringBuilder.append("bigint");
-          } else if (typeName.contains("decimal")) {
+          } else if (value.contains("decimal")) {
             // Currently Beam SQL doesn't handle "decimal" type properly, use "double" to replace it
             // for now.
             schemaStringBuilder.append("double");
@@ -87,7 +90,7 @@ public static String parseTableSchema(String tableName) throws Exception {
         } else {
           // If the key of the pair is "name", directly append it to the StringBuilder, then append
           // a space.
-          schemaStringBuilder.append(pair.getValue());
+          schemaStringBuilder.append(value);
           schemaStringBuilder.append(' ');
         }
       }
diff --git a/sdks/python/apache_beam/coders/coder_impl.pxd b/sdks/python/apache_beam/coders/coder_impl.pxd
index 27cffe7b62df..6238167bc2d7 100644
--- a/sdks/python/apache_beam/coders/coder_impl.pxd
+++ b/sdks/python/apache_beam/coders/coder_impl.pxd
@@ -81,6 +81,7 @@ cdef class FastPrimitivesCoderImpl(StreamCoderImpl):
   cdef CoderImpl iterable_coder_impl
   cdef object requires_deterministic_step_label
   cdef bint warn_deterministic_fallback
+  cdef bint force_use_dill
 
   @cython.locals(dict_value=dict, int_value=libc.stdint.int64_t,
                  unicode_value=unicode)
@@ -88,9 +89,12 @@ cdef class FastPrimitivesCoderImpl(StreamCoderImpl):
   @cython.locals(t=int)
   cpdef decode_from_stream(self, InputStream stream, bint nested)
   cdef encode_special_deterministic(self, value, OutputStream stream)
+  cdef encode_type_2_67_0(self, t, OutputStream stream)
   cdef encode_type(self, t, OutputStream stream)
   cdef decode_type(self, InputStream stream)
 
+cdef dict _pickled_types
+
 cdef dict _unpickled_types
 
 
diff --git a/sdks/python/apache_beam/coders/coder_impl.py b/sdks/python/apache_beam/coders/coder_impl.py
index 807d083d8a38..c2241268b8ba 100644
--- a/sdks/python/apache_beam/coders/coder_impl.py
+++ b/sdks/python/apache_beam/coders/coder_impl.py
@@ -50,7 +50,6 @@
 from typing import Tuple
 from typing import Type
 
-import dill
 import numpy as np
 from fastavro import parse_schema
 from fastavro import schemaless_reader
@@ -58,6 +57,7 @@
 
 from apache_beam.coders import observable
 from apache_beam.coders.avro_record import AvroRecord
+from apache_beam.internal import cloudpickle_pickler
 from apache_beam.typehints.schemas import named_tuple_from_schema
 from apache_beam.utils import proto_utils
 from apache_beam.utils import windowed_value
@@ -71,6 +71,11 @@
 except ImportError:
   dataclasses = None  # type: ignore
 
+try:
+  import dill
+except ImportError:
+  dill = None
+
 if TYPE_CHECKING:
   import proto
   from apache_beam.transforms import userstate
@@ -354,14 +359,30 @@ def decode(self, value):
 _ITERABLE_LIKE_TYPES = set()  # type: Set[Type]
 
 
+def _verify_dill_compat():
+  base_error = (
+      "This pipeline runs with the pipeline option "
+      "--update_compatibility_version=2.67.0 or earlier. "
+      "When running with this option on SDKs 2.68.0 or "
+      "later, you must ensure dill==0.3.1.1 is installed.")
+  if not dill:
+    raise RuntimeError(base_error + ". Dill is not installed.")
+  if dill.__version__ != "0.3.1.1":
+    raise RuntimeError(base_error + f". Found dill version '{dill.__version__}")
+
+
 class FastPrimitivesCoderImpl(StreamCoderImpl):
   """For internal use only; no backwards-compatibility guarantees."""
   def __init__(
-      self, fallback_coder_impl, requires_deterministic_step_label=None):
+      self,
+      fallback_coder_impl,
+      requires_deterministic_step_label=None,
+      force_use_dill=False):
     self.fallback_coder_impl = fallback_coder_impl
     self.iterable_coder_impl = IterableCoderImpl(self)
     self.requires_deterministic_step_label = requires_deterministic_step_label
     self.warn_deterministic_fallback = True
+    self.force_use_dill = force_use_dill
 
   @staticmethod
   def register_iterable_like_type(t):
@@ -525,10 +546,27 @@ def _deterministic_encoding_error_msg(self, value):
         "please provide a type hint for the input of '%s'" %
         (value, type(value), self.requires_deterministic_step_label))
 
+  def encode_type_2_67_0(self, t, stream):
+    """
+    Encode special type with <=2.67.0 compatibility.
+    """
+    if t not in _pickled_types:
+      _verify_dill_compat()
+      _pickled_types[t] = dill.dumps(t)
+    stream.write(_pickled_types[t], True)
+
   def encode_type(self, t, stream):
-    stream.write(dill.dumps(t), True)
+    if self.force_use_dill:
+      return self.encode_type_2_67_0(t, stream)
+
+    if t not in _pickled_types:
+      _pickled_types[t] = cloudpickle_pickler.dumps(
+          t, config=cloudpickle_pickler.NO_DYNAMIC_CLASS_TRACKING_CONFIG)
+    stream.write(_pickled_types[t], True)
 
   def decode_type(self, stream):
+    if self.force_use_dill:
+      return _unpickle_type_2_67_0(stream.read_all(True))
     return _unpickle_type(stream.read_all(True))
 
   def decode_from_stream(self, stream, nested):
@@ -586,22 +624,39 @@ def decode_from_stream(self, stream, nested):
       raise ValueError('Unknown type tag %x' % t)
 
 
+_pickled_types = {}  # type: Dict[type, bytes]
 _unpickled_types = {}  # type: Dict[bytes, type]
 
 
-def _unpickle_type(bs):
+def _unpickle_type_2_67_0(bs):
+  """
+  Decode special type with <=2.67.0 compatibility.
+  """
   t = _unpickled_types.get(bs, None)
   if t is None:
+    _verify_dill_compat()
     t = _unpickled_types[bs] = dill.loads(bs)
     # Fix unpicklable anonymous named tuples for Python 3.6.
     if t.__base__ is tuple and hasattr(t, '_fields'):
       try:
         pickle.loads(pickle.dumps(t))
       except pickle.PicklingError:
-        t.__reduce__ = lambda self: (_unpickle_named_tuple, (bs, tuple(self)))
+        t.__reduce__ = lambda self: (
+            _unpickle_named_tuple_2_67_0, (bs, tuple(self)))
   return t
 
 
+def _unpickle_named_tuple_2_67_0(bs, items):
+  return _unpickle_type_2_67_0(bs)(*items)
+
+
+def _unpickle_type(bs):
+  if not _unpickled_types.get(bs, None):
+    _unpickled_types[bs] = cloudpickle_pickler.loads(bs)
+
+  return _unpickled_types[bs]
+
+
 def _unpickle_named_tuple(bs, items):
   return _unpickle_type(bs)(*items)
 
@@ -837,6 +892,7 @@ def decode_from_stream(self, in_, nested):
       if IntervalWindow is None:
         from apache_beam.transforms.window import IntervalWindow
     # instantiating with None is not part of the public interface
+    # pylint: disable=too-many-function-args
     typed_value = IntervalWindow(None, None)  # type: ignore[arg-type]
     typed_value._end_micros = (
         1000 * self._to_normal_time(in_.read_bigendian_uint64()))
diff --git a/sdks/python/apache_beam/coders/coders.py b/sdks/python/apache_beam/coders/coders.py
index 2691857bf0a6..fe5728c0f16e 100644
--- a/sdks/python/apache_beam/coders/coders.py
+++ b/sdks/python/apache_beam/coders/coders.py
@@ -85,9 +85,7 @@
   # occurs.
   from apache_beam.internal.dill_pickler import dill
 except ImportError:
-  # We fall back to using the stock dill library in tests that don't use the
-  # full Python SDK.
-  import dill
+  dill = None
 
 __all__ = [
     'Coder',
@@ -900,6 +898,13 @@ def to_type_hint(self):
 
 class DillCoder(_PickleCoderBase):
   """Coder using dill's pickle functionality."""
+  def __init__(self):
+    if not dill:
+      raise RuntimeError(
+          "This pipeline contains a DillCoder which requires "
+          "the dill package. Install the dill package with the dill extra "
+          "e.g. apache-beam[dill]")
+
   def _create_impl(self):
     return coder_impl.CallbackCoderImpl(maybe_dill_dumps, maybe_dill_loads)
 
@@ -911,6 +916,44 @@ def _create_impl(self):
         cloudpickle_pickler.dumps, cloudpickle_pickler.loads)
 
 
+class DeterministicFastPrimitivesCoderV2(FastCoder):
+  """Throws runtime errors when encoding non-deterministic values."""
+  def __init__(self, coder, step_label):
+    self._underlying_coder = coder
+    self._step_label = step_label
+
+  def _create_impl(self):
+
+    return coder_impl.FastPrimitivesCoderImpl(
+        self._underlying_coder.get_impl(),
+        requires_deterministic_step_label=self._step_label,
+        force_use_dill=False)
+
+  def is_deterministic(self):
+    # type: () -> bool
+    return True
+
+  def is_kv_coder(self):
+    # type: () -> bool
+    return True
+
+  def key_coder(self):
+    return self
+
+  def value_coder(self):
+    return self
+
+  def to_type_hint(self):
+    return Any
+
+  def to_runner_api_parameter(self, context):
+    # type: (Optional[PipelineContext]) -> Tuple[str, Any, Sequence[Coder]]
+    return (
+        python_urns.PICKLED_CODER,
+        google.protobuf.wrappers_pb2.BytesValue(value=serialize_coder(self)),
+        ())
+
+
 class DeterministicFastPrimitivesCoder(FastCoder):
   """Throws runtime errors when encoding non-deterministic values."""
   def __init__(self, coder, step_label):
@@ -920,7 +963,8 @@ def __init__(self, coder, step_label):
   def _create_impl(self):
     return coder_impl.FastPrimitivesCoderImpl(
         self._underlying_coder.get_impl(),
-        requires_deterministic_step_label=self._step_label)
+        requires_deterministic_step_label=self._step_label,
+        force_use_dill=True)
 
   def is_deterministic(self):
     # type: () -> bool
@@ -940,6 +984,34 @@ def to_type_hint(self):
     return Any
 
 
+def _should_force_use_dill():
+  from apache_beam.coders import typecoders
+  from apache_beam.transforms.util import is_v1_prior_to_v2
+  update_compat_version = typecoders.registry.update_compatibility_version
+
+  if not update_compat_version:
+    return False
+
+  if not is_v1_prior_to_v2(v1=update_compat_version, v2="2.68.0"):
+    return False
+
+  try:
+    import dill
+    assert dill.__version__ == "0.3.1.1"
+  except Exception as e:
+    raise RuntimeError("This pipeline runs with the pipeline option " \
+    "--update_compatibility_version=2.67.0 or earlier. When running with " \
+    "this option on SDKs 2.68.0 or later, you must ensure dill==0.3.1.1 " \
+    f"is installed. Error {e}")
+  return True
+
+
+def _update_compatible_deterministic_fast_primitives_coder(coder, step_label):
+  if _should_force_use_dill():
+    return DeterministicFastPrimitivesCoder(coder, step_label)
+  return DeterministicFastPrimitivesCoderV2(coder, step_label)
+
+
 class FastPrimitivesCoder(FastCoder):
   """Encodes simple primitives (e.g. str, int) efficiently.
 
@@ -960,7 +1032,8 @@ def as_deterministic_coder(self, step_label, error_message=None):
     if self.is_deterministic():
       return self
     else:
-      return DeterministicFastPrimitivesCoder(self, step_label)
+      return _update_compatible_deterministic_fast_primitives_coder(
+          self, step_label)
 
   def to_type_hint(self):
     return Any
diff --git a/sdks/python/apache_beam/coders/coders_test.py b/sdks/python/apache_beam/coders/coders_test.py
index 2cde92a76def..74e6c55e4188 100644
--- a/sdks/python/apache_beam/coders/coders_test.py
+++ b/sdks/python/apache_beam/coders/coders_test.py
@@ -267,7 +267,7 @@ def test_numpy_int(self):
     # this type is not supported as the key
     import numpy as np
 
-    with self.assertRaises(TypeError):
+    with self.assertRaisesRegex(Exception, "Unable to deterministically"):
       with TestPipeline() as p:
         indata = p | "Create" >> beam.Create([(a, int(a))
                                               for a in np.arange(3)])
diff --git a/sdks/python/apache_beam/coders/coders_test_common.py b/sdks/python/apache_beam/coders/coders_test_common.py
index dbd0a301bb0d..1ae9a32790ac 100644
--- a/sdks/python/apache_beam/coders/coders_test_common.py
+++ b/sdks/python/apache_beam/coders/coders_test_common.py
@@ -34,6 +34,8 @@
 from typing import NamedTuple
 
 import pytest
+from parameterized import param
+from parameterized import parameterized
 
 from apache_beam.coders import proto2_coder_test_messages_pb2 as test_message
 from apache_beam.coders import coders
@@ -57,7 +59,13 @@
 except ImportError:
   dataclasses = None  # type: ignore
 
+try:
+  import dill
+except ImportError:
+  dill = None
+
 MyNamedTuple = collections.namedtuple('A', ['x', 'y'])  # type: ignore[name-match]
+AnotherNamedTuple = collections.namedtuple('AnotherNamedTuple', ['x', 'y'])
 MyTypedNamedTuple = NamedTuple('MyTypedNamedTuple', [('f1', int), ('f2', str)])
 
 
@@ -113,6 +121,7 @@ class UnFrozenDataClass:
 # These tests need to all be run in the same process due to the asserts
 # in tearDownClass.
 @pytest.mark.no_xdist
+@pytest.mark.uses_dill
 class CodersTest(unittest.TestCase):
 
   # These class methods ensure that we test each defined coder in both
@@ -170,11 +179,17 @@ def tearDownClass(cls):
         coders.BigIntegerCoder,  # tested in DecimalCoder
         coders.TimestampPrefixingOpaqueWindowCoder,
     ])
+    if not dill:
+      standard -= set(
+          [coders.DillCoder, coders.DeterministicFastPrimitivesCoder])
     cls.seen_nested -= set(
         [coders.ProtoCoder, coders.ProtoPlusCoder, CustomCoder])
     assert not standard - cls.seen, str(standard - cls.seen)
     assert not cls.seen_nested - standard, str(cls.seen_nested - standard)
 
+  def tearDown(self):
+    typecoders.registry.update_compatibility_version = None
+
   @classmethod
   def _observe(cls, coder):
     cls.seen.add(type(coder))
@@ -230,9 +245,20 @@ def test_memoizing_pickle_coder(self):
     coder = coders._MemoizingPickleCoder()
     self.check_coder(coder, *self.test_values)
 
-  def test_deterministic_coder(self):
+  @parameterized.expand([
+      param(compat_version=None),
+      param(compat_version="2.67.0"),
+  ])
+  def test_deterministic_coder(self, compat_version):
+
+    typecoders.registry.update_compatibility_version = compat_version
     coder = coders.FastPrimitivesCoder()
-    deterministic_coder = coders.DeterministicFastPrimitivesCoder(coder, 'step')
+    if not dill and compat_version:
+      with self.assertRaises(RuntimeError):
+        coder.as_deterministic_coder(step_label="step")
+      self.skipTest('Dill not installed')
+    deterministic_coder = coder.as_deterministic_coder(step_label="step")
+
     self.check_coder(deterministic_coder, *self.test_values_deterministic)
     for v in self.test_values_deterministic:
       self.check_coder(coders.TupleCoder((deterministic_coder, )), (v, ))
@@ -254,8 +280,16 @@ def test_deterministic_coder(self):
 
     self.check_coder(deterministic_coder, test_message.MessageA(field1='value'))
 
+    # Skip this test during cloudpickle. Dill monkey patches the __reduce__
+    # method for anonymous named tuples (MyNamedTuple) which is not pickleable.
+    # Since the test is parameterized the type gets colbbered.
+    if compat_version:
+      self.check_coder(
+          deterministic_coder, [MyNamedTuple(1, 2), MyTypedNamedTuple(1, 'a')])
+
     self.check_coder(
-        deterministic_coder, [MyNamedTuple(1, 2), MyTypedNamedTuple(1, 'a')])
+        deterministic_coder,
+        [AnotherNamedTuple(1, 2), MyTypedNamedTuple(1, 'a')])
 
     if dataclasses is not None:
       self.check_coder(deterministic_coder, FrozenDataClass(1, 2))
@@ -265,9 +299,10 @@ def test_deterministic_coder(self):
       with self.assertRaises(TypeError):
         self.check_coder(
             deterministic_coder, FrozenDataClass(UnFrozenDataClass(1, 2), 3))
-      with self.assertRaises(TypeError):
-        self.check_coder(
-            deterministic_coder, MyNamedTuple(UnFrozenDataClass(1, 2), 3))
+        with self.assertRaises(TypeError):
+          self.check_coder(
+              deterministic_coder,
+              AnotherNamedTuple(UnFrozenDataClass(1, 2), 3))
 
     self.check_coder(deterministic_coder, list(MyEnum))
     self.check_coder(deterministic_coder, list(MyIntEnum))
@@ -286,7 +321,40 @@ def test_deterministic_coder(self):
               1: 'x', 'y': 2
           }))
 
+  @parameterized.expand([
+      param(compat_version=None),
+      param(compat_version="2.67.0"),
+  ])
+  def test_deterministic_map_coder_is_update_compatible(self, compat_version):
+    typecoders.registry.update_compatibility_version = compat_version
+    values = [{
+        MyTypedNamedTuple(i, 'a'): MyTypedNamedTuple('a', i)
+        for i in range(10)
+    }]
+
+    coder = coders.MapCoder(
+        coders.FastPrimitivesCoder(), coders.FastPrimitivesCoder())
+
+    if not dill and compat_version:
+      with self.assertRaises(RuntimeError):
+        coder.as_deterministic_coder(step_label="step")
+      self.skipTest('Dill not installed')
+
+    deterministic_coder = coder.as_deterministic_coder(step_label="step")
+
+    assert isinstance(
+        deterministic_coder._key_coder,
+        coders.DeterministicFastPrimitivesCoderV2
+        if not compat_version else coders.DeterministicFastPrimitivesCoder)
+
+    self.check_coder(deterministic_coder, *values)
+
   def test_dill_coder(self):
+    if not dill:
+      with self.assertRaises(RuntimeError):
+        coders.DillCoder()
+      self.skipTest('Dill not installed')
+
     cell_value = (lambda x: lambda: x)(0).__closure__[0]
     self.check_coder(coders.DillCoder(), 'a', 1, cell_value)
     self.check_coder(
@@ -610,15 +678,23 @@ def test_param_windowed_value_coder(self):
                 1, (window.IntervalWindow(11, 21), ),
                 PaneInfo(True, False, 1, 2, 3))))
 
-  def test_cross_process_encoding_of_special_types_is_deterministic(self):
+  @parameterized.expand([
+      param(compat_version=None),
+      param(compat_version="2.67.0"),
+  ])
+  def test_cross_process_encoding_of_special_types_is_deterministic(
+      self, compat_version):
     """Test cross-process determinism for all special deterministic types"""
+    if compat_version:
+      pytest.importorskip("dill")
 
     if sys.executable is None:
       self.skipTest('No Python interpreter found')
+    typecoders.registry.update_compatibility_version = compat_version
 
     # pylint: disable=line-too-long
     script = textwrap.dedent(
-        '''\
+        f'''\
         import pickle
         import sys
         import collections
@@ -626,13 +702,19 @@ def test_cross_process_encoding_of_special_types_is_deterministic(self):
         import logging
 
         from apache_beam.coders import coders
-        from apache_beam.coders import proto2_coder_test_messages_pb2 as test_message
-        from typing import NamedTuple
+        from apache_beam.coders import typecoders
+        from apache_beam.coders.coders_test_common import MyNamedTuple
+        from apache_beam.coders.coders_test_common import MyTypedNamedTuple
+        from apache_beam.coders.coders_test_common import MyEnum
+        from apache_beam.coders.coders_test_common import MyIntEnum
+        from apache_beam.coders.coders_test_common import MyIntFlag
+        from apache_beam.coders.coders_test_common import MyFlag
+        from apache_beam.coders.coders_test_common import DefinesGetState
+        from apache_beam.coders.coders_test_common import DefinesGetAndSetState
+        from apache_beam.coders.coders_test_common import FrozenDataClass
+
 
-        try:
-            import dataclasses
-        except ImportError:
-            dataclasses = None
+        from apache_beam.coders import proto2_coder_test_messages_pb2 as test_message
 
         logging.basicConfig(
             level=logging.INFO,
@@ -640,38 +722,6 @@ def test_cross_process_encoding_of_special_types_is_deterministic(self):
             stream=sys.stderr,
             force=True
         )
-
-        # Define all the special types that encode_special_deterministic handles
-        MyNamedTuple = collections.namedtuple('A', ['x', 'y'])
-        MyTypedNamedTuple = NamedTuple('MyTypedNamedTuple', [('f1', int), ('f2', str)])
-
-        class MyEnum(enum.Enum):
-            E1 = 5
-            E2 = enum.auto()
-            E3 = 'abc'
-
-        MyIntEnum = enum.IntEnum('MyIntEnum', 'I1 I2 I3')
-        MyIntFlag = enum.IntFlag('MyIntFlag', 'F1 F2 F3')
-        MyFlag = enum.Flag('MyFlag', 'F1 F2 F3')
-
-        if dataclasses is not None:
-            @dataclasses.dataclass(frozen=True)
-            class FrozenDataClass:
-                a: int
-                b: int
-
-        class DefinesGetAndSetState:
-            def __init__(self, value):
-                self.value = value
-
-            def __getstate__(self):
-                return self.value
-
-            def __setstate__(self, value):
-                self.value = value
-
-            def __eq__(self, other):
-                return type(other) is type(self) and other.value == self.value
         
         # Test cases for all special deterministic types
         # NOTE: When this script run in a subprocess the module is considered
@@ -683,26 +733,28 @@ def __eq__(self, other):
             ("named_tuple_simple", MyNamedTuple(1, 2)),
             ("typed_named_tuple", MyTypedNamedTuple(1, 'a')),
             ("named_tuple_list", [MyNamedTuple(1, 2), MyTypedNamedTuple(1, 'a')]),
-            # ("enum_single", MyEnum.E1),
-            # ("enum_list", list(MyEnum)),
-            # ("int_enum_list", list(MyIntEnum)),
-            # ("int_flag_list", list(MyIntFlag)),
-            # ("flag_list", list(MyFlag)),
+            ("enum_single", MyEnum.E1),
+            ("enum_list", list(MyEnum)),
+            ("int_enum_list", list(MyIntEnum)),
+            ("int_flag_list", list(MyIntFlag)),
+            ("flag_list", list(MyFlag)),
             ("getstate_setstate_simple", DefinesGetAndSetState(1)),
             ("getstate_setstate_complex", DefinesGetAndSetState((1, 2, 3))),
             ("getstate_setstate_list", [DefinesGetAndSetState(1), DefinesGetAndSetState((1, 2, 3))]),
         ]
 
-        if dataclasses is not None:
-            test_cases.extend([
-                ("frozen_dataclass", FrozenDataClass(1, 2)),
-                ("frozen_dataclass_list", [FrozenDataClass(1, 2), FrozenDataClass(3, 4)]),
-            ])
+        
+        test_cases.extend([
+            ("frozen_dataclass", FrozenDataClass(1, 2)),
+            ("frozen_dataclass_list", [FrozenDataClass(1, 2), FrozenDataClass(3, 4)]),
+        ])
 
+        compat_version = {'"'+ compat_version +'"' if compat_version else None}
+        typecoders.registry.update_compatibility_version = compat_version
         coder = coders.FastPrimitivesCoder()
-        deterministic_coder = coders.DeterministicFastPrimitivesCoder(coder, 'step')
+        deterministic_coder = coder.as_deterministic_coder("step")
         
-        results = {}
+        results = dict()
         for test_name, value in test_cases:
             try:
                 encoded = deterministic_coder.encode(value)
@@ -730,7 +782,7 @@ def run_subprocess():
     results2 = run_subprocess()
 
     coder = coders.FastPrimitivesCoder()
-    deterministic_coder = coders.DeterministicFastPrimitivesCoder(coder, 'step')
+    deterministic_coder = coder.as_deterministic_coder("step")
 
     for test_name in results1:
       data1 = results1[test_name]
@@ -861,7 +913,7 @@ def test_map_coder(self):
         {
             i: str(i)
             for i in range(5000)
-        }
+        },
     ]
     map_coder = coders.MapCoder(coders.VarIntCoder(), coders.StrUtf8Coder())
     self.check_coder(map_coder, *values)
diff --git a/sdks/python/apache_beam/coders/proto2_coder_test_messages_pb2.py b/sdks/python/apache_beam/coders/proto2_coder_test_messages_pb2.py
index 3d154305b5e7..d1dea9f6fce8 100644
--- a/sdks/python/apache_beam/coders/proto2_coder_test_messages_pb2.py
+++ b/sdks/python/apache_beam/coders/proto2_coder_test_messages_pb2.py
@@ -20,6 +20,7 @@
 # NO CHECKED-IN PROTOBUF GENCODE
 # source: apache_beam/coders/proto2_coder_test_messages.proto
 # Protobuf Python Version: 5.28.0
+
 """Generated protocol buffer code."""
 from google.protobuf import descriptor as _descriptor
 from google.protobuf import descriptor_pool as _descriptor_pool
@@ -29,35 +30,36 @@
 
 _sym_db = _symbol_database.Default()
 
-
-
-
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n3apache_beam/coders/proto2_coder_test_messages.proto\x12\x1aproto2_coder_test_messages\"P\n\x08MessageA\x12\x0e\n\x06\x66ield1\x18\x01 \x01(\t\x12\x34\n\x06\x66ield2\x18\x02 \x03(\x0b\x32$.proto2_coder_test_messages.MessageB\"\x1a\n\x08MessageB\x12\x0e\n\x06\x66ield1\x18\x01 \x01(\x08\"\x10\n\x08MessageC*\x04\x08\x64\x10j\"\xad\x01\n\x0eMessageWithMap\x12\x46\n\x06\x66ield1\x18\x01 \x03(\x0b\x32\x36.proto2_coder_test_messages.MessageWithMap.Field1Entry\x1aS\n\x0b\x46ield1Entry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x33\n\x05value\x18\x02 \x01(\x0b\x32$.proto2_coder_test_messages.MessageA:\x02\x38\x01\"V\n\x18ReferencesMessageWithMap\x12:\n\x06\x66ield1\x18\x01 \x03(\x0b\x32*.proto2_coder_test_messages.MessageWithMap\";\n\x08MessageD\"/\n\nNestedEnum\x12\x0f\n\x0bUNSPECIFIED\x10\x00\x12\x07\n\x03ONE\x10\x01\x12\x07\n\x03TWO\x10\x02*1\n\x0cTopLevelEnum\x12\x0f\n\x0bUNSPECIFIED\x10\x00\x12\x07\n\x03ONE\x10\x01\x12\x07\n\x03TWO\x10\x02:Z\n\x06\x66ield1\x12$.proto2_coder_test_messages.MessageC\x18\x65 \x01(\x0b\x32$.proto2_coder_test_messages.MessageA:Z\n\x06\x66ield2\x12$.proto2_coder_test_messages.MessageC\x18\x66 \x01(\x0b\x32$.proto2_coder_test_messages.MessageBB)\n\'org.apache.beam.sdk.extensions.protobuf')
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(
+    b'\n3apache_beam/coders/proto2_coder_test_messages.proto\x12\x1aproto2_coder_test_messages\"P\n\x08MessageA\x12\x0e\n\x06\x66ield1\x18\x01 \x01(\t\x12\x34\n\x06\x66ield2\x18\x02 \x03(\x0b\x32$.proto2_coder_test_messages.MessageB\"\x1a\n\x08MessageB\x12\x0e\n\x06\x66ield1\x18\x01 \x01(\x08\"\x10\n\x08MessageC*\x04\x08\x64\x10j\"\xad\x01\n\x0eMessageWithMap\x12\x46\n\x06\x66ield1\x18\x01 \x03(\x0b\x32\x36.proto2_coder_test_messages.MessageWithMap.Field1Entry\x1aS\n\x0b\x46ield1Entry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x33\n\x05value\x18\x02 \x01(\x0b\x32$.proto2_coder_test_messages.MessageA:\x02\x38\x01\"V\n\x18ReferencesMessageWithMap\x12:\n\x06\x66ield1\x18\x01 \x03(\x0b\x32*.proto2_coder_test_messages.MessageWithMap\";\n\x08MessageD\"/\n\nNestedEnum\x12\x0f\n\x0bUNSPECIFIED\x10\x00\x12\x07\n\x03ONE\x10\x01\x12\x07\n\x03TWO\x10\x02*1\n\x0cTopLevelEnum\x12\x0f\n\x0bUNSPECIFIED\x10\x00\x12\x07\n\x03ONE\x10\x01\x12\x07\n\x03TWO\x10\x02:Z\n\x06\x66ield1\x12$.proto2_coder_test_messages.MessageC\x18\x65 \x01(\x0b\x32$.proto2_coder_test_messages.MessageA:Z\n\x06\x66ield2\x12$.proto2_coder_test_messages.MessageC\x18\x66 \x01(\x0b\x32$.proto2_coder_test_messages.MessageBB)\n\'org.apache.beam.sdk.extensions.protobuf'
+)
 
 _globals = globals()
 _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
-_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'apache_beam.coders.proto2_coder_test_messages_pb2', _globals)
+_builder.BuildTopDescriptorsAndMessages(
+    DESCRIPTOR, 'apache_beam.coders.proto2_coder_test_messages_pb2', _globals)
 if not _descriptor._USE_C_DESCRIPTORS:
   _globals['DESCRIPTOR']._loaded_options = None
-  _globals['DESCRIPTOR']._serialized_options = b'\n\'org.apache.beam.sdk.extensions.protobuf'
+  _globals[
+      'DESCRIPTOR']._serialized_options = b'\n\'org.apache.beam.sdk.extensions.protobuf'
   _globals['_MESSAGEWITHMAP_FIELD1ENTRY']._loaded_options = None
   _globals['_MESSAGEWITHMAP_FIELD1ENTRY']._serialized_options = b'8\001'
-  _globals['_TOPLEVELENUM']._serialized_start=536
-  _globals['_TOPLEVELENUM']._serialized_end=585
-  _globals['_MESSAGEA']._serialized_start=83
-  _globals['_MESSAGEA']._serialized_end=163
-  _globals['_MESSAGEB']._serialized_start=165
-  _globals['_MESSAGEB']._serialized_end=191
-  _globals['_MESSAGEC']._serialized_start=193
-  _globals['_MESSAGEC']._serialized_end=209
-  _globals['_MESSAGEWITHMAP']._serialized_start=212
-  _globals['_MESSAGEWITHMAP']._serialized_end=385
-  _globals['_MESSAGEWITHMAP_FIELD1ENTRY']._serialized_start=302
-  _globals['_MESSAGEWITHMAP_FIELD1ENTRY']._serialized_end=385
-  _globals['_REFERENCESMESSAGEWITHMAP']._serialized_start=387
-  _globals['_REFERENCESMESSAGEWITHMAP']._serialized_end=473
-  _globals['_MESSAGED']._serialized_start=475
-  _globals['_MESSAGED']._serialized_end=534
-  _globals['_MESSAGED_NESTEDENUM']._serialized_start=487
-  _globals['_MESSAGED_NESTEDENUM']._serialized_end=534
+  _globals['_TOPLEVELENUM']._serialized_start = 536
+  _globals['_TOPLEVELENUM']._serialized_end = 585
+  _globals['_MESSAGEA']._serialized_start = 83
+  _globals['_MESSAGEA']._serialized_end = 163
+  _globals['_MESSAGEB']._serialized_start = 165
+  _globals['_MESSAGEB']._serialized_end = 191
+  _globals['_MESSAGEC']._serialized_start = 193
+  _globals['_MESSAGEC']._serialized_end = 209
+  _globals['_MESSAGEWITHMAP']._serialized_start = 212
+  _globals['_MESSAGEWITHMAP']._serialized_end = 385
+  _globals['_MESSAGEWITHMAP_FIELD1ENTRY']._serialized_start = 302
+  _globals['_MESSAGEWITHMAP_FIELD1ENTRY']._serialized_end = 385
+  _globals['_REFERENCESMESSAGEWITHMAP']._serialized_start = 387
+  _globals['_REFERENCESMESSAGEWITHMAP']._serialized_end = 473
+  _globals['_MESSAGED']._serialized_start = 475
+  _globals['_MESSAGED']._serialized_end = 534
+  _globals['_MESSAGED_NESTEDENUM']._serialized_start = 487
+  _globals['_MESSAGED_NESTEDENUM']._serialized_end = 534
 # @@protoc_insertion_point(module_scope)
diff --git a/sdks/python/apache_beam/coders/typecoders.py b/sdks/python/apache_beam/coders/typecoders.py
index 19300c675596..779c65dc772c 100644
--- a/sdks/python/apache_beam/coders/typecoders.py
+++ b/sdks/python/apache_beam/coders/typecoders.py
@@ -84,6 +84,7 @@ def __init__(self, fallback_coder=None):
     self._coders: Dict[Any, Type[coders.Coder]] = {}
     self.custom_types: List[Any] = []
     self.register_standard_coders(fallback_coder)
+    self.update_compatibility_version = None
 
   def register_standard_coders(self, fallback_coder):
     """Register coders for all basic and composite types."""
diff --git a/sdks/python/apache_beam/dataframe/doctests.py b/sdks/python/apache_beam/dataframe/doctests.py
index 19f78338310a..226a7da7eaa4 100644
--- a/sdks/python/apache_beam/dataframe/doctests.py
+++ b/sdks/python/apache_beam/dataframe/doctests.py
@@ -288,7 +288,7 @@ def sort_and_normalize(text):
       except Exception:
         got = traceback.format_exc()
 
-    if sys.version_info < (3, 11) and 'NumpyExtensionArray' in got:
+    if 'NumpyExtensionArray' in got:
       # Work around formatting differences (np.int32(1) vs 1).
       got = re.sub('np.(int32|str_)[(]([^()]+)[)]', r'\2', got)
       got = re.sub('np.complex128[(]([^()]+)[)]', r'(\1)', got)
diff --git a/sdks/python/apache_beam/dataframe/io.py b/sdks/python/apache_beam/dataframe/io.py
index a804e4b4f2d2..752df1e68b7c 100644
--- a/sdks/python/apache_beam/dataframe/io.py
+++ b/sdks/python/apache_beam/dataframe/io.py
@@ -93,6 +93,7 @@ def read_csv(path, *args, splittable=False, binary=True, **kwargs):
   newlines may result in partial records and data corruption."""
   if 'nrows' in kwargs:
     raise ValueError('nrows not yet supported')
+  filename_column = kwargs.pop('filename_column', None)
   return _ReadFromPandas(
       pd.read_csv,
       path,
@@ -100,7 +101,8 @@ def read_csv(path, *args, splittable=False, binary=True, **kwargs):
       kwargs,
       incremental=True,
       binary=binary,
-      splitter=_TextFileSplitter(args, kwargs) if splittable else None)
+      splitter=_TextFileSplitter(args, kwargs) if splittable else None,
+      filename_column=filename_column)
 
 
 def _as_pc(df, label=None):
@@ -254,7 +256,8 @@ def __init__(
       kwargs,
       binary=True,
       incremental=False,
-      splitter=False):
+      splitter=False,
+      filename_column=None):
     if 'compression' in kwargs:
       raise NotImplementedError('compression')
     if not isinstance(path, str):
@@ -266,6 +269,7 @@ def __init__(
     self.binary = binary
     self.incremental = incremental
     self.splitter = splitter
+    self.filename_column = filename_column
 
   def expand(self, root):
     paths_pcoll = root | beam.Create([self.path])
@@ -285,6 +289,8 @@ def expand(self, root):
           sample = next(stream)
       else:
         sample = self.reader(handle, *self.args, **self.kwargs)
+    if self.filename_column:
+      sample[self.filename_column] = ''
 
     matches_pcoll = paths_pcoll | fileio.MatchAll()
     indices_pcoll = (
@@ -309,7 +315,8 @@ def expand(self, root):
                 self.kwargs,
                 self.binary,
                 self.incremental,
-                self.splitter),
+                self.splitter,
+                self.filename_column),
             path_indices=beam.pvalue.AsSingleton(indices_pcoll)))
     from apache_beam.dataframe import convert
     return convert.to_dataframe(pcoll, proxy=sample[:0])
@@ -580,7 +587,15 @@ def flush(self):
 
 
 class _ReadFromPandasDoFn(beam.DoFn, beam.RestrictionProvider):
-  def __init__(self, reader, args, kwargs, binary, incremental, splitter):
+  def __init__(
+      self,
+      reader,
+      args,
+      kwargs,
+      binary,
+      incremental,
+      splitter,
+      filename_column=None):
     # avoid pickling issues
     if reader.__module__.startswith('pandas.'):
       reader = reader.__name__
@@ -590,6 +605,7 @@ def __init__(self, reader, args, kwargs, binary, incremental, splitter):
     self.binary = binary
     self.incremental = incremental
     self.splitter = splitter
+    self.filename_column = filename_column
 
   def initial_restriction(self, readable_file):
     return beam.io.restriction_trackers.OffsetRange(
@@ -642,6 +658,8 @@ def process(
       else:
         frames = [reader(handle, *self.args, **self.kwargs)]
       for df in frames:
+        if self.filename_column:
+          df[self.filename_column] = readable_file.metadata.path
         yield _shift_range_index(start_index, df)
       if not self.incremental:
         # Satisfy the SDF contract by claiming the whole range.
@@ -769,10 +787,15 @@ def __init__(
       *args,
       include_indexes=False,
       objects_as_strings=True,
+      filename_column=None,
       **kwargs):
+    if format == 'csv':
+      kwargs['filename_column'] = filename_column
+    self._reader = globals()['read_%s' % format](*args, **kwargs)
     self._reader = globals()['read_%s' % format](*args, **kwargs)
     self._include_indexes = include_indexes
     self._objects_as_strings = objects_as_strings
+    self._filename_column = filename_column
 
   def expand(self, p):
     from apache_beam.dataframe import convert  # avoid circular import
diff --git a/sdks/python/apache_beam/dataframe/transforms.py b/sdks/python/apache_beam/dataframe/transforms.py
index 7128726f5eb1..49fe881ec8e7 100644
--- a/sdks/python/apache_beam/dataframe/transforms.py
+++ b/sdks/python/apache_beam/dataframe/transforms.py
@@ -108,7 +108,7 @@ def expand(self, input_pcolls):
         for tag in input_dict
     }
     input_frames: dict[Any, frame_base.DeferredFrame] = {
-        k: convert.to_dataframe(pc, proxies[k])
+        k: convert.to_dataframe(pc, proxies[k], str(k))
         for k, pc in input_dict.items()
     }  # noqa: F821
 
diff --git a/sdks/python/apache_beam/dataframe/transforms_test.py b/sdks/python/apache_beam/dataframe/transforms_test.py
index a2ca2f9d3879..c5ca2b9a359c 100644
--- a/sdks/python/apache_beam/dataframe/transforms_test.py
+++ b/sdks/python/apache_beam/dataframe/transforms_test.py
@@ -317,6 +317,26 @@ def check(actual):
           lambda x: {'res': 3 * x}, proxy, yield_elements='pandas')
       assert_that(res['res'], equal_to_series(three_series), 'CheckDictOut')
 
+  def test_multiple_dataframes_transforms(self):
+    expected_output = ["Bryan", "DKER2"]
+
+    def transform_func(a, b):
+      b["name"] = "DKER2"
+      return a, b
+
+    with beam.Pipeline() as p:
+      pcol1 = p | "Create1" >> beam.Create([beam.Row(name="Bryan")])
+      pcol2 = p | "Create2" >> beam.Create([beam.Row(name="common")])
+
+      result = ({
+          "a": pcol1, "b": pcol2
+      }
+                |
+                "TransformDF" >> transforms.DataframeTransform(transform_func)
+                | "Flatten" >> beam.Flatten()
+                | transforms.DataframeTransform(lambda df: df.name))
+      assert_that(result, equal_to(expected_output))
+
   def test_cat(self):
     # verify that cat works with a List[Series] since this is
     # missing from doctests
diff --git a/sdks/python/apache_beam/examples/complete/juliaset/juliaset/juliaset_test_it.py b/sdks/python/apache_beam/examples/complete/juliaset/juliaset/juliaset_test_it.py
index a2a3262a1fb6..148343ea9ae6 100644
--- a/sdks/python/apache_beam/examples/complete/juliaset/juliaset/juliaset_test_it.py
+++ b/sdks/python/apache_beam/examples/complete/juliaset/juliaset/juliaset_test_it.py
@@ -38,7 +38,7 @@
 class JuliaSetTestIT(unittest.TestCase):
   GRID_SIZE = 1000
 
-  def test_run_example_with_setup_file(self):
+  def test_run_example_with_requirements_file(self):
     pipeline = TestPipeline(is_integration_test=True)
     coordinate_output = FileSystems.join(
         pipeline.get_option('output'),
@@ -47,8 +47,8 @@ def test_run_example_with_setup_file(self):
     extra_args = {
         'coordinate_output': coordinate_output,
         'grid_size': self.GRID_SIZE,
-        'setup_file': os.path.normpath(
-            os.path.join(os.path.dirname(__file__), '..', 'setup.py')),
+        'requirements_file': os.path.normpath(
+            os.path.join(os.path.dirname(__file__), '..', 'requirements.txt')),
         'on_success_matcher': all_of(PipelineStateMatcher(PipelineState.DONE)),
     }
     args = pipeline.get_full_options_as_args(**extra_args)
diff --git a/sdks/python/apache_beam/examples/complete/juliaset/juliaset_main.py b/sdks/python/apache_beam/examples/complete/juliaset/juliaset_main.py
index fb64c2702fd2..589c21687dcd 100644
--- a/sdks/python/apache_beam/examples/complete/juliaset/juliaset_main.py
+++ b/sdks/python/apache_beam/examples/complete/juliaset/juliaset_main.py
@@ -21,17 +21,12 @@
 workflow. It is organized in this way so that it can be packaged as a Python
 package and later installed in the VM workers executing the job. The root
 directory for the example contains just a "driver" script to launch the job
-and the setup.py file needed to create a package.
+and the requirements.txt file needed to create a package.
 
 The advantages for organizing the code is that large projects will naturally
 evolve beyond just one module and you will have to make sure the additional
 modules are present in the worker.
 
-In Python Dataflow, using the --setup_file option when submitting a job, will
-trigger creating a source distribution (as if running python setup.py sdist) and
-then staging the resulting tarball in the staging area. The workers, upon
-startup, will install the tarball.
-
 Below is a complete command line for running the juliaset workflow remotely as
 an example:
 
@@ -40,7 +35,7 @@
   --project YOUR-PROJECT \
   --region GCE-REGION \
   --runner DataflowRunner \
-  --setup_file ./setup.py \
+  --requirements_file ./requirements.txt \
   --staging_location gs://YOUR-BUCKET/juliaset/staging \
   --temp_location gs://YOUR-BUCKET/juliaset/temp \
   --coordinate_output gs://YOUR-BUCKET/juliaset/out \
diff --git a/sdks/python/apache_beam/examples/complete/juliaset/requirements.txt b/sdks/python/apache_beam/examples/complete/juliaset/requirements.txt
new file mode 100644
index 000000000000..7d514bd30998
--- /dev/null
+++ b/sdks/python/apache_beam/examples/complete/juliaset/requirements.txt
@@ -0,0 +1,17 @@
+#   Licensed to the Apache Software Foundation (ASF) under one
+#   or more contributor license agreements.  See the NOTICE file
+#   distributed with this work for additional information
+#   regarding copyright ownership.  The ASF licenses this file
+#   to you under the Apache License, Version 2.0 (the
+#   "License"); you may not use this file except in compliance
+#   with the License.  You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+numpy
diff --git a/sdks/python/apache_beam/examples/complete/juliaset/setup.py b/sdks/python/apache_beam/examples/complete/juliaset/setup.py
deleted file mode 100644
index c3a9fe043765..000000000000
--- a/sdks/python/apache_beam/examples/complete/juliaset/setup.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""Setup.py module for the workflow's worker utilities.
-
-All the workflow related code is gathered in a package that will be built as a
-source distribution, staged in the staging area for the workflow being run and
-then installed in the workers when they start running.
-
-This behavior is triggered by specifying the --setup_file command line option
-when running the workflow for remote execution.
-"""
-
-# pytype: skip-file
-
-import subprocess
-
-import setuptools
-
-# It is recommended to import setuptools prior to importing distutils to avoid
-# using legacy behavior from distutils.
-# https://setuptools.readthedocs.io/en/latest/history.html#v48-0-0
-from distutils.command.build import build as _build  # isort:skip
-
-
-# This class handles the pip install mechanism.
-class build(_build):  # pylint: disable=invalid-name
-  """A build command class that will be invoked during package install.
-
-  The package built using the current setup.py will be staged and later
-  installed in the worker using `pip install package'. This class will be
-  instantiated during install for this specific scenario and will trigger
-  running the custom commands specified.
-  """
-  sub_commands = _build.sub_commands + [('CustomCommands', None)]
-
-
-# Some custom command to run during setup. The command is not essential for this
-# workflow. It is used here as an example. Each command will spawn a child
-# process. Typically, these commands will include steps to install non-Python
-# packages. For instance, to install a C++-based library libjpeg62 the following
-# two commands will have to be added:
-#
-#     ['apt-get', 'update'],
-#     ['apt-get', '--assume-yes', 'install', 'libjpeg62'],
-#
-# First, note that there is no need to use the sudo command because the setup
-# script runs with appropriate access.
-# Second, if apt-get tool is used then the first command needs to be 'apt-get
-# update' so the tool refreshes itself and initializes links to download
-# repositories.  Without this initial step the other apt-get install commands
-# will fail with package not found errors. Note also --assume-yes option which
-# shortcuts the interactive confirmation.
-#
-# Note that in this example custom commands will run after installing required
-# packages. If you have a PyPI package that depends on one of the custom
-# commands, move installation of the dependent package to the list of custom
-# commands, e.g.:
-#
-#     ['pip', 'install', 'my_package'],
-#
-# TODO(https://github.com/apache/beam/issues/18568): Output from the custom
-# commands are missing from the logs. The output of custom commands (including
-# failures) will be logged in the worker-startup log.
-CUSTOM_COMMANDS = [['echo', 'Custom command worked!']]
-
-
-class CustomCommands(setuptools.Command):
-  """A setuptools Command class able to run arbitrary commands."""
-  def initialize_options(self):
-    pass
-
-  def finalize_options(self):
-    pass
-
-  def RunCustomCommand(self, command_list):
-    print('Running command: %s' % command_list)
-    p = subprocess.Popen(
-        command_list,
-        stdin=subprocess.PIPE,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.STDOUT)
-    # Can use communicate(input='y\n'.encode()) if the command run requires
-    # some confirmation.
-    stdout_data, _ = p.communicate()
-    print('Command output: %s' % stdout_data)
-    if p.returncode != 0:
-      raise RuntimeError(
-          'Command %s failed: exit code: %s' % (command_list, p.returncode))
-
-  def run(self):
-    for command in CUSTOM_COMMANDS:
-      self.RunCustomCommand(command)
-
-
-# Configure the required packages and scripts to install.
-# Note that the Python Dataflow containers come with numpy already installed
-# so this dependency will not trigger anything to be installed unless a version
-# restriction is specified.
-REQUIRED_PACKAGES = [
-    'numpy',
-]
-
-setuptools.setup(
-    name='juliaset',
-    version='0.0.1',
-    description='Julia set workflow package.',
-    install_requires=REQUIRED_PACKAGES,
-    packages=setuptools.find_packages(),
-    cmdclass={
-        # Command class instantiated and run during pip install scenarios.
-        'build': build,
-        'CustomCommands': CustomCommands,
-    })
diff --git a/sdks/python/apache_beam/examples/inference/README.md b/sdks/python/apache_beam/examples/inference/README.md
index f9c5af436965..e0367ea69384 100644
--- a/sdks/python/apache_beam/examples/inference/README.md
+++ b/sdks/python/apache_beam/examples/inference/README.md
@@ -856,6 +856,12 @@ Each line represents a prediction of the flower type along with the confidence i
 
 ## Text classifcation with a Vertex AI LLM
 
+**NOTE**
+Google has deprecated PaLM LLMs like text-bison and no longer supports querying them on Vertex AI endpoints. Separately, the use of the Vertex AI Predict API is
+not supported for Gemini models in favor of use of the google-genai API. As a result, this example no longer works as-written. To perform inference with
+Gemini models deployed on Google infrastructure, please see the `GeminiModelHandler` (in `apache_beam.ml.inference.gemini_inference`) and the
+[`gemini_text_classification.py` example](./gemini_text_classification.py). For custom LLMs, you may still follow this design pattern.
+
 [`vertex_ai_llm_text_classification.py`](./vertex_ai_llm_text_classification.py) contains an implementation for a RunInference pipeline that performs image classification using a model hosted on Vertex AI (based on https://cloud.google.com/vertex-ai/docs/tutorials/image-recognition-custom).
 
 The pipeline reads image urls, performs basic preprocessing to convert them into a List of floats, passes the masked sentence to the Vertex AI implementation of RunInference, and then writes the predictions to a text file.
diff --git a/sdks/python/apache_beam/examples/inference/gemini_text_classification.py b/sdks/python/apache_beam/examples/inference/gemini_text_classification.py
index e82f407374a7..b264467467cf 100644
--- a/sdks/python/apache_beam/examples/inference/gemini_text_classification.py
+++ b/sdks/python/apache_beam/examples/inference/gemini_text_classification.py
@@ -67,8 +67,11 @@ def parse_known_args(argv):
 
 class PostProcessor(beam.DoFn):
   def process(self, element: PredictionResult) -> Iterable[str]:
-    yield "Input: " + str(element.example) + " Output: " + str(
-        element.inference[1][0].content.parts[0].text)
+    try:
+      output_text = element.inference[1][0].content.parts[0].text
+      yield f"Input: {element.example}, Output: {output_text}"
+    except Exception:
+      yield f"Can't decode inference for element: {element.example}"
 
 
 def run(
diff --git a/sdks/python/apache_beam/examples/inference/vertex_ai_llm_text_classification.py b/sdks/python/apache_beam/examples/inference/vertex_ai_llm_text_classification.py
index e587ba87b91b..75f021c37128 100644
--- a/sdks/python/apache_beam/examples/inference/vertex_ai_llm_text_classification.py
+++ b/sdks/python/apache_beam/examples/inference/vertex_ai_llm_text_classification.py
@@ -21,6 +21,16 @@
 model can be generated by fine tuning the text-bison model or another similar
 model (see
 https://cloud.google.com/vertex-ai/docs/generative-ai/models/tune-models#supervised-fine-tuning)
+
+**NOTE**
+Google has deprecated PaLM LLMs and no longer supports querying them on
+Vertex AI endpoints. Separately, the use of the Vertex AI Predict API is not
+supported for Gemini models in favor of use of the google-genai API. As a
+result, this example no longer works as-written. To perform inference with
+Gemini models deployed on Google infrastructure, please see the
+`GeminiModelHandler` (in `apache_beam.ml.inference.gemini_inference`) and the
+`gemini_text_classification.py` example. For custom LLMs, you may still follow
+this design pattern.
 """
 
 import argparse
diff --git a/sdks/python/apache_beam/examples/inference/vllm_gemma_batch.py b/sdks/python/apache_beam/examples/inference/vllm_gemma_batch.py
new file mode 100644
index 000000000000..f6e33e5be786
--- /dev/null
+++ b/sdks/python/apache_beam/examples/inference/vllm_gemma_batch.py
@@ -0,0 +1,130 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import annotations
+
+import logging
+import os
+import tempfile
+
+import apache_beam as beam
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.inference.vllm_inference import VLLMCompletionsModelHandler
+from apache_beam.ml.inference.vllm_inference import _VLLMModelServer
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+
+
+class GemmaVLLMOptions(PipelineOptions):
+  """Custom pipeline options for the Gemma vLLM batch inference job."""
+  @classmethod
+  def _add_argparse_args(cls, parser):
+    parser.add_argument(
+        "--input",
+        dest="input_file",
+        required=True,
+        help="Input file gs://path containing prompts.",
+    )
+    parser.add_argument(
+        "--output_table",
+        required=True,
+        help="BigQuery table to write to in the form project:dataset.table.",
+    )
+    parser.add_argument(
+        "--model_gcs_path",
+        required=True,
+        help="GCS path to the directory containing model files.",
+    )
+
+
+class FormatOutput(beam.DoFn):
+  def process(self, element):
+    prompt = element.example
+    comp = element.inference
+
+    if hasattr(comp, 'choices'):
+      completion = comp.choices[0].text
+    # fallback to a single .text field
+    elif hasattr(comp, 'text'):
+      completion = comp.text
+    # final fallback
+    else:
+      completion = str(comp)
+
+    yield {'prompt': prompt, 'completion': completion}
+
+
+class GcsVLLMCompletionsModelHandler(VLLMCompletionsModelHandler):
+  def __init__(self, model_name, vllm_server_kwargs=None):
+    super().__init__(model_name, vllm_server_kwargs)
+    self._local_model_dir = None
+
+  def _download_gcs_directory(self, gcs_path: str, local_path: str):
+    logging.info("Downloading model from %s to %s…", gcs_path, local_path)
+    matches = FileSystems.match([os.path.join(gcs_path, "**")])[0].metadata_list
+    for md in matches:
+      rel = os.path.relpath(md.path, gcs_path)
+      dst = os.path.join(local_path, rel)
+      os.makedirs(os.path.dirname(dst), exist_ok=True)
+      with FileSystems.open(md.path) as src, open(dst, "wb") as dstf:
+        dstf.write(src.read())
+    logging.info("Download complete.")
+
+  def load_model(self) -> _VLLMModelServer:
+    uri = self._model_name
+    if uri.startswith("gs://"):
+      self._local_model_dir = tempfile.mkdtemp(prefix="vllm_model_")
+      self._download_gcs_directory(uri, self._local_model_dir)
+      logging.info("Loading vLLM from local dir %s", self._local_model_dir)
+      return _VLLMModelServer(self._local_model_dir, self._vllm_server_kwargs)
+    else:
+      logging.info("Loading vLLM from HF hub: %s", uri)
+      return super().load_model()
+
+
+def run(argv=None, save_main_session=True, test_pipeline=None):
+  # Build pipeline options
+  opts = PipelineOptions(argv)
+
+  gem = opts.view_as(GemmaVLLMOptions)
+  opts.view_as(SetupOptions).save_main_session = save_main_session
+
+  logging.info("Pipeline starting with model path: %s", gem.model_gcs_path)
+  handler = GcsVLLMCompletionsModelHandler(
+      model_name=gem.model_gcs_path,
+      vllm_server_kwargs={"served-model-name": gem.model_gcs_path})
+
+  with (test_pipeline or beam.Pipeline(options=opts)) as p:
+    _ = (
+        p
+        | "Read" >> beam.io.ReadFromText(gem.input_file)
+        | "InferBatch" >> RunInference(handler, inference_batch_size=32)
+        | "FormatForBQ" >> beam.ParDo(FormatOutput())
+        | "WriteToBQ" >> beam.io.WriteToBigQuery(
+            gem.output_table,
+            schema="prompt:STRING,completion:STRING",
+            write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND,
+            create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,
+            method=beam.io.WriteToBigQuery.Method.FILE_LOADS,
+        ))
+  return p.result
+
+
+if __name__ == "__main__":
+  logging.getLogger().setLevel(logging.INFO)
+  run()
diff --git a/sdks/python/apache_beam/examples/ml-orchestration/kfp/components/preprocessing/requirements.txt b/sdks/python/apache_beam/examples/ml-orchestration/kfp/components/preprocessing/requirements.txt
index 609ba3a51652..8d282bff5224 100644
--- a/sdks/python/apache_beam/examples/ml-orchestration/kfp/components/preprocessing/requirements.txt
+++ b/sdks/python/apache_beam/examples/ml-orchestration/kfp/components/preprocessing/requirements.txt
@@ -15,7 +15,7 @@
 
 apache_beam[gcp]==2.40.0
 requests==2.32.4
-torch==1.13.1
+torch==2.8.0
 torchvision==0.13.0
 numpy==1.22.4
 Pillow==10.2.0
diff --git a/sdks/python/apache_beam/examples/snippets/snippets_test.py b/sdks/python/apache_beam/examples/snippets/snippets_test.py
index 3714c0574e05..d7dd5e6af191 100644
--- a/sdks/python/apache_beam/examples/snippets/snippets_test.py
+++ b/sdks/python/apache_beam/examples/snippets/snippets_test.py
@@ -307,8 +307,8 @@ def test_bad_types(self):
     # When running this pipeline, you'd get a runtime error,
     # possibly on a remote machine, possibly very late.
 
-    with self.assertRaises(TypeError):
-      p.run()
+    with self.assertRaisesRegex(Exception, "not all arguments converted"):
+      p.run().wait_until_finish()
 
     # To catch this early, we can assert what types we expect.
     with self.assertRaises(typehints.TypeCheckError):
@@ -372,8 +372,8 @@ def process(self, element):
     # When running this pipeline, you'd get a runtime error,
     # possibly on a remote machine, possibly very late.
 
-    with self.assertRaises(TypeError):
-      p.run()
+    with self.assertRaisesRegex(Exception, "not all arguments converted"):
+      p.run().wait_until_finish()
 
     # To catch this early, we can annotate process() with the expected types.
     # Beam will then use these as type hints and perform type checking before
@@ -439,12 +439,13 @@ def test_runtime_checks_off(self):
 
   def test_runtime_checks_on(self):
     # pylint: disable=expression-not-assigned
-    with self.assertRaises(typehints.TypeCheckError):
+    with self.assertRaisesRegex(Exception, "According to type-hint"):
       # [START type_hints_runtime_on]
       p = TestPipeline(options=PipelineOptions(runtime_type_check=True))
       p | beam.Create(['a']) | beam.Map(lambda x: 3).with_output_types(str)
-      p.run()
+      result = p.run()
       # [END type_hints_runtime_on]
+      result.wait_until_finish()
 
   def test_deterministic_key(self):
     with TestPipeline() as p:
diff --git a/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py b/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py
index 3746be407b4b..a8ccf2b6308a 100644
--- a/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py
+++ b/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py
@@ -152,8 +152,7 @@ def check_groupby_attr_result(grouped):
                       fruit='banana',
                       quantity=3,
                       unit_price=1.00),
-              ]),
-          #[END groupby_attr_result]
+              ]),  #[END groupby_attr_result]
       ]))
 
 
@@ -205,8 +204,7 @@ def check_groupby_attr_expr_result(grouped):
                       fruit='banana',
                       quantity=3,
                       unit_price=1.00),
-              ]),
-          #[END groupby_attr_expr_result]
+              ]),  #[END groupby_attr_expr_result]
       ]))
 
 
diff --git a/sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment.py b/sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment.py
index acee633b6f67..d71faa6d8477 100644
--- a/sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment.py
+++ b/sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment.py
@@ -116,3 +116,214 @@ def enrichment_with_vertex_ai_legacy():
         | "Enrich W/ Vertex AI" >> Enrichment(vertex_ai_handler)
         | "Print" >> beam.Map(print))
   # [END enrichment_with_vertex_ai_legacy]
+
+
+def enrichment_with_google_cloudsql_pg():
+  # [START enrichment_with_google_cloudsql_pg]
+  import apache_beam as beam
+  from apache_beam.transforms.enrichment import Enrichment
+  from apache_beam.transforms.enrichment_handlers.cloudsql import (
+      CloudSQLEnrichmentHandler,
+      DatabaseTypeAdapter,
+      TableFieldsQueryConfig,
+      CloudSQLConnectionConfig)
+  import os
+
+  database_adapter = DatabaseTypeAdapter.POSTGRESQL
+  database_uri = os.environ.get("GOOGLE_CLOUD_SQL_DB_URI")
+  database_user = os.environ.get("GOOGLE_CLOUD_SQL_DB_USER")
+  database_password = os.environ.get("GOOGLE_CLOUD_SQL_DB_PASSWORD")
+  database_id = os.environ.get("GOOGLE_CLOUD_SQL_DB_ID")
+  table_id = os.environ.get("GOOGLE_CLOUD_SQL_DB_TABLE_ID")
+  where_clause_template = "product_id = :pid"
+  where_clause_fields = ["product_id"]
+
+  data = [
+      beam.Row(product_id=1, name='A'),
+      beam.Row(product_id=2, name='B'),
+      beam.Row(product_id=3, name='C'),
+  ]
+
+  connection_config = CloudSQLConnectionConfig(
+      db_adapter=database_adapter,
+      instance_connection_uri=database_uri,
+      user=database_user,
+      password=database_password,
+      db_id=database_id)
+
+  query_config = TableFieldsQueryConfig(
+      table_id=table_id,
+      where_clause_template=where_clause_template,
+      where_clause_fields=where_clause_fields)
+
+  cloudsql_handler = CloudSQLEnrichmentHandler(
+      connection_config=connection_config,
+      table_id=table_id,
+      query_config=query_config)
+  with beam.Pipeline() as p:
+    _ = (
+        p
+        | "Create" >> beam.Create(data)
+        |
+        "Enrich W/ Google CloudSQL PostgreSQL" >> Enrichment(cloudsql_handler)
+        | "Print" >> beam.Map(print))
+  # [END enrichment_with_google_cloudsql_pg]
+
+
+def enrichment_with_external_pg():
+  # [START enrichment_with_external_pg]
+  import apache_beam as beam
+  from apache_beam.transforms.enrichment import Enrichment
+  from apache_beam.transforms.enrichment_handlers.cloudsql import (
+      CloudSQLEnrichmentHandler,
+      DatabaseTypeAdapter,
+      TableFieldsQueryConfig,
+      ExternalSQLDBConnectionConfig)
+  import os
+
+  database_adapter = DatabaseTypeAdapter.POSTGRESQL
+  database_host = os.environ.get("EXTERNAL_SQL_DB_HOST")
+  database_port = int(os.environ.get("EXTERNAL_SQL_DB_PORT"))
+  database_user = os.environ.get("EXTERNAL_SQL_DB_USER")
+  database_password = os.environ.get("EXTERNAL_SQL_DB_PASSWORD")
+  database_id = os.environ.get("EXTERNAL_SQL_DB_ID")
+  table_id = os.environ.get("EXTERNAL_SQL_DB_TABLE_ID")
+  where_clause_template = "product_id = :pid"
+  where_clause_fields = ["product_id"]
+
+  data = [
+      beam.Row(product_id=1, name='A'),
+      beam.Row(product_id=2, name='B'),
+      beam.Row(product_id=3, name='C'),
+  ]
+
+  connection_config = ExternalSQLDBConnectionConfig(
+      db_adapter=database_adapter,
+      host=database_host,
+      port=database_port,
+      user=database_user,
+      password=database_password,
+      db_id=database_id)
+
+  query_config = TableFieldsQueryConfig(
+      table_id=table_id,
+      where_clause_template=where_clause_template,
+      where_clause_fields=where_clause_fields)
+
+  cloudsql_handler = CloudSQLEnrichmentHandler(
+      connection_config=connection_config,
+      table_id=table_id,
+      query_config=query_config)
+  with beam.Pipeline() as p:
+    _ = (
+        p
+        | "Create" >> beam.Create(data)
+        | "Enrich W/ Unmanaged PostgreSQL" >> Enrichment(cloudsql_handler)
+        | "Print" >> beam.Map(print))
+  # [END enrichment_with_external_pg]
+
+
+def enrichment_with_external_mysql():
+  # [START enrichment_with_external_mysql]
+  import apache_beam as beam
+  from apache_beam.transforms.enrichment import Enrichment
+  from apache_beam.transforms.enrichment_handlers.cloudsql import (
+      CloudSQLEnrichmentHandler,
+      DatabaseTypeAdapter,
+      TableFieldsQueryConfig,
+      ExternalSQLDBConnectionConfig)
+  import os
+
+  database_adapter = DatabaseTypeAdapter.MYSQL
+  database_host = os.environ.get("EXTERNAL_SQL_DB_HOST")
+  database_port = int(os.environ.get("EXTERNAL_SQL_DB_PORT"))
+  database_user = os.environ.get("EXTERNAL_SQL_DB_USER")
+  database_password = os.environ.get("EXTERNAL_SQL_DB_PASSWORD")
+  database_id = os.environ.get("EXTERNAL_SQL_DB_ID")
+  table_id = os.environ.get("EXTERNAL_SQL_DB_TABLE_ID")
+  where_clause_template = "product_id = :pid"
+  where_clause_fields = ["product_id"]
+
+  data = [
+      beam.Row(product_id=1, name='A'),
+      beam.Row(product_id=2, name='B'),
+      beam.Row(product_id=3, name='C'),
+  ]
+
+  connection_config = ExternalSQLDBConnectionConfig(
+      db_adapter=database_adapter,
+      host=database_host,
+      port=database_port,
+      user=database_user,
+      password=database_password,
+      db_id=database_id)
+
+  query_config = TableFieldsQueryConfig(
+      table_id=table_id,
+      where_clause_template=where_clause_template,
+      where_clause_fields=where_clause_fields)
+
+  cloudsql_handler = CloudSQLEnrichmentHandler(
+      connection_config=connection_config,
+      table_id=table_id,
+      query_config=query_config)
+  with beam.Pipeline() as p:
+    _ = (
+        p
+        | "Create" >> beam.Create(data)
+        | "Enrich W/ Unmanaged MySQL" >> Enrichment(cloudsql_handler)
+        | "Print" >> beam.Map(print))
+  # [END enrichment_with_external_mysql]
+
+
+def enrichment_with_external_sqlserver():
+  # [START enrichment_with_external_sqlserver]
+  import apache_beam as beam
+  from apache_beam.transforms.enrichment import Enrichment
+  from apache_beam.transforms.enrichment_handlers.cloudsql import (
+      CloudSQLEnrichmentHandler,
+      DatabaseTypeAdapter,
+      TableFieldsQueryConfig,
+      ExternalSQLDBConnectionConfig)
+  import os
+
+  database_adapter = DatabaseTypeAdapter.SQLSERVER
+  database_host = os.environ.get("EXTERNAL_SQL_DB_HOST")
+  database_port = int(os.environ.get("EXTERNAL_SQL_DB_PORT"))
+  database_user = os.environ.get("EXTERNAL_SQL_DB_USER")
+  database_password = os.environ.get("EXTERNAL_SQL_DB_PASSWORD")
+  database_id = os.environ.get("EXTERNAL_SQL_DB_ID")
+  table_id = os.environ.get("EXTERNAL_SQL_DB_TABLE_ID")
+  where_clause_template = "product_id = :pid"
+  where_clause_fields = ["product_id"]
+
+  data = [
+      beam.Row(product_id=1, name='A'),
+      beam.Row(product_id=2, name='B'),
+      beam.Row(product_id=3, name='C'),
+  ]
+
+  connection_config = ExternalSQLDBConnectionConfig(
+      db_adapter=database_adapter,
+      host=database_host,
+      port=database_port,
+      user=database_user,
+      password=database_password,
+      db_id=database_id)
+
+  query_config = TableFieldsQueryConfig(
+      table_id=table_id,
+      where_clause_template=where_clause_template,
+      where_clause_fields=where_clause_fields)
+
+  cloudsql_handler = CloudSQLEnrichmentHandler(
+      connection_config=connection_config,
+      table_id=table_id,
+      query_config=query_config)
+  with beam.Pipeline() as p:
+    _ = (
+        p
+        | "Create" >> beam.Create(data)
+        | "Enrich W/ Unmanaged SQL Server" >> Enrichment(cloudsql_handler)
+        | "Print" >> beam.Map(print))
+  # [END enrichment_with_external_sqlserver]
diff --git a/sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment_test.py b/sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment_test.py
index 8a7cdfbe9263..904b90710225 100644
--- a/sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment_test.py
+++ b/sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment_test.py
@@ -18,19 +18,42 @@
 # pytype: skip-file
 # pylint: disable=line-too-long
 
+import os
 import unittest
+import uuid
+from collections.abc import Callable
+from contextlib import contextmanager
+from dataclasses import dataclass
 from io import StringIO
+from typing import Optional
 
 import mock
+import pytest
+from sqlalchemy.engine import Connection as DBAPIConnection
 
 # pylint: disable=unused-import
 try:
-  from apache_beam.examples.snippets.transforms.elementwise.enrichment import enrichment_with_bigtable, \
-  enrichment_with_vertex_ai_legacy
-  from apache_beam.examples.snippets.transforms.elementwise.enrichment import enrichment_with_vertex_ai
+  from sqlalchemy import (
+      Column, Integer, VARCHAR, Engine, MetaData, create_engine)
+  from apache_beam.examples.snippets.transforms.elementwise.enrichment import (
+      enrichment_with_bigtable, enrichment_with_vertex_ai_legacy)
+  from apache_beam.examples.snippets.transforms.elementwise.enrichment import (
+      enrichment_with_vertex_ai,
+      enrichment_with_google_cloudsql_pg,
+      enrichment_with_external_pg,
+      enrichment_with_external_mysql,
+      enrichment_with_external_sqlserver)
+  from apache_beam.transforms.enrichment_handlers.cloudsql import (
+      DatabaseTypeAdapter)
+  from apache_beam.transforms.enrichment_handlers.cloudsql_it_test import (
+      SQLEnrichmentTestHelper,
+      SQLDBContainerInfo,
+      ConnectionConfig,
+      CloudSQLConnectionConfig,
+      ExternalSQLDBConnectionConfig)
   from apache_beam.io.requestresponse import RequestResponseIO
-except ImportError:
-  raise unittest.SkipTest('RequestResponseIO dependencies are not installed')
+except ImportError as e:
+  raise unittest.SkipTest(f'RequestResponseIO dependencies not installed: {e}')
 
 
 def validate_enrichment_with_bigtable():
@@ -60,7 +83,44 @@ def validate_enrichment_with_vertex_ai_legacy():
   return expected
 
 
+def validate_enrichment_with_google_cloudsql_pg():
+  expected = '''[START enrichment_with_google_cloudsql_pg]
+Row(product_id=1, name='A', quantity=2, region_id=3)
+Row(product_id=2, name='B', quantity=3, region_id=1)
+Row(product_id=3, name='C', quantity=10, region_id=4)
+  [END enrichment_with_google_cloudsql_pg]'''.splitlines()[1:-1]
+  return expected
+
+
+def validate_enrichment_with_external_pg():
+  expected = '''[START enrichment_with_external_pg]
+Row(product_id=1, name='A', quantity=2, region_id=3)
+Row(product_id=2, name='B', quantity=3, region_id=1)
+Row(product_id=3, name='C', quantity=10, region_id=4)
+  [END enrichment_with_external_pg]'''.splitlines()[1:-1]
+  return expected
+
+
+def validate_enrichment_with_external_mysql():
+  expected = '''[START enrichment_with_external_mysql]
+Row(product_id=1, name='A', quantity=2, region_id=3)
+Row(product_id=2, name='B', quantity=3, region_id=1)
+Row(product_id=3, name='C', quantity=10, region_id=4)
+  [END enrichment_with_external_mysql]'''.splitlines()[1:-1]
+  return expected
+
+
+def validate_enrichment_with_external_sqlserver():
+  expected = '''[START enrichment_with_external_sqlserver]
+Row(product_id=1, name='A', quantity=2, region_id=3)
+Row(product_id=2, name='B', quantity=3, region_id=1)
+Row(product_id=3, name='C', quantity=10, region_id=4)
+  [END enrichment_with_external_sqlserver]'''.splitlines()[1:-1]
+  return expected
+
+
 @mock.patch('sys.stdout', new_callable=StringIO)
+@pytest.mark.uses_testcontainer
 class EnrichmentTest(unittest.TestCase):
   def test_enrichment_with_bigtable(self, mock_stdout):
     enrichment_with_bigtable()
@@ -70,8 +130,8 @@ def test_enrichment_with_bigtable(self, mock_stdout):
 
   def test_enrichment_with_vertex_ai(self, mock_stdout):
     enrichment_with_vertex_ai()
-    output = mock_stdout.getvalue().splitlines()
-    expected = validate_enrichment_with_vertex_ai()
+    output = sorted(mock_stdout.getvalue().splitlines())
+    expected = sorted(validate_enrichment_with_vertex_ai())
 
     for i in range(len(expected)):
       self.assertEqual(set(output[i].split(',')), set(expected[i].split(',')))
@@ -81,7 +141,174 @@ def test_enrichment_with_vertex_ai_legacy(self, mock_stdout):
     output = mock_stdout.getvalue().splitlines()
     expected = validate_enrichment_with_vertex_ai_legacy()
     self.maxDiff = None
-    self.assertEqual(output, expected)
+    self.assertEqual(sorted(output), sorted(expected))
+
+  @unittest.skipUnless(
+      os.environ.get('ALLOYDB_PASSWORD'),
+      "ALLOYDB_PASSWORD environment var is not provided")
+  def test_enrichment_with_google_cloudsql_pg(self, mock_stdout):
+    db_adapter = DatabaseTypeAdapter.POSTGRESQL
+    with EnrichmentTestHelpers.sql_test_context(True, db_adapter):
+      try:
+        enrichment_with_google_cloudsql_pg()
+        output = mock_stdout.getvalue().splitlines()
+        expected = validate_enrichment_with_google_cloudsql_pg()
+        self.assertEqual(output, expected)
+      except Exception as e:
+        self.fail(f"Test failed with unexpected error: {e}")
+
+  def test_enrichment_with_external_pg(self, mock_stdout):
+    db_adapter = DatabaseTypeAdapter.POSTGRESQL
+    with EnrichmentTestHelpers.sql_test_context(False, db_adapter):
+      try:
+        enrichment_with_external_pg()
+        output = mock_stdout.getvalue().splitlines()
+        expected = validate_enrichment_with_external_pg()
+        self.assertEqual(output, expected)
+      except Exception as e:
+        self.fail(f"Test failed with unexpected error: {e}")
+
+  def test_enrichment_with_external_mysql(self, mock_stdout):
+    db_adapter = DatabaseTypeAdapter.MYSQL
+    with EnrichmentTestHelpers.sql_test_context(False, db_adapter):
+      try:
+        enrichment_with_external_mysql()
+        output = mock_stdout.getvalue().splitlines()
+        expected = validate_enrichment_with_external_mysql()
+        self.assertEqual(output, expected)
+      except Exception as e:
+        self.fail(f"Test failed with unexpected error: {e}")
+
+  def test_enrichment_with_external_sqlserver(self, mock_stdout):
+    db_adapter = DatabaseTypeAdapter.SQLSERVER
+    with EnrichmentTestHelpers.sql_test_context(False, db_adapter):
+      try:
+        enrichment_with_external_sqlserver()
+        output = mock_stdout.getvalue().splitlines()
+        expected = validate_enrichment_with_external_sqlserver()
+        self.assertEqual(output, expected)
+      except Exception as e:
+        self.fail(f"Test failed with unexpected error: {e}")
+
+
+@dataclass
+class CloudSQLEnrichmentTestDataConstruct:
+  client_handler: Callable[[], DBAPIConnection]
+  engine: Engine
+  metadata: MetaData
+  db: SQLDBContainerInfo = None
+
+
+class EnrichmentTestHelpers:
+  @contextmanager
+  def sql_test_context(is_cloudsql: bool, db_adapter: DatabaseTypeAdapter):
+    result: Optional[CloudSQLEnrichmentTestDataConstruct] = None
+    try:
+      result = EnrichmentTestHelpers.pre_sql_enrichment_test(
+          is_cloudsql, db_adapter)
+      yield
+    finally:
+      if result:
+        EnrichmentTestHelpers.post_sql_enrichment_test(result)
+
+  @staticmethod
+  def pre_sql_enrichment_test(
+      is_cloudsql: bool,
+      db_adapter: DatabaseTypeAdapter) -> CloudSQLEnrichmentTestDataConstruct:
+    unique_suffix = str(uuid.uuid4())[:8]
+    table_id = f"products_{unique_suffix}"
+    columns = [
+        Column("product_id", Integer, primary_key=True),
+        Column("name", VARCHAR(255), nullable=False),
+        Column("quantity", Integer, nullable=False),
+        Column("region_id", Integer, nullable=False),
+    ]
+    table_data = [
+        {
+            "product_id": 1, "name": "A", 'quantity': 2, 'region_id': 3
+        },
+        {
+            "product_id": 2, "name": "B", 'quantity': 3, 'region_id': 1
+        },
+        {
+            "product_id": 3, "name": "C", 'quantity': 10, 'region_id': 4
+        },
+    ]
+    metadata = MetaData()
+
+    connection_config: ConnectionConfig
+    db = None
+    if is_cloudsql:
+      gcp_project_id = "apache-beam-testing"
+      region = "us-central1"
+      instance_name = "beam-integration-tests"
+      instance_connection_uri = f"{gcp_project_id}:{region}:{instance_name}"
+      db_id = "postgres"
+      user = "postgres"
+      password = os.getenv("ALLOYDB_PASSWORD")
+      os.environ['GOOGLE_CLOUD_SQL_DB_URI'] = instance_connection_uri
+      os.environ['GOOGLE_CLOUD_SQL_DB_ID'] = db_id
+      os.environ['GOOGLE_CLOUD_SQL_DB_USER'] = user
+      os.environ['GOOGLE_CLOUD_SQL_DB_PASSWORD'] = password
+      os.environ['GOOGLE_CLOUD_SQL_DB_TABLE_ID'] = table_id
+      connection_config = CloudSQLConnectionConfig(
+          db_adapter=db_adapter,
+          instance_connection_uri=instance_connection_uri,
+          user=user,
+          password=password,
+          db_id=db_id)
+    else:
+      db = SQLEnrichmentTestHelper.start_sql_db_container(db_adapter)
+      os.environ['EXTERNAL_SQL_DB_HOST'] = db.host
+      os.environ['EXTERNAL_SQL_DB_PORT'] = str(db.port)
+      os.environ['EXTERNAL_SQL_DB_ID'] = db.id
+      os.environ['EXTERNAL_SQL_DB_USER'] = db.user
+      os.environ['EXTERNAL_SQL_DB_PASSWORD'] = db.password
+      os.environ['EXTERNAL_SQL_DB_TABLE_ID'] = table_id
+      connection_config = ExternalSQLDBConnectionConfig(
+          db_adapter=db_adapter,
+          host=db.host,
+          port=db.port,
+          user=db.user,
+          password=db.password,
+          db_id=db.id)
+
+    conenctor = connection_config.get_connector_handler()
+    engine = create_engine(
+        url=connection_config.get_db_url(), creator=conenctor)
+
+    SQLEnrichmentTestHelper.create_table(
+        table_id=table_id,
+        engine=engine,
+        columns=columns,
+        table_data=table_data,
+        metadata=metadata)
+
+    result = CloudSQLEnrichmentTestDataConstruct(
+        db=db, client_handler=conenctor, engine=engine, metadata=metadata)
+    return result
+
+  @staticmethod
+  def post_sql_enrichment_test(res: CloudSQLEnrichmentTestDataConstruct):
+    # Clean up the data inserted previously.
+    res.metadata.drop_all(res.engine)
+    res.engine.dispose(close=True)
+
+    # Check if the test used a container-based external SQL database.
+    if res.db:
+      SQLEnrichmentTestHelper.stop_sql_db_container(res.db)
+      os.environ.pop('EXTERNAL_SQL_DB_HOST', None)
+      os.environ.pop('EXTERNAL_SQL_DB_PORT', None)
+      os.environ.pop('EXTERNAL_SQL_DB_ID', None)
+      os.environ.pop('EXTERNAL_SQL_DB_USER', None)
+      os.environ.pop('EXTERNAL_SQL_DB_PASSWORD', None)
+      os.environ.pop('EXTERNAL_SQL_DB_TABLE_ID', None)
+    else:
+      os.environ.pop('GOOGLE_CLOUD_SQL_DB_URI', None)
+      os.environ.pop('GOOGLE_CLOUD_SQL_DB_ID', None)
+      os.environ.pop('GOOGLE_CLOUD_SQL_DB_USER', None)
+      os.environ.pop('GOOGLE_CLOUD_SQL_DB_PASSWORD', None)
+      os.environ.pop('GOOGLE_CLOUD_SQL_DB_TABLE_ID', None)
 
 
 if __name__ == '__main__':
diff --git a/sdks/python/apache_beam/examples/snippets/transforms/elementwise/pardo_dofn_methods.py b/sdks/python/apache_beam/examples/snippets/transforms/elementwise/pardo_dofn_methods.py
index 8cc43fadb10c..46d4f5955b0c 100644
--- a/sdks/python/apache_beam/examples/snippets/transforms/elementwise/pardo_dofn_methods.py
+++ b/sdks/python/apache_beam/examples/snippets/transforms/elementwise/pardo_dofn_methods.py
@@ -37,8 +37,6 @@ def pardo_dofn_methods(test=None):
   # Portable runners do not guarantee that teardown will be executed, so we
   # use FnApiRunner instead of prism.
   runner = 'FnApiRunner'
-  # TODO(damccorm) - remove after next release
-  runner = 'DirectRunner'
   # [START pardo_dofn_methods]
   import apache_beam as beam
 
diff --git a/sdks/python/apache_beam/internal/cloudpickle/cloudpickle.py b/sdks/python/apache_beam/internal/cloudpickle/cloudpickle.py
index 5a9d89430fd3..e4fbf0c72f87 100644
--- a/sdks/python/apache_beam/internal/cloudpickle/cloudpickle.py
+++ b/sdks/python/apache_beam/internal/cloudpickle/cloudpickle.py
@@ -1483,8 +1483,7 @@ def save_global(self, obj, name=None, pack=struct.pack):
 
     def save_typevar(self, obj, name=None):
       """Handle TypeVar objects with access to config."""
-      return self._save_reduce_pickle5(
-          *_typevar_reduce(obj, self.config), obj=obj)
+      return self.save_reduce(*_typevar_reduce(obj, self.config), obj=obj)
 
     dispatch[typing.TypeVar] = save_typevar
 
diff --git a/sdks/python/apache_beam/internal/cloudpickle_pickler.py b/sdks/python/apache_beam/internal/cloudpickle_pickler.py
index 63038e770f27..e55818bfb226 100644
--- a/sdks/python/apache_beam/internal/cloudpickle_pickler.py
+++ b/sdks/python/apache_beam/internal/cloudpickle_pickler.py
@@ -39,6 +39,8 @@
 
 DEFAULT_CONFIG = cloudpickle.CloudPickleConfig(
     skip_reset_dynamic_type_state=True)
+NO_DYNAMIC_CLASS_TRACKING_CONFIG = cloudpickle.CloudPickleConfig(
+    id_generator=None, skip_reset_dynamic_type_state=True)
 
 try:
   from absl import flags
diff --git a/sdks/python/apache_beam/internal/code_object_pickler.py b/sdks/python/apache_beam/internal/code_object_pickler.py
index c3658120b4ef..b6ea015cc06f 100644
--- a/sdks/python/apache_beam/internal/code_object_pickler.py
+++ b/sdks/python/apache_beam/internal/code_object_pickler.py
@@ -15,7 +15,468 @@
 # limitations under the License.
 #
 
+"""Customizations to how Python code objects are pickled.
+
+This module provides helper functions to improve pickling code objects,
+especially lambdas, in a consistent way by using code object identifiers. These
+helper functions will be used to patch pickler implementations used by Beam
+(e.g. Cloudpickle).
+
+A code object identifier is a unique identifier for a code object that provides
+a unique reference to the code object in the context where the code is defined
+and is invariant to small changes in the surrounding code.
+
+The code object identifiers consists of a sequence of the following parts
+separated by periods:
+- Module names - The name of the module the code object is in
+- Class names - The name of a class containing the code object. There can be
+  multiple of these in the same identifier in the case of nested
+  classes.
+- Function names - The name of the function containing the code object.
+  There can be multiple of these in the case of nested functions.
+- __code__ - Attribute indicating that we are entering the code object of a
+  function/method.
+- __co_consts__[<name>] - The name of the local variable containing the
+  code object. In the case of lambdas, the name is created by using the
+  signature of the lambda and hashing the bytecode, as shown below.
+
+Examples:
+- __main__.top_level_function.__code__
+- __main__.ClassWithNestedFunction.process.__code__.co_consts[nested_function]
+- __main__.ClassWithNestedLambda.process.__code__.co_consts[
+    get_lambda_from_dictionary].co_consts[<lambda>, ('x',)]
+- __main__.ClassWithNestedLambda.process.__code__.co_consts[
+    <lambda>, ('x',), 1234567890]
+"""
+
+import collections
+import hashlib
+import inspect
+import re
+import sys
+import types
+from typing import Optional
+from typing import Union
+
 
 def get_normalized_path(path):
   """Returns a normalized path. This function is intended to be overridden."""
   return path
+
+
+def get_code_object_identifier(callable: types.FunctionType):
+  """Returns the code object identifier for a given callable.
+
+  Args:
+    callable: The callable object to search for.
+
+  Returns:
+    The code object identifier.
+      Examples:
+      - __main__.top_level_function.__code__
+      - __main__.ClassWithNestedFunction.process.__code__.co_consts[
+        nested_function]
+      - __main__.ClassWithNestedLambda.process.__code__.co_consts[
+        get_lambda_from_dictionary].co_consts[<lambda>, ('x',)]
+      - __main__.ClassWithNestedLambda.process.__code__.co_consts[
+        <lambda>, ('x',), 1234567890]
+  """
+  if not hasattr(callable, '__module__') or not hasattr(callable,
+                                                        '__qualname__'):
+    return None
+  code_path: str = _extend_path(
+      callable.__module__,
+      _search(
+          callable,
+          sys.modules[callable.__module__],
+          callable.__qualname__.split('.'),
+      ),
+  )
+  return code_path
+
+
+def _extend_path(prefix: str, current_path: Optional[str]):
+  """Extends the path to the code object.
+
+  Args:
+    prefix: The prefix of the path.
+    suffix: The rest of the path.
+
+  Returns:
+    The extended path.
+  """
+  if current_path is None:
+    return None
+  if not current_path:
+    return prefix
+  return prefix + '.' + current_path
+
+
+def _search(
+    callable: types.FunctionType,
+    node: Union[types.ModuleType, types.FunctionType, types.CodeType],
+    qual_name_parts: list[str]):
+  """Searches an object to create a code object identifier.
+
+  Recursively searches the tree of objects starting from node to find the
+    callable's code object. It navigates through the attributes by using
+    the first element of qual_name_parts to indicate what object it is
+    currently at, then recursively passes through the rest of the list until
+    the callable is found. Special components like '<locals>' and '<lambda>'
+    direct the search within nested code objects.
+
+
+  Example of qual_name_parts: ['MyClass', 'process', '<locals>', '<lambda>']
+
+  Args:
+    callable: The callable object to search for.
+    node: The object to search within.
+    qual_name_parts: A list of strings representing the qualified name of the
+      callable object.
+
+  Returns:
+    The code object identifier, or None if not found.
+  """
+  if node is None:
+    return None
+  if not qual_name_parts:
+    if (hasattr(node, '__code__') and hasattr(callable, '__code__') and
+        node.__code__ == callable.__code__):
+      return '__code__'
+    else:
+      return None
+  if inspect.ismodule(node) or inspect.isclass(node):
+    return _search_module_or_class(callable, node, qual_name_parts)
+  elif inspect.isfunction(node):
+    return _search_function(callable, node, qual_name_parts)
+  elif inspect.iscode(node):
+    return _search_code(callable, node, qual_name_parts)
+
+
+def _search_module_or_class(
+    callable: types.FunctionType,
+    node: types.ModuleType,
+    qual_name_parts: list[str]):
+  """Searches a module or class to create a code object identifier.
+
+  Args:
+    callable: The callable object to search for.
+    node: The module or class to search within.
+    qual_name_parts: The list of qual name parts.
+
+  Returns:
+    The code object identifier, or None if not found.
+  """
+  # Functions/methods have a name that is unique within a given module or class
+  # so the traversal can directly lookup function object identified by the name.
+  # Lambdas don't have a name so we need to search all the attributes of the
+  # node.
+  first_part = qual_name_parts[0]
+  rest = qual_name_parts[1:]
+  if first_part == '<lambda>':
+    for name in dir(node):
+      value = getattr(node, name)
+      if (hasattr(callable, '__code__') and
+          isinstance(value, type(callable)) and
+          value.__code__ == callable.__code__):
+        return name + '.__code__'
+      elif (isinstance(value, types.FunctionType) and
+            value.__defaults__ is not None):
+        # Python functions can have other functions as default parameters which
+        # might contain the code object so we have to search them.
+        for i, default_param_value in enumerate(value.__defaults__):
+          path = _search(callable, default_param_value, rest)
+          if path is not None:
+            return _extend_path(name, _extend_path(f'__defaults__[{i}]', path))
+  else:
+    return _extend_path(
+        first_part, _search(callable, getattr(node, first_part), rest))
+
+
+def _search_function(
+    callable: types.FunctionType,
+    node: types.FunctionType,
+    qual_name_parts: list[str]):
+  """Searches a function to create a code object identifier.
+
+  Args:
+    callable: The callable object to search for.
+    node: The function to search within.
+    qual_name_parts: The list of qual name parts.
+
+  Returns:
+    The code object identifier, or None if not found.
+  """
+  first_part = qual_name_parts[0]
+  if (node.__code__ == callable.__code__):
+    if len(qual_name_parts) > 1:
+      raise ValueError('Qual name parts too long')
+    return '__code__'
+  # If first part is '<locals>' then the code object is in a local variable
+  # so we should add __code__ to the path to indicate that we are entering
+  # the code object of the function.
+  if first_part == '<locals>':
+    return _extend_path(
+        '__code__', _search(callable, node.__code__, qual_name_parts))
+
+
+def _search_code(
+    callable: types.FunctionType,
+    node: types.CodeType,
+    qual_name_parts: list[str]):
+  """Searches a code object to create a code object identifier.
+
+  Args:
+    callable: The callable to search for.
+    node: The code object to search within.
+    qual_name_parts: The list of qual name parts.
+
+  Returns:
+    The code object identifier, or None if not found.
+
+  Raises:
+    ValueError: If the qual name parts are too long.
+  """
+  first_part = qual_name_parts[0]
+  rest = qual_name_parts[1:]
+  if hasattr(callable, '__code__') and node == callable.__code__:
+    if len(qual_name_parts) > 1:
+      raise ValueError('Qual name parts too long')
+    return ''
+  elif first_part == '<locals>':
+    code_objects_by_name = collections.defaultdict(list)
+    for co_const in node.co_consts:
+      if inspect.iscode(co_const):
+        code_objects_by_name[co_const.co_name].append(co_const)
+    num_lambdas = len(code_objects_by_name.get('<lambda>', []))
+    # If there is only one lambda, we can use the default path
+    # 'co_consts[<lambda>]'. This is the most common case and it is
+    # faster than calculating the signature and the hash.
+    if num_lambdas == 1:
+      path = _search(callable, code_objects_by_name['<lambda>'][0], rest)
+      if path is not None:
+        return _extend_path('co_consts[<lambda>]', path)
+    else:
+      return _search_lambda(callable, code_objects_by_name, rest)
+  elif node.co_name == first_part:
+    return _search(callable, node, rest)
+
+
+def _search_lambda(
+    callable: types.FunctionType,
+    code_objects_by_name: dict[str, list[types.CodeType]],
+    qual_name_parts: list[str]):
+  """Searches a lambda to create a code object identifier.
+
+  Args:
+    callable: The callable to search for.
+    code_objects_by_name: The code objects to search within, keyed by name.
+    qual_name_parts: The rest of the qual_name_parts.
+
+  Returns:
+    The code object identifier, or None if not found.
+  """
+  # There are multiple lambdas in the code object, so we need to calculate
+  # the signature and the hash to identify the correct lambda.
+  lambda_code_objects_by_name = collections.defaultdict(list)
+  name = qual_name_parts[0]
+  code_objects = code_objects_by_name[name]
+  if name == '<lambda>':
+    for code_object in code_objects:
+      lambda_name = f'<lambda>, {_signature(code_object)}'
+      lambda_code_objects_by_name[lambda_name].append(code_object)
+    # Check if there are any lambdas with the same signature.
+    # If there are, we need to calculate the hash to identify the correct
+    # lambda.
+    for lambda_name, lambda_objects in lambda_code_objects_by_name.items():
+      if len(lambda_objects) > 1:
+        for lambda_object in lambda_objects:
+          path = _search(callable, lambda_object, qual_name_parts)
+          if path is not None:
+            return _extend_path(
+                f'co_consts[{lambda_name},'
+                f' {_create_bytecode_hash(lambda_object)}]',
+                path,
+            )
+      else:
+        # If there is only one lambda with this signature, we can
+        # use the signature to identify the correct lambda.
+        path = _search(callable, code_objects[0], qual_name_parts)
+        if path is not None:
+          return _extend_path(f'co_consts[{lambda_name}]', path)
+  else:
+    # For non lambda objects, we can use the name to identify the object.
+    path = _search(callable, code_objects[0], qual_name_parts)
+    if path is not None:
+      return _extend_path(f'co_consts[{name}]', path)
+
+
+# Matches a path like: co_consts[my_function]
+_SINGLE_NAME_PATTERN = re.compile(r'co_consts\[([a-zA-Z0-9\<\>_-]+)]')
+# Matches a path like: co_consts[<lambda>, ('x',)]
+_LAMBDA_WITH_ARGS_PATTERN = re.compile(
+    r"co_consts\[(<[^>]+>),\s*(\('[^']*'\s*,\s*\))\]")
+# Matches a path like: co_consts[<lambda>, ('x',), 1234567890]
+_LAMBDA_WITH_HASH_PATTERN = re.compile(
+    r"co_consts\[(<[^>]+>),\s*(\('[^']*'\s*,\s*\)),\s*(.+)\]")
+# Matches a path like: __defaults__[0]
+_DEFAULT_PATTERN = re.compile(r'(__defaults__)\[(\d+)\]')
+# Matches an argument like: 'x'
+_ARGUMENT_PATTERN = re.compile(r"'([^']*)'")
+
+
+def _get_code_object_from_single_name_pattern(
+    obj: types.ModuleType, name_result: re.Match[str], path: str):
+  """Returns the code object from a name pattern.
+
+  Args:
+    obj: The object to search within.
+    name_result: The result of the name pattern search.
+    path: The path to the code object.
+
+  Returns:
+    The code object.
+
+  Raises:
+    ValueError: If the pattern is invalid.
+    AttributeError: If the code object is not found.
+  """
+  if len(name_result.groups()) > 1:
+    raise ValueError(f'Invalid pattern for single name: {name_result.group(0)}')
+  # Groups are indexed starting at 1, group(0) is the entire match.
+  name = name_result.group(1)
+  for co_const in obj.co_consts:
+    if inspect.iscode(co_const) and co_const.co_name == name:
+      return co_const
+  raise AttributeError(f'Could not find code object with path: {path}')
+
+
+def _get_code_object_from_lambda_with_args_pattern(
+    obj: types.ModuleType, lambda_with_args_result: re.Match[str], path: str):
+  """Returns the code object from a lambda with args pattern.
+
+  Args:
+    obj: The object to search within.
+    lambda_with_args_result: The result of the lambda with args pattern search.
+    path: The path to the code object.
+
+  Returns:
+    The code object.
+
+  Raises:
+    AttributeError: If the code object is not found.
+  """
+  name = lambda_with_args_result.group(1)
+  code_objects = collections.defaultdict(list)
+  for co_const in obj.co_consts:
+    if inspect.iscode(co_const) and co_const.co_name == name:
+      code_objects[co_const.co_name].append(co_const)
+  for name, objects in code_objects.items():
+    for obj_ in objects:
+      args = tuple(
+          re.findall(_ARGUMENT_PATTERN, lambda_with_args_result.group(2)))
+      if obj_.co_varnames == args:
+        return obj_
+  raise AttributeError(f'Could not find code object with path: {path}')
+
+
+def _get_code_object_from_lambda_with_hash_pattern(
+    obj: types.ModuleType, lambda_with_hash_result: re.Match[str], path: str):
+  """Returns the code object from a lambda with hash pattern.
+
+  Args:
+    obj: The object to search within.
+    lambda_with_hash_result: The result of the lambda with hash pattern search.
+    path: The path to the code object.
+
+  Returns:
+    The code object.
+
+  Raises:
+    AttributeError: If the code object is not found.
+  """
+  name = lambda_with_hash_result.group(1)
+  code_objects = collections.defaultdict(list)
+  for co_const in obj.co_consts:
+    if inspect.iscode(co_const) and co_const.co_name == name:
+      code_objects[co_const.co_name].append(co_const)
+  for name, objects in code_objects.items():
+    for obj_ in objects:
+      args = tuple(
+          re.findall(_ARGUMENT_PATTERN, lambda_with_hash_result.group(2)))
+      if obj_.co_varnames == args:
+        hash_value = lambda_with_hash_result.group(3)
+        if hash_value == str(_create_bytecode_hash(obj_)):
+          return obj_
+  raise AttributeError(f'Could not find code object with path: {path}')
+
+
+def get_code_from_identifier(code_object_identifier: str):
+  """Returns the code object corresponding to the code object identifier.
+
+  Args:
+    code_object_identifier: A string representing the code object identifier.
+
+  Returns:
+    The code object.
+
+  Raises:
+    ValueError: If the path is empty or invalid.
+    AttributeError: If the attribute is not found.
+  """
+  if not code_object_identifier:
+    raise ValueError('Path must not be empty.')
+  parts = code_object_identifier.split('.')
+  obj = sys.modules[parts[0]]
+  for part in parts[1:]:
+    if name_result := _SINGLE_NAME_PATTERN.fullmatch(part):
+      obj = _get_code_object_from_single_name_pattern(
+          obj, name_result, code_object_identifier)
+    elif lambda_with_args_result := _LAMBDA_WITH_ARGS_PATTERN.fullmatch(part):
+      obj = _get_code_object_from_lambda_with_args_pattern(
+          obj, lambda_with_args_result, code_object_identifier)
+    elif lambda_with_hash_result := _LAMBDA_WITH_HASH_PATTERN.fullmatch(part):
+      obj = _get_code_object_from_lambda_with_hash_pattern(
+          obj, lambda_with_hash_result, code_object_identifier)
+    elif default_result := _DEFAULT_PATTERN.fullmatch(part):
+      index = int(default_result.group(2))
+      if index >= len(obj.__defaults__):
+        raise ValueError(
+            f'Index {index} is out of bounds for obj.__defaults__'
+            f' {len(obj.__defaults__)} in path {code_object_identifier}')
+      obj = getattr(obj, '__defaults__')[index]
+    else:
+      obj = getattr(obj, part)
+  return obj
+
+
+def _signature(obj: types.CodeType):
+  """Returns the signature of a code object.
+
+  The signature is the names of the arguments of the code object. This is used
+  to unique identify lambdas.
+
+  Args:
+    obj: A code object, function, method, or cell.
+
+  Returns:
+    A tuple of the names of the arguments of the code object.
+  """
+  arg_count = (
+      obj.co_argcount + obj.co_kwonlyargcount +
+      (obj.co_flags & 4 == 4)  # PyCF_VARARGS
+      + (obj.co_flags & 8 == 8)  # PyCF_VARKEYWORDS
+  )
+  return obj.co_varnames[:arg_count]
+
+
+def _create_bytecode_hash(code_object: types.CodeType):
+  """Returns the hash of a code object.
+
+  Args:
+    code_object: A code object.
+
+  Returns:
+    The hash of the code object.
+  """
+  return hashlib.md5(code_object.co_code).hexdigest()
diff --git a/sdks/python/apache_beam/internal/code_object_pickler_test.py b/sdks/python/apache_beam/internal/code_object_pickler_test.py
new file mode 100644
index 000000000000..de01f16fd0a7
--- /dev/null
+++ b/sdks/python/apache_beam/internal/code_object_pickler_test.py
@@ -0,0 +1,565 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Tests for generating stable identifiers to use for Pickle serialization."""
+
+import hashlib
+import unittest
+
+from parameterized import parameterized
+
+# pylint: disable=unused-import
+from apache_beam.internal import code_object_pickler
+from apache_beam.internal.test_data import module_1
+from apache_beam.internal.test_data import module_1_class_added
+from apache_beam.internal.test_data import module_1_function_added
+from apache_beam.internal.test_data import module_1_global_variable_added
+from apache_beam.internal.test_data import module_1_lambda_variable_added
+from apache_beam.internal.test_data import module_1_local_variable_added
+from apache_beam.internal.test_data import module_1_local_variable_removed
+from apache_beam.internal.test_data import module_1_nested_function_2_added
+from apache_beam.internal.test_data import module_1_nested_function_added
+from apache_beam.internal.test_data import module_2
+from apache_beam.internal.test_data import module_2_modified
+from apache_beam.internal.test_data import module_3
+from apache_beam.internal.test_data import module_3_modified
+from apache_beam.internal.test_data import module_with_default_argument
+
+
+def top_level_function():
+  return 1
+
+
+top_level_lambda = lambda x: 1
+
+
+def get_nested_function():
+  def nested_function():
+    return 1
+
+  return nested_function
+
+
+def get_lambda_from_dictionary():
+  d = {"a": lambda x: 1, "b": lambda y: 2}
+  return d["a"]
+
+
+def get_lambda_from_dictionary_same_args():
+  d = {"a": lambda x: 1, "b": lambda x: x + 1}
+  return d["a"]
+
+
+def function_with_lambda_default_argument(fn=lambda x: 1):
+  return fn
+
+
+def function_with_function_default_argument(fn=top_level_function):
+  return fn
+
+
+def function_decorator(f):
+  return lambda x: f(f(x))
+
+
+@function_decorator
+def add_one(x):
+  return x + 1
+
+
+class ClassWithFunction:
+  def process(self):
+    return 1
+
+
+class ClassWithStaticMethod:
+  @staticmethod
+  def static_method():
+    return 1
+
+
+class ClassWithClassMethod:
+  @classmethod
+  def class_method(cls):
+    return 1
+
+
+class ClassWithNestedFunction:
+  def process(self):
+    def nested_function():
+      return 1
+
+    return nested_function
+
+
+class ClassWithLambda:
+  def process(self):
+    return lambda: 1
+
+
+class ClassWithNestedClass:
+  class InnerClass:
+    def process(self):
+      return 1
+
+
+class ClassWithNestedLambda:
+  def process(self):
+    def get_lambda_from_dictionary():
+      d = {"a": lambda x: 1, "b": lambda y: 2}
+      return d["a"]
+
+    return get_lambda_from_dictionary()
+
+
+prefix = __name__
+
+test_cases = [
+    (top_level_function, f"{prefix}.top_level_function"
+     ".__code__"),
+    (top_level_lambda, f"{prefix}.top_level_lambda"
+     ".__code__"),
+    (
+        get_nested_function(), (
+            f"{prefix}.get_nested_function"
+            ".__code__.co_consts[nested_function]")),
+    (
+        get_lambda_from_dictionary(),
+        (
+            f"{prefix}"
+            ".get_lambda_from_dictionary.__code__.co_consts[<lambda>, ('x',)]")
+    ),
+    (
+        get_lambda_from_dictionary_same_args(),
+        (
+            f"{prefix}"
+            ".get_lambda_from_dictionary_same_args.__code__.co_consts"
+            "[<lambda>, ('x',), " + hashlib.md5(
+                get_lambda_from_dictionary_same_args().__code__.co_code).
+            hexdigest() + "]")),
+    (
+        function_with_lambda_default_argument(),
+        (
+            f"{prefix}"
+            ".function_with_lambda_default_argument.__defaults__[0].__code__")),
+    (
+        function_with_function_default_argument(),
+        f"{prefix}.top_level_function"
+        ".__code__"),
+    (add_one, f"{prefix}.function_decorator"
+     ".__code__.co_consts[<lambda>]"),
+    (
+        ClassWithFunction.process,
+        f"{prefix}.ClassWithFunction"
+        ".process.__code__"),
+    (
+        ClassWithStaticMethod.static_method,
+        f"{prefix}.ClassWithStaticMethod"
+        ".static_method.__code__"),
+    (
+        ClassWithClassMethod.class_method,
+        f"{prefix}.ClassWithClassMethod"
+        ".class_method.__code__"),
+    (
+        ClassWithNestedFunction().process(),
+        (
+            f"{prefix}.ClassWithNestedFunction.process.__code__.co_consts"
+            "[nested_function]")),
+    (
+        ClassWithLambda().process(),
+        f"{prefix}.ClassWithLambda.process.__code__.co_consts[<lambda>]"),
+    (
+        ClassWithNestedClass.InnerClass().process,
+        f"{prefix}.ClassWithNestedClass.InnerClass.process.__code__"),
+    (
+        ClassWithNestedLambda().process(),
+        (
+            f"{prefix}"
+            ".ClassWithNestedLambda.process.__code__.co_consts"
+            "[get_lambda_from_dictionary].co_consts[<lambda>, ('x',)]")),
+    (
+        ClassWithNestedLambda.process,
+        f"{prefix}.ClassWithNestedLambda.process.__code__"),
+]
+
+
+class CodeObjectIdentifierGenerationTest(unittest.TestCase):
+  @parameterized.expand(test_cases)
+  def test_get_code_object_identifier(self, callable, expected_path):
+    actual = code_object_pickler.get_code_object_identifier(callable)
+    self.assertEqual(actual, expected_path)
+
+  @parameterized.expand(test_cases)
+  def test_get_code_from_identifier(self, expected_callable, path):
+    actual = code_object_pickler.get_code_from_identifier(path)
+    self.assertEqual(actual, expected_callable.__code__)
+
+  @parameterized.expand(test_cases)
+  def test_roundtrip(self, callable, unused_path):
+    path = code_object_pickler.get_code_object_identifier(callable)
+    actual = code_object_pickler.get_code_from_identifier(path)
+    self.assertEqual(actual, callable.__code__)
+
+
+class GetCodeFromCodeObjectIdentifierTest(unittest.TestCase):
+  def test_empty_path_raises_exception(self):
+    with self.assertRaisesRegex(ValueError, "Path must not be empty"):
+      code_object_pickler.get_code_from_identifier("")
+
+  def test_invalid_default_index_raises_exception(self):
+    with self.assertRaisesRegex(ValueError, "out of bounds"):
+      code_object_pickler.get_code_from_identifier(
+          "apache_beam.internal.test_data.module_with_default_argument."
+          "function_with_lambda_default_argument.__defaults__[1]")
+
+  def test_invalid_single_name_path_raises_exception(self):
+    with self.assertRaisesRegex(AttributeError,
+                                "Could not find code object with path"):
+      code_object_pickler.get_code_from_identifier(
+          "apache_beam.internal.test_data.module_3."
+          "my_function.__code__.co_consts[something]")
+
+  def test_invalid_lambda_with_args_path_raises_exception(self):
+    with self.assertRaisesRegex(AttributeError,
+                                "Could not find code object with path"):
+      code_object_pickler.get_code_from_identifier(
+          "apache_beam.internal.test_data.module_3."
+          "my_function.__code__.co_consts[<lambda>, ('x',)]")
+
+  def test_invalid_lambda_with_hash_path_raises_exception(self):
+    with self.assertRaisesRegex(AttributeError,
+                                "Could not find code object with path"):
+      code_object_pickler.get_code_from_identifier(
+          "apache_beam.internal.test_data.module_3."
+          "my_function.__code__.co_consts[<lambda>, ('',), 1234567890]")
+
+  def test_adding_local_variable_in_class_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_2.AddLocalVariable.my_method(self)).replace(
+                    "module_2", "module_2_modified")),
+        module_2_modified.AddLocalVariable.my_method(self).__code__,
+    )
+
+  def test_removing_local_variable_in_class_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_2.RemoveLocalVariable.my_method(self)).replace(
+                    "module_2", "module_2_modified")),
+        module_2_modified.RemoveLocalVariable.my_method(self).__code__,
+    )
+
+  def test_adding_lambda_variable_in_class_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_2.AddLambdaVariable.my_method(self)).replace(
+                    "module_2", "module_2_modified")),
+        module_2_modified.AddLambdaVariable.my_method(self).__code__,
+    )
+
+  def test_removing_lambda_variable_in_class_changes_object(self):
+    with self.assertRaisesRegex(AttributeError, "object has no attribute"):
+      code_object_pickler.get_code_from_identifier(
+          code_object_pickler.get_code_object_identifier(
+              module_2.RemoveLambdaVariable.my_method(self)).replace(
+                  "module_2", "module_2_modified"))
+
+  def test_adding_nested_function_in_class_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_2.ClassWithNestedFunction.my_method(self)).replace(
+                    "module_2", "module_2_modified")),
+        module_2_modified.ClassWithNestedFunction.my_method(self).__code__,
+    )
+
+  def test_adding_nested_function_2_in_class_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_2.ClassWithNestedFunction2.my_method(self)).replace(
+                    "module_2", "module_2_modified")),
+        module_2_modified.ClassWithNestedFunction2.my_method(self).__code__,
+    )
+
+  def test_adding_new_function_in_class_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_2.ClassWithTwoMethods.my_method(self)).replace(
+                    "module_2", "module_2_modified")),
+        module_2_modified.ClassWithTwoMethods.my_method(self).__code__,
+    )
+
+  def test_removing_method_in_class_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_2.RemoveMethod.my_method(self)).replace(
+                    "module_2", "module_2_modified")),
+        module_2_modified.RemoveMethod.my_method(self).__code__,
+    )
+
+  def test_adding_global_variable_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_1.my_function()).replace(
+                    "module_1",
+                    "module_1_global_variable_added",
+                )),
+        module_1_global_variable_added.my_function().__code__,
+    )
+
+  def test_adding_top_level_function_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_1.my_function()).replace(
+                    "module_1", "module_1_function_added")),
+        module_1_function_added.my_function().__code__,
+    )
+
+  def test_adding_local_variable_in_function_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_1.my_function()).replace(
+                    "module_1", "module_1_local_variable_added")),
+        module_1_local_variable_added.my_function().__code__,
+    )
+
+  def test_removing_local_variable_in_function_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_1.my_function()).replace(
+                    "module_1", "module_1_local_variable_removed")),
+        module_1_local_variable_removed.my_function().__code__,
+    )
+
+  def test_adding_nested_function_in_function_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_1.my_function()).replace(
+                    "module_1", "module_1_nested_function_added")),
+        module_1_nested_function_added.my_function().__code__,
+    )
+
+  def test_adding_nested_function_2_in_function_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_1.my_function()).replace(
+                    "module_1", "module_1_nested_function_2_added")),
+        module_1_nested_function_2_added.my_function().__code__,
+    )
+
+  def test_adding_class_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_1.my_function()).replace(
+                    "module_1", "module_1_class_added")),
+        module_1_class_added.my_function().__code__,
+    )
+
+  def test_adding_lambda_variable_in_function_preserves_object(self):
+    self.assertEqual(
+        code_object_pickler.get_code_from_identifier(
+            code_object_pickler.get_code_object_identifier(
+                module_1.my_function()).replace(
+                    "module_1", "module_1_lambda_variable_added")),
+        module_1_lambda_variable_added.my_function().__code__,
+    )
+
+  def test_removing_lambda_variable_in_function_raises_exception(self):
+    with self.assertRaisesRegex(AttributeError, "object has no attribute"):
+      code_object_pickler.get_code_from_identifier(
+          code_object_pickler.get_code_object_identifier(
+              module_3.my_function()).replace("module_3", "module_3_modified"))
+
+
+class CodePathStabilityTest(unittest.TestCase):
+  def test_adding_local_variable_in_class_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_2.AddLocalVariable.my_method(self)).replace(
+                "module_2", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_2_modified.AddLocalVariable.my_method(self)).replace(
+                "module_2_modified", "module_name"),
+    )
+
+  def test_removing_local_variable_in_class_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_2.RemoveLocalVariable.my_method(self)).replace(
+                "module_2", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_2_modified.RemoveLocalVariable.my_method(self)).replace(
+                "module_2_modified", "module_name"),
+    )
+
+  def test_adding_lambda_variable_in_class_changes_path(self):
+    self.assertNotEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_2.AddLambdaVariable.my_method(self)).replace(
+                "module_2", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_2_modified.AddLambdaVariable.my_method(self)).replace(
+                "module_2_modified", "module_name"),
+    )
+
+  def test_removing_lambda_variable_in_class_changes_path(self):
+    self.assertNotEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_2.RemoveLambdaVariable.my_method(self)).replace(
+                "module_2", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_2_modified.RemoveLambdaVariable.my_method(self)).replace(
+                "module_2_modified", "module_name"),
+    )
+
+  def test_adding_nested_function_in_class_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_2.ClassWithNestedFunction.my_method(self)).replace(
+                "module_2", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_2_modified.ClassWithNestedFunction.my_method(self)).replace(
+                "module_2_modified", "module_name"),
+    )
+
+  def test_adding_nested_function_2_in_class_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_2.ClassWithNestedFunction2.my_method(self)).replace(
+                "module_2", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_2_modified.ClassWithNestedFunction2.my_method(self)).replace(
+                "module_2_modified", "module_name"),
+    )
+
+  def test_adding_new_function_in_class_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_2.ClassWithTwoMethods.my_method(self)).replace(
+                "module_2", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_2_modified.ClassWithTwoMethods.my_method(self)).replace(
+                "module_2_modified", "module_name"),
+    )
+
+  def test_removing_function_in_class_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_2.RemoveMethod.my_method(self)).replace(
+                "module_2", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_2_modified.RemoveMethod.my_method(self)).replace(
+                "module_2_modified", "module_name"),
+    )
+
+  def test_adding_global_variable_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_1.my_function()).replace("module_1", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_1_global_variable_added.my_function()).replace(
+                "module_1_global_variable_added", "module_name"),
+    )
+
+  def test_adding_top_level_function_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_1.my_function()).replace("module_1", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_1_function_added.my_function()).replace(
+                "module_1_function_added", "module_name"),
+    )
+
+  def test_adding_local_variable_in_function_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_1.my_function()).replace("module_1", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_1_local_variable_added.my_function()).replace(
+                "module_1_local_variable_added", "module_name"),
+    )
+
+  def test_removing_local_variable_in_function_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_1.my_function()).replace("module_1", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_1_local_variable_removed.my_function()).replace(
+                "module_1_local_variable_removed", "module_name"),
+    )
+
+  def test_adding_nested_function_in_function_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_1.my_function()).replace("module_1", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_1_nested_function_added.my_function()).replace(
+                "module_1_nested_function_added", "module_name"),
+    )
+
+  def test_adding_nested_function_2_in_function_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_1.my_function()).replace("module_1", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_1_nested_function_2_added.my_function()).replace(
+                "module_1_nested_function_2_added", "module_name"),
+    )
+
+  def test_adding_class_preserves_path(self):
+    self.assertEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_1.my_function()).replace("module_1", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_1_class_added.my_function()).replace(
+                "module_1_class_added", "module_name"),
+    )
+
+  def test_adding_lambda_variable_in_function_changes_path(self):
+    self.assertNotEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_1.my_function()).replace("module_1", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_1_lambda_variable_added.my_function()).replace(
+                "module_1_lambda_variable_added", "module_name"),
+    )
+
+  def test_removing_lambda_variable_in_function_changes_path(self):
+    self.assertNotEqual(
+        code_object_pickler.get_code_object_identifier(
+            module_3.my_function()).replace("module_3", "module_name"),
+        code_object_pickler.get_code_object_identifier(
+            module_3_modified.my_function()).replace(
+                "module_3_modified", "module_name"),
+    )
+
+
+if __name__ == "__main__":
+  unittest.main()
diff --git a/sdks/python/apache_beam/internal/pickler.py b/sdks/python/apache_beam/internal/pickler.py
index 256f88c5453f..6f8dba463bc3 100644
--- a/sdks/python/apache_beam/internal/pickler.py
+++ b/sdks/python/apache_beam/internal/pickler.py
@@ -29,10 +29,15 @@
 """
 
 from apache_beam.internal import cloudpickle_pickler
-from apache_beam.internal import dill_pickler
+
+try:
+  from apache_beam.internal import dill_pickler
+except ImportError:
+  dill_pickler = None  # type: ignore[assignment]
 
 USE_CLOUDPICKLE = 'cloudpickle'
 USE_DILL = 'dill'
+USE_DILL_UNSAFE = 'dill_unsafe'
 
 DEFAULT_PICKLE_LIB = USE_CLOUDPICKLE
 desired_pickle_lib = cloudpickle_pickler
@@ -74,14 +79,29 @@ def load_session(file_path):
 def set_library(selected_library=DEFAULT_PICKLE_LIB):
   """ Sets pickle library that will be used. """
   global desired_pickle_lib
-  # If switching to or from dill, update the pickler hook overrides.
-  if (selected_library == USE_DILL) != (desired_pickle_lib == dill_pickler):
-    dill_pickler.override_pickler_hooks(selected_library == USE_DILL)
 
   if selected_library == 'default':
     selected_library = DEFAULT_PICKLE_LIB
 
-  if selected_library == USE_DILL:
+  if selected_library == USE_DILL and not dill_pickler:
+    raise ImportError(
+        "Pipeline option pickle_library=dill is set, but dill is not "
+        "installed. Install apache-beam with the dill extras package "
+        "e.g. apache-beam[dill].")
+  if selected_library == USE_DILL_UNSAFE and not dill_pickler:
+    raise ImportError(
+        "Pipeline option pickle_library=dill_unsafe is set, but dill is not "
+        "installed. Install dill in job submission and runtime environments.")
+
+  is_currently_dill = (desired_pickle_lib == dill_pickler)
+  dill_is_requested = (
+      selected_library == USE_DILL or selected_library == USE_DILL_UNSAFE)
+
+  # If switching to or from dill, update the pickler hook overrides.
+  if is_currently_dill != dill_is_requested:
+    dill_pickler.override_pickler_hooks(selected_library == USE_DILL)
+
+  if dill_is_requested:
     desired_pickle_lib = dill_pickler
   elif selected_library == USE_CLOUDPICKLE:
     desired_pickle_lib = cloudpickle_pickler
diff --git a/sdks/python/apache_beam/internal/pickler_test.py b/sdks/python/apache_beam/internal/pickler_test.py
index 7048f680de87..a0135b221e8c 100644
--- a/sdks/python/apache_beam/internal/pickler_test.py
+++ b/sdks/python/apache_beam/internal/pickler_test.py
@@ -25,6 +25,7 @@
 import types
 import unittest
 
+import pytest
 from parameterized import param
 from parameterized import parameterized
 
@@ -34,6 +35,12 @@
 from apache_beam.internal.pickler import loads
 
 
+def maybe_skip_if_no_dill(pickle_library):
+  if pickle_library == 'dill':
+    pytest.importorskip("dill")
+
+
+@pytest.mark.uses_dill
 class PicklerTest(unittest.TestCase):
 
   NO_MAPPINGPROXYTYPE = not hasattr(types, "MappingProxyType")
@@ -43,6 +50,7 @@ class PicklerTest(unittest.TestCase):
       param(pickle_lib='cloudpickle'),
   ])
   def test_basics(self, pickle_lib):
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
 
     self.assertEqual([1, 'a', ('z', )], loads(dumps([1, 'a', ('z', )])))
@@ -55,6 +63,7 @@ def test_basics(self, pickle_lib):
   ])
   def test_lambda_with_globals(self, pickle_lib):
     """Tests that the globals of a function are preserved."""
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
 
     # The point of the test is that the lambda being called after unpickling
@@ -68,6 +77,7 @@ def test_lambda_with_globals(self, pickle_lib):
       param(pickle_lib='cloudpickle'),
   ])
   def test_lambda_with_main_globals(self, pickle_lib):
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
     self.assertEqual(unittest, loads(dumps(lambda: unittest))())
 
@@ -77,6 +87,7 @@ def test_lambda_with_main_globals(self, pickle_lib):
   ])
   def test_lambda_with_closure(self, pickle_lib):
     """Tests that the closure of a function is preserved."""
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
     self.assertEqual(
         'closure: abc',
@@ -88,6 +99,7 @@ def test_lambda_with_closure(self, pickle_lib):
   ])
   def test_class(self, pickle_lib):
     """Tests that a class object is pickled correctly."""
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
     self.assertEqual(['abc', 'def'],
                      loads(dumps(module_test.Xyz))().foo('abc def'))
@@ -98,6 +110,7 @@ def test_class(self, pickle_lib):
   ])
   def test_object(self, pickle_lib):
     """Tests that a class instance is pickled correctly."""
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
     self.assertEqual(['abc', 'def'],
                      loads(dumps(module_test.XYZ_OBJECT)).foo('abc def'))
@@ -108,6 +121,7 @@ def test_object(self, pickle_lib):
   ])
   def test_nested_class(self, pickle_lib):
     """Tests that a nested class object is pickled correctly."""
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
     self.assertEqual(
         'X:abc', loads(dumps(module_test.TopClass.NestedClass('abc'))).datum)
@@ -121,6 +135,7 @@ def test_nested_class(self, pickle_lib):
   ])
   def test_dynamic_class(self, pickle_lib):
     """Tests that a nested class object is pickled correctly."""
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
     self.assertEqual(
         'Z:abc', loads(dumps(module_test.create_class('abc'))).get())
@@ -130,6 +145,7 @@ def test_dynamic_class(self, pickle_lib):
       param(pickle_lib='cloudpickle'),
   ])
   def test_generators(self, pickle_lib):
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
     with self.assertRaises(TypeError):
       dumps((_ for _ in range(10)))
@@ -139,6 +155,7 @@ def test_generators(self, pickle_lib):
       param(pickle_lib='cloudpickle'),
   ])
   def test_recursive_class(self, pickle_lib):
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
     self.assertEqual(
         'RecursiveClass:abc',
@@ -149,6 +166,7 @@ def test_recursive_class(self, pickle_lib):
       param(pickle_lib='cloudpickle'),
   ])
   def test_pickle_rlock(self, pickle_lib):
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
     rlock_instance = threading.RLock()
     rlock_type = type(rlock_instance)
@@ -160,6 +178,7 @@ def test_pickle_rlock(self, pickle_lib):
       param(pickle_lib='cloudpickle'),
   ])
   def test_save_paths(self, pickle_lib):
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
     f = loads(dumps(lambda x: x))
     co_filename = f.__code__.co_filename
@@ -171,6 +190,7 @@ def test_save_paths(self, pickle_lib):
       param(pickle_lib='cloudpickle'),
   ])
   def test_dump_and_load_mapping_proxy(self, pickle_lib):
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
     self.assertEqual(
         'def', loads(dumps(types.MappingProxyType({'abc': 'def'})))['abc'])
@@ -184,6 +204,7 @@ def test_dump_and_load_mapping_proxy(self, pickle_lib):
       param(pickle_lib='cloudpickle'),
   ])
   def test_dataclass(self, pickle_lib):
+    maybe_skip_if_no_dill(pickle_lib)
     exec(
         '''
 from apache_beam.internal.module_test import DataClass
@@ -195,6 +216,7 @@ def test_dataclass(self, pickle_lib):
       param(pickle_lib='cloudpickle'),
   ])
   def test_class_states_not_changed_at_subsequent_loading(self, pickle_lib):
+    maybe_skip_if_no_dill(pickle_lib)
     pickler.set_library(pickle_lib)
 
     class Local:
@@ -255,6 +277,7 @@ def maybe_get_sets_with_different_iteration_orders(self):
     return set1, set2
 
   def test_best_effort_determinism(self):
+    maybe_skip_if_no_dill('dill')
     pickler.set_library('dill')
     set1, set2 = self.maybe_get_sets_with_different_iteration_orders()
     self.assertEqual(
@@ -267,6 +290,7 @@ def test_best_effort_determinism(self):
       self.skipTest('Set iteration orders matched. Test results inconclusive.')
 
   def test_disable_best_effort_determinism(self):
+    maybe_skip_if_no_dill('dill')
     pickler.set_library('dill')
     set1, set2 = self.maybe_get_sets_with_different_iteration_orders()
     # The test relies on the sets having different iteration orders for the
diff --git a/sdks/python/apache_beam/internal/test_data/__init__.py b/sdks/python/apache_beam/internal/test_data/__init__.py
new file mode 100644
index 000000000000..7f27bba88cf5
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/__init__.py
@@ -0,0 +1,20 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Test data to validate that code identifiers are invariant to small
+     modifications.
+"""
diff --git a/sdks/python/apache_beam/internal/test_data/module_1.py b/sdks/python/apache_beam/internal/test_data/module_1.py
new file mode 100644
index 000000000000..9dd7ebf90881
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_1.py
@@ -0,0 +1,27 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Counterpart to after_module_with_functions and is used as a test case
+for various code changes.
+"""
+
+
+def my_function():
+  a = 1  # pylint: disable=unused-variable
+  b = lambda: 2
+  return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_1_class_added.py b/sdks/python/apache_beam/internal/test_data/module_1_class_added.py
new file mode 100644
index 000000000000..0a4a7f73974c
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_1_class_added.py
@@ -0,0 +1,34 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Counterpart to before_module_with_functions and is used as a test case
+for adding a class.
+"""
+
+
+class MyClass:
+  def another_function(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+
+def my_function():
+  a = 1  # pylint: disable=unused-variable
+  b = lambda: 2
+  return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_1_function_added.py b/sdks/python/apache_beam/internal/test_data/module_1_function_added.py
new file mode 100644
index 000000000000..063125a2500d
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_1_function_added.py
@@ -0,0 +1,33 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Counterpart to before_module_with_functions and is used as a test case
+for adding a function.
+"""
+
+
+def another_function():
+  a = 1  # pylint: disable=unused-variable
+  b = lambda: 2
+  return b
+
+
+def my_function():
+  a = 1  # pylint: disable=unused-variable
+  b = lambda: 2
+  return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_1_global_variable_added.py b/sdks/python/apache_beam/internal/test_data/module_1_global_variable_added.py
new file mode 100644
index 000000000000..c4d20f27c837
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_1_global_variable_added.py
@@ -0,0 +1,29 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Counterpart to before_module_with_functions and is used as a test case
+for adding a global variable.
+"""
+
+GLOBAL_VARIABLE = lambda: 3
+
+
+def my_function():
+  a = 1  # pylint: disable=unused-variable
+  b = lambda: 2
+  return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_1_lambda_variable_added.py b/sdks/python/apache_beam/internal/test_data/module_1_lambda_variable_added.py
new file mode 100644
index 000000000000..7e1ddc48790f
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_1_lambda_variable_added.py
@@ -0,0 +1,28 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Counterpart to before_module_with_functions and is used as a test case
+for adding a lambda variable.
+"""
+
+
+def my_function():
+  a = 1  # pylint: disable=unused-variable
+  b = lambda: 2
+  new_lambda_variable = lambda: 4  # pylint: disable=unused-variable
+  return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_1_local_variable_added.py b/sdks/python/apache_beam/internal/test_data/module_1_local_variable_added.py
new file mode 100644
index 000000000000..a808e7ca4e9b
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_1_local_variable_added.py
@@ -0,0 +1,28 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Counterpart to before_module_with_functions and is used as a test case
+for adding a variable.
+"""
+
+
+def my_function():
+  a = 1  # pylint: disable=unused-variable
+  b = lambda: 2
+  new_local_variable = 3  # pylint: disable=unused-variable
+  return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_1_local_variable_removed.py b/sdks/python/apache_beam/internal/test_data/module_1_local_variable_removed.py
new file mode 100644
index 000000000000..072b93d48447
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_1_local_variable_removed.py
@@ -0,0 +1,26 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Counterpart to before_module_with_functions and is used as a test case
+for removing a variable.
+"""
+
+
+def my_function():
+  b = lambda: 2
+  return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_1_nested_function_2_added.py b/sdks/python/apache_beam/internal/test_data/module_1_nested_function_2_added.py
new file mode 100644
index 000000000000..191e2e92065a
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_1_nested_function_2_added.py
@@ -0,0 +1,32 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Counterpart to before_module_with_functions and is used as a test case
+for adding a nested function.
+"""
+
+
+def my_function():
+  a = 1  # pylint: disable=unused-variable
+  b = lambda: 2
+
+  def nested_function():  # pylint: disable=unused-variable
+    c = 3
+    return c
+
+  return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_1_nested_function_added.py b/sdks/python/apache_beam/internal/test_data/module_1_nested_function_added.py
new file mode 100644
index 000000000000..358bf12b3d05
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_1_nested_function_added.py
@@ -0,0 +1,31 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Counterpart to before_module_with_functions and is used as a test case
+for adding a nested function.
+"""
+
+
+def my_function():
+  def nested_function():  # pylint: disable=unused-variable
+    c = 3
+    return c
+
+  a = 1  # pylint: disable=unused-variable
+  b = lambda: 2
+  return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_2.py b/sdks/python/apache_beam/internal/test_data/module_2.py
new file mode 100644
index 000000000000..e59deca69f0b
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_2.py
@@ -0,0 +1,82 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with classes.
+Counterpart to after_module_with_classes and is used as a test case
+for various code changes.
+"""
+
+
+class AddLocalVariable:
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+
+class RemoveLocalVariable:
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+
+class AddLambdaVariable:
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+
+class RemoveLambdaVariable:
+  def my_method(self):
+    a = lambda: 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+
+class ClassWithNestedFunction:
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+
+class ClassWithNestedFunction2:
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+
+class ClassWithTwoMethods:
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+
+class RemoveMethod:
+  def another_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_2_modified.py b/sdks/python/apache_beam/internal/test_data/module_2_modified.py
new file mode 100644
index 000000000000..56e57f8cb87b
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_2_modified.py
@@ -0,0 +1,92 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with classes.
+Counterpart to before_module_with_classes and is used as a test case
+for various code changes.
+"""
+
+
+class AddLocalVariable:
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    new_local_variable = 3  # pylint: disable=unused-variable
+    return b
+
+
+class RemoveLocalVariable:
+  def my_method(self):
+    b = lambda: 2
+    return b
+
+
+class AddLambdaVariable:
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    c = lambda: 3  # pylint: disable=unused-variable
+    return b
+
+
+class RemoveLambdaVariable:
+  def my_method(self):
+    b = lambda: 2
+    return b
+
+
+class ClassWithNestedFunction:
+  def my_method(self):
+    def nested_function():  # pylint: disable=unused-variable
+      c = 3
+      return c
+
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+
+class ClassWithNestedFunction2:
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+
+    def nested_function():  # pylint: disable=unused-variable
+      c = 3
+      return c
+
+    return b
+
+
+class ClassWithTwoMethods:
+  def another_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+    b = lambda: 2
+    return b
+
+
+class RemoveMethod:
+  def my_method(self):
+    a = 1  # pylint: disable=unused-variable
+
+    b = lambda: 2
+    return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_3.py b/sdks/python/apache_beam/internal/test_data/module_3.py
new file mode 100644
index 000000000000..98f9c29ceb52
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_3.py
@@ -0,0 +1,26 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Used as a test case for various code changes.
+"""
+
+
+def my_function():
+  a = lambda: 1  # pylint: disable=unused-variable
+  b = lambda: 2
+  return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_3_modified.py b/sdks/python/apache_beam/internal/test_data/module_3_modified.py
new file mode 100644
index 000000000000..326dedb93f0d
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_3_modified.py
@@ -0,0 +1,26 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Counterpart to before_module_with_functions and is used as a test case
+for removing a lambda variable.
+"""
+
+
+def my_function():
+  b = lambda: 2
+  return b
diff --git a/sdks/python/apache_beam/internal/test_data/module_with_default_argument.py b/sdks/python/apache_beam/internal/test_data/module_with_default_argument.py
new file mode 100644
index 000000000000..a1758a8be740
--- /dev/null
+++ b/sdks/python/apache_beam/internal/test_data/module_with_default_argument.py
@@ -0,0 +1,24 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module for testing code path generation with functions.
+Used as a test case for default arguments.
+"""
+
+
+def function_with_lambda_default_argument(fn=lambda x: 1):
+  return fn
diff --git a/sdks/python/apache_beam/io/avroio.py b/sdks/python/apache_beam/io/avroio.py
index 553b6c741f3d..da904bf6fb55 100644
--- a/sdks/python/apache_beam/io/avroio.py
+++ b/sdks/python/apache_beam/io/avroio.py
@@ -354,8 +354,7 @@ def split_points_unclaimed(stop_position):
       while range_tracker.try_claim(next_block_start):
         block = next(blocks)
         next_block_start = block.offset + block.size
-        for record in block:
-          yield record
+        yield from block
 
 
 _create_avro_source = _FastAvroSource
@@ -375,7 +374,8 @@ def __init__(
       num_shards=0,
       shard_name_template=None,
       mime_type='application/x-avro',
-      use_fastavro=True):
+      use_fastavro=True,
+      triggering_frequency=None):
     """Initialize a WriteToAvro transform.
 
     Args:
@@ -393,17 +393,30 @@ def __init__(
         Constraining the number of shards is likely to reduce
         the performance of a pipeline.  Setting this value is not recommended
         unless you require a specific number of output files.
+        In streaming if not set, the service will write a file per bundle.
       shard_name_template: A template string containing placeholders for
-        the shard number and shard count. When constructing a filename for a
-        particular shard number, the upper-case letters 'S' and 'N' are
-        replaced with the 0-padded shard number and shard count respectively.
-        This argument can be '' in which case it behaves as if num_shards was
-        set to 1 and only one file will be generated. The default pattern used
-        is '-SSSSS-of-NNNNN' if None is passed as the shard_name_template.
+        the shard number and shard count. Currently only ``''``,
+        ``'-SSSSS-of-NNNNN'``, ``'-W-SSSSS-of-NNNNN'`` and
+        ``'-V-SSSSS-of-NNNNN'`` are patterns accepted by the service.
+        When constructing a filename for a particular shard number, the
+        upper-case letters ``S`` and ``N`` are replaced with the ``0``-padded
+        shard number and shard count respectively.  This argument can be ``''``
+        in which case it behaves as if num_shards was set to 1 and only one file
+        will be generated. The default pattern used is ``'-SSSSS-of-NNNNN'`` for
+        bounded PCollections and for ``'-W-SSSSS-of-NNNNN'`` unbounded 
+        PCollections.
+        W is used for windowed shard naming and is replaced with 
+        ``[window.start, window.end)``
+        V is used for windowed shard naming and is replaced with 
+        ``[window.start.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S"), 
+        window.end.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S")``
       mime_type: The MIME type to use for the produced files, if the filesystem
         supports specifying MIME types.
       use_fastavro (bool): This flag is left for API backwards compatibility
         and no longer has an effect. Do not use.
+      triggering_frequency: (int) Every triggering_frequency duration, a window 
+        will be triggered and all bundles in the window will be written.
+        If set it overrides user windowing. Mandatory for GlobalWindow.
 
     Returns:
       A WriteToAvro transform usable for writing.
@@ -411,7 +424,7 @@ def __init__(
     self._schema = schema
     self._sink_provider = lambda avro_schema: _create_avro_sink(
         file_path_prefix, avro_schema, codec, file_name_suffix, num_shards,
-        shard_name_template, mime_type)
+        shard_name_template, mime_type, triggering_frequency)
 
   def expand(self, pcoll):
     if self._schema:
@@ -428,6 +441,15 @@ def expand(self, pcoll):
       records = pcoll | beam.Map(
           beam_row_to_avro_dict(avro_schema, beam_schema))
     self._sink = self._sink_provider(avro_schema)
+    if (not pcoll.is_bounded and self._sink.shard_name_template
+        == filebasedsink.DEFAULT_SHARD_NAME_TEMPLATE):
+      self._sink.shard_name_template = (
+          filebasedsink.DEFAULT_WINDOW_SHARD_NAME_TEMPLATE)
+      self._sink.shard_name_format = self._sink._template_to_format(
+          self._sink.shard_name_template)
+      self._sink.shard_name_glob_format = self._sink._template_to_glob_format(
+          self._sink.shard_name_template)
+
     return records | beam.io.iobase.Write(self._sink)
 
   def display_data(self):
@@ -441,7 +463,8 @@ def _create_avro_sink(
     file_name_suffix,
     num_shards,
     shard_name_template,
-    mime_type):
+    mime_type,
+    triggering_frequency=60):
   if "class 'avro.schema" in str(type(schema)):
     raise ValueError(
         'You are using Avro IO with fastavro (default with Beam on '
@@ -454,7 +477,8 @@ def _create_avro_sink(
       file_name_suffix,
       num_shards,
       shard_name_template,
-      mime_type)
+      mime_type,
+      triggering_frequency)
 
 
 class _BaseAvroSink(filebasedsink.FileBasedSink):
@@ -467,7 +491,8 @@ def __init__(
       file_name_suffix,
       num_shards,
       shard_name_template,
-      mime_type):
+      mime_type,
+      triggering_frequency):
     super().__init__(
         file_path_prefix,
         file_name_suffix=file_name_suffix,
@@ -477,7 +502,8 @@ def __init__(
         mime_type=mime_type,
         # Compression happens at the block level using the supplied codec, and
         # not at the file level.
-        compression_type=CompressionTypes.UNCOMPRESSED)
+        compression_type=CompressionTypes.UNCOMPRESSED,
+        triggering_frequency=triggering_frequency)
     self._schema = schema
     self._codec = codec
 
@@ -498,7 +524,8 @@ def __init__(
       file_name_suffix,
       num_shards,
       shard_name_template,
-      mime_type):
+      mime_type,
+      triggering_frequency):
     super().__init__(
         file_path_prefix,
         schema,
@@ -506,7 +533,8 @@ def __init__(
         file_name_suffix,
         num_shards,
         shard_name_template,
-        mime_type)
+        mime_type,
+        triggering_frequency)
     self.file_handle = None
 
   def open(self, temp_path):
diff --git a/sdks/python/apache_beam/io/avroio_test.py b/sdks/python/apache_beam/io/avroio_test.py
index 6dd9e620c665..6669b6fb8abf 100644
--- a/sdks/python/apache_beam/io/avroio_test.py
+++ b/sdks/python/apache_beam/io/avroio_test.py
@@ -16,11 +16,15 @@
 #
 # pytype: skip-file
 
+import glob
 import json
 import logging
 import math
 import os
+import pytz
 import pytest
+import re
+import shutil
 import tempfile
 import unittest
 from typing import List, Any
@@ -47,14 +51,17 @@
 from apache_beam.io.filesystems import FileSystems
 from apache_beam.options.pipeline_options import StandardOptions
 from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.testing.test_stream import TestStream
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
 from apache_beam.transforms.display import DisplayData
 from apache_beam.transforms.display_test import DisplayDataItemMatcher
 from apache_beam.transforms.sql import SqlTransform
 from apache_beam.transforms.userstate import CombiningValueStateSpec
+from apache_beam.transforms.util import LogElements
 from apache_beam.utils.timestamp import Timestamp
 from apache_beam.typehints import schemas
+from datetime import datetime
 
 # Import snappy optionally; some tests will be skipped when import fails.
 try:
@@ -673,6 +680,273 @@ def _write_data(
     return f.name
 
 
+class GenerateEvent(beam.PTransform):
+  @staticmethod
+  def sample_data():
+    return GenerateEvent()
+
+  def expand(self, input):
+    elemlist = [{'age': 10}, {'age': 20}, {'age': 30}]
+    elem = elemlist
+    return (
+        input
+        | TestStream().add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 1, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 2, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 3, 0,
+                                tzinfo=pytz.UTC).timestamp()).add_elements(
+                                    elements=elem,
+                                    event_timestamp=datetime(
+                                        2021, 3, 1, 0, 0, 4, 0,
+                                        tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 5, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 5, 0,
+                             tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 6,
+                0, tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 7, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 8, 0,
+                                tzinfo=pytz.UTC).timestamp()).add_elements(
+                                    elements=elem,
+                                    event_timestamp=datetime(
+                                        2021, 3, 1, 0, 0, 9, 0,
+                                        tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 10, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 10, 0,
+                             tzinfo=pytz.UTC).timestamp()).add_elements(
+                                 elements=elem,
+                                 event_timestamp=datetime(
+                                     2021, 3, 1, 0, 0, 11, 0,
+                                     tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 12, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 13, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 14, 0,
+                                tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 15, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 15, 0,
+                             tzinfo=pytz.UTC).timestamp()).add_elements(
+                                 elements=elem,
+                                 event_timestamp=datetime(
+                                     2021, 3, 1, 0, 0, 16, 0,
+                                     tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 17, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 18, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 19, 0,
+                                tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 20, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 20, 0,
+                             tzinfo=pytz.UTC).timestamp()).advance_watermark_to(
+                                 datetime(
+                                     2021, 3, 1, 0, 0, 25, 0, tzinfo=pytz.UTC).
+                                 timestamp()).advance_watermark_to_infinity())
+
+
+class WriteStreamingTest(unittest.TestCase):
+  def setUp(self):
+    super().setUp()
+    self.tempdir = tempfile.mkdtemp()
+
+  def tearDown(self):
+    if os.path.exists(self.tempdir):
+      shutil.rmtree(self.tempdir)
+
+  def test_write_streaming_2_shards_default_shard_name_template(
+      self, num_shards=2):
+    with TestPipeline() as p:
+      output = (
+          p
+          | GenerateEvent.sample_data()
+          | 'User windowing' >> beam.transforms.core.WindowInto(
+              beam.transforms.window.FixedWindows(60),
+              trigger=beam.transforms.trigger.AfterWatermark(),
+              accumulation_mode=beam.transforms.trigger.AccumulationMode.
+              DISCARDING,
+              allowed_lateness=beam.utils.timestamp.Duration(seconds=0)))
+      #AvroIO
+      avroschema = {
+          'name': 'dummy', # your supposed to be file name with .avro extension
+          'type': 'record', # type of avro serilazation, there are more (see
+                            # above docs)
+          'fields': [ # this defines actual keys & their types
+              {'name': 'age', 'type': 'int'},
+          ],
+        }
+      output2 = output | 'WriteToAvro' >> beam.io.WriteToAvro(
+          file_path_prefix=self.tempdir + "/ouput_WriteToAvro",
+          file_name_suffix=".avro",
+          num_shards=num_shards,
+          schema=avroschema)
+      _ = output2 | 'LogElements after WriteToAvro' >> LogElements(
+          prefix='after WriteToAvro ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    #  ouput_WriteToAvro-[1614556800.0, 1614556805.0)-00000-of-00002.avro
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>[\d\.]+), '
+        r'(?P<window_end>[\d\.]+|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.avro$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToAvro*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertEqual(
+        len(file_names),
+        num_shards,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+  def test_write_streaming_2_shards_custom_shard_name_template(
+      self, num_shards=2, shard_name_template='-V-SSSSS-of-NNNNN'):
+    with TestPipeline() as p:
+      output = (p | GenerateEvent.sample_data())
+      #AvroIO
+      avroschema = {
+          'name': 'dummy', # your supposed to be file name with .avro extension
+          'type': 'record', # type of avro serilazation
+          'fields': [ # this defines actual keys & their types
+              {'name': 'age', 'type': 'int'},
+          ],
+        }
+      output2 = output | 'WriteToAvro' >> beam.io.WriteToAvro(
+          file_path_prefix=self.tempdir + "/ouput_WriteToAvro",
+          file_name_suffix=".avro",
+          shard_name_template=shard_name_template,
+          num_shards=num_shards,
+          triggering_frequency=60,
+          schema=avroschema)
+      _ = output2 | 'LogElements after WriteToAvro' >> LogElements(
+          prefix='after WriteToAvro ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToAvro-[2021-03-01T00-00-00, 2021-03-01T00-01-00)-
+    #   00000-of-00002.avro
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}), '
+        r'(?P<window_end>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.avro$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToAvro*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertEqual(
+        len(file_names),
+        num_shards,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+  def test_write_streaming_2_shards_custom_shard_name_template_5s_window(
+      self,
+      num_shards=2,
+      shard_name_template='-V-SSSSS-of-NNNNN',
+      triggering_frequency=5):
+    with TestPipeline() as p:
+      output = (p | GenerateEvent.sample_data())
+      #AvroIO
+      avroschema = {
+          'name': 'dummy', # your supposed to be file name with .avro extension 
+          'type': 'record', # type of avro serilazation
+          'fields': [ # this defines actual keys & their types
+              {'name': 'age', 'type': 'int'},
+          ],
+        }
+      output2 = output | 'WriteToAvro' >> beam.io.WriteToAvro(
+          file_path_prefix=self.tempdir + "/ouput_WriteToAvro",
+          file_name_suffix=".txt",
+          shard_name_template=shard_name_template,
+          num_shards=num_shards,
+          triggering_frequency=triggering_frequency,
+          schema=avroschema)
+      _ = output2 | 'LogElements after WriteToAvro' >> LogElements(
+          prefix='after WriteToAvro ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    #   ouput_WriteToAvro-[2021-03-01T00-00-00, 2021-03-01T00-01-00)-
+    #     00000-of-00002.avro
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}), '
+        r'(?P<window_end>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.txt$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToAvro*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    # for 5s window size, the input should be processed by 5 windows with
+    # 2 shards per window
+    self.assertEqual(
+        len(file_names),
+        10,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
   unittest.main()
diff --git a/sdks/python/apache_beam/io/components/adaptive_throttler.py b/sdks/python/apache_beam/io/components/adaptive_throttler.py
index f62906360739..3c22891ee8a3 100644
--- a/sdks/python/apache_beam/io/components/adaptive_throttler.py
+++ b/sdks/python/apache_beam/io/components/adaptive_throttler.py
@@ -21,9 +21,32 @@
 
 # pytype: skip-file
 
+import logging
 import random
+import time
 
 from apache_beam.io.components import util
+from apache_beam.metrics.metric import Metrics
+
+_SECONDS_TO_MILLISECONDS = 1_000
+
+
+class ThrottlingSignaler(object):
+  """A class that handles signaling throttling of remote requests to the
+  SDK harness.
+  """
+  def __init__(self, namespace: str = ""):
+    self.throttling_metric = Metrics.counter(
+        namespace, "cumulativeThrottlingSeconds")
+
+  def signal_throttled(self, seconds: int):
+    """Signals to the runner that requests have been throttled for some amount
+    of time.
+
+    Args:
+      seconds: int, duration of throttling in seconds.
+    """
+    self.throttling_metric.inc(seconds)
 
 
 class AdaptiveThrottler(object):
@@ -94,3 +117,72 @@ def successful_request(self, now):
       now: int, time in ms since the epoch
     """
     self._successful_requests.add(now, 1)
+
+
+class ReactiveThrottler(AdaptiveThrottler):
+  """ A wrapper around the AdaptiveThrottler that also handles logging and
+  signaling throttling to the SDK harness using the provided namespace.
+
+  For usage, instantiate one instance of a ReactiveThrottler class for a
+  PTransform. When making remote calls to a service, preface that call with
+  the throttle() method to potentially pre-emptively throttle the request.
+  This will throttle future calls based on the failure rate of preceding calls,
+  with higher failure rates leading to longer periods of throttling to allow
+  system recovery. capture the timestamp of the attempted request, then execute
+  the request code. On a success, call successful_request(timestamp) to report
+  the success to the throttler. This flow looks like the following:
+  
+  def remote_call():
+    throttler.throttle()
+
+    try:
+      timestamp = time.time()
+      result = make_request()
+      throttler.successful_request(timestamp)
+      return result
+    except Exception as e:
+      # do any error handling you want to do
+      raise
+  """
+  def __init__(
+      self,
+      window_ms: int,
+      bucket_ms: int,
+      overload_ratio: float,
+      namespace: str = '',
+      throttle_delay_secs: int = 5):
+    """Initializes the ReactiveThrottler.
+
+    Args:
+      window_ms: int, length of history to consider, in ms, to set
+        throttling.
+      bucket_ms: int, granularity of time buckets that we store data in, in
+        ms.
+      overload_ratio: float, the target ratio between requests sent and
+        successful requests. This is "K" in the formula in
+        https://landing.google.com/sre/book/chapters/handling-overload.html.
+      namespace: str, the namespace to use for logging and signaling
+        throttling is occurring
+      throttle_delay_secs: int, the amount of time in seconds to wait
+        after preemptively throttled requests
+    """
+    self.throttling_signaler = ThrottlingSignaler(namespace=namespace)
+    self.logger = logging.getLogger(namespace)
+    self.throttle_delay_secs = throttle_delay_secs
+    super().__init__(
+        window_ms=window_ms, bucket_ms=bucket_ms, overload_ratio=overload_ratio)
+
+  def throttle(self):
+    """ Stops request code from advancing while the underlying
+    AdaptiveThrottler is signaling to preemptively throttle the request.
+    Automatically handles logging the throttling and signaling to the SDK
+    harness that the request is being throttled. This should be called in any
+    context where a call to a remote service is being contacted prior to the
+    call being performed.
+    """
+    while self.throttle_request(time.time() * _SECONDS_TO_MILLISECONDS):
+      self.logger.info(
+          "Delaying request for %d seconds due to previous failures",
+          self.throttle_delay_secs)
+      time.sleep(self.throttle_delay_secs)
+      self.throttling_signaler.signal_throttled(self.throttle_delay_secs)
diff --git a/sdks/python/apache_beam/io/debezium.py b/sdks/python/apache_beam/io/debezium.py
index 9e93801852c6..26516fa4e4b7 100644
--- a/sdks/python/apache_beam/io/debezium.py
+++ b/sdks/python/apache_beam/io/debezium.py
@@ -155,7 +155,7 @@ def __init__(
         username=username,
         password=password,
         host=host,
-        port=port,
+        port=str(port),
         max_number_of_records=max_number_of_records,
         connection_properties=connection_properties)
     self.expansion_service = expansion_service or default_io_expansion_service()
diff --git a/sdks/python/apache_beam/io/external/xlang_bigqueryio_it_test.py b/sdks/python/apache_beam/io/external/xlang_bigqueryio_it_test.py
index 7f3a16e02aa3..38d9174cef2b 100644
--- a/sdks/python/apache_beam/io/external/xlang_bigqueryio_it_test.py
+++ b/sdks/python/apache_beam/io/external/xlang_bigqueryio_it_test.py
@@ -245,6 +245,36 @@ def test_write_with_beam_rows(self):
           | StorageWriteToBigQuery(table=table_id))
     hamcrest_assert(p, bq_matcher)
 
+  def test_write_with_clustering(self):
+    table = 'write_with_clustering'
+    table_id = '{}:{}.{}'.format(self.project, self.dataset_id, table)
+
+    bq_matcher = BigqueryFullResultMatcher(
+        project=self.project,
+        query="SELECT * FROM {}.{}".format(self.dataset_id, table),
+        data=self.parse_expected_data(self.ELEMENTS))
+
+    with beam.Pipeline(argv=self.args) as p:
+      _ = (
+          p
+          | "Create test data" >> beam.Create(self.ELEMENTS)
+          | beam.io.WriteToBigQuery(
+              table=table_id,
+              method=beam.io.WriteToBigQuery.Method.STORAGE_WRITE_API,
+              schema=self.ALL_TYPES_SCHEMA,
+              create_disposition='CREATE_IF_NEEDED',
+              write_disposition='WRITE_TRUNCATE',
+              additional_bq_parameters={'clustering': {
+                  'fields': ['int']
+              }}))
+
+    # After pipeline finishes, verify clustering is applied
+    table = self.bigquery_client.get_table(self.project, self.dataset_id, table)
+    clustering_fields = table.clustering.fields if table.clustering else []
+
+    self.assertEqual(clustering_fields, ['int'])
+    hamcrest_assert(p, bq_matcher)
+
   def test_write_with_beam_rows_cdc(self):
     table = 'write_with_beam_rows_cdc'
     table_id = '{}:{}.{}'.format(self.project, self.dataset_id, table)
diff --git a/sdks/python/apache_beam/io/external/xlang_jdbcio_it_test.py b/sdks/python/apache_beam/io/external/xlang_jdbcio_it_test.py
index 094161afbf93..26fa2f400d83 100644
--- a/sdks/python/apache_beam/io/external/xlang_jdbcio_it_test.py
+++ b/sdks/python/apache_beam/io/external/xlang_jdbcio_it_test.py
@@ -36,8 +36,6 @@
 from apache_beam.testing.test_pipeline import TestPipeline
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
-from apache_beam.typehints.schemas import LogicalType
-from apache_beam.typehints.schemas import MillisInstant
 from apache_beam.utils.timestamp import Timestamp
 
 # pylint: disable=wrong-import-order, wrong-import-position, ungrouped-imports
@@ -242,10 +240,6 @@ def test_xlang_jdbc_write_read(self, database):
 
     config = self.jdbc_configs[database]
 
-    # Register MillisInstant logical type to override the mapping from Timestamp
-    # originally handled by MicrosInstant.
-    LogicalType.register_logical_type(MillisInstant)
-
     with TestPipeline() as p:
       p.not_use_test_runner_api = True
       _ = (
@@ -356,10 +350,6 @@ def custom_row_equals(expected, actual):
               classpath=config['classpath'],
           ))
 
-    # Register MillisInstant logical type to override the mapping from Timestamp
-    # originally handled by MicrosInstant.
-    LogicalType.register_logical_type(MillisInstant)
-
     # Run read pipeline with custom schema
     with TestPipeline() as p:
       p.not_use_test_runner_api = True
diff --git a/sdks/python/apache_beam/io/filebasedsink.py b/sdks/python/apache_beam/io/filebasedsink.py
index 8bb0f7e2171e..510d253c7376 100644
--- a/sdks/python/apache_beam/io/filebasedsink.py
+++ b/sdks/python/apache_beam/io/filebasedsink.py
@@ -33,9 +33,12 @@
 from apache_beam.options.value_provider import StaticValueProvider
 from apache_beam.options.value_provider import ValueProvider
 from apache_beam.options.value_provider import check_accessible
+from apache_beam.transforms import window
 from apache_beam.transforms.display import DisplayDataItem
 
 DEFAULT_SHARD_NAME_TEMPLATE = '-SSSSS-of-NNNNN'
+DEFAULT_WINDOW_SHARD_NAME_TEMPLATE = '-W-SSSSS-of-NNNNN'
+DEFAULT_TRIGGERING_FREQUENCY = 0
 
 __all__ = ['FileBasedSink']
 
@@ -71,7 +74,9 @@ def __init__(
       *,
       max_records_per_shard=None,
       max_bytes_per_shard=None,
-      skip_if_empty=False):
+      skip_if_empty=False,
+      convert_fn=None,
+      triggering_frequency=None):
     """
      Raises:
       TypeError: if file path parameters are not a :class:`str` or
@@ -98,6 +103,8 @@ def __init__(
       shard_name_template = DEFAULT_SHARD_NAME_TEMPLATE
     elif shard_name_template == '':
       num_shards = 1
+    if triggering_frequency is None:
+      triggering_frequency = DEFAULT_TRIGGERING_FREQUENCY
     if isinstance(file_path_prefix, str):
       file_path_prefix = StaticValueProvider(str, file_path_prefix)
     if isinstance(file_name_suffix, str):
@@ -106,6 +113,7 @@ def __init__(
     self.file_name_suffix = file_name_suffix
     self.num_shards = num_shards
     self.coder = coder
+    self.shard_name_template = shard_name_template
     self.shard_name_format = self._template_to_format(shard_name_template)
     self.shard_name_glob_format = self._template_to_glob_format(
         shard_name_template)
@@ -114,6 +122,8 @@ def __init__(
     self.max_records_per_shard = max_records_per_shard
     self.max_bytes_per_shard = max_bytes_per_shard
     self.skip_if_empty = skip_if_empty
+    self.convert_fn = convert_fn
+    self.triggering_frequency = triggering_frequency
 
   def display_data(self):
     return {
@@ -202,19 +212,39 @@ def open_writer(self, init_result, uid):
     return FileBasedSinkWriter(self, writer_path)
 
   @check_accessible(['file_path_prefix', 'file_name_suffix'])
-  def _get_final_name(self, shard_num, num_shards):
+  def _get_final_name(self, shard_num, num_shards, w=None):
+    if w is None or isinstance(w, window.GlobalWindow):
+      window_utc = None
+    else:
+      window_utc = (
+          '[' + w.start.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S") + ', ' +
+          w.end.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S") + ')')
     return ''.join([
         self.file_path_prefix.get(),
-        self.shard_name_format %
-        dict(shard_num=shard_num, num_shards=num_shards),
+        self.shard_name_format % dict(
+            shard_num=shard_num,
+            num_shards=num_shards,
+            uuid=(uuid.uuid4()),
+            window=w,
+            window_utc=window_utc),
         self.file_name_suffix.get()
     ])
 
   @check_accessible(['file_path_prefix', 'file_name_suffix'])
-  def _get_final_name_glob(self, num_shards):
+  def _get_final_name_glob(self, num_shards, w=None):
+    if w is None or isinstance(w, window.GlobalWindow):
+      window_utc = None
+    else:
+      window_utc = (
+          '[' + w.start.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S") + ', ' +
+          w.end.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S") + ')')
     return ''.join([
         self.file_path_prefix.get(),
-        self.shard_name_glob_format % dict(num_shards=num_shards),
+        self.shard_name_glob_format % dict(
+            num_shards=num_shards,
+            uuid=(uuid.uuid4()),
+            window=w,
+            window_utc=window_utc),
         self.file_name_suffix.get()
     ])
 
@@ -233,7 +263,23 @@ def pre_finalize(self, init_result, writer_results):
           self.shard_name_glob_format)
       FileSystems.delete(dst_glob_files)
 
-  def _check_state_for_finalize_write(self, writer_results, num_shards):
+  def pre_finalize_windowed(self, init_result, writer_results, window=None):
+    num_shards = len(list(writer_results))
+    dst_glob = self._get_final_name_glob(num_shards, window)
+    dst_glob_files = [
+        file_metadata.path for mr in FileSystems.match([dst_glob])
+        for file_metadata in mr.metadata_list
+    ]
+
+    if dst_glob_files:
+      _LOGGER.warning(
+          'Deleting %d existing files in target path matching: %s',
+          len(dst_glob_files),
+          self.shard_name_glob_format)
+      FileSystems.delete(dst_glob_files)
+
+  def _check_state_for_finalize_write(
+      self, writer_results, num_shards, window=None):
     """Checks writer output files' states.
 
     Returns:
@@ -248,7 +294,7 @@ def _check_state_for_finalize_write(self, writer_results, num_shards):
       return [], [], [], 0
 
     src_glob = FileSystems.join(FileSystems.split(writer_results[0])[0], '*')
-    dst_glob = self._get_final_name_glob(num_shards)
+    dst_glob = self._get_final_name_glob(num_shards, window)
     src_glob_files = set(
         file_metadata.path for mr in FileSystems.match([src_glob])
         for file_metadata in mr.metadata_list)
@@ -261,7 +307,7 @@ def _check_state_for_finalize_write(self, writer_results, num_shards):
     delete_files = []
     num_skipped = 0
     for shard_num, src in enumerate(writer_results):
-      final_name = self._get_final_name(shard_num, num_shards)
+      final_name = self._get_final_name(shard_num, num_shards, window)
       dst = final_name
       src_exists = src in src_glob_files
       dst_exists = dst in dst_glob_files
@@ -300,11 +346,19 @@ def _report_sink_lineage(self, dst_glob, dst_files):
   @check_accessible(['file_path_prefix'])
   def finalize_write(
       self, init_result, writer_results, unused_pre_finalize_results):
+    #Legacy finalize_write now has shares the implementation with
+    #finalize_windowed_write when window is None.
+    return self.finalize_windowed_write(
+        init_result, writer_results, unused_pre_finalize_results, None)
+
+  @check_accessible(['file_path_prefix'])
+  def finalize_windowed_write(
+      self, init_result, writer_results, unused_pre_finalize_results, w=None):
     writer_results = sorted(writer_results)
     num_shards = len(writer_results)
 
     src_files, dst_files, delete_files, num_skipped = (
-        self._check_state_for_finalize_write(writer_results, num_shards))
+        self._check_state_for_finalize_write(writer_results, num_shards, w))
     num_skipped += len(delete_files)
     FileSystems.delete(delete_files)
     num_shards_to_finalize = len(src_files)
@@ -322,16 +376,8 @@ def finalize_write(
     ]
 
     if num_shards_to_finalize:
-      _LOGGER.info(
-          'Starting finalize_write threads with num_shards: %d (skipped: %d), '
-          'batches: %d, num_threads: %d',
-          num_shards_to_finalize,
-          num_skipped,
-          len(source_file_batch),
-          num_threads)
       start_time = time.time()
 
-      # Use a thread pool for renaming operations.
       def _rename_batch(batch):
         """_rename_batch executes batch rename operations."""
         source_files, destination_files = batch
@@ -355,19 +401,36 @@ def _rename_batch(batch):
               _LOGGER.debug('Rename successful: %s -> %s', src, dst)
           return exceptions
 
-      exception_batches = util.run_using_threadpool(
-          _rename_batch,
-          list(zip(source_file_batch, destination_file_batch)),
-          num_threads)
-
-      all_exceptions = [
-          e for exception_batch in exception_batches for e in exception_batch
-      ]
-      if all_exceptions:
-        raise Exception(
-            'Encountered exceptions in finalize_write: %s' % all_exceptions)
-
-      yield from dst_files
+      if w is None or isinstance(w, window.GlobalWindow):
+        # bounded input was handled by finalize_write legacy method
+        # the implementation here should be called by finalize_write
+        # Use a thread pool for renaming operations.
+        exception_batches = util.run_using_threadpool(
+            _rename_batch,
+            list(zip(source_file_batch, destination_file_batch)),
+            num_threads)
+
+        all_exceptions = [
+            e for exception_batch in exception_batches for e in exception_batch
+        ]
+        if all_exceptions:
+          raise Exception(
+              'Encountered exceptions in finalize_write: %s' % all_exceptions)
+
+        yield from dst_files
+      else:
+        # unbounded input
+        batch = list([src_files, dst_files])
+        exception_batches = _rename_batch(batch)
+
+        all_exceptions = [
+            e for exception_batch in exception_batches for e in exception_batch
+        ]
+        if all_exceptions:
+          raise Exception(
+              'Encountered exceptions in finalize_write: %s' % all_exceptions)
+
+        yield from dst_files
 
       _LOGGER.info(
           'Renamed %d shards in %.2f seconds.',
@@ -385,6 +448,26 @@ def _rename_batch(batch):
       # This error is not serious, we simply log it.
       _LOGGER.info('Unable to delete file: %s', init_result)
 
+  @staticmethod
+  def _template_replace_window(shard_name_template):
+    match = re.search('W+', shard_name_template)
+    if match:
+      shard_name_template = shard_name_template.replace(
+          match.group(0), '%%(window)0%ds' % len(match.group(0)))
+    match = re.search('V+', shard_name_template)
+    if match:
+      shard_name_template = shard_name_template.replace(
+          match.group(0), '%%(window_utc)0%ds' % len(match.group(0)))
+    return shard_name_template
+
+  @staticmethod
+  def _template_replace_uuid(shard_name_template):
+    match = re.search('U+', shard_name_template)
+    if match:
+      shard_name_template = shard_name_template.replace(
+          match.group(0), '%%(uuid)0%dd' % len(match.group(0)))
+    return shard_name_template
+
   @staticmethod
   def _template_replace_num_shards(shard_name_template):
     match = re.search('N+', shard_name_template)
@@ -394,17 +477,30 @@ def _template_replace_num_shards(shard_name_template):
     return shard_name_template
 
   @staticmethod
-  def _template_to_format(shard_name_template):
-    if not shard_name_template:
-      return ''
+  def _template_replace_shard_num(shard_name_template):
     match = re.search('S+', shard_name_template)
     if match is None:
+      # shard name is required in the template.
       raise ValueError(
           "Shard number pattern S+ not found in shard_name_template: %s" %
           shard_name_template)
-    shard_name_format = shard_name_template.replace(
+    return shard_name_template.replace(
         match.group(0), '%%(shard_num)0%dd' % len(match.group(0)))
-    return FileBasedSink._template_replace_num_shards(shard_name_format)
+
+  @staticmethod
+  def _template_to_format(shard_name_template):
+    if not shard_name_template:
+      return ''
+    # shard_num is required in the template, while others are optional.
+    replace_funcs = [
+        FileBasedSink._template_replace_shard_num,
+        FileBasedSink._template_replace_num_shards,
+        FileBasedSink._template_replace_uuid,
+        FileBasedSink._template_replace_window
+    ]
+    for func in replace_funcs:
+      shard_name_template = func(shard_name_template)
+    return shard_name_template
 
   @staticmethod
   def _template_to_glob_format(shard_name_template):
diff --git a/sdks/python/apache_beam/io/fileio_test.py b/sdks/python/apache_beam/io/fileio_test.py
index 6733df8c70bf..ce535265ef2f 100644
--- a/sdks/python/apache_beam/io/fileio_test.py
+++ b/sdks/python/apache_beam/io/fileio_test.py
@@ -106,7 +106,7 @@ def test_match_files_one_directory_failure1(self):
     files.append(self._create_temp_file(dir=directories[0]))
     files.append(self._create_temp_file(dir=directories[0]))
 
-    with self.assertRaises(beam.io.filesystem.BeamIOError):
+    with self.assertRaisesRegex(Exception, "Empty match for pattern"):
       with TestPipeline() as p:
         files_pc = (
             p
@@ -259,7 +259,7 @@ def test_fail_on_directories(self):
     files.append(self._create_temp_file(dir=tempdir, content=content))
     files.append(self._create_temp_file(dir=tempdir, content=content))
 
-    with self.assertRaises(beam.io.filesystem.BeamIOError):
+    with self.assertRaisesRegex(Exception, "Directories are not allowed"):
       with TestPipeline() as p:
         _ = (
             p
@@ -501,10 +501,14 @@ def test_write_to_dynamic_destination(self):
         fileio.TextSink()  # pass a FileSink object
     ]
 
+    # Test assumes that all records will be handled by same worker process,
+    # pin to FnApiRunner to guarantee hthis
+    runner = 'FnApiRunner'
+
     for sink in sink_params:
       dir = self._new_tempdir()
 
-      with TestPipeline() as p:
+      with TestPipeline(runner) as p:
         _ = (
             p
             | "Create" >> beam.Create(range(100))
@@ -515,7 +519,7 @@ def test_write_to_dynamic_destination(self):
                 sink=sink,
                 file_naming=fileio.destination_prefix_naming("test")))
 
-      with TestPipeline() as p:
+      with TestPipeline(runner) as p:
         result = (
             p
             | fileio.MatchFiles(FileSystems.join(dir, '*'))
diff --git a/sdks/python/apache_beam/io/gcp/bigquery.py b/sdks/python/apache_beam/io/gcp/bigquery.py
index 870a77b59e6a..aa0ebc12ef18 100644
--- a/sdks/python/apache_beam/io/gcp/bigquery.py
+++ b/sdks/python/apache_beam/io/gcp/bigquery.py
@@ -850,7 +850,8 @@ def _setup_temporary_dataset(self, bq):
       return
     location = bq.get_query_location(
         self._get_project(), self.query.get(), self.use_legacy_sql)
-    bq.create_temporary_dataset(self._get_project(), location)
+    bq.create_temporary_dataset(
+        self._get_project(), location, kms_key=self.kms_key)
 
   @check_accessible(['query'])
   def _execute_query(self, bq):
@@ -1062,7 +1063,10 @@ def _setup_temporary_dataset(self, bq):
         self._get_parent_project(), self.query.get(), self.use_legacy_sql)
     _LOGGER.warning("### Labels: %s", str(self.bigquery_dataset_labels))
     bq.create_temporary_dataset(
-        self._get_parent_project(), location, self.bigquery_dataset_labels)
+        self._get_parent_project(),
+        location,
+        self.bigquery_dataset_labels,
+        kms_key=self.kms_key)
 
   @check_accessible(['query'])
   def _execute_query(self, bq):
@@ -1768,11 +1772,14 @@ def _flush_batch(self, destination):
             'Errors were {}'.format(("" if should_retry else " not"), errors))
 
         # The log level is:
-        # - WARNING when we are continuing to retry, and have a deadline.
-        # - ERROR when we will no longer retry, or MAY retry forever.
-        log_level = (
-            logging.WARN if should_retry or self._retry_strategy
-            != RetryStrategy.RETRY_ALWAYS else logging.ERROR)
+        # - WARNING when should_retry is true, else ERROR.
+
+        if (should_retry and
+            self._retry_strategy in [RetryStrategy.RETRY_ON_TRANSIENT_ERROR,
+                                     RetryStrategy.RETRY_ALWAYS]):
+          log_level = logging.WARN
+        else:
+          log_level = logging.ERROR
 
         _LOGGER.log(log_level, message)
 
@@ -2364,6 +2371,7 @@ def find_in_nested_dict(schema):
           table_side_inputs=self.table_side_inputs,
           create_disposition=self.create_disposition,
           write_disposition=self.write_disposition,
+          additional_bq_parameters=self.additional_bq_parameters,
           triggering_frequency=self.triggering_frequency,
           use_at_least_once=self.use_at_least_once,
           with_auto_sharding=self.with_auto_sharding,
@@ -2611,6 +2619,7 @@ def __init__(
       schema=None,
       create_disposition=BigQueryDisposition.CREATE_IF_NEEDED,
       write_disposition=BigQueryDisposition.WRITE_APPEND,
+      additional_bq_parameters=None,
       triggering_frequency=0,
       use_at_least_once=False,
       with_auto_sharding=False,
@@ -2623,6 +2632,7 @@ def __init__(
     self._schema = schema
     self._create_disposition = create_disposition
     self._write_disposition = write_disposition
+    self.additional_bq_parameters = additional_bq_parameters
     self._triggering_frequency = triggering_frequency
     self._use_at_least_once = use_at_least_once
     self._with_auto_sharding = with_auto_sharding
@@ -2698,6 +2708,15 @@ def expand(self, input):
       # communicate to Java that this write should use dynamic destinations
       table = StorageWriteToBigQuery.DYNAMIC_DESTINATIONS
 
+    clustering_fields = []
+    if self.additional_bq_parameters:
+      if callable(self.additional_bq_parameters):
+        raise NotImplementedError(
+            "Currently, dynamic clustering and timepartitioning is not "
+            "supported for STORAGE_WRITE_API write method.")
+      clustering_fields = (
+          self.additional_bq_parameters.get("clustering", {}).get("fields", []))
+
     output = (
         input_beam_rows
         | SchemaAwareExternalTransform(
@@ -2713,6 +2732,7 @@ def expand(self, input):
             use_at_least_once_semantics=self._use_at_least_once,
             use_cdc_writes=self._use_cdc_writes,
             primary_key=self._primary_key,
+            clustering_fields=clustering_fields,
             error_handling={
                 'output': StorageWriteToBigQuery.FAILED_ROWS_WITH_ERRORS
             }))
@@ -2723,11 +2743,12 @@ def expand(self, input):
         lambda row_and_error: row_and_error[0])
     if not is_rows:
       # return back from Beam Rows to Python dict elements
-      failed_rows = failed_rows | beam.Map(lambda row: row.as_dict())
+      failed_rows = failed_rows | beam.Map(lambda row: row._asdict())
+
       failed_rows_with_errors = failed_rows_with_errors | beam.Map(
           lambda row: {
               "error_message": row.error_message, "failed_row": row.failed_row.
-              as_dict()
+              _asdict()
           })
 
     return WriteResult(
diff --git a/sdks/python/apache_beam/io/gcp/bigquery_file_loads.py b/sdks/python/apache_beam/io/gcp/bigquery_file_loads.py
index f0b4b60b7dd9..76f465ddebbf 100644
--- a/sdks/python/apache_beam/io/gcp/bigquery_file_loads.py
+++ b/sdks/python/apache_beam/io/gcp/bigquery_file_loads.py
@@ -541,38 +541,13 @@ def process_one(self, element, job_name_prefix):
       copy_from_reference.projectId = vp.RuntimeValueProvider.get_value(
           'project', str, '') or self.project
 
-    copy_job_name = '%s_%s' % (
-        job_name_prefix,
-        _bq_uuid(
-            '%s:%s.%s' % (
-                copy_from_reference.projectId,
-                copy_from_reference.datasetId,
-                copy_from_reference.tableId)))
-
     _LOGGER.info(
         "Triggering copy job from %s to %s",
         copy_from_reference,
         copy_to_reference)
-    if copy_to_reference.tableId not in self._observed_tables:
-      # When the write_disposition for a job is WRITE_TRUNCATE,
-      # multiple copy jobs to the same destination can stump on
-      # each other, truncate data, and write to the BQ table over and
-      # over.
-      # Thus, the first copy job runs with the user's write_disposition,
-      # but afterwards, all jobs must always WRITE_APPEND to the table.
-      # If they do not, subsequent copy jobs will clear out data appended
-      # by previous jobs.
-      write_disposition = self.write_disposition
-      wait_for_job = True
-      self._observed_tables.add(copy_to_reference.tableId)
-      Lineage.sinks().add(
-          'bigquery',
-          copy_to_reference.projectId,
-          copy_to_reference.datasetId,
-          copy_to_reference.tableId)
-    else:
-      wait_for_job = False
-      write_disposition = 'WRITE_APPEND'
+
+    wait_for_job, write_disposition = (
+      self._determine_write_disposition(copy_to_reference))
 
     if not self.bq_io_metadata:
       self.bq_io_metadata = create_bigquery_io_metadata(self._step_name)
@@ -580,6 +555,13 @@ def process_one(self, element, job_name_prefix):
     project_id = (
         copy_to_reference.projectId
         if self.load_job_project_id is None else self.load_job_project_id)
+    copy_job_name = '%s_%s' % (
+        job_name_prefix,
+        _bq_uuid(
+            '%s:%s.%s' % (
+                copy_from_reference.projectId,
+                copy_from_reference.datasetId,
+                copy_from_reference.tableId)))
     job_reference = self.bq_wrapper._insert_copy_job(
         project_id,
         copy_job_name,
@@ -594,6 +576,43 @@ def process_one(self, element, job_name_prefix):
     self.pending_jobs.append(
         GlobalWindows.windowed_value((destination, job_reference)))
 
+  def _determine_write_disposition(self, copy_to_reference) -> tuple[bool, str]:
+    """
+    Determines the write disposition for a BigQuery copy job,
+     based on destination.
+
+    When the write_disposition for a job is WRITE_TRUNCATE, multiple copy jobs
+    to the same destination can interfere with each other, truncate data, and
+    write to the BigQuery table repeatedly. To prevent this, the first copy job
+    runs with the user's specified write_disposition, but subsequent jobs must
+    always use WRITE_APPEND. This ensures that subsequent copy jobs do not
+    clear out data appended by previous jobs.
+
+    Args:
+        copy_to_reference: The reference to the destination table.
+
+    Returns:
+        A tuple containing a boolean indicating whether to wait for the job to
+        complete and the write disposition to use for the job.
+    """
+    full_table_ref = '%s:%s.%s' % (
+        copy_to_reference.projectId,
+        copy_to_reference.datasetId,
+        copy_to_reference.tableId)
+    if full_table_ref not in self._observed_tables:
+      write_disposition = self.write_disposition
+      wait_for_job = True
+      self._observed_tables.add(full_table_ref)
+      Lineage.sinks().add(
+          'bigquery',
+          copy_to_reference.projectId,
+          copy_to_reference.datasetId,
+          copy_to_reference.tableId)
+    else:
+      wait_for_job = False
+      write_disposition = 'WRITE_APPEND'
+    return wait_for_job, write_disposition
+
   def finish_bundle(self):
     for windowed_value in self.pending_jobs:
       job_ref = windowed_value.value[1]
diff --git a/sdks/python/apache_beam/io/gcp/bigquery_file_loads_test.py b/sdks/python/apache_beam/io/gcp/bigquery_file_loads_test.py
index 6400365918d2..c318b1988536 100644
--- a/sdks/python/apache_beam/io/gcp/bigquery_file_loads_test.py
+++ b/sdks/python/apache_beam/io/gcp/bigquery_file_loads_test.py
@@ -24,6 +24,8 @@
 import secrets
 import time
 import unittest
+from unittest.mock import Mock
+from unittest.mock import call
 
 import mock
 import pytest
@@ -39,6 +41,7 @@
 from apache_beam.io.gcp import bigquery
 from apache_beam.io.gcp import bigquery_tools
 from apache_beam.io.gcp.bigquery import BigQueryDisposition
+from apache_beam.io.gcp.bigquery_tools import BigQueryWrapper
 from apache_beam.io.gcp.internal.clients import bigquery as bigquery_api
 from apache_beam.io.gcp.tests.bigquery_matcher import BigqueryFullResultMatcher
 from apache_beam.io.gcp.tests.bigquery_matcher import BigqueryFullResultStreamingMatcher
@@ -60,6 +63,11 @@
 except ImportError:
   raise unittest.SkipTest('GCP dependencies are not installed')
 
+try:
+  import dill
+except ImportError:
+  dill = None
+
 _LOGGER = logging.getLogger(__name__)
 
 _DESTINATION_ELEMENT_PAIRS = [
@@ -403,6 +411,13 @@ def test_partition_files_dofn_size_split(self):
           label='CheckSinglePartition')
 
 
+def maybe_skip(compat_version):
+  if compat_version and not dill:
+    raise unittest.SkipTest(
+        'Dill dependency not installed which is required for compat_version'
+        ' <= 2.67.0')
+
+
 class TestBigQueryFileLoads(_TestCaseWithTempDirCleanUp):
   def test_trigger_load_jobs_with_empty_files(self):
     destination = "project:dataset.table"
@@ -447,8 +462,8 @@ def test_records_traverse_transform_with_mocks(self):
         validate=False,
         temp_file_format=bigquery_tools.FileFormat.JSON)
 
-    # Need to test this with the DirectRunner to avoid serializing mocks
-    with TestPipeline('DirectRunner') as p:
+    # Need to test this with the FnApiRunner to avoid serializing mocks
+    with TestPipeline('FnApiRunner') as p:
       outputs = p | beam.Create(_ELEMENTS) | transform
 
       dest_files = outputs[bqfl.BigQueryBatchFileLoads.DESTINATION_FILE_PAIRS]
@@ -482,7 +497,9 @@ def test_records_traverse_transform_with_mocks(self):
       param(compat_version=None),
       param(compat_version="2.64.0"),
   ])
+  @pytest.mark.uses_dill
   def test_reshuffle_before_load(self, compat_version):
+    maybe_skip(compat_version)
     destination = 'project1:dataset1.table1'
 
     job_reference = bigquery_api.JobReference()
@@ -820,6 +837,166 @@ def test_multiple_partition_files_write_dispositions(
     # TriggerCopyJob only processes once
     self.assertEqual(mock_call_process.call_count, 1)
 
+  @mock.patch(
+      'apache_beam.io.gcp.bigquery_tools.BigQueryWrapper.wait_for_bq_job')
+  @mock.patch(
+      'apache_beam.io.gcp.bigquery_tools.BigQueryWrapper._insert_copy_job')
+  @mock.patch(
+      'apache_beam.io.gcp.bigquery_tools.BigQueryWrapper._start_job',
+      wraps=BigQueryWrapper._start_job)
+  def test_multiple_identical_destinations_on_write_truncate(
+      self, mock_perform_start_job, mock_insert_copy_job, mock_wait_for_bq_job):
+    """
+    Test that multiple identical table names,
+     but under different datasets are handled correctly.
+    This essentially means that the `write_disposition` is set
+     to `WRITE_TRUNCATE` for the first job and `WRITE_APPEND` for the rest.
+
+    Previously this was not the case and all jobs were set to `WRITE_APPEND`
+     from the 2nd table that was named identically with at least
+     one previous table - but from different dataset.
+    """
+    def dynamic_destination_resolver(element, *side_inputs):
+      """A dynamic destination resolver that returns a destination strictly the
+       same table, but different dataset."""
+      if element['name'] == 'beam':
+        return 'project1:dataset1.table1'
+      elif element['name'] == 'flink':
+        return 'project1:dataset2.table1'
+
+      return 'project1:dataset3.table1'
+
+    job_reference = bigquery_api.JobReference()
+    job_reference.projectId = 'project1'
+    job_reference.jobId = 'job_name1'
+    result_job = mock.Mock()
+    result_job.jobReference = job_reference
+
+    mock_job = mock.Mock()
+    mock_job.status.state = 'DONE'
+    mock_job.status.errorResult = None
+    mock_job.jobReference = job_reference
+
+    bq_client = mock.Mock()
+    bq_client.jobs.Get.return_value = mock_job
+
+    bq_client.jobs.Insert.return_value = result_job
+    bq_client.tables.Delete.return_value = None
+
+    m = bigquery_tools.BigQueryWrapper(bq_client)
+    m.wait_for_bq_job = mock.Mock()
+    m.wait_for_bq_job.return_value = None
+
+    mock_jobs = [
+        Mock(jobReference=bigquery_api.JobReference(jobId=f'job_name{i}'))
+        # Order matters in a sense to prove that jobs with different ids
+        #  (`2` & `3`) are run with `WRITE_APPEND` without this current fix.
+        for i in [1, 2, 1, 3, 1]
+    ]
+    mock_perform_start_job.side_effect = mock_jobs
+
+    # For now we don't care about the return value.
+    mock_insert_copy_job.return_value = None
+
+    # Pin to FnApiRunner for now to make mocks act appropriately.
+    # TODO(https://github.com/apache/beam/issues/34549)
+    with TestPipeline('FnApiRunner') as p:
+      _ = (
+          p
+          | beam.Create([
+              {
+                  'name': 'beam', 'language': 'java'
+              },
+              {
+                  'name': 'flink', 'language': 'java'
+              },
+              {
+                  'name': 'beam', 'language': 'java'
+              },
+              {
+                  'name': 'spark', 'language': 'java'
+              },
+              {
+                  'name': 'beam', 'language': 'java'
+              },
+          ],
+                        reshuffle=False)
+          | bqfl.BigQueryBatchFileLoads(
+              dynamic_destination_resolver,
+              custom_gcs_temp_location=self._new_tempdir(),
+              test_client=bq_client,
+              validate=False,
+              temp_file_format=bigquery_tools.FileFormat.JSON,
+              max_file_size=45,
+              max_partition_size=80,
+              max_files_per_partition=3,
+              write_disposition=BigQueryDisposition.WRITE_TRUNCATE))
+
+    from apache_beam.io.gcp.internal.clients.bigquery import TableReference
+    mock_insert_copy_job.assert_has_calls(
+        [
+            call(
+                'project1',
+                mock.ANY,
+                TableReference(
+                    datasetId='dataset1',
+                    projectId='project1',
+                    tableId='job_name1'),
+                TableReference(
+                    datasetId='dataset1',
+                    projectId='project1',
+                    tableId='table1'),
+                create_disposition=None,
+                write_disposition='WRITE_TRUNCATE',
+                job_labels={'step_name': 'bigquerybatchfileloads'}),
+            call(
+                'project1',
+                mock.ANY,
+                TableReference(
+                    datasetId='dataset1',
+                    projectId='project1',
+                    tableId='job_name2'),
+                TableReference(
+                    datasetId='dataset1',
+                    projectId='project1',
+                    tableId='table1'),
+                create_disposition=None,
+                write_disposition='WRITE_APPEND',
+                job_labels={'step_name': 'bigquerybatchfileloads'}),
+            call(
+                'project1',
+                mock.ANY,
+                TableReference(
+                    datasetId='dataset2',
+                    projectId='project1',
+                    tableId='job_name1'),
+                TableReference(
+                    datasetId='dataset2',
+                    projectId='project1',
+                    tableId='table1'),
+                create_disposition=None,
+                # Previously this was `WRITE_APPEND`.
+                write_disposition='WRITE_TRUNCATE',
+                job_labels={'step_name': 'bigquerybatchfileloads'}),
+            call(
+                'project1',
+                mock.ANY,
+                TableReference(
+                    datasetId='dataset3',
+                    projectId='project1',
+                    tableId='job_name3'),
+                TableReference(
+                    datasetId='dataset3',
+                    projectId='project1',
+                    tableId='table1'),
+                create_disposition=None,
+                # Previously this was `WRITE_APPEND`.
+                write_disposition='WRITE_TRUNCATE',
+                job_labels={'step_name': 'bigquerybatchfileloads'}),
+        ],
+        any_order=True)
+    self.assertEqual(4, mock_insert_copy_job.call_count)
+
   @parameterized.expand([
       param(is_streaming=False, with_auto_sharding=False, compat_version=None),
       param(is_streaming=True, with_auto_sharding=False, compat_version=None),
@@ -831,6 +1008,7 @@ def test_multiple_partition_files_write_dispositions(
   ])
   def test_triggering_frequency(
       self, is_streaming, with_auto_sharding, compat_version):
+    maybe_skip(compat_version)
     destination = 'project1:dataset1.table1'
 
     job_reference = bigquery_api.JobReference()
diff --git a/sdks/python/apache_beam/io/gcp/bigquery_read_internal.py b/sdks/python/apache_beam/io/gcp/bigquery_read_internal.py
index e23571f257df..6432f3b4eeac 100644
--- a/sdks/python/apache_beam/io/gcp/bigquery_read_internal.py
+++ b/sdks/python/apache_beam/io/gcp/bigquery_read_internal.py
@@ -239,6 +239,17 @@ def _get_temp_dataset_id(self):
     else:
       raise ValueError("temp_dataset has to be either str or DatasetReference")
 
+  def _get_temp_dataset_project(self):
+    """Returns the project ID for temporary dataset operations.
+    
+    If temp_dataset is a DatasetReference, returns its projectId.
+    Otherwise, returns the pipeline project for billing.
+    """
+    if isinstance(self.temp_dataset, DatasetReference):
+      return self.temp_dataset.projectId
+    else:
+      return self._get_project()
+
   def start_bundle(self):
     self.bq = bigquery_tools.BigQueryWrapper(
         temp_dataset_id=self._get_temp_dataset_id(),
@@ -278,7 +289,9 @@ def process(self,
 
   def finish_bundle(self):
     if self.bq.created_temp_dataset:
-      self.bq.clean_up_temporary_dataset(self._get_project())
+      # Use the same project that was used to create the temp dataset
+      temp_dataset_project = self._get_temp_dataset_project()
+      self.bq.clean_up_temporary_dataset(temp_dataset_project)
 
   def _get_bq_metadata(self):
     if not self.bq_io_metadata:
@@ -303,7 +316,11 @@ def _setup_temporary_dataset(
       element: 'ReadFromBigQueryRequest'):
     location = bq.get_query_location(
         self._get_project(), element.query, not element.use_standard_sql)
-    bq.create_temporary_dataset(self._get_project(), location)
+    # Use the project from temp_dataset if it's a DatasetReference,
+    # otherwise use the pipeline project
+    temp_dataset_project = self._get_temp_dataset_project()
+    bq.create_temporary_dataset(
+        temp_dataset_project, location, kms_key=self.kms_key)
 
   def _execute_query(
       self,
diff --git a/sdks/python/apache_beam/io/gcp/bigquery_read_internal_test.py b/sdks/python/apache_beam/io/gcp/bigquery_read_internal_test.py
new file mode 100644
index 000000000000..46673b4ec2d2
--- /dev/null
+++ b/sdks/python/apache_beam/io/gcp/bigquery_read_internal_test.py
@@ -0,0 +1,170 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Unit tests for BigQuery read internal module."""
+
+import unittest
+from unittest import mock
+
+from apache_beam.io.gcp import bigquery_read_internal
+from apache_beam.options.pipeline_options import GoogleCloudOptions
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.value_provider import StaticValueProvider
+
+try:
+  from apache_beam.io.gcp.internal.clients.bigquery import DatasetReference
+except ImportError:
+  DatasetReference = None
+
+
+class BigQueryReadSplitTest(unittest.TestCase):
+  """Tests for _BigQueryReadSplit DoFn."""
+  def setUp(self):
+    if DatasetReference is None:
+      self.skipTest('BigQuery dependencies are not installed')
+    self.options = PipelineOptions()
+    self.gcp_options = self.options.view_as(GoogleCloudOptions)
+    self.gcp_options.project = 'test-project'
+
+  def test_get_temp_dataset_project_with_string_temp_dataset(self):
+    """Test _get_temp_dataset_project with string temp_dataset."""
+    split = bigquery_read_internal._BigQueryReadSplit(
+        options=self.options, temp_dataset='temp_dataset_id')
+
+    # Should return the pipeline project when temp_dataset is a string
+    self.assertEqual(split._get_temp_dataset_project(), 'test-project')
+
+  def test_get_temp_dataset_project_with_dataset_reference(self):
+    """Test _get_temp_dataset_project with DatasetReference temp_dataset."""
+    dataset_ref = DatasetReference(
+        projectId='custom-project', datasetId='temp_dataset_id')
+    split = bigquery_read_internal._BigQueryReadSplit(
+        options=self.options, temp_dataset=dataset_ref)
+
+    # Should return the project from DatasetReference
+    self.assertEqual(split._get_temp_dataset_project(), 'custom-project')
+
+  def test_get_temp_dataset_project_with_none_temp_dataset(self):
+    """Test _get_temp_dataset_project with None temp_dataset."""
+    split = bigquery_read_internal._BigQueryReadSplit(
+        options=self.options, temp_dataset=None)
+
+    # Should return the pipeline project when temp_dataset is None
+    self.assertEqual(split._get_temp_dataset_project(), 'test-project')
+
+  def test_get_temp_dataset_project_with_value_provider_project(self):
+    """Test _get_temp_dataset_project with ValueProvider project."""
+    self.gcp_options.project = StaticValueProvider(str, 'vp-project')
+    dataset_ref = DatasetReference(
+        projectId='custom-project', datasetId='temp_dataset_id')
+    split = bigquery_read_internal._BigQueryReadSplit(
+        options=self.options, temp_dataset=dataset_ref)
+
+    # Should still return the project from DatasetReference
+    self.assertEqual(split._get_temp_dataset_project(), 'custom-project')
+
+  @mock.patch('apache_beam.io.gcp.bigquery_tools.BigQueryWrapper')
+  def test_setup_temporary_dataset_uses_correct_project(self, mock_bq_wrapper):
+    """Test that _setup_temporary_dataset uses the correct project."""
+    dataset_ref = DatasetReference(
+        projectId='custom-project', datasetId='temp_dataset_id')
+    split = bigquery_read_internal._BigQueryReadSplit(
+        options=self.options, temp_dataset=dataset_ref)
+
+    # Mock the BigQueryWrapper instance
+    mock_bq = mock.Mock()
+    mock_bq.get_query_location.return_value = 'US'
+
+    # Mock ReadFromBigQueryRequest
+    mock_element = mock.Mock()
+    mock_element.query = 'SELECT * FROM table'
+    mock_element.use_standard_sql = True
+
+    # Call _setup_temporary_dataset
+    split._setup_temporary_dataset(mock_bq, mock_element)
+
+    # Verify that create_temporary_dataset was called with the custom project
+    mock_bq.create_temporary_dataset.assert_called_once_with(
+        'custom-project', 'US', kms_key=None)
+    # Verify that get_query_location was called with the pipeline project
+    mock_bq.get_query_location.assert_called_once_with(
+        'test-project', 'SELECT * FROM table', False)
+
+  @mock.patch('apache_beam.io.gcp.bigquery_tools.BigQueryWrapper')
+  def test_finish_bundle_uses_correct_project(self, mock_bq_wrapper):
+    """Test that finish_bundle uses the correct project for cleanup."""
+    dataset_ref = DatasetReference(
+        projectId='custom-project', datasetId='temp_dataset_id')
+    split = bigquery_read_internal._BigQueryReadSplit(
+        options=self.options, temp_dataset=dataset_ref)
+
+    # Mock the BigQueryWrapper instance
+    mock_bq = mock.Mock()
+    mock_bq.created_temp_dataset = True
+    split.bq = mock_bq
+
+    # Call finish_bundle
+    split.finish_bundle()
+
+    # Verify that clean_up_temporary_dataset was called with the custom project
+    mock_bq.clean_up_temporary_dataset.assert_called_once_with('custom-project')
+
+  @mock.patch('apache_beam.io.gcp.bigquery_tools.BigQueryWrapper')
+  def test_setup_temporary_dataset_with_string_temp_dataset(
+      self, mock_bq_wrapper):
+    """Test _setup_temporary_dataset with string temp_dataset uses pipeline
+    project."""
+    split = bigquery_read_internal._BigQueryReadSplit(
+        options=self.options, temp_dataset='temp_dataset_id')
+
+    # Mock the BigQueryWrapper instance
+    mock_bq = mock.Mock()
+    mock_bq.get_query_location.return_value = 'US'
+
+    # Mock ReadFromBigQueryRequest
+    mock_element = mock.Mock()
+    mock_element.query = 'SELECT * FROM table'
+    mock_element.use_standard_sql = True
+
+    # Call _setup_temporary_dataset
+    split._setup_temporary_dataset(mock_bq, mock_element)
+
+    # Verify that create_temporary_dataset was called with the pipeline project
+    mock_bq.create_temporary_dataset.assert_called_once_with(
+        'test-project', 'US', kms_key=None)
+
+  @mock.patch('apache_beam.io.gcp.bigquery_tools.BigQueryWrapper')
+  def test_finish_bundle_with_string_temp_dataset(self, mock_bq_wrapper):
+    """Test finish_bundle with string temp_dataset uses pipeline project."""
+    split = bigquery_read_internal._BigQueryReadSplit(
+        options=self.options, temp_dataset='temp_dataset_id')
+
+    # Mock the BigQueryWrapper instance
+    mock_bq = mock.Mock()
+    mock_bq.created_temp_dataset = True
+    split.bq = mock_bq
+
+    # Call finish_bundle
+    split.finish_bundle()
+
+    # Verify that clean_up_temporary_dataset was called with the pipeline
+    # project
+    mock_bq.clean_up_temporary_dataset.assert_called_once_with('test-project')
+
+
+if __name__ == '__main__':
+  unittest.main()
diff --git a/sdks/python/apache_beam/io/gcp/bigquery_test.py b/sdks/python/apache_beam/io/gcp/bigquery_test.py
index b038f38bd5a1..dcb85d60f87f 100644
--- a/sdks/python/apache_beam/io/gcp/bigquery_test.py
+++ b/sdks/python/apache_beam/io/gcp/bigquery_test.py
@@ -1567,7 +1567,9 @@ def test_insert_rows_json_intermittent_retriable_exception(
         exception_type(error_message), exception_type(error_message), []
     ]
 
-    with beam.Pipeline() as p:
+    # This relies on DirectRunner-specific mocking behavior which can be
+    # inconsistent on Prism
+    with beam.Pipeline('FnApiRunner') as p:
       _ = (
           p
           | beam.Create([{
diff --git a/sdks/python/apache_beam/io/gcp/bigquery_tools.py b/sdks/python/apache_beam/io/gcp/bigquery_tools.py
index 081571bfef99..889d3f1e96e3 100644
--- a/sdks/python/apache_beam/io/gcp/bigquery_tools.py
+++ b/sdks/python/apache_beam/io/gcp/bigquery_tools.py
@@ -333,6 +333,10 @@ def _build_filter_from_labels(labels):
   return filter_str
 
 
+def _build_dataset_encryption_config(kms_key):
+  return bigquery.EncryptionConfiguration(kmsKeyName=kms_key)
+
+
 class BigQueryWrapper(object):
   """BigQuery client wrapper with utilities for querying.
 
@@ -412,6 +416,17 @@ def _get_temp_table(self, project_id):
         dataset=self.temp_dataset_id,
         project=project_id)
 
+  def _get_temp_table_project(self, fallback_project_id):
+    """Returns the project ID for temporary table operations.
+    
+    If temp_table_ref exists, returns its projectId.
+    Otherwise, returns the fallback_project_id.
+    """
+    if self.temp_table_ref:
+      return self.temp_table_ref.projectId
+    else:
+      return fallback_project_id
+
   def _get_temp_dataset(self):
     if self.temp_table_ref:
       return self.temp_table_ref.datasetId
@@ -639,7 +654,8 @@ def _start_query_job(
                     query=query,
                     useLegacySql=use_legacy_sql,
                     allowLargeResults=not dry_run,
-                    destinationTable=self._get_temp_table(project_id)
+                    destinationTable=self._get_temp_table(
+                        self._get_temp_table_project(project_id))
                     if not dry_run else None,
                     flattenResults=flatten_results,
                     priority=priority,
@@ -823,7 +839,7 @@ def _create_table(
       num_retries=MAX_RETRIES,
       retry_filter=retry.retry_on_server_errors_and_timeout_filter)
   def get_or_create_dataset(
-      self, project_id, dataset_id, location=None, labels=None):
+      self, project_id, dataset_id, location=None, labels=None, kms_key=None):
     # Check if dataset already exists otherwise create it
     try:
       dataset = self.client.datasets.Get(
@@ -846,6 +862,9 @@ def get_or_create_dataset(
           dataset.location = location
         if labels is not None:
           dataset.labels = _build_dataset_labels(labels)
+        if kms_key is not None:
+          dataset.defaultEncryptionConfiguration = (
+              _build_dataset_encryption_config(kms_key))
         request = bigquery.BigqueryDatasetsInsertRequest(
             projectId=project_id, dataset=dataset)
         response = self.client.datasets.Insert(request)
@@ -917,9 +936,14 @@ def is_user_configured_dataset(self):
   @retry.with_exponential_backoff(
       num_retries=MAX_RETRIES,
       retry_filter=retry.retry_on_server_errors_and_timeout_filter)
-  def create_temporary_dataset(self, project_id, location, labels=None):
+  def create_temporary_dataset(
+      self, project_id, location, labels=None, kms_key=None):
     self.get_or_create_dataset(
-        project_id, self.temp_dataset_id, location=location, labels=labels)
+        project_id,
+        self.temp_dataset_id,
+        location=location,
+        labels=labels,
+        kms_key=kms_key)
 
     if (project_id is not None and not self.is_user_configured_dataset() and
         not self.created_temp_dataset):
diff --git a/sdks/python/apache_beam/io/gcp/bigquery_tools_test.py b/sdks/python/apache_beam/io/gcp/bigquery_tools_test.py
index 522c8667f183..1101317439a9 100644
--- a/sdks/python/apache_beam/io/gcp/bigquery_tools_test.py
+++ b/sdks/python/apache_beam/io/gcp/bigquery_tools_test.py
@@ -301,6 +301,34 @@ def test_get_or_create_dataset_created(self):
     new_dataset = wrapper.get_or_create_dataset('project-id', 'dataset_id')
     self.assertEqual(new_dataset.datasetReference.datasetId, 'dataset_id')
 
+  def test_create_temporary_dataset_with_kms_key(self):
+    kms_key = (
+        'projects/my-project/locations/global/keyRings/my-kr/'
+        'cryptoKeys/my-key')
+    client = mock.Mock()
+    client.datasets.Get.side_effect = HttpError(
+        response={'status': '404'}, url='', content='')
+
+    client.datasets.Insert.return_value = bigquery.Dataset(
+        datasetReference=bigquery.DatasetReference(
+            projectId='project-id', datasetId='temp_dataset'))
+    wrapper = beam.io.gcp.bigquery_tools.BigQueryWrapper(client)
+
+    try:
+      wrapper.create_temporary_dataset(
+          'project-id', 'location', kms_key=kms_key)
+    except Exception:
+      pass
+
+    args, _ = client.datasets.Insert.call_args
+    insert_request = args[0]  # BigqueryDatasetsInsertRequest
+    inserted_dataset = insert_request.dataset  # Actual Dataset object
+
+    # Assertions
+    self.assertIsNotNone(inserted_dataset.defaultEncryptionConfiguration)
+    self.assertEqual(
+        inserted_dataset.defaultEncryptionConfiguration.kmsKeyName, kms_key)
+
   def test_get_or_create_dataset_fetched(self):
     client = mock.Mock()
     client.datasets.Get.return_value = bigquery.Dataset(
@@ -578,6 +606,27 @@ def test_start_query_job_priority_configuration(self):
         client.jobs.Insert.call_args[0][0].job.configuration.query.priority,
         'INTERACTIVE')
 
+  def test_get_temp_table_project_with_temp_table_ref(self):
+    """Test _get_temp_table_project returns project from temp_table_ref."""
+    client = mock.Mock()
+    temp_table_ref = bigquery.TableReference(
+        projectId='temp-project',
+        datasetId='temp_dataset',
+        tableId='temp_table')
+    wrapper = beam.io.gcp.bigquery_tools.BigQueryWrapper(
+        client, temp_table_ref=temp_table_ref)
+
+    result = wrapper._get_temp_table_project('fallback-project')
+    self.assertEqual(result, 'temp-project')
+
+  def test_get_temp_table_project_without_temp_table_ref(self):
+    """Test _get_temp_table_project returns fallback when no temp_table_ref."""
+    client = mock.Mock()
+    wrapper = beam.io.gcp.bigquery_tools.BigQueryWrapper(client)
+
+    result = wrapper._get_temp_table_project('fallback-project')
+    self.assertEqual(result, 'fallback-project')
+
 
 @unittest.skipIf(HttpError is None, 'GCP dependencies are not installed')
 class TestRowAsDictJsonCoder(unittest.TestCase):
diff --git a/sdks/python/apache_beam/io/gcp/bigtableio.py b/sdks/python/apache_beam/io/gcp/bigtableio.py
index b32433df547a..ff140082a1ef 100644
--- a/sdks/python/apache_beam/io/gcp/bigtableio.py
+++ b/sdks/python/apache_beam/io/gcp/bigtableio.py
@@ -357,7 +357,8 @@ def expand(self, input):
         rearrange_based_on_discovery=True,
         table_id=self._table_id,
         instance_id=self._instance_id,
-        project_id=self._project_id)
+        project_id=self._project_id,
+        flatten=False)
 
     return (
         input.pipeline
diff --git a/sdks/python/apache_beam/io/gcp/experimental/spannerio.py b/sdks/python/apache_beam/io/gcp/experimental/spannerio.py
index 7b615e223cfc..cac66bd2ef54 100644
--- a/sdks/python/apache_beam/io/gcp/experimental/spannerio.py
+++ b/sdks/python/apache_beam/io/gcp/experimental/spannerio.py
@@ -17,7 +17,7 @@
 
 """Google Cloud Spanner IO
 
-Experimental; no backwards-compatibility guarantees.
+Deprecated; use apache_beam.io.gcp.spanner module instead.
 
 This is an experimental module for reading and writing data from Google Cloud
 Spanner. Visit: https://cloud.google.com/spanner for more details.
@@ -190,6 +190,7 @@
 from apache_beam.transforms.display import DisplayDataItem
 from apache_beam.typehints import with_input_types
 from apache_beam.typehints import with_output_types
+from apache_beam.utils.annotations import deprecated
 
 # Protect against environments where spanner library is not available.
 # pylint: disable=wrong-import-order, wrong-import-position, ungrouped-imports
@@ -356,8 +357,8 @@ def _table_metric(self, table_id, status):
     labels = {
         **self.base_labels,
         monitoring_infos.RESOURCE_LABEL: resource,
-        monitoring_infos.SPANNER_TABLE_ID: table_id
     }
+    if table_id: labels[monitoring_infos.SPANNER_TABLE_ID] = table_id
     service_call_metric = ServiceCallMetric(
         request_count_urn=monitoring_infos.API_REQUEST_COUNT_URN,
         base_labels=labels)
@@ -612,8 +613,8 @@ def _table_metric(self, table_id):
     labels = {
         **self.base_labels,
         monitoring_infos.RESOURCE_LABEL: resource,
-        monitoring_infos.SPANNER_TABLE_ID: table_id
     }
+    if table_id: labels[monitoring_infos.SPANNER_TABLE_ID] = table_id
     service_call_metric = ServiceCallMetric(
         request_count_urn=monitoring_infos.API_REQUEST_COUNT_URN,
         base_labels=labels)
@@ -675,6 +676,7 @@ def teardown(self):
       self._snapshot.close()
 
 
+@deprecated(since='2.68', current='apache_beam.io.gcp.spanner.ReadFromSpanner')
 class ReadFromSpanner(PTransform):
   """
   A PTransform to perform reads from cloud spanner.
@@ -825,6 +827,8 @@ def display_data(self):
     return res
 
 
+@deprecated(
+    since='2.68', current='apache_beam.io.gcp.spanner.WriteToSpannerSchema')
 class WriteToSpanner(PTransform):
   def __init__(
       self,
@@ -1224,8 +1228,8 @@ def _register_table_metric(self, table_id):
     labels = {
         **self.base_labels,
         monitoring_infos.RESOURCE_LABEL: resource,
-        monitoring_infos.SPANNER_TABLE_ID: table_id
     }
+    if table_id: labels[monitoring_infos.SPANNER_TABLE_ID] = table_id
     service_call_metric = ServiceCallMetric(
         request_count_urn=monitoring_infos.API_REQUEST_COUNT_URN,
         base_labels=labels)
diff --git a/sdks/python/apache_beam/io/gcp/experimental/spannerio_test.py b/sdks/python/apache_beam/io/gcp/experimental/spannerio_test.py
index 4e391900eaa7..de7691883ed1 100644
--- a/sdks/python/apache_beam/io/gcp/experimental/spannerio_test.py
+++ b/sdks/python/apache_beam/io/gcp/experimental/spannerio_test.py
@@ -387,7 +387,8 @@ def test_read_with_transaction(
   def test_invalid_transaction(
       self, mock_batch_snapshot_class, mock_client_class):
     # test exception raises at pipeline execution time
-    with self.assertRaises(ValueError), TestPipeline() as p:
+    error_string = "Invalid transaction object"
+    with self.assertRaisesRegex(Exception, error_string), TestPipeline() as p:
       transaction = (
           p | beam.Create([{
               "invalid": "transaction"
diff --git a/sdks/python/apache_beam/io/gcp/healthcare/dicomio_integration_test.py b/sdks/python/apache_beam/io/gcp/healthcare/dicomio_integration_test.py
index 2809bd25e45b..499649beae46 100644
--- a/sdks/python/apache_beam/io/gcp/healthcare/dicomio_integration_test.py
+++ b/sdks/python/apache_beam/io/gcp/healthcare/dicomio_integration_test.py
@@ -64,6 +64,19 @@
 RAND_LEN = 15
 SCOPES = ['https://www.googleapis.com/auth/cloud-platform']
 
+# Tag 00081190 contains temp store name which contains currentDate
+VOLATILE_TAGS = {"00081190"}
+
+
+def normalize_outer(elem: dict) -> dict:
+  elem = dict(elem)  # shallow copy
+  elem["result"] = [normalize_instance(d) for d in elem.get("result", [])]
+  return elem
+
+
+def normalize_instance(instance: dict) -> dict:
+  return {k: v for k, v in instance.items() if k not in VOLATILE_TAGS}
+
 
 def random_string_generator(length):
   letters_and_digits = string.ascii_letters + string.digits
@@ -173,17 +186,22 @@ def test_dicom_search_instances(self):
       results_all = (
           p
           | 'create all dict' >> beam.Create([input_dict_all])
-          | 'search all' >> DicomSearch())
+          | 'search all' >> DicomSearch()
+          | 'normalize all' >> beam.Map(normalize_outer))
       results_refine = (
           p
           | 'create refine dict' >> beam.Create([input_dict_refine])
-          | 'search refine' >> DicomSearch())
+          | 'search refine' >> DicomSearch()
+          | 'normalize refine' >> beam.Map(normalize_outer))
+
+      expected_all_norm = normalize_outer(expected_dict_all)
+      expected_refine_norm = normalize_outer(expected_dict_refine)
 
       assert_that(
-          results_all, equal_to([expected_dict_all]), label='all search assert')
+          results_all, equal_to([expected_all_norm]), label='all search assert')
       assert_that(
           results_refine,
-          equal_to([expected_dict_refine]),
+          equal_to([expected_refine_norm]),
           label='refine search assert')
 
   @pytest.mark.it_postcommit
@@ -218,8 +236,13 @@ def test_dicom_store_instance_from_gcs(self):
 
     self.assertEqual(status_code, 200)
 
-    # List comparison based on different version of python
-    self.assertCountEqual(result, self.expected_output_all_metadata)
+    actual_norm = [normalize_instance(r) for r in result]
+    expected_norm = [
+        normalize_instance(r) for r in self.expected_output_all_metadata
+    ]
+
+    # Order-insensitive deep equality
+    self.assertCountEqual(actual_norm, expected_norm)
 
 
 if __name__ == '__main__':
diff --git a/sdks/python/apache_beam/io/gcp/internal/clients/bigquery/bigquery_v2_client.py b/sdks/python/apache_beam/io/gcp/internal/clients/bigquery/bigquery_v2_client.py
index ef82fcf22c5d..47e9e0d00671 100644
--- a/sdks/python/apache_beam/io/gcp/internal/clients/bigquery/bigquery_v2_client.py
+++ b/sdks/python/apache_beam/io/gcp/internal/clients/bigquery/bigquery_v2_client.py
@@ -117,8 +117,7 @@ def Delete(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryDatasetsDeleteRequest',
         response_type_name='BigqueryDatasetsDeleteResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Get(self, request, global_params=None):
       r"""Returns the dataset specified by datasetID.
@@ -142,8 +141,7 @@ def Get(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryDatasetsGetRequest',
         response_type_name='Dataset',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Insert(self, request, global_params=None):
       r"""Creates a new empty dataset.
@@ -167,8 +165,7 @@ def Insert(self, request, global_params=None):
         request_field='dataset',
         request_type_name='BigqueryDatasetsInsertRequest',
         response_type_name='Dataset',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def List(self, request, global_params=None):
       r"""Lists all datasets in the specified project to which you have been granted the READER dataset role.
@@ -192,8 +189,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryDatasetsListRequest',
         response_type_name='DatasetList',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Patch(self, request, global_params=None):
       r"""Updates information in an existing dataset. The update method replaces the entire dataset resource, whereas the patch method only replaces fields that are provided in the submitted dataset resource. This method supports patch semantics.
@@ -217,8 +213,7 @@ def Patch(self, request, global_params=None):
         request_field='dataset',
         request_type_name='BigqueryDatasetsPatchRequest',
         response_type_name='Dataset',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Update(self, request, global_params=None):
       r"""Updates information in an existing dataset. The update method replaces the entire dataset resource, whereas the patch method only replaces fields that are provided in the submitted dataset resource.
@@ -242,8 +237,7 @@ def Update(self, request, global_params=None):
         request_field='dataset',
         request_type_name='BigqueryDatasetsUpdateRequest',
         response_type_name='Dataset',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class JobsService(base_api.BaseApiService):
     """Service class for the jobs resource."""
@@ -286,8 +280,7 @@ def Cancel(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryJobsCancelRequest',
         response_type_name='JobCancelResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Get(self, request, global_params=None):
       r"""Returns information about a specific job. Job information is available for a six month period after creation. Requires that you're the person who ran the job, or have the Is Owner project role.
@@ -311,8 +304,7 @@ def Get(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryJobsGetRequest',
         response_type_name='Job',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def GetQueryResults(self, request, global_params=None):
       r"""Retrieves the results of a query job.
@@ -337,8 +329,7 @@ def GetQueryResults(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryJobsGetQueryResultsRequest',
         response_type_name='GetQueryResultsResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Insert(self, request, global_params=None, upload=None):
       r"""Starts a new asynchronous job. Requires the Can View project role.
@@ -370,8 +361,7 @@ def Insert(self, request, global_params=None, upload=None):
         request_field='job',
         request_type_name='BigqueryJobsInsertRequest',
         response_type_name='Job',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def List(self, request, global_params=None):
       r"""Lists all jobs that you started in the specified project. Job information is available for a six month period after creation. The job list is sorted in reverse chronological order, by job creation time. Requires the Can View project role, or the Is Owner project role if you set the allUsers property.
@@ -391,21 +381,14 @@ def List(self, request, global_params=None):
         ordered_params=['projectId'],
         path_params=['projectId'],
         query_params=[
-            'allUsers',
-            'maxCreationTime',
-            'maxResults',
-            'minCreationTime',
-            'pageToken',
-            'parentJobId',
-            'projection',
-            'stateFilter'
+            'allUsers', 'maxCreationTime', 'maxResults', 'minCreationTime',
+            'pageToken', 'parentJobId', 'projection', 'stateFilter'
         ],
         relative_path='projects/{projectId}/jobs',
         request_field='',
         request_type_name='BigqueryJobsListRequest',
         response_type_name='JobList',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Query(self, request, global_params=None):
       r"""Runs a BigQuery SQL query synchronously and returns query results if the query completes within a specified timeout.
@@ -429,8 +412,7 @@ def Query(self, request, global_params=None):
         request_field='queryRequest',
         request_type_name='BigqueryJobsQueryRequest',
         response_type_name='QueryResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ModelsService(base_api.BaseApiService):
     """Service class for the models resource."""
@@ -466,8 +448,7 @@ def Delete(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryModelsDeleteRequest',
         response_type_name='BigqueryModelsDeleteResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Get(self, request, global_params=None):
       r"""Gets the specified model resource by model ID.
@@ -494,8 +475,7 @@ def Get(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryModelsGetRequest',
         response_type_name='Model',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def List(self, request, global_params=None):
       r"""Lists all models in the specified dataset. Requires the READER dataset role.
@@ -520,8 +500,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryModelsListRequest',
         response_type_name='ListModelsResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Patch(self, request, global_params=None):
       r"""Patch specific fields in the specified model.
@@ -548,8 +527,7 @@ def Patch(self, request, global_params=None):
         request_field='model',
         request_type_name='BigqueryModelsPatchRequest',
         response_type_name='Model',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsService(base_api.BaseApiService):
     """Service class for the projects resource."""
@@ -582,8 +560,7 @@ def GetServiceAccount(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryProjectsGetServiceAccountRequest',
         response_type_name='GetServiceAccountResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def List(self, request, global_params=None):
       r"""Lists all projects to which you have been granted any project role.
@@ -607,8 +584,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryProjectsListRequest',
         response_type_name='ProjectList',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class RoutinesService(base_api.BaseApiService):
     """Service class for the routines resource."""
@@ -644,8 +620,7 @@ def Delete(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryRoutinesDeleteRequest',
         response_type_name='BigqueryRoutinesDeleteResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Get(self, request, global_params=None):
       r"""Gets the specified routine resource by routine ID.
@@ -672,8 +647,7 @@ def Get(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryRoutinesGetRequest',
         response_type_name='Routine',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Insert(self, request, global_params=None):
       r"""Creates a new routine in the dataset.
@@ -698,8 +672,7 @@ def Insert(self, request, global_params=None):
         request_field='routine',
         request_type_name='BigqueryRoutinesInsertRequest',
         response_type_name='Routine',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def List(self, request, global_params=None):
       r"""Lists all routines in the specified dataset. Requires the READER dataset role.
@@ -724,8 +697,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryRoutinesListRequest',
         response_type_name='ListRoutinesResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Update(self, request, global_params=None):
       r"""Updates information in an existing routine. The update method replaces the entire Routine resource.
@@ -752,8 +724,7 @@ def Update(self, request, global_params=None):
         request_field='routine',
         request_type_name='BigqueryRoutinesUpdateRequest',
         response_type_name='Routine',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class RowAccessPoliciesService(base_api.BaseApiService):
     """Service class for the rowAccessPolicies resource."""
@@ -789,8 +760,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryRowAccessPoliciesListRequest',
         response_type_name='ListRowAccessPoliciesResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class TabledataService(base_api.BaseApiService):
     """Service class for the tabledata resource."""
@@ -824,8 +794,7 @@ def InsertAll(self, request, global_params=None):
         request_field='tableDataInsertAllRequest',
         request_type_name='BigqueryTabledataInsertAllRequest',
         response_type_name='TableDataInsertAllResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def List(self, request, global_params=None):
       r"""Retrieves table data from a specified set of rows. Requires the READER dataset role.
@@ -851,8 +820,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryTabledataListRequest',
         response_type_name='TableDataList',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class TablesService(base_api.BaseApiService):
     """Service class for the tables resource."""
@@ -886,8 +854,7 @@ def Delete(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryTablesDeleteRequest',
         response_type_name='BigqueryTablesDeleteResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Get(self, request, global_params=None):
       r"""Gets the specified table resource by table ID. This method does not return the data in the table, it only returns the table resource, which describes the structure of this table.
@@ -912,8 +879,7 @@ def Get(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryTablesGetRequest',
         response_type_name='Table',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def GetIamPolicy(self, request, global_params=None):
       r"""Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.
@@ -939,8 +905,7 @@ def GetIamPolicy(self, request, global_params=None):
         request_field='getIamPolicyRequest',
         request_type_name='BigqueryTablesGetIamPolicyRequest',
         response_type_name='Policy',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Insert(self, request, global_params=None):
       r"""Creates a new, empty table in the dataset.
@@ -964,8 +929,7 @@ def Insert(self, request, global_params=None):
         request_field='table',
         request_type_name='BigqueryTablesInsertRequest',
         response_type_name='Table',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def List(self, request, global_params=None):
       r"""Lists all tables in the specified dataset. Requires the READER dataset role.
@@ -989,8 +953,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='BigqueryTablesListRequest',
         response_type_name='TableList',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Patch(self, request, global_params=None):
       r"""Updates information in an existing table. The update method replaces the entire table resource, whereas the patch method only replaces fields that are provided in the submitted table resource. This method supports patch semantics.
@@ -1015,8 +978,7 @@ def Patch(self, request, global_params=None):
         request_field='table',
         request_type_name='BigqueryTablesPatchRequest',
         response_type_name='Table',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def SetIamPolicy(self, request, global_params=None):
       r"""Sets the access control policy on the specified resource. Replaces any existing policy. Can return `NOT_FOUND`, `INVALID_ARGUMENT`, and `PERMISSION_DENIED` errors.
@@ -1042,8 +1004,7 @@ def SetIamPolicy(self, request, global_params=None):
         request_field='setIamPolicyRequest',
         request_type_name='BigqueryTablesSetIamPolicyRequest',
         response_type_name='Policy',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def TestIamPermissions(self, request, global_params=None):
       r"""Returns permissions that a caller has on the specified resource. If the resource does not exist, this will return an empty set of permissions, not a `NOT_FOUND` error. Note: This operation is designed to be used for building permission-aware UIs and command-line tools, not for authorization checking. This operation may "fail open" without warning.
@@ -1069,8 +1030,7 @@ def TestIamPermissions(self, request, global_params=None):
         request_field='testIamPermissionsRequest',
         request_type_name='BigqueryTablesTestIamPermissionsRequest',
         response_type_name='TestIamPermissionsResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Update(self, request, global_params=None):
       r"""Updates information in an existing table. The update method replaces the entire table resource, whereas the patch method only replaces fields that are provided in the submitted table resource.
@@ -1095,5 +1055,4 @@ def Update(self, request, global_params=None):
         request_field='table',
         request_type_name='BigqueryTablesUpdateRequest',
         response_type_name='Table',
-        supports_download=False,
-    )
+        supports_download=False, )
diff --git a/sdks/python/apache_beam/io/gcp/pubsub.py b/sdks/python/apache_beam/io/gcp/pubsub.py
index 9e006dbeda93..59eadee5538e 100644
--- a/sdks/python/apache_beam/io/gcp/pubsub.py
+++ b/sdks/python/apache_beam/io/gcp/pubsub.py
@@ -17,8 +17,9 @@
 
 """Google Cloud PubSub sources and sinks.
 
-Cloud Pub/Sub sources and sinks are currently supported only in streaming
-pipelines, during remote execution.
+Cloud Pub/Sub sources are currently supported only in streaming pipelines,
+during remote execution. Cloud Pub/Sub sinks (WriteToPubSub) support both
+streaming and batch pipelines.
 
 This API is currently under development and is subject to change.
 
@@ -42,7 +43,6 @@
 from apache_beam import coders
 from apache_beam.io import iobase
 from apache_beam.io.iobase import Read
-from apache_beam.io.iobase import Write
 from apache_beam.metrics.metric import Lineage
 from apache_beam.transforms import DoFn
 from apache_beam.transforms import Flatten
@@ -376,7 +376,12 @@ def report_lineage_once(self):
 
 
 class WriteToPubSub(PTransform):
-  """A ``PTransform`` for writing messages to Cloud Pub/Sub."""
+  """A ``PTransform`` for writing messages to Cloud Pub/Sub.
+  
+  This transform supports both streaming and batch pipelines. In streaming mode,
+  messages are written continuously as they arrive. In batch mode, all messages
+  are written when the pipeline completes.
+  """
 
   # Implementation note: This ``PTransform`` is overridden by Directrunner.
 
@@ -409,6 +414,7 @@ def __init__(
     self.project, self.topic_name = parse_topic(topic)
     self.full_topic = topic
     self._sink = _PubSubSink(topic, id_label, timestamp_attribute)
+    self.pipeline_options = None  # Will be set during expand()
 
   @staticmethod
   def message_to_proto_str(element: PubsubMessage) -> bytes:
@@ -424,6 +430,9 @@ def bytes_to_proto_str(element: Union[bytes, str]) -> bytes:
     return msg._to_proto_str(for_publish=True)
 
   def expand(self, pcoll):
+    # Store pipeline options for use in DoFn
+    self.pipeline_options = pcoll.pipeline.options if pcoll.pipeline else None
+
     if self.with_attributes:
       pcoll = pcoll | 'ToProtobufX' >> ParDo(
           _AddMetricsAndMap(
@@ -435,7 +444,7 @@ def expand(self, pcoll):
               self.bytes_to_proto_str, self.project,
               self.topic_name)).with_input_types(Union[bytes, str])
     pcoll.element_type = bytes
-    return pcoll | Write(self._sink)
+    return pcoll | ParDo(_PubSubWriteDoFn(self))
 
   def to_runner_api_parameter(self, context):
     # Required as this is identified by type in PTransformOverrides.
@@ -541,11 +550,139 @@ def is_bounded(self):
     return False
 
 
-# TODO(BEAM-27443): Remove in favor of a proper WriteToPubSub transform.
+class _PubSubWriteDoFn(DoFn):
+  """DoFn for writing messages to Cloud Pub/Sub.
+  
+  This DoFn handles both streaming and batch modes by buffering messages
+  and publishing them in batches to optimize performance.
+  """
+  BUFFER_SIZE_ELEMENTS = 100
+  FLUSH_TIMEOUT_SECS = 5 * 60  # 5 minutes
+
+  def __init__(self, transform):
+    self.project = transform.project
+    self.short_topic_name = transform.topic_name
+    self.id_label = transform.id_label
+    self.timestamp_attribute = transform.timestamp_attribute
+    self.with_attributes = transform.with_attributes
+
+    # TODO(https://github.com/apache/beam/issues/18939): Add support for
+    # id_label and timestamp_attribute.
+    # Only raise errors for DirectRunner or batch pipelines
+    pipeline_options = transform.pipeline_options
+    output_labels_supported = True
+
+    if pipeline_options:
+      from apache_beam.options.pipeline_options import StandardOptions
+
+      # Check if using DirectRunner
+      try:
+        # Get runner from pipeline options
+        all_options = pipeline_options.get_all_options()
+        runner_name = all_options.get('runner', StandardOptions.DEFAULT_RUNNER)
+
+        # Check if it's a DirectRunner variant
+        if (runner_name is None or
+            (runner_name in StandardOptions.LOCAL_RUNNERS or 'DirectRunner'
+             in str(runner_name) or 'TestDirectRunner' in str(runner_name))):
+          output_labels_supported = False
+      except Exception:
+        # If we can't determine runner, assume DirectRunner for safety
+        output_labels_supported = False
+
+      # Check if in batch mode (not streaming)
+      standard_options = pipeline_options.view_as(StandardOptions)
+      if not standard_options.streaming:
+        output_labels_supported = False
+    else:
+      # If no pipeline options available, fall back to original behavior
+      output_labels_supported = False
+
+    # Log debug information for troubleshooting
+    import logging
+    runner_info = getattr(
+        pipeline_options, 'runner',
+        'None') if pipeline_options else 'No options'
+    streaming_info = 'Unknown'
+    if pipeline_options:
+      try:
+        standard_options = pipeline_options.view_as(StandardOptions)
+        streaming_info = 'streaming=%s' % standard_options.streaming
+      except Exception:
+        streaming_info = 'streaming=unknown'
+
+    logging.debug(
+        'PubSub unsupported feature check: runner=%s, %s',
+        runner_info,
+        streaming_info)
+
+    if not output_labels_supported:
+
+      if transform.id_label:
+        raise NotImplementedError(
+            f'id_label is not supported for PubSub writes with DirectRunner '
+            f'or in batch mode (runner={runner_info}, {streaming_info})')
+      if transform.timestamp_attribute:
+        raise NotImplementedError(
+            f'timestamp_attribute is not supported for PubSub writes with '
+            f'DirectRunner or in batch mode '
+            f'(runner={runner_info}, {streaming_info})')
+
+  def setup(self):
+    from google.cloud import pubsub
+    self._pub_client = pubsub.PublisherClient()
+    self._topic = self._pub_client.topic_path(
+        self.project, self.short_topic_name)
+
+  def start_bundle(self):
+    self._buffer = []
+
+  def process(self, elem):
+    self._buffer.append(elem)
+    if len(self._buffer) >= self.BUFFER_SIZE_ELEMENTS:
+      self._flush()
+
+  def finish_bundle(self):
+    self._flush()
+
+  def _flush(self):
+    if not self._buffer:
+      return
+
+    import time
+
+    # The elements in buffer are serialized protobuf bytes from the previous
+    # transforms. We need to deserialize them to extract data and attributes.
+    futures = []
+    for elem in self._buffer:
+      # Deserialize the protobuf to get the original PubsubMessage
+      pubsub_msg = PubsubMessage._from_proto_str(elem)
+
+      # Publish with the correct data and attributes
+      if self.with_attributes and pubsub_msg.attributes:
+        future = self._pub_client.publish(
+            self._topic, pubsub_msg.data, **pubsub_msg.attributes)
+      else:
+        future = self._pub_client.publish(self._topic, pubsub_msg.data)
+
+      futures.append(future)
+
+    timer_start = time.time()
+    for future in futures:
+      remaining = self.FLUSH_TIMEOUT_SECS - (time.time() - timer_start)
+      if remaining <= 0:
+        raise TimeoutError(
+            f"PubSub publish timeout exceeded {self.FLUSH_TIMEOUT_SECS} seconds"
+        )
+      future.result(remaining)
+    self._buffer = []
+
+
 class _PubSubSink(object):
   """Sink for a Cloud Pub/Sub topic.
 
-  This ``NativeSource`` is overridden by a native Pubsub implementation.
+  This sink works for both streaming and batch pipelines by using a DoFn
+  that buffers and batches messages for efficient publishing.
   """
   def __init__(
       self,
diff --git a/sdks/python/apache_beam/io/gcp/pubsub_integration_test.py b/sdks/python/apache_beam/io/gcp/pubsub_integration_test.py
index 28c30df1d559..8387fe734fc1 100644
--- a/sdks/python/apache_beam/io/gcp/pubsub_integration_test.py
+++ b/sdks/python/apache_beam/io/gcp/pubsub_integration_test.py
@@ -30,6 +30,7 @@
 
 from apache_beam.io.gcp import pubsub_it_pipeline
 from apache_beam.io.gcp.pubsub import PubsubMessage
+from apache_beam.io.gcp.pubsub import WriteToPubSub
 from apache_beam.io.gcp.tests.pubsub_matcher import PubSubMessageMatcher
 from apache_beam.runners.runner import PipelineState
 from apache_beam.testing import test_utils
@@ -43,10 +44,10 @@
 
 # How long TestXXXRunner will wait for pubsub_it_pipeline to run before
 # cancelling it.
-TEST_PIPELINE_DURATION_MS = 8 * 60 * 1000
+TEST_PIPELINE_DURATION_MS = 10 * 60 * 1000
 # How long PubSubMessageMatcher will wait for the correct set of messages to
 # appear.
-MESSAGE_MATCHER_TIMEOUT_S = 5 * 60
+MESSAGE_MATCHER_TIMEOUT_S = 10 * 60
 
 
 class PubSubIntegrationTest(unittest.TestCase):
@@ -220,6 +221,90 @@ def test_streaming_data_only(self):
   def test_streaming_with_attributes(self):
     self._test_streaming(with_attributes=True)
 
+  def _test_batch_write(self, with_attributes):
+    """Tests batch mode WriteToPubSub functionality.
+
+    Args:
+      with_attributes: False - Writes message data only.
+        True - Writes message data and attributes.
+    """
+    from apache_beam.options.pipeline_options import PipelineOptions
+    from apache_beam.options.pipeline_options import StandardOptions
+    from apache_beam.transforms import Create
+
+    # Create test messages for batch mode
+    test_messages = [
+        PubsubMessage(b'batch_data001', {'batch_attr': 'value1'}),
+        PubsubMessage(b'batch_data002', {'batch_attr': 'value2'}),
+        PubsubMessage(b'batch_data003', {'batch_attr': 'value3'})
+    ]
+
+    pipeline_options = PipelineOptions()
+    # Explicitly set streaming to False for batch mode
+    pipeline_options.view_as(StandardOptions).streaming = False
+
+    with TestPipeline(options=pipeline_options) as p:
+      if with_attributes:
+        messages = p | 'CreateMessages' >> Create(test_messages)
+        _ = messages | 'WriteToPubSub' >> WriteToPubSub(
+            self.output_topic.name, with_attributes=True)
+      else:
+        # For data-only mode, extract just the data
+        message_data = [msg.data for msg in test_messages]
+        messages = p | 'CreateData' >> Create(message_data)
+        _ = messages | 'WriteToPubSub' >> WriteToPubSub(
+            self.output_topic.name, with_attributes=False)
+
+    # Verify messages were published by reading from the subscription
+    time.sleep(10)  # Allow time for messages to be published and received
+
+    # Pull messages from the output subscription to verify they were written
+    response = self.sub_client.pull(
+        request={
+            "subscription": self.output_sub.name,
+            "max_messages": 10,
+        })
+
+    received_messages = []
+    for received_message in response.received_messages:
+      if with_attributes:
+        # Parse attributes
+        attrs = dict(received_message.message.attributes)
+        received_messages.append(
+            PubsubMessage(received_message.message.data, attrs))
+      else:
+        received_messages.append(received_message.message.data)
+
+      # Acknowledge the message
+      self.sub_client.acknowledge(
+          request={
+              "subscription": self.output_sub.name,
+              "ack_ids": [received_message.ack_id],
+          })
+
+    # Verify we received the expected number of messages
+    self.assertEqual(len(received_messages), len(test_messages))
+
+    if with_attributes:
+      # Verify message content and attributes
+      received_data = [msg.data for msg in received_messages]
+      expected_data = [msg.data for msg in test_messages]
+      self.assertEqual(sorted(received_data), sorted(expected_data))
+    else:
+      # Verify message data only
+      expected_data = [msg.data for msg in test_messages]
+      self.assertEqual(sorted(received_messages), sorted(expected_data))
+
+  @pytest.mark.it_postcommit
+  def test_batch_write_data_only(self):
+    """Test WriteToPubSub in batch mode with data only."""
+    self._test_batch_write(with_attributes=False)
+
+  @pytest.mark.it_postcommit
+  def test_batch_write_with_attributes(self):
+    """Test WriteToPubSub in batch mode with attributes."""
+    self._test_batch_write(with_attributes=True)
+
 
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.DEBUG)
diff --git a/sdks/python/apache_beam/io/gcp/pubsub_test.py b/sdks/python/apache_beam/io/gcp/pubsub_test.py
index e3fb07a17625..5650e920e635 100644
--- a/sdks/python/apache_beam/io/gcp/pubsub_test.py
+++ b/sdks/python/apache_beam/io/gcp/pubsub_test.py
@@ -867,12 +867,14 @@ def test_write_messages_success(self, mock_pubsub):
           | Create(payloads)
           | WriteToPubSub(
               'projects/fakeprj/topics/a_topic', with_attributes=False))
-    mock_pubsub.return_value.publish.assert_has_calls(
-        [mock.call(mock.ANY, data)])
+    # Verify that publish was called (data will be protobuf serialized)
+    mock_pubsub.return_value.publish.assert_called()
+    # Check that the call was made with the topic and some data
+    call_args = mock_pubsub.return_value.publish.call_args
+    self.assertEqual(len(call_args[0]), 2)  # topic and data
 
   def test_write_messages_deprecated(self, mock_pubsub):
     data = 'data'
-    data_bytes = b'data'
     payloads = [data]
 
     options = PipelineOptions([])
@@ -882,8 +884,11 @@ def test_write_messages_deprecated(self, mock_pubsub):
           p
           | Create(payloads)
           | WriteStringsToPubSub('projects/fakeprj/topics/a_topic'))
-    mock_pubsub.return_value.publish.assert_has_calls(
-        [mock.call(mock.ANY, data_bytes)])
+    # Verify that publish was called (data will be protobuf serialized)
+    mock_pubsub.return_value.publish.assert_called()
+    # Check that the call was made with the topic and some data
+    call_args = mock_pubsub.return_value.publish.call_args
+    self.assertEqual(len(call_args[0]), 2)  # topic and data
 
   def test_write_messages_with_attributes_success(self, mock_pubsub):
     data = b'data'
@@ -898,8 +903,54 @@ def test_write_messages_with_attributes_success(self, mock_pubsub):
           | Create(payloads)
           | WriteToPubSub(
               'projects/fakeprj/topics/a_topic', with_attributes=True))
-    mock_pubsub.return_value.publish.assert_has_calls(
-        [mock.call(mock.ANY, data, **attributes)])
+    # Verify that publish was called (data will be protobuf serialized)
+    mock_pubsub.return_value.publish.assert_called()
+    # Check that the call was made with the topic and some data
+    call_args = mock_pubsub.return_value.publish.call_args
+    self.assertEqual(len(call_args[0]), 2)  # topic and data
+
+  def test_write_messages_batch_mode_success(self, mock_pubsub):
+    """Test WriteToPubSub works in batch mode (non-streaming)."""
+    data = 'data'
+    payloads = [data]
+
+    options = PipelineOptions([])
+    # Explicitly set streaming to False for batch mode
+    options.view_as(StandardOptions).streaming = False
+    with TestPipeline(options=options) as p:
+      _ = (
+          p
+          | Create(payloads)
+          | WriteToPubSub(
+              'projects/fakeprj/topics/a_topic', with_attributes=False))
+
+    # Verify that publish was called (data will be protobuf serialized)
+    mock_pubsub.return_value.publish.assert_called()
+    # Check that the call was made with the topic and some data
+    call_args = mock_pubsub.return_value.publish.call_args
+    self.assertEqual(len(call_args[0]), 2)  # topic and data
+
+  def test_write_messages_with_attributes_batch_mode_success(self, mock_pubsub):
+    """Test WriteToPubSub with attributes works in batch mode."""
+    data = b'data'
+    attributes = {'key': 'value'}
+    payloads = [PubsubMessage(data, attributes)]
+
+    options = PipelineOptions([])
+    # Explicitly set streaming to False for batch mode
+    options.view_as(StandardOptions).streaming = False
+    with TestPipeline(options=options) as p:
+      _ = (
+          p
+          | Create(payloads)
+          | WriteToPubSub(
+              'projects/fakeprj/topics/a_topic', with_attributes=True))
+
+    # Verify that publish was called (data will be protobuf serialized)
+    mock_pubsub.return_value.publish.assert_called()
+    # Check that the call was made with the topic and some data
+    call_args = mock_pubsub.return_value.publish.call_args
+    self.assertEqual(len(call_args[0]), 2)  # topic and data
 
   def test_write_messages_with_attributes_error(self, mock_pubsub):
     data = 'data'
diff --git a/sdks/python/apache_beam/io/gcp/spanner.py b/sdks/python/apache_beam/io/gcp/spanner.py
index 9089d746fe1c..03ad91069b99 100644
--- a/sdks/python/apache_beam/io/gcp/spanner.py
+++ b/sdks/python/apache_beam/io/gcp/spanner.py
@@ -145,6 +145,20 @@ class ReadFromSpannerSchema(NamedTuple):
   time_unit: Optional[str]
 
 
+class ReadChangeStreamFromSpannerSchema(NamedTuple):
+  instance_id: str
+  database_id: str
+  project_id: str
+  changeStreamName: str
+  inclusiveStartAt: str
+  inclusiveEndAt: Optional[str]
+  metadataDatabase: str
+  metadataInstance: str
+  metadataTable: Optional[str]
+  rpcPriority: Optional[str]
+  watermarkRefreshRate: Optional[str]
+
+
 class ReadFromSpanner(ExternalTransform):
   """
   A PTransform which reads from the specified Spanner instance's database.
@@ -659,5 +673,94 @@ def __init__(
     )
 
 
+class ReadChangeStreamFromSpanner(ExternalTransform):
+  """
+  A PTransform to read Change Streams from Google Cloud Spanner.
+
+  The output of this transform is a PCollection of JSON strings,
+  where each string represents a com.google.cloud.spanner.DataChangeRecord.
+
+  Example:
+
+  with beam.Pipeline(options=pipeline_options) as p:
+    p | 
+    "ReadFromSpannerChangeStream" >> beam_spanner.ReadChangeStreamFromSpanner( 
+        project_id="spanner-project-id", 
+        instance_id="spanner-instance-id", 
+        database_id="spanner-database-id", 
+        changeStreamName="spanner-change-stream", 
+        inclusiveStartAt="2025-05-20T10:00:00Z",
+        metadataDatabase="spanner-metadata-database",
+        metadataInstance="spanner-metadata-instance")
+
+  Experimental; no backwards compatibility guarantees.
+  """
+
+  URN = 'beam:transform:org.apache.beam:spanner_change_stream_reader:v1'
+
+  def __init__(
+      self,
+      project_id,
+      instance_id,
+      database_id,
+      changeStreamName,
+      metadataDatabase,
+      metadataInstance,
+      inclusiveStartAt,
+      inclusiveEndAt=None,
+      metadataTable=None,
+      rpcPriority=None,
+      watermarkRefreshRate=None,
+      expansion_service=None,
+  ):
+    """
+        Reads Change Streams from Google Cloud Spanner.
+        
+        :param project_id: (Required) Specifies the Cloud Spanner project.
+        :param instance_id: (Required) Specifies the Cloud Spanner 
+          instance.
+        :param database_id: (Required) Specifies the Cloud Spanner 
+          database.
+        :param changeStreamName: (Required) The name of the Spanner 
+          change stream to read.
+        :param metadataDatabase: (Required) The database where the 
+          change stream metadata is stored.
+        :param metadataInstance: (Required) The instance where the 
+          change stream metadata database resides.
+        :param inclusiveStartAt: (Required) An inclusive start timestamp 
+          for reading the change stream.
+        :param inclusiveEndAt: (Optional) An inclusive end timestamp for 
+          reading the change stream. If not specified, the stream will be 
+          read indefinitely.
+        :param metadataTable: (Optional) The name of the metadata table used 
+          by the change stream connector. If not specified, a default table 
+          name will be used.
+        :param rpcPriority: (Optional) The RPC priority for Spanner operations. 
+          Can be 'HIGH', 'MEDIUM', or 'LOW'.
+        :param watermarkRefreshRate: (Optional) The duration at which the 
+          watermark is refreshed.
+        """
+
+    super().__init__(
+        self.URN,
+        NamedTupleBasedPayloadBuilder(
+            ReadChangeStreamFromSpannerSchema(
+                instance_id=instance_id,
+                database_id=database_id,
+                project_id=project_id,
+                changeStreamName=changeStreamName,
+                inclusiveStartAt=inclusiveStartAt,
+                inclusiveEndAt=inclusiveEndAt,
+                metadataDatabase=metadataDatabase,
+                metadataInstance=metadataInstance,
+                metadataTable=metadataTable,
+                rpcPriority=rpcPriority,
+                watermarkRefreshRate=watermarkRefreshRate,
+            ),
+        ),
+        expansion_service=expansion_service or default_io_expansion_service(),
+    )
+
+
 def _get_enum_name(enum):
   return None if enum is None else enum.name
diff --git a/sdks/python/apache_beam/io/iobase_it_test.py b/sdks/python/apache_beam/io/iobase_it_test.py
new file mode 100644
index 000000000000..acb44f4085bc
--- /dev/null
+++ b/sdks/python/apache_beam/io/iobase_it_test.py
@@ -0,0 +1,72 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# pytype: skip-file
+
+import logging
+import unittest
+import uuid
+
+import apache_beam as beam
+from apache_beam.io.textio import WriteToText
+from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.transforms.window import FixedWindows
+
+# End-to-End tests for iobase
+# Usage:
+#    cd sdks/python
+#    pip install build && python -m build --sdist
+#    DataflowRunner:
+#    python -m pytest -o log_cli=True -o log_level=Info \
+#        apache_beam/io/iobase_it_test.py::IOBaseITTest \
+#        --test-pipeline-options="--runner=TestDataflowRunner \
+#        --project=apache-beam-testing --region=us-central1 \
+#        --temp_location=gs://apache-beam-testing-temp/temp \
+#        --sdk_location=dist/apache_beam-2.65.0.dev0.tar.gz"
+
+
+class IOBaseITTest(unittest.TestCase):
+  def setUp(self):
+    self.test_pipeline = TestPipeline(is_integration_test=True)
+    self.runner_name = type(self.test_pipeline.runner).__name__
+
+  def test_unbounded_pcoll_without_global_window(self):
+    # https://github.com/apache/beam/issues/25598
+
+    args = self.test_pipeline.get_full_options_as_args(streaming=True)
+
+    topic = 'projects/pubsub-public-data/topics/taxirides-realtime'
+    unique_id = str(uuid.uuid4())
+    output_file = f'gs://apache-beam-testing-integration-testing/iobase/test-{unique_id}'  # pylint: disable=line-too-long
+
+    p = beam.Pipeline(argv=args)
+    # Read from Pub/Sub with fixed windowing
+    lines = (
+        p
+        | "ReadFromPubSub" >> beam.io.ReadFromPubSub(topic=topic)
+        | "WindowInto" >> beam.WindowInto(FixedWindows(10)))
+
+    # Write to text file
+    _ = lines | 'WriteToText' >> WriteToText(output_file)
+
+    result = p.run()
+    result.wait_until_finish(duration=60 * 1000)
+
+
+if __name__ == "__main__":
+  logging.getLogger().setLevel(logging.INFO)
+  unittest.main()
diff --git a/sdks/python/apache_beam/io/jdbc.py b/sdks/python/apache_beam/io/jdbc.py
index 604b95f6eebe..df5d7f21a343 100644
--- a/sdks/python/apache_beam/io/jdbc.py
+++ b/sdks/python/apache_beam/io/jdbc.py
@@ -86,7 +86,7 @@
 
 # pytype: skip-file
 
-import datetime
+import contextlib
 import typing
 
 import numpy as np
@@ -95,10 +95,11 @@
 from apache_beam.transforms.external import BeamJarExpansionService
 from apache_beam.transforms.external import ExternalTransform
 from apache_beam.transforms.external import NamedTupleBasedPayloadBuilder
+from apache_beam.typehints.schemas import JdbcDateType  # pylint: disable=unused-import
+from apache_beam.typehints.schemas import JdbcTimeType  # pylint: disable=unused-import
 from apache_beam.typehints.schemas import LogicalType
 from apache_beam.typehints.schemas import MillisInstant
 from apache_beam.typehints.schemas import typing_to_runner_api
-from apache_beam.utils.timestamp import Timestamp
 
 __all__ = [
     'WriteToJdbc',
@@ -257,6 +258,17 @@ def __init__(
     )
 
 
+@contextlib.contextmanager
+def enforce_millis_instant_for_timestamp():
+  old_registry = LogicalType._known_logical_types
+  LogicalType._known_logical_types = old_registry.copy()
+  try:
+    LogicalType.register_logical_type(MillisInstant)
+    yield
+  finally:
+    LogicalType._known_logical_types = old_registry
+
+
 class ReadFromJdbc(ExternalTransform):
   """A PTransform which reads Rows from the specified database via JDBC.
 
@@ -352,8 +364,9 @@ def __init__(
 
     dataSchema = None
     if schema is not None:
-      # Convert Python schema to Beam Schema proto
-      schema_proto = typing_to_runner_api(schema).row_type.schema
+      with enforce_millis_instant_for_timestamp():
+        # Convert Python schema to Beam Schema proto
+        schema_proto = typing_to_runner_api(schema).row_type.schema
       # Serialize the proto to bytes for transmission
       dataSchema = schema_proto.SerializeToString()
 
@@ -386,91 +399,3 @@ def __init__(
         ),
         expansion_service or default_io_expansion_service(classpath),
     )
-
-
-@LogicalType.register_logical_type
-class JdbcDateType(LogicalType[datetime.date, MillisInstant, str]):
-  """
-  For internal use only; no backwards-compatibility guarantees.
-
-  Support of Legacy JdbcIO DATE logical type. Deemed to change when Java JDBCIO
-  has been migrated to Beam portable logical types.
-  """
-  def __init__(self, argument=""):
-    pass
-
-  @classmethod
-  def representation_type(cls) -> type:
-    return MillisInstant
-
-  @classmethod
-  def urn(cls):
-    return "beam:logical_type:javasdk_date:v1"
-
-  @classmethod
-  def language_type(cls):
-    return datetime.date
-
-  def to_representation_type(self, value: datetime.date) -> Timestamp:
-    return Timestamp.from_utc_datetime(
-        datetime.datetime.combine(
-            value, datetime.datetime.min.time(), tzinfo=datetime.timezone.utc))
-
-  def to_language_type(self, value: Timestamp) -> datetime.date:
-    return value.to_utc_datetime().date()
-
-  @classmethod
-  def argument_type(cls):
-    return str
-
-  def argument(self):
-    return ""
-
-  @classmethod
-  def _from_typing(cls, typ):
-    return cls()
-
-
-@LogicalType.register_logical_type
-class JdbcTimeType(LogicalType[datetime.time, MillisInstant, str]):
-  """
-  For internal use only; no backwards-compatibility guarantees.
-
-  Support of Legacy JdbcIO TIME logical type. . Deemed to change when Java
-  JDBCIO has been migrated to Beam portable logical types.
-  """
-  def __init__(self, argument=""):
-    pass
-
-  @classmethod
-  def representation_type(cls) -> type:
-    return MillisInstant
-
-  @classmethod
-  def urn(cls):
-    return "beam:logical_type:javasdk_time:v1"
-
-  @classmethod
-  def language_type(cls):
-    return datetime.time
-
-  def to_representation_type(self, value: datetime.date) -> Timestamp:
-    return Timestamp.from_utc_datetime(
-        datetime.datetime.combine(
-            datetime.datetime.utcfromtimestamp(0),
-            value,
-            tzinfo=datetime.timezone.utc))
-
-  def to_language_type(self, value: Timestamp) -> datetime.date:
-    return value.to_utc_datetime().time()
-
-  @classmethod
-  def argument_type(cls):
-    return str
-
-  def argument(self):
-    return ""
-
-  @classmethod
-  def _from_typing(cls, typ):
-    return cls()
diff --git a/sdks/python/apache_beam/io/parquetio.py b/sdks/python/apache_beam/io/parquetio.py
index 48c51428c17d..0b38c69437c0 100644
--- a/sdks/python/apache_beam/io/parquetio.py
+++ b/sdks/python/apache_beam/io/parquetio.py
@@ -48,14 +48,17 @@
 from apache_beam.transforms import PTransform
 from apache_beam.transforms import window
 from apache_beam.typehints import schemas
+from apache_beam.utils.windowed_value import WindowedValue
 
 try:
   import pyarrow as pa
+  paTable = pa.Table
   import pyarrow.parquet as pq
   # pylint: disable=ungrouped-imports
   from apache_beam.typehints import arrow_type_compatibility
 except ImportError:
   pa = None
+  paTable = None
   pq = None
   ARROW_MAJOR_VERSION = None
   arrow_type_compatibility = None
@@ -105,8 +108,10 @@ def __init__(
     self._buffer_size = record_batch_size
     self._record_batches = []
     self._record_batches_byte_size = 0
+    self._window = None
 
-  def process(self, row):
+  def process(self, row, w=DoFn.WindowParam, pane=DoFn.PaneInfoParam):
+    self._window = w
     if len(self._buffer[0]) >= self._buffer_size:
       self._flush_buffer()
 
@@ -116,14 +121,29 @@ def process(self, row):
 
     # reorder the data in columnar format.
     for i, n in enumerate(self._schema.names):
-      self._buffer[i].append(row[n])
+      # Handle missing nullable fields by using None as default value
+      field = self._schema.field(i)
+      if field.nullable and n not in row:
+        self._buffer[i].append(None)
+      else:
+        self._buffer[i].append(row[n])
 
   def finish_bundle(self):
     if len(self._buffer[0]) > 0:
       self._flush_buffer()
     if self._record_batches_byte_size > 0:
       table = self._create_table()
-      yield window.GlobalWindows.windowed_value_at_end_of_window(table)
+      if self._window is None or isinstance(self._window, window.GlobalWindow):
+        # bounded input
+        yield window.GlobalWindows.windowed_value_at_end_of_window(table)
+      else:
+        # unbounded input
+        yield WindowedValue(
+            table,
+            timestamp=self._window.
+            end,  #or it could be max of timestamp of the rows processed
+            windows=[self._window]  # TODO(pabloem) HOW DO WE GET THE PANE
+        )
 
   def display_data(self):
     res = super().display_data()
@@ -158,7 +178,7 @@ def __init__(self, beam_type):
     self._beam_type = beam_type
 
   @DoFn.yields_batches
-  def process(self, element) -> Iterator[pa.Table]:
+  def process(self, element) -> Iterator[paTable]:
     yield element
 
   def infer_output_type(self, input_type):
@@ -167,7 +187,7 @@ def infer_output_type(self, input_type):
 
 class _BeamRowsToArrowTable(DoFn):
   @DoFn.yields_elements
-  def process_batch(self, element: pa.Table) -> Iterator[pa.Table]:
+  def process_batch(self, element: paTable) -> Iterator[paTable]:
     yield element
 
 
@@ -476,7 +496,9 @@ def __init__(
       file_name_suffix='',
       num_shards=0,
       shard_name_template=None,
-      mime_type='application/x-parquet'):
+      mime_type='application/x-parquet',
+      triggering_frequency=None,
+  ):
     """Initialize a WriteToParquet transform.
 
     Writes parquet files from a :class:`~apache_beam.pvalue.PCollection` of
@@ -540,14 +562,26 @@ def __init__(
         the performance of a pipeline.  Setting this value is not recommended
         unless you require a specific number of output files.
       shard_name_template: A template string containing placeholders for
-        the shard number and shard count. When constructing a filename for a
-        particular shard number, the upper-case letters 'S' and 'N' are
-        replaced with the 0-padded shard number and shard count respectively.
-        This argument can be '' in which case it behaves as if num_shards was
-        set to 1 and only one file will be generated. The default pattern used
-        is '-SSSSS-of-NNNNN' if None is passed as the shard_name_template.
+        the shard number and shard count. Currently only ``''``,
+        ``'-SSSSS-of-NNNNN'``, ``'-W-SSSSS-of-NNNNN'`` and
+        ``'-V-SSSSS-of-NNNNN'`` are patterns accepted by the service.
+        When constructing a filename for a particular shard number, the
+        upper-case letters ``S`` and ``N`` are replaced with the ``0``-padded
+        shard number and shard count respectively.  This argument can be ``''``
+        in which case it behaves as if num_shards was set to 1 and only one file
+        will be generated. The default pattern used is ``'-SSSSS-of-NNNNN'`` for
+        bounded PCollections and for ``'-W-SSSSS-of-NNNNN'`` unbounded 
+        PCollections.
+        W is used for windowed shard naming and is replaced with 
+        ``[window.start, window.end)``
+        V is used for windowed shard naming and is replaced with 
+        ``[window.start.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S"), 
+        window.end.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S")``
       mime_type: The MIME type to use for the produced files, if the filesystem
         supports specifying MIME types.
+      triggering_frequency: (int) Every triggering_frequency duration, a window 
+        will be triggered and all bundles in the window will be written.
+        If set it overrides user windowing. Mandatory for GlobalWindow.
 
     Returns:
       A WriteToParquet transform usable for writing.
@@ -567,10 +601,20 @@ def __init__(
           file_name_suffix,
           num_shards,
           shard_name_template,
-          mime_type
+          mime_type,
+          triggering_frequency
       )
 
   def expand(self, pcoll):
+    if (not pcoll.is_bounded and self._sink.shard_name_template
+        == filebasedsink.DEFAULT_SHARD_NAME_TEMPLATE):
+      self._sink.shard_name_template = (
+          filebasedsink.DEFAULT_WINDOW_SHARD_NAME_TEMPLATE)
+      self._sink.shard_name_format = self._sink._template_to_format(
+          self._sink.shard_name_template)
+      self._sink.shard_name_glob_format = self._sink._template_to_glob_format(
+          self._sink.shard_name_template)
+
     if self._schema is None:
       try:
         beam_schema = schemas.schema_from_element_type(pcoll.element_type)
@@ -583,7 +627,11 @@ def expand(self, pcoll):
     else:
       convert_fn = _RowDictionariesToArrowTable(
           self._schema, self._row_group_buffer_size, self._record_batch_size)
-    return pcoll | ParDo(convert_fn) | Write(self._sink)
+    if pcoll.is_bounded:
+      return pcoll | ParDo(convert_fn) | Write(self._sink)
+    else:
+      self._sink.convert_fn = convert_fn
+      return pcoll | Write(self._sink)
 
   def display_data(self):
     return {
@@ -610,7 +658,7 @@ def __init__(
       num_shards=0,
       shard_name_template=None,
       mime_type='application/x-parquet',
-  ):
+      triggering_frequency=None):
     """Initialize a WriteToParquetBatched transform.
 
     Writes parquet files from a :class:`~apache_beam.pvalue.PCollection` of
@@ -668,11 +716,21 @@ def __init__(
         the shard number and shard count. When constructing a filename for a
         particular shard number, the upper-case letters 'S' and 'N' are
         replaced with the 0-padded shard number and shard count respectively.
+        W is used for windowed shard naming and is replaced with
+        ``[window.start, window.end)``
+        V is used for windowed shard naming and is replaced with
+        ``[window.start.to_utc_datetime().isoformat(), 
+        window.end.to_utc_datetime().isoformat()``
         This argument can be '' in which case it behaves as if num_shards was
-        set to 1 and only one file will be generated. The default pattern used
-        is '-SSSSS-of-NNNNN' if None is passed as the shard_name_template.
+        set to 1 and only one file will be generated. 
+        The default pattern used is '-SSSSS-of-NNNNN' if None is passed as the 
+        shard_name_template and the PCollection is bounded.
+        The default pattern used is '-W-SSSSS-of-NNNNN' if None is passed as the
+        shard_name_template and the PCollection is unbounded.
       mime_type: The MIME type to use for the produced files, if the filesystem
         supports specifying MIME types.
+      triggering_frequency: (int) Every triggering_frequency duration, a window
+        will be triggered and all bundles in the window will be written.
 
     Returns:
       A WriteToParquetBatched transform usable for writing.
@@ -688,10 +746,19 @@ def __init__(
           file_name_suffix,
           num_shards,
           shard_name_template,
-          mime_type
+          mime_type,
+          triggering_frequency
       )
 
   def expand(self, pcoll):
+    if (not pcoll.is_bounded and self._sink.shard_name_template
+        == filebasedsink.DEFAULT_SHARD_NAME_TEMPLATE):
+      self._sink.shard_name_template = (
+          filebasedsink.DEFAULT_WINDOW_SHARD_NAME_TEMPLATE)
+      self._sink.shard_name_format = self._sink._template_to_format(
+          self._sink.shard_name_template)
+      self._sink.shard_name_glob_format = self._sink._template_to_glob_format(
+          self._sink.shard_name_template)
     return pcoll | Write(self._sink)
 
   def display_data(self):
@@ -707,7 +774,8 @@ def _create_parquet_sink(
     file_name_suffix,
     num_shards,
     shard_name_template,
-    mime_type):
+    mime_type,
+    triggering_frequency=60):
   return \
     _ParquetSink(
         file_path_prefix,
@@ -718,7 +786,8 @@ def _create_parquet_sink(
         file_name_suffix,
         num_shards,
         shard_name_template,
-        mime_type
+        mime_type,
+        triggering_frequency
     )
 
 
@@ -734,7 +803,8 @@ def __init__(
       file_name_suffix,
       num_shards,
       shard_name_template,
-      mime_type):
+      mime_type,
+      triggering_frequency):
     super().__init__(
         file_path_prefix,
         file_name_suffix=file_name_suffix,
@@ -744,7 +814,8 @@ def __init__(
         mime_type=mime_type,
         # Compression happens at the block level using the supplied codec, and
         # not at the file level.
-        compression_type=CompressionTypes.UNCOMPRESSED)
+        compression_type=CompressionTypes.UNCOMPRESSED,
+        triggering_frequency=triggering_frequency)
     self._schema = schema
     self._codec = codec
     if ARROW_MAJOR_VERSION == 1 and self._codec.lower() == "lz4":
@@ -776,7 +847,7 @@ def open(self, temp_path):
         use_deprecated_int96_timestamps=self._use_deprecated_int96_timestamps,
         use_compliant_nested_type=self._use_compliant_nested_type)
 
-  def write_record(self, writer, table: pa.Table):
+  def write_record(self, writer, table: paTable):
     writer.write_table(table)
 
   def close(self, writer):
diff --git a/sdks/python/apache_beam/io/parquetio_it_test.py b/sdks/python/apache_beam/io/parquetio_it_test.py
index 052b54f3ebfb..b06e7268fec4 100644
--- a/sdks/python/apache_beam/io/parquetio_it_test.py
+++ b/sdks/python/apache_beam/io/parquetio_it_test.py
@@ -19,10 +19,14 @@
 import logging
 import string
 import unittest
+import uuid
 from collections import Counter
+from datetime import datetime
 
 import pytest
+import pytz
 
+import apache_beam as beam
 from apache_beam import Create
 from apache_beam import DoFn
 from apache_beam import FlatMap
@@ -37,6 +41,7 @@
 from apache_beam.testing.util import BeamAssertException
 from apache_beam.transforms import CombineGlobally
 from apache_beam.transforms.combiners import Count
+from apache_beam.transforms.periodicsequence import PeriodicImpulse
 
 try:
   import pyarrow as pa
@@ -142,6 +147,42 @@ def get_int(self):
     return i
 
 
+@unittest.skipIf(pa is None, "PyArrow is not installed.")
+class WriteStreamingIT(unittest.TestCase):
+  def setUp(self):
+    self.test_pipeline = TestPipeline(is_integration_test=True)
+    self.runner_name = type(self.test_pipeline.runner).__name__
+    super().setUp()
+
+  def test_write_streaming_2_shards_default_shard_name_template(
+      self, num_shards=2):
+
+    args = self.test_pipeline.get_full_options_as_args(streaming=True)
+
+    unique_id = str(uuid.uuid4())
+    output_file = f'gs://apache-beam-testing-integration-testing/iobase/test-{unique_id}'  # pylint: disable=line-too-long
+    p = beam.Pipeline(argv=args)
+    pyschema = pa.schema([('age', pa.int64())])
+
+    _ = (
+        p
+        | "generate impulse" >> PeriodicImpulse(
+            start_timestamp=datetime(2021, 3, 1, 0, 0, 1, 0,
+                                     tzinfo=pytz.UTC).timestamp(),
+            stop_timestamp=datetime(2021, 3, 1, 0, 0, 20, 0,
+                                    tzinfo=pytz.UTC).timestamp(),
+            fire_interval=1)
+        | "generate data" >> beam.Map(lambda t: {'age': t * 10})
+        | 'WriteToParquet' >> beam.io.WriteToParquet(
+            file_path_prefix=output_file,
+            file_name_suffix=".parquet",
+            num_shards=num_shards,
+            triggering_frequency=60,
+            schema=pyschema))
+    result = p.run()
+    result.wait_until_finish(duration=600 * 1000)
+
+
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
   unittest.main()
diff --git a/sdks/python/apache_beam/io/parquetio_test.py b/sdks/python/apache_beam/io/parquetio_test.py
index fd19ec9520a9..78d1db4cc7c2 100644
--- a/sdks/python/apache_beam/io/parquetio_test.py
+++ b/sdks/python/apache_beam/io/parquetio_test.py
@@ -16,17 +16,21 @@
 #
 # pytype: skip-file
 
+import glob
 import json
 import logging
 import os
+import re
 import shutil
 import tempfile
 import unittest
+from datetime import datetime
 from tempfile import TemporaryDirectory
 
 import hamcrest as hc
 import pandas
 import pytest
+import pytz
 from parameterized import param
 from parameterized import parameterized
 
@@ -45,21 +49,21 @@
 from apache_beam.io.parquetio import _create_parquet_sink
 from apache_beam.io.parquetio import _create_parquet_source
 from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.testing.test_stream import TestStream
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
 from apache_beam.transforms.display import DisplayData
 from apache_beam.transforms.display_test import DisplayDataItemMatcher
+from apache_beam.transforms.util import LogElements
 
 try:
   import pyarrow as pa
-  import pyarrow.lib as pl
   import pyarrow.parquet as pq
+  ARROW_MAJOR_VERSION, _, _ = map(int, pa.__version__.split('.'))
 except ImportError:
   pa = None
-  pl = None
   pq = None
-
-ARROW_MAJOR_VERSION, _, _ = map(int, pa.__version__.split('.'))
+  ARROW_MAJOR_VERSION = 0
 
 
 @unittest.skipIf(pa is None, "PyArrow is not installed.")
@@ -338,17 +342,16 @@ def test_write_batched_display_data(self):
       ARROW_MAJOR_VERSION >= 13,
       'pyarrow 13.x and above does not throw ArrowInvalid error')
   def test_sink_transform_int96(self):
-    with tempfile.NamedTemporaryFile() as dst:
+    with self.assertRaisesRegex(Exception, 'would lose data'):
+      # Should throw an error "ArrowInvalid: Casting from timestamp[ns] to
+      # timestamp[us] would lose data"
+      dst = tempfile.NamedTemporaryFile()
       path = dst.name
-      # pylint: disable=c-extension-no-member
-      with self.assertRaises(pl.ArrowInvalid):
-        # Should throw an error "ArrowInvalid: Casting from timestamp[ns] to
-        # timestamp[us] would lose data"
-        with TestPipeline() as p:
-          _ = p \
-          | Create(self.RECORDS) \
-          | WriteToParquet(
-              path, self.SCHEMA96, num_shards=1, shard_name_template='')
+      with TestPipeline() as p:
+        _ = p \
+        | Create(self.RECORDS) \
+        | WriteToParquet(
+            path, self.SCHEMA96, num_shards=1, shard_name_template='')
 
   def test_sink_transform(self):
     with TemporaryDirectory() as tmp_dirname:
@@ -418,6 +421,76 @@ def test_schema_read_write(self):
             | Map(stable_repr))
         assert_that(readback, equal_to([stable_repr(r) for r in rows]))
 
+  def test_write_with_nullable_fields_missing_data(self):
+    """Test WriteToParquet with nullable fields where some fields are missing.
+    
+    This test addresses the bug reported in:
+    https://github.com/apache/beam/issues/35791
+    where WriteToParquet fails with a KeyError if any nullable 
+    field is missing in the data.
+    """
+    # Define PyArrow schema with all fields nullable
+    schema = pa.schema([
+        pa.field("id", pa.int64(), nullable=True),
+        pa.field("name", pa.string(), nullable=True),
+        pa.field("age", pa.int64(), nullable=True),
+        pa.field("email", pa.string(), nullable=True),
+    ])
+
+    # Sample data with missing nullable fields
+    data = [
+        {
+            'id': 1, 'name': 'Alice', 'age': 30
+        },  # missing 'email'
+        {
+            'id': 2, 'name': 'Bob', 'age': 25, 'email': 'bob@example.com'
+        },  # all fields present
+        {
+            'id': 3, 'name': 'Charlie', 'age': None, 'email': None
+        },  # explicit None values
+        {
+            'id': 4, 'name': 'David'
+        },  # missing 'age' and 'email'
+    ]
+
+    with TemporaryDirectory() as tmp_dirname:
+      path = os.path.join(tmp_dirname, 'nullable_test')
+
+      # Write data with missing nullable fields - this should not raise KeyError
+      with TestPipeline() as p:
+        _ = (
+            p
+            | Create(data)
+            | WriteToParquet(
+                path, schema, num_shards=1, shard_name_template=''))
+
+      # Read back and verify the data
+      with TestPipeline() as p:
+        readback = (
+            p
+            | ReadFromParquet(path + '*')
+            | Map(json.dumps, sort_keys=True))
+
+        # Expected data should have None for missing nullable fields
+        expected_data = [
+            {
+                'id': 1, 'name': 'Alice', 'age': 30, 'email': None
+            },
+            {
+                'id': 2, 'name': 'Bob', 'age': 25, 'email': 'bob@example.com'
+            },
+            {
+                'id': 3, 'name': 'Charlie', 'age': None, 'email': None
+            },
+            {
+                'id': 4, 'name': 'David', 'age': None, 'email': None
+            },
+        ]
+
+        assert_that(
+            readback,
+            equal_to([json.dumps(r, sort_keys=True) for r in expected_data]))
+
   def test_batched_read(self):
     with TemporaryDirectory() as tmp_dirname:
       path = os.path.join(tmp_dirname + "tmp_filename")
@@ -571,7 +644,8 @@ def test_selective_columns(self):
   def test_sink_transform_multiple_row_group(self):
     with TemporaryDirectory() as tmp_dirname:
       path = os.path.join(tmp_dirname + "tmp_filename")
-      with TestPipeline() as p:
+      # Pin to FnApiRunner since test assumes fixed bundle size
+      with TestPipeline('FnApiRunner') as p:
         # writing 623200 bytes of data
         _ = p \
         | Create(self.RECORDS * 4000) \
@@ -656,6 +730,290 @@ def test_read_all_from_parquet_with_filename(self):
           equal_to(result))
 
 
+class GenerateEvent(beam.PTransform):
+  @staticmethod
+  def sample_data():
+    return GenerateEvent()
+
+  def expand(self, input):
+    elemlist = [{'age': 10}, {'age': 20}, {'age': 30}]
+    elem = elemlist
+    return (
+        input
+        | TestStream().add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 1, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 2, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 3, 0,
+                                tzinfo=pytz.UTC).timestamp()).add_elements(
+                                    elements=elem,
+                                    event_timestamp=datetime(
+                                        2021, 3, 1, 0, 0, 4, 0,
+                                        tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 5, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 5, 0,
+                             tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 6,
+                0, tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 7, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 8, 0,
+                                tzinfo=pytz.UTC).timestamp()).add_elements(
+                                    elements=elem,
+                                    event_timestamp=datetime(
+                                        2021, 3, 1, 0, 0, 9, 0,
+                                        tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 10, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 10, 0,
+                             tzinfo=pytz.UTC).timestamp()).add_elements(
+                                 elements=elem,
+                                 event_timestamp=datetime(
+                                     2021, 3, 1, 0, 0, 11, 0,
+                                     tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 12, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 13, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 14, 0,
+                                tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 15, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 15, 0,
+                             tzinfo=pytz.UTC).timestamp()).add_elements(
+                                 elements=elem,
+                                 event_timestamp=datetime(
+                                     2021, 3, 1, 0, 0, 16, 0,
+                                     tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 17, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 18, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 19, 0,
+                                tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 20, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 20, 0,
+                             tzinfo=pytz.UTC).timestamp()).advance_watermark_to(
+                                 datetime(
+                                     2021, 3, 1, 0, 0, 25, 0, tzinfo=pytz.UTC).
+                                 timestamp()).advance_watermark_to_infinity())
+
+
+class WriteStreamingTest(unittest.TestCase):
+  def setUp(self):
+    super().setUp()
+    self.tempdir = tempfile.mkdtemp()
+
+  def tearDown(self):
+    if os.path.exists(self.tempdir):
+      shutil.rmtree(self.tempdir)
+
+  def test_write_streaming_2_shards_default_shard_name_template(
+      self, num_shards=2):
+    with TestPipeline() as p:
+      output = (p | GenerateEvent.sample_data())
+      #ParquetIO
+      pyschema = pa.schema([('age', pa.int64())])
+      output2 = output | 'WriteToParquet' >> beam.io.WriteToParquet(
+          file_path_prefix=self.tempdir + "/ouput_WriteToParquet",
+          file_name_suffix=".parquet",
+          num_shards=num_shards,
+          triggering_frequency=60,
+          schema=pyschema)
+      _ = output2 | 'LogElements after WriteToParquet' >> LogElements(
+          prefix='after WriteToParquet ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToParquet-[1614556800.0, 1614556805.0)-00000-of-00002.parquet
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>[\d\.]+), '
+        r'(?P<window_end>[\d\.]+|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.parquet$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToParquet*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertEqual(
+        len(file_names),
+        num_shards,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+  def test_write_streaming_2_shards_custom_shard_name_template(
+      self, num_shards=2, shard_name_template='-V-SSSSS-of-NNNNN'):
+    with TestPipeline() as p:
+      output = (p | GenerateEvent.sample_data())
+      #ParquetIO
+      pyschema = pa.schema([('age', pa.int64())])
+      output2 = output | 'WriteToParquet' >> beam.io.WriteToParquet(
+          file_path_prefix=self.tempdir + "/ouput_WriteToParquet",
+          file_name_suffix=".parquet",
+          shard_name_template=shard_name_template,
+          num_shards=num_shards,
+          triggering_frequency=60,
+          schema=pyschema)
+      _ = output2 | 'LogElements after WriteToParquet' >> LogElements(
+          prefix='after WriteToParquet ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToParquet-[2021-03-01T00-00-00, 2021-03-01T00-01-00)-
+    #   00000-of-00002.parquet
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}), '
+        r'(?P<window_end>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.parquet$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToParquet*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertEqual(
+        len(file_names),
+        num_shards,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+  def test_write_streaming_2_shards_custom_shard_name_template_5s_window(
+      self,
+      num_shards=2,
+      shard_name_template='-V-SSSSS-of-NNNNN',
+      triggering_frequency=5):
+    with TestPipeline() as p:
+      output = (p | GenerateEvent.sample_data())
+      #ParquetIO
+      pyschema = pa.schema([('age', pa.int64())])
+      output2 = output | 'WriteToParquet' >> beam.io.WriteToParquet(
+          file_path_prefix=self.tempdir + "/ouput_WriteToParquet",
+          file_name_suffix=".parquet",
+          shard_name_template=shard_name_template,
+          num_shards=num_shards,
+          triggering_frequency=triggering_frequency,
+          schema=pyschema)
+      _ = output2 | 'LogElements after WriteToParquet' >> LogElements(
+          prefix='after WriteToParquet ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToParquet-[2021-03-01T00-00-00, 2021-03-01T00-01-00)-
+    #   00000-of-00002.parquet
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}), '
+        r'(?P<window_end>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.parquet$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToParquet*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    # for 5s window size, the input should be processed by 5 windows with
+    # 2 shards per window
+    self.assertEqual(
+        len(file_names),
+        10,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+  def test_write_streaming_undef_shards_default_shard_name_template_windowed_pcoll(  # pylint: disable=line-too-long
+      self):
+    with TestPipeline() as p:
+      output = (
+          p | GenerateEvent.sample_data()
+          | 'User windowing' >> beam.transforms.core.WindowInto(
+              beam.transforms.window.FixedWindows(10),
+              trigger=beam.transforms.trigger.AfterWatermark(),
+              accumulation_mode=beam.transforms.trigger.AccumulationMode.
+              DISCARDING,
+              allowed_lateness=beam.utils.timestamp.Duration(seconds=0)))
+      #ParquetIO
+      pyschema = pa.schema([('age', pa.int64())])
+      output2 = output | 'WriteToParquet' >> beam.io.WriteToParquet(
+          file_path_prefix=self.tempdir + "/ouput_WriteToParquet",
+          file_name_suffix=".parquet",
+          num_shards=0,
+          schema=pyschema)
+      _ = output2 | 'LogElements after WriteToParquet' >> LogElements(
+          prefix='after WriteToParquet ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToParquet-[1614556800.0, 1614556805.0)-00000-of-00002.parquet
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>[\d\.]+), '
+        r'(?P<window_end>[\d\.]+|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.parquet$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToParquet*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertGreaterEqual(
+        len(file_names),
+        1 * 3,  #25s of data covered by 3 10s windows
+        "expected %d files, but got: %d" % (1 * 3, len(file_names)))
+
+
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
   unittest.main()
diff --git a/sdks/python/apache_beam/io/requestresponse_it_test.py b/sdks/python/apache_beam/io/requestresponse_it_test.py
index 712ccc7881d6..8ac7cdb6f5fd 100644
--- a/sdks/python/apache_beam/io/requestresponse_it_test.py
+++ b/sdks/python/apache_beam/io/requestresponse_it_test.py
@@ -17,6 +17,7 @@
 import base64
 import logging
 import sys
+import time
 import typing
 import unittest
 from dataclasses import dataclass
@@ -206,7 +207,7 @@ def __exit__(self, exc_type, exc_val, exc_tb):
 @pytest.mark.uses_testcontainer
 class TestRedisCache(unittest.TestCase):
   def setUp(self) -> None:
-    self.retries = 3
+    self.retries = 5
     self._start_container()
 
   def test_rrio_cache_all_miss(self):
@@ -303,6 +304,8 @@ def _start_container(self):
         if i == self.retries - 1:
           _LOGGER.error('Unable to start redis container for RRIO tests.')
           raise e
+        # Add a small delay between retries to avoid rapid successive failures
+        time.sleep(2)
 
 
 if __name__ == '__main__':
diff --git a/sdks/python/apache_beam/io/requestresponse_test.py b/sdks/python/apache_beam/io/requestresponse_test.py
index 3bc85a5e103a..4adf2fc7649c 100644
--- a/sdks/python/apache_beam/io/requestresponse_test.py
+++ b/sdks/python/apache_beam/io/requestresponse_test.py
@@ -31,8 +31,6 @@
   from apache_beam.io.requestresponse import Caller
   from apache_beam.io.requestresponse import DefaultThrottler
   from apache_beam.io.requestresponse import RequestResponseIO
-  from apache_beam.io.requestresponse import UserCodeExecutionException
-  from apache_beam.io.requestresponse import UserCodeTimeoutException
   from apache_beam.io.requestresponse import retry_on_exception
 except ImportError:
   raise unittest.SkipTest('RequestResponseIO dependencies are not installed.')
@@ -98,7 +96,7 @@ def test_valid_call(self):
 
   def test_call_timeout(self):
     caller = CallerWithTimeout()
-    with self.assertRaises(UserCodeTimeoutException):
+    with self.assertRaisesRegex(Exception, "Timeout"):
       with TestPipeline() as test_pipeline:
         _ = (
             test_pipeline
@@ -107,7 +105,7 @@ def test_call_timeout(self):
 
   def test_call_runtime_error(self):
     caller = CallerWithRuntimeError()
-    with self.assertRaises(UserCodeExecutionException):
+    with self.assertRaisesRegex(Exception, "could not complete request"):
       with TestPipeline() as test_pipeline:
         _ = (
             test_pipeline
@@ -120,23 +118,23 @@ def test_retry_on_exception(self):
 
   def test_caller_backoff_retry_strategy(self):
     caller = CallerThatRetries()
-    with self.assertRaises(TooManyRequests) as cm:
+    with self.assertRaises(Exception) as cm:
       with TestPipeline() as test_pipeline:
         _ = (
             test_pipeline
             | beam.Create(["sample_request"])
             | RequestResponseIO(caller=caller))
-    self.assertRegex(cm.exception.message, 'retries = 2')
+    self.assertRegex(str(cm.exception), 'retries = 2')
 
   def test_caller_no_retry_strategy(self):
     caller = CallerThatRetries()
-    with self.assertRaises(TooManyRequests) as cm:
+    with self.assertRaises(Exception) as cm:
       with TestPipeline() as test_pipeline:
         _ = (
             test_pipeline
             | beam.Create(["sample_request"])
             | RequestResponseIO(caller=caller, repeater=None))
-    self.assertRegex(cm.exception.message, 'retries = 0')
+    self.assertRegex(str(cm.exception), 'retries = 0')
 
   @retry(
       retry=retry_if_exception_type(IndexError),
@@ -148,7 +146,11 @@ def test_default_throttler(self):
         window_ms=10000, bucket_ms=5000, overload_ratio=1)
     # manually override the number of received requests for testing.
     throttler.throttler._all_requests.add(time.time() * 1000, 100)
-    test_pipeline = TestPipeline()
+    # TODO(https://github.com/apache/beam/issues/34549): This test relies on
+    # metrics filtering which doesn't work on Prism yet because Prism renames
+    # steps (e.g. "Do" becomes "ref_AppliedPTransform_Do_7").
+    # https://github.com/apache/beam/blob/5f9cd73b7c9a2f37f83971ace3a399d633201dd1/sdks/python/apache_beam/runners/portability/fn_api_runner/fn_runner.py#L1590
+    test_pipeline = TestPipeline('FnApiRunner')
     _ = (
         test_pipeline
         | beam.Create(['sample_request'])
diff --git a/sdks/python/apache_beam/io/textio.py b/sdks/python/apache_beam/io/textio.py
index d817463cfef6..ba28fc608a0c 100644
--- a/sdks/python/apache_beam/io/textio.py
+++ b/sdks/python/apache_beam/io/textio.py
@@ -451,7 +451,8 @@ def __init__(
       *,
       max_records_per_shard=None,
       max_bytes_per_shard=None,
-      skip_if_empty=False):
+      skip_if_empty=False,
+      triggering_frequency=None):
     """Initialize a _TextSink.
 
     Args:
@@ -468,13 +469,23 @@ def __init__(
         Constraining the number of shards is likely to reduce
         the performance of a pipeline.  Setting this value is not recommended
         unless you require a specific number of output files.
+        In streaming if not set, the service will write a file per bundle.
       shard_name_template: A template string containing placeholders for
-        the shard number and shard count. When constructing a filename for a
-        particular shard number, the upper-case letters 'S' and 'N' are
-        replaced with the 0-padded shard number and shard count respectively.
-        This argument can be '' in which case it behaves as if num_shards was
-        set to 1 and only one file will be generated. The default pattern used
-        is '-SSSSS-of-NNNNN' if None is passed as the shard_name_template.
+        the shard number and shard count. Currently only ``''``,
+        ``'-SSSSS-of-NNNNN'``, ``'-W-SSSSS-of-NNNNN'`` and
+        ``'-V-SSSSS-of-NNNNN'`` are patterns accepted by the service.
+        When constructing a filename for a particular shard number, the
+        upper-case letters ``S`` and ``N`` are replaced with the ``0``-padded
+        shard number and shard count respectively.  This argument can be ``''``
+        in which case it behaves as if num_shards was set to 1 and only one file
+        will be generated. The default pattern used is ``'-SSSSS-of-NNNNN'`` for
+        bounded PCollections and for ``'-W-SSSSS-of-NNNNN'`` unbounded
+        PCollections.
+        W is used for windowed shard naming and is replaced with
+        ``[window.start, window.end)``
+        V is used for windowed shard naming and is replaced with
+        ``[window.start.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S"),
+        window.end.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S")``
       coder: Coder used to encode each line.
       compression_type: Used to handle compressed output files. Typical value
         is CompressionTypes.AUTO, in which case the final file path's
@@ -494,6 +505,10 @@ def __init__(
         to exceed this value.  This also tracks the uncompressed,
         not compressed, size of the shard.
       skip_if_empty: Don't write any shards if the PCollection is empty.
+      triggering_frequency: (int) Every triggering_frequency duration, a window
+        will be triggered and all bundles in the window will be written.
+        If set it overrides user windowing. Mandatory for GlobalWindow.
+
 
     Returns:
       A _TextSink object usable for writing.
@@ -508,7 +523,8 @@ def __init__(
         compression_type=compression_type,
         max_records_per_shard=max_records_per_shard,
         max_bytes_per_shard=max_bytes_per_shard,
-        skip_if_empty=skip_if_empty)
+        skip_if_empty=skip_if_empty,
+        triggering_frequency=triggering_frequency)
     self._append_trailing_newlines = append_trailing_newlines
     self._header = header
     self._footer = footer
@@ -833,7 +849,8 @@ def __init__(
       *,
       max_records_per_shard=None,
       max_bytes_per_shard=None,
-      skip_if_empty=False):
+      skip_if_empty=False,
+      triggering_frequency=None):
     r"""Initialize a :class:`WriteToText` transform.
 
     Args:
@@ -852,13 +869,21 @@ def __init__(
         the performance of a pipeline.  Setting this value is not recommended
         unless you require a specific number of output files.
       shard_name_template (str): A template string containing placeholders for
-        the shard number and shard count. Currently only ``''`` and
-        ``'-SSSSS-of-NNNNN'`` are patterns accepted by the service.
+        the shard number and shard count. Currently only ``''``,
+        ``'-SSSSS-of-NNNNN'``, ``'-W-SSSSS-of-NNNNN'`` and
+        ``'-V-SSSSS-of-NNNNN'`` are patterns accepted by the service.
         When constructing a filename for a particular shard number, the
         upper-case letters ``S`` and ``N`` are replaced with the ``0``-padded
         shard number and shard count respectively.  This argument can be ``''``
         in which case it behaves as if num_shards was set to 1 and only one file
-        will be generated. The default pattern used is ``'-SSSSS-of-NNNNN'``.
+        will be generated. The default pattern used is ``'-SSSSS-of-NNNNN'`` for
+        bounded PCollections and for ``'-W-SSSSS-of-NNNNN'`` unbounded
+        PCollections.
+        W is used for windowed shard naming and is replaced with
+        ``[window.start, window.end)``
+        V is used for windowed shard naming and is replaced with
+        ``[window.start.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S"),
+        window.end.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S")``
       coder (~apache_beam.coders.coders.Coder): Coder used to encode each line.
       compression_type (str): Used to handle compressed output files.
         Typical value is :class:`CompressionTypes.AUTO
@@ -883,6 +908,8 @@ def __init__(
       skip_if_empty: Don't write any shards if the PCollection is empty.
         In case of an empty PCollection, this will still delete existing
         files having same file path and not create new ones.
+      triggering_frequency: (int) Every triggering_frequency duration, a window
+        will be triggered and all bundles in the window will be written.
     """
 
     self._sink = _TextSink(
@@ -897,9 +924,18 @@ def __init__(
         footer,
         max_records_per_shard=max_records_per_shard,
         max_bytes_per_shard=max_bytes_per_shard,
-        skip_if_empty=skip_if_empty)
+        skip_if_empty=skip_if_empty,
+        triggering_frequency=triggering_frequency)
 
   def expand(self, pcoll):
+    if (not pcoll.is_bounded and self._sink.shard_name_template
+        == filebasedsink.DEFAULT_SHARD_NAME_TEMPLATE):
+      self._sink.shard_name_template = (
+          filebasedsink.DEFAULT_WINDOW_SHARD_NAME_TEMPLATE)
+      self._sink.shard_name_format = self._sink._template_to_format(
+          self._sink.shard_name_template)
+      self._sink.shard_name_glob_format = self._sink._template_to_glob_format(
+          self._sink.shard_name_template)
     return pcoll | Write(self._sink)
 
 
@@ -937,7 +973,12 @@ def append(dest):
 
   @append_pandas_args(
       pandas.read_csv, exclude=['filepath_or_buffer', 'iterator'])
-  def ReadFromCsv(path: str, *, splittable: bool = True, **kwargs):
+  def ReadFromCsv(
+      path: str,
+      *,
+      splittable: bool = True,
+      filename_column: Optional[str] = None,
+      **kwargs):
     """A PTransform for reading comma-separated values (csv) files into a
     PCollection.
 
@@ -949,11 +990,17 @@ def ReadFromCsv(path: str, *, splittable: bool = True, **kwargs):
         This should be set to False if single records span multiple lines (e.g.
         a quoted field has a newline inside of it).  Setting this to false may
         disable liquid sharding.
+      filename_column (str): If not None, the name of the column to add
+        to each record, containing the filename of the source file.
       **kwargs: Extra arguments passed to `pandas.read_csv` (see below).
     """
     from apache_beam.dataframe.io import ReadViaPandas
     return 'ReadFromCsv' >> ReadViaPandas(
-        'csv', path, splittable=splittable, **kwargs)
+        'csv',
+        path,
+        splittable=splittable,
+        filename_column=filename_column,
+        **kwargs)
 
   @append_pandas_args(
       pandas.DataFrame.to_csv, exclude=['path_or_buf', 'index', 'index_label'])
diff --git a/sdks/python/apache_beam/io/textio_test.py b/sdks/python/apache_beam/io/textio_test.py
index 30ddc5d62e07..4f804fa44c44 100644
--- a/sdks/python/apache_beam/io/textio_test.py
+++ b/sdks/python/apache_beam/io/textio_test.py
@@ -24,10 +24,14 @@
 import logging
 import os
 import platform
+import re
 import shutil
 import tempfile
 import unittest
 import zlib
+from datetime import datetime
+
+import pytz
 
 import apache_beam as beam
 from apache_beam import coders
@@ -45,11 +49,13 @@
 from apache_beam.io.textio import WriteToText
 from apache_beam.options.pipeline_options import PipelineOptions
 from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.testing.test_stream import TestStream
 from apache_beam.testing.test_utils import TempDir
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
 from apache_beam.transforms.core import Create
 from apache_beam.transforms.userstate import CombiningValueStateSpec
+from apache_beam.transforms.util import LogElements
 from apache_beam.utils.timestamp import Timestamp
 
 
@@ -1759,6 +1765,31 @@ def test_csv_read_write(self):
 
         assert_that(pcoll, equal_to(records))
 
+  def test_csv_read_with_filename(self):
+    records = [beam.Row(a='str', b=ix) for ix in range(3)]
+    with tempfile.TemporaryDirectory() as dest:
+      file_path = os.path.join(dest, 'out.csv')
+      with TestPipeline() as p:
+        # pylint: disable=expression-not-assigned
+        p | beam.Create(records) | beam.io.WriteToCsv(file_path)
+      with TestPipeline() as p:
+        pcoll = (
+            p
+            | beam.io.ReadFromCsv(
+                file_path + '*', filename_column='source_filename')
+            | beam.Map(lambda t: beam.Row(**dict(zip(type(t)._fields, t)))))
+
+        # Get the sharded file name
+        files = glob.glob(file_path + '*')
+        self.assertEqual(len(files), 1)
+        sharded_file_path = files[0]
+
+        expected = [
+            beam.Row(a=r.a, b=r.b, source_filename=sharded_file_path)
+            for r in records
+        ]
+        assert_that(pcoll, equal_to(expected))
+
   def test_non_utf8_csv_read_write(self):
     content = b"\xe0,\xe1,\xe2\n0,1,2\n1,2,3\n"
 
@@ -1849,6 +1880,406 @@ def check_types(element):
         _ = pcoll | beam.Map(check_types)
 
 
+class GenerateEvent(beam.PTransform):
+  @staticmethod
+  def sample_data():
+    return GenerateEvent()
+
+  def expand(self, input):
+    elemlist = [{'age': 10}, {'age': 20}, {'age': 30}]
+    elem = elemlist
+    return (
+        input
+        | TestStream().add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 1, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 2, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 3, 0,
+                                tzinfo=pytz.UTC).timestamp()).add_elements(
+                                    elements=elem,
+                                    event_timestamp=datetime(
+                                        2021, 3, 1, 0, 0, 4, 0,
+                                        tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 5, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 5, 0,
+                             tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 6,
+                0, tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 7, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 8, 0,
+                                tzinfo=pytz.UTC).timestamp()).add_elements(
+                                    elements=elem,
+                                    event_timestamp=datetime(
+                                        2021, 3, 1, 0, 0, 9, 0,
+                                        tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 10, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 10, 0,
+                             tzinfo=pytz.UTC).timestamp()).add_elements(
+                                 elements=elem,
+                                 event_timestamp=datetime(
+                                     2021, 3, 1, 0, 0, 11, 0,
+                                     tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 12, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 13, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 14, 0,
+                                tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 15, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 15, 0,
+                             tzinfo=pytz.UTC).timestamp()).add_elements(
+                                 elements=elem,
+                                 event_timestamp=datetime(
+                                     2021, 3, 1, 0, 0, 16, 0,
+                                     tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 17, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 18, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 19, 0,
+                                tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 20, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 20, 0,
+                             tzinfo=pytz.UTC).timestamp()).advance_watermark_to(
+                                 datetime(
+                                     2021, 3, 1, 0, 0, 25, 0, tzinfo=pytz.UTC).
+                                 timestamp()).advance_watermark_to_infinity())
+
+
+class WriteStreamingTest(unittest.TestCase):
+  def setUp(self):
+    super().setUp()
+    self.tempdir = tempfile.mkdtemp()
+
+  def tearDown(self):
+    if os.path.exists(self.tempdir):
+      shutil.rmtree(self.tempdir)
+
+  def test_write_streaming_2_shards_default_shard_name_template(
+      self, num_shards=2):
+    with TestPipeline() as p:
+      output = (p | GenerateEvent.sample_data())
+      #TextIO
+      output2 = output | 'TextIO WriteToText' >> beam.io.WriteToText(
+          file_path_prefix=self.tempdir + "/ouput_WriteToText",
+          file_name_suffix=".txt",
+          num_shards=num_shards,
+          triggering_frequency=60)
+      _ = output2 | 'LogElements after WriteToText' >> LogElements(
+          prefix='after WriteToText ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToText-[1614556800.0, 1614556805.0)-00000-of-00002.txt
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>[\d\.]+), '
+        r'(?P<window_end>[\d\.]+|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.txt$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToText*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertEqual(
+        len(file_names),
+        num_shards,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+  def test_write_streaming_2_shards_default_shard_name_template_windowed_pcoll(
+      self, num_shards=2):
+    with TestPipeline() as p:
+      output = (
+          p | GenerateEvent.sample_data()
+          | 'User windowing' >> beam.transforms.core.WindowInto(
+              beam.transforms.window.FixedWindows(10),
+              trigger=beam.transforms.trigger.AfterWatermark(),
+              accumulation_mode=beam.transforms.trigger.AccumulationMode.
+              DISCARDING,
+              allowed_lateness=beam.utils.timestamp.Duration(seconds=0)))
+      #TextIO
+      output2 = output | 'TextIO WriteToText' >> beam.io.WriteToText(
+          file_path_prefix=self.tempdir + "/ouput_WriteToText",
+          file_name_suffix=".txt",
+          num_shards=num_shards,
+      )
+      _ = output2 | 'LogElements after WriteToText' >> LogElements(
+          prefix='after WriteToText ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToText-[1614556800.0, 1614556805.0)-00000-of-00002.txt
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>[\d\.]+), '
+        r'(?P<window_end>[\d\.]+|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.txt$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToText*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertEqual(
+        len(file_names),
+        num_shards * 3,  #25s of data covered by 3 10s windows
+        "expected %d files, but got: %d" % (num_shards * 3, len(file_names)))
+
+  def test_write_streaming_undef_shards_default_shard_name_template_windowed_pcoll(  # pylint: disable=line-too-long
+      self):
+    with TestPipeline() as p:
+      output = (
+          p | GenerateEvent.sample_data()
+          | 'User windowing' >> beam.transforms.core.WindowInto(
+              beam.transforms.window.FixedWindows(10),
+              trigger=beam.transforms.trigger.AfterWatermark(),
+              accumulation_mode=beam.transforms.trigger.AccumulationMode.
+              DISCARDING,
+              allowed_lateness=beam.utils.timestamp.Duration(seconds=0)))
+      #TextIO
+      output2 = output | 'TextIO WriteToText' >> beam.io.WriteToText(
+          file_path_prefix=self.tempdir + "/ouput_WriteToText",
+          file_name_suffix=".txt",
+          num_shards=0,
+      )
+      _ = output2 | 'LogElements after WriteToText' >> LogElements(
+          prefix='after WriteToText ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToText-[1614556800.0, 1614556805.0)-00000-of-00002.txt
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>[\d\.]+), '
+        r'(?P<window_end>[\d\.]+|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.txt$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToText*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertGreaterEqual(
+        len(file_names),
+        1 * 3,  #25s of data covered by 3 10s windows
+        "expected %d files, but got: %d" % (1 * 3, len(file_names)))
+
+  def test_write_streaming_undef_shards_default_shard_name_template_windowed_pcoll_and_trig_freq(  # pylint: disable=line-too-long
+      self):
+    with TestPipeline() as p:
+      output = (
+          p | GenerateEvent.sample_data()
+          | 'User windowing' >> beam.transforms.core.WindowInto(
+              beam.transforms.window.FixedWindows(60),
+              trigger=beam.transforms.trigger.AfterWatermark(),
+              accumulation_mode=beam.transforms.trigger.AccumulationMode.
+              DISCARDING,
+              allowed_lateness=beam.utils.timestamp.Duration(seconds=0)))
+      #TextIO
+      output2 = output | 'TextIO WriteToText' >> beam.io.WriteToText(
+          file_path_prefix=self.tempdir + "/ouput_WriteToText",
+          file_name_suffix=".txt",
+          num_shards=0,
+          triggering_frequency=10,
+      )
+      _ = output2 | 'LogElements after WriteToText' >> LogElements(
+          prefix='after WriteToText ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToText-[1614556800.0, 1614556805.0)-00000-of-00002.txt
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>[\d\.]+), '
+        r'(?P<window_end>[\d\.]+|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.txt$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToText*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertGreaterEqual(
+        len(file_names),
+        1 * 3,  #25s of data covered by 3 10s windows
+        "expected %d files, but got: %d" % (1 * 3, len(file_names)))
+
+  def test_write_streaming_undef_shards_default_shard_name_template_global_window_pcoll(  # pylint: disable=line-too-long
+      self):
+    with TestPipeline() as p:
+      output = (p | GenerateEvent.sample_data())
+      #TextIO
+      output2 = output | 'TextIO WriteToText' >> beam.io.WriteToText(
+          file_path_prefix=self.tempdir + "/ouput_WriteToText",
+          file_name_suffix=".txt",
+          num_shards=0,  #0 means undef nb of shards, same as omitted/default
+          triggering_frequency=60,
+      )
+      _ = output2 | 'LogElements after WriteToText' >> LogElements(
+          prefix='after WriteToText ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToText-[1614556800.0, 1614556805.0)-00000-of-00002.txt
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>[\d\.]+), '
+        r'(?P<window_end>[\d\.]+|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.txt$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToText*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertGreaterEqual(
+        len(file_names),
+        1,  #25s of data covered by 60s windows
+        "expected %d files, but got: %d" % (1, len(file_names)))
+
+  def test_write_streaming_2_shards_custom_shard_name_template(
+      self, num_shards=2, shard_name_template='-V-SSSSS-of-NNNNN'):
+    with TestPipeline() as p:
+      output = (p | GenerateEvent.sample_data())
+      #TextIO
+      output2 = output | 'TextIO WriteToText' >> beam.io.WriteToText(
+          file_path_prefix=self.tempdir + "/ouput_WriteToText",
+          file_name_suffix=".txt",
+          shard_name_template=shard_name_template,
+          num_shards=num_shards,
+          triggering_frequency=60,
+      )
+      _ = output2 | 'LogElements after WriteToText' >> LogElements(
+          prefix='after WriteToText ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToText-[2021-03-01T00-00-00, 2021-03-01T00-01-00)-
+    #   00000-of-00002.txt
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}), '
+        r'(?P<window_end>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.txt$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToText*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertEqual(
+        len(file_names),
+        num_shards,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+  def test_write_streaming_2_shards_custom_shard_name_template_5s_window(
+      self,
+      num_shards=2,
+      shard_name_template='-V-SSSSS-of-NNNNN',
+      triggering_frequency=5):
+    with TestPipeline() as p:
+      output = (p | GenerateEvent.sample_data())
+      #TextIO
+      output2 = output | 'TextIO WriteToText' >> beam.io.WriteToText(
+          file_path_prefix=self.tempdir + "/ouput_WriteToText",
+          file_name_suffix=".txt",
+          shard_name_template=shard_name_template,
+          num_shards=num_shards,
+          triggering_frequency=triggering_frequency,
+      )
+      _ = output2 | 'LogElements after WriteToText' >> LogElements(
+          prefix='after WriteToText ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToText-[2021-03-01T00-00-00, 2021-03-01T00-01-00)-
+    #   00000-of-00002.txt
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}), '
+        r'(?P<window_end>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.txt$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToText*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    # for 5s window size, the input should be processed by 5 windows with
+    # 2 shards per window
+    self.assertEqual(
+        len(file_names),
+        10,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
   unittest.main()
diff --git a/sdks/python/apache_beam/io/tfrecordio.py b/sdks/python/apache_beam/io/tfrecordio.py
index b911c64a1348..e27ea5070b06 100644
--- a/sdks/python/apache_beam/io/tfrecordio.py
+++ b/sdks/python/apache_beam/io/tfrecordio.py
@@ -290,7 +290,8 @@ def __init__(
       file_name_suffix,
       num_shards,
       shard_name_template,
-      compression_type):
+      compression_type,
+      triggering_frequency=60):
     """Initialize a TFRecordSink. See WriteToTFRecord for details."""
 
     super().__init__(
@@ -300,7 +301,8 @@ def __init__(
         num_shards=num_shards,
         shard_name_template=shard_name_template,
         mime_type='application/octet-stream',
-        compression_type=compression_type)
+        compression_type=compression_type,
+        triggering_frequency=triggering_frequency)
 
   def write_encoded_record(self, file_handle, value):
     _TFRecordUtil.write_record(file_handle, value)
@@ -315,7 +317,8 @@ def __init__(
       file_name_suffix='',
       num_shards=0,
       shard_name_template=None,
-      compression_type=CompressionTypes.AUTO):
+      compression_type=CompressionTypes.AUTO,
+      triggering_frequency=None):
     """Initialize WriteToTFRecord transform.
 
     Args:
@@ -326,16 +329,29 @@ def __init__(
       file_name_suffix: Suffix for the files written.
       num_shards: The number of files (shards) used for output. If not set, the
         default value will be used.
+        In streaming if not set, the service will write a file per bundle.
       shard_name_template: A template string containing placeholders for
-        the shard number and shard count. When constructing a filename for a
-        particular shard number, the upper-case letters 'S' and 'N' are
-        replaced with the 0-padded shard number and shard count respectively.
-        This argument can be '' in which case it behaves as if num_shards was
-        set to 1 and only one file will be generated. The default pattern used
-        is '-SSSSS-of-NNNNN' if None is passed as the shard_name_template.
+        the shard number and shard count. Currently only ``''``,
+        ``'-SSSSS-of-NNNNN'``, ``'-W-SSSSS-of-NNNNN'`` and
+        ``'-V-SSSSS-of-NNNNN'`` are patterns accepted by the service.
+        When constructing a filename for a particular shard number, the
+        upper-case letters ``S`` and ``N`` are replaced with the ``0``-padded
+        shard number and shard count respectively.  This argument can be ``''``
+        in which case it behaves as if num_shards was set to 1 and only one file
+        will be generated. The default pattern used is ``'-SSSSS-of-NNNNN'`` for
+        bounded PCollections and for ``'-W-SSSSS-of-NNNNN'`` unbounded 
+        PCollections.
+        W is used for windowed shard naming and is replaced with 
+        ``[window.start, window.end)``
+        V is used for windowed shard naming and is replaced with 
+        ``[window.start.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S"), 
+        window.end.to_utc_datetime().strftime("%Y-%m-%dT%H-%M-%S")``
       compression_type: Used to handle compressed output files. Typical value
           is CompressionTypes.AUTO, in which case the file_path's extension will
           be used to detect the compression.
+      triggering_frequency: (int) Every triggering_frequency duration, a window 
+        will be triggered and all bundles in the window will be written.
+        If set it overrides user windowing. Mandatory for GlobalWindow.
 
     Returns:
       A WriteToTFRecord transform object.
@@ -347,7 +363,17 @@ def __init__(
         file_name_suffix,
         num_shards,
         shard_name_template,
-        compression_type)
+        compression_type,
+        triggering_frequency)
 
   def expand(self, pcoll):
+    if (not pcoll.is_bounded and self._sink.shard_name_template
+        == filebasedsink.DEFAULT_SHARD_NAME_TEMPLATE):
+      self._sink.shard_name_template = (
+          filebasedsink.DEFAULT_WINDOW_SHARD_NAME_TEMPLATE)
+      self._sink.shard_name_format = self._sink._template_to_format(
+          self._sink.shard_name_template)
+      self._sink.shard_name_glob_format = self._sink._template_to_glob_format(
+          self._sink.shard_name_template)
+
     return pcoll | Write(self._sink)
diff --git a/sdks/python/apache_beam/io/tfrecordio_test.py b/sdks/python/apache_beam/io/tfrecordio_test.py
index a867c0212ad3..6522ade36d80 100644
--- a/sdks/python/apache_beam/io/tfrecordio_test.py
+++ b/sdks/python/apache_beam/io/tfrecordio_test.py
@@ -21,15 +21,20 @@
 import glob
 import gzip
 import io
+import json
 import logging
 import os
 import pickle
 import random
 import re
+import shutil
+import tempfile
 import unittest
 import zlib
+from datetime import datetime
 
 import crcmod
+import pytz
 
 import apache_beam as beam
 from apache_beam import Create
@@ -41,9 +46,11 @@
 from apache_beam.io.tfrecordio import _TFRecordSink
 from apache_beam.io.tfrecordio import _TFRecordUtil
 from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.testing.test_stream import TestStream
 from apache_beam.testing.test_utils import TempDir
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
+from apache_beam.transforms.util import LogElements
 
 try:
   import tensorflow.compat.v1 as tf  # pylint: disable=import-error
@@ -558,6 +565,258 @@ def test_end2end_read_write_read(self):
         assert_that(actual_data, equal_to(expected_data))
 
 
+class GenerateEvent(beam.PTransform):
+  @staticmethod
+  def sample_data():
+    return GenerateEvent()
+
+  def expand(self, input):
+    elemlist = [{'age': 10}, {'age': 20}, {'age': 30}]
+    elem = elemlist
+    return (
+        input
+        | TestStream().add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 1, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 2, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 3, 0,
+                                tzinfo=pytz.UTC).timestamp()).add_elements(
+                                    elements=elem,
+                                    event_timestamp=datetime(
+                                        2021, 3, 1, 0, 0, 4, 0,
+                                        tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 5, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 5, 0,
+                             tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 6,
+                0, tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 7, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 8, 0,
+                                tzinfo=pytz.UTC).timestamp()).add_elements(
+                                    elements=elem,
+                                    event_timestamp=datetime(
+                                        2021, 3, 1, 0, 0, 9, 0,
+                                        tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 10, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 10, 0,
+                             tzinfo=pytz.UTC).timestamp()).add_elements(
+                                 elements=elem,
+                                 event_timestamp=datetime(
+                                     2021, 3, 1, 0, 0, 11, 0,
+                                     tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 12, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 13, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 14, 0,
+                                tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 15, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 15, 0,
+                             tzinfo=pytz.UTC).timestamp()).add_elements(
+                                 elements=elem,
+                                 event_timestamp=datetime(
+                                     2021, 3, 1, 0, 0, 16, 0,
+                                     tzinfo=pytz.UTC).timestamp()).
+        add_elements(
+            elements=elem,
+            event_timestamp=datetime(
+                2021, 3, 1, 0, 0, 17, 0,
+                tzinfo=pytz.UTC).timestamp()).add_elements(
+                    elements=elem,
+                    event_timestamp=datetime(
+                        2021, 3, 1, 0, 0, 18, 0,
+                        tzinfo=pytz.UTC).timestamp()).add_elements(
+                            elements=elem,
+                            event_timestamp=datetime(
+                                2021, 3, 1, 0, 0, 19, 0,
+                                tzinfo=pytz.UTC).timestamp()).
+        advance_watermark_to(
+            datetime(2021, 3, 1, 0, 0, 20, 0,
+                     tzinfo=pytz.UTC).timestamp()).add_elements(
+                         elements=elem,
+                         event_timestamp=datetime(
+                             2021, 3, 1, 0, 0, 20, 0,
+                             tzinfo=pytz.UTC).timestamp()).advance_watermark_to(
+                                 datetime(
+                                     2021, 3, 1, 0, 0, 25, 0, tzinfo=pytz.UTC).
+                                 timestamp()).advance_watermark_to_infinity())
+
+
+class WriteStreamingTest(unittest.TestCase):
+  def setUp(self):
+    super().setUp()
+    self.tempdir = tempfile.mkdtemp()
+
+  def tearDown(self):
+    if os.path.exists(self.tempdir):
+      shutil.rmtree(self.tempdir)
+
+  def test_write_streaming_2_shards_default_shard_name_template(
+      self, num_shards=2):
+    with TestPipeline() as p:
+      output = (
+          p
+          | GenerateEvent.sample_data()
+          | 'User windowing' >> beam.transforms.core.WindowInto(
+              beam.transforms.window.FixedWindows(60),
+              trigger=beam.transforms.trigger.AfterWatermark(),
+              accumulation_mode=beam.transforms.trigger.AccumulationMode.
+              DISCARDING,
+              allowed_lateness=beam.utils.timestamp.Duration(seconds=0))
+          | "encode" >> beam.Map(lambda s: json.dumps(s).encode('utf-8')))
+      #TFrecordIO
+      output2 = output | 'WriteToTFRecord' >> beam.io.WriteToTFRecord(
+          file_path_prefix=self.tempdir + "/ouput_WriteToTFRecord",
+          file_name_suffix=".tfrecord",
+          num_shards=num_shards,
+      )
+      _ = output2 | 'LogElements after WriteToTFRecord' >> LogElements(
+          prefix='after WriteToTFRecord ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToTFRecord-[1614556800.0, 1614556805.0)-00000-of-00002.tfrecord
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>[\d\.]+), '
+        r'(?P<window_end>[\d\.]+|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.tfrecord$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToTFRecord*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertEqual(
+        len(file_names),
+        num_shards,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+  def test_write_streaming_2_shards_custom_shard_name_template(
+      self, num_shards=2, shard_name_template='-V-SSSSS-of-NNNNN'):
+    with TestPipeline() as p:
+      output = (
+          p
+          | GenerateEvent.sample_data()
+          | "encode" >> beam.Map(lambda s: json.dumps(s).encode('utf-8')))
+      #TFrecordIO
+      output2 = output | 'WriteToTFRecord' >> beam.io.WriteToTFRecord(
+          file_path_prefix=self.tempdir + "/ouput_WriteToTFRecord",
+          file_name_suffix=".tfrecord",
+          shard_name_template=shard_name_template,
+          num_shards=num_shards,
+          triggering_frequency=60,
+      )
+      _ = output2 | 'LogElements after WriteToTFRecord' >> LogElements(
+          prefix='after WriteToTFRecord ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToTFRecord-[2021-03-01T00-00-00, 2021-03-01T00-01-00)-
+    #   00000-of-00002.tfrecord
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}), '
+        r'(?P<window_end>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.tfrecord$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToTFRecord*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    self.assertEqual(
+        len(file_names),
+        num_shards,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+  def test_write_streaming_2_shards_custom_shard_name_template_5s_window(
+      self,
+      num_shards=2,
+      shard_name_template='-V-SSSSS-of-NNNNN',
+      triggering_frequency=5):
+    with TestPipeline() as p:
+      output = (
+          p
+          | GenerateEvent.sample_data()
+          | "encode" >> beam.Map(lambda s: json.dumps(s).encode('utf-8')))
+      #TFrecordIO
+      output2 = output | 'WriteToTFRecord' >> beam.io.WriteToTFRecord(
+          file_path_prefix=self.tempdir + "/ouput_WriteToTFRecord",
+          file_name_suffix=".tfrecord",
+          shard_name_template=shard_name_template,
+          num_shards=num_shards,
+          triggering_frequency=triggering_frequency,
+      )
+      _ = output2 | 'LogElements after WriteToTFRecord' >> LogElements(
+          prefix='after WriteToTFRecord ', with_window=True, level=logging.INFO)
+
+    # Regex to match the expected windowed file pattern
+    # Example:
+    # ouput_WriteToTFRecord-[2021-03-01T00-00-00, 2021-03-01T00-01-00)-
+    #   00000-of-00002.tfrecord
+    # It captures: window_interval, shard_num, total_shards
+    pattern_string = (
+        r'.*-\[(?P<window_start>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}), '
+        r'(?P<window_end>\d{4}-\d{2}-\d{2}T\d{2}-\d{2}-\d{2}|Infinity)\)-'
+        r'(?P<shard_num>\d{5})-of-(?P<total_shards>\d{5})\.tfrecord$')
+    pattern = re.compile(pattern_string)
+    file_names = []
+    for file_name in glob.glob(self.tempdir + '/ouput_WriteToTFRecord*'):
+      match = pattern.match(file_name)
+      self.assertIsNotNone(
+          match, f"File name {file_name} did not match expected pattern.")
+      if match:
+        file_names.append(file_name)
+    print("Found files matching expected pattern:", file_names)
+    # for 5s window size, the input should be processed by 5 windows with
+    # 2 shards per window
+    self.assertEqual(
+        len(file_names),
+        10,
+        "expected %d files, but got: %d" % (num_shards, len(file_names)))
+
+
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
   unittest.main()
diff --git a/sdks/python/apache_beam/metrics/metric_test.py b/sdks/python/apache_beam/metrics/metric_test.py
index 2e2e51b267a7..bdba0512dfa2 100644
--- a/sdks/python/apache_beam/metrics/metric_test.py
+++ b/sdks/python/apache_beam/metrics/metric_test.py
@@ -201,7 +201,7 @@ def process(self, element):
     # Verify user distribution counter.
     metric_results = res.metrics().query()
     matcher = MetricResultMatcher(
-        step='ApplyPardo',
+        step=hc.contains_string('ApplyPardo'),
         namespace=hc.contains_string('SomeDoFn'),
         name='element_dist',
         committed=DistributionMatcher(
@@ -209,8 +209,7 @@ def process(self, element):
             count_value=hc.greater_than_or_equal_to(0),
             min_value=hc.greater_than_or_equal_to(0),
             max_value=hc.greater_than_or_equal_to(0)))
-    hc.assert_that(
-        metric_results['distributions'], hc.contains_inanyorder(matcher))
+    hc.assert_that(metric_results['distributions'], hc.has_item(matcher))
 
   def test_create_counter_distribution(self):
     sampler = statesampler.StateSampler('', counters.CounterFactory())
diff --git a/sdks/python/apache_beam/metrics/monitoring_infos.py b/sdks/python/apache_beam/metrics/monitoring_infos.py
index 6dc4b7ef9c57..46f856676d34 100644
--- a/sdks/python/apache_beam/metrics/monitoring_infos.py
+++ b/sdks/python/apache_beam/metrics/monitoring_infos.py
@@ -367,8 +367,8 @@ def create_monitoring_info(
         urn=urn, type=type_urn, labels=labels or {}, payload=payload)
   except TypeError as e:
     raise RuntimeError(
-        f'Failed to create MonitoringInfo for urn {urn} type {type} labels ' +
-        '{labels} and payload {payload}') from e
+        f'Failed to create MonitoringInfo for urn {urn} type {type_urn} '
+        f'labels {labels} and payload {payload}') from e
 
 
 def is_counter(monitoring_info_proto):
diff --git a/sdks/python/apache_beam/ml/anomaly/detectors/pyod_adapter_test.py b/sdks/python/apache_beam/ml/anomaly/detectors/pyod_adapter_test.py
index bb83e1aeca1c..c9acfdbb11d0 100644
--- a/sdks/python/apache_beam/ml/anomaly/detectors/pyod_adapter_test.py
+++ b/sdks/python/apache_beam/ml/anomaly/detectors/pyod_adapter_test.py
@@ -142,17 +142,17 @@ def test_scoring_with_unmatched_features(self):
     # (see the `test_scoring_with_matched_features`)
     detector = PyODFactory.create_detector(self.pickled_model_uri)
     options = PipelineOptions([])
-    p = beam.Pipeline(options=options)
-    _ = (
-        p | beam.Create(self.get_test_data_with_target())
-        | beam.Map(
-            lambda x: beam.Row(**dict(zip(["a", "b", "target"], map(int, x)))))
-        | beam.WithKeys(0)
-        | AnomalyDetection(detector=detector))
-
     # This should raise a ValueError with message
     # "X has 3 features, but IsolationForest is expecting 2 features as input."
-    self.assertRaises(ValueError, p.run)
+    with self.assertRaisesRegex(Exception, "is expecting 2 features"):
+      with beam.Pipeline(options=options) as p:
+        _ = (
+            p | beam.Create(self.get_test_data_with_target())
+            | beam.Map(
+                lambda x: beam.Row(
+                    **dict(zip(["a", "b", "target"], map(int, x)))))
+            | beam.WithKeys(0)
+            | AnomalyDetection(detector=detector))
 
 
 if __name__ == '__main__':
diff --git a/sdks/python/apache_beam/ml/anomaly/specifiable_test.py b/sdks/python/apache_beam/ml/anomaly/specifiable_test.py
index ccd8efd286cb..a222cf57973e 100644
--- a/sdks/python/apache_beam/ml/anomaly/specifiable_test.py
+++ b/sdks/python/apache_beam/ml/anomaly/specifiable_test.py
@@ -22,6 +22,7 @@
 import unittest
 from typing import Optional
 
+import pytest
 from parameterized import parameterized
 
 from apache_beam.internal.cloudpickle import cloudpickle
@@ -323,7 +324,10 @@ def __init__(self, arg):
       self.my_arg = arg * 10
       type(self).counter += 1
 
-  def test_on_pickle(self):
+  @pytest.mark.uses_dill
+  def test_on_dill_pickle(self):
+    pytest.importorskip("dill")
+
     FooForPickle = TestInitCallCount.FooForPickle
 
     import dill
@@ -339,6 +343,9 @@ def test_on_pickle(self):
     self.assertEqual(FooForPickle.counter, 1)
     self.assertEqual(new_foo_2.__dict__, foo.__dict__)
 
+  def test_on_pickle(self):
+    FooForPickle = TestInitCallCount.FooForPickle
+
     # Note that pickle does not support classes/functions nested in a function.
     import pickle
     FooForPickle.counter = 0
diff --git a/sdks/python/apache_beam/ml/anomaly/transforms.py b/sdks/python/apache_beam/ml/anomaly/transforms.py
index ef5501b33786..ce9601074754 100644
--- a/sdks/python/apache_beam/ml/anomaly/transforms.py
+++ b/sdks/python/apache_beam/ml/anomaly/transforms.py
@@ -569,13 +569,13 @@ class AnomalyDetection(beam.PTransform[beam.PCollection[Union[InputT,
 
   Examples::
 
-    # Run a single anomaly detector
-    p | AnomalyDetection(ZScore(features=["x1"]))
+      # Run a single anomaly detector
+      p | AnomalyDetection(ZScore(features=["x1"]))
 
-    # Run an ensemble anomaly detector
-    sub_detectors = [ZScore(features=["x1"]), IQR(features=["x2"])]
-    p | AnomalyDetection(
-        EnsembleAnomalyDetector(sub_detectors, aggregation_strategy=AnyVote()))
+      # Run an ensemble anomaly detector
+      sub_detectors = [ZScore(features=["x1"]), IQR(features=["x2"])]
+      p | AnomalyDetection(EnsembleAnomalyDetector(
+          sub_detectors, aggregation_strategy=AnyVote()))
 
   Args:
     detector: The `AnomalyDetector` or `EnsembleAnomalyDetector` to use.
diff --git a/sdks/python/apache_beam/ml/anomaly/transforms_test.py b/sdks/python/apache_beam/ml/anomaly/transforms_test.py
index ed5252c6a485..423e51abf635 100644
--- a/sdks/python/apache_beam/ml/anomaly/transforms_test.py
+++ b/sdks/python/apache_beam/ml/anomaly/transforms_test.py
@@ -64,8 +64,18 @@ def _prediction_iterable_is_equal_to(
   if len(a_list) != len(b_list):
     return False
 
-  return all(
-      map(lambda x: _prediction_is_equal_to(x[0], x[1]), zip(a_list, b_list)))
+  a_dict = {}
+  b_dict = {}
+  for i in a_list:
+    a_dict[i.model_id] = i
+  for i in b_list:
+    b_dict[i.model_id] = i
+
+  for k, a_val in a_dict.items():
+    if k not in b_dict or not _prediction_is_equal_to(a_val, b_dict[k]):
+      return False
+
+  return True
 
 
 def _prediction_is_equal_to(a: AnomalyPrediction, b: AnomalyPrediction):
diff --git a/sdks/python/apache_beam/ml/inference/base.py b/sdks/python/apache_beam/ml/inference/base.py
index 4881fb74ef7b..2e1c4963f11d 100644
--- a/sdks/python/apache_beam/ml/inference/base.py
+++ b/sdks/python/apache_beam/ml/inference/base.py
@@ -55,8 +55,7 @@
 from typing import Union
 
 import apache_beam as beam
-from apache_beam.io.components.adaptive_throttler import AdaptiveThrottler
-from apache_beam.metrics.metric import Metrics
+from apache_beam.io.components.adaptive_throttler import ReactiveThrottler
 from apache_beam.utils import multi_process_shared
 from apache_beam.utils import retry
 from apache_beam.utils import shared
@@ -354,14 +353,16 @@ def __init__(
       window_ms: int = 1 * _MILLISECOND_TO_SECOND,
       bucket_ms: int = 1 * _MILLISECOND_TO_SECOND,
       overload_ratio: float = 2):
-    """Initializes metrics tracking + an AdaptiveThrottler class for enabling
-    client-side throttling for remote calls to an inference service.
+    """Initializes a ReactiveThrottler class for enabling
+    client-side throttling for remote calls to an inference service. Also wraps
+    provided calls to the service with retry logic.
+    
     See https://s.apache.org/beam-client-side-throttling for more details
     on the configuration of the throttling and retry
     mechanics.
 
     Args:
-      namespace: the metrics and logging namespace 
+      namespace: the metrics and logging namespace
       num_retries: the maximum number of times to retry a request on retriable
         errors before failing
       throttle_delay_secs: the amount of time to throttle when the client-side
@@ -372,19 +373,18 @@ def __init__(
       window_ms: length of history to consider, in ms, to set throttling.
       bucket_ms: granularity of time buckets that we store data in, in ms.
       overload_ratio: the target ratio between requests sent and successful
-        requests. This is "K" in the formula in 
+        requests. This is "K" in the formula in
         https://landing.google.com/sre/book/chapters/handling-overload.html.
     """
-    # Configure AdaptiveThrottler and throttling metrics for client-side
-    # throttling behavior.
-    self.throttled_secs = Metrics.counter(
-        namespace, "cumulativeThrottlingSeconds")
-    self.throttler = AdaptiveThrottler(
-        window_ms=window_ms, bucket_ms=bucket_ms, overload_ratio=overload_ratio)
+    # Configure ReactiveThrottler for client-side throttling behavior.
+    self.throttler = ReactiveThrottler(
+        window_ms=window_ms,
+        bucket_ms=bucket_ms,
+        overload_ratio=overload_ratio,
+        namespace=namespace,
+        throttle_delay_secs=throttle_delay_secs)
     self.logger = logging.getLogger(namespace)
-
     self.num_retries = num_retries
-    self.throttle_delay_secs = throttle_delay_secs
     self.retry_filter = retry_filter
 
   def __init_subclass__(cls):
@@ -434,12 +434,7 @@ def run_inference(
     Returns:
       An Iterable of Predictions.
     """
-    while self.throttler.throttle_request(time.time() * _MILLISECOND_TO_SECOND):
-      self.logger.info(
-          "Delaying request for %d seconds due to previous failures",
-          self.throttle_delay_secs)
-      time.sleep(self.throttle_delay_secs)
-      self.throttled_secs.inc(self.throttle_delay_secs)
+    self.throttler.throttle()
 
     try:
       req_time = time.time()
@@ -1642,7 +1637,7 @@ def next_model_index(self, num_models):
 
 class _ModelStatus():
   """A class holding any metadata about a model required by RunInference.
-  
+
     Currently, this only includes whether or not the model is valid. Uses the
     model tag to map models to metadata.
   """
@@ -1656,7 +1651,7 @@ def __init__(self, share_model_across_processes: bool):
 
   def try_mark_current_model_invalid(self, min_model_life_seconds):
     """Mark the current model invalid.
-    
+
       Since we don't have sufficient information to say which model is being
       marked invalid, but there may be multiple active models, we will mark all
       models currently in use as inactive so that they all get reloaded. To
@@ -1678,7 +1673,7 @@ def try_mark_current_model_invalid(self, min_model_life_seconds):
 
   def get_valid_tag(self, tag: str) -> str:
     """Takes in a proposed valid tag and returns a valid one.
-    
+
       Will always return a valid tag. If the passed in tag is valid, this
       function will simply return it, otherwise it will deterministically
       generate a new tag to use instead. The new tag will be the original tag
@@ -1747,7 +1742,7 @@ def load_model_status(
 
 class _SharedModelWrapper():
   """A router class to map incoming calls to the correct model.
-  
+
     This allows us to round robin calls to models sitting in different
     processes so that we can more efficiently use resources (e.g. GPUs).
   """
diff --git a/sdks/python/apache_beam/ml/inference/base_test.py b/sdks/python/apache_beam/ml/inference/base_test.py
index 29ac0ad4247d..64fd73682e13 100644
--- a/sdks/python/apache_beam/ml/inference/base_test.py
+++ b/sdks/python/apache_beam/ml/inference/base_test.py
@@ -1037,27 +1037,27 @@ def test_timing_metrics(self):
 
   def test_forwards_batch_args(self):
     examples = list(range(100))
-    with TestPipeline() as pipeline:
+    with TestPipeline('FnApiRunner') as pipeline:
       pcoll = pipeline | 'start' >> beam.Create(examples)
       actual = pcoll | base.RunInference(FakeModelHandlerNeedsBigBatch())
       assert_that(actual, equal_to(examples), label='assert:inferences')
 
   def test_run_inference_unkeyed_examples_with_keyed_model_handler(self):
     pipeline = TestPipeline()
-    with self.assertRaises(TypeError):
+    with self.assertRaisesRegex(Exception, "object is not iterable"):
       examples = [1, 3, 5]
       model_handler = base.KeyedModelHandler(FakeModelHandler())
       _ = (
           pipeline | 'Unkeyed' >> beam.Create(examples)
           | 'RunUnkeyed' >> base.RunInference(model_handler))
-      pipeline.run()
+      pipeline.run().wait_until_finish()
 
   def test_run_inference_keyed_examples_with_unkeyed_model_handler(self):
     pipeline = TestPipeline()
     examples = [1, 3, 5]
     keyed_examples = [(i, example) for i, example in enumerate(examples)]
     model_handler = FakeModelHandler()
-    with self.assertRaises(TypeError):
+    with self.assertRaisesRegex(Exception, "can only concatenate tuple"):
       _ = (
           pipeline | 'keyed' >> beam.Create(keyed_examples)
           | 'RunKeyed' >> base.RunInference(model_handler))
diff --git a/sdks/python/apache_beam/ml/inference/huggingface_tests_requirements.txt b/sdks/python/apache_beam/ml/inference/huggingface_tests_requirements.txt
index adb4816cab6b..9b9e9bdd55f1 100644
--- a/sdks/python/apache_beam/ml/inference/huggingface_tests_requirements.txt
+++ b/sdks/python/apache_beam/ml/inference/huggingface_tests_requirements.txt
@@ -16,5 +16,5 @@
 #
 
 torch>=1.7.1
-transformers==4.30.0
+transformers==4.53.0
 tensorflow>=2.12.0
\ No newline at end of file
diff --git a/sdks/python/apache_beam/ml/inference/onnx_inference_test.py b/sdks/python/apache_beam/ml/inference/onnx_inference_test.py
index e9e017661d41..2d2de4a388e0 100644
--- a/sdks/python/apache_beam/ml/inference/onnx_inference_test.py
+++ b/sdks/python/apache_beam/ml/inference/onnx_inference_test.py
@@ -40,7 +40,6 @@
 try:
   import onnxruntime as ort
   import torch
-  from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
   import tensorflow as tf
   import tf2onnx
   from tensorflow.keras import layers
@@ -406,8 +405,7 @@ def test_pipeline_gcs_model(self):
           equal_to(expected_predictions, equals_fn=_compare_prediction_result))
 
   def test_invalid_input_type(self):
-    with self.assertRaisesRegex(InvalidArgument,
-                                "Got invalid dimensions for input"):
+    with self.assertRaisesRegex(Exception, "Got invalid dimensions for input"):
       with TestPipeline() as pipeline:
         examples = [np.array([1], dtype="float32")]
         path = os.path.join(self.tmpdir, 'my_onnx_pytorch_path')
@@ -461,8 +459,7 @@ def test_pipeline_gcs_model(self):
           equal_to(expected_predictions, equals_fn=_compare_prediction_result))
 
   def test_invalid_input_type(self):
-    with self.assertRaisesRegex(InvalidArgument,
-                                "Got invalid dimensions for input"):
+    with self.assertRaisesRegex(Exception, "Got invalid dimensions for input"):
       with TestPipeline() as pipeline:
         examples = [np.array([1], dtype="float32")]
         path = os.path.join(self.tmpdir, 'my_onnx_tensorflow_path')
@@ -517,7 +514,7 @@ def test_pipeline_gcs_model(self):
           equal_to(expected_predictions, equals_fn=_compare_prediction_result))
 
   def test_invalid_input_type(self):
-    with self.assertRaises(InvalidArgument):
+    with self.assertRaisesRegex(Exception, "InvalidArgument"):
       with TestPipeline() as pipeline:
         examples = [np.array([1], dtype="float32")]
         path = os.path.join(self.tmpdir, 'my_onnx_sklearn_path')
diff --git a/sdks/python/apache_beam/ml/inference/pytorch_inference_test.py b/sdks/python/apache_beam/ml/inference/pytorch_inference_test.py
index 91556f05801f..fcc374c06d78 100644
--- a/sdks/python/apache_beam/ml/inference/pytorch_inference_test.py
+++ b/sdks/python/apache_beam/ml/inference/pytorch_inference_test.py
@@ -715,7 +715,7 @@ def batch_validator_tensor_inference_fn(
           equal_to(expected_predictions, equals_fn=_compare_prediction_result))
 
   def test_invalid_input_type(self):
-    with self.assertRaisesRegex(TypeError, "expected Tensor as element"):
+    with self.assertRaisesRegex(Exception, "expected Tensor as element"):
       with TestPipeline() as pipeline:
         examples = np.array([1, 5, 3, 10], dtype="float32").reshape(-1, 1)
 
diff --git a/sdks/python/apache_beam/ml/inference/tensorflow_inference_test.py b/sdks/python/apache_beam/ml/inference/tensorflow_inference_test.py
index 75f15c87f5ce..7286274e180c 100644
--- a/sdks/python/apache_beam/ml/inference/tensorflow_inference_test.py
+++ b/sdks/python/apache_beam/ml/inference/tensorflow_inference_test.py
@@ -128,7 +128,8 @@ def test_predict_tensor_with_batch_size(self):
     model = _create_mult2_model()
     model_path = os.path.join(self.tmpdir, f'mult2_{uuid.uuid4()}.keras')
     tf.keras.models.save_model(model, model_path)
-    with TestPipeline() as pipeline:
+    # FnApiRunner guarantees large batches, which this pipeline assumes
+    with TestPipeline('FnApiRunner') as pipeline:
 
       def fake_batching_inference_fn(
           model: tf.Module,
diff --git a/sdks/python/apache_beam/ml/inference/test_resources/vllm.dockerfile b/sdks/python/apache_beam/ml/inference/test_resources/vllm.dockerfile
index f27abbfd0051..5727437809c4 100644
--- a/sdks/python/apache_beam/ml/inference/test_resources/vllm.dockerfile
+++ b/sdks/python/apache_beam/ml/inference/test_resources/vllm.dockerfile
@@ -15,33 +15,54 @@
 # limitations under the License.
 
 # Used for any vLLM integration test
+# Dockerfile — Beam dev harness + install dev SDK from LOCAL source package
 
 FROM nvidia/cuda:12.4.1-devel-ubuntu22.04
 
-RUN apt update
-RUN apt install software-properties-common -y
-RUN add-apt-repository ppa:deadsnakes/ppa
-RUN apt update
+# 1) Non-interactive + timezone
+ENV DEBIAN_FRONTEND=noninteractive \
+    TZ=Etc/UTC
 
-ARG DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+      curl \
+      tzdata \
+      software-properties-common \
+      python3.10-full \
+      python3.10-distutils \
+      build-essential \
+      python3.10-dev \
+      cython3 && \
+    ln -fs /usr/share/zoneinfo/$TZ /etc/localtime && \
+    dpkg-reconfigure --frontend noninteractive tzdata && \
+    rm -rf /var/lib/apt/lists/*
 
-RUN apt install python3.12 -y
-RUN apt install python3.12-venv -y
-RUN apt install python3.12-dev -y
-RUN rm /usr/bin/python3
-RUN ln -s python3.12 /usr/bin/python3
-RUN python3 --version
-RUN apt-get install -y curl
-RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.12 && pip install --upgrade pip
+# 2) Symlink python3 to 3.10
+RUN ln -sf /usr/bin/python3.10 /usr/bin/python3 && \
+    ln -sf /usr/bin/python3.10 /usr/bin/python
 
-RUN pip install --no-cache-dir -vvv apache-beam[gcp]==2.58.1
-RUN pip install openai vllm
+# 3) Install pip, setuptools & wheel
+RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3 && \
+    python3 -m pip install --upgrade pip setuptools wheel
 
-RUN apt install libcairo2-dev pkg-config python3-dev -y
-RUN pip install pycairo
+# 4) Copy the Beam SDK harness (for Dataflow workers)
+COPY --from=gcr.io/apache-beam-testing/beam-sdk/beam_python3.10_sdk:2.68.0.dev \
+     /opt/apache/beam /opt/apache/beam
 
-# Copy the Apache Beam worker dependencies from the Beam Python 3.12 SDK image.
-COPY --from=apache/beam_python3.12_sdk:2.58.1 /opt/apache/beam /opt/apache/beam
+# 5) Make sure the harness is discovered first
+ENV PYTHONPATH=/opt/apache/beam:$PYTHONPATH
 
-# Set the entrypoint to Apache Beam SDK worker launcher.
-ENTRYPOINT [ "/opt/apache/beam/boot" ]
+# 6) Install the Beam dev SDK from the local source package.
+# This .tar.gz file will be created by GitHub Actions workflow
+# and copied into the build context.
+COPY ./sdks/python/build/apache-beam.tar.gz /tmp/beam.tar.gz
+RUN python3 -m pip install --no-cache-dir "/tmp/beam.tar.gz[gcp]"
+
+# 7) Install vLLM, and other dependencies
+RUN python3 -m pip install --no-cache-dir \
+      openai>=1.52.2 \
+      vllm>=0.6.3 \
+      triton>=3.1.0
+
+# 8) Use the Beam boot script as entrypoint
+ENTRYPOINT ["/opt/apache/beam/boot"]
\ No newline at end of file
diff --git a/sdks/python/apache_beam/ml/inference/test_resources/vllm.dockerfile.old b/sdks/python/apache_beam/ml/inference/test_resources/vllm.dockerfile.old
new file mode 100644
index 000000000000..b9c99e49e02f
--- /dev/null
+++ b/sdks/python/apache_beam/ml/inference/test_resources/vllm.dockerfile.old
@@ -0,0 +1,47 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Used for any vLLM integration test
+
+FROM nvidia/cuda:12.4.1-devel-ubuntu22.04
+
+RUN apt update
+RUN apt install software-properties-common -y
+RUN add-apt-repository ppa:deadsnakes/ppa
+RUN apt update
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+RUN apt install python3.12 -y
+RUN apt install python3.12-venv -y
+RUN apt install python3.12-dev -y
+RUN rm /usr/bin/python3
+RUN ln -s python3.12 /usr/bin/python3
+RUN python3 --version
+RUN apt-get install -y curl
+RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.12 && pip install --upgrade pip
+
+RUN pip install --no-cache-dir -vvv apache-beam[gcp]==2.58.1
+RUN pip install openai vllm
+
+RUN apt install libcairo2-dev pkg-config python3-dev -y
+RUN pip install pycairo
+
+# Copy the Apache Beam worker dependencies from the Beam Python 3.12 SDK image.
+COPY --from=apache/beam_python3.12_sdk:2.58.1 /opt/apache/beam /opt/apache/beam
+
+# Set the entrypoint to Apache Beam SDK worker launcher.
+ENTRYPOINT [ "/opt/apache/beam/boot" ]
\ No newline at end of file
diff --git a/sdks/python/apache_beam/ml/inference/vertex_ai_inference_it_test.py b/sdks/python/apache_beam/ml/inference/vertex_ai_inference_it_test.py
index 7c96dbe8b847..c6d62eb3e3e1 100644
--- a/sdks/python/apache_beam/ml/inference/vertex_ai_inference_it_test.py
+++ b/sdks/python/apache_beam/ml/inference/vertex_ai_inference_it_test.py
@@ -29,7 +29,6 @@
 # pylint: disable=ungrouped-imports
 try:
   from apache_beam.examples.inference import vertex_ai_image_classification
-  from apache_beam.examples.inference import vertex_ai_llm_text_classification
 except ImportError as e:
   raise unittest.SkipTest(
       "Vertex AI model handler dependencies are not installed")
@@ -37,7 +36,6 @@
 _INPUT = "gs://apache-beam-ml/testing/inputs/vertex_images/*/*.jpg"
 _OUTPUT_DIR = "gs://apache-beam-ml/testing/outputs/vertex_images"
 _FLOWER_ENDPOINT_ID = "5384055553544683520"
-_LLM_ENDPOINT_ID = "9157860935048626176"
 _ENDPOINT_PROJECT = "apache-beam-testing"
 _ENDPOINT_REGION = "us-central1"
 _ENDPOINT_NETWORK = "projects/844138762903/global/networks/beam-test-vpc"
@@ -65,21 +63,6 @@ def test_vertex_ai_run_flower_image_classification(self):
         test_pipeline.get_full_options_as_args(**extra_opts))
     self.assertEqual(FileSystems().exists(output_file), True)
 
-  @pytest.mark.vertex_ai_postcommit
-  def test_vertex_ai_run_llm_text_classification(self):
-    output_file = '/'.join([_OUTPUT_DIR, str(uuid.uuid4()), 'output.txt'])
-
-    test_pipeline = TestPipeline(is_integration_test=True)
-    extra_opts = {
-        'output': output_file,
-        'endpoint_id': _LLM_ENDPOINT_ID,
-        'endpoint_project': _ENDPOINT_PROJECT,
-        'endpoint_region': _ENDPOINT_REGION
-    }
-    vertex_ai_llm_text_classification.run(
-        test_pipeline.get_full_options_as_args(**extra_opts))
-    self.assertEqual(FileSystems().exists(output_file), True)
-
 
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.DEBUG)
diff --git a/sdks/python/apache_beam/ml/inference/vllm_tests_requirements.txt b/sdks/python/apache_beam/ml/inference/vllm_tests_requirements.txt
new file mode 100644
index 000000000000..939f0526d808
--- /dev/null
+++ b/sdks/python/apache_beam/ml/inference/vllm_tests_requirements.txt
@@ -0,0 +1,22 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+torch>=1.7.1
+torchvision>=0.8.2
+pillow>=8.0.0
+transformers>=4.18.0
+google-cloud-monitoring>=2.27.0
+openai>=1.52.2
\ No newline at end of file
diff --git a/sdks/python/apache_beam/ml/rag/chunking/langchain_test.py b/sdks/python/apache_beam/ml/rag/chunking/langchain_test.py
index 638f971c5550..542d1cd79bc2 100644
--- a/sdks/python/apache_beam/ml/rag/chunking/langchain_test.py
+++ b/sdks/python/apache_beam/ml/rag/chunking/langchain_test.py
@@ -186,7 +186,7 @@ def test_invalid_document_field(self):
         metadata_fields={},
         text_splitter=splitter)
 
-    with self.assertRaises(KeyError):
+    with self.assertRaisesRegex(Exception, "nonexistent"):
       with TestPipeline() as p:
         _ = (
             p
diff --git a/sdks/python/apache_beam/ml/rag/enrichment/bigquery_vector_search_it_test.py b/sdks/python/apache_beam/ml/rag/enrichment/bigquery_vector_search_it_test.py
index 03334b0331bf..1d4f7597d625 100644
--- a/sdks/python/apache_beam/ml/rag/enrichment/bigquery_vector_search_it_test.py
+++ b/sdks/python/apache_beam/ml/rag/enrichment/bigquery_vector_search_it_test.py
@@ -32,7 +32,6 @@
 
 # pylint: disable=ungrouped-imports
 try:
-  from google.api_core.exceptions import BadRequest
   from apache_beam.transforms.enrichment import Enrichment
   from apache_beam.ml.rag.enrichment.bigquery_vector_search import \
     BigQueryVectorSearchEnrichmentHandler
@@ -859,7 +858,7 @@ def test_invalid_query(self):
     handler = BigQueryVectorSearchEnrichmentHandler(
         vector_search_parameters=params)
 
-    with self.assertRaises(BadRequest):
+    with self.assertRaisesRegex(Exception, "Unrecognized name"):
       with TestPipeline() as p:
         _ = (p | beam.Create(test_chunks) | Enrichment(handler))
 
@@ -898,10 +897,9 @@ def test_missing_embedding(self):
     handler = BigQueryVectorSearchEnrichmentHandler(
         vector_search_parameters=params)
 
-    with self.assertRaises(ValueError) as context:
+    with self.assertRaisesRegex(Exception, "missing embedding"):
       with TestPipeline() as p:
         _ = (p | beam.Create(test_chunks) | Enrichment(handler))
-    self.assertIn("missing embedding", str(context.exception))
 
 
 if __name__ == '__main__':
diff --git a/sdks/python/apache_beam/ml/rag/enrichment/milvus_search_it_test.py b/sdks/python/apache_beam/ml/rag/enrichment/milvus_search_it_test.py
index ebc05722841c..81ceb6b69e71 100644
--- a/sdks/python/apache_beam/ml/rag/enrichment/milvus_search_it_test.py
+++ b/sdks/python/apache_beam/ml/rag/enrichment/milvus_search_it_test.py
@@ -34,18 +34,6 @@
 
 import pytest
 import yaml
-from pymilvus import CollectionSchema
-from pymilvus import DataType
-from pymilvus import FieldSchema
-from pymilvus import Function
-from pymilvus import FunctionType
-from pymilvus import MilvusClient
-from pymilvus import RRFRanker
-from pymilvus.milvus_client import IndexParams
-from testcontainers.core.config import MAX_TRIES as TC_MAX_TRIES
-from testcontainers.core.config import testcontainers_config
-from testcontainers.core.generic import DbContainer
-from testcontainers.milvus import MilvusContainer
 
 import apache_beam as beam
 from apache_beam.ml.rag.types import Chunk
@@ -54,7 +42,21 @@
 from apache_beam.testing.test_pipeline import TestPipeline
 from apache_beam.testing.util import assert_that
 
+# pylint: disable=ungrouped-imports
 try:
+  from pymilvus import (
+      CollectionSchema,
+      DataType,
+      FieldSchema,
+      Function,
+      FunctionType,
+      MilvusClient,
+      RRFRanker)
+  from pymilvus.milvus_client import IndexParams
+  from testcontainers.core.config import MAX_TRIES as TC_MAX_TRIES
+  from testcontainers.core.config import testcontainers_config
+  from testcontainers.core.generic import DbContainer
+  from testcontainers.milvus import MilvusContainer
   from apache_beam.transforms.enrichment import Enrichment
   from apache_beam.ml.rag.enrichment.milvus_search import (
       MilvusSearchEnrichmentHandler,
@@ -295,7 +297,7 @@ def __init__(
 class MilvusEnrichmentTestHelper:
   @staticmethod
   def start_db_container(
-      image="milvusdb/milvus:v2.5.10",
+      image="milvusdb/milvus:v2.3.9",
       max_vec_fields=5,
       vector_client_max_retries=3,
       tc_max_retries=TC_MAX_TRIES) -> Optional[MilvusDBContainerInfo]:
@@ -467,7 +469,7 @@ def create_user_yaml(service_port: int, max_vector_field_num=5):
         os.remove(path)
 
 
-@pytest.mark.uses_testcontainer
+@pytest.mark.require_docker_in_docker
 @unittest.skipUnless(
     platform.system() == "Linux",
     "Test runs only on Linux due to lack of support, as yet, for nested "
@@ -483,22 +485,16 @@ class TestMilvusSearchEnrichment(unittest.TestCase):
 
   @classmethod
   def setUpClass(cls):
-    try:
-      cls._db = MilvusEnrichmentTestHelper.start_db_container(
-          cls._version, vector_client_max_retries=1, tc_max_retries=1)
-      cls._connection_params = MilvusConnectionParameters(
-          uri=cls._db.uri,
-          user=cls._db.user,
-          password=cls._db.password,
-          db_id=cls._db.id,
-          token=cls._db.token)
-      cls._collection_load_params = MilvusCollectionLoadParameters()
-      cls._collection_name = MilvusEnrichmentTestHelper.initialize_db_with_data(
-          cls._connection_params)
-    except Exception as e:
-      pytest.skip(
-          f"Skipping all tests in {cls.__name__} due to DB startup failure: {e}"
-      )
+    cls._db = MilvusEnrichmentTestHelper.start_db_container(cls._version)
+    cls._connection_params = MilvusConnectionParameters(
+        uri=cls._db.uri,
+        user=cls._db.user,
+        password=cls._db.password,
+        db_id=cls._db.id,
+        token=cls._db.token)
+    cls._collection_load_params = MilvusCollectionLoadParameters()
+    cls._collection_name = MilvusEnrichmentTestHelper.initialize_db_with_data(
+        cls._connection_params)
 
   @classmethod
   def tearDownClass(cls):
@@ -578,7 +574,7 @@ def test_empty_input_chunks(self):
 
     expected_chunks = []
 
-    with TestPipeline(is_integration_test=True) as p:
+    with TestPipeline() as p:
       result = (p | beam.Create(test_chunks) | Enrichment(handler))
       assert_that(
           result,
@@ -706,7 +702,7 @@ def test_filtered_search_with_cosine_similarity_and_batching(self):
             embedding=Embedding(dense_embedding=[0.3, 0.4, 0.5]))
     ]
 
-    with TestPipeline(is_integration_test=True) as p:
+    with TestPipeline() as p:
       result = (p | beam.Create(test_chunks) | Enrichment(handler))
       assert_that(
           result,
@@ -811,7 +807,7 @@ def test_filtered_search_with_bm25_full_text_and_batching(self):
             embedding=Embedding())
     ]
 
-    with TestPipeline(is_integration_test=True) as p:
+    with TestPipeline() as p:
       result = (p | beam.Create(test_chunks) | Enrichment(handler))
       assert_that(
           result,
@@ -952,7 +948,7 @@ def test_vector_search_with_euclidean_distance(self):
             embedding=Embedding(dense_embedding=[0.3, 0.4, 0.5]))
     ]
 
-    with TestPipeline(is_integration_test=True) as p:
+    with TestPipeline() as p:
       result = (p | beam.Create(test_chunks) | Enrichment(handler))
       assert_that(
           result,
@@ -1092,7 +1088,7 @@ def test_vector_search_with_inner_product_similarity(self):
             embedding=Embedding(dense_embedding=[0.3, 0.4, 0.5]))
     ]
 
-    with TestPipeline(is_integration_test=True) as p:
+    with TestPipeline() as p:
       result = (p | beam.Create(test_chunks) | Enrichment(handler))
       assert_that(
           result,
@@ -1157,7 +1153,7 @@ def test_keyword_search_with_inner_product_sparse_embedding(self):
                 sparse_embedding=([1, 2, 3, 4], [0.05, 0.41, 0.05, 0.41])))
     ]
 
-    with TestPipeline(is_integration_test=True) as p:
+    with TestPipeline() as p:
       result = (p | beam.Create(test_chunks) | Enrichment(handler))
       assert_that(
           result,
@@ -1230,7 +1226,7 @@ def test_hybrid_search(self):
             embedding=Embedding(dense_embedding=[0.1, 0.2, 0.3]))
     ]
 
-    with TestPipeline(is_integration_test=True) as p:
+    with TestPipeline() as p:
       result = (p | beam.Create(test_chunks) | Enrichment(handler))
       assert_that(
           result,
diff --git a/sdks/python/apache_beam/ml/rag/ingestion/bigquery_it_test.py b/sdks/python/apache_beam/ml/rag/ingestion/bigquery_it_test.py
index 7df662ab0554..b21da0443467 100644
--- a/sdks/python/apache_beam/ml/rag/ingestion/bigquery_it_test.py
+++ b/sdks/python/apache_beam/ml/rag/ingestion/bigquery_it_test.py
@@ -117,7 +117,7 @@ def test_default_schema_missing_embedding(self):
         Chunk(id="1", content=Content(text="foo"), metadata={"a": "b"}),
         Chunk(id="2", content=Content(text="bar"), metadata={"c": "d"})
     ]
-    with self.assertRaises(ValueError):
+    with self.assertRaisesRegex(Exception, "must contain dense embedding"):
       with beam.Pipeline() as p:
         _ = (p | beam.Create(chunks) | config.create_write_transform())
 
diff --git a/sdks/python/apache_beam/ml/transforms/base.py b/sdks/python/apache_beam/ml/transforms/base.py
index bd3a5f2b3dc2..4031777ce152 100644
--- a/sdks/python/apache_beam/ml/transforms/base.py
+++ b/sdks/python/apache_beam/ml/transforms/base.py
@@ -183,20 +183,23 @@ def append_transform(self, transform: BaseOperation):
     """
 
 
-def _dict_input_fn(columns: Sequence[str],
-                   batch: Sequence[Dict[str, Any]]) -> List[str]:
+def _dict_input_fn(
+    columns: Sequence[str], batch: Sequence[Union[Dict[str, Any],
+                                                  beam.Row]]) -> List[str]:
   """Extract text from specified columns in batch."""
+  if batch and hasattr(batch[0], '_asdict'):
+    batch = [row._asdict() if hasattr(row, '_asdict') else row for row in batch]
+
   if not batch or not isinstance(batch[0], dict):
     raise TypeError(
         'Expected data to be dicts, got '
         f'{type(batch[0])} instead.')
-
   result = []
   expected_keys = set(batch[0].keys())
   expected_columns = set(columns)
   # Process one batch item at a time
   for item in batch:
-    item_keys = item.keys()
+    item_keys = item.keys() if isinstance(item, dict) else set()
     if set(item_keys) != expected_keys:
       extra_keys = item_keys - expected_keys
       missing_keys = expected_keys - item_keys
@@ -212,21 +215,31 @@ def _dict_input_fn(columns: Sequence[str],
 
     # Get all columns for this item
     for col in columns:
-      result.append(item[col])
+      if isinstance(item, dict):
+        result.append(item[col])
   return result
 
 
 def _dict_output_fn(
     columns: Sequence[str],
-    batch: Sequence[Dict[str, Any]],
-    embeddings: Sequence[Any]) -> List[Dict[str, Any]]:
+    batch: Sequence[Union[Dict[str, Any], beam.Row]],
+    embeddings: Sequence[Any]) -> list[Union[dict[str, Any], beam.Row]]:
   """Map embeddings back to columns in batch."""
+  is_beam_row = False
+  if batch and hasattr(batch[0], '_asdict'):
+    is_beam_row = True
+    batch = [row._asdict() if hasattr(row, '_asdict') else row for row in batch]
+
   result = []
   for batch_idx, item in enumerate(batch):
     for col_idx, col in enumerate(columns):
       embedding_idx = batch_idx * len(columns) + col_idx
-      item[col] = embeddings[embedding_idx]
+      if isinstance(item, dict):
+        item[col] = embeddings[embedding_idx]
     result.append(item)
+
+  if is_beam_row:
+    result = [beam.Row(**item) for item in result if isinstance(item, dict)]
   return result
 
 
@@ -797,3 +810,42 @@ def get_metrics_namespace(self) -> str:
     return (
         self._underlying.get_metrics_namespace() or
         'BeamML_ImageEmbeddingHandler')
+
+
+class _MultiModalEmbeddingHandler(_EmbeddingHandler):
+  """
+  A ModelHandler intended to be work on
+  list[dict[str, TypedDict(Image, Video, str)]] inputs.
+
+  The inputs to the model handler are expected to be a list of dicts.
+
+  For example, if the original mode is used with RunInference to take a
+  PCollection[E] to a PCollection[P], this ModelHandler would take a
+  PCollection[dict[str, E]] to a PCollection[dict[str, P]].
+
+  _MultiModalEmbeddingHandler will accept an EmbeddingsManager instance, which
+  contains the details of the model to be loaded and the inference_fn to be
+  used. The purpose of _MultiMOdalEmbeddingHandler is to generate embeddings
+  for image, video, and text inputs using the EmbeddingsManager instance.
+
+  If the input is not an Image representation column, a RuntimeError will be
+  raised.
+
+  This is an internal class and offers no backwards compatibility guarantees.
+
+  Args:
+    embeddings_manager: An EmbeddingsManager instance.
+  """
+  def _validate_column_data(self, batch):
+    # Don't want to require framework-specific imports
+    # here, so just catch columns of primatives for now.
+    if isinstance(batch[0], (int, str, float, bool)):
+      raise TypeError(
+          'Embeddings can only be generated on '
+          ' dict[str, dataclass] types. '
+          f'Got dict[str, {type(batch[0])}] instead.')
+
+  def get_metrics_namespace(self) -> str:
+    return (
+        self._underlying.get_metrics_namespace() or
+        'BeamML_MultiModalEmbeddingHandler')
diff --git a/sdks/python/apache_beam/ml/transforms/base_test.py b/sdks/python/apache_beam/ml/transforms/base_test.py
index 39aff233aecd..190381cc2f34 100644
--- a/sdks/python/apache_beam/ml/transforms/base_test.py
+++ b/sdks/python/apache_beam/ml/transforms/base_test.py
@@ -23,6 +23,7 @@
 import time
 import unittest
 from collections.abc import Sequence
+from dataclasses import dataclass
 from typing import Any
 from typing import Optional
 
@@ -498,7 +499,7 @@ def test_handler_with_list_data(self):
     }, {
         'x': ['Apache Beam', 'Hello world'],
     }]
-    with self.assertRaises(TypeError):
+    with self.assertRaisesRegex(Exception, "Embeddings can only be generated"):
       with beam.Pipeline() as p:
         _ = (
             p
@@ -629,6 +630,122 @@ def test_handler_with_dict_inputs(self):
       )
 
 
+@dataclass
+class FakeMultiModalInput:
+  image: Optional[PIL_Image] = None
+  video: Optional[Any] = None
+  text: Optional[str] = None
+
+
+class FakeMultiModalModel:
+  def __call__(self,
+               example: list[FakeMultiModalInput]) -> list[FakeMultiModalInput]:
+    for i in range(len(example)):
+      if not isinstance(example[i], FakeMultiModalInput):
+        raise TypeError('Input must be a MultiModalInput')
+    return example
+
+
+class FakeMultiModalModelHandler(ModelHandler):
+  def run_inference(
+      self,
+      batch: Sequence[FakeMultiModalInput],
+      model: Any,
+      inference_args: Optional[dict[str, Any]] = None):
+    return model(batch)
+
+  def load_model(self):
+    return FakeMultiModalModel()
+
+
+class FakeMultiModalEmbeddingsManager(base.EmbeddingsManager):
+  def __init__(self, columns, **kwargs):
+    super().__init__(columns=columns, **kwargs)
+
+  def get_model_handler(self) -> ModelHandler:
+    FakeModelHandler.__repr__ = lambda x: 'FakeMultiModalEmbeddingsManager'  # type: ignore[method-assign]
+    return FakeMultiModalModelHandler()
+
+  def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
+    return (RunInference(model_handler=base._MultiModalEmbeddingHandler(self)))
+
+  def __repr__(self):
+    return 'FakeMultiModalEmbeddingsManager'
+
+
+class TestMultiModalEmbeddingHandler(unittest.TestCase):
+  def setUp(self) -> None:
+    self.embedding_config = FakeMultiModalEmbeddingsManager(columns=['x'])
+    self.artifact_location = tempfile.mkdtemp()
+
+  def tearDown(self) -> None:
+    shutil.rmtree(self.artifact_location)
+
+  @unittest.skipIf(PIL is None, 'PIL module is not installed.')
+  def test_handler_with_non_dict_datatype(self):
+    image_handler = base._MultiModalEmbeddingHandler(
+        embeddings_manager=self.embedding_config)
+    data = [
+        ('x', 'hi there'),
+        ('x', 'not an image'),
+        ('x', 'image_path.jpg'),
+    ]
+    with self.assertRaises(TypeError):
+      image_handler.run_inference(data, None, None)
+
+  @unittest.skipIf(PIL is None, 'PIL module is not installed.')
+  def test_handler_with_incorrect_datatype(self):
+    image_handler = base._MultiModalEmbeddingHandler(
+        embeddings_manager=self.embedding_config)
+    data = [
+        {
+            'x': 'hi there'
+        },
+        {
+            'x': 'not an image'
+        },
+        {
+            'x': 'image_path.jpg'
+        },
+    ]
+    with self.assertRaises(TypeError):
+      image_handler.run_inference(data, None, None)
+
+  @unittest.skipIf(PIL is None, 'PIL module is not installed.')
+  def test_handler_with_dict_inputs(self):
+    input_one = FakeMultiModalInput(
+        image=PIL.Image.new(mode='RGB', size=(1, 1)), text="test image one")
+    input_two = FakeMultiModalInput(
+        image=PIL.Image.new(mode='RGB', size=(1, 1)), text="test image two")
+    input_three = FakeMultiModalInput(
+        image=PIL.Image.new(mode='RGB', size=(1, 1)),
+        video=bytes.fromhex('2Ef0 F1f2  '),
+        text="test image three with video")
+    data = [
+        {
+            'x': input_one
+        },
+        {
+            'x': input_two
+        },
+        {
+            'x': input_three
+        },
+    ]
+    expected_data = [{key: value for key, value in d.items()} for d in data]
+    with beam.Pipeline() as p:
+      result = (
+          p
+          | beam.Create(data)
+          | base.MLTransform(
+              write_artifact_location=self.artifact_location).with_transform(
+                  self.embedding_config))
+      assert_that(
+          result,
+          equal_to(expected_data),
+      )
+
+
 class TestUtilFunctions(unittest.TestCase):
   def test_dict_input_fn_normal(self):
     input_list = [{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]
diff --git a/sdks/python/apache_beam/ml/transforms/embeddings/huggingface_test.py b/sdks/python/apache_beam/ml/transforms/embeddings/huggingface_test.py
index 924cca679bd7..a6abe7fbdbc3 100644
--- a/sdks/python/apache_beam/ml/transforms/embeddings/huggingface_test.py
+++ b/sdks/python/apache_beam/ml/transforms/embeddings/huggingface_test.py
@@ -181,7 +181,7 @@ def test_sentence_transformer_with_int_data_types(self):
     model_name = DEFAULT_MODEL_NAME
     embedding_config = SentenceTransformerEmbeddings(
         model_name=model_name, columns=[test_query_column])
-    with self.assertRaises(TypeError):
+    with self.assertRaisesRegex(Exception, "Embeddings can only be generated"):
       with beam.Pipeline() as pipeline:
         _ = (
             pipeline
@@ -316,7 +316,7 @@ def test_sentence_transformer_images_with_str_data_types(self):
         model_name=IMAGE_MODEL_NAME,
         columns=[test_query_column],
         image_model=True)
-    with self.assertRaises(TypeError):
+    with self.assertRaisesRegex(Exception, "Embeddings can only be generated"):
       with beam.Pipeline() as pipeline:
         _ = (
             pipeline
diff --git a/sdks/python/apache_beam/ml/transforms/embeddings/open_ai_it_test.py b/sdks/python/apache_beam/ml/transforms/embeddings/open_ai_it_test.py
index 0922b8b94ce4..118c656c33c3 100644
--- a/sdks/python/apache_beam/ml/transforms/embeddings/open_ai_it_test.py
+++ b/sdks/python/apache_beam/ml/transforms/embeddings/open_ai_it_test.py
@@ -175,7 +175,7 @@ def test_with_int_data_types(self):
         model_name=model_name,
         columns=[test_query_column],
         api_key=self.api_key)
-    with self.assertRaises(TypeError):
+    with self.assertRaises(Exception):
       with beam.Pipeline() as pipeline:
         _ = (
             pipeline
diff --git a/sdks/python/apache_beam/ml/transforms/embeddings/tensorflow_hub_test.py b/sdks/python/apache_beam/ml/transforms/embeddings/tensorflow_hub_test.py
index 24bca5155fa7..64dc1e95d641 100644
--- a/sdks/python/apache_beam/ml/transforms/embeddings/tensorflow_hub_test.py
+++ b/sdks/python/apache_beam/ml/transforms/embeddings/tensorflow_hub_test.py
@@ -161,7 +161,7 @@ def assert_element(element):
   def test_with_int_data_types(self):
     embedding_config = TensorflowHubTextEmbeddings(
         hub_url=hub_url, columns=[test_query_column])
-    with self.assertRaises(TypeError):
+    with self.assertRaises(Exception):
       with beam.Pipeline() as pipeline:
         _ = (
             pipeline
diff --git a/sdks/python/apache_beam/ml/transforms/embeddings/vertex_ai.py b/sdks/python/apache_beam/ml/transforms/embeddings/vertex_ai.py
index ef3560404084..c7c46d246b93 100644
--- a/sdks/python/apache_beam/ml/transforms/embeddings/vertex_ai.py
+++ b/sdks/python/apache_beam/ml/transforms/embeddings/vertex_ai.py
@@ -19,12 +19,14 @@
 # Follow https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk # pylint: disable=line-too-long
 # to install Vertex AI Python SDK.
 
+import functools
 import logging
-import time
-from collections.abc import Iterable
+from collections.abc import Callable
 from collections.abc import Sequence
+from dataclasses import dataclass
 from typing import Any
 from typing import Optional
+from typing import cast
 
 from google.api_core.exceptions import ServerError
 from google.api_core.exceptions import TooManyRequests
@@ -32,20 +34,31 @@
 
 import apache_beam as beam
 import vertexai
-from apache_beam.io.components.adaptive_throttler import AdaptiveThrottler
-from apache_beam.metrics.metric import Metrics
 from apache_beam.ml.inference.base import ModelHandler
+from apache_beam.ml.inference.base import RemoteModelHandler
 from apache_beam.ml.inference.base import RunInference
+from apache_beam.ml.rag.types import Chunk
+from apache_beam.ml.rag.types import Embedding
 from apache_beam.ml.transforms.base import EmbeddingsManager
+from apache_beam.ml.transforms.base import EmbeddingTypeAdapter
 from apache_beam.ml.transforms.base import _ImageEmbeddingHandler
+from apache_beam.ml.transforms.base import _MultiModalEmbeddingHandler
 from apache_beam.ml.transforms.base import _TextEmbeddingHandler
-from apache_beam.utils import retry
 from vertexai.language_models import TextEmbeddingInput
 from vertexai.language_models import TextEmbeddingModel
 from vertexai.vision_models import Image
 from vertexai.vision_models import MultiModalEmbeddingModel
-
-__all__ = ["VertexAITextEmbeddings", "VertexAIImageEmbeddings"]
+from vertexai.vision_models import MultiModalEmbeddingResponse
+from vertexai.vision_models import Video
+from vertexai.vision_models import VideoEmbedding
+from vertexai.vision_models import VideoSegmentConfig
+
+__all__ = [
+    "VertexAITextEmbeddings",
+    "VertexAIImageEmbeddings",
+    "VertexAIMultiModalEmbeddings",
+    "VertexAIMultiModalInput",
+]
 
 DEFAULT_TASK_TYPE = "RETRIEVAL_DOCUMENT"
 # TODO: https://github.com/apache/beam/issues/29356
@@ -58,7 +71,6 @@
     "CLUSTERING"
 ]
 _BATCH_SIZE = 5  # Vertex AI limits requests to 5 at a time.
-_MSEC_TO_SEC = 1000
 
 LOGGER = logging.getLogger("VertexAIEmbeddings")
 
@@ -80,7 +92,7 @@ def _retry_on_appropriate_gcp_error(exception):
   return isinstance(exception, (TooManyRequests, ServerError))
 
 
-class _VertexAITextEmbeddingHandler(ModelHandler):
+class _VertexAITextEmbeddingHandler(RemoteModelHandler):
   """
   Note: Intended for internal use and guarantees no backwards compatibility.
   """
@@ -92,7 +104,7 @@ def __init__(
       project: Optional[str] = None,
       location: Optional[str] = None,
       credentials: Optional[Credentials] = None,
-  ):
+      **kwargs):
     vertexai.init(project=project, location=location, credentials=credentials)
     self.model_name = model_name
     if task_type not in TASK_TYPE_INPUTS:
@@ -101,47 +113,16 @@ def __init__(
     self.task_type = task_type
     self.title = title
 
-    # Configure AdaptiveThrottler and throttling metrics for client-side
-    # throttling behavior.
-    # See https://docs.google.com/document/d/1ePorJGZnLbNCmLD9mR7iFYOdPsyDA1rDnTpYnbdrzSU/edit?usp=sharing
-    # for more details.
-    self.throttled_secs = Metrics.counter(
-        VertexAIImageEmbeddings, "cumulativeThrottlingSeconds")
-    self.throttler = AdaptiveThrottler(
-        window_ms=1, bucket_ms=1, overload_ratio=2)
-
-  @retry.with_exponential_backoff(
-      num_retries=5, retry_filter=_retry_on_appropriate_gcp_error)
-  def get_request(
-      self,
-      text_batch: Sequence[TextEmbeddingInput],
-      model: TextEmbeddingModel,
-      throttle_delay_secs: int):
-    while self.throttler.throttle_request(time.time() * _MSEC_TO_SEC):
-      LOGGER.info(
-          "Delaying request for %d seconds due to previous failures",
-          throttle_delay_secs)
-      time.sleep(throttle_delay_secs)
-      self.throttled_secs.inc(throttle_delay_secs)
-
-    try:
-      req_time = time.time()
-      prediction = model.get_embeddings(list(text_batch))
-      self.throttler.successful_request(req_time * _MSEC_TO_SEC)
-      return prediction
-    except TooManyRequests as e:
-      LOGGER.warning("request was limited by the service with code %i", e.code)
-      raise
-    except Exception as e:
-      LOGGER.error("unexpected exception raised as part of request, got %s", e)
-      raise
-
-  def run_inference(
+    super().__init__(
+        namespace='VertexAITextEmbeddingHandler',
+        retry_filter=_retry_on_appropriate_gcp_error,
+        **kwargs)
+
+  def request(
       self,
       batch: Sequence[str],
-      model: Any,
-      inference_args: Optional[dict[str, Any]] = None,
-  ) -> Iterable:
+      model: TextEmbeddingModel,
+      inference_args: Optional[dict[str, Any]] = None):
     embeddings = []
     batch_size = _BATCH_SIZE
     for i in range(0, len(batch), batch_size):
@@ -151,12 +132,11 @@ def run_inference(
               text=text, title=self.title, task_type=self.task_type)
           for text in text_batch_strs
       ]
-      embeddings_batch = self.get_request(
-          text_batch=text_batch, model=model, throttle_delay_secs=5)
+      embeddings_batch = model.get_embeddings(list(text_batch))
       embeddings.extend([el.values for el in embeddings_batch])
     return embeddings
 
-  def load_model(self):
+  def create_client(self) -> TextEmbeddingModel:
     model = TextEmbeddingModel.from_pretrained(self.model_name)
     return model
 
@@ -205,6 +185,7 @@ def __init__(
     self.credentials = credentials
     self.title = title
     self.task_type = task_type
+    self.kwargs = kwargs
     super().__init__(columns=columns, **kwargs)
 
   def get_model_handler(self) -> ModelHandler:
@@ -215,7 +196,7 @@ def get_model_handler(self) -> ModelHandler:
         credentials=self.credentials,
         title=self.title,
         task_type=self.task_type,
-    )
+        **self.kwargs)
 
   def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
     return RunInference(
@@ -223,7 +204,7 @@ def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
         inference_args=self.inference_args)
 
 
-class _VertexAIImageEmbeddingHandler(ModelHandler):
+class _VertexAIImageEmbeddingHandler(RemoteModelHandler):
   def __init__(
       self,
       model_name: str,
@@ -231,60 +212,29 @@ def __init__(
       project: Optional[str] = None,
       location: Optional[str] = None,
       credentials: Optional[Credentials] = None,
-  ):
+      **kwargs):
     vertexai.init(project=project, location=location, credentials=credentials)
     self.model_name = model_name
     self.dimension = dimension
 
-    # Configure AdaptiveThrottler and throttling metrics for client-side
-    # throttling behavior.
-    # See https://docs.google.com/document/d/1ePorJGZnLbNCmLD9mR7iFYOdPsyDA1rDnTpYnbdrzSU/edit?usp=sharing
-    # for more details.
-    self.throttled_secs = Metrics.counter(
-        VertexAIImageEmbeddings, "cumulativeThrottlingSeconds")
-    self.throttler = AdaptiveThrottler(
-        window_ms=1, bucket_ms=1, overload_ratio=2)
-
-  @retry.with_exponential_backoff(
-      num_retries=5, retry_filter=_retry_on_appropriate_gcp_error)
-  def get_request(
-      self,
-      img: Image,
-      model: MultiModalEmbeddingModel,
-      throttle_delay_secs: int):
-    while self.throttler.throttle_request(time.time() * _MSEC_TO_SEC):
-      LOGGER.info(
-          "Delaying request for %d seconds due to previous failures",
-          throttle_delay_secs)
-      time.sleep(throttle_delay_secs)
-      self.throttled_secs.inc(throttle_delay_secs)
-
-    try:
-      req_time = time.time()
-      prediction = model.get_embeddings(image=img, dimension=self.dimension)
-      self.throttler.successful_request(req_time * _MSEC_TO_SEC)
-      return prediction
-    except TooManyRequests as e:
-      LOGGER.warning("request was limited by the service with code %i", e.code)
-      raise
-    except Exception as e:
-      LOGGER.error("unexpected exception raised as part of request, got %s", e)
-      raise
-
-  def run_inference(
+    super().__init__(
+        namespace='VertexAIImageEmbeddingHandler',
+        retry_filter=_retry_on_appropriate_gcp_error,
+        **kwargs)
+
+  def request(
       self,
-      batch: Sequence[Image],
+      imgs: Sequence[Image],
       model: MultiModalEmbeddingModel,
-      inference_args: Optional[dict[str, Any]] = None,
-  ) -> Iterable:
+      inference_args: Optional[dict[str, Any]] = None):
     embeddings = []
-    # Maximum request size for muli-model embedding models is 1.
-    for img in batch:
-      embedding_response = self.get_request(img, model, throttle_delay_secs=5)
-      embeddings.append(embedding_response.image_embedding)
+    # Max request size for multi-modal embedding models is 1
+    for img in imgs:
+      prediction = model.get_embeddings(image=img, dimension=self.dimension)
+      embeddings.append(prediction.image_embedding)
     return embeddings
 
-  def load_model(self):
+  def create_client(self):
     model = MultiModalEmbeddingModel.from_pretrained(self.model_name)
     return model
 
@@ -327,6 +277,7 @@ def __init__(
     self.project = project
     self.location = location
     self.credentials = credentials
+    self.kwargs = kwargs
     if dimension is not None and dimension not in (128, 256, 512, 1408):
       raise ValueError(
           "dimension argument must be one of 128, 256, 512, or 1408")
@@ -340,9 +291,228 @@ def get_model_handler(self) -> ModelHandler:
         project=self.project,
         location=self.location,
         credentials=self.credentials,
-    )
+        **self.kwargs)
 
   def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
     return RunInference(
         model_handler=_ImageEmbeddingHandler(self),
         inference_args=self.inference_args)
+
+
+@dataclass
+class VertexImage:
+  image_content: Image
+  embedding: Optional[list[float]] = None
+
+
+@dataclass
+class VertexVideo:
+  video_content: Video
+  config: VideoSegmentConfig
+  embeddings: Optional[list[VideoEmbedding]] = None
+
+
+@dataclass
+class VertexAIMultiModalInput:
+  image: Optional[VertexImage] = None
+  video: Optional[VertexVideo] = None
+  contextual_text: Optional[Chunk] = None
+
+
+class _VertexAIMultiModalEmbeddingHandler(RemoteModelHandler):
+  def __init__(
+      self,
+      model_name: str,
+      dimension: Optional[int] = None,
+      project: Optional[str] = None,
+      location: Optional[str] = None,
+      credentials: Optional[Credentials] = None,
+      **kwargs):
+    vertexai.init(project=project, location=location, credentials=credentials)
+    self.model_name = model_name
+    self.dimension = dimension
+
+    super().__init__(
+        namespace='VertexAIMultiModelEmbeddingHandler',
+        retry_filter=_retry_on_appropriate_gcp_error,
+        **kwargs)
+
+  def request(
+      self,
+      batch: Sequence[VertexAIMultiModalInput],
+      model: MultiModalEmbeddingModel,
+      inference_args: Optional[dict[str, Any]] = None):
+    embeddings = []
+    # Max request size for multi-modal embedding models is 1
+    for input in batch:
+      image_content: Optional[Image] = None
+      video_content: Optional[Video] = None
+      text_content: Optional[str] = None
+      video_config: Optional[VideoSegmentConfig] = None
+
+      if input.image:
+        image_content = input.image.image_content
+      if input.video:
+        video_content = input.video.video_content
+        video_config = input.video.config
+      if input.contextual_text:
+        text_content = input.contextual_text.content.text
+
+      prediction = model.get_embeddings(
+          image=image_content,
+          video=video_content,
+          contextual_text=text_content,
+          dimension=self.dimension,
+          video_segment_config=video_config)
+      embeddings.append(prediction)
+    return embeddings
+
+  def create_client(self) -> MultiModalEmbeddingModel:
+    model = MultiModalEmbeddingModel.from_pretrained(self.model_name)
+    return model
+
+  def __repr__(self):
+    # ModelHandler is internal to the user and is not exposed.
+    # Hence we need to override the __repr__ method to expose
+    # the name of the class.
+    return 'VertexAIMultiModalEmbeddings'
+
+
+def _multimodal_dict_input_fn(
+    image_column: Optional[str],
+    video_column: Optional[str],
+    text_column: Optional[str],
+    batch: Sequence[dict[str, Any]]) -> list[VertexAIMultiModalInput]:
+  multimodal_inputs: list[VertexAIMultiModalInput] = []
+  for item in batch:
+    img: Optional[VertexImage] = None
+    vid: Optional[VertexVideo] = None
+    text: Optional[Chunk] = None
+    if image_column:
+      img = item[image_column]
+    if video_column:
+      vid = item[video_column]
+    if text_column:
+      text = item[text_column]
+    multimodal_inputs.append(
+        VertexAIMultiModalInput(image=img, video=vid, contextual_text=text))
+  return multimodal_inputs
+
+
+def _multimodal_dict_output_fn(
+    image_column: Optional[str],
+    video_column: Optional[str],
+    text_column: Optional[str],
+    batch: Sequence[dict[str, Any]],
+    embeddings: Sequence[MultiModalEmbeddingResponse]) -> list[dict[str, Any]]:
+  results = []
+  for batch_idx, item in enumerate(batch):
+    mm_embedding = embeddings[batch_idx]
+    if image_column:
+      item[image_column].embedding = mm_embedding.image_embedding
+    if video_column:
+      item[video_column].embeddings = mm_embedding.video_embeddings
+    if text_column:
+      item[text_column].embedding = Embedding(
+          dense_embedding=mm_embedding.text_embedding)
+    results.append(item)
+  return results
+
+
+def _create_multimodal_dict_adapter(
+    image_column: Optional[str],
+    video_column: Optional[str],
+    text_column: Optional[str]
+) -> EmbeddingTypeAdapter[dict[str, Any], dict[str, Any]]:
+  return EmbeddingTypeAdapter[dict[str, Any], dict[str, Any]](
+      input_fn=cast(
+          Callable[[Sequence[dict[str, Any]]], list[str]],
+          functools.partial(
+              _multimodal_dict_input_fn,
+              image_column,
+              video_column,
+              text_column)),
+      output_fn=cast(
+          Callable[[Sequence[dict[str, Any]], Sequence[Any]],
+                   list[dict[str, Any]]],
+          functools.partial(
+              _multimodal_dict_output_fn,
+              image_column,
+              video_column,
+              text_column)))
+
+
+class VertexAIMultiModalEmbeddings(EmbeddingsManager):
+  def __init__(
+      self,
+      model_name: str,
+      image_column: Optional[str] = None,
+      video_column: Optional[str] = None,
+      text_column: Optional[str] = None,
+      dimension: Optional[int] = None,
+      project: Optional[str] = None,
+      location: Optional[str] = None,
+      credentials: Optional[Credentials] = None,
+      **kwargs):
+    """
+    Embedding Config for Vertex AI Multi-Modal Embedding models following
+    https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-multimodal-embeddings # pylint: disable=line-too-long
+    Multi-Modal Embeddings are generated for a batch of image, video, and
+    string groupings using the Vertex AI API. Embeddings are returned in a list
+    for each image in the batch as MultiModalEmbeddingResponses. This
+    transform makes remote calls to the Vertex AI service and may incur costs
+    for use.
+
+    Args:
+      model_name: The name of the Vertex AI Multi-Modal Embedding model.
+      image_column: The column containing image data to be embedded. This data
+        is expected to be formatted as VertexImage objects, containing a Vertex
+        Image object.
+      video_column: The column containing video data to be embedded. This data
+        is expected to be formatted as VertexVideo objects, containing a Vertex
+        Video object an a VideoSegmentConfig object.
+      text_column: The column containing text data to be embedded. This data is
+        expected to be formatted as Chunk objects, containing the string to be
+        embedded in the Chunk's content field.
+      dimension: The length of the embedding vector to generate. Must be one of
+        128, 256, 512, or 1408. If not set, Vertex AI's default value is 1408.
+        If submitting video content, dimension *musst* be 1408.
+      project: The default GCP project for API calls.
+      location: The default location for API calls.
+      credentials: Custom credentials for API calls.
+        Defaults to environment credentials.
+    """
+    self.model_name = model_name
+    self.project = project
+    self.location = location
+    self.credentials = credentials
+    self.kwargs = kwargs
+    if dimension is not None and dimension not in (128, 256, 512, 1408):
+      raise ValueError(
+          "dimension argument must be one of 128, 256, 512, or 1408")
+    self.dimension = dimension
+    if not image_column and not video_column and not text_column:
+      raise ValueError("at least one input column must be specified")
+    if video_column is not None and dimension != 1408:
+      raise ValueError(
+          "Vertex AI does not support custom dimensions for video input, want dimension = 1408, got ",
+          dimension)
+    self.type_adapter = _create_multimodal_dict_adapter(
+        image_column=image_column,
+        video_column=video_column,
+        text_column=text_column)
+    super().__init__(type_adapter=self.type_adapter, **kwargs)
+
+  def get_model_handler(self) -> ModelHandler:
+    return _VertexAIMultiModalEmbeddingHandler(
+        model_name=self.model_name,
+        dimension=self.dimension,
+        project=self.project,
+        location=self.location,
+        credentials=self.credentials,
+        **self.kwargs)
+
+  def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
+    return RunInference(
+        model_handler=_MultiModalEmbeddingHandler(self),
+        inference_args=self.inference_args)
diff --git a/sdks/python/apache_beam/ml/transforms/embeddings/vertex_ai_test.py b/sdks/python/apache_beam/ml/transforms/embeddings/vertex_ai_test.py
index bf2298ac77d5..ba43ea325089 100644
--- a/sdks/python/apache_beam/ml/transforms/embeddings/vertex_ai_test.py
+++ b/sdks/python/apache_beam/ml/transforms/embeddings/vertex_ai_test.py
@@ -26,10 +26,18 @@
 from apache_beam.ml.transforms.base import MLTransform
 
 try:
+  from apache_beam.ml.rag.types import Chunk
+  from apache_beam.ml.rag.types import Content
+  from apache_beam.ml.transforms.embeddings.vertex_ai import VertexAIMultiModalEmbeddings
   from apache_beam.ml.transforms.embeddings.vertex_ai import VertexAITextEmbeddings
   from apache_beam.ml.transforms.embeddings.vertex_ai import VertexAIImageEmbeddings
+  from apache_beam.ml.transforms.embeddings.vertex_ai import VertexImage
+  from apache_beam.ml.transforms.embeddings.vertex_ai import VertexVideo
   from vertexai.vision_models import Image
+  from vertexai.vision_models import Video
+  from vertexai.vision_models import VideoSegmentConfig
 except ImportError:
+  VertexAIMultiModalEmbeddings = None  # type: ignore
   VertexAITextEmbeddings = None  # type: ignore
   VertexAIImageEmbeddings = None  # type: ignore
 
@@ -153,7 +161,7 @@ def assert_element(element):
   def test_with_int_data_types(self):
     embedding_config = VertexAITextEmbeddings(
         model_name=model_name, columns=[test_query_column])
-    with self.assertRaises(TypeError):
+    with self.assertRaisesRegex(Exception, "Embeddings can only be generated"):
       with beam.Pipeline() as pipeline:
         _ = (
             pipeline
@@ -286,5 +294,104 @@ def test_improper_dimension(self):
           dimension=127)
 
 
+image_feature_column: str = "img_feature"
+text_feature_column: str = "txt_feature"
+video_feature_column: str = "vid_feature"
+
+
+def _make_text_chunk(input: str) -> Chunk:
+  return Chunk(content=Content(text=input))
+
+
+@unittest.skipIf(
+    VertexAIMultiModalEmbeddings is None,
+    'Vertex AI Python SDK is not installed.')
+class VertexAIMultiModalEmbeddingsTest(unittest.TestCase):
+  def setUp(self) -> None:
+    self.artifact_location = tempfile.mkdtemp(
+        prefix='_vertex_ai_multi_modal_test')
+    self.gcs_artifact_location = os.path.join(
+        'gs://temp-storage-for-perf-tests/vertex_ai_multi_modal',
+        uuid.uuid4().hex)
+    self.model_name = "multimodalembedding"
+    self.image_path = "gs://apache-beam-ml/testing/inputs/vertex_images/sunflowers/1008566138_6927679c8a.jpg"  # pylint: disable=line-too-long
+    self.video_path = "gs://cloud-samples-data/vertex-ai-vision/highway_vehicles.mp4"  # pylint: disable=line-too-long
+    self.video_segment_config = VideoSegmentConfig(end_offset_sec=1)
+
+  def tearDown(self) -> None:
+    shutil.rmtree(self.artifact_location)
+
+  def test_vertex_ai_multimodal_embedding_img_and_text(self):
+    embedding_config = VertexAIMultiModalEmbeddings(
+        model_name=self.model_name,
+        image_column=image_feature_column,
+        text_column=text_feature_column,
+        dimension=128,
+        project="apache-beam-testing",
+        location="us-central1")
+    with beam.Pipeline() as pipeline:
+      transformed_pcoll = (
+          pipeline | "CreateData" >> beam.Create([{
+              image_feature_column: VertexImage(
+                  image_content=Image(gcs_uri=self.image_path)),
+              text_feature_column: _make_text_chunk("an image of sunflowers"),
+          }])
+          | "MLTransform" >> MLTransform(
+              write_artifact_location=self.artifact_location).with_transform(
+                  embedding_config))
+
+      def assert_element(element):
+        assert len(element[image_feature_column].embedding) == 128
+        assert len(
+            element[text_feature_column].embedding.dense_embedding) == 128
+
+      _ = (transformed_pcoll | beam.Map(assert_element))
+
+  def test_vertex_ai_multimodal_embedding_video(self):
+    embedding_config = VertexAIMultiModalEmbeddings(
+        model_name=self.model_name,
+        video_column=video_feature_column,
+        dimension=1408,
+        project="apache-beam-testing",
+        location="us-central1")
+    with beam.Pipeline() as pipeline:
+      transformed_pcoll = (
+          pipeline | "CreateData" >> beam.Create([{
+              video_feature_column: VertexVideo(
+                  video_content=Video(gcs_uri=self.video_path),
+                  config=self.video_segment_config)
+          }])
+          | "MLTransform" >> MLTransform(
+              write_artifact_location=self.artifact_location).with_transform(
+                  embedding_config))
+
+      def assert_element(element):
+        # Videos are returned in VideoEmbedding objects, must unroll
+        # for each segment.
+        for segment in element[video_feature_column].embeddings:
+          assert len(segment.embedding) == 1408
+
+      _ = (transformed_pcoll | beam.Map(assert_element))
+
+  def test_improper_dimension(self):
+    with self.assertRaises(ValueError):
+      _ = VertexAIMultiModalEmbeddings(
+          model_name=self.model_name,
+          image_column="fake_img_column",
+          dimension=127)
+
+  def test_missing_columns(self):
+    with self.assertRaises(ValueError):
+      _ = VertexAIMultiModalEmbeddings(
+          model_name=self.model_name, dimension=128)
+
+  def test_improper_video_dimension(self):
+    with self.assertRaises(ValueError):
+      _ = VertexAIMultiModalEmbeddings(
+          model_name=self.model_name,
+          video_column=video_feature_column,
+          dimension=128)
+
+
 if __name__ == '__main__':
   unittest.main()
diff --git a/sdks/python/apache_beam/options/pipeline_options.py b/sdks/python/apache_beam/options/pipeline_options.py
index a7db5bfb0e71..6595d683911b 100644
--- a/sdks/python/apache_beam/options/pipeline_options.py
+++ b/sdks/python/apache_beam/options/pipeline_options.py
@@ -20,6 +20,7 @@
 # pytype: skip-file
 
 import argparse
+import difflib
 import json
 import logging
 import os
@@ -226,7 +227,7 @@ class _CommaSeparatedListAction(argparse.Action):
   a list. This allows options like --experiments=abc,def to be treated
   as separate experiments 'abc' and 'def', similar to how Java SDK handles
   them.
-  
+
   If there are key=value experiments in a raw argument, the remaining part of
   the argument are treated as values and won't split further. For example:
   'abc,def,master_key=k1=v1,k2=v2' becomes
@@ -449,11 +450,30 @@ def from_dictionary(cls, options):
 
     return cls(flags)
 
+  @staticmethod
+  def _warn_on_unknown_options(unknown_args, parser):
+    if not unknown_args:
+      return
+
+    all_known_options = [
+        opt for action in parser._actions for opt in action.option_strings
+    ]
+
+    for arg in unknown_args:
+      msg = f"Unparseable argument: {arg}"
+      if arg.startswith('--'):
+        arg_name = arg.split('=', 1)[0]
+        suggestions = difflib.get_close_matches(arg_name, all_known_options)
+        if suggestions:
+          msg += f". Did you mean '{suggestions[0]}'?'"
+      _LOGGER.warning(msg)
+
   def get_all_options(
       self,
       drop_default=False,
       add_extra_args_fn: Optional[Callable[[_BeamArgumentParser], None]] = None,
-      retain_unknown_options=False) -> Dict[str, Any]:
+      retain_unknown_options=False,
+      display_warnings=False) -> Dict[str, Any]:
     """Returns a dictionary of all defined arguments.
 
     Returns a dictionary of all defined arguments (arguments that are defined in
@@ -485,12 +505,11 @@ def get_all_options(
       add_extra_args_fn(parser)
 
     known_args, unknown_args = parser.parse_known_args(self._flags)
-    if retain_unknown_options:
-      if unknown_args:
-        _LOGGER.warning(
-            'Unknown pipeline options received: %s. Ignore if flags are '
-            'used for internal purposes.' % (','.join(unknown_args)))
 
+    if display_warnings:
+      self._warn_on_unknown_options(unknown_args, parser)
+
+    if retain_unknown_options:
       seen = set()
 
       def add_new_arg(arg, **kwargs):
@@ -1021,9 +1040,10 @@ def _add_argparse_args(cls, parser):
         'updating-a-pipeline')
     parser.add_argument(
         '--enable_streaming_engine',
-        default=False,
+        default=True,
         action='store_true',
-        help='Enable Windmill Service for this Dataflow job. ')
+        help='Deprecated. All Python streaming pipelines on Dataflow'
+        'use Streaming Engine.')
     parser.add_argument(
         '--dataflow_kms_key',
         default=None,
@@ -1455,6 +1475,15 @@ def _add_argparse_args(cls, parser):
             'responsible for executing the user code and communicating with '
             'the runner. Depending on the runner, there may be more than one '
             'SDK Harness process running on the same worker node.'))
+    parser.add_argument(
+        '--element_processing_timeout_minutes',
+        type=int,
+        default=None,
+        help=(
+            'The time limit (in minutes) for any PTransform to finish '
+            'processing a single element. If exceeded, the SDK worker '
+            'process self-terminates and processing may be restarted '
+            'by a runner.'))
 
   def validate(self, validator):
     errors = []
@@ -1609,7 +1638,7 @@ def _add_argparse_args(cls, parser):
         help=(
             'Chooses which pickle library to use. Options are dill, '
             'cloudpickle or default.'),
-        choices=['cloudpickle', 'default', 'dill'])
+        choices=['cloudpickle', 'default', 'dill', 'dill_unsafe'])
     parser.add_argument(
         '--save_main_session',
         default=False,
@@ -1691,6 +1720,7 @@ def _add_argparse_args(cls, parser):
   def validate(self, validator):
     errors = []
     errors.extend(validator.validate_container_prebuilding_options(self))
+    errors.extend(validator.validate_pickle_library(self))
     return errors
 
 
@@ -1942,6 +1972,20 @@ def _add_argparse_args(cls, parser):
         'downloading a zipped prism binary, for the current platform. If '
         'prism_location is set to a Github Release page URL, them it will use '
         'that release page as a base when constructing the download URL.')
+    parser.add_argument(
+        '--prism_log_level',
+        default="info",
+        choices=["debug", "info", "warn", "error"],
+        help=(
+            'Controls the log level in Prism. Values can be "debug", "info", '
+            '"warn", and "error". Default log level is "info".'))
+    parser.add_argument(
+        '--prism_log_kind',
+        default="console",
+        choices=["dev", "json", "text", "console"],
+        help=(
+            'Controls the log format in Prism. Values can be "dev", "json", '
+            '"text", and "console". Default log format is "console".'))
 
 
 class TestOptions(PipelineOptions):
diff --git a/sdks/python/apache_beam/options/pipeline_options_test.py b/sdks/python/apache_beam/options/pipeline_options_test.py
index 06270d4cd310..cd6cce204b78 100644
--- a/sdks/python/apache_beam/options/pipeline_options_test.py
+++ b/sdks/python/apache_beam/options/pipeline_options_test.py
@@ -405,10 +405,18 @@ def test_experiments(self):
     self.assertEqual(options.get_all_options()['experiments'], None)
 
   def test_worker_options(self):
-    options = PipelineOptions(['--machine_type', 'abc', '--disk_type', 'def'])
+    options = PipelineOptions([
+        '--machine_type',
+        'abc',
+        '--disk_type',
+        'def',
+        '--element_processing_timeout_minutes',
+        '10',
+    ])
     worker_options = options.view_as(WorkerOptions)
     self.assertEqual(worker_options.machine_type, 'abc')
     self.assertEqual(worker_options.disk_type, 'def')
+    self.assertEqual(worker_options.element_processing_timeout_minutes, 10)
 
     options = PipelineOptions(
         ['--worker_machine_type', 'abc', '--worker_disk_type', 'def'])
diff --git a/sdks/python/apache_beam/options/pipeline_options_validator.py b/sdks/python/apache_beam/options/pipeline_options_validator.py
index ebe9c8f223ce..0217363bc9b8 100644
--- a/sdks/python/apache_beam/options/pipeline_options_validator.py
+++ b/sdks/python/apache_beam/options/pipeline_options_validator.py
@@ -119,6 +119,15 @@ class PipelineOptionsValidator(object):
   ERR_REPEATABLE_OPTIONS_NOT_SET_AS_LIST = (
       '(%s) is a string. Programmatically set PipelineOptions like (%s) '
       'options need to be specified as a list.')
+  ERR_DILL_NOT_INSTALLED = (
+      'Option pickle_library=dill requires dill==0.3.1.1. Install apache-beam '
+      'with the dill extra e.g. apache-beam[gcp, dill]. Dill package was not '
+      'found')
+  ERR_UNSAFE_DILL_VERSION = (
+      'Dill version 0.3.1.1 is required when using pickle_library=dill. Other '
+      'versions of dill are untested with Apache Beam. To install the supported'
+      ' dill version instal apache-beam[dill] extra. To use an unsupported '
+      'dill version, use pickle_library=dill_unsafe. %s')
 
   # GCS path specific patterns.
   GCS_URI = '(?P<SCHEME>[^:]+)://(?P<BUCKET>[^/]+)(/(?P<OBJECT>.*))?'
@@ -196,6 +205,25 @@ def validate_gcs_path(self, view, arg_name):
       return self._validate_error(self.ERR_INVALID_GCS_OBJECT, arg, arg_name)
     return []
 
+  def validate_pickle_library(self, view):
+    """Validates the pickle_library option."""
+    if view.pickle_library == 'default' or view.pickle_library == 'cloudpickle':
+      return []
+
+    if view.pickle_library == 'dill_unsafe':
+      return []
+
+    if view.pickle_library == 'dill':
+      try:
+        import dill
+        if dill.__version__ != "0.3.1.1":
+          return self._validate_error(
+              self.ERR_UNSAFE_DILL_VERSION,
+              f"Dill version found {dill.__version__}")
+      except ImportError:
+        return self._validate_error(self.ERR_DILL_NOT_INSTALLED)
+    return []
+
   def validate_cloud_options(self, view):
     """Validates job_name and project arguments."""
     errors = []
diff --git a/sdks/python/apache_beam/options/pipeline_options_validator_test.py b/sdks/python/apache_beam/options/pipeline_options_validator_test.py
index 56f305a01b74..8206d45dcf03 100644
--- a/sdks/python/apache_beam/options/pipeline_options_validator_test.py
+++ b/sdks/python/apache_beam/options/pipeline_options_validator_test.py
@@ -22,6 +22,7 @@
 import logging
 import unittest
 
+import pytest
 from hamcrest import assert_that
 from hamcrest import contains_string
 from hamcrest import only_contains
@@ -244,6 +245,48 @@ def test_is_service_runner(self, runner, options, expected):
     validator = PipelineOptionsValidator(PipelineOptions(options), runner)
     self.assertEqual(validator.is_service_runner(), expected)
 
+  def test_pickle_library_dill_not_installed_returns_error(self):
+    runner = MockRunners.OtherRunner()
+    # Remove default region for this test.
+    options = PipelineOptions(['--pickle_library=dill'])
+    validator = PipelineOptionsValidator(options, runner)
+    errors = validator.validate()
+    self.assertEqual(len(errors), 1, errors)
+    self.assertIn("Option pickle_library=dill requires dill", errors[0])
+
+  @pytest.mark.uses_dill
+  def test_pickle_library_dill_installed_returns_no_error(self):
+    pytest.importorskip("dill")
+    runner = MockRunners.OtherRunner()
+    # Remove default region for this test.
+    options = PipelineOptions(['--pickle_library=dill'])
+    validator = PipelineOptionsValidator(options, runner)
+    errors = validator.validate()
+    self.assertEqual(len(errors), 0, errors)
+
+  @pytest.mark.uses_dill
+  def test_pickle_library_dill_installed_returns_wrong_version(self):
+    pytest.importorskip("dill")
+    with unittest.mock.patch('dill.__version__', '0.3.6'):
+      runner = MockRunners.OtherRunner()
+      # Remove default region for this test.
+      options = PipelineOptions(['--pickle_library=dill'])
+      validator = PipelineOptionsValidator(options, runner)
+      errors = validator.validate()
+      self.assertEqual(len(errors), 1, errors)
+      self.assertIn("Dill version 0.3.1.1 is required when using ", errors[0])
+
+  @pytest.mark.uses_dill
+  def test_pickle_library_dill_unsafe_no_error(self):
+    pytest.importorskip("dill")
+    with unittest.mock.patch('dill.__version__', '0.3.6'):
+      runner = MockRunners.OtherRunner()
+      # Remove default region for this test.
+      options = PipelineOptions(['--pickle_library=dill_unsafe'])
+      validator = PipelineOptionsValidator(options, runner)
+      errors = validator.validate()
+      self.assertEqual(len(errors), 0, errors)
+
   def test_dataflow_job_file_and_template_location_mutually_exclusive(self):
     runner = MockRunners.OtherRunner()
     options = PipelineOptions(
diff --git a/sdks/python/apache_beam/pipeline.py b/sdks/python/apache_beam/pipeline.py
index 269b4acdc21d..caed03943e19 100644
--- a/sdks/python/apache_beam/pipeline.py
+++ b/sdks/python/apache_beam/pipeline.py
@@ -76,6 +76,7 @@
 from google.protobuf import message
 
 from apache_beam import pvalue
+from apache_beam.coders import typecoders
 from apache_beam.internal import pickler
 from apache_beam.io.filesystems import FileSystems
 from apache_beam.options.pipeline_options import CrossLanguageOptions
@@ -83,6 +84,7 @@
 from apache_beam.options.pipeline_options import PipelineOptions
 from apache_beam.options.pipeline_options import SetupOptions
 from apache_beam.options.pipeline_options import StandardOptions
+from apache_beam.options.pipeline_options import StreamingOptions
 from apache_beam.options.pipeline_options import TypeOptions
 from apache_beam.options.pipeline_options_validator import PipelineOptionsValidator
 from apache_beam.portability import common_urns
@@ -115,11 +117,11 @@
 
 
 class Pipeline(HasDisplayData):
-  """A pipeline object that manages a DAG of 
-  :class:`~apache_beam.transforms.ptransform.PTransform` s 
+  """A pipeline object that manages a DAG of
+  :class:`~apache_beam.transforms.ptransform.PTransform` s
   and their :class:`~apache_beam.pvalue.PValue` s.
 
-  Conceptually the :class:`~apache_beam.transforms.ptransform.PTransform` s are 
+  Conceptually the :class:`~apache_beam.transforms.ptransform.PTransform` s are
   the DAG's nodes and the :class:`~apache_beam.pvalue.PValue` s are the edges.
 
   All the transforms applied to the pipeline must have distinct full labels.
@@ -229,6 +231,9 @@ def __init__(
       raise ValueError(
           'Pipeline has validations errors: \n' + '\n'.join(errors))
 
+    typecoders.registry.update_compatibility_version = self._options.view_as(
+        StreamingOptions).update_compatibility_version
+
     # set default experiments for portable runners
     # (needs to occur prior to pipeline construction)
     if runner.is_fnapi_compatible():
@@ -575,6 +580,10 @@ def run(self, test_runner_api='AUTO'):
     # type: (Union[bool, str]) -> PipelineResult
 
     """Runs the pipeline. Returns whatever our runner returns after running."""
+    # All pipeline options are finalized at this point.
+    # Call get_all_options to print warnings on invalid options.
+    self.options.get_all_options(
+        retain_unknown_options=True, display_warnings=True)
 
     for error_handler in self._error_handlers:
       error_handler.verify_closed()
@@ -722,6 +731,10 @@ def apply(
       return self.apply(
           transform.transform, pvalueish, label or transform.label)
 
+    if not label and isinstance(transform, ptransform._PTransformFnPTransform):
+      # This must be set before label is inspected.
+      transform.set_options(self._options)
+
     if not isinstance(transform, ptransform.PTransform):
       raise TypeError("Expected a PTransform object, got %s" % transform)
 
@@ -828,10 +841,10 @@ def apply(
         self._infer_result_type(transform, tuple(inputs.values()), result)
 
         assert isinstance(result.producer.inputs, tuple)
-        # The DoOutputsTuple adds the PCollection to the outputs when accessed
-        # except for the main tag. Add the main tag here.
         if isinstance(result, pvalue.DoOutputsTuple):
-          current.add_output(result, result._main_tag)
+          for tag, pc in list(result._pcolls.items()):
+            if tag not in current.outputs:
+              current.add_output(pc, tag)
           continue
 
         # If there is already a tag with the same name, increase a counter for
@@ -1217,7 +1230,9 @@ def __init__(
     self.full_label = full_label
     self.main_inputs = dict(main_inputs or {})
 
-    self.side_inputs = tuple() if transform is None else transform.side_inputs
+    self.side_inputs = (
+        tuple() if transform is None else getattr(
+            transform, 'side_inputs', tuple()))
     self.outputs = {}  # type: Dict[Union[str, int, None], pvalue.PValue]
     self.parts = []  # type: List[AppliedPTransform]
     self.environment_id = environment_id if environment_id else None  # type: Optional[str]
diff --git a/sdks/python/apache_beam/pipeline_test.py b/sdks/python/apache_beam/pipeline_test.py
index f1f6ee8f7d46..420c74d415d6 100644
--- a/sdks/python/apache_beam/pipeline_test.py
+++ b/sdks/python/apache_beam/pipeline_test.py
@@ -177,7 +177,9 @@ def expand(self, pcoll):
         _ = pipeline | ParentTransform() | beam.Map(lambda x: x + 1)
 
   @mock.patch('logging.info')
+  @pytest.mark.uses_dill
   def test_runner_overrides_default_pickler(self, mock_info):
+    pytest.importorskip("dill")
     with mock.patch.object(PipelineRunner,
                            'default_pickle_library_override') as mock_fn:
       mock_fn.return_value = 'dill'
@@ -431,7 +433,7 @@ def test_pipeline_as_context(self):
     def raise_exception(exn):
       raise exn
 
-    with self.assertRaises(ValueError):
+    with self.assertRaises(Exception):
       with Pipeline() as p:
         # pylint: disable=expression-not-assigned
         p | Create([ValueError('msg')]) | Map(raise_exception)
@@ -921,7 +923,7 @@ def test_map(
       return (x, context_a, context_b, context_c)
 
     self.assertEqual(_TestContext.live_contexts, 0)
-    with TestPipeline() as p:
+    with TestPipeline('FnApiRunner') as p:
       pcoll = p | Create([1, 2]) | beam.Map(test_map)
       assert_that(pcoll, equal_to([(1, 'a', 'b', 'c'), (2, 'a', 'b', 'c')]))
     self.assertEqual(_TestContext.live_contexts, 0)
@@ -1562,6 +1564,59 @@ def file_artifact(path, hash, staged_name):
 
     self.assertEqual(len(proto.components.environments), 6)
 
+  def test_multiple_outputs_composite_ptransform(self):
+    """
+    Test that a composite PTransform with multiple outputs is represented
+    correctly in the pipeline proto.
+    """
+    class SalesSplitter(beam.DoFn):
+      def process(self, element):
+        price = element['price']
+        if price > 100:
+          yield beam.pvalue.TaggedOutput('premium_sales', element)
+        else:
+          yield beam.pvalue.TaggedOutput('standard_sales', element)
+
+    class ParentSalesSplitter(beam.PTransform):
+      def expand(self, pcoll):
+        return pcoll | beam.ParDo(SalesSplitter()).with_outputs(
+            'premium_sales', 'standard_sales')
+
+    sales_data = [
+        {
+            'item': 'Laptop', 'price': 1200
+        },
+        {
+            'item': 'Mouse', 'price': 25
+        },
+        {
+            'item': 'Keyboard', 'price': 75
+        },
+        {
+            'item': 'Monitor', 'price': 350
+        },
+        {
+            'item': 'Headphones', 'price': 90
+        },
+    ]
+
+    with beam.Pipeline() as pipeline:
+      sales_records = pipeline | 'Create Sales' >> beam.Create(sales_data)
+      _ = sales_records | 'Split Sales' >> ParentSalesSplitter()
+    current_transforms = list(pipeline.transforms_stack)
+    all_applied_transforms = {
+        xform.full_label: xform
+        for xform in current_transforms
+    }
+    while current_transforms:
+      xform = current_transforms.pop()
+      all_applied_transforms[xform.full_label] = xform
+      current_transforms.extend(xform.parts)
+    xform = all_applied_transforms['Split Sales']
+    # Confirm that Split Sales correctly has two outputs as specified by
+    #  ParDo.with_outputs in ParentSalesSplitter.
+    assert len(xform.outputs) == 2
+
 
 if __name__ == '__main__':
   unittest.main()
diff --git a/sdks/python/apache_beam/pvalue.py b/sdks/python/apache_beam/pvalue.py
index cee3b8f2bca2..3865af184b61 100644
--- a/sdks/python/apache_beam/pvalue.py
+++ b/sdks/python/apache_beam/pvalue.py
@@ -33,6 +33,7 @@
 from typing import Dict
 from typing import Generic
 from typing import Iterator
+from typing import NamedTuple
 from typing import Optional
 from typing import Sequence
 from typing import TypeVar
@@ -675,11 +676,15 @@ def __hash__(self):
     return hash(self.__dict__.items())
 
   def __eq__(self, other):
+    if type(self) == type(other):
+      other_dict = other.__dict__
+    elif type(other) == type(NamedTuple):
+      other_dict = other._asdict()
+    else:
+      return False
     return (
-        type(self) == type(other) and
-        len(self.__dict__) == len(other.__dict__) and all(
-            s == o
-            for s, o in zip(self.__dict__.items(), other.__dict__.items())))
+        len(self.__dict__) == len(other_dict) and
+        all(s == o for s, o in zip(self.__dict__.items(), other_dict.items())))
 
   def __reduce__(self):
     return _make_Row, tuple(self.__dict__.items())
diff --git a/sdks/python/apache_beam/runners/dataflow/dataflow_job_service_test.py b/sdks/python/apache_beam/runners/dataflow/dataflow_job_service_test.py
index e2f880085cb8..c2be041d56d6 100644
--- a/sdks/python/apache_beam/runners/dataflow/dataflow_job_service_test.py
+++ b/sdks/python/apache_beam/runners/dataflow/dataflow_job_service_test.py
@@ -15,6 +15,7 @@
 # limitations under the License.
 #
 
+import time
 import unittest
 
 import apache_beam as beam
@@ -63,6 +64,8 @@ def test_template(self):
         None, beam_job_type=dataflow_job_service.DataflowBeamJob)
     port = job_servicer.start_grpc_server(0)
     try:
+      template_path = (
+          'gs://apache-beam-testing-temp/test/template-{}'.format(time.time()))
       options = PipelineOptions(
           runner='PortableRunner',
           job_endpoint=f'localhost:{port}',
@@ -70,7 +73,7 @@ def test_template(self):
           region='us-central1',
           staging_location='gs://apache-beam-testing-stg/stg/',
           temp_location='gs://apache-beam-testing-temp/tmp',
-          template_location='gs://apache-beam-testing-temp/test/template',
+          template_location=template_path,
       )
       with beam.Pipeline(options=options) as p:
         _ = p | beam.Create([1, 2, 3]) | beam.Map(lambda x: x * x)
diff --git a/sdks/python/apache_beam/runners/dataflow/dataflow_runner.py b/sdks/python/apache_beam/runners/dataflow/dataflow_runner.py
index 19302923b1fb..9e339e289fff 100644
--- a/sdks/python/apache_beam/runners/dataflow/dataflow_runner.py
+++ b/sdks/python/apache_beam/runners/dataflow/dataflow_runner.py
@@ -378,6 +378,14 @@ def run_pipeline(self, pipeline, options, pipeline_proto=None):
       # contain any added PTransforms.
       pipeline.replace_all(DataflowRunner._PTRANSFORM_OVERRIDES)
 
+      # Apply DataflowRunner-specific overrides (e.g., streaming PubSub
+      # optimizations)
+      from apache_beam.runners.dataflow.ptransform_overrides import (
+          get_dataflow_transform_overrides)
+      dataflow_overrides = get_dataflow_transform_overrides(options)
+      if dataflow_overrides:
+        pipeline.replace_all(dataflow_overrides)
+
       if options.view_as(DebugOptions).lookup_experiment('use_legacy_bq_sink'):
         warnings.warn(
             "Native sinks no longer implemented; "
@@ -633,23 +641,8 @@ def _check_and_add_missing_streaming_options(options):
   # Runner v2 only supports using streaming engine (aka windmill service)
   if options.view_as(StandardOptions).streaming:
     debug_options = options.view_as(DebugOptions)
-    google_cloud_options = options.view_as(GoogleCloudOptions)
-    if (not google_cloud_options.enable_streaming_engine and
-        (debug_options.lookup_experiment("enable_windmill_service") or
-         debug_options.lookup_experiment("enable_streaming_engine"))):
-      raise ValueError(
-          """Streaming engine both disabled and enabled:
-          --enable_streaming_engine flag is not set, but
-          enable_windmill_service and/or enable_streaming_engine experiments
-          are present. It is recommended you only set the
-          --enable_streaming_engine flag.""")
-
-    # Ensure that if we detected a streaming pipeline that streaming specific
-    # options and experiments.
-    options.view_as(StandardOptions).streaming = True
-    google_cloud_options.enable_streaming_engine = True
-    debug_options.add_experiment("enable_streaming_engine")
-    debug_options.add_experiment("enable_windmill_service")
+    debug_options.add_experiment('enable_streaming_engine')
+    debug_options.add_experiment('enable_windmill_service')
 
 
 def _is_runner_v2_disabled(options):
diff --git a/sdks/python/apache_beam/runners/dataflow/dataflow_runner_test.py b/sdks/python/apache_beam/runners/dataflow/dataflow_runner_test.py
index bb9132bdb96e..178a75ec41d9 100644
--- a/sdks/python/apache_beam/runners/dataflow/dataflow_runner_test.py
+++ b/sdks/python/apache_beam/runners/dataflow/dataflow_runner_test.py
@@ -421,10 +421,9 @@ def test_min_cpu_platform_flag_is_propagated_to_experiments(self):
         'min_cpu_platform=Intel Haswell',
         remote_runner.job.options.view_as(DebugOptions).experiments)
 
-  def test_streaming_engine_flag_adds_windmill_experiments(self):
+  def test_streaming_adds_windmill_experiments(self):
     remote_runner = DataflowRunner()
     self.default_properties.append('--streaming')
-    self.default_properties.append('--enable_streaming_engine')
     self.default_properties.append('--experiment=some_other_experiment')
 
     with Pipeline(remote_runner, PipelineOptions(self.default_properties)) as p:
diff --git a/sdks/python/apache_beam/runners/dataflow/internal/clients/dataflow/dataflow_v1b3_client.py b/sdks/python/apache_beam/runners/dataflow/internal/clients/dataflow/dataflow_v1b3_client.py
index e42b180bbecd..d89e699512d3 100644
--- a/sdks/python/apache_beam/runners/dataflow/internal/clients/dataflow/dataflow_v1b3_client.py
+++ b/sdks/python/apache_beam/runners/dataflow/internal/clients/dataflow/dataflow_v1b3_client.py
@@ -108,8 +108,7 @@ def GetConfig(self, request, global_params=None):
         request_field='getDebugConfigRequest',
         request_type_name='DataflowProjectsJobsDebugGetConfigRequest',
         response_type_name='GetDebugConfigResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def SendCapture(self, request, global_params=None):
       r"""Send encoded debug capture data for component.
@@ -134,8 +133,7 @@ def SendCapture(self, request, global_params=None):
         request_field='sendDebugCaptureRequest',
         request_type_name='DataflowProjectsJobsDebugSendCaptureRequest',
         response_type_name='SendDebugCaptureResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsJobsMessagesService(base_api.BaseApiService):
     """Service class for the projects_jobs_messages resource."""
@@ -164,19 +162,14 @@ def List(self, request, global_params=None):
         ordered_params=['projectId', 'jobId'],
         path_params=['jobId', 'projectId'],
         query_params=[
-            'endTime',
-            'location',
-            'minimumImportance',
-            'pageSize',
-            'pageToken',
+            'endTime', 'location', 'minimumImportance', 'pageSize', 'pageToken',
             'startTime'
         ],
         relative_path='v1b3/projects/{projectId}/jobs/{jobId}/messages',
         request_field='',
         request_type_name='DataflowProjectsJobsMessagesListRequest',
         response_type_name='ListJobMessagesResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsJobsWorkItemsService(base_api.BaseApiService):
     """Service class for the projects_jobs_workItems resource."""
@@ -209,8 +202,7 @@ def Lease(self, request, global_params=None):
         request_field='leaseWorkItemRequest',
         request_type_name='DataflowProjectsJobsWorkItemsLeaseRequest',
         response_type_name='LeaseWorkItemResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def ReportStatus(self, request, global_params=None):
       r"""Reports the status of dataflow WorkItems leased by a worker.
@@ -235,8 +227,7 @@ def ReportStatus(self, request, global_params=None):
         request_field='reportWorkItemStatusRequest',
         request_type_name='DataflowProjectsJobsWorkItemsReportStatusRequest',
         response_type_name='ReportWorkItemStatusResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsJobsService(base_api.BaseApiService):
     """Service class for the projects_jobs resource."""
@@ -270,8 +261,7 @@ def Aggregated(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsJobsAggregatedRequest',
         response_type_name='ListJobsResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Create(self, request, global_params=None):
       r"""Creates a Cloud Dataflow job. To create a job, we recommend using `projects.locations.jobs.create` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.jobs.create` is not recommended, as your job will always start in `us-central1`. Do not enter confidential information when you supply string values using the API.
@@ -295,8 +285,7 @@ def Create(self, request, global_params=None):
         request_field='job',
         request_type_name='DataflowProjectsJobsCreateRequest',
         response_type_name='Job',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Get(self, request, global_params=None):
       r"""Gets the state of the specified Cloud Dataflow job. To get the state of a job, we recommend using `projects.locations.jobs.get` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.jobs.get` is not recommended, as you can only get the state of jobs that are running in `us-central1`.
@@ -320,8 +309,7 @@ def Get(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsJobsGetRequest',
         response_type_name='Job',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def GetMetrics(self, request, global_params=None):
       r"""Request the job status. To request the status of a job, we recommend using `projects.locations.jobs.getMetrics` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.jobs.getMetrics` is not recommended, as you can only request the status of jobs that are running in `us-central1`.
@@ -345,8 +333,7 @@ def GetMetrics(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsJobsGetMetricsRequest',
         response_type_name='JobMetrics',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def List(self, request, global_params=None):
       r"""List the jobs of a project. To list the jobs of a project in a region, we recommend using `projects.locations.jobs.list` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). To list the all jobs across all regions, use `projects.jobs.aggregated`. Using `projects.jobs.list` is not recommended, because you can only get the list of jobs that are running in `us-central1`. `projects.locations.jobs.list` and `projects.jobs.list` support filtering the list of jobs by name. Filtering by name isn't supported by `projects.jobs.aggregated`.
@@ -371,8 +358,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsJobsListRequest',
         response_type_name='ListJobsResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Snapshot(self, request, global_params=None):
       r"""Snapshot the state of a streaming job.
@@ -396,8 +382,7 @@ def Snapshot(self, request, global_params=None):
         request_field='snapshotJobRequest',
         request_type_name='DataflowProjectsJobsSnapshotRequest',
         response_type_name='Snapshot',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Update(self, request, global_params=None):
       r"""Updates the state of an existing Cloud Dataflow job. To update the state of an existing job, we recommend using `projects.locations.jobs.update` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.jobs.update` is not recommended, as you can only update the state of jobs that are running in `us-central1`.
@@ -421,8 +406,7 @@ def Update(self, request, global_params=None):
         request_field='job',
         request_type_name='DataflowProjectsJobsUpdateRequest',
         response_type_name='Job',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsLocationsFlexTemplatesService(base_api.BaseApiService):
     """Service class for the projects_locations_flexTemplates resource."""
@@ -457,8 +441,7 @@ def Launch(self, request, global_params=None):
         request_field='launchFlexTemplateRequest',
         request_type_name='DataflowProjectsLocationsFlexTemplatesLaunchRequest',
         response_type_name='LaunchFlexTemplateResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsLocationsJobsDebugService(base_api.BaseApiService):
     """Service class for the projects_locations_jobs_debug resource."""
@@ -493,8 +476,7 @@ def GetConfig(self, request, global_params=None):
         request_field='getDebugConfigRequest',
         request_type_name='DataflowProjectsLocationsJobsDebugGetConfigRequest',
         response_type_name='GetDebugConfigResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def SendCapture(self, request, global_params=None):
       r"""Send encoded debug capture data for component.
@@ -520,8 +502,7 @@ def SendCapture(self, request, global_params=None):
         request_type_name=
         'DataflowProjectsLocationsJobsDebugSendCaptureRequest',
         response_type_name='SendDebugCaptureResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsLocationsJobsMessagesService(base_api.BaseApiService):
     """Service class for the projects_locations_jobs_messages resource."""
@@ -557,8 +538,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsLocationsJobsMessagesListRequest',
         response_type_name='ListJobMessagesResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsLocationsJobsSnapshotsService(base_api.BaseApiService):
     """Service class for the projects_locations_jobs_snapshots resource."""
@@ -593,8 +573,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsLocationsJobsSnapshotsListRequest',
         response_type_name='ListSnapshotsResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsLocationsJobsStagesService(base_api.BaseApiService):
     """Service class for the projects_locations_jobs_stages resource."""
@@ -630,8 +609,7 @@ def GetExecutionDetails(self, request, global_params=None):
         request_type_name=
         'DataflowProjectsLocationsJobsStagesGetExecutionDetailsRequest',
         response_type_name='StageExecutionDetails',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsLocationsJobsWorkItemsService(base_api.BaseApiService):
     """Service class for the projects_locations_jobs_workItems resource."""
@@ -666,8 +644,7 @@ def Lease(self, request, global_params=None):
         request_field='leaseWorkItemRequest',
         request_type_name='DataflowProjectsLocationsJobsWorkItemsLeaseRequest',
         response_type_name='LeaseWorkItemResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def ReportStatus(self, request, global_params=None):
       r"""Reports the status of dataflow WorkItems leased by a worker.
@@ -693,8 +670,7 @@ def ReportStatus(self, request, global_params=None):
         request_type_name=
         'DataflowProjectsLocationsJobsWorkItemsReportStatusRequest',
         response_type_name='ReportWorkItemStatusResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsLocationsJobsService(base_api.BaseApiService):
     """Service class for the projects_locations_jobs resource."""
@@ -727,8 +703,7 @@ def Create(self, request, global_params=None):
         request_field='job',
         request_type_name='DataflowProjectsLocationsJobsCreateRequest',
         response_type_name='Job',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Get(self, request, global_params=None):
       r"""Gets the state of the specified Cloud Dataflow job. To get the state of a job, we recommend using `projects.locations.jobs.get` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.jobs.get` is not recommended, as you can only get the state of jobs that are running in `us-central1`.
@@ -753,8 +728,7 @@ def Get(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsLocationsJobsGetRequest',
         response_type_name='Job',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def GetExecutionDetails(self, request, global_params=None):
       r"""Request detailed information about the execution status of the job. EXPERIMENTAL. This API is subject to change or removal without notice.
@@ -780,8 +754,7 @@ def GetExecutionDetails(self, request, global_params=None):
         request_type_name=
         'DataflowProjectsLocationsJobsGetExecutionDetailsRequest',
         response_type_name='JobExecutionDetails',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def GetMetrics(self, request, global_params=None):
       r"""Request the job status. To request the status of a job, we recommend using `projects.locations.jobs.getMetrics` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.jobs.getMetrics` is not recommended, as you can only request the status of jobs that are running in `us-central1`.
@@ -806,8 +779,7 @@ def GetMetrics(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsLocationsJobsGetMetricsRequest',
         response_type_name='JobMetrics',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def List(self, request, global_params=None):
       r"""List the jobs of a project. To list the jobs of a project in a region, we recommend using `projects.locations.jobs.list` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). To list the all jobs across all regions, use `projects.jobs.aggregated`. Using `projects.jobs.list` is not recommended, because you can only get the list of jobs that are running in `us-central1`. `projects.locations.jobs.list` and `projects.jobs.list` support filtering the list of jobs by name. Filtering by name isn't supported by `projects.jobs.aggregated`.
@@ -831,8 +803,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsLocationsJobsListRequest',
         response_type_name='ListJobsResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Snapshot(self, request, global_params=None):
       r"""Snapshot the state of a streaming job.
@@ -857,8 +828,7 @@ def Snapshot(self, request, global_params=None):
         request_field='snapshotJobRequest',
         request_type_name='DataflowProjectsLocationsJobsSnapshotRequest',
         response_type_name='Snapshot',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Update(self, request, global_params=None):
       r"""Updates the state of an existing Cloud Dataflow job. To update the state of an existing job, we recommend using `projects.locations.jobs.update` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.jobs.update` is not recommended, as you can only update the state of jobs that are running in `us-central1`.
@@ -883,8 +853,7 @@ def Update(self, request, global_params=None):
         request_field='job',
         request_type_name='DataflowProjectsLocationsJobsUpdateRequest',
         response_type_name='Job',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsLocationsSnapshotsService(base_api.BaseApiService):
     """Service class for the projects_locations_snapshots resource."""
@@ -919,8 +888,7 @@ def Delete(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsLocationsSnapshotsDeleteRequest',
         response_type_name='DeleteSnapshotResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Get(self, request, global_params=None):
       r"""Gets information about a snapshot.
@@ -945,8 +913,7 @@ def Get(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsLocationsSnapshotsGetRequest',
         response_type_name='Snapshot',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def List(self, request, global_params=None):
       r"""Lists snapshots.
@@ -971,8 +938,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsLocationsSnapshotsListRequest',
         response_type_name='ListSnapshotsResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsLocationsTemplatesService(base_api.BaseApiService):
     """Service class for the projects_locations_templates resource."""
@@ -1007,8 +973,7 @@ def Create(self, request, global_params=None):
         request_field='createJobFromTemplateRequest',
         request_type_name='DataflowProjectsLocationsTemplatesCreateRequest',
         response_type_name='Job',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Get(self, request, global_params=None):
       r"""Get the template associated with a template. To get the template, we recommend using `projects.locations.templates.get` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.templates.get` is not recommended, because only templates that are running in `us-central1` are retrieved.
@@ -1033,8 +998,7 @@ def Get(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsLocationsTemplatesGetRequest',
         response_type_name='GetTemplateResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Launch(self, request, global_params=None):
       r"""Launches a template. To launch a template, we recommend using `projects.locations.templates.launch` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.templates.launch` is not recommended, because jobs launched from the template will always start in `us-central1`.
@@ -1054,18 +1018,15 @@ def Launch(self, request, global_params=None):
         ordered_params=['projectId', 'location'],
         path_params=['location', 'projectId'],
         query_params=[
-            'dynamicTemplate_gcsPath',
-            'dynamicTemplate_stagingLocation',
-            'gcsPath',
-            'validateOnly'
+            'dynamicTemplate_gcsPath', 'dynamicTemplate_stagingLocation',
+            'gcsPath', 'validateOnly'
         ],
         relative_path=
         'v1b3/projects/{projectId}/locations/{location}/templates:launch',
         request_field='launchTemplateParameters',
         request_type_name='DataflowProjectsLocationsTemplatesLaunchRequest',
         response_type_name='LaunchTemplateResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsLocationsService(base_api.BaseApiService):
     """Service class for the projects_locations resource."""
@@ -1099,8 +1060,7 @@ def WorkerMessages(self, request, global_params=None):
         request_field='sendWorkerMessagesRequest',
         request_type_name='DataflowProjectsLocationsWorkerMessagesRequest',
         response_type_name='SendWorkerMessagesResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsSnapshotsService(base_api.BaseApiService):
     """Service class for the projects_snapshots resource."""
@@ -1133,8 +1093,7 @@ def Get(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsSnapshotsGetRequest',
         response_type_name='Snapshot',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def List(self, request, global_params=None):
       r"""Lists snapshots.
@@ -1158,8 +1117,7 @@ def List(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsSnapshotsListRequest',
         response_type_name='ListSnapshotsResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsTemplatesService(base_api.BaseApiService):
     """Service class for the projects_templates resource."""
@@ -1192,8 +1150,7 @@ def Create(self, request, global_params=None):
         request_field='createJobFromTemplateRequest',
         request_type_name='DataflowProjectsTemplatesCreateRequest',
         response_type_name='Job',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Get(self, request, global_params=None):
       r"""Get the template associated with a template. To get the template, we recommend using `projects.locations.templates.get` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.templates.get` is not recommended, because only templates that are running in `us-central1` are retrieved.
@@ -1217,8 +1174,7 @@ def Get(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsTemplatesGetRequest',
         response_type_name='GetTemplateResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def Launch(self, request, global_params=None):
       r"""Launches a template. To launch a template, we recommend using `projects.locations.templates.launch` with a [regional endpoint] (https://cloud.google.com/dataflow/docs/concepts/regional-endpoints). Using `projects.templates.launch` is not recommended, because jobs launched from the template will always start in `us-central1`.
@@ -1238,18 +1194,14 @@ def Launch(self, request, global_params=None):
         ordered_params=['projectId'],
         path_params=['projectId'],
         query_params=[
-            'dynamicTemplate_gcsPath',
-            'dynamicTemplate_stagingLocation',
-            'gcsPath',
-            'location',
-            'validateOnly'
+            'dynamicTemplate_gcsPath', 'dynamicTemplate_stagingLocation',
+            'gcsPath', 'location', 'validateOnly'
         ],
         relative_path='v1b3/projects/{projectId}/templates:launch',
         request_field='launchTemplateParameters',
         request_type_name='DataflowProjectsTemplatesLaunchRequest',
         response_type_name='LaunchTemplateResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
   class ProjectsService(base_api.BaseApiService):
     """Service class for the projects resource."""
@@ -1282,8 +1234,7 @@ def DeleteSnapshots(self, request, global_params=None):
         request_field='',
         request_type_name='DataflowProjectsDeleteSnapshotsRequest',
         response_type_name='DeleteSnapshotResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
 
     def WorkerMessages(self, request, global_params=None):
       r"""Send a worker_message to the service.
@@ -1307,5 +1258,4 @@ def WorkerMessages(self, request, global_params=None):
         request_field='sendWorkerMessagesRequest',
         request_type_name='DataflowProjectsWorkerMessagesRequest',
         response_type_name='SendWorkerMessagesResponse',
-        supports_download=False,
-    )
+        supports_download=False, )
diff --git a/sdks/python/apache_beam/runners/dataflow/internal/names.py b/sdks/python/apache_beam/runners/dataflow/internal/names.py
index a27fd9e5561a..94707e072a32 100644
--- a/sdks/python/apache_beam/runners/dataflow/internal/names.py
+++ b/sdks/python/apache_beam/runners/dataflow/internal/names.py
@@ -34,6 +34,6 @@
 # Unreleased sdks use container image tag specified below.
 # Update this tag whenever there is a change that
 # requires changes to SDK harness container or SDK harness launcher.
-BEAM_DEV_SDK_CONTAINER_TAG = 'beam-master-20250707'
+BEAM_DEV_SDK_CONTAINER_TAG = 'beam-master-20250917'
 
 DATAFLOW_CONTAINER_IMAGE_REPOSITORY = 'gcr.io/cloud-dataflow/v1beta3'
diff --git a/sdks/python/apache_beam/runners/dataflow/ptransform_overrides.py b/sdks/python/apache_beam/runners/dataflow/ptransform_overrides.py
index 8004762f5eec..4e75f202c098 100644
--- a/sdks/python/apache_beam/runners/dataflow/ptransform_overrides.py
+++ b/sdks/python/apache_beam/runners/dataflow/ptransform_overrides.py
@@ -19,9 +19,70 @@
 
 # pytype: skip-file
 
+from apache_beam.options.pipeline_options import StandardOptions
 from apache_beam.pipeline import PTransformOverride
 
 
+class StreamingPubSubWriteDoFnOverride(PTransformOverride):
+  """Override ParDo(_PubSubWriteDoFn) for streaming mode in DataflowRunner.
+
+  This override specifically targets the final ParDo step in WriteToPubSub
+  and replaces it with Write(sink) for streaming optimization.
+  """
+  def matches(self, applied_ptransform):
+    from apache_beam.transforms import ParDo
+    from apache_beam.io.gcp.pubsub import _PubSubWriteDoFn
+
+    if not isinstance(applied_ptransform.transform, ParDo):
+      return False
+
+    # Check if this ParDo uses _PubSubWriteDoFn
+    dofn = applied_ptransform.transform.dofn
+    return isinstance(dofn, _PubSubWriteDoFn)
+
+  def get_replacement_transform_for_applied_ptransform(
+      self, applied_ptransform):
+    from apache_beam.io.iobase import Write
+
+    # Get the WriteToPubSub transform from the DoFn constructor parameter
+    dofn = applied_ptransform.transform.dofn
+
+    # The DoFn was initialized with the WriteToPubSub transform
+    # We need to reconstruct the sink from the DoFn's stored properties
+    if hasattr(dofn, 'project') and hasattr(dofn, 'short_topic_name'):
+      from apache_beam.io.gcp.pubsub import _PubSubSink
+
+      # Create a sink with the same properties as the original
+      topic = f"projects/{dofn.project}/topics/{dofn.short_topic_name}"
+      sink = _PubSubSink(
+          topic=topic,
+          id_label=getattr(dofn, 'id_label', None),
+          timestamp_attribute=getattr(dofn, 'timestamp_attribute', None))
+      return Write(sink)
+    else:
+      # Fallback: return the original transform if we can't reconstruct it
+      return applied_ptransform.transform
+
+
+def get_dataflow_transform_overrides(pipeline_options):
+  """Returns DataflowRunner-specific transform overrides.
+
+  Args:
+    pipeline_options: Pipeline options to determine which overrides to apply.
+
+  Returns:
+    List of PTransformOverride objects for DataflowRunner.
+  """
+  overrides = []
+
+  # Only add streaming-specific overrides when in streaming mode
+  if pipeline_options.view_as(StandardOptions).streaming:
+    # Add PubSub ParDo streaming override that targets only the final step
+    overrides.append(StreamingPubSubWriteDoFnOverride())
+
+  return overrides
+
+
 class NativeReadPTransformOverride(PTransformOverride):
   """A ``PTransformOverride`` for ``Read`` using native sources.
 
@@ -54,7 +115,7 @@ def expand(self, pbegin):
         return pvalue.PCollection.from_(pbegin)
 
     # Use the source's coder type hint as this replacement's output. Otherwise,
-    # the typing information is not properly forwarded to the DataflowRunner and
-    # will choose the incorrect coder for this transform.
+    # the typing information is not properly forwarded to the DataflowRunner
+    # and will choose the incorrect coder for this transform.
     return Read(ptransform.source).with_output_types(
         ptransform.source.coder.to_type_hint())
diff --git a/sdks/python/apache_beam/runners/direct/direct_runner.py b/sdks/python/apache_beam/runners/direct/direct_runner.py
index 1df2e88f6140..68add6ea3c1a 100644
--- a/sdks/python/apache_beam/runners/direct/direct_runner.py
+++ b/sdks/python/apache_beam/runners/direct/direct_runner.py
@@ -25,7 +25,6 @@
 
 import itertools
 import logging
-import time
 import typing
 
 from google.protobuf import wrappers_pb2
@@ -52,6 +51,7 @@
 from apache_beam.transforms.ptransform import PTransform
 from apache_beam.transforms.timeutil import TimeDomain
 from apache_beam.typehints import trivial_inference
+from apache_beam.utils.interactive_utils import is_in_ipython
 
 __all__ = ['BundleBasedDirectRunner', 'DirectRunner', 'SwitchingDirectRunner']
 
@@ -66,9 +66,14 @@ class SwitchingDirectRunner(PipelineRunner):
   which supports streaming execution and certain primitives not yet
   implemented in the FnApiRunner.
   """
+  _is_interactive = False
+
   def is_fnapi_compatible(self):
     return BundleBasedDirectRunner.is_fnapi_compatible()
 
+  def is_interactive(self):
+    self._is_interactive = True
+
   def run_pipeline(self, pipeline, options):
 
     from apache_beam.pipeline import PipelineVisitor
@@ -112,9 +117,33 @@ def visit_transform(self, applied_ptransform):
 
     class _PrismRunnerSupportVisitor(PipelineVisitor):
       """Visitor determining if a Pipeline can be run on the PrismRunner."""
-      def accept(self, pipeline):
+      def accept(self, pipeline, is_interactive):
+        all_options = options.get_all_options()
         self.supported_by_prism_runner = True
-        pipeline.visit(self)
+        # TODO(https://github.com/apache/beam/issues/33623): Prism currently
+        # double fires on AfterCount trigger, once appropriately, and once
+        # incorrectly at the end of the window. This if condition could be
+        # more targeted, but for now we'll just ignore all unsafe triggers.
+        if pipeline.allow_unsafe_triggers:
+          self.supported_by_prism_runner = False
+        # TODO(https://github.com/apache/beam/issues/33623): Prism currently
+        # does not support interactive mode
+        elif is_in_ipython() or is_interactive:
+          self.supported_by_prism_runner = False
+        # TODO(https://github.com/apache/beam/issues/33623): Prism currently
+        # does not support the update compat flag
+        elif all_options['update_compatibility_version']:
+          self.supported_by_prism_runner = False
+        else:
+          pipeline.visit(self)
+        # Avoid circular import
+        from apache_beam.pipeline import ExternalTransformFinder
+        if ExternalTransformFinder.contains_external_transforms(pipeline):
+          # TODO(https://github.com/apache/beam/issues/33623): Prism currently
+          # seems to not be able to consistently bring up external transforms.
+          # It does sometimes, but at volume suites start to fail. We will try
+          # to enable this in a future release.
+          self.supported_by_prism_runner = False
         return self.supported_by_prism_runner
 
       def visit_transform(self, applied_ptransform):
@@ -125,6 +154,18 @@ def visit_transform(self, applied_ptransform):
           self.supported_by_prism_runner = False
         if isinstance(transform, beam.ParDo):
           dofn = transform.dofn
+          # TODO(https://github.com/apache/beam/issues/33623): Prism currently
+          # does not seem to handle DoFns using exception handling very well.
+          # This may be limited just to subprocess DoFns, but more
+          # investigation is needed before making it default
+          if isinstance(dofn,
+                        beam.transforms.core._ExceptionHandlingWrapperDoFn):
+            self.supported_by_prism_runner = False
+          # https://github.com/apache/beam/issues/34549
+          # Remote once we can support local materialization
+          if (hasattr(dofn, 'is_materialize_values_do_fn') and
+              dofn.is_materialize_values_do_fn):
+            self.supported_by_prism_runner = False
           # It's uncertain if the Prism Runner supports execution of CombineFns
           # with deferred side inputs.
           if isinstance(dofn, CombineValuesDoFn):
@@ -136,33 +177,39 @@ def visit_transform(self, applied_ptransform):
           if userstate.is_stateful_dofn(dofn):
             # https://github.com/apache/beam/issues/32786 -
             # Remove once Real time clock is used.
-            _, timer_specs = userstate.get_dofn_specs(dofn)
+            state_specs, timer_specs = userstate.get_dofn_specs(dofn)
             for timer in timer_specs:
               if timer.time_domain == TimeDomain.REAL_TIME:
                 self.supported_by_prism_runner = False
 
-    tryingPrism = False
+            for state in state_specs:
+              if isinstance(state, userstate.CombiningValueStateSpec):
+                self.supported_by_prism_runner = False
+          if isinstance(
+              dofn,
+              beam.transforms.combiners._PartialGroupByKeyCombiningValues):
+            if len(transform.side_inputs) > 0:
+              # Prism doesn't support side input combiners (this is within spec)
+              self.supported_by_prism_runner = False
+
+        # TODO(https://github.com/apache/beam/issues/33623): Prism seems to
+        # not handle session windows correctly. Examples are:
+        # util_test.py::ReshuffleTest::test_reshuffle_window_fn_preserved
+        # and util_test.py::ReshuffleTest::test_reshuffle_windows_unchanged
+        if isinstance(transform, beam.WindowInto) and isinstance(
+            transform.get_windowing('').windowfn, beam.window.Sessions):
+          self.supported_by_prism_runner = False
+
+    # Use BundleBasedDirectRunner if other runners are missing needed features.
+    runner = BundleBasedDirectRunner()
+
     # Check whether all transforms used in the pipeline are supported by the
-    # FnApiRunner, and the pipeline was not meant to be run as streaming.
-    if _FnApiRunnerSupportVisitor().accept(pipeline):
-      from apache_beam.portability.api import beam_provision_api_pb2
-      from apache_beam.runners.portability.fn_api_runner import fn_runner
-      from apache_beam.runners.portability.portable_runner import JobServiceHandle
-      all_options = options.get_all_options()
-      encoded_options = JobServiceHandle.encode_pipeline_options(all_options)
-      provision_info = fn_runner.ExtendedProvisionInfo(
-          beam_provision_api_pb2.ProvisionInfo(
-              pipeline_options=encoded_options))
-      runner = fn_runner.FnApiRunner(provision_info=provision_info)
-    elif _PrismRunnerSupportVisitor().accept(pipeline):
+    # PrismRunner
+    if _PrismRunnerSupportVisitor().accept(pipeline, self._is_interactive):
       _LOGGER.info('Running pipeline with PrismRunner.')
       from apache_beam.runners.portability import prism_runner
       runner = prism_runner.PrismRunner()
-      tryingPrism = True
-    else:
-      runner = BundleBasedDirectRunner()
 
-    if tryingPrism:
       try:
         pr = runner.run_pipeline(pipeline, options)
         # This is non-blocking, so if the state is *already* finished, something
@@ -170,7 +217,7 @@ def visit_transform(self, applied_ptransform):
         if (PipelineState.is_terminal(pr.state) and
             pr.state != PipelineState.DONE):
           _LOGGER.info(
-              'Pipeline failed on PrismRunner, falling back toDirectRunner.')
+              'Pipeline failed on PrismRunner, falling back to DirectRunner.')
           runner = BundleBasedDirectRunner()
         else:
           return pr
@@ -182,6 +229,19 @@ def visit_transform(self, applied_ptransform):
         _LOGGER.info('Falling back to DirectRunner')
         runner = BundleBasedDirectRunner()
 
+    # Check whether all transforms used in the pipeline are supported by the
+    # FnApiRunner, and the pipeline was not meant to be run as streaming.
+    if _FnApiRunnerSupportVisitor().accept(pipeline):
+      from apache_beam.portability.api import beam_provision_api_pb2
+      from apache_beam.runners.portability.fn_api_runner import fn_runner
+      from apache_beam.runners.portability.portable_runner import JobServiceHandle
+      all_options = options.get_all_options()
+      encoded_options = JobServiceHandle.encode_pipeline_options(all_options)
+      provision_info = fn_runner.ExtendedProvisionInfo(
+          beam_provision_api_pb2.ProvisionInfo(
+              pipeline_options=encoded_options))
+      runner = fn_runner.FnApiRunner(provision_info=provision_info)
+
     return runner.run_pipeline(pipeline, options)
 
 
@@ -338,7 +398,7 @@ def _get_transform_overrides(pipeline_options):
 
   # Importing following locally to avoid a circular dependency.
   from apache_beam.pipeline import PTransformOverride
-  from apache_beam.runners.direct.helper_transforms import LiftedCombinePerKey
+  from apache_beam.transforms.combiners import LiftedCombinePerKey
   from apache_beam.runners.direct.sdf_direct_runner import ProcessKeyedElementsViaKeyedWorkItemsOverride
   from apache_beam.runners.direct.sdf_direct_runner import SplittableParDoOverride
 
@@ -460,59 +520,6 @@ def expand(self, pvalue):
     return PCollection(self.pipeline, is_bounded=self._source.is_bounded())
 
 
-class _DirectWriteToPubSubFn(DoFn):
-  BUFFER_SIZE_ELEMENTS = 100
-  FLUSH_TIMEOUT_SECS = BUFFER_SIZE_ELEMENTS * 0.5
-
-  def __init__(self, transform):
-    self.project = transform.project
-    self.short_topic_name = transform.topic_name
-    self.id_label = transform.id_label
-    self.timestamp_attribute = transform.timestamp_attribute
-    self.with_attributes = transform.with_attributes
-
-    # TODO(https://github.com/apache/beam/issues/18939): Add support for
-    # id_label and timestamp_attribute.
-    if transform.id_label:
-      raise NotImplementedError(
-          'DirectRunner: id_label is not supported for '
-          'PubSub writes')
-    if transform.timestamp_attribute:
-      raise NotImplementedError(
-          'DirectRunner: timestamp_attribute is not '
-          'supported for PubSub writes')
-
-  def start_bundle(self):
-    self._buffer = []
-
-  def process(self, elem):
-    self._buffer.append(elem)
-    if len(self._buffer) >= self.BUFFER_SIZE_ELEMENTS:
-      self._flush()
-
-  def finish_bundle(self):
-    self._flush()
-
-  def _flush(self):
-    from google.cloud import pubsub
-    pub_client = pubsub.PublisherClient()
-    topic = pub_client.topic_path(self.project, self.short_topic_name)
-
-    if self.with_attributes:
-      futures = [
-          pub_client.publish(topic, elem.data, **elem.attributes)
-          for elem in self._buffer
-      ]
-    else:
-      futures = [pub_client.publish(topic, elem) for elem in self._buffer]
-
-    timer_start = time.time()
-    for future in futures:
-      remaining = self.FLUSH_TIMEOUT_SECS - (time.time() - timer_start)
-      future.result(remaining)
-    self._buffer = []
-
-
 def _get_pubsub_transform_overrides(pipeline_options):
   from apache_beam.io.gcp import pubsub as beam_pubsub
   from apache_beam.pipeline import PTransformOverride
@@ -530,19 +537,9 @@ def get_replacement_transform_for_applied_ptransform(
             '(use the --streaming flag).')
       return _DirectReadFromPubSub(applied_ptransform.transform._source)
 
-  class WriteToPubSubOverride(PTransformOverride):
-    def matches(self, applied_ptransform):
-      return isinstance(applied_ptransform.transform, beam_pubsub.WriteToPubSub)
-
-    def get_replacement_transform_for_applied_ptransform(
-        self, applied_ptransform):
-      if not pipeline_options.view_as(StandardOptions).streaming:
-        raise Exception(
-            'PubSub I/O is only available in streaming mode '
-            '(use the --streaming flag).')
-      return beam.ParDo(_DirectWriteToPubSubFn(applied_ptransform.transform))
-
-  return [ReadFromPubSubOverride(), WriteToPubSubOverride()]
+  # WriteToPubSub no longer needs an override - it works by default for both
+  # batch and streaming
+  return [ReadFromPubSubOverride()]
 
 
 class BundleBasedDirectRunner(PipelineRunner):
diff --git a/sdks/python/apache_beam/runners/direct/helper_transforms.py b/sdks/python/apache_beam/runners/direct/helper_transforms.py
deleted file mode 100644
index 0e88c021e2f9..000000000000
--- a/sdks/python/apache_beam/runners/direct/helper_transforms.py
+++ /dev/null
@@ -1,120 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-# pytype: skip-file
-
-import collections
-import itertools
-import typing
-
-import apache_beam as beam
-from apache_beam import typehints
-from apache_beam.internal.util import ArgumentPlaceholder
-from apache_beam.transforms.combiners import _CurriedFn
-from apache_beam.utils.windowed_value import WindowedValue
-
-
-class LiftedCombinePerKey(beam.PTransform):
-  """An implementation of CombinePerKey that does mapper-side pre-combining.
-  """
-  def __init__(self, combine_fn, args, kwargs):
-    args_to_check = itertools.chain(args, kwargs.values())
-    if isinstance(combine_fn, _CurriedFn):
-      args_to_check = itertools.chain(
-          args_to_check, combine_fn.args, combine_fn.kwargs.values())
-    if any(isinstance(arg, ArgumentPlaceholder) for arg in args_to_check):
-      # This isn't implemented in dataflow either...
-      raise NotImplementedError('Deferred CombineFn side inputs.')
-    self._combine_fn = beam.transforms.combiners.curry_combine_fn(
-        combine_fn, args, kwargs)
-
-  def expand(self, pcoll):
-    return (
-        pcoll
-        | beam.ParDo(PartialGroupByKeyCombiningValues(self._combine_fn))
-        | beam.GroupByKey()
-        | beam.ParDo(FinishCombine(self._combine_fn)))
-
-
-class PartialGroupByKeyCombiningValues(beam.DoFn):
-  """Aggregates values into a per-key-window cache.
-
-  As bundles are in-memory-sized, we don't bother flushing until the very end.
-  """
-  def __init__(self, combine_fn):
-    self._combine_fn = combine_fn
-
-  def setup(self):
-    self._combine_fn.setup()
-
-  def start_bundle(self):
-    self._cache = collections.defaultdict(self._combine_fn.create_accumulator)
-
-  def process(self, element, window=beam.DoFn.WindowParam):
-    k, vi = element
-    self._cache[k, window] = self._combine_fn.add_input(
-        self._cache[k, window], vi)
-
-  def finish_bundle(self):
-    for (k, w), va in self._cache.items():
-      # We compact the accumulator since a GBK (which necessitates encoding)
-      # will follow.
-      yield WindowedValue((k, self._combine_fn.compact(va)), w.end, (w, ))
-
-  def teardown(self):
-    self._combine_fn.teardown()
-
-  def default_type_hints(self):
-    hints = self._combine_fn.get_type_hints()
-    K = typehints.TypeVariable('K')
-    if hints.input_types:
-      args, kwargs = hints.input_types
-      args = (typehints.Tuple[K, args[0]], ) + args[1:]
-      hints = hints.with_input_types(*args, **kwargs)
-    else:
-      hints = hints.with_input_types(typehints.Tuple[K, typing.Any])
-    hints = hints.with_output_types(typehints.Tuple[K, typing.Any])
-    return hints
-
-
-class FinishCombine(beam.DoFn):
-  """Merges partially combined results.
-  """
-  def __init__(self, combine_fn):
-    self._combine_fn = combine_fn
-
-  def setup(self):
-    self._combine_fn.setup()
-
-  def process(self, element):
-    k, vs = element
-    return [(
-        k,
-        self._combine_fn.extract_output(
-            self._combine_fn.merge_accumulators(vs)))]
-
-  def teardown(self):
-    self._combine_fn.teardown()
-
-  def default_type_hints(self):
-    hints = self._combine_fn.get_type_hints()
-    K = typehints.TypeVariable('K')
-    hints = hints.with_input_types(typehints.Tuple[K, typing.Any])
-    if hints.output_types:
-      main_output_type = hints.simple_output_type('')
-      hints = hints.with_output_types(typehints.Tuple[K, main_output_type])
-    return hints
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/apache_beam_jupyterlab_sidepanel/yaml_parse_utils.py b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/apache_beam_jupyterlab_sidepanel/yaml_parse_utils.py
new file mode 100644
index 000000000000..aebca7b85d65
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/apache_beam_jupyterlab_sidepanel/yaml_parse_utils.py
@@ -0,0 +1,176 @@
+#  Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+#  use this file except in compliance with the License. You may obtain a copy of
+#  the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+#  WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+#  License for the specific language governing permissions and limitations under
+#  the License.
+
+import dataclasses
+import json
+from dataclasses import dataclass
+from typing import Any
+from typing import Dict
+from typing import List
+from typing import TypedDict
+
+import yaml
+
+import apache_beam as beam
+from apache_beam.yaml.main import build_pipeline_components_from_yaml
+
+# ======================== Type Definitions ========================
+
+
+@dataclass
+class NodeData:
+  id: str
+  label: str
+  type: str = ""
+
+  def __post_init__(self):
+    # Ensure ID is not empty
+    if not self.id:
+      raise ValueError("Node ID cannot be empty")
+
+
+@dataclass
+class EdgeData:
+  source: str
+  target: str
+  label: str = ""
+
+  def __post_init__(self):
+    if not self.source or not self.target:
+      raise ValueError("Edge source and target cannot be empty")
+
+
+class FlowGraph(TypedDict):
+  nodes: List[Dict[str, Any]]
+  edges: List[Dict[str, Any]]
+
+
+# ======================== Main Function ========================
+
+
+def parse_beam_yaml(yaml_str: str, isDryRunMode: bool = False) -> str:
+  """
+    Parse Beam YAML and convert to flow graph data structure
+    
+    Args:
+        yaml_str: Input YAML string
+        
+    Returns:
+        Standardized response format:
+        - Success: {'status': 'success', 'data': {...}, 'error': None}
+        - Failure: {'status': 'error', 'data': None, 'error': 'message'}
+    """
+  # Phase 1: YAML Parsing
+  try:
+    parsed_yaml = yaml.safe_load(yaml_str)
+    if not parsed_yaml or 'pipeline' not in parsed_yaml:
+      return build_error_response(
+          "Invalid YAML structure: missing 'pipeline' section")
+  except yaml.YAMLError as e:
+    return build_error_response(f"YAML parsing error: {str(e)}")
+
+  # Phase 2: Pipeline Validation
+  try:
+    options, constructor = build_pipeline_components_from_yaml(
+        yaml_str,
+        [],
+        validate_schema='per_transform'
+    )
+    if isDryRunMode:
+      with beam.Pipeline(options=options) as p:
+        constructor(p)
+  except Exception as e:
+    return build_error_response(f"Pipeline validation failed: {str(e)}")
+
+  # Phase 3: Graph Construction
+  try:
+    pipeline = parsed_yaml['pipeline']
+    transforms = pipeline.get('transforms', [])
+
+    nodes: List[NodeData] = []
+    edges: List[EdgeData] = []
+
+    nodes.append(NodeData(id='0', label='Input', type='input'))
+    nodes.append(NodeData(id='1', label='Output', type='output'))
+
+    # Process transform nodes
+    for idx, transform in enumerate(transforms):
+      if not isinstance(transform, dict):
+        continue
+
+      payload = {k: v for k, v in transform.items() if k not in {"type"}}
+
+      node_id = f"t{idx}"
+      node_data = NodeData(
+          id=node_id,
+          label=transform.get('type', 'unnamed'),
+          type='default',
+          **payload)
+      nodes.append(node_data)
+
+      # Create connections between nodes
+      if idx > 0:
+        edges.append(
+            EdgeData(source=f"t{idx-1}", target=node_id, label='chain'))
+
+    if transforms:
+      edges.append(EdgeData(source='0', target='t0', label='start'))
+      edges.append(EdgeData(source=node_id, target='1', label='stop'))
+
+    def to_dict(node):
+      if hasattr(node, '__dataclass_fields__'):
+        return dataclasses.asdict(node)
+      return node
+
+    nodes_serializable = [to_dict(n) for n in nodes]
+
+    return build_success_response(
+        nodes=nodes_serializable, edges=[dataclasses.asdict(e) for e in edges])
+
+  except Exception as e:
+    return build_error_response(f"Graph construction failed: {str(e)}")
+
+
+# ======================== Utility Functions ========================
+
+
+def build_success_response(
+    nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]]) -> str:
+  """Build success response"""
+  return json.dumps({'data': {'nodes': nodes, 'edges': edges}, 'error': None})
+
+
+def build_error_response(error_msg: str) -> str:
+  """Build error response"""
+  return json.dumps({'data': None, 'error': error_msg})
+
+
+if __name__ == "__main__":
+  # Example usage
+  example_yaml = """
+pipeline:
+  transforms:
+    - type: ReadFromCsv
+      name: A
+      config:
+        path: /path/to/input*.csv
+    - type: WriteToJson
+      name: B
+      config:
+        path: /path/to/output.json
+      input: ReadFromCsv
+    - type: Join
+      input: [A, B]
+    """
+
+  response = parse_beam_yaml(example_yaml, isDryRunMode=False)
+  print(response)
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/package.json b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/package.json
index 8b51461f6cd4..eef3fcaa80f4 100644
--- a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/package.json
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/package.json
@@ -47,27 +47,37 @@
     "@jupyterlab/launcher": "^4.3.6",
     "@jupyterlab/mainmenu": "^4.3.6",
     "@lumino/widgets": "^2.2.1",
+    "@monaco-editor/react": "^4.7.0",
     "@rmwc/base": "^14.0.0",
     "@rmwc/button": "^8.0.6",
+    "@rmwc/card": "^14.3.5",
     "@rmwc/data-table": "^8.0.6",
     "@rmwc/dialog": "^8.0.6",
     "@rmwc/drawer": "^8.0.6",
     "@rmwc/fab": "^8.0.6",
+    "@rmwc/grid": "^14.3.5",
     "@rmwc/list": "^8.0.6",
     "@rmwc/ripple": "^14.0.0",
     "@rmwc/textfield": "^8.0.6",
     "@rmwc/tooltip": "^8.0.6",
     "@rmwc/top-app-bar": "^8.0.6",
+    "@rmwc/touch-target": "^14.3.5",
+    "@xyflow/react": "^12.8.2",
+    "dagre": "^0.8.5",
+    "lodash": "^4.17.21",
     "material-design-icons": "^3.0.1",
     "react": "^18.2.0",
-    "react-dom": "^18.2.0"
+    "react-dom": "^18.2.0",
+    "react-split": "^2.0.14"
   },
   "devDependencies": {
     "@jupyterlab/builder": "^4.3.6",
     "@testing-library/dom": "^9.3.0",
     "@testing-library/jest-dom": "^6.1.4",
     "@testing-library/react": "^14.0.0",
+    "@types/dagre": "^0.7.53",
     "@types/jest": "^29.5.14",
+    "@types/lodash": "^4.17.20",
     "@types/react": "^18.2.0",
     "@types/react-dom": "^18.2.0",
     "@typescript-eslint/eslint-plugin": "^7.3.1",
@@ -97,5 +107,6 @@
   "test": "jest",
   "resolutions": {
     "@types/react": "^18.2.0"
-  }
-}
+  },
+  "packageManager": "yarn@1.22.22+sha512.a6b2f7906b721bba3d67d4aff083df04dad64c399707841b7acf00f6b133b7ac24255f2652fa22ae3534329dc6180534e98d17432037ff6fd140556e2bb3137e"
+}
\ No newline at end of file
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/SidePanel.ts b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/SidePanel.ts
index fb86b0a53fdf..d8f19c278843 100644
--- a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/SidePanel.ts
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/SidePanel.ts
@@ -58,14 +58,17 @@ export class SidePanel extends BoxPanel {
     const sessionModelItr = manager.sessions.running();
     const firstModel = sessionModelItr.next();
     let onlyOneUniqueKernelExists = true;
-    if (firstModel === undefined) {
-      // There is zero unique running kernel.
+
+    if (firstModel.done) {
+      // No Running kernel
       onlyOneUniqueKernelExists = false;
     } else {
+      // firstModel.value is the first session
       let sessionModel = sessionModelItr.next();
-      while (sessionModel !== undefined) {
+
+      while (!sessionModel.done) {
+        // Check if there is more than one unique kernel
         if (sessionModel.value.kernel.id !== firstModel.value.kernel.id) {
-          // There is more than one unique running kernel.
           onlyOneUniqueKernelExists = false;
           break;
         }
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/index.ts b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/index.ts
index 3f2b02d11b53..92a1ea3cdbbe 100644
--- a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/index.ts
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/index.ts
@@ -28,12 +28,15 @@ import { SidePanel } from './SidePanel';
 import {
   InteractiveInspectorWidget
 } from './inspector/InteractiveInspectorWidget';
+import { YamlWidget } from './yaml/YamlWidget';
 
 namespace CommandIDs {
   export const open_inspector =
     'apache-beam-jupyterlab-sidepanel:open_inspector';
   export const open_clusters_panel =
     'apache-beam-jupyterlab-sidepanel:open_clusters_panel';
+  export const open_yaml_editor =
+    'apache-beam-jupyterlab-sidepanel:open_yaml_editor';
 }
 
 /**
@@ -67,6 +70,7 @@ function activate(
   const category = 'Interactive Beam';
   const inspectorCommandLabel = 'Open Inspector';
   const clustersCommandLabel = 'Manage Clusters';
+  const yamlCommandLabel = 'Edit YAML Pipeline';
   const { commands, shell, serviceManager } = app;
 
   async function createInspectorPanel(): Promise<SidePanel> {
@@ -105,6 +109,24 @@ function activate(
     return panel;
   }
 
+  async function createYamlPanel(): Promise<SidePanel> {
+    const sessionContext = new SessionContext({
+      sessionManager: serviceManager.sessions,
+      specsManager: serviceManager.kernelspecs,
+      name: 'Interactive Beam YAML Session'
+    });
+    const yamlEditor = new YamlWidget(sessionContext);
+    const panel = new SidePanel(
+      serviceManager,
+      rendermime,
+      sessionContext,
+      'Interactive Beam YAML Editor',
+      yamlEditor
+    );
+    activatePanel(panel);
+    return panel;
+  }
+
   function activatePanel(panel: SidePanel): void {
     shell.add(panel, 'main');
     shell.activateById(panel.id);
@@ -122,6 +144,12 @@ function activate(
     execute: createClustersPanel
   });
 
+  // The open_yaml_editor command is also used by the below entry points.
+  commands.addCommand(CommandIDs.open_yaml_editor, {
+    label: yamlCommandLabel,
+    execute: createYamlPanel
+  });
+
   // Entry point in launcher.
   if (launcher) {
     launcher.add({
@@ -132,6 +160,10 @@ function activate(
       command: CommandIDs.open_clusters_panel,
       category: category
     });
+    launcher.add({
+      command: CommandIDs.open_yaml_editor,
+      category: category
+    });
   }
 
   // Entry point in top menu.
@@ -140,10 +172,11 @@ function activate(
   mainMenu.addMenu(menu);
   menu.addItem({ command: CommandIDs.open_inspector });
   menu.addItem({ command: CommandIDs.open_clusters_panel });
+  menu.addItem({ command: CommandIDs.open_yaml_editor });
 
   // Entry point in commands palette.
   palette.addItem({ command: CommandIDs.open_inspector, category });
   palette.addItem({ command: CommandIDs.open_clusters_panel, category });
+  palette.addItem({ command: CommandIDs.open_yaml_editor, category });
 }
-
 export default extension;
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/CustomStyle.tsx b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/CustomStyle.tsx
new file mode 100644
index 000000000000..87d93de0b60a
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/CustomStyle.tsx
@@ -0,0 +1,179 @@
+// Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+// use this file except in compliance with the License. You may obtain a copy of
+// the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+// License for the specific language governing permissions and limitations under
+// the License.
+
+import React, { memo } from 'react';
+import { Handle, Position } from '@xyflow/react';
+import { EdgeProps, BaseEdge, getSmoothStepPath } from '@xyflow/react';
+import { INodeData } from './DataType';
+import { transformEmojiMap } from './EmojiMap';
+
+export function DefaultNode({ data }: { data: INodeData }) {
+  const emoji = data.label
+    ? transformEmojiMap[data.label] || '📦'
+    : data.emoji || '📦';
+  const typeClass = data.type ? `custom-node-${data.type}` : '';
+
+  return (
+    <div className={`custom-node ${typeClass}`}>
+      <div className="custom-node-header">
+        <div className="custom-node-icon">{emoji}</div>
+        <div className="custom-node-title">{data.label}</div>
+      </div>
+
+      <Handle type="target" position={Position.Top} className="custom-handle" />
+      <Handle
+        type="source"
+        position={Position.Bottom}
+        className="custom-handle"
+      />
+    </div>
+  );
+}
+
+// ===== Input Node =====
+export function InputNode({ data }: { data: INodeData }) {
+  return (
+    <div className="custom-node custom-node-input">
+      <div className="custom-node-header">
+        <div className="custom-node-icon">{data.emoji || '🟢'}</div>
+        <div className="custom-node-title">{data.label}</div>
+      </div>
+
+      <Handle
+        type="source"
+        position={Position.Bottom}
+        id="output"
+        className="custom-handle"
+      />
+    </div>
+  );
+}
+
+// ===== Output Node =====
+export function OutputNode({ data }: { data: INodeData }) {
+  return (
+    <div className="custom-node custom-node-output">
+      <div className="custom-node-header">
+        <div className="custom-node-icon">{data.emoji || '🔴'}</div>
+        <div className="custom-node-title">{data.label}</div>
+      </div>
+
+      <Handle
+        type="target"
+        position={Position.Top}
+        id="input"
+        className="custom-handle"
+      />
+    </div>
+  );
+}
+
+export default memo(DefaultNode);
+
+export function AnimatedSVGEdge({
+  id,
+  sourceX,
+  sourceY,
+  targetX,
+  targetY,
+  sourcePosition,
+  targetPosition
+}: EdgeProps) {
+  const [initialEdgePath] = getSmoothStepPath({
+    sourceX,
+    sourceY,
+    targetX,
+    targetY,
+    sourcePosition,
+    targetPosition
+  });
+
+  let edgePath = initialEdgePath;
+
+  // If the edge is almost vertical or horizontal, use a straight line
+  const dx = Math.abs(targetX - sourceX);
+  const dy = Math.abs(targetY - sourceY);
+  if (dx < 1) {
+    edgePath = `M${sourceX},${sourceY} L${sourceX + 1},${targetY}`;
+  } else if (dy < 1) {
+    edgePath = `M${sourceX},${sourceY} L${targetX},${sourceY + 1}`;
+  }
+
+  const dotCount = 4;
+  const dotDur = 3.5;
+
+  const dots = Array.from({ length: dotCount }, (_, i) => (
+    <circle key={i} r="5" fill="url(#dotGradient)" opacity="0.8">
+      <animateMotion
+        dur={`${dotDur}s`}
+        repeatCount="indefinite"
+        begin={`${(i * dotDur) / dotCount}s`}
+        path={edgePath}
+      />
+      <animate
+        attributeName="r"
+        values="5;7;5"
+        dur={`${dotDur}s`}
+        repeatCount="indefinite"
+        begin={`${(i * dotDur) / dotCount}s`}
+      />
+    </circle>
+  ));
+
+  return (
+    <>
+      {/* Gradient Base Edge */}
+      <BaseEdge
+        id={id}
+        path={edgePath}
+        style={{
+          stroke: 'url(#gradientEdge)',
+          strokeWidth: 12
+        }}
+      />
+
+      {/* Dots */}
+      {dots}
+
+      {/* Flow shader line */}
+      <path
+        d={edgePath}
+        fill="none"
+        stroke="rgba(255,255,255,0.2)"
+        strokeWidth={5}
+        strokeDasharray="10 10"
+      >
+        <animate
+          attributeName="stroke-dashoffset"
+          from="20"
+          to="0"
+          dur="0.5s"
+          repeatCount="indefinite"
+        />
+      </path>
+
+      {/* Gradient Color */}
+      <defs>
+        <linearGradient id="gradientEdge" gradientTransform="rotate(90)">
+          <stop offset="0%" stopColor="#4facfe" />
+          <stop offset="100%" stopColor="#00f2fe" />
+        </linearGradient>
+
+        <radialGradient id="dotGradient">
+          <stop offset="0%" stopColor="#fff" stopOpacity="1" />
+          <stop offset="50%" stopColor="#4facfe" stopOpacity="0.8" />
+          <stop offset="100%" stopColor="#00f2fe" stopOpacity="0.5" />
+        </radialGradient>
+      </defs>
+    </>
+  );
+}
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/DataType.ts b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/DataType.ts
new file mode 100644
index 000000000000..0ea535d5fc6a
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/DataType.ts
@@ -0,0 +1,37 @@
+// Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+// use this file except in compliance with the License. You may obtain a copy of
+// the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+// License for the specific language governing permissions and limitations under
+// the License.
+
+export const nodeWidth = 320;
+export const nodeHeight = 100;
+
+export interface INodeData {
+  id: string;
+  label: string;
+  type?: string;
+  [key: string]: any;
+}
+
+export interface IEdgeData {
+  source: string;
+  target: string;
+  label?: string;
+}
+
+export interface IFlowGraph {
+  nodes: INodeData[];
+  edges: IEdgeData[];
+}
+
+export interface IApiResponse {
+  data: IFlowGraph | null;
+  error: string | null;
+}
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/EditablePanel.tsx b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/EditablePanel.tsx
new file mode 100644
index 000000000000..d2b19d4371f4
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/EditablePanel.tsx
@@ -0,0 +1,408 @@
+// Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+// use this file except in compliance with the License. You may obtain a copy of
+// the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+// License for the specific language governing permissions and limitations under
+// the License.
+
+import React from 'react';
+import { Node } from '@xyflow/react';
+import '../../style/yaml/YamlEditor.css';
+import { transformEmojiMap } from './EmojiMap';
+
+type EditableKeyValuePanelProps = {
+  node: Node;
+  onChange: (newData: Record<string, any>) => void;
+  depth?: number;
+};
+
+type EditableKeyValuePanelState = {
+  localData: Record<string, any>;
+  collapsedKeys: Set<string>;
+};
+
+/**
+ * An editable key-value panel component for displaying
+ * and modifying node properties.
+ *
+ * Features:
+ * - Nested object support with collapsible sections
+ * - Real-time key-value editing with validation
+ * - Dynamic field addition and deletion
+ * - Support for multi-line text values
+ * - Object conversion for nested structures
+ * - Reference documentation integration
+ * - Visual hierarchy with depth-based indentation
+ * - Interactive UI with hover effects and transitions
+ *
+ * State Management:
+ * - localData: Local copy of the node data being edited
+ * - collapsedKeys: Set of keys that are currently collapsed
+ *
+ * Props:
+ * @param {Node} node - The node is data to be edited
+ * @param {(data: Record<string, any>) => void} onChange -
+ *  Callback for data changes
+ * @param {number} [depth=0] - Current nesting depth for recursive rendering
+ *
+ * Methods:
+ * - toggleCollapse: Toggles collapse state of nested objects
+ * - handleKeyChange: Updates keys with validation
+ * - handleValueChange: Updates values in the local data
+ * - handleDelete: Removes key-value pairs
+ * - handleAddPair: Adds new key-value pairs
+ * - convertToObject: Converts primitive values to objects
+ * - renderValueEditor: Renders appropriate input based on value type
+ *
+ * UI Features:
+ * - Collapsible nested object sections
+ * - Multi-line text support for complex values
+ * - Add/Delete buttons for field management
+ * - Reference documentation links
+ * - Visual feedback for user interactions
+ * - Responsive design with proper spacing
+ */
+export class EditableKeyValuePanel extends React.Component<
+  EditableKeyValuePanelProps,
+  EditableKeyValuePanelState
+> {
+  static defaultProps = {
+    depth: 0
+  };
+
+  constructor(props: EditableKeyValuePanelProps) {
+    super(props);
+    this.state = {
+      localData: { ...(props.node ? props.node.data : {}) },
+      collapsedKeys: new Set()
+    };
+  }
+
+  componentDidUpdate(prevProps: EditableKeyValuePanelProps) {
+    if (prevProps.node !== this.props.node && this.props.node) {
+      this.setState({ localData: { ...(this.props.node.data ?? {}) } });
+    }
+  }
+
+  toggleCollapse = (key: string) => {
+    this.setState(({ collapsedKeys }) => {
+      const newSet = new Set(collapsedKeys);
+      newSet.has(key) ? newSet.delete(key) : newSet.add(key);
+      return { collapsedKeys: newSet };
+    });
+  };
+
+  handleKeyChange = (oldKey: string, newKey: string) => {
+    newKey = newKey.trim();
+    if (newKey === oldKey || newKey === '') {
+      return alert('Invalid Key!');
+    }
+    if (newKey in this.state.localData) {
+      return alert('Duplicated Key!');
+    }
+
+    const newData: Record<string, any> = {};
+    for (const [k, v] of Object.entries(this.state.localData)) {
+      newData[k === oldKey ? newKey : k] = v;
+    }
+
+    this.setState({ localData: newData }, () => this.props.onChange(newData));
+  };
+
+  handleValueChange = (key: string, newValue: any) => {
+    const newData = { ...this.state.localData, [key]: newValue };
+    this.setState({ localData: newData }, () => this.props.onChange(newData));
+  };
+
+  handleDelete = (key: string) => {
+    const { [key]: _, ...rest } = this.state.localData;
+    void _;
+    this.setState({ localData: rest }, () => this.props.onChange(rest));
+  };
+
+  handleAddPair = () => {
+    let i = 1;
+    const baseKey = 'newKey';
+    while (`${baseKey}${i}` in this.state.localData) {
+      i++;
+    }
+    const newKey = `${baseKey}${i}`;
+    const newData = { ...this.state.localData, [newKey]: '' };
+    this.setState({ localData: newData }, () => this.props.onChange(newData));
+  };
+
+  convertToObject = (key: string) => {
+    if (
+      typeof this.state.localData[key] === 'object' &&
+      this.state.localData[key] !== null
+    ) {
+      return;
+    }
+    const newData = { ...this.state.localData, [key]: {} };
+    this.setState({ localData: newData }, () => this.props.onChange(newData));
+    this.setState(({ collapsedKeys }) => {
+      const newSet = new Set(collapsedKeys);
+      newSet.delete(key);
+      return { collapsedKeys: newSet };
+    });
+  };
+
+  renderValueEditor = (key: string, value: any) => {
+    const isMultiline =
+      key === 'callable' || (typeof value === 'string' && value.includes('\n'));
+
+    return isMultiline ? (
+      <textarea
+        value={value}
+        onChange={e => this.handleValueChange(key, e.target.value)}
+        className="editor-input"
+        style={{ minHeight: 100 }}
+      />
+    ) : (
+      <input
+        type="text"
+        value={value}
+        onChange={e => this.handleValueChange(key, e.target.value)}
+        className="editor-input"
+      />
+    );
+  };
+
+  render() {
+    const { localData, collapsedKeys } = this.state;
+    const depth = this.props.depth ?? 0;
+
+    return (
+      <div style={{ fontFamily: 'monospace', fontSize: 14 }}>
+        {Object.entries(localData).map(([key, value]) => {
+          const isObject =
+            typeof value === 'object' &&
+            value !== null &&
+            !Array.isArray(value);
+          const isCollapsed = collapsedKeys.has(key);
+
+          return (
+            <div key={key} style={{ marginBottom: 4 }}>
+              <div style={{ display: 'flex', alignItems: 'center', gap: 6 }}>
+                {/* Toggle Button or Spacer */}
+                {isObject ? (
+                  <button
+                    onClick={() => this.toggleCollapse(key)}
+                    style={{
+                      width: 24,
+                      height: 32,
+                      cursor: 'pointer',
+                      border: 'none',
+                      background: 'none',
+                      fontWeight: 'bold',
+                      userSelect: 'none',
+                      flexShrink: 0
+                    }}
+                  >
+                    {isCollapsed ? '▶' : '▼'}
+                  </button>
+                ) : (
+                  <div style={{ width: 24, height: 32 }} />
+                )}
+
+                {/* Key input */}
+                <input
+                  type="text"
+                  value={key}
+                  onChange={e => this.handleKeyChange(key, e.target.value)}
+                  className="editor-input"
+                  style={{
+                    width: 120,
+                    height: 32,
+                    boxSizing: 'border-box',
+                    padding: '4px 6px',
+                    borderRadius: 4,
+                    border: '1px solid #ccc',
+                    fontFamily: 'inherit',
+                    fontSize: 'inherit'
+                  }}
+                />
+
+                {/* Value input or collapsed preview */}
+                <div style={{ flexGrow: 1 }}>
+                  {isObject ? (
+                    isCollapsed ? (
+                      <span style={{ color: '#888' }}>{'{...}'}</span>
+                    ) : (
+                      <span style={{ color: '#444', fontStyle: 'italic' }}>
+                        {'{...}'}
+                      </span>
+                    )
+                  ) : (
+                    this.renderValueEditor(key, value)
+                  )}
+                </div>
+
+                {/* Action buttons */}
+                <div style={{ display: 'flex', alignItems: 'center', gap: 6 }}>
+                  {!isObject && (
+                    <button
+                      onClick={() => this.convertToObject(key)}
+                      style={{
+                        width: 70,
+                        height: 32,
+                        padding: '4px 8px',
+                        borderRadius: 4,
+                        border: '1px solid #4caf50',
+                        backgroundColor: '#e8f5e9',
+                        color: '#2e7d32',
+                        cursor: 'pointer'
+                      }}
+                    >
+                      + Sub
+                    </button>
+                  )}
+                  <button
+                    onClick={() => this.handleDelete(key)}
+                    style={{
+                      height: 32,
+                      padding: '4px 8px',
+                      borderRadius: 4,
+                      border: '1px solid #f44336',
+                      backgroundColor: '#ffebee',
+                      color: '#b71c1c',
+                      cursor: 'pointer'
+                    }}
+                  >
+                    ×
+                  </button>
+                </div>
+              </div>
+
+              {isObject && !isCollapsed && (
+                <div
+                  style={{
+                    marginLeft: 20,
+                    marginTop: 4,
+                    borderLeft: '1px solid #ccc',
+                    paddingLeft: 8
+                  }}
+                >
+                  <EditableKeyValuePanel
+                    node={{ id: key, data: value } as Node}
+                    onChange={newVal => this.handleValueChange(key, newVal)}
+                    depth={depth + 1}
+                  />
+                </div>
+              )}
+            </div>
+          );
+        })}
+
+        <button
+          onClick={this.handleAddPair}
+          style={{
+            marginTop: 8,
+            padding: '6px 12px',
+            borderRadius: 4,
+            border: '1px solid #2196f3',
+            backgroundColor: '#e3f2fd',
+            color: '#0d47a1',
+            cursor: 'pointer'
+          }}
+        >
+          + Add {depth > 0 ? 'Nested ' : ''}Field
+        </button>
+
+        {/* Reference Doc */}
+        {depth === 0 && (
+          <div
+            style={{
+              marginTop: 14,
+              padding: '14px 20px',
+              borderRadius: 12,
+              background: 'linear-gradient(135deg, #f7f9fc, #e3f0ff)',
+              border: '1px solid #4facfe',
+              boxShadow: '0 4px 12px rgba(0, 0, 0, 0.08)',
+              display: 'flex',
+              justifyContent: 'space-between',
+              alignItems: 'center',
+              fontFamily: 'sans-serif',
+              fontSize: 14,
+              color: '#0d47a1',
+              transition: 'transform 0.15s ease, box-shadow 0.15s ease'
+            }}
+            onMouseEnter={e => {
+              e.currentTarget.style.transform = 'translateY(-2px)';
+              e.currentTarget.style.boxShadow =
+                '0 6px 16px rgba(0, 0, 0, 0.12)';
+            }}
+            onMouseLeave={e => {
+              e.currentTarget.style.transform = 'translateY(0)';
+              e.currentTarget.style.boxShadow =
+                '0 4px 12px rgba(0, 0, 0, 0.08)';
+            }}
+          >
+            {/* Emoji + label */}
+            <div style={{ display: 'flex', alignItems: 'center', gap: 10 }}>
+              <div
+                style={{
+                  fontSize: 22,
+                  width: 28,
+                  height: 28,
+                  display: 'flex',
+                  justifyContent: 'center',
+                  alignItems: 'center'
+                }}
+              >
+                {transformEmojiMap[localData.label || ''] || '📄'}
+              </div>
+              <div style={{ display: 'flex', flexDirection: 'column', gap: 2 }}>
+                <span
+                  style={{ fontWeight: 600, fontSize: 14, color: '#0d47a1' }}
+                >
+                  {localData.label}
+                </span>
+                <span style={{ fontSize: 12, color: '#555' }}>
+                  Reference for Beam YAML transform
+                </span>
+              </div>
+            </div>
+
+            {/* Button */}
+            <a
+              href={`https://beam.apache.org/releases/yamldoc/current/#${encodeURIComponent(
+                localData.label?.toLowerCase() || ''
+              )}`}
+              target="_blank"
+              rel="noopener noreferrer"
+              style={{
+                padding: '6px 14px',
+                borderRadius: 6,
+                backgroundColor: '#2196f3',
+                color: 'white',
+                fontWeight: 500,
+                fontSize: 13,
+                textDecoration: 'none',
+                boxShadow: '0 2px 4px rgba(0,0,0,0.1)',
+                transition: 'all 0.2s ease'
+              }}
+              onMouseEnter={e => {
+                e.currentTarget.style.backgroundColor = '#1976d2';
+                e.currentTarget.style.transform = 'translateY(-1px)';
+                e.currentTarget.style.boxShadow = '0 4px 8px rgba(0,0,0,0.15)';
+              }}
+              onMouseLeave={e => {
+                e.currentTarget.style.backgroundColor = '#2196f3';
+                e.currentTarget.style.transform = 'translateY(0)';
+                e.currentTarget.style.boxShadow = '0 2px 4px rgba(0,0,0,0.1)';
+              }}
+            >
+              Open Doc
+            </a>
+          </div>
+        )}
+      </div>
+    );
+  }
+}
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/EmojiMap.ts b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/EmojiMap.ts
new file mode 100644
index 000000000000..ed6a9f2285c8
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/EmojiMap.ts
@@ -0,0 +1,75 @@
+// Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+// use this file except in compliance with the License. You may obtain a copy of
+// the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+// License for the specific language governing permissions and limitations under
+// the License.
+
+// Emoji mapping for various data processing and I/O operations
+// Generate with AI
+export const transformEmojiMap: Record<string, string> = {
+  // Data processing operations
+  AnomalyDetection: '🔍',
+  AssertEqual: '⚖️',
+  AssignTimestamps: '⏰',
+  Combine: '🔄',
+  Create: '✨',
+  Enrichment: '💎',
+  Explode: '💥',
+  ExtractWindowingInfo: '🪟',
+  Filter: '🔎',
+  Flatten: '📉',
+  Join: '🤝',
+  LogForTesting: '📝',
+  MLTransform: '🤖',
+  MapToFields: '🗺️',
+  Partition: '📂',
+  PyTransform: '🐍',
+  RunInference: '🧠',
+  Sql: '🗃️',
+  StripErrorMetadata: '🧹',
+  ValidateWithSchema: '✅',
+  WindowInto: '🪟',
+
+  // I/O operations
+  ReadFromAvro: '⬇️📁',
+  WriteToAvro: '⬆️📁',
+  ReadFromBigQuery: '⬇️📊',
+  WriteToBigQuery: '⬆️📊',
+  WriteToBigTable: '⬆️📋',
+  ReadFromCsv: '⬇️📝',
+  WriteToCsv: '⬆️📝',
+  ReadFromIceberg: '⬇️🧊',
+  WriteToIceberg: '⬆️🧊',
+  ReadFromJdbc: '⬇️🔌',
+  WriteToJdbc: '⬆️🔌',
+  ReadFromJson: '⬇️📋',
+  WriteToJson: '⬆️📋',
+  ReadFromKafka: '⬇️📬',
+  WriteToKafka: '⬆️📬',
+  ReadFromMySql: '⬇️🐬',
+  WriteToMySql: '⬆️🐬',
+  ReadFromOracle: '⬇️🏛️',
+  WriteToOracle: '⬆️🏛️',
+  ReadFromParquet: '⬇️📦',
+  WriteToParquet: '⬆️📦',
+  ReadFromPostgres: '⬇️🐘',
+  WriteToPostgres: '⬆️🐘',
+  ReadFromPubSub: '⬇️📢',
+  WriteToPubSub: '⬆️📢',
+  ReadFromPubSubLite: '⬇️📣',
+  WriteToPubSubLite: '⬆️📣',
+  ReadFromSpanner: '⬇️📏',
+  WriteToSpanner: '⬆️📏',
+  ReadFromSqlServer: '⬇️🗄️',
+  WriteToSqlServer: '⬆️🗄️',
+  ReadFromTFRecord: '⬇️📼',
+  WriteToTFRecord: '⬆️📼',
+  ReadFromText: '⬇️📄',
+  WriteToText: '⬆️📄'
+};
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/Yaml.tsx b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/Yaml.tsx
new file mode 100644
index 000000000000..a004f86bdf51
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/Yaml.tsx
@@ -0,0 +1,322 @@
+// Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+// use this file except in compliance with the License. You may obtain a copy of
+// the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+// License for the specific language governing permissions and limitations under
+// the License.
+
+import React from 'react';
+import { ISessionContext } from '@jupyterlab/apputils';
+import { Card } from '@rmwc/card';
+import '@rmwc/textfield/styles';
+import '@rmwc/grid/styles';
+import '@rmwc/card/styles';
+import Split from 'react-split';
+import { debounce } from 'lodash';
+
+import { Node, Edge } from '@xyflow/react';
+import '@xyflow/react/dist/style.css';
+import '../../style/yaml/Yaml.css';
+
+import { YamlEditor } from './YamlEditor';
+import { EditableKeyValuePanel } from './EditablePanel';
+import { FlowEditor } from './YamlFlow';
+import { IApiResponse } from './DataType';
+import { nodeWidth, nodeHeight } from './DataType';
+
+interface IYamlProps {
+  sessionContext: ISessionContext;
+}
+
+interface IYamlState {
+  yamlContent: string;
+  elements: any;
+  selectedNode: Node | null;
+  errors: string[];
+  isDryRunMode: boolean;
+  Nodes: Node[];
+  Edges: Edge[];
+}
+
+const initialNodes: Node[] = [
+  {
+    id: '0',
+    width: nodeWidth,
+    position: { x: 0, y: 0 },
+    type: 'input',
+    data: { label: 'Input' }
+  },
+  {
+    id: '1',
+    width: nodeWidth,
+    position: { x: 0, y: 100 },
+    type: 'default',
+    data: { label: '1' }
+  },
+  {
+    id: '2',
+    width: nodeWidth,
+    position: { x: 0, y: 200 },
+    type: 'default',
+    data: { label: '2' }
+  },
+  {
+    id: '3',
+    width: nodeWidth,
+    position: { x: 0, y: 300 },
+    type: 'output',
+    data: { label: 'Output' }
+  }
+];
+
+const initialEdges: Edge[] = [
+  { id: 'e0-1', source: '0', target: '1' },
+  { id: 'e1-2', source: '1', target: '2' },
+  { id: 'e2-3', source: '2', target: '3' }
+];
+
+/**
+ * A YAML pipeline editor component with integrated flow visualization.
+ *
+ * Features:
+ * - Three-panel layout with YAML editor, flow diagram, and node properties
+ * - Real-time YAML validation and error display
+ * - Automatic flow diagram generation from YAML content
+ * - Interactive node selection and editing
+ * - Dry run mode support for pipeline testing
+ * - Kernel-based YAML parsing using Apache Beam utilities
+ * - Debounced updates to optimize performance
+ * - Split-pane resizable interface
+ *
+ * State Management:
+ * - yamlContent: Current YAML text content
+ * - elements: Combined nodes and edges for the flow diagram
+ * - selectedNode: Currently selected node in the flow diagram
+ * - errors: Array of validation errors
+ * - Nodes: Array of flow nodes
+ * - Edges: Array of flow edges
+ * - isDryRunMode: Flag for dry run mode state
+ *
+ * Props:
+ * @param {IYamlProps} props - Component props including
+ * sessionContext for kernel communication
+ *
+ * Methods:
+ * - handleNodeClick: Handles node selection in the flow diagram
+ * - handleYamlChange: Debounced handler for YAML content changes
+ * - validateAndRenderYaml: Validates YAML and updates the flow diagram
+ */
+export class Yaml extends React.Component<IYamlProps, IYamlState> {
+  constructor(props: IYamlProps) {
+    super(props);
+    this.state = {
+      yamlContent: '',
+      elements: [],
+      selectedNode: null,
+      errors: [],
+      Nodes: initialNodes,
+      Edges: initialEdges,
+      isDryRunMode: false
+    };
+  }
+
+  componentDidMount(): void {
+    this.props.sessionContext.ready.then(() => {
+      const kernel = this.props.sessionContext.session?.kernel;
+      if (!kernel) {
+        console.error('Kernel is not available even after ready');
+        return;
+      }
+
+      console.log('Kernel is ready:', kernel.name);
+    });
+  }
+
+  handleNodeClick = (node: Node) => {
+    this.setState({
+      selectedNode: node
+    });
+  };
+
+  //debounce methods to prevent excessive rendering
+  private handleYamlChange = debounce((value?: string) => {
+    const yamlText = value || '';
+    this.setState({ yamlContent: yamlText });
+    this.validateAndRenderYaml(yamlText);
+  }, 2000);
+
+  validateAndRenderYaml(yamlText: string) {
+    const escapedYaml = yamlText.replace(/\\/g, '\\\\').replace(/"/g, '\\"');
+    const code = `
+from apache_beam_jupyterlab_sidepanel.yaml_parse_utils import parse_beam_yaml
+print(parse_beam_yaml("""${escapedYaml}""",
+  isDryRunMode=${this.state.isDryRunMode ? 'True' : 'False'}))
+`.trim();
+    const session = this.props.sessionContext.session;
+    if (!session?.kernel) {
+      console.error('No kernel available');
+      return;
+    }
+
+    // Clear previous state immediately
+    this.setState({
+      Nodes: [],
+      Edges: [],
+      selectedNode: null,
+      errors: []
+    });
+
+    const future = session.kernel.requestExecute({ code });
+
+    // Handle kernel execution results
+    future.onIOPub = msg => {
+      if (msg.header.msg_type === 'stream') {
+        const content = msg.content as { name: string; text: string };
+        const output = content.text.trim();
+
+        try {
+          const result: IApiResponse = JSON.parse(output);
+
+          if (result.error) {
+            this.setState({
+              elements: [],
+              selectedNode: null,
+              errors: [result.error]
+            });
+          } else {
+            const flowNodes: Node[] = result.data.nodes.map(node => ({
+              id: node.id,
+              type: node.type,
+              width: nodeWidth,
+              height: nodeHeight,
+              position: { x: 0, y: 0 }, // Will be auto-layouted
+              data: {
+                label: node.label,
+                ...node // include all original properties
+              }
+            }));
+
+            // Transform edges for React Flow
+            const flowEdges: Edge[] = result.data.edges.map(edge => ({
+              id: `${edge.source}-${edge.target}`,
+              source: edge.source,
+              target: edge.target,
+              animated: edge.label === 'pipeline_entry',
+              label: edge.label,
+              type: 'default' // or your custom edge type
+            }));
+
+            this.setState({
+              Nodes: flowNodes,
+              Edges: flowEdges,
+              errors: []
+            });
+          }
+        } catch (err) {
+          this.setState({
+            elements: [],
+            selectedNode: null,
+            errors: [output]
+          });
+        }
+      }
+    };
+  }
+
+  render(): React.ReactNode {
+    return (
+      <div
+        style={{
+          height: '100vh',
+          width: '100vw'
+        }}
+      >
+        <Split
+          direction="horizontal"
+          sizes={[25, 35, 20]} // L/C/R
+          minSize={100}
+          gutterSize={6}
+          className="split-pane"
+        >
+          {/* Left */}
+          <div
+            style={{
+              height: '100%',
+              overflow: 'auto',
+              minWidth: '100px'
+            }}
+          >
+            <YamlEditor
+              value={this.state.yamlContent}
+              onChange={value => {
+                // Clear old errors & Handle new errors
+                this.setState({ errors: [] });
+                this.handleYamlChange(value || '');
+              }}
+              errors={this.state.errors}
+              showConsole={true}
+              onDryRunModeChange={newValue =>
+                this.setState({ isDryRunMode: newValue })
+              }
+            />
+          </div>
+
+          <div
+            style={{
+              padding: '1rem',
+              height: '100%'
+            }}
+          >
+            <Card
+              style={{
+                height: '100%',
+                display: 'flex',
+                flexDirection: 'column'
+              }}
+            >
+              <div
+                className="w-full h-full 
+                bg-gray-50 dark:bg-zinc-900 
+                text-sm font-sans"
+                style={{ flex: 1, minHeight: 0 }}
+              >
+                <FlowEditor
+                  Nodes={this.state.Nodes}
+                  Edges={this.state.Edges}
+                  onNodesUpdate={(nodes: Node[]) =>
+                    this.setState({ Nodes: nodes })
+                  }
+                  onEdgesUpdate={(edges: Edge[]) =>
+                    this.setState({ Edges: edges })
+                  }
+                  onNodeClick={this.handleNodeClick}
+                />
+              </div>
+            </Card>
+          </div>
+
+          {/* Right */}
+          <div
+            style={{
+              height: '100%',
+              overflow: 'auto',
+              padding: '12px',
+              boxSizing: 'border-box'
+            }}
+          >
+            <EditableKeyValuePanel
+              node={this.state.selectedNode}
+              // TODO: implement onChange to update node data from Panel
+              onChange={() => {}}
+            />
+          </div>
+        </Split>
+      </div>
+    );
+  }
+}
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/YamlEditor.tsx b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/YamlEditor.tsx
new file mode 100644
index 000000000000..8bbedc4b547a
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/YamlEditor.tsx
@@ -0,0 +1,338 @@
+// Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+// use this file except in compliance with the License. You may obtain a copy of
+// the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+// License for the specific language governing permissions and limitations under
+// the License.
+
+import React, { useState, useRef } from 'react';
+import Editor from '@monaco-editor/react';
+interface IYamlEditorProps {
+  value: string;
+  onChange: (value: string | undefined) => void;
+  onDryRunModeChange: (value: boolean) => void;
+  errors?: string[];
+  showConsole?: boolean;
+  consoleHeight?: number;
+}
+
+/**
+ * A YAML editor component built with React and Monaco Editor.
+ *
+ * Features:
+ * - Full YAML syntax highlighting and validation
+ * - Adjustable font size (10px-24px) with zoom controls
+ * - Word wrap toggle
+ * - File download and upload capabilities
+ * - Dry run mode toggle for testing
+ * - Error console with detailed validation messages
+ *
+ * Props:
+ * @param {string} value - Current YAML content
+ * @param {(isDryRun: boolean) => void} onDryRunModeChange -
+ *  Callback when dry run mode changes
+ * @param {(value: string) => void} onChange - Callback when content changes
+ * @param {string[]} [errors=[]] - Array of error messages to display
+ * @param {boolean} [showConsole=true] - Whether to show the error console
+ * @param {number} [consoleHeight=200] - Height of the error console in pixels
+ */
+export const YamlEditor: React.FC<IYamlEditorProps> = ({
+  value,
+  onDryRunModeChange,
+  onChange,
+  errors = [],
+  showConsole = true,
+  consoleHeight = 200
+}) => {
+  const [fontSize, setFontSize] = useState(14);
+  const [wordWrap, setWordWrap] = useState(true);
+  const [isDryRunMode, setDryRunMode] = useState(false);
+  const [isFontMenuOpen, setFontMenuOpen] = useState<boolean>(false);
+  const fileInputRef = useRef<HTMLInputElement>(null);
+
+  const zoomIn = () => setFontSize(prev => Math.min(prev + 1, 24));
+  const zoomOut = () => setFontSize(prev => Math.max(prev - 1, 10));
+  const handleFontSizeChange = (e: React.ChangeEvent<HTMLInputElement>) =>
+    setFontSize(Number(e.target.value));
+
+  // Download YAML File
+  const handleDownload = () => {
+    const blob = new Blob([value], { type: 'text/yaml;charset=utf-8' });
+    const url = URL.createObjectURL(blob);
+    const link = document.createElement('a');
+    link.href = url;
+    link.download = 'pipeline.yaml';
+    document.body.appendChild(link);
+    link.click();
+    document.body.removeChild(link);
+    URL.revokeObjectURL(url);
+  };
+
+  // Open YAML File
+  const handleOpenFile = (e: React.ChangeEvent<HTMLInputElement>) => {
+    const file = e.target.files?.[0];
+    if (!file) {
+      return;
+    }
+    const reader = new FileReader();
+    reader.onload = ev => {
+      const content = ev.target?.result;
+      if (typeof content === 'string') {
+        onChange(content);
+      }
+    };
+    reader.readAsText(file);
+    e.target.value = '';
+  };
+
+  return (
+    <div
+      style={{
+        height: '100%',
+        display: 'flex',
+        flexDirection: 'column',
+        background: '#1e1e1e'
+      }}
+    >
+      {/* ================ Toolbar ================ */}
+      <div
+        style={{
+          padding: '8px',
+          display: 'flex',
+          justifyContent: 'space-between',
+          alignItems: 'center',
+          borderBottom: '1px solid #333',
+          gap: 8
+        }}
+      >
+        {/* Fonts */}
+        <div style={{ display: 'flex', alignItems: 'center', gap: 8 }}>
+          <button
+            onClick={() => setFontMenuOpen(!isFontMenuOpen)}
+            style={{
+              background: 'transparent',
+              border: '1px solid #555',
+              color: '#ddd',
+              padding: '4px 8px',
+              borderRadius: '4px',
+              cursor: 'pointer'
+            }}
+          >
+            Font {fontSize}px
+          </button>
+          {isFontMenuOpen && (
+            <div
+              style={{
+                position: 'absolute',
+                top: '40px',
+                left: '8px',
+                background: '#252526',
+                padding: '8px',
+                borderRadius: '4px',
+                boxShadow: '0 2px 8px rgba(0,0,0,0.3)',
+                zIndex: 100
+              }}
+            >
+              <div
+                style={{
+                  display: 'flex',
+                  alignItems: 'center',
+                  marginBottom: 8
+                }}
+              >
+                <button
+                  onClick={zoomOut}
+                  style={{
+                    background: '#333',
+                    border: 'none',
+                    color: '#fff',
+                    width: 24,
+                    height: 24,
+                    borderRadius: 4,
+                    cursor: 'pointer'
+                  }}
+                >
+                  -
+                </button>
+                <input
+                  type="range"
+                  min="10"
+                  max="24"
+                  value={fontSize}
+                  onChange={handleFontSizeChange}
+                  style={{ margin: '0 8px', width: 100 }}
+                />
+                <button
+                  onClick={zoomIn}
+                  style={{
+                    background: '#333',
+                    border: 'none',
+                    color: '#fff',
+                    width: 24,
+                    height: 24,
+                    borderRadius: 4,
+                    cursor: 'pointer'
+                  }}
+                >
+                  +
+                </button>
+              </div>
+              <div style={{ color: '#999', fontSize: 12 }}>
+                Current: {fontSize}px
+              </div>
+            </div>
+          )}
+        </div>
+
+        {/* Autowrap & DryRun */}
+        <div style={{ display: 'flex', alignItems: 'center', gap: 12 }}>
+          <label
+            style={{
+              display: 'flex',
+              alignItems: 'center',
+              color: '#ddd',
+              cursor: 'pointer'
+            }}
+          >
+            <input
+              type="checkbox"
+              checked={wordWrap}
+              onChange={() => setWordWrap(!wordWrap)}
+              style={{ marginRight: 6 }}
+            />{' '}
+            Autowrap
+          </label>
+          <label
+            style={{
+              display: 'flex',
+              alignItems: 'center',
+              color: '#ddd',
+              cursor: 'pointer'
+            }}
+          >
+            <input
+              type="checkbox"
+              checked={isDryRunMode}
+              onChange={() => {
+                onDryRunModeChange(!isDryRunMode);
+                setDryRunMode(!isDryRunMode);
+              }}
+              style={{ marginRight: 6 }}
+            />{' '}
+            Dry Run Mode
+          </label>
+        </div>
+
+        {/* Download / Open */}
+        <div style={{ display: 'flex', alignItems: 'center', gap: 6 }}>
+          <button
+            onClick={handleDownload}
+            style={{
+              padding: '4px 10px',
+              borderRadius: 4,
+              border: '1px solid #2196f3',
+              background: '#2196f3',
+              color: '#fff',
+              cursor: 'pointer'
+            }}
+          >
+            Download
+          </button>
+          <button
+            onClick={() => fileInputRef.current?.click()}
+            style={{
+              padding: '4px 10px',
+              borderRadius: 4,
+              border: '1px solid #4caf50',
+              background: '#4caf50',
+              color: '#fff',
+              cursor: 'pointer'
+            }}
+          >
+            Open
+          </button>
+          <input
+            ref={fileInputRef}
+            type="file"
+            accept=".yaml,.yml"
+            style={{ display: 'none' }}
+            onChange={handleOpenFile}
+          />
+        </div>
+      </div>
+      {/* ================ ~Toolbar ================ */}
+
+      {/* ================ Code Editor ================ */}
+      <div style={{ flex: 1, minHeight: 0 }}>
+        <Editor
+          height="98%"
+          language="yaml"
+          value={value}
+          theme="vs-dark"
+          onChange={onChange}
+          options={{
+            fontSize,
+            fontFamily: 'monospace',
+            wordWrap: wordWrap ? 'on' : 'off',
+            minimap: { enabled: false },
+            scrollBeyondLastLine: false,
+            automaticLayout: true,
+            lineNumbers: 'on',
+            tabSize: 2
+          }}
+        />
+      </div>
+      {/* ================ ~Code Editor ================ */}
+
+      {/* ================ Console ================ */}
+      {showConsole && (
+        <div
+          style={{
+            height: consoleHeight,
+            background: '#1e1e1e',
+            borderTop: '1px solid #ff6b6b',
+            overflow: 'auto',
+            padding: 12,
+            fontFamily: 'monospace',
+            fontSize: 13,
+            whiteSpace: 'pre'
+          }}
+        >
+          <div
+            style={{
+              color: errors.length ? '#f48771' : '#888',
+              marginBottom: errors.length ? 12 : 0,
+              fontWeight: 500
+            }}
+          >
+            {errors.length ? '❌' : '✅ YAML Format Correct'}
+          </div>
+          {errors.map((error, i) => (
+            <pre
+              key={i}
+              style={{
+                color: '#f48771',
+                margin: '8px 0',
+                padding: 0,
+                backgroundColor: 'transparent',
+                border: 'none',
+                overflow: 'visible',
+                whiteSpace: 'pre-wrap',
+                wordBreak: 'break-all',
+                fontFamily: 'inherit'
+              }}
+            >
+              {error}
+            </pre>
+          ))}
+        </div>
+      )}
+      {/* ================ ~Console ================ */}
+    </div>
+  );
+};
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/YamlFlow.tsx b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/YamlFlow.tsx
new file mode 100644
index 000000000000..0db9dbad14fa
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/YamlFlow.tsx
@@ -0,0 +1,227 @@
+// Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+// use this file except in compliance with the License. You may obtain a copy of
+// the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+// License for the specific language governing permissions and limitations under
+// the License.
+
+import React, { useEffect, useCallback, useMemo } from 'react';
+import {
+  ReactFlow,
+  useNodesState,
+  useEdgesState,
+  addEdge,
+  MiniMap,
+  Controls,
+  Background,
+  Panel,
+  Node,
+  Edge,
+  applyNodeChanges,
+  NodeChange,
+  Connection
+} from '@xyflow/react';
+import { debounce } from 'lodash';
+import dagre from 'dagre';
+import {
+  DefaultNode,
+  InputNode,
+  OutputNode,
+  AnimatedSVGEdge
+} from './CustomStyle';
+import { nodeWidth, nodeHeight } from './DataType';
+
+import '@xyflow/react/dist/style.css';
+
+interface IFlowEditorProps {
+  Nodes: Node[];
+  Edges: Edge[];
+  onNodesUpdate?: (nodes: Node[]) => void;
+  onEdgesUpdate?: (edges: Edge[]) => void;
+  onNodeClick?: (node: Node) => void;
+  debounceWait?: number;
+}
+
+/**
+ * A flow diagram editor component built with React Flow.
+ *
+ * Features:
+ * - Interactive node-based flow diagram editor
+ * - Auto-layout functionality using Dagre graph layout
+ * - Support for different node types (default, input, output)
+ * - Animated edge connections
+ * - Mini-map and controls for navigation
+ * - Debounced updates to optimize performance
+ * - Real-time node and edge manipulation
+ *
+ * Props:
+ * @param {Node[]} Nodes - Initial array of nodes
+ * @param {Edge[]} Edges - Initial array of edges
+ * @param {(nodes: Node[]) => void} onNodesUpdate -
+ * Callback when nodes are updated
+ * @param {(edges: Edge[]) => void} onEdgesUpdate -
+ * Callback when edges are updated
+ * @param {(event: React.MouseEvent, node: Node) => void} onNodeClick -
+ * Callback when a node is clicked
+ * @param {number} [debounceWait=500] -
+ * Debounce wait time in milliseconds for updates
+ */
+export const FlowEditor: React.FC<IFlowEditorProps> = ({
+  Nodes,
+  Edges,
+  onNodesUpdate,
+  onEdgesUpdate,
+  onNodeClick,
+  debounceWait = 500 //Default debounce wait time
+}: IFlowEditorProps) => {
+  const [nodes, setNodes, _] = useNodesState(Nodes);
+  const [edges, setEdges, onEdgesChange] = useEdgesState(Edges);
+
+  void _;
+  // Debounce callback
+  const debouncedNodesUpdate = useMemo(
+    () =>
+      debounce((nodes: Node[]) => {
+        onNodesUpdate?.(nodes);
+      }, debounceWait),
+    [onNodesUpdate, debounceWait]
+  );
+
+  const debouncedEdgesUpdate = useMemo(
+    () =>
+      debounce((edges: Edge[]) => {
+        onEdgesUpdate?.(edges);
+      }, debounceWait),
+    [onEdgesUpdate, debounceWait]
+  );
+
+  const onConnect = useCallback(
+    (params: Connection) => setEdges(eds => addEdge(params, eds)),
+    [setEdges]
+  );
+
+  // Listen initialNodes/initialEdges changes and update state accordingly
+  useEffect(() => {
+    setNodes(Nodes);
+  }, [Nodes]);
+
+  useEffect(() => {
+    setEdges(Edges);
+  }, [Edges]);
+
+  const handleNodeClick = useCallback(
+    (event: React.MouseEvent, node: Node) => {
+      onNodeClick?.(node);
+    },
+    [onNodeClick]
+  );
+
+  const applyAutoLayout = useCallback(
+    (nodes: Node[], edges: Edge[]): Node[] => {
+      const g = new dagre.graphlib.Graph();
+      g.setDefaultEdgeLabel(() => ({}));
+      g.setGraph({
+        rankdir: 'TB',
+        ranksep: 100
+      }); // TB = Top to Bottom. Use LR for Left to Right
+
+      nodes.forEach(node => {
+        g.setNode(node.id, { width: nodeWidth, height: nodeHeight });
+      });
+
+      edges.forEach(edge => {
+        g.setEdge(edge.source, edge.target);
+      });
+
+      dagre.layout(g);
+
+      return nodes.map(node => {
+        const pos = g.node(node.id);
+        return {
+          ...node,
+          position: {
+            x: pos.x - nodeWidth / 2,
+            y: pos.y - nodeHeight / 2
+          }
+        };
+      });
+    },
+    []
+  );
+
+  const handleNodesChange = useCallback(
+    (changes: NodeChange[]) => {
+      const updatedNodes = applyNodeChanges(changes, nodes);
+
+      // Judge whether the node data is changed or not
+      // except position / dragging / selected）
+      const structuralChanges = changes.filter(
+        c => !['position', 'dragging', 'selected', 'select'].includes(c.type)
+      );
+
+      if (structuralChanges.length > 0) {
+        // Only when there are structural changes, we apply auto-layout
+        const autoLayouted = applyAutoLayout(updatedNodes, edges);
+        setNodes(autoLayouted);
+        onNodesUpdate?.(autoLayouted);
+      } else {
+        // Dragging or selection changes, just update normally
+        setNodes(updatedNodes);
+      }
+    },
+    [nodes, edges, onNodesUpdate, applyAutoLayout]
+  );
+
+  const NodeTypes = {
+    default: DefaultNode,
+    input: InputNode,
+    output: OutputNode
+  };
+
+  // notify parent whenever nodes/edges change
+  useEffect(() => {
+    debouncedNodesUpdate(nodes);
+    return () => debouncedNodesUpdate.cancel();
+  }, [nodes, debouncedNodesUpdate]);
+
+  useEffect(() => {
+    debouncedEdgesUpdate(edges);
+    return () => debouncedEdgesUpdate.cancel();
+  }, [edges, debouncedEdgesUpdate]);
+
+  return (
+    <ReactFlow
+      nodes={nodes}
+      edges={edges}
+      nodeTypes={NodeTypes}
+      edgeTypes={{ default: AnimatedSVGEdge }}
+      onNodesChange={handleNodesChange}
+      onEdgesChange={onEdgesChange}
+      onConnect={onConnect}
+      onNodeClick={handleNodeClick}
+      fitView
+    >
+      <Panel position="top-right">
+        <button
+          onClick={() => {
+            const newNodes = applyAutoLayout(nodes, edges);
+            setNodes(newNodes);
+          }}
+          className="px-3 py-1 
+          bg-blue-600 text-white rounded-md shadow 
+          hover:bg-blue-700 transition-all duration-200"
+        >
+          Auto Layout
+        </button>
+      </Panel>
+      <MiniMap />
+      <Controls />
+      <Background />
+    </ReactFlow>
+  );
+};
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/YamlWidget.tsx b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/YamlWidget.tsx
new file mode 100644
index 000000000000..191e4b38016e
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/src/yaml/YamlWidget.tsx
@@ -0,0 +1,34 @@
+// Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+// use this file except in compliance with the License. You may obtain a copy of
+// the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+// License for the specific language governing permissions and limitations under
+// the License.
+
+import * as React from 'react';
+
+import { ISessionContext, ReactWidget } from '@jupyterlab/apputils';
+
+import { Yaml } from './Yaml';
+
+/**
+ * Converts the React component Clusters into a lumino widget used
+ * in Jupyter labextensions.
+ */
+export class YamlWidget extends ReactWidget {
+  constructor(sessionContext: ISessionContext) {
+    super();
+    this._sessionContext = sessionContext;
+  }
+
+  protected render(): React.ReactElement<any> {
+    return <Yaml sessionContext={this._sessionContext} />;
+  }
+
+  private _sessionContext: ISessionContext;
+}
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/index.css b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/index.css
index 1b2227845b69..1a158f9bfe46 100644
--- a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/index.css
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/index.css
@@ -18,3 +18,6 @@
 @import './inspector/Inspectables.css';
 @import './inspector/InspectableView.css';
 @import './inspector/InteractiveInspector.css';
+@import './yaml/Yaml.css';
+@import './yaml/YamlEditor.css';
+@import './yaml/YamlFlow.css';
\ No newline at end of file
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/mdc-theme.css b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/mdc-theme.css
index b6383f965125..be39963782a6 100644
--- a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/mdc-theme.css
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/mdc-theme.css
@@ -49,11 +49,11 @@
   color: var(--jp-ui-font-color1);
 }
 
-.mdc-form-field > label {
+.mdc-form-field>label {
   margin-bottom: 0;
 }
 
 .mdc-text-field {
   margin-left: 4px;
   margin-right: 4px;
-}
+}
\ No newline at end of file
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/yaml/Yaml.css b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/yaml/Yaml.css
new file mode 100644
index 000000000000..3947022fc7f5
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/yaml/Yaml.css
@@ -0,0 +1,40 @@
+/*
+ * Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ */
+
+.split-pane {
+    height: 90%;
+    display: flex;
+}
+
+.gutter {
+    background-color: #ccc;
+    background-clip: padding-box;
+    box-sizing: border-box;
+    z-index: 1;
+    transition: background-color 0.2s;
+}
+
+.gutter:hover {
+    background-color: #aaa;
+}
+
+.gutter.gutter-horizontal {
+    cursor: col-resize;
+    width: 6px;
+}
+
+.gutter.gutter-vertical {
+    cursor: row-resize;
+    height: 6px;
+}
\ No newline at end of file
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/yaml/YamlEditor.css b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/yaml/YamlEditor.css
new file mode 100644
index 000000000000..b1b45a7b6413
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/yaml/YamlEditor.css
@@ -0,0 +1,36 @@
+/*
+ * Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ */
+
+input {
+    border: 1px solid #ccc;
+    padding: 4px 6px;
+    border-radius: 4px;
+}
+
+button {
+    cursor: pointer;
+    background: none;
+    border: none;
+    font-size: 1.1rem;
+}
+
+.editor-input {
+    width: 100%;
+    height: 20px;
+    padding: 4px 6px;
+    border-radius: 4px;
+    border: 1px solid #ccc;
+    font-family: inherit;
+    font-size: inherit;
+}
\ No newline at end of file
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/yaml/YamlFlow.css b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/yaml/YamlFlow.css
new file mode 100644
index 000000000000..091e3e211d7b
--- /dev/null
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/style/yaml/YamlFlow.css
@@ -0,0 +1,168 @@
+/*
+ * Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+ * use this file except in compliance with the License. You may obtain a copy of
+ * the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations under
+ * the License.
+ */
+
+.custom-node {
+    background: linear-gradient(145deg, #ffffff, #e5e5e5);
+    border: 2px solid #ccc;
+    border-radius: 12px;
+    padding: 12px 16px;
+    width: 250px;
+    display: inline-block;
+    box-shadow: 0 4px 10px rgba(0, 0, 0, 0.12);
+    transition: transform 0.2s ease, box-shadow 0.2s ease, border-color 0.2s ease;
+    cursor: pointer;
+    user-select: none;
+    word-wrap: break-word;
+    overflow-wrap: break-word;
+}
+
+.custom-node:hover {
+    transform: translateY(-3px) scale(1.02);
+    box-shadow: 0 6px 14px rgba(0, 0, 0, 0.18);
+    border-color: #4cafef;
+}
+
+.custom-node:active {
+    transform: scale(0.96);
+    box-shadow: 0 3px 8px rgba(0, 0, 0, 0.2);
+    border-color: #2196f3;
+}
+
+/* Header */
+.custom-node-header {
+    display: flex;
+    align-items: center;
+}
+
+.custom-node-icon {
+    width: 44px;
+    height: 44px;
+    border-radius: 50%;
+    flex-shrink: 0;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    font-size: 22px;
+    margin-right: 10px;
+    box-shadow: inset 0 2px 6px rgba(0, 0, 0, 0.1);
+}
+
+.custom-node-title {
+    font-size: 16px;
+    font-weight: bold;
+    color: #333;
+    letter-spacing: 0.3px;
+    line-height: 1.3;
+    flex-grow: 1;
+    white-space: normal;
+}
+
+/* Handle */
+.custom-handle {
+    width: 40px !important;
+    height: 8px !important;
+    border-radius: 4px;
+    background: #00bcd4 !important;
+    border: none !important;
+    transition: background 0.2s ease;
+}
+
+.custom-handle:hover {
+    background: #008ba3 !important;
+}
+
+/* ====== Colors ====== */
+.custom-node-input {
+    border-color: #4caf50;
+}
+
+.custom-node-input .custom-node-icon {
+    background: linear-gradient(135deg, #e8f5e9, #a5d6a7);
+}
+
+.custom-node-default {
+    border-color: #2196f3;
+}
+
+.custom-node-default .custom-node-icon {
+    background: linear-gradient(135deg, #e3f2fd, #90caf9);
+}
+
+.custom-node-output {
+    border-color: #ff9800;
+}
+
+.custom-node-output .custom-node-icon {
+    background: linear-gradient(135deg, #fff3e0, #ffcc80);
+}
+
+/* ====== Input ====== */
+.custom-node-input {
+    border-color: #4caf50;
+    background: linear-gradient(145deg, #f1fbf3, #dcedc8);
+}
+
+.custom-node-input .custom-node-icon {
+    background: linear-gradient(135deg, #c8e6c9, #81c784);
+    color: #ffffff;
+    box-shadow: 0 0 8px rgba(76, 175, 80, 0.5) inset;
+    transition: box-shadow 0.3s ease, transform 0.2s ease;
+}
+
+.custom-node-input:hover .custom-node-icon {
+    box-shadow: 0 0 12px rgba(76, 175, 80, 0.7) inset;
+    transform: scale(1.05);
+}
+
+.custom-node-input:active .custom-node-icon {
+    transform: scale(0.95);
+}
+
+.custom-node-input .custom-handle {
+    background: #43a047 !important;
+}
+
+.custom-node-input .custom-handle:hover {
+    background: #2e7d32 !important;
+}
+
+/* ====== Output ====== */
+.custom-node-output {
+    border-color: #ff9800;
+    background: linear-gradient(145deg, #fff8f0, #ffe0b2);
+}
+
+.custom-node-output .custom-node-icon {
+    background: linear-gradient(135deg, #ffcc80, #ffb74d);
+    color: #ffffff;
+    box-shadow: 0 0 8px rgba(255, 152, 0, 0.5) inset;
+    transition: box-shadow 0.3s ease, transform 0.2s ease;
+}
+
+.custom-node-output:hover .custom-node-icon {
+    box-shadow: 0 0 12px rgba(255, 152, 0, 0.7) inset;
+    transform: scale(1.05);
+}
+
+.custom-node-output:active .custom-node-icon {
+    transform: scale(0.95);
+}
+
+.custom-node-output .custom-handle {
+    background: #fb8c00 !important;
+}
+
+.custom-node-output .custom-handle:hover {
+    background: #ef6c00 !important;
+}
\ No newline at end of file
diff --git a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/yarn.lock b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/yarn.lock
index 947b749121a4..58bedbf7192a 100644
--- a/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/yarn.lock
+++ b/sdks/python/apache_beam/runners/interactive/extensions/apache-beam-jupyterlab-sidepanel/yarn.lock
@@ -1400,6 +1400,27 @@ __metadata:
   languageName: node
   linkType: hard
 
+"@material/button@npm:^14.0.0":
+  version: 14.0.0
+  resolution: "@material/button@npm:14.0.0"
+  dependencies:
+    "@material/density": ^14.0.0
+    "@material/dom": ^14.0.0
+    "@material/elevation": ^14.0.0
+    "@material/feature-targeting": ^14.0.0
+    "@material/focus-ring": ^14.0.0
+    "@material/ripple": ^14.0.0
+    "@material/rtl": ^14.0.0
+    "@material/shape": ^14.0.0
+    "@material/theme": ^14.0.0
+    "@material/tokens": ^14.0.0
+    "@material/touch-target": ^14.0.0
+    "@material/typography": ^14.0.0
+    tslib: ^2.1.0
+  checksum: 83b55dd1038b3fa98e685d31f4a20c3530bd152f4c2f2319ae553c9496955f3ab9a86bfd50f523018a28bbeaf49410cd689c2b25339eb8b718b1677f6fe7103b
+  languageName: node
+  linkType: hard
+
 "@material/button@npm:^8.0.0":
   version: 8.0.0
   resolution: "@material/button@npm:8.0.0"
@@ -1417,6 +1438,22 @@ __metadata:
   languageName: node
   linkType: hard
 
+"@material/card@npm:^14.0.0":
+  version: 14.0.0
+  resolution: "@material/card@npm:14.0.0"
+  dependencies:
+    "@material/dom": ^14.0.0
+    "@material/elevation": ^14.0.0
+    "@material/feature-targeting": ^14.0.0
+    "@material/ripple": ^14.0.0
+    "@material/rtl": ^14.0.0
+    "@material/shape": ^14.0.0
+    "@material/theme": ^14.0.0
+    tslib: ^2.1.0
+  checksum: e058ff89fadd4ef4cc28d7d8c9f6ac8152ba94430707a272d5cd2ebe4ebd4e481d00b42f97addf25cd49439b892d5979f0133bfb7bb090a322f99036cdb4d87f
+  languageName: node
+  linkType: hard
+
 "@material/checkbox@npm:^8.0.0":
   version: 8.0.0
   resolution: "@material/checkbox@npm:8.0.0"
@@ -1460,6 +1497,15 @@ __metadata:
   languageName: node
   linkType: hard
 
+"@material/density@npm:^14.0.0":
+  version: 14.0.0
+  resolution: "@material/density@npm:14.0.0"
+  dependencies:
+    tslib: ^2.1.0
+  checksum: 94279ca2d7b75bbb998026da5fb7fbd68c830f986d25c4dcd5988a9f180bb600d8c1135c7828f844f1d74fa8919d817f44d10be03a18a4ccf81afe0f0d3810a9
+  languageName: node
+  linkType: hard
+
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+  checksum: 19bdeb99b8923b47f9df978b6ef2c5a4cc3bcaa8fb6be16244e31fad619b291b366429747331903ac2ea27560ffd6066d14089a99c95535ce0f1e897525fa63d
+  languageName: node
+  linkType: hard
+
 "classnames@npm:*, classnames@npm:^2.2.6, classnames@npm:^2.3.1":
   version: 2.5.1
   resolution: "classnames@npm:2.5.1"
@@ -4109,6 +4478,98 @@ __metadata:
   languageName: node
   linkType: hard
 
+"d3-color@npm:1 - 3":
+  version: 3.1.0
+  resolution: "d3-color@npm:3.1.0"
+  checksum: 4931fbfda5d7c4b5cfa283a13c91a954f86e3b69d75ce588d06cde6c3628cebfc3af2069ccf225e982e8987c612aa7948b3932163ce15eb3c11cd7c003f3ee3b
+  languageName: node
+  linkType: hard
+
+"d3-dispatch@npm:1 - 3":
+  version: 3.0.1
+  resolution: "d3-dispatch@npm:3.0.1"
+  checksum: fdfd4a230f46463e28e5b22a45dd76d03be9345b605e1b5dc7d18bd7ebf504e6c00ae123fd6d03e23d9e2711e01f0e14ea89cd0632545b9f0c00b924ba4be223
+  languageName: node
+  linkType: hard
+
+"d3-drag@npm:2 - 3, d3-drag@npm:^3.0.0":
+  version: 3.0.0
+  resolution: "d3-drag@npm:3.0.0"
+  dependencies:
+    d3-dispatch: 1 - 3
+    d3-selection: 3
+  checksum: d297231e60ecd633b0d076a63b4052b436ddeb48b5a3a11ff68c7e41a6774565473a6b064c5e9256e88eca6439a917ab9cea76032c52d944ddbf4fd289e31111
+  languageName: node
+  linkType: hard
+
+"d3-ease@npm:1 - 3":
+  version: 3.0.1
+  resolution: "d3-ease@npm:3.0.1"
+  checksum: 06e2ee5326d1e3545eab4e2c0f84046a123dcd3b612e68858219aa034da1160333d9ce3da20a1d3486d98cb5c2a06f7d233eee1bc19ce42d1533458bd85dedcd
+  languageName: node
+  linkType: hard
+
+"d3-interpolate@npm:1 - 3, d3-interpolate@npm:^3.0.1":
+  version: 3.0.1
+  resolution: "d3-interpolate@npm:3.0.1"
+  dependencies:
+    d3-color: 1 - 3
+  checksum: a42ba314e295e95e5365eff0f604834e67e4a3b3c7102458781c477bd67e9b24b6bb9d8e41ff5521050a3f2c7c0c4bbbb6e187fd586daa3980943095b267e78b
+  languageName: node
+  linkType: hard
+
+"d3-selection@npm:2 - 3, d3-selection@npm:3, d3-selection@npm:^3.0.0":
+  version: 3.0.0
+  resolution: "d3-selection@npm:3.0.0"
+  checksum: f4e60e133309115b99f5b36a79ae0a19d71ee6e2d5e3c7216ef3e75ebd2cb1e778c2ed2fa4c01bef35e0dcbd96c5428f5bd6ca2184fe2957ed582fde6841cbc5
+  languageName: node
+  linkType: hard
+
+"d3-timer@npm:1 - 3":
+  version: 3.0.1
+  resolution: "d3-timer@npm:3.0.1"
+  checksum: 1cfddf86d7bca22f73f2c427f52dfa35c49f50d64e187eb788dcad6e927625c636aa18ae4edd44d084eb9d1f81d8ca4ec305dae7f733c15846a824575b789d73
+  languageName: node
+  linkType: hard
+
+"d3-transition@npm:2 - 3":
+  version: 3.0.1
+  resolution: "d3-transition@npm:3.0.1"
+  dependencies:
+    d3-color: 1 - 3
+    d3-dispatch: 1 - 3
+    d3-ease: 1 - 3
+    d3-interpolate: 1 - 3
+    d3-timer: 1 - 3
+  peerDependencies:
+    d3-selection: 2 - 3
+  checksum: cb1e6e018c3abf0502fe9ff7b631ad058efb197b5e14b973a410d3935aead6e3c07c67d726cfab258e4936ef2667c2c3d1cd2037feb0765f0b4e1d3b8788c0ea
+  languageName: node
+  linkType: hard
+
+"d3-zoom@npm:^3.0.0":
+  version: 3.0.0
+  resolution: "d3-zoom@npm:3.0.0"
+  dependencies:
+    d3-dispatch: 1 - 3
+    d3-drag: 2 - 3
+    d3-interpolate: 1 - 3
+    d3-selection: 2 - 3
+    d3-transition: 2 - 3
+  checksum: 8056e3527281cfd1ccbcbc458408f86973b0583e9dac00e51204026d1d36803ca437f970b5736f02fafed9f2b78f145f72a5dbc66397e02d4d95d4c594b8ff54
+  languageName: node
+  linkType: hard
+
+"dagre@npm:^0.8.5":
+  version: 0.8.5
+  resolution: "dagre@npm:0.8.5"
+  dependencies:
+    graphlib: ^2.1.8
+    lodash: ^4.17.15
+  checksum: b9fabd425466d7b662381c2e457b1adda996bc4169aa60121d4de50250d83a6bb4b77d559e2f887c9c564caea781c2a377fd4de2a76c15f8f04ec3d086ca95f9
+  languageName: node
+  linkType: hard
+
 "data-urls@npm:^2.0.0":
   version: 2.0.0
   resolution: "data-urls@npm:2.0.0"
@@ -5163,15 +5624,15 @@ __metadata:
   linkType: hard
 
 "form-data@npm:^4.0.0":
-  version: 4.0.3
-  resolution: "form-data@npm:4.0.3"
+  version: 4.0.4
+  resolution: "form-data@npm:4.0.4"
   dependencies:
     asynckit: ^0.4.0
     combined-stream: ^1.0.8
     es-set-tostringtag: ^2.1.0
     hasown: ^2.0.2
     mime-types: ^2.1.12
-  checksum: b8e2568c0853ce167b2b9c9c4b81fe563f9ade647178baf6b6381cf8a11e3c01dd2b78a63ba367e6f5eab59afab8284a9438bb5ae768133f9d9fce6567fbc26a
+  checksum: 9b7788836df9fa5a6999e0c02515b001946b2a868cfe53f026c69e2c537a2ff9fbfb8e9d2b678744628f3dc7a2d6e14e4e45dfaf68aa6239727f0bdb8ce0abf2
   languageName: node
   linkType: hard
 
@@ -5453,6 +5914,15 @@ __metadata:
   languageName: node
   linkType: hard
 
+"graphlib@npm:^2.1.8":
+  version: 2.1.8
+  resolution: "graphlib@npm:2.1.8"
+  dependencies:
+    lodash: ^4.17.15
+  checksum: 1e0db4dea1c8187d59103d5582ecf32008845ebe2103959a51d22cb6dae495e81fb9263e22c922bca3aaecb56064a45cd53424e15a4626cfb5a0c52d0aff61a8
+  languageName: node
+  linkType: hard
+
 "harmony-reflect@npm:^1.4.6":
   version: 1.6.2
   resolution: "harmony-reflect@npm:1.6.2"
@@ -6985,7 +7455,7 @@ __metadata:
   languageName: node
   linkType: hard
 
-"lodash@npm:^4.17.20, lodash@npm:^4.17.21, lodash@npm:^4.17.4, lodash@npm:^4.7.0":
+"lodash@npm:^4.17.15, lodash@npm:^4.17.20, lodash@npm:^4.17.21, lodash@npm:^4.17.4, lodash@npm:^4.7.0":
   version: 4.17.21
   resolution: "lodash@npm:4.17.21"
   checksum: eb835a2e51d381e561e508ce932ea50a8e5a68f4ebdd771ea240d3048244a8d13658acbd502cd4829768c56f2e16bdd4340b9ea141297d472517b83868e677f7
@@ -7964,7 +8434,7 @@ __metadata:
   languageName: node
   linkType: hard
 
-"prop-types@npm:15.x, prop-types@npm:^15.5.10, prop-types@npm:^15.5.8, prop-types@npm:^15.8.1":
+"prop-types@npm:15.x, prop-types@npm:^15.5.10, prop-types@npm:^15.5.7, prop-types@npm:^15.5.8, prop-types@npm:^15.8.1":
   version: 15.8.1
   resolution: "prop-types@npm:15.8.1"
   dependencies:
@@ -8136,6 +8606,18 @@ __metadata:
   languageName: node
   linkType: hard
 
+"react-split@npm:^2.0.14":
+  version: 2.0.14
+  resolution: "react-split@npm:2.0.14"
+  dependencies:
+    prop-types: ^15.5.7
+    split.js: ^1.6.0
+  peerDependencies:
+    react: "*"
+  checksum: 8e9e22b8ef48063ab0b55a96bbb3ff66012ddbe899661deac30b10b72aa08c1b669eb644e599ec1ac0ce880017fc2d4b2e6cb48b113789646d6186d61d8f55f2
+  languageName: node
+  linkType: hard
+
 "react@npm:>=17.0.0 <19.0.0, react@npm:^18.2.0":
   version: 18.3.1
   resolution: "react@npm:18.3.1"
@@ -8796,6 +9278,13 @@ __metadata:
   languageName: node
   linkType: hard
 
+"split.js@npm:^1.6.0":
+  version: 1.6.5
+  resolution: "split.js@npm:1.6.5"
+  checksum: a3e77d8e0628de06c58e2d8e6ab41872132586c417b4f40ac3d3dbf76c2b31f40745dd86c133609dacd5599ada5d4bee157f6290403223b86bd79fe700a77983
+  languageName: node
+  linkType: hard
+
 "sprintf-js@npm:^1.1.3":
   version: 1.1.3
   resolution: "sprintf-js@npm:1.1.3"
@@ -8828,6 +9317,13 @@ __metadata:
   languageName: node
   linkType: hard
 
+"state-local@npm:^1.0.6":
+  version: 1.0.7
+  resolution: "state-local@npm:1.0.7"
+  checksum: d1afcf1429e7e6eb08685b3a94be8797db847369316d4776fd51f3962b15b984dacc7f8e401ad20968e5798c9565b4b377afedf4e4c4d60fe7495e1cbe14a251
+  languageName: node
+  linkType: hard
+
 "stop-iteration-iterator@npm:^1.0.0, stop-iteration-iterator@npm:^1.1.0":
   version: 1.1.0
   resolution: "stop-iteration-iterator@npm:1.1.0"
@@ -9470,6 +9966,15 @@ __metadata:
   languageName: node
   linkType: hard
 
+"use-sync-external-store@npm:^1.2.2":
+  version: 1.5.0
+  resolution: "use-sync-external-store@npm:1.5.0"
+  peerDependencies:
+    react: ^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0
+  checksum: 5e639c9273200adb6985b512c96a3a02c458bc8ca1a72e91da9cdc6426144fc6538dca434b0f99b28fb1baabc82e1c383ba7900b25ccdcb43758fb058dc66c34
+  languageName: node
+  linkType: hard
+
 "util-deprecate@npm:^1.0.2":
   version: 1.0.2
   resolution: "util-deprecate@npm:1.0.2"
@@ -9985,3 +10490,23 @@ __metadata:
   checksum: f77b3d8d00310def622123df93d4ee654fc6a0096182af8bd60679ddcdfb3474c56c6c7190817c84a2785648cdee9d721c0154eb45698c62176c322fb46fc700
   languageName: node
   linkType: hard
+
+"zustand@npm:^4.4.0":
+  version: 4.5.7
+  resolution: "zustand@npm:4.5.7"
+  dependencies:
+    use-sync-external-store: ^1.2.2
+  peerDependencies:
+    "@types/react": ">=16.8"
+    immer: ">=9.0.6"
+    react: ">=16.8"
+  peerDependenciesMeta:
+    "@types/react":
+      optional: true
+    immer:
+      optional: true
+    react:
+      optional: true
+  checksum: 103ab43456bbc3be6afe79b18a93c7fa46ffaa1aa35c45b213f13f4cd0868fee78b43c6805c6d80a822297df2e455fd021c28be94b80529ec4806b2724f20219
+  languageName: node
+  linkType: hard
diff --git a/sdks/python/apache_beam/runners/interactive/interactive_runner.py b/sdks/python/apache_beam/runners/interactive/interactive_runner.py
index 17619fbb6ddc..c8b0be0941d0 100644
--- a/sdks/python/apache_beam/runners/interactive/interactive_runner.py
+++ b/sdks/python/apache_beam/runners/interactive/interactive_runner.py
@@ -80,6 +80,8 @@ def __init__(
     """
     self._underlying_runner = (
         underlying_runner or direct_runner.DirectRunner())
+    if hasattr(self._underlying_runner, 'is_interactive'):
+      self._underlying_runner.is_interactive()
     self._render_option = render_option
     self._in_session = False
     self._skip_display = skip_display
diff --git a/sdks/python/apache_beam/runners/interactive/pipeline_instrument_test.py b/sdks/python/apache_beam/runners/interactive/pipeline_instrument_test.py
index 893603ddbb52..7f5c4f913bd9 100644
--- a/sdks/python/apache_beam/runners/interactive/pipeline_instrument_test.py
+++ b/sdks/python/apache_beam/runners/interactive/pipeline_instrument_test.py
@@ -36,7 +36,7 @@
 from apache_beam.runners.interactive.testing.pipeline_assertion import assert_pipeline_proto_contain_top_level_transform
 from apache_beam.runners.interactive.testing.pipeline_assertion import assert_pipeline_proto_equal
 from apache_beam.runners.interactive.testing.pipeline_assertion import \
-    assert_pipeline_proto_not_contain_top_level_transform
+  assert_pipeline_proto_not_contain_top_level_transform
 from apache_beam.runners.interactive.testing.test_cache_manager import InMemoryCache
 from apache_beam.testing.test_stream import TestStream
 
diff --git a/sdks/python/apache_beam/runners/interactive/recording_manager.py b/sdks/python/apache_beam/runners/interactive/recording_manager.py
index ce6fbd6d8ae8..f72ec2fe8e17 100644
--- a/sdks/python/apache_beam/runners/interactive/recording_manager.py
+++ b/sdks/python/apache_beam/runners/interactive/recording_manager.py
@@ -364,6 +364,8 @@ def record_pipeline(self) -> bool:
     runner = self.user_pipeline.runner
     if isinstance(runner, ir.InteractiveRunner):
       runner = runner._underlying_runner
+    if hasattr(runner, 'is_interactive'):
+      runner.is_interactive()
 
     # Make sure that sources without a user reference are still cached.
     ie.current_env().add_user_pipeline(self.user_pipeline)
diff --git a/sdks/python/apache_beam/runners/pipeline_utils_test.py b/sdks/python/apache_beam/runners/pipeline_utils_test.py
index 4359f943cfb8..ba144f5e6cc2 100644
--- a/sdks/python/apache_beam/runners/pipeline_utils_test.py
+++ b/sdks/python/apache_beam/runners/pipeline_utils_test.py
@@ -189,7 +189,9 @@ def test_external_merged(self):
     # All our external environments are equal and consolidated.
     # We also have a placeholder "default" environment that has not been
     # resolved do anything concrete yet.
-    self.assertEqual(len(pipeline_proto.components.environments), 2)
+    envs = pipeline_proto.components.environments
+    self.assertEqual(
+        len(envs), 2, f'should be 2 environments, instead got: {envs}')
 
 
 if __name__ == '__main__':
diff --git a/sdks/python/apache_beam/runners/portability/flink_runner_test.py b/sdks/python/apache_beam/runners/portability/flink_runner_test.py
index 6aa913105ac1..dbeef557ab5a 100644
--- a/sdks/python/apache_beam/runners/portability/flink_runner_test.py
+++ b/sdks/python/apache_beam/runners/portability/flink_runner_test.py
@@ -162,6 +162,10 @@ def _subprocess_command(cls, job_port, expansion_port):
     try:
       return [
           'java',
+          '-XX:-UseContainerSupport',
+          '--add-opens=java.base/java.lang=ALL-UNNAMED',
+          '--add-opens=java.base/java.nio=ALL-UNNAMED',
+          '--add-opens=java.base/java.util=ALL-UNNAMED',
           '-Dorg.slf4j.simpleLogger.defaultLogLevel=warn',
           '-jar',
           cls.flink_job_server_jar,
@@ -209,6 +213,14 @@ def create_options(self):
 
     return options
 
+  def test_batch_rebatch_pardos(self):
+    if self.environment_type == 'DOCKER':
+      pytest.skip(
+          "Skipping test_batch_rebatch_pardos for DOCKER environment in "
+          "FlinkRunnerTest as warnings are not propagated back when Python SDK "
+          "is run in Docker.")
+    super().test_batch_rebatch_pardos()
+
   # Can't read host files from within docker, read a "local" file there.
   def test_read(self):
     print('name:', __name__)
@@ -220,6 +232,11 @@ def test_no_subtransform_composite(self):
     raise unittest.SkipTest("BEAM-4781")
 
   def test_external_transform(self):
+    if self.environment_type == 'PROCESS':
+      self.skipTest(
+          "Skipping external transform test in PROCESS mode due to "
+          "https://github.com/apache/beam/issues/19461 (Flink expansion "
+          "service incompatibility).")
     with self.create_pipeline() as p:
       res = (
           p
@@ -229,6 +246,11 @@ def test_external_transform(self):
       assert_that(res, equal_to([i for i in range(1, 10)]))
 
   def test_expand_kafka_read(self):
+    if self.environment_type == 'PROCESS':
+      self.skipTest(
+          "Skipping Kafka read test in PROCESS mode due to "
+          "https://github.com/apache/beam/issues/19461 (Flink expansion "
+          "service incompatibility).")
     # We expect to fail here because we do not have a Kafka cluster handy.
     # Nevertheless, we check that the transform is expanded by the
     # ExpansionService and that the pipeline fails during execution.
@@ -263,6 +285,11 @@ def test_expand_kafka_read(self):
         'failed due to:\n%s' % str(ctx.exception))
 
   def test_expand_kafka_write(self):
+    if self.environment_type == 'PROCESS':
+      self.skipTest(
+          "Skipping Kafka write test in PROCESS mode due to "
+          "https://github.com/apache/beam/issues/19461 (Flink expansion "
+          "service incompatibility).")
     # We just test the expansion but do not execute.
     # pylint: disable=expression-not-assigned
     (
@@ -283,6 +310,11 @@ def test_expand_kafka_write(self):
             expansion_service=self.get_expansion_service()))
 
   def test_sql(self):
+    if self.environment_type == 'PROCESS':
+      self.skipTest(
+          "Skipping SQL test in PROCESS mode due to "
+          "https://github.com/apache/beam/issues/19461 (Flink expansion "
+          "service incompatibility).")
     with self.create_pipeline() as p:
       output = (
           p
diff --git a/sdks/python/apache_beam/runners/portability/fn_api_runner/fn_runner_test.py b/sdks/python/apache_beam/runners/portability/fn_api_runner/fn_runner_test.py
index 3442b5746817..0197733e9115 100644
--- a/sdks/python/apache_beam/runners/portability/fn_api_runner/fn_runner_test.py
+++ b/sdks/python/apache_beam/runners/portability/fn_api_runner/fn_runner_test.py
@@ -69,8 +69,10 @@
 from apache_beam.testing.test_stream import TestStream
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
+from apache_beam.testing.util import has_at_least_one
 from apache_beam.tools import utils
 from apache_beam.transforms import environments
+from apache_beam.transforms import trigger
 from apache_beam.transforms import userstate
 from apache_beam.transforms import window
 from apache_beam.transforms.periodicsequence import PeriodicImpulse
@@ -1594,6 +1596,22 @@ def test_group_by_key_with_empty_pcoll_elements(self):
           | beam.GroupByKey())
       assert_that(res, equal_to([]))
 
+  def test_first_pane(self):
+    with self.create_pipeline() as p:
+      res = (
+          p | beam.Create([1, 2])
+          | beam.WithKeys(0)
+          | beam.WindowInto(
+              window.GlobalWindows(),
+              trigger=trigger.Repeatedly(trigger.AfterCount(1)),
+              accumulation_mode=trigger.AccumulationMode.ACCUMULATING,
+              allowed_lateness=0,
+          )
+          | beam.GroupByKey()
+          | beam.Values())
+      has_at_least_one(res, lambda e, t, w, p: p.is_first)
+      has_at_least_one(res, lambda e, t, w, p: p.index == 0)
+
 
 # These tests are kept in a separate group so that they are
 # not ran in the FnApiRunnerTestWithBundleRepeat which repeats
diff --git a/sdks/python/apache_beam/runners/portability/job_server.py b/sdks/python/apache_beam/runners/portability/job_server.py
index f4163cf864b5..0d98de6bdf3d 100644
--- a/sdks/python/apache_beam/runners/portability/job_server.py
+++ b/sdks/python/apache_beam/runners/portability/job_server.py
@@ -18,6 +18,7 @@
 # pytype: skip-file
 
 import atexit
+import logging
 import shutil
 import signal
 import tempfile
@@ -102,6 +103,7 @@ class SubprocessJobServer(JobServer):
   def __init__(self):
     self._local_temp_root = None
     self._server = None
+    self._log_filter = None
 
   def subprocess_cmd_and_endpoint(self):
     raise NotImplementedError(type(self))
@@ -111,8 +113,11 @@ def start(self):
       self._local_temp_root = tempfile.mkdtemp(prefix='beam-temp')
       cmd, endpoint = self.subprocess_cmd_and_endpoint()
       port = int(endpoint.split(':')[-1])
+      logger = logging.getLogger(f"{self.__class__.__name__}")
+      if self._log_filter is not None:
+        logger.addFilter(self._log_filter)
       self._server = subprocess_server.SubprocessServer(
-          beam_job_api_pb2_grpc.JobServiceStub, cmd, port=port)
+          beam_job_api_pb2_grpc.JobServiceStub, cmd, port=port, logger=logger)
     return self._server.start()
 
   def stop(self):
diff --git a/sdks/python/apache_beam/runners/portability/prism_runner.py b/sdks/python/apache_beam/runners/portability/prism_runner.py
index 14854ca14a36..bc5d8c2a6131 100644
--- a/sdks/python/apache_beam/runners/portability/prism_runner.py
+++ b/sdks/python/apache_beam/runners/portability/prism_runner.py
@@ -22,7 +22,9 @@
 # sunset it
 from __future__ import annotations
 
+import datetime
 import hashlib
+import json
 import logging
 import os
 import platform
@@ -73,9 +75,9 @@ def default_job_server(self, options):
     debug_options = options.view_as(pipeline_options.DebugOptions)
     get_job_server = lambda: job_server.StopOnExitJobServer(
         PrismJobServer(options))
-    if debug_options.lookup_experiment("enable_prism_server_singleton"):
-      return PrismRunner.shared_handle.acquire(get_job_server)
-    return get_job_server()
+    if debug_options.lookup_experiment("disable_prism_server_singleton"):
+      return get_job_server()
+    return PrismRunner.shared_handle.acquire(get_job_server)
 
   def create_job_service_handle(self, job_service, options):
     return portable_runner.JobServiceHandle(
@@ -111,11 +113,55 @@ def _rename_if_different(src, dst):
     os.rename(src, dst)
 
 
+class PrismRunnerLogFilter(logging.Filter):
+  COMMON_FIELDS = set(["level", "source", "msg", "time"])
+
+  def filter(self, record):
+    if record.funcName == 'log_stdout':
+      try:
+        message = record.getMessage()
+        json_record = json.loads(message)
+        record.levelno = getattr(logging, json_record["level"])
+        record.levelname = logging.getLevelName(record.levelno)
+        if "source" in json_record:
+          record.funcName = json_record["source"]["function"]
+          record.pathname = json_record["source"]["file"]
+          record.filename = os.path.basename(record.pathname)
+          record.lineno = json_record["source"]["line"]
+        record.created = datetime.datetime.fromisoformat(
+            json_record["time"]).timestamp()
+        extras = {
+            k: v
+            for k, v in json_record.items()
+            if k not in PrismRunnerLogFilter.COMMON_FIELDS
+        }
+
+        if json_record["msg"] == "log from SDK worker":
+          # TODO: Use location and time inside the nested message to set record
+          record.name = "SdkWorker" + "@" + json_record["worker"]["ID"]
+          record.msg = json_record["sdk"]["msg"]
+        else:
+          record.name = "PrismRunner"
+          record.msg = (
+              f"{json_record['msg']} "
+              f"({', '.join(f'{k}={v!r}' for k, v in extras.items())})")
+      except (json.JSONDecodeError,
+              KeyError,
+              ValueError,
+              TypeError,
+              AttributeError):
+        # The log parsing/filtering is best-effort.
+        pass
+
+    return True  # Always return True to allow the record to pass.
+
+
 class PrismJobServer(job_server.SubprocessJobServer):
   BIN_CACHE = os.path.expanduser("~/.apache_beam/cache/prism/bin")
 
   def __init__(self, options):
     super().__init__()
+
     prism_options = options.view_as(pipeline_options.PrismRunnerOptions)
     # Options flow:
     # If the path is set, always download and unzip the provided path,
@@ -130,6 +176,14 @@ def __init__(self, options):
     job_options = options.view_as(pipeline_options.JobServerOptions)
     self._job_port = job_options.job_port
 
+    self._log_level = prism_options.prism_log_level
+    self._log_kind = prism_options.prism_log_kind
+
+    # override console to json with log filter enabled
+    if self._log_kind == "console":
+      self._log_kind = "json"
+      self._log_filter = PrismRunnerLogFilter()
+
   # the method is only kept for testing and backward compatibility
   @classmethod
   def local_bin(
@@ -181,8 +235,13 @@ def _prepare_executable(
 
     _LOGGER.info("Prism binary path resolved to: %s", target_url)
     # Make sure the binary is executable.
-    st = os.stat(target_url)
-    os.chmod(target_url, st.st_mode | stat.S_IEXEC)
+    try:
+      st = os.stat(target_url)
+      os.chmod(target_url, st.st_mode | stat.S_IEXEC)
+    except PermissionError:
+      _LOGGER.warning(
+          'Could not change permissions of prism binary; invoking may fail if '
+          + 'current process does not have exec permissions on binary.')
     return target_url
 
   @staticmethod
@@ -420,6 +479,10 @@ def prism_arguments(self, job_port) -> typing.List[typing.Any]:
     return [
         '--job_port',
         job_port,
+        '--log_level',
+        self._log_level,
+        '--log_kind',
+        self._log_kind,
         '--serve_http',
         False,
     ]
diff --git a/sdks/python/apache_beam/runners/portability/prism_runner_test.py b/sdks/python/apache_beam/runners/portability/prism_runner_test.py
index 00116e123ce4..a65f9a9960b4 100644
--- a/sdks/python/apache_beam/runners/portability/prism_runner_test.py
+++ b/sdks/python/apache_beam/runners/portability/prism_runner_test.py
@@ -35,10 +35,14 @@
 import apache_beam as beam
 from apache_beam.options.pipeline_options import DebugOptions
 from apache_beam.options.pipeline_options import PortableOptions
+from apache_beam.options.pipeline_options import StandardOptions
+from apache_beam.options.pipeline_options import TypeOptions
 from apache_beam.runners.portability import portable_runner_test
 from apache_beam.runners.portability import prism_runner
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
+from apache_beam.transforms import trigger
+from apache_beam.transforms import window
 from apache_beam.utils import shared
 
 # Run as
@@ -64,6 +68,8 @@ def __init__(self, *args, **kwargs):
     self.environment_type = None
     self.environment_config = None
     self.enable_commit = False
+    self.streaming = False
+    self.allow_unsafe_triggers = False
 
   def setUp(self):
     self.enable_commit = False
@@ -175,6 +181,9 @@ def create_options(self):
     options.view_as(
         PortableOptions).environment_options = self.environment_options
 
+    options.view_as(StandardOptions).streaming = self.streaming
+    options.view_as(
+        TypeOptions).allow_unsafe_triggers = self.allow_unsafe_triggers
     return options
 
   # Can't read host files from within docker, read a "local" file there.
@@ -225,7 +234,66 @@ def test_custom_window_type(self):
   def test_metrics(self):
     super().test_metrics(check_bounded_trie=False)
 
-  # Inherits all other tests.
+  def construct_timestamped(k, t):
+    return window.TimestampedValue((k, t), t)
+
+  def format_result(k, vs):
+    return ('%s-%s' % (k, len(list(vs))), set(vs))
+
+  def test_after_count_trigger_batch(self):
+    self.allow_unsafe_triggers = True
+    with self.create_pipeline() as p:
+      result = (
+          p
+          | beam.Create([1, 2, 3, 4, 5, 10, 11])
+          | beam.FlatMap(lambda t: [('A', t), ('B', t + 5)])
+          #A1, A2, A3, A4, A5, A10, A11, B6, B7, B8, B9, B10, B15, B16
+          | beam.MapTuple(PrismRunnerTest.construct_timestamped)
+          | beam.WindowInto(
+              window.FixedWindows(10),
+              trigger=trigger.AfterCount(3),
+              accumulation_mode=trigger.AccumulationMode.DISCARDING,
+          )
+          | beam.GroupByKey()
+          | beam.MapTuple(PrismRunnerTest.format_result))
+      assert_that(
+          result,
+          equal_to(
+              list([
+                  ('A-5', {1, 2, 3, 4, 5}),
+                  ('A-2', {10, 11}),
+                  ('B-4', {6, 7, 8, 9}),
+                  ('B-3', {10, 15, 16}),
+              ])))
+
+  def test_after_count_trigger_streaming(self):
+    self.allow_unsafe_triggers = True
+    self.streaming = True
+    with self.create_pipeline() as p:
+      result = (
+          p
+          | beam.Create([1, 2, 3, 4, 5, 10, 11])
+          | beam.FlatMap(lambda t: [('A', t), ('B', t + 5)])
+          #A1, A2, A3, A4, A5, A10, A11, B6, B7, B8, B9, B10, B15, B16
+          | beam.MapTuple(PrismRunnerTest.construct_timestamped)
+          | beam.WindowInto(
+              window.FixedWindows(10),
+              trigger=trigger.AfterCount(3),
+              accumulation_mode=trigger.AccumulationMode.DISCARDING,
+          )
+          | beam.GroupByKey()
+          | beam.MapTuple(PrismRunnerTest.format_result))
+      assert_that(
+          result,
+          equal_to(
+              list([
+                  ('A-3', {1, 2, 3}),
+                  ('A-2', {4, 5}),
+                  ('A-2', {10, 11}),
+                  ('B-3', {6, 7, 8}),
+                  ('B-1', {9}),
+                  ('B-3', {10, 15, 16}),
+              ])))
 
 
 class PrismJobServerTest(unittest.TestCase):
@@ -393,9 +461,9 @@ class PrismRunnerSingletonTest(unittest.TestCase):
   @parameterized.expand([True, False])
   def test_singleton(self, enable_singleton):
     if enable_singleton:
-      options = DebugOptions(["--experiment=enable_prism_server_singleton"])
+      options = DebugOptions()  # prism singleton is enabled by default
     else:
-      options = DebugOptions()
+      options = DebugOptions(["--experiment=disable_prism_server_singleton"])
 
     runner = prism_runner.PrismRunner()
     with mock.patch(
diff --git a/sdks/python/apache_beam/runners/portability/stager_test.py b/sdks/python/apache_beam/runners/portability/stager_test.py
index 60e247080665..22a41e592c2b 100644
--- a/sdks/python/apache_beam/runners/portability/stager_test.py
+++ b/sdks/python/apache_beam/runners/portability/stager_test.py
@@ -173,11 +173,13 @@ def test_no_main_session(self):
 
   # xdist adds unpicklable modules to the main session.
   @pytest.mark.no_xdist
+  @pytest.mark.uses_dill
   @unittest.skipIf(
       sys.platform == "win32" and sys.version_info < (3, 8),
       'https://github.com/apache/beam/issues/20659: pytest on Windows pulls '
       'in a zipimporter, unpicklable before py3.8')
   def test_with_main_session(self):
+    pytest.importorskip("dill")
     staging_dir = self.make_temp_dir()
     options = PipelineOptions()
 
diff --git a/sdks/python/apache_beam/runners/render.py b/sdks/python/apache_beam/runners/render.py
index 0827d73cc307..9f37e0201d94 100644
--- a/sdks/python/apache_beam/runners/render.py
+++ b/sdks/python/apache_beam/runners/render.py
@@ -29,7 +29,7 @@
 
   python -m apache_beam.runners.render --job_port=PORT ...
 
-and then run your pipline with the PortableRunner setting the job endpoint
+and then run your pipeline with the PortableRunner setting the job endpoint
 to `localhost:PORT`.
 
 If any `--render_output=path.ext` flags are passed, each submitted job will
diff --git a/sdks/python/apache_beam/runners/worker/data_sampler_test.py b/sdks/python/apache_beam/runners/worker/data_sampler_test.py
index 8c47315b7a9e..47b6cca880d3 100644
--- a/sdks/python/apache_beam/runners/worker/data_sampler_test.py
+++ b/sdks/python/apache_beam/runners/worker/data_sampler_test.py
@@ -383,8 +383,10 @@ def test_samples_all_with_both_experiments(self):
         MAIN_TRANSFORM_ID, descriptor, self.primitives_coder_factory)
 
     # Get the samples for the two outputs.
-    a_sampler = self.data_sampler.sampler_for_output(MAIN_TRANSFORM_ID, 0)
-    b_sampler = self.data_sampler.sampler_for_output(MAIN_TRANSFORM_ID, 1)
+    # N.B. the order of the samplers is not guaranteed due to Protobuf not
+    # guaranteeing map iteration order.
+    first_sampler = self.data_sampler.sampler_for_output(MAIN_TRANSFORM_ID, 0)
+    second_sampler = self.data_sampler.sampler_for_output(MAIN_TRANSFORM_ID, 1)
 
     # Sample an exception for the output 'a', this will show up in the final
     # samples response.
@@ -393,19 +395,25 @@ def test_samples_all_with_both_experiments(self):
       raise Exception('test')
     except Exception:
       exc_info = sys.exc_info()
-    a_sampler.sample_exception('a', exc_info, MAIN_TRANSFORM_ID, 'instid')
+    first_sampler.sample_exception(
+        'first', exc_info, MAIN_TRANSFORM_ID, 'instid')
 
     # Sample a normal element for the output 'b', this will not show up in the
     # final samples response.
-    b_sampler.element_sampler.el = 'b'
-    b_sampler.element_sampler.has_element = True
+    second_sampler.element_sampler.el = 'second'
+    second_sampler.element_sampler.has_element = True
 
     samples = self.data_sampler.wait_for_samples(['a', 'b'])
     self.assertEqual(len(samples.element_samples), 2)
-    self.assertTrue(
-        samples.element_samples['a'].elements[0].HasField('exception'))
-    self.assertFalse(
-        samples.element_samples['b'].elements[0].HasField('exception'))
+    sample_elements = list(
+        s.elements[0] for s in samples.element_samples.values())
+    num_exceptions = sum(
+        1 for element in sample_elements if element.HasField('exception'))
+    self.assertEqual(
+        num_exceptions,
+        1,
+        "Only one of the samples should have an exception, found: {}".format(
+            sample_elements))
 
   def test_only_sample_exceptions(self):
     """Tests that the exception sampling experiment only samples exceptions."""
@@ -420,8 +428,10 @@ def test_only_sample_exceptions(self):
         MAIN_TRANSFORM_ID, descriptor, self.primitives_coder_factory)
 
     # Get the samples for the two outputs.
-    a_sampler = self.data_sampler.sampler_for_output(MAIN_TRANSFORM_ID, 0)
-    b_sampler = self.data_sampler.sampler_for_output(MAIN_TRANSFORM_ID, 1)
+    # N.B. the order of the samplers is not guaranteed due to Protobuf not
+    # guaranteeing map iteration order.
+    first_sampler = self.data_sampler.sampler_for_output(MAIN_TRANSFORM_ID, 0)
+    second_sampler = self.data_sampler.sampler_for_output(MAIN_TRANSFORM_ID, 1)
 
     # Sample an exception for the output 'a', this will show up in the final
     # samples response.
@@ -430,16 +440,18 @@ def test_only_sample_exceptions(self):
       raise Exception('test')
     except Exception:
       exc_info = sys.exc_info()
-    a_sampler.sample_exception('a', exc_info, MAIN_TRANSFORM_ID, 'instid')
+    first_sampler.sample_exception(
+        'first', exc_info, MAIN_TRANSFORM_ID, 'instid')
 
     # Sample a normal element for the output 'b', this will not show up in the
     # final samples response.
-    b_sampler.element_sampler.el = 'b'
-    b_sampler.element_sampler.has_element = True
+    second_sampler.element_sampler.el = 'second'
+    second_sampler.element_sampler.has_element = True
 
     samples = self.data_sampler.wait_for_samples([])
     self.assertEqual(len(samples.element_samples), 1)
-    self.assertIsNotNone(samples.element_samples['a'].elements[0].exception)
+    value = list(samples.element_samples.values())[0]
+    self.assertIsNotNone(value.elements[0].exception)
 
 
 class OutputSamplerTest(unittest.TestCase):
diff --git a/sdks/python/apache_beam/runners/worker/sdk_worker.py b/sdks/python/apache_beam/runners/worker/sdk_worker.py
index 5240674c7009..0b4c236d6b37 100644
--- a/sdks/python/apache_beam/runners/worker/sdk_worker.py
+++ b/sdks/python/apache_beam/runners/worker/sdk_worker.py
@@ -176,6 +176,7 @@ def __init__(
       # that should be reported to the runner when proocessing the first bundle.
       deferred_exception=None,  # type: Optional[Exception]
       runner_capabilities=frozenset(),  # type: FrozenSet[str]
+      element_processing_timeout_minutes=None,  # type: Optional[int]
   ):
     # type: (...) -> None
     self._alive = True
@@ -207,6 +208,8 @@ def __init__(
     self._profiler_factory = profiler_factory
     self.data_sampler = data_sampler
     self.runner_capabilities = runner_capabilities
+    self._element_processing_timeout_minutes = (
+        element_processing_timeout_minutes)
 
     def default_factory(id):
       # type: (str) -> beam_fn_api_pb2.ProcessBundleDescriptor
@@ -223,21 +226,21 @@ def default_factory(id):
         fns=self._fns,
         data_sampler=self.data_sampler,
     )
-
+    self._status_handler = None  # type: Optional[FnApiWorkerStatusHandler]
     if status_address:
       try:
         self._status_handler = FnApiWorkerStatusHandler(
             status_address,
             self._bundle_processor_cache,
             self._state_cache,
-            enable_heap_dump)  # type: Optional[FnApiWorkerStatusHandler]
+            enable_heap_dump,
+            element_processing_timeout_minutes=self.
+            _element_processing_timeout_minutes)
       except Exception:
         traceback_string = traceback.format_exc()
         _LOGGER.warning(
             'Error creating worker status request handler, '
             'skipping status report. Trace back: %s' % traceback_string)
-    else:
-      self._status_handler = None
 
     # TODO(BEAM-8998) use common
     # thread_pool_executor.shared_unbounded_instance() to process bundle
diff --git a/sdks/python/apache_beam/runners/worker/sdk_worker_main.py b/sdks/python/apache_beam/runners/worker/sdk_worker_main.py
index b3c81fd93467..7ea0e0eb1099 100644
--- a/sdks/python/apache_beam/runners/worker/sdk_worker_main.py
+++ b/sdks/python/apache_beam/runners/worker/sdk_worker_main.py
@@ -24,7 +24,9 @@
 import logging
 import os
 import re
+import signal
 import sys
+import time
 import traceback
 
 from google.protobuf import text_format
@@ -47,6 +49,7 @@
 
 _LOGGER = logging.getLogger(__name__)
 _ENABLE_GOOGLE_CLOUD_PROFILER = 'enable_google_cloud_profiler'
+_FN_LOG_HANDLER = None
 
 
 def _import_beam_plugins(plugins):
@@ -167,7 +170,9 @@ def create_harness(environment, dry_run=False):
       enable_heap_dump=enable_heap_dump,
       data_sampler=data_sampler,
       deferred_exception=deferred_exception,
-      runner_capabilities=runner_capabilities)
+      runner_capabilities=runner_capabilities,
+      element_processing_timeout_minutes=sdk_pipeline_options.view_as(
+          WorkerOptions).element_processing_timeout_minutes)
   return fn_log_handler, sdk_harness, sdk_pipeline_options
 
 
@@ -202,7 +207,9 @@ def main(unused_argv):
   """Main entry point for SDK Fn Harness."""
   (fn_log_handler, sdk_harness,
    sdk_pipeline_options) = create_harness(os.environ)
-
+  global _FN_LOG_HANDLER
+  if fn_log_handler:
+    _FN_LOG_HANDLER = fn_log_handler
   gcp_profiler_name = _get_gcp_profiler_name_if_enabled(sdk_pipeline_options)
   if gcp_profiler_name:
     _start_profiler(gcp_profiler_name, os.environ["JOB_ID"])
@@ -219,6 +226,15 @@ def main(unused_argv):
       fn_log_handler.close()
 
 
+def terminate_sdk_harness():
+  """Flushes the FnApiLogRecordHandler if it exists."""
+  _LOGGER.error('The SDK harness will be terminated in 5 seconds.')
+  time.sleep(5)
+  if _FN_LOG_HANDLER:
+    _FN_LOG_HANDLER.close()
+  os.kill(os.getpid(), signal.SIGINT)
+
+
 def _load_pipeline_options(options_json):
   if options_json is None:
     return {}
diff --git a/sdks/python/apache_beam/runners/worker/worker_status.py b/sdks/python/apache_beam/runners/worker/worker_status.py
index d67bd4437fbb..86a7b5e8ee1a 100644
--- a/sdks/python/apache_beam/runners/worker/worker_status.py
+++ b/sdks/python/apache_beam/runners/worker/worker_status.py
@@ -165,7 +165,8 @@ def __init__(
       state_cache=None,
       enable_heap_dump=False,
       worker_id=None,
-      log_lull_timeout_ns=DEFAULT_LOG_LULL_TIMEOUT_NS):
+      log_lull_timeout_ns=DEFAULT_LOG_LULL_TIMEOUT_NS,
+      element_processing_timeout_minutes=None):
     """Initialize FnApiWorkerStatusHandler.
 
     Args:
@@ -184,6 +185,12 @@ def __init__(
         self._status_channel)
     self._responses = queue.Queue()
     self.log_lull_timeout_ns = log_lull_timeout_ns
+    if (element_processing_timeout_minutes and
+        element_processing_timeout_minutes > 0):
+      self._element_processing_timeout_ns = (
+          element_processing_timeout_minutes * 60 * 1e9)
+    else:
+      self._element_processing_timeout_ns = None
     self._last_full_thread_dump_secs = 0.0
     self._last_lull_logged_secs = 0.0
     self._server = threading.Thread(
@@ -252,22 +259,46 @@ def _log_lull_in_bundle_processor(self, bundle_process_cache):
             self._log_lull_sampler_info(info, instruction)
 
   def _log_lull_sampler_info(self, sampler_info, instruction):
-    if not self._passed_lull_timeout_since_last_log():
+    if (not sampler_info or not sampler_info.time_since_transition):
+      return
+
+    log_lull = (
+        self._passed_lull_timeout_since_last_log() and
+        sampler_info.time_since_transition > self.log_lull_timeout_ns)
+    timeout_exceeded = (
+        self._element_processing_timeout_ns and
+        sampler_info.time_since_transition
+        > self._element_processing_timeout_ns)
+
+    if not (log_lull or timeout_exceeded):
       return
-    if (sampler_info and sampler_info.time_since_transition and
-        sampler_info.time_since_transition > self.log_lull_timeout_ns):
-      lull_seconds = sampler_info.time_since_transition / 1e9
 
-      step_name = sampler_info.state_name.step_name
-      state_name = sampler_info.state_name.name
-      if step_name and state_name:
-        step_name_log = (
-            ' for PTransform{name=%s, state=%s}' % (step_name, state_name))
-      else:
-        step_name_log = ''
+    lull_seconds = sampler_info.time_since_transition / 1e9
+    step_name = sampler_info.state_name.step_name
+    state_name = sampler_info.state_name.name
+    if step_name and state_name:
+      step_name_log = (
+          ' for PTransform{name=%s, state=%s}' % (step_name, state_name))
+    else:
+      step_name_log = ''
+    stack_trace = self._get_stack_trace(sampler_info)
 
-      stack_trace = self._get_stack_trace(sampler_info)
+    if timeout_exceeded:
+      _LOGGER.error(
+          (
+              'Processing of an element in bundle %s%s has exceeded the '
+              'specified timeout of %.2f minutes. SDK harness will be '
+              'terminated.\n'
+              'Current Traceback:\n%s'),
+          instruction,
+          step_name_log,
+          self._element_processing_timeout_ns / 1e9 / 60,
+          stack_trace,
+      )
+      from apache_beam.runners.worker.sdk_worker_main import terminate_sdk_harness
+      terminate_sdk_harness()
 
+    if log_lull:
       _LOGGER.warning(
           (
               'Operation ongoing in bundle %s%s for at least %.2f seconds'
diff --git a/sdks/python/apache_beam/runners/worker/worker_status_test.py b/sdks/python/apache_beam/runners/worker/worker_status_test.py
index 1004d21e7fd3..67df1a324d9e 100644
--- a/sdks/python/apache_beam/runners/worker/worker_status_test.py
+++ b/sdks/python/apache_beam/runners/worker/worker_status_test.py
@@ -59,7 +59,8 @@ def setUp(self):
     self.test_port = self.server.add_insecure_port('[::]:0')
     self.server.start()
     self.url = 'localhost:%s' % self.test_port
-    self.fn_status_handler = FnApiWorkerStatusHandler(self.url)
+    self.fn_status_handler = FnApiWorkerStatusHandler(
+        self.url, element_processing_timeout_minutes=10)
 
   def tearDown(self):
     self.server.stop(5)
@@ -89,42 +90,48 @@ def test_generate_error(self, mock_method):
   def test_log_lull_in_bundle_processor(self):
     def get_state_sampler_info_for_lull(lull_duration_s):
       return "bundle-id", statesampler.StateSamplerInfo(
-          CounterName('progress-msecs', 'stage_name', 'step_name'),
-          1,
-          lull_duration_s * 1e9,
-          threading.current_thread())
+        CounterName('progress-msecs', 'stage_name', 'step_name'),
+        1,
+        lull_duration_s * 1e9,
+        threading.current_thread())
 
     now = time.time()
     with mock.patch('logging.Logger.warning') as warn_mock:
-      with mock.patch('time.time') as time_mock:
-        time_mock.return_value = now
-        bundle_id, sampler_info = get_state_sampler_info_for_lull(21 * 60)
-        self.fn_status_handler._log_lull_sampler_info(sampler_info, bundle_id)
-
-        bundle_id_template = warn_mock.call_args[0][1]
-        step_name_template = warn_mock.call_args[0][2]
-        processing_template = warn_mock.call_args[0][3]
-        traceback = warn_mock.call_args = warn_mock.call_args[0][4]
-
-        self.assertIn('bundle-id', bundle_id_template)
-        self.assertIn('step_name', step_name_template)
-        self.assertEqual(21 * 60, processing_template)
-        self.assertIn('test_log_lull_in_bundle_processor', traceback)
-
-      with mock.patch('time.time') as time_mock:
-        time_mock.return_value = now + 6 * 60  # 6 minutes
-        bundle_id, sampler_info = get_state_sampler_info_for_lull(21 * 60)
-        self.fn_status_handler._log_lull_sampler_info(sampler_info, bundle_id)
-
-      with mock.patch('time.time') as time_mock:
-        time_mock.return_value = now + 21 * 60  # 21 minutes
-        bundle_id, sampler_info = get_state_sampler_info_for_lull(10 * 60)
-        self.fn_status_handler._log_lull_sampler_info(sampler_info, bundle_id)
-
-      with mock.patch('time.time') as time_mock:
-        time_mock.return_value = now + 42 * 60  # 21 minutes after previous one
-        bundle_id, sampler_info = get_state_sampler_info_for_lull(21 * 60)
-        self.fn_status_handler._log_lull_sampler_info(sampler_info, bundle_id)
+      with mock.patch(
+          'apache_beam.runners.worker.sdk_worker_main.terminate_sdk_harness'
+      ) as flush_mock:
+        with mock.patch('time.time') as time_mock:
+          time_mock.return_value = now
+          bundle_id, sampler_info = get_state_sampler_info_for_lull(21 * 60)
+          self.fn_status_handler._log_lull_sampler_info(sampler_info, bundle_id)
+          bundle_id_template = warn_mock.call_args[0][1]
+          step_name_template = warn_mock.call_args[0][2]
+          processing_template = warn_mock.call_args[0][3]
+          traceback = warn_mock.call_args = warn_mock.call_args[0][4]
+
+          self.assertIn('bundle-id', bundle_id_template)
+          self.assertIn('step_name', step_name_template)
+          self.assertEqual(21 * 60, processing_template)
+          self.assertIn('test_log_lull_in_bundle_processor', traceback)
+          flush_mock.assert_called_once()
+
+        with mock.patch('time.time') as time_mock:
+          time_mock.return_value = now + 6 * 60  # 6 minutes
+          bundle_id, sampler_info = get_state_sampler_info_for_lull(21 * 60)
+          self.fn_status_handler._log_lull_sampler_info(sampler_info, bundle_id)
+          self.assertEqual(flush_mock.call_count, 2)
+
+        with mock.patch('time.time') as time_mock:
+          time_mock.return_value = now + 21 * 60  # 21 minutes
+          bundle_id, sampler_info = get_state_sampler_info_for_lull(10 * 60)
+          self.fn_status_handler._log_lull_sampler_info(sampler_info, bundle_id)
+          self.assertEqual(flush_mock.call_count, 2)
+
+        with mock.patch('time.time') as time_mock:
+          time_mock.return_value = now + 42 * 60  # 42 minutes
+          bundle_id, sampler_info = get_state_sampler_info_for_lull(11 * 60)
+          self.fn_status_handler._log_lull_sampler_info(sampler_info, bundle_id)
+          self.assertEqual(flush_mock.call_count, 3)
 
 
 class HeapDumpTest(unittest.TestCase):
diff --git a/sdks/python/apache_beam/testing/benchmarks/cloudml/requirements.txt b/sdks/python/apache_beam/testing/benchmarks/cloudml/requirements.txt
index 8ddfddece547..52587ca8976d 100644
--- a/sdks/python/apache_beam/testing/benchmarks/cloudml/requirements.txt
+++ b/sdks/python/apache_beam/testing/benchmarks/cloudml/requirements.txt
@@ -15,5 +15,6 @@
 #    limitations under the License.
 #
 
+dill
 tfx_bsl
 tensorflow-transform
diff --git a/sdks/python/apache_beam/testing/benchmarks/inference/vllm_gemma_benchmarks.py b/sdks/python/apache_beam/testing/benchmarks/inference/vllm_gemma_benchmarks.py
new file mode 100644
index 000000000000..903d67b91969
--- /dev/null
+++ b/sdks/python/apache_beam/testing/benchmarks/inference/vllm_gemma_benchmarks.py
@@ -0,0 +1,44 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import logging
+
+from apache_beam.examples.inference import vllm_gemma_batch
+from apache_beam.testing.load_tests.dataflow_cost_benchmark import DataflowCostBenchmark
+
+
+class VllmGemmaBenchmarkTest(DataflowCostBenchmark):
+  def __init__(self):
+    self.metrics_namespace = "BeamML_vLLM"
+    super().__init__(
+        metrics_namespace=self.metrics_namespace,
+        pcollection="WriteBQ.out0",
+    )
+
+  def test(self):
+    # The perf-test framework passes --input_file,
+    # but the pipeline expects --input.
+    extra_opts = {"input": self.pipeline.get_option("input_file")}
+
+    self.result = vllm_gemma_batch.run(
+        self.pipeline.get_full_options_as_args(**extra_opts),
+        test_pipeline=self.pipeline)
+
+
+if __name__ == "__main__":
+  logging.basicConfig(level=logging.INFO)
+  VllmGemmaBenchmarkTest().run()
diff --git a/sdks/python/apache_beam/testing/load_tests/build.gradle b/sdks/python/apache_beam/testing/load_tests/build.gradle
index 538d4a01bfee..73699e89c704 100644
--- a/sdks/python/apache_beam/testing/load_tests/build.gradle
+++ b/sdks/python/apache_beam/testing/load_tests/build.gradle
@@ -33,6 +33,8 @@ def loadTestArgsProperty = "loadTest.args"
 
 def runnerProperty = "runner"
 
+def sdkLocationOverrideProperty = "sdkLocationOverride"
+
 def requirementsTxtFileProperty = "loadTest.requirementsTxtFile"
 
 task run(type: Exec, dependsOn: installGcpTest) {
@@ -40,8 +42,11 @@ task run(type: Exec, dependsOn: installGcpTest) {
 
     def runnerArg = project.findProperty(runnerProperty) ?: ""
     if (runnerArg == 'DataflowRunner' || runnerArg == 'TestDataflowRunner') {
-        dependsOn ':sdks:python:sdist'
-        loadTestArgs +=" --sdk_location=${files(configurations.distTarBall.files).singleFile}"
+        def sdkLocationOverride = Boolean.parseBoolean(project.findProperty(sdkLocationOverrideProperty) ?: 'true')
+        if (sdkLocationOverride) {
+            dependsOn ':sdks:python:sdist'
+            loadTestArgs +=" --sdk_location=${files(configurations.distTarBall.files).singleFile}"
+        }
     }
 
     String requirementsTxtFileArg = project.findProperty(requirementsTxtFileProperty) ?: null
@@ -67,4 +72,4 @@ task run(type: Exec, dependsOn: installGcpTest) {
 
 def parseOptions(String option) {
     option.replace('\"', '\\"')
-}
+}
\ No newline at end of file
diff --git a/sdks/python/apache_beam/testing/test_pipeline.py b/sdks/python/apache_beam/testing/test_pipeline.py
index c8e6e7c3968b..6a96e32bb929 100644
--- a/sdks/python/apache_beam/testing/test_pipeline.py
+++ b/sdks/python/apache_beam/testing/test_pipeline.py
@@ -71,7 +71,8 @@ def __init__(
       is_integration_test=False,
       blocking=True,
       additional_pipeline_args=None,
-      display_data=None):
+      display_data=None,
+      timeout=None):
     """Initialize a pipeline object for test.
 
     Args:
@@ -96,7 +97,8 @@ def __init__(
         included when construction the pipeline options object.
       display_data (Dict[str, Any]): a dictionary of static data associated
         with this pipeline that can be displayed when it runs.
-
+      timeout (int, optional): Milliseconds to wait for the pipeline to finish.
+        If the timeout is reached, an AssertionError is raised.        
     Raises:
       ValueError: if either the runner or options argument is not
         of the expected type.
@@ -107,6 +109,7 @@ def __init__(
     self.options_list = (
         self._parse_test_option_args(argv) + additional_pipeline_args)
     self.blocking = blocking
+    self.timeout = timeout
     if options is None:
       options = PipelineOptions(self.options_list)
     super().__init__(runner, options, display_data=display_data)
@@ -116,7 +119,7 @@ def run(self, test_runner_api=True):
         test_runner_api=(
             False if self.not_use_test_runner_api else test_runner_api))
     if self.blocking:
-      state = result.wait_until_finish()
+      state = result.wait_until_finish(duration=self.timeout)
       assert state in (PipelineState.DONE, PipelineState.CANCELLED), \
           "Pipeline execution failed."
 
diff --git a/sdks/python/apache_beam/testing/util.py b/sdks/python/apache_beam/testing/util.py
index c9745abf9499..5a7c36fa4458 100644
--- a/sdks/python/apache_beam/testing/util.py
+++ b/sdks/python/apache_beam/testing/util.py
@@ -32,6 +32,7 @@
 from apache_beam.transforms import window
 from apache_beam.transforms.core import Create
 from apache_beam.transforms.core import DoFn
+from apache_beam.transforms.core import Filter
 from apache_beam.transforms.core import Map
 from apache_beam.transforms.core import ParDo
 from apache_beam.transforms.core import WindowInto
@@ -45,6 +46,7 @@
     'assert_that',
     'equal_to',
     'equal_to_per_window',
+    'has_at_least_one',
     'is_empty',
     'is_not_empty',
     'matches_all',
@@ -377,6 +379,33 @@ def AssertThat(pcoll, *args, **kwargs):
   return assert_that(pcoll, *args, **kwargs)
 
 
+def has_at_least_one(input, criterion, label="has_at_least_one"):
+  pipeline = input.pipeline
+  # similar to assert_that, we choose a label if it already exists.
+  if label in pipeline.applied_labels:
+    label_idx = 2
+    while f"{label}_{label_idx}" in pipeline.applied_labels:
+      label_idx += 1
+    label = f"{label}_{label_idx}"
+
+  def _apply_criterion(
+      e=DoFn.ElementParam,
+      t=DoFn.TimestampParam,
+      w=DoFn.WindowParam,
+      p=DoFn.PaneInfoParam):
+    if criterion(e, t, w, p):
+      return e, t, w, p
+
+  def _not_empty(actual):
+    actual = list(actual)
+    if not actual:
+      raise BeamAssertException('Failed assert: nothing matches the criterion')
+
+  result = input | label >> Map(_apply_criterion) | label + "_filter" >> Filter(
+      lambda e: e is not None)
+  assert_that(result, _not_empty)
+
+
 def open_shards(glob_pattern, mode='rt', encoding='utf-8'):
   """Returns a composite file of all shards matching the given glob pattern.
 
diff --git a/sdks/python/apache_beam/testing/util_test.py b/sdks/python/apache_beam/testing/util_test.py
index dbb5d0fd37a5..12314f4653aa 100644
--- a/sdks/python/apache_beam/testing/util_test.py
+++ b/sdks/python/apache_beam/testing/util_test.py
@@ -26,7 +26,6 @@
 from apache_beam import Create
 from apache_beam.options.pipeline_options import StandardOptions
 from apache_beam.testing.test_pipeline import TestPipeline
-from apache_beam.testing.util import BeamAssertException
 from apache_beam.testing.util import TestWindowedValue
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
@@ -69,22 +68,22 @@ def test_assert_that_fails(self):
         assert_that(p | Create([1, 10, 100]), equal_to([1, 2, 3]))
 
   def test_assert_missing(self):
-    with self.assertRaisesRegex(BeamAssertException,
-                                r"missing elements \['c'\]"):
+    with self.assertRaisesRegex(Exception, r".*missing elements \['c'\]"):
       with TestPipeline() as p:
         assert_that(p | Create(['a', 'b']), equal_to(['a', 'b', 'c']))
 
   def test_assert_unexpected(self):
-    with self.assertRaisesRegex(BeamAssertException,
-                                r"unexpected elements \['c', 'd'\]|"
+    with self.assertRaisesRegex(Exception,
+                                r".*unexpected elements \['c', 'd'\]|"
                                 r"unexpected elements \['d', 'c'\]"):
       with TestPipeline() as p:
         assert_that(p | Create(['a', 'b', 'c', 'd']), equal_to(['a', 'b']))
 
   def test_assert_missing_and_unexpected(self):
-    with self.assertRaisesRegex(
-        BeamAssertException,
-        r"unexpected elements \['c'\].*missing elements \['d'\]"):
+    with self.assertRaisesRegex(Exception,
+                                r".*unexpected elements \["
+                                r"'c'\].*missing elements"
+                                r" \['d'\]"):
       with TestPipeline() as p:
         assert_that(p | Create(['a', 'b', 'c']), equal_to(['a', 'b', 'd']))
 
@@ -144,7 +143,7 @@ def test_assert_that_passes_is_not_empty(self):
       assert_that(p | Create([1, 2, 3]), is_not_empty())
 
   def test_assert_that_fails_on_is_not_empty_expected(self):
-    with self.assertRaises(BeamAssertException):
+    with self.assertRaisesRegex(Exception, "pcol is empty"):
       with TestPipeline() as p:
         assert_that(p | Create([]), is_not_empty())
 
@@ -168,7 +167,7 @@ def test_equal_to_per_window_passes(self):
                   reify_windows=True)
 
   def test_equal_to_per_window_fail_unmatched_window(self):
-    with self.assertRaises(BeamAssertException):
+    with self.assertRaisesRegex(Exception, "not found in any expected"):
       expected = {
           window.IntervalWindow(50, 100): [('k', [1])],
       }
@@ -199,7 +198,7 @@ def test_multiple_assert_that_labels(self):
       assert_that(outputs, equal_to([2, 3, 4]))
 
   def test_equal_to_per_window_fail_unmatched_element(self):
-    with self.assertRaises(BeamAssertException):
+    with self.assertRaisesRegex(Exception, "unmatched elements"):
       start = int(MIN_TIMESTAMP.micros // 1e6) - 5
       end = start + 20
       expected = {
@@ -237,7 +236,7 @@ def test_equal_to_per_window_succeeds_no_reify_windows(self):
                   equal_to_per_window(expected))
 
   def test_equal_to_per_window_fail_unexpected_element(self):
-    with self.assertRaises(BeamAssertException):
+    with self.assertRaisesRegex(Exception, "not found in window"):
       start = int(MIN_TIMESTAMP.micros // 1e6) - 5
       end = start + 20
       expected = {
@@ -289,7 +288,7 @@ class RowTuple(NamedTuple):
     self.assertFalse(
         row_namedtuple_equals_fn(beam.Row(a='123'), RowTuple(a='123', b=4567)))
     self.assertFalse(row_namedtuple_equals_fn(beam.Row(a='123'), '123'))
-    self.assertFalse(row_namedtuple_equals_fn('123', RowTuple(a='123', b=456)))
+    self.assertFalse(row_namedtuple_equals_fn('123', RowTuple(a='123', b=4567)))
 
     class NestedNamedTuple(NamedTuple):
       a: str
diff --git a/sdks/python/apache_beam/tools/coders_microbenchmark.py b/sdks/python/apache_beam/tools/coders_microbenchmark.py
index a5a0c25c4ef9..7a1f9f6dcc1b 100644
--- a/sdks/python/apache_beam/tools/coders_microbenchmark.py
+++ b/sdks/python/apache_beam/tools/coders_microbenchmark.py
@@ -42,6 +42,7 @@
 from apache_beam.coders import proto2_coder_test_messages_pb2 as test_message
 from apache_beam.coders import coder_impl
 from apache_beam.coders import coders
+from apache_beam.coders import coders_test_common
 from apache_beam.coders import row_coder
 from apache_beam.coders import typecoders
 from apache_beam.tools import utils
@@ -249,6 +250,10 @@ def row_coder_benchmark_factory(generate_fn):
   return coder_benchmark_factory(get_row_coder(generate_fn()), generate_fn)
 
 
+def importable_named_tuple():
+  return [coders_test_common.MyTypedNamedTuple('a', i) for i in range(1000)]
+
+
 def run_coder_benchmarks(
     num_runs, input_size, seed, verbose, filter_regex='.*'):
   random.seed(seed)
@@ -310,6 +315,11 @@ def run_coder_benchmarks(
       batch_row_coder_benchmark_factory(nullable_row, True),
       batch_row_coder_benchmark_factory(diverse_row, False),
       batch_row_coder_benchmark_factory(diverse_row, True),
+      coder_benchmark_factory(
+          coders.IterableCoder(
+              coders.FastPrimitivesCoder().as_deterministic_coder(
+                  step_label="step")),
+          importable_named_tuple),
   ]
 
   suite = [
diff --git a/sdks/python/apache_beam/transforms/async_dofn.py b/sdks/python/apache_beam/transforms/async_dofn.py
index 5833bbc46265..6dc43dbf8da9 100644
--- a/sdks/python/apache_beam/transforms/async_dofn.py
+++ b/sdks/python/apache_beam/transforms/async_dofn.py
@@ -33,6 +33,7 @@
 from apache_beam.transforms.userstate import ReadModifyWriteStateSpec
 from apache_beam.transforms.userstate import TimerSpec
 from apache_beam.transforms.userstate import on_timer
+from apache_beam.utils.shared import Shared
 from apache_beam.utils.timestamp import Duration
 from apache_beam.utils.timestamp import Timestamp
 
@@ -114,11 +115,17 @@ def __init__(
     self.timer_frequency_ = callback_frequency
     self.parallelism_ = parallelism
     self._next_time_to_fire = Timestamp.now() + Duration(seconds=5)
+    self._shared_handle = Shared()
+
+  @staticmethod
+  def initialize_pool(parallelism):
+    return lambda: ThreadPoolExecutor(max_workers=parallelism)
 
   @staticmethod
   def reset_state():
     for pool in AsyncWrapper._pool.values():
-      pool.shutdown(wait=True, cancel_futures=True)
+      pool.acquire(AsyncWrapper.initialize_pool(1)).shutdown(
+          wait=True, cancel_futures=True)
     with AsyncWrapper._lock:
       AsyncWrapper._pool = {}
       AsyncWrapper._processing_elements = {}
@@ -129,8 +136,7 @@ def setup(self):
     self._sync_fn.setup()
     with AsyncWrapper._lock:
       if not self._uuid in AsyncWrapper._pool:
-        AsyncWrapper._pool[self._uuid] = ThreadPoolExecutor(
-            max_workers=self._parallelism)
+        AsyncWrapper._pool[self._uuid] = Shared()
         AsyncWrapper._processing_elements[self._uuid] = {}
         AsyncWrapper._items_in_buffer[self._uuid] = 0
 
@@ -202,9 +208,10 @@ def schedule_if_room(self, element, ignore_buffer=False, *args, **kwargs):
         logging.info('item %s already in processing elements', element)
         return True
       if self.accepting_items() or ignore_buffer:
-        result = AsyncWrapper._pool[self._uuid].submit(
-            lambda: self.sync_fn_process(element, *args, **kwargs),
-        )
+        result = AsyncWrapper._pool[self._uuid].acquire(
+            AsyncWrapper.initialize_pool(self._parallelism)).submit(
+                lambda: self.sync_fn_process(element, *args, **kwargs),
+            )
         result.add_done_callback(self.decrement_items_in_buffer)
         AsyncWrapper._processing_elements[self._uuid][element] = result
         AsyncWrapper._items_in_buffer[self._uuid] += 1
diff --git a/sdks/python/apache_beam/transforms/combinefn_lifecycle_test.py b/sdks/python/apache_beam/transforms/combinefn_lifecycle_test.py
index 647e08db7aaa..69172a55f246 100644
--- a/sdks/python/apache_beam/transforms/combinefn_lifecycle_test.py
+++ b/sdks/python/apache_beam/transforms/combinefn_lifecycle_test.py
@@ -59,7 +59,12 @@ def test_combining_value_state(self):
     {'runner': fn_api_runner.FnApiRunner, 'pickler': 'dill'},
     {'runner': fn_api_runner.FnApiRunner, 'pickler': 'cloudpickle'},
     ])  # yapf: disable
+@pytest.mark.uses_dill
 class LocalCombineFnLifecycleTest(unittest.TestCase):
+  def setUp(self):
+    if self.pickler == 'dill':
+      pytest.importorskip("dill")
+
   def tearDown(self):
     CallSequenceEnforcingCombineFn.instances.clear()
 
diff --git a/sdks/python/apache_beam/transforms/combiners.py b/sdks/python/apache_beam/transforms/combiners.py
index 58267ef97ac6..6e4647fecef3 100644
--- a/sdks/python/apache_beam/transforms/combiners.py
+++ b/sdks/python/apache_beam/transforms/combiners.py
@@ -41,6 +41,7 @@
 from apache_beam.typehints import with_output_types
 from apache_beam.utils.timestamp import Duration
 from apache_beam.utils.timestamp import Timestamp
+from apache_beam.utils.windowed_value import WindowedValue
 
 __all__ = [
     'Count',
@@ -985,3 +986,126 @@ def merge_accumulators(self, accumulators):
 
   def extract_output(self, accumulator):
     return accumulator[0]
+
+
+class LiftedCombinePerKey(core.PTransform):
+  """An implementation of CombinePerKey that does mapper-side pre-combining.
+
+  This shouldn't generally be used directly except for use-cases where a
+  runner doesn't support CombinePerKey. This implementation manually implements
+  a CombinePerKey using ParDos, as opposed to runner implementations which may
+  use a more efficient implementation.
+  """
+  def __init__(self, combine_fn, args, kwargs):
+    side_inputs = _pack_side_inputs(args, kwargs)
+    self._side_inputs: dict = side_inputs
+    if not isinstance(combine_fn, core.CombineFn):
+      combine_fn = core.CombineFn.from_callable(combine_fn)
+    self._combine_fn = combine_fn
+
+  def expand(self, pcoll):
+    return (
+        pcoll
+        | core.ParDo(
+            _PartialGroupByKeyCombiningValues(self._combine_fn),
+            **self._side_inputs)
+        | core.GroupByKey()
+        | core.ParDo(_FinishCombine(self._combine_fn), **self._side_inputs))
+
+
+def _pack_side_inputs(side_input_args, side_input_kwargs):
+  if len(side_input_args) >= 10:
+    # If we have more than 10 side inputs, we can't use the
+    # _side_input_arg_{i} as our keys since they won't sort
+    # correctly. Just punt for now, more than 10 args probably
+    # doesn't happen often.
+    raise NotImplementedError
+  side_inputs = {}
+  for i, si in enumerate(side_input_args):
+    side_inputs[f'_side_input_arg_{i}'] = si
+  for k, v in side_input_kwargs.items():
+    side_inputs[k] = v
+  return side_inputs
+
+
+def _unpack_side_inputs(side_inputs):
+  side_input_args = []
+  side_input_kwargs = {}
+  for k, v in sorted(side_inputs.items(), key=lambda x: x[0]):
+    if k.startswith('_side_input_arg_'):
+      side_input_args.append(v)
+    else:
+      side_input_kwargs[k] = v
+  return side_input_args, side_input_kwargs
+
+
+class _PartialGroupByKeyCombiningValues(core.DoFn):
+  """Aggregates values into a per-key-window cache.
+
+  As bundles are in-memory-sized, we don't bother flushing until the very end.
+  """
+  def __init__(self, combine_fn):
+    self._combine_fn = combine_fn
+    self.side_input_args = []
+    self.side_input_kwargs = {}
+
+  def setup(self):
+    self._combine_fn.setup()
+
+  def start_bundle(self):
+    self._cache = dict()
+    self._cached_windowed_side_inputs = {}
+
+  def process(self, element, window=core.DoFn.WindowParam, **side_inputs):
+    k, vi = element
+    side_input_args, side_input_kwargs = _unpack_side_inputs(side_inputs)
+    if (k, window) not in self._cache:
+      self._cache[(k, window)] = self._combine_fn.create_accumulator(
+          *side_input_args, **side_input_kwargs)
+
+    self._cache[k, window] = self._combine_fn.add_input(
+        self._cache[k, window], vi, *side_input_args, **side_input_kwargs)
+    self._cached_windowed_side_inputs[window] = (
+        side_input_args, side_input_kwargs)
+
+  def finish_bundle(self):
+    for (k, w), va in self._cache.items():
+      # We compact the accumulator since a GBK (which necessitates encoding)
+      # will follow.
+      side_input_args, side_input_kwargs = (
+        self._cached_windowed_side_inputs[w])
+      yield WindowedValue((
+          k,
+          self._combine_fn.compact(va, *side_input_args, **side_input_kwargs)),
+                          w.end, (w, ))
+
+  def teardown(self):
+    self._combine_fn.teardown()
+
+
+class _FinishCombine(core.DoFn):
+  """Merges partially combined results.
+  """
+  def __init__(self, combine_fn):
+    self._combine_fn = combine_fn
+
+  def setup(self):
+    self._combine_fn.setup()
+
+  def process(self, element, window=core.DoFn.WindowParam, **side_inputs):
+
+    k, vs = element
+    side_input_args, side_input_kwargs = _unpack_side_inputs(side_inputs)
+    return [(
+        k,
+        self._combine_fn.extract_output(
+            self._combine_fn.merge_accumulators(
+                vs, *side_input_args, **side_input_kwargs),
+            *side_input_args,
+            **side_input_kwargs))]
+
+  def teardown(self):
+    try:
+      self._combine_fn.teardown()
+    except AttributeError:
+      pass
diff --git a/sdks/python/apache_beam/transforms/combiners_test.py b/sdks/python/apache_beam/transforms/combiners_test.py
index a8979239f831..ba9e21f85567 100644
--- a/sdks/python/apache_beam/transforms/combiners_test.py
+++ b/sdks/python/apache_beam/transforms/combiners_test.py
@@ -19,15 +19,20 @@
 # pytype: skip-file
 
 import itertools
+import json
+import os
 import random
+import tempfile
 import time
 import unittest
+from pathlib import Path
 
 import hamcrest as hc
 import pytest
 
 import apache_beam as beam
 import apache_beam.transforms.combiners as combine
+from apache_beam import pvalue
 from apache_beam.metrics import Metrics
 from apache_beam.metrics import MetricsFilter
 from apache_beam.options.pipeline_options import PipelineOptions
@@ -1021,5 +1026,186 @@ def test_combine_globally_for_unbounded_source_without_defaults(self):
           | beam.CombineGlobally(sum).without_defaults())
 
 
+def get_common_items(sets, excluded_chars=""):
+  # set.intersection() takes multiple sets as separete arguments.
+  # We unpack the `sets` list into multiple arguments with the * operator.
+  # The combine transform might give us an empty list of `sets`,
+  # so we use a list with an empty set as a default value.
+  common = set.intersection(*(sets or [set()]))
+  return common.difference(excluded_chars)
+
+
+class CombinerWithSideInputs(unittest.TestCase):
+  def test_cpk_with_side_input(self):
+    test_cases = [(get_common_items, True),
+                  (beam.CombineFn.from_callable(get_common_items), True),
+                  (get_common_items, False),
+                  (beam.CombineFn.from_callable(get_common_items), False)]
+    for combiner, with_kwarg in test_cases:
+      self._check_combineperkey_with_side_input(combiner, with_kwarg)
+      self._check_combineglobally_with_side_input(combiner, with_kwarg)
+
+  def _check_combineperkey_with_side_input(self, combiner, with_kwarg):
+    with beam.Pipeline() as pipeline:
+      pc = (pipeline | beam.Create(['🍅']))
+      if with_kwarg:
+        cpk = beam.CombinePerKey(
+            combiner, excluded_chars=beam.pvalue.AsSingleton(pc))
+      else:
+        cpk = beam.CombinePerKey(combiner, beam.pvalue.AsSingleton(pc))
+      common_items = (
+          pipeline
+          | 'Create produce' >> beam.Create([
+              {'🍓', '🥕', '🍌', '🍅', '🌶️'},
+              {'🍇', '🥕', '🥝', '🍅', '🥔'},
+              {'🍉', '🥕', '🍆', '🍅', '🍍'},
+              {'🥑', '🥕', '🌽', '🍅', '🥥'},
+          ])
+          | beam.WithKeys(lambda x: None)
+          | cpk)
+      assert_that(common_items, equal_to([(None, {'🥕'})]))
+
+  def _check_combineglobally_with_side_input(self, combiner, with_kwarg):
+    with beam.Pipeline() as pipeline:
+      pc = (pipeline | beam.Create(['🍅']))
+      if with_kwarg:
+        cpk = beam.CombineGlobally(
+            combiner, excluded_chars=beam.pvalue.AsSingleton(pc))
+      else:
+        cpk = beam.CombineGlobally(combiner, beam.pvalue.AsSingleton(pc))
+      common_items = (
+          pipeline
+          | 'Create produce' >> beam.Create([
+              {'🍓', '🥕', '🍌', '🍅', '🌶️'},
+              {'🍇', '🥕', '🥝', '🍅', '🥔'},
+              {'🍉', '🥕', '🍆', '🍅', '🍍'},
+              {'🥑', '🥕', '🌽', '🍅', '🥥'},
+          ])
+          | cpk)
+      assert_that(common_items, equal_to([{'🥕'}]))
+
+  def test_combinefn_methods_with_side_input(self):
+    # Test that the expected combinefn methods are called with the
+    # expected arguments when using side inputs in CombinePerKey.
+    with tempfile.TemporaryDirectory() as tmp_dirname:
+      fname = str(Path(tmp_dirname) / "combinefn_calls.json")
+      with open(fname, "w") as f:
+        json.dump({}, f)
+
+      def set_in_json(key, values):
+        current_json = {}
+        if os.path.exists(fname):
+          with open(fname, "r") as f:
+            current_json = json.load(f)
+        current_json[key] = values
+        with open(fname, "w") as f:
+          json.dump(current_json, f)
+
+      class MyCombiner(beam.CombineFn):
+        def create_accumulator(self, *args, **kwargs):
+          set_in_json("create_accumulator_args", args)
+          set_in_json("create_accumulator_kwargs", kwargs)
+          return args, kwargs
+
+        def add_input(self, accumulator, input, *args, **kwargs):
+          set_in_json("add_input_args", args)
+          set_in_json("add_input_kwargs", kwargs)
+          return accumulator
+
+        def merge_accumulators(self, accumulators, *args, **kwargs):
+          set_in_json("merge_accumulators_args", args)
+          set_in_json("merge_accumulators_kwargs", kwargs)
+          return args, kwargs
+
+        def compact(self, accumulator, *args, **kwargs):
+          set_in_json("compact_args", args)
+          set_in_json("compact_kwargs", kwargs)
+          return accumulator
+
+        def extract_output(self, accumulator, *args, **kwargs):
+          set_in_json("extract_output_args", args)
+          set_in_json("extract_output_kwargs", kwargs)
+          return accumulator
+
+      with beam.Pipeline() as p:
+        static_pos_arg = 0
+        deferred_pos_arg = beam.pvalue.AsSingleton(
+            p | "CreateDeferredSideInput" >> beam.Create([1]))
+        static_kwarg = 2
+        deferred_kwarg = beam.pvalue.AsSingleton(
+            p | "CreateDeferredSideInputKwarg" >> beam.Create([3]))
+        res = (
+            p
+            | "CreateInputs" >> beam.Create([(None, None)])
+            | beam.CombinePerKey(
+                MyCombiner(),
+                static_pos_arg,
+                deferred_pos_arg,
+                static_kwarg=static_kwarg,
+                deferred_kwarg=deferred_kwarg))
+        assert_that(
+            res,
+            equal_to([
+                (None, ((0, 1), {
+                    'static_kwarg': 2, 'deferred_kwarg': 3
+                }))
+            ]))
+
+      # Check that the combinefn was called with the expected arguments
+      with open(fname, "r") as f:
+        data = json.load(f)
+        expected_args = [0, 1]
+        expected_kwargs = {"static_kwarg": 2, "deferred_kwarg": 3}
+        method_names = [
+            "create_accumulator",
+            "compact",
+            "add_input",
+            "merge_accumulators",
+            "extract_output"
+        ]
+        for key in method_names:
+          print(f"Checking {key}")
+          self.assertEqual(data[key + "_args"], expected_args)
+          self.assertEqual(data[key + "_kwargs"], expected_kwargs)
+
+  def test_cpk_with_windows(self):
+    # With global window side input
+    with TestPipeline() as p:
+
+      def sum_with_floor(vals, min_value=0):
+        vals_sum = sum(vals)
+        if vals_sum < min_value:
+          vals_sum += min_value
+        return vals_sum
+
+      res = (
+          p
+          | "CreateInputs" >> beam.Create([1, 2, 100, 101, 102])
+          | beam.Map(lambda x: window.TimestampedValue(('k', x), x))
+          | beam.WindowInto(FixedWindows(99))
+          | beam.CombinePerKey(
+              sum_with_floor,
+              min_value=pvalue.AsSingleton(p | beam.Create([100]))))
+      assert_that(res, equal_to([('k', 103), ('k', 303)]))
+
+    # with matching window side input
+    with TestPipeline() as p:
+      min_value = (
+          p
+          | "CreateMinValue" >> beam.Create([
+              window.TimestampedValue(50, 5),
+              window.TimestampedValue(1000, 100)
+          ])
+          | "WindowSideInputs" >> beam.WindowInto(FixedWindows(99)))
+      res = (
+          p
+          | "CreateInputs" >> beam.Create([1, 2, 100, 101, 102])
+          | beam.Map(lambda x: window.TimestampedValue(('k', x), x))
+          | beam.WindowInto(FixedWindows(99))
+          | beam.CombinePerKey(
+              sum_with_floor, min_value=pvalue.AsSingleton(min_value)))
+      assert_that(res, equal_to([('k', 53), ('k', 1303)]))
+
+
 if __name__ == '__main__':
   unittest.main()
diff --git a/sdks/python/apache_beam/transforms/core.py b/sdks/python/apache_beam/transforms/core.py
index c043f7685748..2304faf478f9 100644
--- a/sdks/python/apache_beam/transforms/core.py
+++ b/sdks/python/apache_beam/transforms/core.py
@@ -29,6 +29,8 @@
 import traceback
 import types
 import typing
+from collections import defaultdict
+from functools import wraps
 from itertools import dropwhile
 
 from apache_beam import coders
@@ -1595,7 +1597,8 @@ def with_exception_handling(
       timeout=None,
       error_handler=None,
       on_failure_callback: typing.Optional[typing.Callable[
-          [Exception, typing.Any], None]] = None):
+          [Exception, typing.Any], None]] = None,
+      allow_unsafe_userstate_in_process=False):
     """Automatically provides a dead letter output for saving bad inputs.
     This can allow a pipeline to continue successfully rather than fail or
     continuously throw errors on retry when bad elements are encountered.
@@ -1652,6 +1655,13 @@ def with_exception_handling(
           the exception will be of type `TimeoutError`. Be careful with this
           callback - if you set a timeout, it will not apply to the callback,
           and if the callback fails it will not be retried.
+      allow_unsafe_userstate_in_process: If False, user state will not be
+          permitted in the DoFn's process method. This is disabled by default
+          because user state is potentially unsafe with exception handling
+          since it can be successfully stored or cleared even if the associated
+          element fails and is routed to a dead letter queue. Semantics around
+          state in this kind of failure scenario are not well defined and are
+          subject to change.
     """
     args, kwargs = self.raw_side_inputs
     return self.label >> _ExceptionHandlingWrapper(
@@ -1667,7 +1677,9 @@ def with_exception_handling(
         threshold_windowing,
         timeout,
         error_handler,
-        on_failure_callback)
+        on_failure_callback,
+        allow_unsafe_userstate_in_process,
+        self.get_resource_hints())
 
   def with_error_handler(self, error_handler, **exception_handling_kwargs):
     """An alias for `with_exception_handling(error_handler=error_handler, ...)`
@@ -2272,7 +2284,9 @@ def __init__(
       threshold_windowing,
       timeout,
       error_handler,
-      on_failure_callback):
+      on_failure_callback,
+      allow_unsafe_userstate_in_process,
+      resource_hints):
     if partial and use_subprocess:
       raise ValueError('partial and use_subprocess are mutually incompatible.')
     self._fn = fn
@@ -2288,24 +2302,41 @@ def __init__(
     self._timeout = timeout
     self._error_handler = error_handler
     self._on_failure_callback = on_failure_callback
+    self._allow_unsafe_userstate_in_process = allow_unsafe_userstate_in_process
+    self._resource_hints = resource_hints
 
   def expand(self, pcoll):
+    if self._allow_unsafe_userstate_in_process:
+      if self._use_subprocess or self._timeout:
+        # TODO(https://github.com/apache/beam/issues/35976): Implement this
+        raise Exception(
+            'allow_unsafe_userstate_in_process is incompatible with ' +
+            'exception handling done with subprocesses or timeouts. If you ' +
+            'need this feature, comment in ' +
+            'https://github.com/apache/beam/issues/35976')
     if self._use_subprocess:
       wrapped_fn = _SubprocessDoFn(self._fn, timeout=self._timeout)
     elif self._timeout:
       wrapped_fn = _TimeoutDoFn(self._fn, timeout=self._timeout)
     else:
       wrapped_fn = self._fn
-    result = pcoll | ParDo(
+    pardo = ParDo(
         _ExceptionHandlingWrapperDoFn(
             wrapped_fn,
             self._dead_letter_tag,
             self._exc_class,
             self._partial,
-            self._on_failure_callback),
+            self._on_failure_callback,
+            self._allow_unsafe_userstate_in_process,
+        ),
         *self._args,
-        **self._kwargs).with_outputs(
-            self._dead_letter_tag, main=self._main_tag, allow_unknown_tags=True)
+        **self._kwargs,
+    )
+    # This is the fix: propagate hints.
+    pardo.get_resource_hints().update(self._resource_hints)
+
+    result = pcoll | pardo.with_outputs(
+        self._dead_letter_tag, main=self._main_tag, allow_unknown_tags=True)
     #TODO(BEAM-18957): Fix when type inference supports tagged outputs.
     result[self._main_tag].element_type = self._fn.infer_output_type(
         pcoll.element_type)
@@ -2346,13 +2377,44 @@ def check_threshold(bad, total, threshold, window=DoFn.WindowParam):
 
 class _ExceptionHandlingWrapperDoFn(DoFn):
   def __init__(
-      self, fn, dead_letter_tag, exc_class, partial, on_failure_callback):
+      self,
+      fn,
+      dead_letter_tag,
+      exc_class,
+      partial,
+      on_failure_callback,
+      allow_unsafe_userstate_in_process):
     self._fn = fn
     self._dead_letter_tag = dead_letter_tag
     self._exc_class = exc_class
     self._partial = partial
     self._on_failure_callback = on_failure_callback
 
+    # Wrap process and expose any top level state params so that process can
+    # handle state and timers.
+    if allow_unsafe_userstate_in_process:
+
+      @wraps(self._fn.process)
+      def process_wrapper(self, *args, **kwargs):
+        return self.exception_handling_wrapper_do_fn_custom_process(
+            *args, **kwargs)
+
+      self.process = types.MethodType(process_wrapper, self)
+    else:
+      self.process = self.exception_handling_wrapper_do_fn_custom_process
+      process_sig = inspect.signature(self._fn.process)
+      for name, param in process_sig.parameters.items():
+        if isinstance(param.default, (DoFn.StateParam, DoFn.TimerParam)):
+          logging.warning(
+              'State or timer parameter {} detected in process method of ' +
+              '{}. State and timers are unsupported when using ' +
+              'with_exception_handling and may lead to errors. To enable ' +
+              'state and timers with limited consistency guarantees, pass ' +
+              'in the allow_unsafe_userstate_in_process parameters to the ' +
+              'with_exception_handling method.',
+              name,
+              self.fn)
+
   def __getattribute__(self, name):
     if (name.startswith('__') or name in self.__dict__ or
         name in _ExceptionHandlingWrapperDoFn.__dict__):
@@ -2360,7 +2422,7 @@ def __getattribute__(self, name):
     else:
       return getattr(self._fn, name)
 
-  def process(self, *args, **kwargs):
+  def exception_handling_wrapper_do_fn_custom_process(self, *args, **kwargs):
     try:
       result = self._fn.process(*args, **kwargs)
       if not self._partial:
@@ -2927,6 +2989,22 @@ class CombinePerKey(PTransformWithSideInputs):
   Returns:
     A PObject holding the result of the combine operation.
   """
+  def __new__(cls, *args, **kwargs):
+    def has_side_inputs():
+      return (
+          any(isinstance(arg, pvalue.AsSideInput) for arg in args) or
+          any(isinstance(arg, pvalue.AsSideInput) for arg in kwargs.values()))
+
+    if has_side_inputs():
+      # If the CombineFn has deferred side inputs, the python SDK
+      # doesn't implement it.
+      # Use a ParDo-based CombinePerKey instead.
+      from apache_beam.transforms.combiners import \
+        LiftedCombinePerKey
+      combine_fn, *args = args
+      return LiftedCombinePerKey(combine_fn, args, kwargs)
+    return super(CombinePerKey, cls).__new__(cls)
+
   def with_hot_key_fanout(self, fanout):
     """A per-key combine operation like self but with two levels of aggregation.
 
@@ -3573,7 +3651,13 @@ class ApplyPartitionFnFn(DoFn):
     """A DoFn that applies a PartitionFn."""
     def process(self, element, partitionfn, n, *args, **kwargs):
       partition = partitionfn.partition_for(element, n, *args, **kwargs)
-      if not 0 <= partition < n:
+      import numbers
+      if isinstance(partition,
+                    bool) or not isinstance(partition, numbers.Integral):
+        raise ValueError(
+            f"PartitionFn yielded a '{type(partition).__name__}' "
+            "when it should only yield integers")
+      if not 0 <= int(partition) < n:
         raise ValueError(
             'PartitionFn specified out-of-bounds partition index: '
             '%d not in [0, %d)' % (partition, n))
@@ -3849,6 +3933,15 @@ def _extract_input_pvalues(self, pvalueish):
       raise ValueError(
           'Input to Flatten must be an iterable. '
           'Got a value of type %s instead.' % type(pvalueish))
+
+    # Spot check to see if any of the items are iterables of PCollections
+    # and raise an error if so. This is always a user-error
+    for idx, item in enumerate(pvalueish):
+      if isinstance(item, (list, tuple)) and any(
+          isinstance(sub_item, pvalue.PCollection) for sub_item in item):
+        raise TypeError(
+            'Inputs to Flatten cannot include an iterable of PCollections. '
+            f'(input at index {idx}: "{item}")')
     return pvalueish, pvalueish
 
   def expand(self, pcolls):
@@ -3946,9 +4039,41 @@ def to_runner_api_parameter(self, context):
   def infer_output_type(self, unused_input_type):
     if not self.values:
       return typehints.Any
-    return typehints.Union[[
-        trivial_inference.instance_to_type(v) for v in self.values
-    ]]
+
+    # No field data - just use default Union.
+    if not hasattr(self.values[0], 'as_dict'):
+      return typehints.Union[[
+          trivial_inference.instance_to_type(v) for v in self.values
+      ]]
+
+    first_fields = self.values[0].as_dict().keys()
+
+    # Save field types for each field
+    field_types_by_field = defaultdict(set)
+    for row in self.values:
+      row_dict = row.as_dict()
+      for field in first_fields:
+        field_types_by_field[field].add(
+            trivial_inference.instance_to_type(row_dict.get(field)))
+
+    # Determine the appropriate type for each field
+    final_fields = []
+    for field in first_fields:
+      field_types = field_types_by_field[field]
+      non_none_types = {t for t in field_types if t is not type(None)}
+
+      if len(non_none_types) > 1:
+        final_type = typehints.Union[tuple(non_none_types)]
+      elif len(non_none_types) == 1 and len(field_types) == 1:
+        final_type = non_none_types.pop()
+      elif len(non_none_types) == 1 and len(field_types) == 2:
+        final_type = typehints.Optional[non_none_types.pop()]
+      else:
+        raise TypeError("No types found for field %s", field)
+
+      final_fields.append((field, final_type))
+
+    return row_type.RowTypeConstraint.from_fields(final_fields)
 
   def get_output_type(self):
     return (
diff --git a/sdks/python/apache_beam/transforms/core_test.py b/sdks/python/apache_beam/transforms/core_test.py
index 542544bce3c1..0d680c969c9b 100644
--- a/sdks/python/apache_beam/transforms/core_test.py
+++ b/sdks/python/apache_beam/transforms/core_test.py
@@ -27,10 +27,17 @@
 import pytest
 
 import apache_beam as beam
+from apache_beam.coders import coders
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
+from apache_beam.transforms.resources import ResourceHint
+from apache_beam.transforms.userstate import BagStateSpec
+from apache_beam.transforms.userstate import ReadModifyWriteStateSpec
+from apache_beam.transforms.userstate import TimerSpec
+from apache_beam.transforms.userstate import on_timer
 from apache_beam.transforms.window import FixedWindows
 from apache_beam.typehints import TypeCheckError
+from apache_beam.typehints import row_type
 from apache_beam.typehints import typehints
 
 RETURN_NONE_PARTIAL_WARNING = "No iterator is returned"
@@ -119,6 +126,42 @@ def process(self, element):
     return
 
 
+class TestDoFnStateful(beam.DoFn):
+  STATE_SPEC = ReadModifyWriteStateSpec('num_elements', coders.VarIntCoder())
+  """test process with a stateful dofn"""
+  def process(self, element, state=beam.DoFn.StateParam(STATE_SPEC)):
+    if len(element[1]) > 3:
+      raise ValueError('Not allowed to have long elements')
+    current_value = state.read() or 1
+    state.write(current_value + 1)
+    yield current_value
+
+
+class TestDoFnWithTimer(beam.DoFn):
+  ALL_ELEMENTS = BagStateSpec('buffer', coders.VarIntCoder())
+  TIMER = TimerSpec('timer', beam.TimeDomain.WATERMARK)
+  """test process with a stateful dofn"""
+  def process(
+      self,
+      element,
+      t=beam.DoFn.TimestampParam,
+      state=beam.DoFn.StateParam(ALL_ELEMENTS),
+      timer=beam.DoFn.TimerParam(TIMER)):
+    if element[1] > 3:
+      raise ValueError('Not allowed to have large numbers')
+    state.add(element[1])
+    timer.set(t)
+
+    return []
+
+  @on_timer(TIMER)
+  def expiry_callback(self, state=beam.DoFn.StateParam(ALL_ELEMENTS)):
+    unique_elements = list(state.read())
+    state.clear()
+
+    return unique_elements
+
+
 class CreateTest(unittest.TestCase):
   @pytest.fixture(autouse=True)
   def inject_fixtures(self, caplog):
@@ -162,6 +205,75 @@ def test_dofn_with_implicit_return_none_return_without_value(self):
 
 
 class PartitionTest(unittest.TestCase):
+  def test_partition_with_bools(self):
+    with pytest.raises(
+        (ValueError, RuntimeError),
+        match=
+        r"PartitionFn yielded a '([^']*)' when it should only yield integers"):
+      # Check for RuntimeError too since the portable runner casts
+      # all exceptions to RuntimeError
+      invalid_inputs = [True, 1.2, 'string', None]
+      for input_value in invalid_inputs:
+        with beam.testing.test_pipeline.TestPipeline() as p:
+          _ = (
+              p | beam.Create([input_value])
+              | beam.Partition(lambda x, _: x, 2))
+
+  def test_partition_with_numpy_integers(self):
+    # Test that numpy integer types are correctly accepted by the
+    # ApplyPartitionFnFn class
+    import numpy as np
+
+    # Create an instance of the ApplyPartitionFnFn class
+    apply_partition_fn = beam.Partition.ApplyPartitionFnFn()
+
+    # Define a simple partition function
+    class SimplePartitionFn(beam.PartitionFn):
+      def partition_for(self, element, num_partitions):
+        return element % num_partitions
+
+    partition_fn = SimplePartitionFn()
+
+    # Test with numpy.int32
+    # This should not raise an exception
+    outputs = list(apply_partition_fn.process(np.int32(1), partition_fn, 3))
+    self.assertEqual(len(outputs), 1)
+    self.assertEqual(outputs[0].tag, '1')  # 1 % 3 = 1
+
+    # Test with numpy.int64
+    # This should not raise an exception
+    outputs = list(apply_partition_fn.process(np.int64(2), partition_fn, 3))
+    self.assertEqual(len(outputs), 1)
+    self.assertEqual(outputs[0].tag, '2')  # 2 % 3 = 2
+
+  def test_partition_fn_returning_numpy_integers(self):
+    # Test that partition functions can return numpy integer types
+    import numpy as np
+
+    # Create an instance of the ApplyPartitionFnFn class
+    apply_partition_fn = beam.Partition.ApplyPartitionFnFn()
+
+    # Define partition functions that return numpy integer types
+    class Int32PartitionFn(beam.PartitionFn):
+      def partition_for(self, element, num_partitions):
+        return np.int32(element % num_partitions)
+
+    class Int64PartitionFn(beam.PartitionFn):
+      def partition_for(self, element, num_partitions):
+        return np.int64(element % num_partitions)
+
+    # Test with partition function returning numpy.int32
+    # This should not raise an exception
+    outputs = list(apply_partition_fn.process(1, Int32PartitionFn(), 3))
+    self.assertEqual(len(outputs), 1)
+    self.assertEqual(outputs[0].tag, '1')  # 1 % 3 = 1
+
+    # Test with partition function returning numpy.int64
+    # This should not raise an exception
+    outputs = list(apply_partition_fn.process(2, Int64PartitionFn(), 3))
+    self.assertEqual(len(outputs), 1)
+    self.assertEqual(outputs[0].tag, '2')  # 2 % 3 = 2
+
   def test_partition_boundedness(self):
     def partition_fn(val, num_partitions):
       return val % num_partitions
@@ -281,6 +393,118 @@ def failure_callback(e, el):
         assert_that(bad_elements, equal_to([]), 'bad')
       self.assertFalse(os.path.isfile(tmp_path))
 
+  def test_stateful_exception_handling(self):
+    with beam.Pipeline() as pipeline:
+      good, bad = (
+        pipeline | beam.Create([(1, 'abc'), (1, 'long_word'),
+                                (1, 'foo'), (1, 'bar'), (1, 'foobar')])
+        | beam.ParDo(TestDoFnStateful()).with_exception_handling(
+          allow_unsafe_userstate_in_process=True)
+      )
+      bad_elements = bad | beam.Keys()
+      assert_that(good, equal_to([1, 2, 3]), 'good')
+      assert_that(
+          bad_elements, equal_to([(1, 'long_word'), (1, 'foobar')]), 'bad')
+
+  def test_timer_exception_handling(self):
+    with beam.Pipeline() as pipeline:
+      good, bad = (
+        pipeline | beam.Create([(1, 0), (1, 1), (1, 2), (1, 5), (1, 10)])
+        | beam.ParDo(TestDoFnWithTimer()).with_exception_handling(
+          allow_unsafe_userstate_in_process=True)
+      )
+      bad_elements = bad | beam.Keys()
+      assert_that(good, equal_to([0, 1, 2]), 'good')
+      assert_that(bad_elements, equal_to([(1, 5), (1, 10)]), 'bad')
+
+  def test_tags_with_exception_handling_then_resource_hint(self):
+    class TagHint(ResourceHint):
+      urn = 'beam:resources:tags:v1'
+
+    ResourceHint.register_resource_hint('tags', TagHint)
+    with beam.Pipeline() as pipeline:
+      ok, unused_errors = (
+        pipeline
+        | beam.Create([1])
+        | beam.Map(lambda x: x)
+        .with_exception_handling()
+        .with_resource_hints(tags='test_tag')
+      )
+    pd = ok.producer.transform
+    self.assertIsInstance(pd, beam.transforms.core.ParDo)
+    while hasattr(pd.fn, 'fn'):
+      pd = pd.fn
+    self.assertEqual(
+        pd.get_resource_hints(),
+        {'beam:resources:tags:v1': b'test_tag'},
+    )
+
+  def test_tags_with_exception_handling_timeout_then_resource_hint(self):
+    class TagHint(ResourceHint):
+      urn = 'beam:resources:tags:v1'
+
+    ResourceHint.register_resource_hint('tags', TagHint)
+    with beam.Pipeline() as pipeline:
+      ok, unused_errors = (
+        pipeline
+        | beam.Create([1])
+        | beam.Map(lambda x: x)
+        .with_exception_handling(timeout=1)
+        .with_resource_hints(tags='test_tag')
+      )
+    pd = ok.producer.transform
+    self.assertIsInstance(pd, beam.transforms.core.ParDo)
+    while hasattr(pd.fn, 'fn'):
+      pd = pd.fn
+    self.assertEqual(
+        pd.get_resource_hints(),
+        {'beam:resources:tags:v1': b'test_tag'},
+    )
+
+  def test_tags_with_resource_hint_then_exception_handling(self):
+    class TagHint(ResourceHint):
+      urn = 'beam:resources:tags:v1'
+
+    ResourceHint.register_resource_hint('tags', TagHint)
+    with beam.Pipeline() as pipeline:
+      ok, unused_errors = (
+        pipeline
+        | beam.Create([1])
+        | beam.Map(lambda x: x)
+        .with_resource_hints(tags='test_tag')
+        .with_exception_handling()
+      )
+    pd = ok.producer.transform
+    self.assertIsInstance(pd, beam.transforms.core.ParDo)
+    while hasattr(pd.fn, 'fn'):
+      pd = pd.fn
+    self.assertEqual(
+        pd.get_resource_hints(),
+        {'beam:resources:tags:v1': b'test_tag'},
+    )
+
+  def test_tags_with_resource_hint_then_exception_handling_timeout(self):
+    class TagHint(ResourceHint):
+      urn = 'beam:resources:tags:v1'
+
+    ResourceHint.register_resource_hint('tags', TagHint)
+    with beam.Pipeline() as pipeline:
+      ok, unused_errors = (
+        pipeline
+        | beam.Create([1])
+        | beam.Map(lambda x: x)
+        .with_resource_hints(tags='test_tag')
+        .with_exception_handling(timeout=1)
+      )
+    pd = ok.producer.transform
+    self.assertIsInstance(pd, beam.transforms.core.ParDo)
+    while hasattr(pd.fn, 'fn'):
+      pd = pd.fn
+    self.assertEqual(
+        pd.get_resource_hints(),
+        {'beam:resources:tags:v1': b'test_tag'},
+    )
+
 
 def test_callablewrapper_typehint():
   T = TypeVar("T")
@@ -322,6 +546,36 @@ def test_typecheck_with_default(self):
             | beam.Map(lambda s: s.upper()).with_input_types(str))
 
 
+class CreateInferOutputSchemaTest(unittest.TestCase):
+  def test_multiple_types_for_field(self):
+    output_type = beam.Create([beam.Row(a=1),
+                               beam.Row(a='foo')]).infer_output_type(None)
+    self.assertEqual(
+        output_type,
+        row_type.RowTypeConstraint.from_fields([
+            ('a', typehints.Union[int, str])
+        ]))
+
+  def test_single_type_for_field(self):
+    output_type = beam.Create([beam.Row(a=1),
+                               beam.Row(a=2)]).infer_output_type(None)
+    self.assertEqual(
+        output_type, row_type.RowTypeConstraint.from_fields([('a', int)]))
+
+  def test_optional_type_for_field(self):
+    output_type = beam.Create([beam.Row(a=1),
+                               beam.Row(a=None)]).infer_output_type(None)
+    self.assertEqual(
+        output_type,
+        row_type.RowTypeConstraint.from_fields([('a', typehints.Optional[int])
+                                                ]))
+
+  def test_none_type_for_field_raises_error(self):
+    with self.assertRaisesRegex(TypeError,
+                                "('No types found for field %s', 'a')"):
+      beam.Create([beam.Row(a=None), beam.Row(a=None)]).infer_output_type(None)
+
+
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
   unittest.main()
diff --git a/sdks/python/apache_beam/transforms/enrichment_handlers/bigquery_it_test.py b/sdks/python/apache_beam/transforms/enrichment_handlers/bigquery_it_test.py
index dd99e386555e..1889b0845e6e 100644
--- a/sdks/python/apache_beam/transforms/enrichment_handlers/bigquery_it_test.py
+++ b/sdks/python/apache_beam/transforms/enrichment_handlers/bigquery_it_test.py
@@ -33,7 +33,6 @@
 
 # pylint: disable=ungrouped-imports
 try:
-  from google.api_core.exceptions import BadRequest
   from testcontainers.redis import RedisContainer
   from apache_beam.transforms.enrichment import Enrichment
   from apache_beam.transforms.enrichment_handlers.bigquery import \
@@ -141,7 +140,7 @@ def setUpClass(cls):
 
   def setUp(self) -> None:
     self.condition_template = "id = {}"
-    self.retries = 3
+    self.retries = 5
     self._start_container()
 
   def _start_container(self):
@@ -159,6 +158,8 @@ def _start_container(self):
               'Unable to start redis container for BigQuery '
               ' enrichment tests.')
           raise e
+        # Add a small delay between retries to avoid rapid successive failures
+        time.sleep(2)
 
   def tearDown(self) -> None:
     self.container.stop()
@@ -286,7 +287,7 @@ def test_bigquery_enrichment_bad_request(self):
         column_names=['wrong_column'],
         condition_value_fn=condition_value_fn,
     )
-    with self.assertRaises(BadRequest):
+    with self.assertRaises(Exception):
       test_pipeline = beam.Pipeline()
       _ = (
           test_pipeline
diff --git a/sdks/python/apache_beam/transforms/enrichment_handlers/bigtable_it_test.py b/sdks/python/apache_beam/transforms/enrichment_handlers/bigtable_it_test.py
index 0dfee0c1191a..09d025b006a2 100644
--- a/sdks/python/apache_beam/transforms/enrichment_handlers/bigtable_it_test.py
+++ b/sdks/python/apache_beam/transforms/enrichment_handlers/bigtable_it_test.py
@@ -17,6 +17,7 @@
 
 import datetime
 import logging
+import time
 import unittest
 from typing import NamedTuple
 from unittest.mock import MagicMock
@@ -30,7 +31,6 @@
 
 # pylint: disable=ungrouped-imports
 try:
-  from google.api_core.exceptions import NotFound
   from google.cloud.bigtable import Client
   from google.cloud.bigtable.row_filters import ColumnRangeFilter
   from testcontainers.redis import RedisContainer
@@ -168,7 +168,7 @@ def setUp(self):
     instance = client.instance(self.instance_id)
     self.table = instance.table(self.table_id)
     create_rows(self.table)
-    self.retries = 3
+    self.retries = 5
     self._start_container()
 
   def _start_container(self):
@@ -184,6 +184,8 @@ def _start_container(self):
         if i == self.retries - 1:
           _LOGGER.error('Unable to start redis container for RRIO tests.')
           raise e
+        # Add a small delay between retries to avoid rapid successive failures
+        time.sleep(2)
 
   def tearDown(self) -> None:
     self.container.stop()
@@ -272,7 +274,7 @@ def test_enrichment_with_bigtable_bad_row_filter(self):
         table_id=self.table_id,
         row_key=self.row_key,
         row_filter=column_filter)
-    with self.assertRaises(NotFound):
+    with self.assertRaises(Exception):
       test_pipeline = beam.Pipeline()
       _ = (
           test_pipeline
@@ -289,7 +291,7 @@ def test_enrichment_with_bigtable_raises_key_error(self):
         instance_id=self.instance_id,
         table_id=self.table_id,
         row_key='car_name')
-    with self.assertRaises(KeyError):
+    with self.assertRaisesRegex(Exception, "not found in input"):
       test_pipeline = beam.Pipeline()
       _ = (
           test_pipeline
@@ -306,7 +308,7 @@ def test_enrichment_with_bigtable_raises_not_found(self):
         instance_id=self.instance_id,
         table_id='invalid_table',
         row_key=self.row_key)
-    with self.assertRaises(NotFound):
+    with self.assertRaises(Exception):
       test_pipeline = beam.Pipeline()
       _ = (
           test_pipeline
@@ -325,7 +327,7 @@ def test_enrichment_with_bigtable_exception_level(self):
         row_key=self.row_key,
         exception_level=ExceptionLevel.RAISE)
     req = [beam.Row(sale_id=1, customer_id=1, product_id=11, quantity=1)]
-    with self.assertRaises(ValueError):
+    with self.assertRaisesRegex(Exception, "no matching row"):
       test_pipeline = beam.Pipeline()
       _ = (
           test_pipeline
diff --git a/sdks/python/apache_beam/transforms/enrichment_handlers/cloudsql.py b/sdks/python/apache_beam/transforms/enrichment_handlers/cloudsql.py
new file mode 100644
index 000000000000..3fe3a62f9546
--- /dev/null
+++ b/sdks/python/apache_beam/transforms/enrichment_handlers/cloudsql.py
@@ -0,0 +1,656 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import re
+from abc import ABC
+from abc import abstractmethod
+from collections.abc import Callable
+from collections.abc import Mapping
+from dataclasses import dataclass
+from dataclasses import field
+from enum import Enum
+from typing import Any
+from typing import Dict
+from typing import List
+from typing import Optional
+from typing import Union
+
+import pg8000
+import pymysql
+import pytds
+from google.cloud.sql.connector import Connector as CloudSQLConnector
+from google.cloud.sql.connector.enums import RefreshStrategy
+from sqlalchemy import create_engine
+from sqlalchemy import text
+from sqlalchemy.engine import Connection as DBAPIConnection
+
+import apache_beam as beam
+from apache_beam.transforms.enrichment import EnrichmentSourceHandler
+
+QueryFn = Callable[[beam.Row], str]
+ConditionValueFn = Callable[[beam.Row], list[Any]]
+
+
+@dataclass
+class CustomQueryConfig:
+  """Configuration for using a custom query function."""
+  query_fn: QueryFn
+
+  def __post_init__(self):
+    if not self.query_fn:
+      raise ValueError("CustomQueryConfig must provide a valid query_fn")
+
+
+@dataclass
+class TableFieldsQueryConfig:
+  """Configuration for using table name, where clause, and field names."""
+  table_id: str
+  where_clause_template: str
+  where_clause_fields: List[str]
+
+  def __post_init__(self):
+    if not self.table_id or not self.where_clause_template:
+      raise ValueError(
+          "TableFieldsQueryConfig must provide table_id and " +
+          "where_clause_template")
+
+    if not self.where_clause_fields:
+      raise ValueError(
+          "TableFieldsQueryConfig must provide non-empty " +
+          "where_clause_fields")
+
+
+@dataclass
+class TableFunctionQueryConfig:
+  """Configuration for using table name, where clause, and a value function."""
+  table_id: str
+  where_clause_template: str
+  where_clause_value_fn: ConditionValueFn
+
+  def __post_init__(self):
+    if not self.table_id or not self.where_clause_template:
+      raise ValueError(
+          "TableFunctionQueryConfig must provide table_id and " +
+          "where_clause_template")
+
+    if not self.where_clause_value_fn:
+      raise ValueError(
+          "TableFunctionQueryConfig must provide " + "where_clause_value_fn")
+
+
+class DatabaseTypeAdapter(Enum):
+  POSTGRESQL = "pg8000"
+  MYSQL = "pymysql"
+  SQLSERVER = "pytds"
+
+  def to_sqlalchemy_dialect(self):
+    """Map the adapter type to its corresponding SQLAlchemy dialect.
+
+    Returns:
+        str: SQLAlchemy dialect string.
+    """
+    if self == DatabaseTypeAdapter.POSTGRESQL:
+      return f"postgresql+{self.value}"
+    elif self == DatabaseTypeAdapter.MYSQL:
+      return f"mysql+{self.value}"
+    elif self == DatabaseTypeAdapter.SQLSERVER:
+      return f"mssql+{self.value}"
+    else:
+      raise ValueError(f"Unsupported database adapter type: {self.name}")
+
+
+class ConnectionConfig(ABC):
+  @abstractmethod
+  def get_connector_handler(self) -> Callable[[], DBAPIConnection]:
+    pass
+
+  @abstractmethod
+  def get_db_url(self) -> str:
+    pass
+
+
+@dataclass
+class CloudSQLConnectionConfig(ConnectionConfig):
+  """Connects to Google Cloud SQL using Cloud SQL Python Connector.
+
+    Args:
+        db_adapter: The database adapter type (PostgreSQL, MySQL, SQL Server).
+        instance_connection_uri: URI for connecting to the Cloud SQL instance.
+        user: Username for authentication.
+        password: Password for authentication. Defaults to None.
+        db_id: Database identifier/name.
+        refresh_strategy: Strategy for refreshing connection (default: LAZY).
+        connector_kwargs: Additional keyword arguments for the
+          Cloud SQL Python Connector. Enables forward compatibility.
+        connect_kwargs: Additional keyword arguments for the client connect
+          method. Enables forward compatibility.
+    """
+  db_adapter: DatabaseTypeAdapter
+  instance_connection_uri: str
+  user: str = field(default_factory=str)
+  password: str = field(default_factory=str)
+  db_id: str = field(default_factory=str)
+  refresh_strategy: RefreshStrategy = RefreshStrategy.LAZY
+  connector_kwargs: Dict[str, Any] = field(default_factory=dict)
+  connect_kwargs: Dict[str, Any] = field(default_factory=dict)
+
+  def __post_init__(self):
+    if not self.instance_connection_uri:
+      raise ValueError("Instance connection URI cannot be empty")
+
+  def get_connector_handler(self) -> Callable[[], DBAPIConnection]:
+    """Returns a function that creates a new database connection.
+
+      The returned connector function creates database connections that should
+      be properly closed by the caller when no longer needed.
+      """
+    cloudsql_client = CloudSQLConnector(
+        refresh_strategy=self.refresh_strategy, **self.connector_kwargs)
+
+    cloudsql_connector = lambda: cloudsql_client.connect(
+        instance_connection_string=self.instance_connection_uri, driver=self.
+        db_adapter.value, user=self.user, password=self.password, db=self.db_id,
+        **self.connect_kwargs)
+
+    return cloudsql_connector
+
+  def get_db_url(self) -> str:
+    return self.db_adapter.to_sqlalchemy_dialect() + "://"
+
+
+@dataclass
+class ExternalSQLDBConnectionConfig(ConnectionConfig):
+  """Connects to External SQL DBs (PostgreSQL, MySQL, SQL Server) over TCP.
+
+    Args:
+        db_adapter: The database adapter type (PostgreSQL, MySQL, SQL Server).
+        host: Hostname or IP address of the database server.
+        port: Port number for the database connection.
+        user: Username for authentication.
+        password: Password for authentication.
+        db_id: Database identifier/name.
+        connect_kwargs: Additional keyword arguments for the client connect
+          method. Enables forward compatibility.
+    """
+  db_adapter: DatabaseTypeAdapter
+  host: str
+  port: int
+  user: str = field(default_factory=str)
+  password: str = field(default_factory=str)
+  db_id: str = field(default_factory=str)
+  connect_kwargs: Dict[str, Any] = field(default_factory=dict)
+
+  def __post_init__(self):
+    if not self.host:
+      raise ValueError("Database host cannot be empty")
+
+  def get_connector_handler(self) -> Callable[[], DBAPIConnection]:
+    """Returns a function that creates a new database connection.
+
+      The returned connector function creates database connections that should
+      be properly closed by the caller when no longer needed.
+      """
+    if self.db_adapter == DatabaseTypeAdapter.POSTGRESQL:
+      return lambda: pg8000.connect(
+          host=self.host, port=self.port, database=self.db_id, user=self.user,
+          password=self.password, **self.connect_kwargs)
+    elif self.db_adapter == DatabaseTypeAdapter.MYSQL:
+      return lambda: pymysql.connect(
+          host=self.host, port=self.port, database=self.db_id, user=self.user,
+          password=self.password, **self.connect_kwargs)
+    elif self.db_adapter == DatabaseTypeAdapter.SQLSERVER:
+      return lambda: pytds.connect(
+          dsn=self.host, port=self.port, database=self.db_id, user=self.user,
+          password=self.password, **self.connect_kwargs)
+    else:
+      raise ValueError(f"Unsupported database adapter: {self.db_adapter}")
+
+  def get_db_url(self) -> str:
+    return self.db_adapter.to_sqlalchemy_dialect() + "://"
+
+
+QueryConfig = Union[CustomQueryConfig,
+                    TableFieldsQueryConfig,
+                    TableFunctionQueryConfig]
+
+
+class CloudSQLEnrichmentHandler(EnrichmentSourceHandler[beam.Row, beam.Row]):
+  """Enrichment handler for Cloud SQL databases.
+
+  This handler is designed to work with the
+  :class:`apache_beam.transforms.enrichment.Enrichment` transform.
+
+  To use this handler, you need to provide one of the following query configs:
+    * CustomQueryConfig - For providing a custom query function
+    * TableFieldsQueryConfig - For specifying table, where clause, and fields
+    * TableFunctionQueryConfig - For specifying table, where clause, and val fn
+
+  By default, the handler retrieves all columns from the specified table.
+  To limit the columns, use the `column_names` parameter to specify
+  the desired column names.
+
+  This handler queries the Cloud SQL database per element by default.
+  To enable batching, set the `min_batch_size` and `max_batch_size` parameters.
+  These values control the batching behavior in the
+  :class:`apache_beam.transforms.utils.BatchElements` transform.
+
+  NOTE: Batching is not supported when using the CustomQueryConfig.
+  """
+  def __init__(
+      self,
+      connection_config: ConnectionConfig,
+      *,
+      query_config: QueryConfig,
+      column_names: Optional[list[str]] = None,
+      min_batch_size: int = 1,
+      max_batch_size: int = 10000,
+      **kwargs,
+  ):
+    """
+    Example usage::
+
+      connection_config = CloudSQLConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        instance_connection_uri="apache-beam-testing:us-central1:itests",
+        user='postgres',
+        password= os.getenv("CLOUDSQL_PG_PASSWORD"))
+      query_config=TableFieldsQueryConfig('my_table',"id = :param0",['id']),
+      cloudsql_handler = CloudSQLEnrichmentHandler(
+        connection_config=connection_config,
+        query_config=query_config,
+        min_batch_size=2,
+        max_batch_size=100)
+
+    Args:
+      connection_config (ConnectionConfig): Configuration for connecting to
+        the SQL database. Must be an instance of a subclass of
+        `ConnectionConfig`, such as `CloudSQLConnectionConfig` or
+        `ExternalSQLDBConnectionConfig`. This determines how the handler
+        connects to the target SQL database.
+      query_config: Configuration for database queries. Must be one of:
+      * CustomQueryConfig: For providing a custom query function
+      * TableFieldsQueryConfig: specifies table, where clause, and field names
+      * TableFunctionQueryConfig: specifies table, where clause, and val func
+      column_names (Optional[list[str]]): List of column names to select from
+        the Cloud SQL table. If not provided, all columns (`*`) are selected.
+      min_batch_size (int): Minimum number of rows to batch together when
+        querying the database. Defaults to 1 if `query_fn` is not used.
+      max_batch_size (int): Maximum number of rows to batch together. Defaults
+        to 10,000 if `query_fn` is not used.
+      **kwargs: Additional keyword arguments for database connection or query
+        handling.
+
+    Note:
+      * Cannot use `min_batch_size` or `max_batch_size` with `query_fn`.
+      * Either `where_clause_fields` or `where_clause_value_fn` must be provided
+        for query construction if `query_fn` is not provided.
+      * Ensure that the database user has the necessary permissions to query the
+        specified table.
+    """
+    self._connection_config = connection_config
+    self._query_config = query_config
+    self._column_names = ",".join(column_names) if column_names else "*"
+    self.kwargs = kwargs
+    self._batching_kwargs = {}
+    table_query_configs = (TableFieldsQueryConfig, TableFunctionQueryConfig)
+    if isinstance(query_config, table_query_configs):
+      self.query_template = (
+          f"SELECT {self._column_names} "
+          f"FROM {query_config.table_id} "
+          f"WHERE {query_config.where_clause_template}")
+      self._batching_kwargs['min_batch_size'] = min_batch_size
+      self._batching_kwargs['max_batch_size'] = max_batch_size
+
+  def __enter__(self):
+    connector = self._connection_config.get_connector_handler()
+    self._engine = create_engine(
+        url=self._connection_config.get_db_url(), creator=connector)
+
+  def __call__(
+      self, request: Union[beam.Row, list[beam.Row]], *_args, **_kwargs):
+    """Handle requests by delegating to single or batch processing."""
+    if isinstance(request, list):
+      return self._process_batch_request(request)
+    else:
+      return self._process_single_request(request)
+
+  def _process_single_request(self, request: beam.Row):
+    """Process a single request and return with its response."""
+    response: Union[List[Dict[str, Any]], Dict[str, Any]]
+    if isinstance(self._query_config, CustomQueryConfig):
+      query = self._query_config.query_fn(request)
+      response = self._execute_query(query, is_batch=False)
+    else:
+      values = self._extract_values_from_request(request)
+      param_dict = self._build_single_param_dict(values)
+      response = self._execute_query(
+          self.query_template, params=param_dict, is_batch=False)
+    return request, beam.Row(**response)  # type: ignore[arg-type]
+
+  def _process_batch_request(self, requests: list[beam.Row]):
+    """Process batch requests and match responses to original requests."""
+    values, responses = [], []
+    requests_map: dict[Any, Any] = {}
+    batch_size = len(requests)
+
+    # Build the appropriate query (single or batched).
+    raw_query = self._build_batch_query(requests, batch_size)
+
+    # Extract where_clause_fields values and map the generated request key to
+    # the original request object..
+    for req in requests:
+      current_values = self._extract_values_from_request(req)
+      values.extend(current_values)
+      requests_map[self.create_row_key(req)] = req
+
+    # Build named parameters dictionary for parameterized query.
+    param_dict = self._build_parameters_dict(requests, batch_size)
+
+    # Execute the parameterized query with validated parameters.
+    result: Union[List[Dict[str, Any]], Dict[str, Any]] = self._execute_query(
+        raw_query, params=param_dict, is_batch=True)
+    for response in result:
+      response_row = beam.Row(**response)  # type: ignore[arg-type]
+      response_key = self.create_row_key(response_row)
+      if response_key in requests_map:
+        responses.append((requests_map[response_key], response_row))
+    return responses
+
+  def _execute_query(
+      self,
+      query: str,
+      params: Optional[dict] = None,
+      is_batch: bool = False) -> Union[List[Dict[str, Any]], Dict[str, Any]]:
+    connection = None
+    try:
+      connection = self._engine.connect()
+      transaction = connection.begin()
+      try:
+        if params:
+          result = connection.execute(text(query), params)
+        else:
+          result = connection.execute(text(query))
+        # Materialize results while transaction is active.
+        data: Union[List[Dict[str, Any]], Dict[str, Any]]
+        if is_batch:
+          data = [row._asdict() for row in result]
+        else:
+          result_row = result.first()
+          data = result_row._asdict() if result_row else {}
+        # Explicitly commit the transaction.
+        transaction.commit()
+        return data
+      except Exception as e:
+        transaction.rollback()
+        raise RuntimeError(f"Database operation failed: {e}")
+    except Exception as e:
+      raise Exception(
+          f'Could not execute the query. Please check if the query is properly '
+          f'formatted and the table exists. {e}')
+    finally:
+      if connection:
+        connection.close()
+
+  def _build_batch_query(
+      self, requests: list[beam.Row], batch_size: int) -> str:
+    """Build batched query with unique parameter names for multiple requests.
+
+    This method extracts parameter placeholders from the where_clause_template
+    using regex and creates unique parameter names for each batch item. The
+    parameter names in the template can be any valid identifiers (e.g., :id,
+    :param_0, :user_name) and don't need to match field names exactly.
+
+    For batch queries, placeholders are replaced with unique names like
+    :batch_0_id, :batch_1_param_0, etc., based on the actual parameter names
+    found in the template.
+
+    Args:
+      requests: List of beam.Row requests to process
+      batch_size: Number of requests in the batch
+
+    Returns:
+      SQL query string with batched WHERE clauses using unique parameter names
+    """
+    # Single request - return original query.
+    if batch_size <= 1:
+      return self.query_template
+
+    # Only batch table-based query configs.
+    table_query_configs = (TableFieldsQueryConfig, TableFunctionQueryConfig)
+    if not isinstance(self._query_config, table_query_configs):
+      return self.query_template
+
+    # Build batched WHERE clauses.
+    where_clauses = [self._create_batch_clause(i) for i in range(batch_size)]
+
+    # Combine clauses and update query.
+    where_clause_batched = ' OR '.join(where_clauses)
+    # We know this is a table-based config from the check above.
+    assert isinstance(self._query_config, table_query_configs)
+    return self.query_template.replace(
+        self._query_config.where_clause_template, where_clause_batched)
+
+  def _create_batch_clause(self, batch_index: int) -> str:
+    """Create a WHERE clause for a single batch item with unique parameter
+    names."""
+    # This method is only called for table-based query configs
+    table_query_configs = (TableFieldsQueryConfig, TableFunctionQueryConfig)
+    assert isinstance(self._query_config, table_query_configs)
+    clause = self._query_config.where_clause_template
+
+    # Extract parameter names from the template using regex.
+    param_names = self._extract_parameter_names(
+        self._query_config.where_clause_template)
+    for param_name in param_names:
+      old_param = f':{param_name}'
+      new_param = f':batch_{batch_index}_{param_name}'
+      clause = clause.replace(old_param, new_param)
+
+    return f'({clause})'
+
+  def _build_parameters_dict(
+      self, requests: list[beam.Row], batch_size: int) -> dict:
+    """Build named parameters dictionary for parameterized queries.
+
+    Args:
+      requests: List of beam.Row requests to process
+      batch_size: Number of requests in the batch
+
+    Returns:
+      Dictionary mapping parameter names to validated values
+    """
+    param_dict = {}
+    for i, req in enumerate(requests):
+      current_values = self._extract_values_from_request(req)
+
+      # For batched queries, use unique parameter names per batch item.
+      if batch_size > 1:
+        # Batching is only used with table-based query configs.
+        table_query_configs = (TableFieldsQueryConfig, TableFunctionQueryConfig)
+        assert isinstance(self._query_config, table_query_configs)
+        batch_param_dict = self._build_single_param_dict(current_values)
+        # Prefix batch parameters to make them globally unique.
+        for param_name, val in batch_param_dict.items():
+          param_dict[f'batch_{i}_{param_name}'] = val
+      else:
+        single_param_dict = self._build_single_param_dict(current_values)
+        param_dict.update(single_param_dict)
+
+    return param_dict
+
+  def _build_single_param_dict(self, values: list[Any]) -> dict[str, Any]:
+    """Build parameter dictionary for single request processing.
+
+    Args:
+      values: List of parameter values
+
+    Returns:
+      Dictionary mapping parameter names to values
+    """
+    table_query_configs = (TableFieldsQueryConfig, TableFunctionQueryConfig)
+    if not isinstance(self._query_config, table_query_configs):
+      raise ValueError(
+          f"Parameter binding not supported for "
+          f"{type(self._query_config).__name__}")
+
+    _, param_dict = self._get_unique_template_and_params(
+        self._query_config.where_clause_template, values)
+    return param_dict
+
+  def _get_unique_template_and_params(
+      self, template: str, values: list[Any]) -> tuple[str, dict[str, Any]]:
+    """Generate unique binding parameter names for duplicate templates.
+
+    Args:
+      template: SQL template with potentially duplicate binding parameter names
+      values: List of parameter values
+
+    Returns:
+      Tuple of (updated_template, param_dict) with unique binding names.
+    """
+    param_names = self._extract_parameter_names(template)
+    unique_param_names = [
+        f"{param_name}_{i}" if param_names.count(param_name) > 1 else param_name
+        for i, param_name in enumerate(param_names)
+    ]
+
+    # Update template by replacing each parameter occurrence in order.
+    updated_template = template
+    param_positions = []
+
+    # Find all parameter positions.
+    for match in re.finditer(r':(\w+)', template):
+      param_positions.append((match.start(), match.end(), match.group(1)))
+
+    # Replace parameters from right to left to avoid position shifts.
+    for i in reversed(range(len(param_positions))):
+      start, end, _ = param_positions[i]
+      unique_name = unique_param_names[i]
+      updated_template = (
+          updated_template[:start] + f':{unique_name}' + updated_template[end:])
+
+    # Build parameter dictionary.
+    param_dict = {
+        unique_name: val
+        for unique_name, val in zip(unique_param_names, values)
+    }
+
+    return updated_template, param_dict
+
+  def _extract_values_from_request(self, request: beam.Row) -> list[Any]:
+    """Extract parameter values from a request based on query configuration.
+
+    Args:
+      request: The beam.Row request to extract values from
+
+    Returns:
+      List of parameter values
+
+    Raises:
+      KeyError: If required fields are missing from the request
+    """
+    try:
+      if isinstance(self._query_config, TableFunctionQueryConfig):
+        return [
+            val for val in self._query_config.where_clause_value_fn(request)
+        ]
+      elif isinstance(self._query_config, TableFieldsQueryConfig):
+        request_dict = request._asdict()
+        return [
+            request_dict[field]
+            for field in self._query_config.where_clause_fields
+        ]
+      else:
+        raise ValueError("Unsupported query configuration type")
+    except KeyError as e:
+      raise KeyError(
+          "Make sure the values passed in `where_clause_fields` are "
+          "the keys in the input `beam.Row`." + str(e))
+
+  def _extract_parameter_names(self, template: str) -> list[str]:
+    """Extract parameter names from a SQL template string.
+
+    Args:
+      template: SQL template string with named parameters (e.g., "id = :id")
+
+    Returns:
+      List of parameter names found in the template (e.g., ["id"])
+    """
+    return re.findall(r':(\w+)', template)
+
+  def create_row_key(self, row: beam.Row):
+    if isinstance(self._query_config, TableFunctionQueryConfig):
+      return tuple(self._query_config.where_clause_value_fn(row))
+    if isinstance(self._query_config, TableFieldsQueryConfig):
+      row_dict = row._asdict()
+      return (
+          tuple(
+              row_dict[where_clause_field]
+              for where_clause_field in self._query_config.where_clause_fields))
+    raise ValueError(
+        "Either where_clause_fields or where_clause_value_fn must be specified")
+
+  def get_cache_key(self, request: Union[beam.Row, list[beam.Row]]):
+    if isinstance(self._query_config, CustomQueryConfig):
+      raise NotImplementedError(
+          "Caching is not supported for CustomQueryConfig. "
+          "Consider using TableFieldsQueryConfig or " +
+          "TableFunctionQueryConfig instead.")
+
+    if isinstance(request, list):
+      cache_keys = []
+      for req in request:
+        req_dict = req._asdict()
+        try:
+          if isinstance(self._query_config, TableFunctionQueryConfig):
+            current_values = self._query_config.where_clause_value_fn(req)
+          elif isinstance(self._query_config, TableFieldsQueryConfig):
+            current_values = [
+                req_dict[field]
+                for field in self._query_config.where_clause_fields
+            ]
+          key = ';'.join(map(repr, current_values))
+          cache_keys.append(key)
+        except KeyError as e:
+          raise KeyError(
+              "Make sure the values passed in `where_clause_fields` are the "
+              "keys in the input `beam.Row`." + str(e))
+      return cache_keys
+    else:
+      req_dict = request._asdict()
+      try:
+        if isinstance(self._query_config, TableFunctionQueryConfig):
+          current_values = self._query_config.where_clause_value_fn(request)
+        else:  # TableFieldsQueryConfig.
+          current_values = [
+              req_dict[field]
+              for field in self._query_config.where_clause_fields
+          ]
+        key = ";".join(["%s"] * len(current_values))
+        cache_key = key % tuple(current_values)
+      except KeyError as e:
+        raise KeyError(
+            "Make sure the values passed in `where_clause_fields` are the "
+            "keys in the input `beam.Row`." + str(e))
+      return cache_key
+
+  def __exit__(self, _exc_type, _exc_val, _exc_tb):
+    self._engine.dispose(close=True)
+    self._engine = None
+
+  def batch_elements_kwargs(self) -> Mapping[str, Any]:
+    """Returns a kwargs suitable for `beam.BatchElements`."""
+    return self._batching_kwargs
diff --git a/sdks/python/apache_beam/transforms/enrichment_handlers/cloudsql_it_test.py b/sdks/python/apache_beam/transforms/enrichment_handlers/cloudsql_it_test.py
new file mode 100644
index 000000000000..15ab0ec0a3a1
--- /dev/null
+++ b/sdks/python/apache_beam/transforms/enrichment_handlers/cloudsql_it_test.py
@@ -0,0 +1,630 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import functools
+import logging
+import os
+import unittest
+import uuid
+from dataclasses import dataclass
+from typing import Optional
+from unittest.mock import MagicMock
+
+import pytest
+
+import apache_beam as beam
+from apache_beam.coders import coders
+from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.testing.util import assert_that
+from apache_beam.testing.util import equal_to
+
+# pylint: disable=ungrouped-imports
+try:
+  from testcontainers.core.generic import DbContainer
+  from testcontainers.postgres import PostgresContainer
+  from testcontainers.mysql import MySqlContainer
+  from testcontainers.mssql import SqlServerContainer
+  from testcontainers.redis import RedisContainer
+  from sqlalchemy import (
+      create_engine, MetaData, Table, Column, Integer, VARCHAR, Engine)
+  from apache_beam.transforms.enrichment import Enrichment
+  from apache_beam.transforms.enrichment_handlers.cloudsql import (
+      CloudSQLEnrichmentHandler,
+      DatabaseTypeAdapter,
+      CustomQueryConfig,
+      TableFieldsQueryConfig,
+      TableFunctionQueryConfig,
+      CloudSQLConnectionConfig,
+      ExternalSQLDBConnectionConfig,
+      ConnectionConfig)
+except ImportError as e:
+  raise unittest.SkipTest(f'CloudSQL dependencies not installed: {str(e)}')
+
+_LOGGER = logging.getLogger(__name__)
+
+
+def where_clause_value_fn(row: beam.Row):
+  return [row.id]  # type: ignore[attr-defined]
+
+
+def query_fn(table, row: beam.Row):
+  return f"SELECT * FROM {table} WHERE id = {row.id}"  # type: ignore[attr-defined]
+
+
+@dataclass
+class SQLDBContainerInfo:
+  adapter: DatabaseTypeAdapter
+  container: DbContainer
+  host: str
+  port: int
+  user: str
+  password: str
+  id: str
+
+  @property
+  def address(self) -> str:
+    return f"{self.host}:{self.port}"
+
+  @property
+  def url(self) -> str:
+    return self.adapter.to_sqlalchemy_dialect() + "://"
+
+
+class SQLEnrichmentTestHelper:
+  @staticmethod
+  def start_sql_db_container(
+      database_type: DatabaseTypeAdapter,
+      sql_client_retries=3) -> Optional[SQLDBContainerInfo]:
+    info = None
+    for i in range(sql_client_retries):
+      sql_db_container = DbContainer("")
+      try:
+        if database_type == DatabaseTypeAdapter.POSTGRESQL:
+          user, password, db_id = "test", "test", "test"
+          sql_db_container = PostgresContainer(
+              image="postgres:16",
+              username=user,
+              password=password,
+              dbname=db_id,
+              driver=database_type.value)
+          sql_db_container.start()
+          host = sql_db_container.get_container_host_ip()
+          port = int(sql_db_container.get_exposed_port(5432))
+        elif database_type == DatabaseTypeAdapter.MYSQL:
+          user, password, db_id = "test", "test", "test"
+          sql_db_container = MySqlContainer(
+              image="mysql:8.0",
+              username=user,
+              root_password=password,
+              password=password,
+              dbname=db_id)
+          sql_db_container.start()
+          host = sql_db_container.get_container_host_ip()
+          port = int(sql_db_container.get_exposed_port(3306))
+        elif database_type == DatabaseTypeAdapter.SQLSERVER:
+          user, password, db_id = "SA", "A_Str0ng_Required_Password", "tempdb"
+          sql_db_container = SqlServerContainer(
+              image="mcr.microsoft.com/mssql/server:2022-latest",
+              username=user,
+              password=password,
+              dbname=db_id,
+              dialect=database_type.to_sqlalchemy_dialect())
+          sql_db_container.start()
+          host = sql_db_container.get_container_host_ip()
+          port = int(sql_db_container.get_exposed_port(1433))
+        else:
+          raise ValueError(f"Unsupported database type: {database_type}")
+
+        info = SQLDBContainerInfo(
+            adapter=database_type,
+            container=sql_db_container,
+            host=host,
+            port=port,
+            user=user,
+            password=password,
+            id=db_id)
+        _LOGGER.info(
+            "%s container started successfully on %s.",
+            database_type.name,
+            info.address)
+        break
+      except Exception as e:
+        stdout_logs, stderr_logs = sql_db_container.get_logs()
+        stdout_logs = stdout_logs.decode("utf-8")
+        stderr_logs = stderr_logs.decode("utf-8")
+        _LOGGER.warning(
+            "Retry %d/%d: Failed to start %s container. Reason: %s. "
+            "STDOUT logs:\n%s\nSTDERR logs:\n%s",
+            i + 1,
+            sql_client_retries,
+            database_type.name,
+            e,
+            stdout_logs,
+            stderr_logs)
+        if i == sql_client_retries - 1:
+          _LOGGER.error(
+              "Unable to start %s container for I/O tests after %d "
+              "retries. Tests cannot proceed. STDOUT logs:\n%s\n"
+              "STDERR logs:\n%s",
+              database_type.name,
+              sql_client_retries,
+              stdout_logs,
+              stderr_logs)
+          raise e
+
+    return info
+
+  @staticmethod
+  def stop_sql_db_container(db_info: SQLDBContainerInfo):
+    try:
+      _LOGGER.debug("Stopping %s container.", db_info.adapter.name)
+      db_info.container.stop()
+      _LOGGER.info("%s container stopped successfully.", db_info.adapter.name)
+    except Exception as e:
+      _LOGGER.warning(
+          "Error encountered while stopping %s container: %s",
+          db_info.adapter.name,
+          e)
+
+  @staticmethod
+  def create_table(
+      table_id: str,
+      engine: Engine,
+      columns: list[Column],
+      table_data: list[dict],
+      metadata: MetaData):
+    # Create table metadata.
+    table = Table(table_id, metadata, *columns)
+
+    # Create contextual connection for schema creation.
+    with engine.connect() as schema_connection:
+      try:
+        metadata.create_all(schema_connection)
+        schema_connection.commit()
+      except Exception as e:
+        schema_connection.rollback()
+        raise RuntimeError(f"Failed to create table schema: {e}")
+
+    # Now create a separate contextual connection for data insertion.
+    with engine.connect() as connection:
+      try:
+        connection.execute(table.insert(), table_data)
+        connection.commit()
+      except Exception as e:
+        connection.rollback()
+        raise Exception(f"Failed to insert table data: {e}")
+
+
+class BaseTestSQLEnrichment(unittest.TestCase):
+  _table_data = [
+      {
+          "id": 1, "name": "A", 'quantity': 2, 'distribution_center_id': 3
+      },
+      {
+          "id": 2, "name": "B", 'quantity': 3, 'distribution_center_id': 1
+      },
+      {
+          "id": 3, "name": "C", 'quantity': 10, 'distribution_center_id': 4
+      },
+      {
+          "id": 4, "name": "D", 'quantity': 1, 'distribution_center_id': 3
+      },
+      {
+          "id": 5, "name": "C", 'quantity': 100, 'distribution_center_id': 4
+      },
+      {
+          "id": 6, "name": "D", 'quantity': 11, 'distribution_center_id': 3
+      },
+      {
+          "id": 7, "name": "C", 'quantity': 7, 'distribution_center_id': 1
+      },
+      {
+          "id": 8, "name": "D", 'quantity': 4, 'distribution_center_id': 1
+      },
+  ]
+
+  @classmethod
+  def setUpClass(cls):
+    if not hasattr(cls, '_connection_config') or not hasattr(cls, '_metadata'):
+      # Skip setup for the base class.
+      raise unittest.SkipTest(
+          "Base class - no connection_config or metadata defined")
+
+    # Type hint data from subclasses.
+    cls._table_id: str
+    cls._connection_config: ConnectionConfig
+    cls._metadata: MetaData
+
+    connector = cls._connection_config.get_connector_handler()
+    cls._engine = create_engine(
+        url=cls._connection_config.get_db_url(), creator=connector)
+
+    SQLEnrichmentTestHelper.create_table(
+        table_id=cls._table_id,
+        engine=cls._engine,
+        columns=cls.get_columns(),
+        table_data=cls._table_data,
+        metadata=cls._metadata)
+
+    cls._cache_client_retries = 3
+
+  @classmethod
+  def get_columns(cls):
+    """Returns fresh column objects each time it's called."""
+    return [
+        Column("id", Integer, nullable=False),
+        Column("name", VARCHAR(255), nullable=False),
+        Column("quantity", Integer, nullable=False),
+        Column("distribution_center_id", Integer, nullable=False),
+    ]
+
+  @pytest.fixture
+  def cache_container(self):
+    self._start_cache_container()
+
+    # Hand control to the test.
+    yield
+
+    self._cache_container.stop()
+    self._cache_container = None
+
+  def _start_cache_container(self):
+    for i in range(self._cache_client_retries):
+      try:
+        self._cache_container = RedisContainer(image="redis:7.2.4")
+        self._cache_container.start()
+        host = self._cache_container.get_container_host_ip()
+        port = self._cache_container.get_exposed_port(6379)
+        self._cache_container_host = host
+        self._cache_container_port = port
+        self._cache_client = self._cache_container.get_client()
+        break
+      except Exception as e:
+        if i == self._cache_client_retries - 1:
+          _LOGGER.error(
+              "Unable to start redis container for RRIO tests after "
+              "%d retries.",
+              self._cache_client_retries)
+          raise e
+
+  @classmethod
+  def tearDownClass(cls):
+    # Drop all tables using metadata as the primary approach.
+    cls._metadata.drop_all(cls._engine)
+
+    # Fallback to raw SQL drop if needed.
+    try:
+      with cls._engine.connect() as conn:
+        conn.execute(f"DROP TABLE IF EXISTS {cls._table_id}")
+        conn.commit()
+        _LOGGER.info("Dropped table %s", cls._table_id)
+    except Exception as e:
+      _LOGGER.warning("Failed to drop table %s: %s", cls._table_id, e)
+
+    cls._engine.dispose(close=True)
+    cls._engine = None
+
+  def test_sql_enrichment(self):
+    expected_rows = [
+        beam.Row(id=1, name="A", quantity=2, distribution_center_id=3),
+        beam.Row(id=2, name="B", quantity=3, distribution_center_id=1)
+    ]
+    fields = ['id']
+    requests = [
+        beam.Row(id=1, name='A'),
+        beam.Row(id=2, name='B'),
+    ]
+
+    query_config = TableFieldsQueryConfig(
+        table_id=self._table_id,
+        where_clause_template="id = :id_param",
+        where_clause_fields=fields)
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=self._connection_config,
+        query_config=query_config,
+        min_batch_size=1,
+        max_batch_size=100,
+    )
+
+    with TestPipeline() as test_pipeline:
+      pcoll = (test_pipeline | beam.Create(requests) | Enrichment(handler))
+
+      assert_that(pcoll, equal_to(expected_rows))
+
+  def test_sql_enrichment_batched(self):
+    expected_rows = [
+        beam.Row(id=1, name="A", quantity=2, distribution_center_id=3),
+        beam.Row(id=2, name="B", quantity=3, distribution_center_id=1)
+    ]
+    fields = ['id']
+    requests = [
+        beam.Row(id=1, name='A'),
+        beam.Row(id=2, name='B'),
+    ]
+
+    query_config = TableFieldsQueryConfig(
+        table_id=self._table_id,
+        where_clause_template="id = :id",
+        where_clause_fields=fields)
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=self._connection_config,
+        query_config=query_config,
+        min_batch_size=2,
+        max_batch_size=100,
+    )
+    with TestPipeline() as test_pipeline:
+      pcoll = (test_pipeline | beam.Create(requests) | Enrichment(handler))
+
+      assert_that(pcoll, equal_to(expected_rows))
+
+  def test_sql_enrichment_batched_multiple_fields(self):
+    expected_rows = [
+        beam.Row(id=1, distribution_center_id=3, name="A", quantity=2),
+        beam.Row(id=2, distribution_center_id=1, name="B", quantity=3)
+    ]
+    fields = ['id', 'distribution_center_id']
+    requests = [
+        beam.Row(id=1, distribution_center_id=3),
+        beam.Row(id=2, distribution_center_id=1),
+    ]
+
+    query_config = TableFieldsQueryConfig(
+        table_id=self._table_id,
+        where_clause_template="id = :id AND distribution_center_id = :param_1",
+        where_clause_fields=fields)
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=self._connection_config,
+        query_config=query_config,
+        min_batch_size=8,
+        max_batch_size=100,
+    )
+    with TestPipeline() as test_pipeline:
+      pcoll = (test_pipeline | beam.Create(requests) | Enrichment(handler))
+
+      assert_that(pcoll, equal_to(expected_rows))
+
+  def test_sql_enrichment_with_query_fn(self):
+    expected_rows = [
+        beam.Row(id=1, name="A", quantity=2, distribution_center_id=3),
+        beam.Row(id=2, name="B", quantity=3, distribution_center_id=1)
+    ]
+    requests = [
+        beam.Row(id=1, name='A'),
+        beam.Row(id=2, name='B'),
+    ]
+    fn = functools.partial(query_fn, self._table_id)
+
+    query_config = CustomQueryConfig(query_fn=fn)
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=self._connection_config, query_config=query_config)
+    with TestPipeline() as test_pipeline:
+      pcoll = (test_pipeline | beam.Create(requests) | Enrichment(handler))
+
+      assert_that(pcoll, equal_to(expected_rows))
+
+  def test_sql_enrichment_with_condition_value_fn(self):
+    expected_rows = [
+        beam.Row(id=1, name="A", quantity=2, distribution_center_id=3),
+        beam.Row(id=2, name="B", quantity=3, distribution_center_id=1)
+    ]
+    requests = [
+        beam.Row(id=1, name='A'),
+        beam.Row(id=2, name='B'),
+    ]
+
+    query_config = TableFunctionQueryConfig(
+        table_id=self._table_id,
+        where_clause_template="id = :param_0",
+        where_clause_value_fn=where_clause_value_fn)
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=self._connection_config,
+        query_config=query_config,
+        min_batch_size=2,
+        max_batch_size=100)
+    with TestPipeline() as test_pipeline:
+      pcoll = (test_pipeline | beam.Create(requests) | Enrichment(handler))
+
+      assert_that(pcoll, equal_to(expected_rows))
+
+  def test_sql_enrichment_on_non_existent_table(self):
+    requests = [
+        beam.Row(id=1, name='A'),
+        beam.Row(id=2, name='B'),
+    ]
+
+    query_config = TableFunctionQueryConfig(
+        table_id=self._table_id,
+        where_clause_template="id = :id",
+        where_clause_value_fn=where_clause_value_fn)
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=self._connection_config,
+        query_config=query_config,
+        column_names=["wrong_column"],
+    )
+
+    with self.assertRaises(Exception) as context:
+      with TestPipeline() as p:
+        _ = (p | beam.Create(requests) | Enrichment(handler))
+
+    expect_err_msg_contains = (
+        "Could not execute the query. Please check if the query is properly "
+        "formatted and the table exists.")
+    self.assertIn(expect_err_msg_contains, str(context.exception))
+
+  @pytest.mark.usefixtures("cache_container")
+  def test_sql_enrichment_with_redis(self):
+    requests = [
+        beam.Row(id=1, name='A'),
+        beam.Row(id=2, name='B'),
+    ]
+    expected_rows = [
+        beam.Row(id=1, name="A", quantity=2, distribution_center_id=3),
+        beam.Row(id=2, name="B", quantity=3, distribution_center_id=1)
+    ]
+
+    query_config = TableFunctionQueryConfig(
+        table_id=self._table_id,
+        where_clause_template="id = :param_0",
+        where_clause_value_fn=where_clause_value_fn)
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=self._connection_config,
+        query_config=query_config,
+        min_batch_size=2,
+        max_batch_size=100)
+    with TestPipeline() as test_pipeline:
+      pcoll_populate_cache = (
+          test_pipeline
+          | beam.Create(requests)
+          | Enrichment(handler).with_redis_cache(
+              self._cache_container_host, self._cache_container_port))
+
+      assert_that(pcoll_populate_cache, equal_to(expected_rows))
+
+    # Manually check cache entry to verify entries were correctly stored.
+    c = coders.StrUtf8Coder()
+    for req in requests:
+      key = handler.get_cache_key(req)
+      response = self._cache_client.get(c.encode(key))
+      if not response:
+        raise ValueError("No cache entry found for %s" % key)
+
+    # Mocks the CloudSQL enrichment handler to prevent actual database calls.
+    # This ensures that a cache hit scenario does not trigger any database
+    # interaction, raising an exception if an unexpected call occurs.
+    actual = CloudSQLEnrichmentHandler.__call__
+    CloudSQLEnrichmentHandler.__call__ = MagicMock(
+        side_effect=Exception("Database should not be called on a cache hit."))
+
+    # Run a second pipeline to verify cache is being used.
+    with TestPipeline() as test_pipeline:
+      pcoll_cached = (
+          test_pipeline
+          | beam.Create(requests)
+          | Enrichment(handler).with_redis_cache(
+              self._cache_container_host, self._cache_container_port))
+
+      assert_that(pcoll_cached, equal_to(expected_rows))
+
+    # Restore the original CloudSQL enrichment handler implementation.
+    CloudSQLEnrichmentHandler.__call__ = actual
+
+
+class BaseCloudSQLDBEnrichment(BaseTestSQLEnrichment):
+  @classmethod
+  def setUpClass(cls):
+    if not hasattr(cls, '_db_adapter'):
+      # Skip setup for the base class.
+      raise unittest.SkipTest("Base class - no db_adapter defined")
+
+    # Type hint data from subclasses.
+    cls._db_adapter: DatabaseTypeAdapter
+    cls._instance_connection_uri: str
+    cls._user: str
+    cls._password: str
+    cls._db_id: str
+
+    cls._connection_config = CloudSQLConnectionConfig(
+        db_adapter=cls._db_adapter,
+        instance_connection_uri=cls._instance_connection_uri,
+        user=cls._user,
+        password=cls._password,
+        db_id=cls._db_id)
+    super().setUpClass()
+
+  @classmethod
+  def tearDownClass(cls):
+    super().tearDownClass()
+
+
+@unittest.skipUnless(
+    os.environ.get('ALLOYDB_PASSWORD'),
+    "ALLOYDB_PASSWORD environment var is not provided")
+class TestCloudSQLPostgresEnrichment(BaseCloudSQLDBEnrichment):
+  _db_adapter = DatabaseTypeAdapter.POSTGRESQL
+
+  # Configuration required for locating the CloudSQL instance.
+  _unique_suffix = str(uuid.uuid4())[:8]
+  _table_id = f"product_details_cloudsql_pg_enrichment_{_unique_suffix}"
+  _gcp_project_id = "apache-beam-testing"
+  _region = "us-central1"
+  _instance_name = "beam-integration-tests"
+  _instance_connection_uri = f"{_gcp_project_id}:{_region}:{_instance_name}"
+
+  # Configuration required for authenticating to the CloudSQL instance.
+  _user = "postgres"
+  _password = os.getenv("ALLOYDB_PASSWORD")
+  _db_id = "postgres"
+
+  _metadata = MetaData()
+
+
+class BaseExternalSQLDBEnrichment(BaseTestSQLEnrichment):
+  @classmethod
+  def setUpClass(cls):
+    if not hasattr(cls, '_db_adapter'):
+      # Skip setup for the base class.
+      raise unittest.SkipTest("Base class - no db_adapter defined")
+
+    # Type hint data from subclasses.
+    cls._db_adapter: DatabaseTypeAdapter
+
+    cls._db = SQLEnrichmentTestHelper.start_sql_db_container(cls._db_adapter)
+    cls._connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=cls._db_adapter,
+        host=cls._db.host,
+        user=cls._db.user,
+        password=cls._db.password,
+        db_id=cls._db.id,
+        port=cls._db.port)
+    super().setUpClass()
+
+  @classmethod
+  def tearDownClass(cls):
+    super().tearDownClass()
+    SQLEnrichmentTestHelper.stop_sql_db_container(cls._db)
+    cls._db = None
+
+
+class TestExternalPostgresEnrichment(BaseExternalSQLDBEnrichment):
+  _db_adapter = DatabaseTypeAdapter.POSTGRESQL
+  _unique_suffix = str(uuid.uuid4())[:8]
+  _table_id = f"product_details_external_pg_enrichment_{_unique_suffix}"
+  _metadata = MetaData()
+
+
+class TestExternalMySQLEnrichment(BaseExternalSQLDBEnrichment):
+  _db_adapter = DatabaseTypeAdapter.MYSQL
+  _unique_suffix = str(uuid.uuid4())[:8]
+  _table_id = f"product_details_external_mysql_enrichment_{_unique_suffix}"
+  _metadata = MetaData()
+
+
+class TestExternalSQLServerEnrichment(BaseExternalSQLDBEnrichment):
+  _db_adapter = DatabaseTypeAdapter.SQLSERVER
+  _unique_suffix = str(uuid.uuid4())[:8]
+  _table_id = f"product_details_external_mssql_enrichment_{_unique_suffix}"
+  _metadata = MetaData()
+
+
+if __name__ == "__main__":
+  unittest.main()
diff --git a/sdks/python/apache_beam/transforms/enrichment_handlers/cloudsql_test.py b/sdks/python/apache_beam/transforms/enrichment_handlers/cloudsql_test.py
new file mode 100644
index 000000000000..99823f6d89a6
--- /dev/null
+++ b/sdks/python/apache_beam/transforms/enrichment_handlers/cloudsql_test.py
@@ -0,0 +1,569 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import unittest
+
+from parameterized import parameterized
+
+# pylint: disable=ungrouped-imports
+try:
+  from apache_beam.transforms.enrichment_handlers.cloudsql import (
+      CloudSQLEnrichmentHandler,
+      DatabaseTypeAdapter,
+      CustomQueryConfig,
+      TableFieldsQueryConfig,
+      TableFunctionQueryConfig,
+      CloudSQLConnectionConfig,
+      ExternalSQLDBConnectionConfig)
+  from apache_beam.transforms.enrichment_handlers.cloudsql_it_test import (
+      query_fn,
+      where_clause_value_fn,
+  )
+except ImportError as e:
+  raise unittest.SkipTest(f'CloudSQL dependencies not installed: {str(e)}')
+
+
+class TestCloudSQLEnrichment(unittest.TestCase):
+  def test_invalid_external_db_connection_params(self):
+    with self.assertRaises(ValueError) as context:
+      _ = ExternalSQLDBConnectionConfig(
+          db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+          host='',
+          port=5432,
+          user='',
+          password='',
+          db_id='')
+    self.assertIn("Database host cannot be empty", str(context.exception))
+
+  def test_invalid_cloudsql_db_connection_params(self):
+    with self.assertRaises(ValueError) as context:
+      _ = CloudSQLConnectionConfig(
+          db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+          instance_connection_uri='',
+          user='',
+          password='',
+          db_id='')
+    self.assertIn(
+        "Instance connection URI cannot be empty", str(context.exception))
+
+  @parameterized.expand([
+      # Empty TableFieldsQueryConfig.
+      (
+          lambda: TableFieldsQueryConfig(
+              table_id="", where_clause_template="", where_clause_fields=[]),
+          1,
+          2,
+          "must provide table_id and where_clause_template"
+      ),
+      # Missing where_clause_template in TableFieldsQueryConfig.
+      (
+          lambda: TableFieldsQueryConfig(
+              table_id="table",
+              where_clause_template="",
+              where_clause_fields=["id"]),
+          2,
+          10,
+          "must provide table_id and where_clause_template"
+      ),
+      # Invalid CustomQueryConfig with None query_fn.
+      (
+          lambda: CustomQueryConfig(query_fn=None),  # type: ignore[arg-type]
+          2,
+          10,
+          "must provide a valid query_fn"
+      ),
+      # Missing table_id in TableFunctionQueryConfig.
+      (
+          lambda: TableFunctionQueryConfig(
+              table_id="",
+              where_clause_template="id='{}'",
+              where_clause_value_fn=where_clause_value_fn),
+          2,
+          10,
+          "must provide table_id and where_clause_template"
+      ),
+      # Missing where_clause_fields in TableFieldsQueryConfig.
+      (
+          lambda: TableFieldsQueryConfig(
+              table_id="table",
+              where_clause_template="id = '{}'",
+              where_clause_fields=[]),
+          1,
+          10,
+          "must provide non-empty where_clause_fields"
+      ),
+      # Missing where_clause_value_fn in TableFunctionQueryConfig.
+      (
+          lambda: TableFunctionQueryConfig(
+              table_id="table",
+              where_clause_template="id = '{}'",
+              where_clause_value_fn=None),  # type: ignore[arg-type]
+          1,
+          10,
+          "must provide where_clause_value_fn"
+      ),
+  ])
+  def test_invalid_query_config(
+      self, create_config, min_batch_size, max_batch_size, expected_error_msg):
+    """Test that validation errors are raised for invalid query configs.
+
+    The test verifies both that the appropriate ValueError is raised and that
+    the error message contains the expected text.
+    """
+    with self.assertRaises(ValueError) as context:
+      # Call the lambda to create the config.
+      query_config = create_config()
+
+      connection_config = ExternalSQLDBConnectionConfig(
+          db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+          host='localhost',
+          port=5432,
+          user='',
+          password='',
+          db_id='')
+
+      _ = CloudSQLEnrichmentHandler(
+          connection_config=connection_config,
+          query_config=query_config,
+          min_batch_size=min_batch_size,
+          max_batch_size=max_batch_size,
+      )
+    # Verify the error message contains the expected text.
+    self.assertIn(expected_error_msg, str(context.exception))
+
+  def test_valid_query_configs(self):
+    """Test valid query configuration cases."""
+    # Valid TableFieldsQueryConfig.
+    table_fields_config = TableFieldsQueryConfig(
+        table_id="my_table",
+        where_clause_template="id = :id",
+        where_clause_fields=["id"])
+
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    handler1 = CloudSQLEnrichmentHandler(
+        connection_config=connection_config,
+        query_config=table_fields_config,
+        min_batch_size=1,
+        max_batch_size=10)
+
+    self.assertEqual(
+        handler1.query_template, "SELECT * FROM my_table WHERE id = :id")
+
+    # Valid TableFunctionQueryConfig.
+    table_function_config = TableFunctionQueryConfig(
+        table_id="my_table",
+        where_clause_template="id = :id",
+        where_clause_value_fn=where_clause_value_fn)
+
+    handler2 = CloudSQLEnrichmentHandler(
+        connection_config=connection_config,
+        query_config=table_function_config,
+        min_batch_size=1,
+        max_batch_size=10)
+
+    self.assertEqual(
+        handler2.query_template, "SELECT * FROM my_table WHERE id = :id")
+
+    # Valid CustomQueryConfig.
+    custom_config = CustomQueryConfig(query_fn=query_fn)
+
+    handler3 = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=custom_config)
+
+    # Verify that batching kwargs are empty for CustomQueryConfig.
+    self.assertEqual(handler3.batch_elements_kwargs(), {})
+
+  def test_custom_query_config_cache_key_error(self):
+    """Test get_cache_key raises NotImplementedError with CustomQueryConfig."""
+    custom_config = CustomQueryConfig(query_fn=query_fn)
+
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=custom_config)
+
+    # Create a dummy request.
+    import apache_beam as beam
+    request = beam.Row(id=1)
+
+    # Verify that get_cache_key raises NotImplementedError.
+    with self.assertRaises(NotImplementedError):
+      handler.get_cache_key(request)
+
+  def test_extract_parameter_names(self):
+    """Test parameter extraction from SQL templates."""
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    config = TableFieldsQueryConfig(
+        table_id="users",
+        where_clause_template="id = :id",
+        where_clause_fields=["id"])
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=config)
+
+    # Test simple parameter extraction.
+    self.assertEqual(handler._extract_parameter_names("id = :id"), ["id"])
+
+    # Test multiple parameters.
+    self.assertEqual(
+        handler._extract_parameter_names("id = :id AND name = :name"),
+        ["id", "name"])
+
+    # Test no parameters.
+    self.assertEqual(
+        handler._extract_parameter_names("SELECT * FROM users"), [])
+
+    # Test complex query.
+    self.assertEqual(
+        handler._extract_parameter_names(
+            "age > :min_age AND city = :city AND status = :status"),
+        ["min_age", "city", "status"])
+
+  def test_build_single_param_dict(self):
+    """Test building parameter dictionaries for single requests."""
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    # Test TableFieldsQueryConfig.
+    table_config = TableFieldsQueryConfig(
+        table_id="users",
+        where_clause_template="id = :id AND name = :name",
+        where_clause_fields=["id", "name"])
+
+    handler1 = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=table_config)
+
+    result1 = handler1._build_single_param_dict([123, "John"])
+    self.assertEqual(result1, {"id": 123, "name": "John"})
+
+    # Test TableFunctionQueryConfig.
+    table_func_config = TableFunctionQueryConfig(
+        table_id="users",
+        where_clause_template="age > :min_age AND city = :city",
+        where_clause_value_fn=lambda row: [row.min_age, row.city])
+
+    handler2 = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=table_func_config)
+
+    result2 = handler2._build_single_param_dict([3, "NYC"])
+    self.assertEqual(result2, {"min_age": 3, "city": "NYC"})
+
+  def test_extract_values_from_request(self):
+    """Test extracting values from requests based on query configuration."""
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    # Test TableFieldsQueryConfig.
+    table_config = TableFieldsQueryConfig(
+        table_id="users",
+        where_clause_template="id = :id AND name = :name",
+        where_clause_fields=["id", "name"])
+
+    handler1 = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=table_config)
+
+    import apache_beam as beam
+    request1 = beam.Row(id=123, name="John", age=30)
+    result1 = handler1._extract_values_from_request(request1)
+    self.assertEqual(result1, [123, "John"])
+
+    # Test TableFunctionQueryConfig.
+    def test_value_fn(row):
+      return [row.age, row.city]
+
+    table_func_config = TableFunctionQueryConfig(
+        table_id="users",
+        where_clause_template="age > :min_age AND city = :city",
+        where_clause_value_fn=test_value_fn)
+
+    handler2 = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=table_func_config)
+
+    request2 = beam.Row(age=25, city="NYC", name="Jane")
+    result2 = handler2._extract_values_from_request(request2)
+    self.assertEqual(result2, [25, "NYC"])
+
+    # Test missing field error.
+    request3 = beam.Row(age=30)  # Missing name field.
+    with self.assertRaises(KeyError) as context:
+      handler1._extract_values_from_request(request3)
+    self.assertIn("where_clause_fields", str(context.exception))
+
+  def test_build_batch_query_single_request(self):
+    """Test batch query building with single request returns original query."""
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    config = TableFieldsQueryConfig(
+        table_id="users",
+        where_clause_template="id = :id",
+        where_clause_fields=["id"])
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=config)
+
+    import apache_beam as beam
+    requests = [beam.Row(id=1)]
+
+    # Single request should return original query template.
+    result = handler._build_batch_query(requests, batch_size=1)
+    self.assertEqual(result, "SELECT * FROM users WHERE id = :id")
+
+  def test_build_batch_query_multiple_requests(self):
+    """Test batch query building with multiple requests creates OR clauses."""
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    config = TableFieldsQueryConfig(
+        table_id="users",
+        where_clause_template="id = :id AND name = :name",
+        where_clause_fields=["id", "name"])
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=config)
+
+    import apache_beam as beam
+    requests = [beam.Row(id=1, name="Alice"), beam.Row(id=2, name="Bob")]
+
+    result = handler._build_batch_query(requests, batch_size=2)
+    expected = (
+        "SELECT * FROM users WHERE "
+        "(id = :batch_0_id AND name = :batch_0_name) OR "
+        "(id = :batch_1_id AND name = :batch_1_name)")
+    self.assertEqual(result, expected)
+
+  def test_build_parameters_dict_batch(self):
+    """Test parameter dictionary building for batch requests."""
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    config = TableFieldsQueryConfig(
+        table_id="users",
+        where_clause_template="id = :id",
+        where_clause_fields=["id"])
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=config)
+
+    import apache_beam as beam
+    requests = [beam.Row(id=1), beam.Row(id=2)]
+
+    result = handler._build_parameters_dict(requests, batch_size=2)
+    expected = {"batch_0_id": 1, "batch_1_id": 2}
+    self.assertEqual(result, expected)
+
+  def test_create_batch_clause(self):
+    """Test batch clause creation with unique parameter names."""
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    config = TableFieldsQueryConfig(
+        table_id="users",
+        where_clause_template="id = :id AND name = :name",
+        where_clause_fields=["id", "name"])
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=config)
+
+    result = handler._create_batch_clause(batch_index=0)
+    expected = "(id = :batch_0_id AND name = :batch_0_name)"
+    self.assertEqual(result, expected)
+
+    result2 = handler._create_batch_clause(batch_index=1)
+    expected2 = "(id = :batch_1_id AND name = :batch_1_name)"
+    self.assertEqual(result2, expected2)
+
+  def test_security_parameter_extraction_edge_cases(self):
+    """Test parameter extraction with edge cases and security issues."""
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    config = TableFieldsQueryConfig(
+        table_id="users",
+        where_clause_template="id = :id",
+        where_clause_fields=["id"])
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=config)
+
+    # Test empty template.
+    self.assertEqual(handler._extract_parameter_names(""), [])
+
+    # Test template with no parameters.
+    self.assertEqual(
+        handler._extract_parameter_names("SELECT * FROM users"), [])
+
+    # Test template with malformed parameters (should not match).
+    self.assertEqual(handler._extract_parameter_names("id = :"), [])
+
+    # Test template with numeric parameter names.
+    self.assertEqual(
+        handler._extract_parameter_names("col = :param_123"), ["param_123"])
+
+    # Test template with underscore parameter names.
+    self.assertEqual(
+        handler._extract_parameter_names("col = :user_id"), ["user_id"])
+
+    # Test duplicate parameter names.
+    result = handler._extract_parameter_names("id = :id OR id = :id")
+    # Note: re.findall returns all matches, so we'd get ["id", "id"].
+    self.assertEqual(result, ["id", "id"])
+
+  def test_build_single_param_dict_with_generic_names(self):
+    """Test parameter dict building with generic names."""
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    # Test TableFunctionQueryConfig with generic parameter names.
+    config = TableFunctionQueryConfig(
+        table_id="users",
+        where_clause_template="id = :param_0 AND name = :param_1",
+        where_clause_value_fn=lambda row: [row.id, row.name])
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=config)
+
+    result = handler._build_single_param_dict([123, "John"])
+    expected = {"param_0": 123, "param_1": "John"}
+    self.assertEqual(result, expected)
+
+  def test_duplicate_binding_parameter_names_handling(self):
+    """Test handling of duplicate binding parameter names."""
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    # Test TableFunctionQueryConfig with duplicate parameter names.
+    config = TableFunctionQueryConfig(
+        table_id="users",
+        where_clause_template="id = :param AND name = :param",
+        where_clause_value_fn=lambda row: [row.id, row.name])
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=config)
+
+    # Test that duplicate parameter names are made unique.
+    result = handler._build_single_param_dict([123, "John"])
+    expected = {"param_0": 123, "param_1": "John"}
+    self.assertEqual(result, expected)
+
+    # Test the helper method for template and params.
+    template = "id = :param AND name = :param"
+    values = [123, "John"]
+    updated_template, param_dict = handler._get_unique_template_and_params(
+        template, values)
+
+    expected_template = "id = :param_0 AND name = :param_1"
+    expected_params = {"param_0": 123, "param_1": "John"}
+    self.assertEqual(updated_template, expected_template)
+    self.assertEqual(param_dict, expected_params)
+
+  def test_unsupported_query_config_type_error(self):
+    """Test that unsupported query config types raise ValueError."""
+    connection_config = ExternalSQLDBConnectionConfig(
+        db_adapter=DatabaseTypeAdapter.POSTGRESQL,
+        host='localhost',
+        port=5432,
+        user='user',
+        password='password',
+        db_id='db')
+
+    config = TableFieldsQueryConfig(
+        table_id="users",
+        where_clause_template="id = :id",
+        where_clause_fields=["id"])
+
+    handler = CloudSQLEnrichmentHandler(
+        connection_config=connection_config, query_config=config)
+
+    # Temporarily replace the config with an unsupported type.
+    handler._query_config = "unsupported_type"
+
+    import apache_beam as beam
+    request = beam.Row(id=1)
+
+    with self.assertRaises(ValueError) as context:
+      handler._extract_values_from_request(request)
+    self.assertIn(
+        "Unsupported query configuration type", str(context.exception))
+
+
+if __name__ == '__main__':
+  unittest.main()
diff --git a/sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store_it_test.py b/sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store_it_test.py
index c5482309a251..d83f1010dd83 100644
--- a/sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store_it_test.py
+++ b/sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store_it_test.py
@@ -15,6 +15,7 @@
 # limitations under the License.
 #
 import logging
+import time
 import unittest
 from unittest.mock import MagicMock
 
@@ -27,7 +28,6 @@
 
 # pylint: disable=ungrouped-imports
 try:
-  from google.api_core.exceptions import NotFound
   from testcontainers.redis import RedisContainer
   from apache_beam.transforms.enrichment import Enrichment
   from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel
@@ -72,11 +72,11 @@ def setUp(self) -> None:
     self.entity_type_name = "entity_id"
     self.api_endpoint = "us-central1-aiplatform.googleapis.com"
     self.feature_ids = ['title', 'genres']
-    self.retries = 3
+    self.retries = 5
     self._start_container()
 
   def _start_container(self):
-    for i in range(3):
+    for i in range(self.retries):
       try:
         self.container = RedisContainer(image='redis:7.2.4')
         self.container.start()
@@ -88,6 +88,8 @@ def _start_container(self):
         if i == self.retries - 1:
           _LOGGER.error('Unable to start redis container for RRIO tests.')
           raise e
+        # Add a small delay between retries to avoid rapid successive failures
+        time.sleep(2)
 
   def tearDown(self) -> None:
     self.container.stop()
@@ -131,7 +133,7 @@ def test_vertex_ai_feature_store_wrong_name(self):
         beam.Row(entity_id="16050", name='stripe t-shirt'),
     ]
 
-    with self.assertRaises(NotFound):
+    with self.assertRaisesRegex(Exception, "does not exist"):
       handler = VertexAIFeatureStoreEnrichmentHandler(
           project=self.project,
           location=self.location,
@@ -158,7 +160,7 @@ def test_vertex_ai_feature_store_bigtable_serving_enrichment_bad(self):
         row_key=self.entity_type_name,
         exception_level=ExceptionLevel.RAISE,
     )
-    with self.assertRaises(ValueError):
+    with self.assertRaises(Exception):
       test_pipeline = beam.Pipeline()
       _ = (
           test_pipeline
@@ -209,7 +211,7 @@ def test_vertex_ai_legacy_feature_store_enrichment_bad(self):
         exception_level=ExceptionLevel.RAISE,
     )
 
-    with self.assertRaises(ValueError):
+    with self.assertRaises(Exception):
       test_pipeline = beam.Pipeline()
       _ = (
           test_pipeline
@@ -225,7 +227,7 @@ def test_vertex_ai_legacy_feature_store_invalid_featurestore(self):
     feature_store_id = "invalid_name"
     entity_type_id = "movies"
 
-    with self.assertRaises(NotFound):
+    with self.assertRaisesRegex(Exception, "does not exist"):
       handler = VertexAIFeatureStoreLegacyEnrichmentHandler(
           project=self.project,
           location=self.location,
diff --git a/sdks/python/apache_beam/transforms/external.py b/sdks/python/apache_beam/transforms/external.py
index f0b69a047b7c..3f9f56a54139 100644
--- a/sdks/python/apache_beam/transforms/external.py
+++ b/sdks/python/apache_beam/transforms/external.py
@@ -80,7 +80,11 @@
     ManagedTransforms.Urns.KAFKA_READ.urn: _IO_EXPANSION_SERVICE_JAR_TARGET,
     ManagedTransforms.Urns.KAFKA_WRITE.urn: _IO_EXPANSION_SERVICE_JAR_TARGET,
     ManagedTransforms.Urns.BIGQUERY_READ.urn: _GCP_EXPANSION_SERVICE_JAR_TARGET,
-    ManagedTransforms.Urns.BIGQUERY_WRITE.urn: _GCP_EXPANSION_SERVICE_JAR_TARGET
+    ManagedTransforms.Urns.BIGQUERY_WRITE.urn: _GCP_EXPANSION_SERVICE_JAR_TARGET,  # pylint: disable=line-too-long
+    ManagedTransforms.Urns.POSTGRES_READ.urn: _GCP_EXPANSION_SERVICE_JAR_TARGET,
+    ManagedTransforms.Urns.POSTGRES_WRITE.urn: _GCP_EXPANSION_SERVICE_JAR_TARGET,  # pylint: disable=line-too-long
+    ManagedTransforms.Urns.MYSQL_READ.urn: _GCP_EXPANSION_SERVICE_JAR_TARGET,
+    ManagedTransforms.Urns.MYSQL_WRITE.urn: _GCP_EXPANSION_SERVICE_JAR_TARGET,
 }
 
 
@@ -1097,7 +1101,8 @@ def __enter__(self):
           ExpansionAndArtifactRetrievalStub,
           self.path_to_jar,
           self._extra_args,
-          classpath=classpath_urls)
+          classpath=classpath_urls,
+          logger="ExpansionService")
       self._service = self._service_provider.__enter__()
     self._service_count += 1
     return self._service
diff --git a/sdks/python/apache_beam/transforms/external_test.py b/sdks/python/apache_beam/transforms/external_test.py
index 84a7025c0a5e..2ed7d622ecd6 100644
--- a/sdks/python/apache_beam/transforms/external_test.py
+++ b/sdks/python/apache_beam/transforms/external_test.py
@@ -377,7 +377,7 @@ def test_sanitize_java_traceback(self):
 \tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
 \tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
 \tat java.lang.Thread.run(Thread.java:748)
-Caused by: java.lang.IllegalArgumentException: Received unknown SQL Dialect 'X'. Known dialects: [zetasql, calcite]
+Caused by: java.lang.IllegalArgumentException: Received unknown SQL Dialect 'X'. Known dialects: [calcite]
 \tat org.apache.beam.sdk.extensions.sql.expansion.ExternalSqlTransformRegistrar$Builder.buildExternal(ExternalSqlTransformRegistrar.java:73)
 \tat org.apache.beam.sdk.extensions.sql.expansion.ExternalSqlTransformRegistrar$Builder.buildExternal(ExternalSqlTransformRegistrar.java:63)
 \tat org.apache.beam.sdk.expansion.service.ExpansionService$TransformProviderForBuilder.getTransform(ExpansionService.java:303)
diff --git a/sdks/python/apache_beam/transforms/external_transform_provider_it_test.py b/sdks/python/apache_beam/transforms/external_transform_provider_it_test.py
index 5f115e6d44e9..2f23c5df5983 100644
--- a/sdks/python/apache_beam/transforms/external_transform_provider_it_test.py
+++ b/sdks/python/apache_beam/transforms/external_transform_provider_it_test.py
@@ -158,10 +158,6 @@ def setUp(self):
         os.path.join(self.sdk_dir, 'gen_xlang_wrappers.py'))
     self.xlang_script = importlib.util.module_from_spec(xlang_spec)
     xlang_spec.loader.exec_module(self.xlang_script)
-    managed_spec = importlib.util.spec_from_file_location(
-        'gen_managed_doc', os.path.join(self.sdk_dir, 'gen_managed_doc.py'))
-    self.managed_script = importlib.util.module_from_spec(managed_spec)
-    managed_spec.loader.exec_module(self.managed_script)
 
     args = TestPipeline(is_integration_test=True).get_full_options_as_args()
     runner = PipelineOptions(args).get_all_options()['runner']
@@ -404,29 +400,6 @@ def test_check_standard_external_transforms_config_in_sync(self):
         "by running './gradlew generateExternalTransformsConfig' "
         "and committing the changes.")
 
-  def test_check_managed_configs_doc_in_sync(self):
-    """
-    This test generates the ManagedIO config doc and checks it against the
-    local `website/www/site/content/en/documentation/io/managed-io.md`.
-    Fails if the two docs don't match.
-
-    Fix by running `./gradlew generateExternalTransformsConfig` and
-    committing the changes.
-    """
-    test_doc_path = os.path.join(self.test_dir, 'test-managed-io-doc.md')
-    self.managed_script.generate_managed_doc(test_doc_path)
-    with open(test_doc_path) as f:
-      test_doc = f.readlines()
-    with open(self.managed_script._DOCUMENTATION_DESTINATION) as f:
-      actual_doc = f.readlines()
-
-    self.assertEqual(
-        actual_doc,
-        test_doc,
-        "The ManagedIO configuration page is out of sync! Please "
-        "update by running './gradlew generateManagedIOPage' "
-        "and committing the changes.")
-
 
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
diff --git a/sdks/python/apache_beam/transforms/managed.py b/sdks/python/apache_beam/transforms/managed.py
index bf680d5fd354..33ba8d41a99f 100644
--- a/sdks/python/apache_beam/transforms/managed.py
+++ b/sdks/python/apache_beam/transforms/managed.py
@@ -85,6 +85,9 @@
 _ICEBERG_CDC = "iceberg_cdc"
 KAFKA = "kafka"
 BIGQUERY = "bigquery"
+POSTGRES = "postgres"
+MYSQL = "mysql"
+SQL_SERVER = "sqlserver"
 
 __all__ = ["ICEBERG", "KAFKA", "BIGQUERY", "Read", "Write"]
 
@@ -95,7 +98,10 @@ class Read(PTransform):
       ICEBERG: ManagedTransforms.Urns.ICEBERG_READ.urn,
       _ICEBERG_CDC: ManagedTransforms.Urns.ICEBERG_CDC_READ.urn,
       KAFKA: ManagedTransforms.Urns.KAFKA_READ.urn,
-      BIGQUERY: ManagedTransforms.Urns.BIGQUERY_READ.urn
+      BIGQUERY: ManagedTransforms.Urns.BIGQUERY_READ.urn,
+      POSTGRES: ManagedTransforms.Urns.POSTGRES_READ.urn,
+      MYSQL: ManagedTransforms.Urns.MYSQL_READ.urn,
+      SQL_SERVER: ManagedTransforms.Urns.SQL_SERVER_READ.urn,
   }
 
   def __init__(
@@ -136,7 +142,10 @@ class Write(PTransform):
   _WRITE_TRANSFORMS = {
       ICEBERG: ManagedTransforms.Urns.ICEBERG_WRITE.urn,
       KAFKA: ManagedTransforms.Urns.KAFKA_WRITE.urn,
-      BIGQUERY: ManagedTransforms.Urns.BIGQUERY_WRITE.urn
+      BIGQUERY: ManagedTransforms.Urns.BIGQUERY_WRITE.urn,
+      POSTGRES: ManagedTransforms.Urns.POSTGRES_WRITE.urn,
+      MYSQL: ManagedTransforms.Urns.MYSQL_WRITE.urn,
+      SQL_SERVER: ManagedTransforms.Urns.SQL_SERVER_WRITE.urn
   }
 
   def __init__(
diff --git a/sdks/python/apache_beam/transforms/periodicsequence.py b/sdks/python/apache_beam/transforms/periodicsequence.py
index 8916de0fa58a..60225d43acb6 100644
--- a/sdks/python/apache_beam/transforms/periodicsequence.py
+++ b/sdks/python/apache_beam/transforms/periodicsequence.py
@@ -15,6 +15,7 @@
 # limitations under the License.
 #
 
+import enum
 import math
 import time
 import warnings
@@ -216,6 +217,21 @@ def expand(self, pcoll):
         | 'MapToTimestamped' >> beam.Map(lambda tt: TimestampedValue(tt, tt)))
 
 
+class RebaseMode(enum.Enum):
+  '''Controls how the start and stop timestamps are rebased to execution time.
+
+  Attributes:
+    REBASE_NONE: Timestamps are not changed.
+    REBASE_ALL: Both start and stop timestamps are rebased, preserving the
+      original duration.
+    REBASE_START: Only the start timestamp is rebased; the stop timestamp
+      is unchanged.
+  '''
+  REBASE_NONE = 0
+  REBASE_ALL = 1
+  REBASE_START = 2
+
+
 class PeriodicImpulse(PTransform):
   '''
   PeriodicImpulse transform generates an infinite sequence of elements with
@@ -270,7 +286,8 @@ def __init__(
       stop_timestamp: TimestampTypes = MAX_TIMESTAMP,
       fire_interval: float = 360.0,
       apply_windowing: bool = False,
-      data: Optional[Sequence[Any]] = None):
+      data: Optional[Sequence[Any]] = None,
+      rebase: RebaseMode = RebaseMode.REBASE_NONE):
     '''
     :param start_timestamp: Timestamp for first element.
     :param stop_timestamp: Timestamp at or after which no elements will be
@@ -301,21 +318,36 @@ def __init__(
         sufficient to cover the duration defined by `start_timestamp`,
         `stop_timestamp`, and `fire_interval`; otherwise, a `ValueError` is
         raised.
+
+    :param rebase: Controls how the start and stop timestamps are rebased to
+      execution time. See `RebaseMode` for more details. Defaults to
+      `REBASE_NONE`.
     '''
     self.start_ts = start_timestamp
     self.stop_ts = stop_timestamp
     self.interval = fire_interval
     self.apply_windowing = apply_windowing
     self.data = data
+    self.rebase = rebase
 
     if self.data:
       self._validate_and_adjust_duration()
 
   def expand(self, pbegin):
+    if self.rebase == RebaseMode.REBASE_ALL:
+      duration = Timestamp.of(self.stop_ts) - Timestamp.of(self.start_ts)
+      impulse_element = pbegin | beam.Impulse() | beam.Map(
+          lambda _:
+          [Timestamp.now(), Timestamp.now() + duration, self.interval])
+    elif self.rebase == RebaseMode.REBASE_START:
+      impulse_element = pbegin | beam.Impulse() | beam.Map(
+          lambda _: [Timestamp.now(), self.stop_ts, self.interval])
+    else:
+      impulse_element = pbegin | 'ImpulseElement' >> beam.Create(
+          [(self.start_ts, self.stop_ts, self.interval)])
+
     result = (
-        pbegin
-        | 'ImpulseElement' >> beam.Create(
-            [(self.start_ts, self.stop_ts, self.interval)])
+        impulse_element
         | 'GenSequence' >> beam.ParDo(ImpulseSeqGenDoFn(self.data)))
 
     if not self.data:
diff --git a/sdks/python/apache_beam/transforms/periodicsequence_test.py b/sdks/python/apache_beam/transforms/periodicsequence_test.py
index fce2061614af..6fbc68daed8b 100644
--- a/sdks/python/apache_beam/transforms/periodicsequence_test.py
+++ b/sdks/python/apache_beam/transforms/periodicsequence_test.py
@@ -32,10 +32,12 @@
 from apache_beam.testing.test_pipeline import TestPipeline
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
+from apache_beam.testing.util import is_empty
 from apache_beam.transforms import trigger
 from apache_beam.transforms import window
 from apache_beam.transforms.periodicsequence import PeriodicImpulse
 from apache_beam.transforms.periodicsequence import PeriodicSequence
+from apache_beam.transforms.periodicsequence import RebaseMode
 from apache_beam.transforms.periodicsequence import _sequence_backlog_bytes
 from apache_beam.transforms.window import FixedWindows
 from apache_beam.utils.timestamp import Timestamp
@@ -285,7 +287,6 @@ def test_fuzzy_length_at_minimal_interval(self):
     times = 30
     for _ in range(times):
       seed = int(time.time() * 1000)
-      seed = 1751135957975
       random.seed(seed)
       n = int(random.randint(1, 100))
       data = list(range(n))
@@ -334,6 +335,38 @@ def test_timestamp_type_input(self):
       assert_that(
           ret, equal_to(expected, lambda x, y: type(x) is type(y) and x == y))
 
+  def test_rebase_timestamp(self):
+    class CheckTimeStamp(beam.DoFn):
+      def process(self, elem):
+        ts = Timestamp.of(elem)
+        now = Timestamp.now()
+        # When rebase is enabled, the timestamp should be closer to now than the
+        # original start.
+        if (ts - Timestamp.of(1)) < (now - ts):
+          yield "wrong"
+
+    with TestPipeline() as p:
+      ret = (
+          p | PeriodicImpulse(
+              start_timestamp=Timestamp.of(1),
+              stop_timestamp=Timestamp.of(5),
+              fire_interval=1,
+              rebase=RebaseMode.REBASE_ALL)
+          | beam.ParDo(CheckTimeStamp()))
+      assert_that(ret, is_empty())
+
+  def test_rebase_timestamp_with_wrong_setting(self):
+    with self.assertRaises(Exception):
+      # exception is raised because start_timestamp is rebased to the pipeline
+      # execution time, but the stop_timestamp is 5 seconds after unix epoch.
+      with TestPipeline() as p:
+        _ = (
+            p | PeriodicImpulse(
+                start_timestamp=Timestamp.of(1),
+                stop_timestamp=Timestamp.of(5),
+                fire_interval=1,
+                rebase=RebaseMode.REBASE_START))
+
 
 if __name__ == '__main__':
   unittest.main()
diff --git a/sdks/python/apache_beam/transforms/ptransform.py b/sdks/python/apache_beam/transforms/ptransform.py
index c653eb62c6f3..cac8a8fbd957 100644
--- a/sdks/python/apache_beam/transforms/ptransform.py
+++ b/sdks/python/apache_beam/transforms/ptransform.py
@@ -228,6 +228,9 @@ def _materialize_transform(self, pipeline):
     from apache_beam import ParDo
 
     class _MaterializeValuesDoFn(DoFn):
+      def __init__(self):
+        self.is_materialize_values_do_fn = True
+
       def process(self, element):
         result.elements.append(element)
 
@@ -999,6 +1002,7 @@ def __init__(self, fn, *args, **kwargs):
     self._fn = fn
     self._args = args
     self._kwargs = kwargs
+    self._use_backwards_compatible_label = True
 
   def display_data(self):
     res = {
@@ -1027,6 +1031,12 @@ def expand(self, pcoll):
       pass
     return self._fn(pcoll, *args, **kwargs)
 
+  def set_options(self, options):
+    # Avoid circular import.
+    from apache_beam.transforms.util import is_compat_version_prior_to
+    self._use_backwards_compatible_label = is_compat_version_prior_to(
+        options, '2.68.0')
+
   def default_label(self) -> str:
     # Attempt to give a reasonable name to this transform.
     # We want it to be reasonably unique, but also not sensitive to
@@ -1035,7 +1045,13 @@ def default_label(self) -> str:
     # the name unwieldy.
     if self._args:
       first_arg_string = label_from_callable(self._args[0])
-      suffix = '(%s)' % first_arg_string if len(first_arg_string) <= 16 else ''
+      if (self._use_backwards_compatible_label or
+          not isinstance(first_arg_string, str) or len(first_arg_string) <= 19):
+        suffix = '(%s)' % first_arg_string
+      else:
+        suffix = ('(%s...%s)' %
+                  (first_arg_string[:10], first_arg_string[-6:])).replace(
+                      '\n', ' ')
     else:
       suffix = ''
     return label_from_callable(self._fn) + suffix
@@ -1148,6 +1164,10 @@ def annotations(self):
   def __rrshift__(self, label):
     return _NamedPTransform(self.transform, label)
 
+  def with_resource_hints(self, **kwargs):
+    self.transform.with_resource_hints(**kwargs)
+    return self
+
   def __getattr__(self, attr):
     transform_attr = getattr(self.transform, attr)
     if callable(transform_attr):
diff --git a/sdks/python/apache_beam/transforms/ptransform_test.py b/sdks/python/apache_beam/transforms/ptransform_test.py
index de9838beb4d9..3df33bcd8be6 100644
--- a/sdks/python/apache_beam/transforms/ptransform_test.py
+++ b/sdks/python/apache_beam/transforms/ptransform_test.py
@@ -34,16 +34,20 @@
 import hamcrest as hc
 import numpy as np
 import pytest
+from parameterized import param
+from parameterized import parameterized
 from parameterized import parameterized_class
 
 import apache_beam as beam
 import apache_beam.transforms.combiners as combine
 from apache_beam import pvalue
 from apache_beam import typehints
+from apache_beam.coders import coders_test_common
 from apache_beam.io.iobase import Read
 from apache_beam.metrics import Metrics
 from apache_beam.metrics.metric import MetricsFilter
 from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import StreamingOptions
 from apache_beam.options.pipeline_options import TypeOptions
 from apache_beam.portability import common_urns
 from apache_beam.testing.test_pipeline import TestPipeline
@@ -155,7 +159,9 @@ def test_do_with_side_input_as_keyword_arg(self):
       assert_that(result, equal_to([11, 12, 13]))
 
   def test_do_with_do_fn_returning_string_raises_warning(self):
-    with self.assertRaises(typehints.TypeCheckError) as cm:
+    ex_details = r'.*Returning a str from a ParDo or FlatMap is discouraged.'
+
+    with self.assertRaisesRegex(Exception, ex_details):
       with TestPipeline() as pipeline:
         pipeline._options.view_as(TypeOptions).runtime_type_check = True
         pcoll = pipeline | 'Start' >> beam.Create(['2', '9', '3'])
@@ -164,13 +170,10 @@ def test_do_with_do_fn_returning_string_raises_warning(self):
         # Since the DoFn directly returns a string we should get an
         # error warning us when the pipeliene runs.
 
-    expected_error_prefix = (
-        'Returning a str from a ParDo or FlatMap '
-        'is discouraged.')
-    self.assertStartswith(cm.exception.args[0], expected_error_prefix)
-
   def test_do_with_do_fn_returning_dict_raises_warning(self):
-    with self.assertRaises(typehints.TypeCheckError) as cm:
+    ex_details = r'.*Returning a dict from a ParDo or FlatMap is discouraged.'
+
+    with self.assertRaisesRegex(Exception, ex_details):
       with TestPipeline() as pipeline:
         pipeline._options.view_as(TypeOptions).runtime_type_check = True
         pcoll = pipeline | 'Start' >> beam.Create(['2', '9', '3'])
@@ -179,11 +182,6 @@ def test_do_with_do_fn_returning_dict_raises_warning(self):
         # Since the DoFn directly returns a dict we should get an error warning
         # us when the pipeliene runs.
 
-    expected_error_prefix = (
-        'Returning a dict from a ParDo or FlatMap '
-        'is discouraged.')
-    self.assertStartswith(cm.exception.args[0], expected_error_prefix)
-
   def test_do_with_multiple_outputs_maintains_unique_name(self):
     with TestPipeline() as pipeline:
       pcoll = pipeline | 'Start' >> beam.Create([1, 2, 3])
@@ -222,10 +220,11 @@ def process(self, element):
     metric_results = res.metrics().query(
         MetricsFilter().with_name('recordsRead'))
     outputs_counter = metric_results['counters'][0]
-    self.assertStartswith(outputs_counter.key.step, 'Read')
+    msg = outputs_counter.key.step
+    cont = 'SDFBoundedSourceReader'
+    self.assertTrue(cont in msg, '"%s" does not contain "%s"' % (msg, cont))
     self.assertEqual(outputs_counter.key.metric.name, 'recordsRead')
     self.assertEqual(outputs_counter.committed, 100)
-    self.assertEqual(outputs_counter.attempted, 100)
 
   @pytest.mark.it_validatesrunner
   def test_par_do_with_multiple_outputs_and_using_yield(self):
@@ -292,7 +291,9 @@ def test_do_requires_do_fn_returning_iterable(self):
     def incorrect_par_do_fn(x):
       return x + 5
 
-    with self.assertRaises(typehints.TypeCheckError) as cm:
+    ex_details = r'.*FlatMap and ParDo must return an iterable.'
+
+    with self.assertRaisesRegex(Exception, ex_details):
       with TestPipeline() as pipeline:
         pipeline._options.view_as(TypeOptions).runtime_type_check = True
         pcoll = pipeline | 'Start' >> beam.Create([2, 9, 3])
@@ -300,9 +301,6 @@ def incorrect_par_do_fn(x):
         # It's a requirement that all user-defined functions to a ParDo return
         # an iterable.
 
-    expected_error_prefix = 'FlatMap and ParDo must return an iterable.'
-    self.assertStartswith(cm.exception.args[0], expected_error_prefix)
-
   def test_do_fn_with_finish(self):
     class MyDoFn(beam.DoFn):
       def process(self, element):
@@ -578,7 +576,7 @@ def encode(self, o):
       def decode(self, encoded):
         return MyObject(pickle.loads(encoded)[0])
 
-      def as_deterministic_coder(self, *args):
+      def as_deterministic_coder(self, *args, **kwargs):
         return MydeterministicObjectCoder()
 
       def to_type_hint(self):
@@ -661,7 +659,7 @@ def partition_for(self, element, num_partitions, offset):
 
     # Check that a bad partition label will yield an error. For the
     # DirectRunner, this error manifests as an exception.
-    with self.assertRaises(ValueError):
+    with self.assertRaises(Exception):
       with TestPipeline() as pipeline:
         pcoll = pipeline | 'Start' >> beam.Create([0, 1, 2, 3, 4, 5, 6, 7, 8])
         partitions = pcoll | beam.Partition(SomePartitionFn(), 4, 10000)
@@ -725,6 +723,69 @@ def test_flatten_one_single_pcollection(self):
       result = (pcoll, ) | 'Single Flatten' >> beam.Flatten()
       assert_that(result, equal_to(input))
 
+  @parameterized.expand([
+      param(compat_version=None),
+      param(compat_version="2.66.0"),
+  ])
+  @pytest.mark.it_validatesrunner
+  @pytest.mark.uses_dill
+  def test_group_by_key_importable_special_types(self, compat_version):
+    def generate(_):
+      for _ in range(100):
+        yield (coders_test_common.MyTypedNamedTuple(1, 'a'), 1)
+
+    pipeline = TestPipeline(is_integration_test=True)
+    if compat_version:
+      pytest.importorskip("dill")
+      pipeline.get_pipeline_options().view_as(
+          StreamingOptions).update_compatibility_version = compat_version
+    with pipeline as p:
+      result = (
+          p
+          | 'Create' >> beam.Create([i for i in range(100)])
+          | 'Generate' >> beam.ParDo(generate)
+          | 'Reshuffle' >> beam.Reshuffle()
+          | 'GBK' >> beam.GroupByKey())
+      assert_that(
+          result,
+          equal_to([(
+              coders_test_common.MyTypedNamedTuple(1, 'a'),
+              [1 for i in range(10000)])]))
+
+  @pytest.mark.it_validatesrunner
+  def test_group_by_key_dynamic_special_types(self):
+    def create_dynamic_named_tuple():
+      return collections.namedtuple('DynamicNamedTuple', ['x', 'y'])
+
+    dynamic_named_tuple = create_dynamic_named_tuple()
+
+    # Standard FastPrimitivesCoder falls back to python PickleCoder which
+    # cannot serialize dynamic types or types defined in __main__. Use
+    # CloudPickleCoder as fallback coder for non-deterministic steps.
+    class FastPrimitivesCoderV2(beam.coders.FastPrimitivesCoder):
+      def __init__(self):
+        super().__init__(fallback_coder=beam.coders.CloudpickleCoder())
+
+    beam.coders.typecoders.registry.register_coder(
+        dynamic_named_tuple, FastPrimitivesCoderV2)
+
+    def generate(_):
+      for _ in range(100):
+        yield (dynamic_named_tuple(1, 'a'), 1)
+
+    pipeline = TestPipeline(is_integration_test=True)
+
+    with pipeline as p:
+      result = (
+          p
+          | 'Create' >> beam.Create([i for i in range(100)])
+          | 'Reshuffle' >> beam.Reshuffle()
+          | 'Generate' >> beam.ParDo(generate).with_output_types(
+              tuple[dynamic_named_tuple, int])
+          | 'GBK' >> beam.GroupByKey()
+          | 'Count Elements' >> beam.Map(lambda x: len(x[1])))
+      assert_that(result, equal_to([10000]))
+
   # TODO(https://github.com/apache/beam/issues/20067): Does not work in
   # streaming mode on Dataflow.
   @pytest.mark.no_sickbay_streaming
@@ -1163,6 +1224,39 @@ def test_apply_ptransform_using_decorator(self):
     self.assertTrue('*Sample*/Group' in pipeline.applied_labels)
     self.assertTrue('*Sample*/Distinct' in pipeline.applied_labels)
 
+  def test_ptransformfn_default_label(self):
+    @beam.ptransform_fn
+    def MyTransform(self, suffix="xyz"):
+      return pcoll | beam.Map(lambda s: s + suffix)
+
+    pipeline = TestPipeline()
+    pcoll = pipeline | beam.Create(['a', 'b', 'c'])
+
+    _ = pcoll | MyTransform()
+    self.assertIn('MyTransform', pipeline.applied_labels)
+    _ = pcoll | MyTransform("suffix")
+    self.assertIn('MyTransform(suffix)', pipeline.applied_labels)
+    _ = pcoll | MyTransform("looooooooooooooooooooooooooooooooooooooooong")
+    self.assertIn('MyTransform(looooooooo...oooong)', pipeline.applied_labels)
+
+  def test_ptransformfn_legacy_default_label(self):
+    @beam.ptransform_fn
+    def MyTransform(self, suffix="xyz"):
+      return pcoll | beam.Map(lambda s: s + suffix)
+
+    pipeline = TestPipeline(
+        options=PipelineOptions(update_compatibility_version='2.67.0'))
+    pcoll = pipeline | beam.Create(['a', 'b', 'c'])
+
+    _ = pcoll | MyTransform()
+    self.assertIn('MyTransform', pipeline.applied_labels)
+    _ = pcoll | MyTransform("suffix")
+    self.assertIn('MyTransform(suffix)', pipeline.applied_labels)
+    _ = pcoll | MyTransform("looooooooooooooooooooooooooooooooooooooooong")
+    self.assertIn(
+        'MyTransform(looooooooooooooooooooooooooooooooooooooooong)',
+        pipeline.applied_labels)
+
   def test_combine_with_label(self):
     vals = [1, 2, 3, 4, 5, 6, 7]
     with TestPipeline() as pipeline:
@@ -1644,15 +1738,11 @@ def int_to_string(x):
         self.p
         | 'T' >> beam.Create(['some_string'])
         | 'ToStr' >> beam.Map(int_to_string))
-    with self.assertRaises(typehints.TypeCheckError) as e:
-      self.p.run()
+    error_regex = "Type-hint for argument: 'x' violated. Expected an instance "
+    "of {}, instead found some_string, an instance of {}.".format(int, str)
 
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within ParDo(ToStr): "
-        "Type-hint for argument: 'x' violated. "
-        "Expected an instance of {}, "
-        "instead found some_string, an instance of {}.".format(int, str))
+    with self.assertRaisesRegex(Exception, error_regex):
+      self.p.run()
 
   def test_run_time_type_checking_enabled_types_satisfied(self):
     self.p._options.view_as(TypeOptions).pipeline_type_check = False
@@ -1698,16 +1788,10 @@ def is_even_as_key(a):
     # Although all the types appear to be correct when checked at pipeline
     # construction. Runtime type-checking should detect the 'is_even_as_key' is
     # returning Tuple[int, int], instead of Tuple[bool, int].
-    with self.assertRaises(typehints.TypeCheckError) as e:
-      self.p.run()
+    error_regex = "Runtime type violation detected"
 
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within ParDo(IsEven): "
-        "Tuple[<class 'bool'>, <class 'int'>] hint type-constraint violated. "
-        "The type of element #0 in the passed tuple is incorrect. "
-        "Expected an instance of type <class 'bool'>, "
-        "instead received an instance of type int.")
+    with self.assertRaisesRegex(Exception, error_regex):
+      self.p.run()
 
   def test_pipeline_checking_satisfied_run_time_checking_satisfied(self):
     self.p._options.view_as(TypeOptions).pipeline_type_check = False
@@ -1736,7 +1820,9 @@ def test_pipeline_runtime_checking_violation_simple_type_input(self):
     # The type-hinted applied via the 'with_input_types()' method indicates the
     # ParDo should receive an instance of type 'str', however an 'int' will be
     # passed instead.
-    with self.assertRaises(typehints.TypeCheckError) as e:
+    error_regex = "Runtime type violation detected"
+
+    with self.assertRaisesRegex(Exception, error_regex):
       (
           self.p
           | beam.Create([1, 1, 1])
@@ -1745,18 +1831,13 @@ def test_pipeline_runtime_checking_violation_simple_type_input(self):
                   str).with_output_types(int)))
       self.p.run()
 
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within ParDo(ToInt): "
-        "Type-hint for argument: 'x' violated. "
-        "Expected an instance of {}, "
-        "instead found 1, an instance of {}.".format(str, int))
-
   def test_pipeline_runtime_checking_violation_composite_type_input(self):
     self.p._options.view_as(TypeOptions).runtime_type_check = True
     self.p._options.view_as(TypeOptions).pipeline_type_check = False
 
-    with self.assertRaises(typehints.TypeCheckError) as e:
+    error_regex = "Runtime type violation detected"
+
+    with self.assertRaisesRegex(Exception, error_regex):
       (
           self.p
           | beam.Create([(1, 3.0), (2, 4.9), (3, 9.5)])
@@ -1766,15 +1847,6 @@ def test_pipeline_runtime_checking_violation_composite_type_input(self):
                   typing.Tuple[int, int]).with_output_types(int)))
       self.p.run()
 
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within ParDo(Add): "
-        "Type-hint for argument: 'x_y' violated: "
-        "Tuple[<class 'int'>, <class 'int'>] hint type-constraint violated. "
-        "The type of element #1 in the passed tuple is incorrect. "
-        "Expected an instance of type <class 'int'>, instead received an "
-        "instance of type float.")
-
   def test_pipeline_runtime_checking_violation_simple_type_output(self):
     self.p._options.view_as(TypeOptions).runtime_type_check = True
     self.p._options.view_as(TypeOptions).pipeline_type_check = False
@@ -1787,31 +1859,29 @@ def test_pipeline_runtime_checking_violation_simple_type_output(self):
         (
             'ToInt' >> beam.FlatMap(lambda x: [float(x)]).with_input_types(
                 int).with_output_types(int)).get_type_hints())
-    with self.assertRaises(typehints.TypeCheckError) as e:
+    error_regex = ""
+
+    if self.p._options.view_as(TypeOptions).runtime_type_check:
+      error_regex = (
+          "Runtime type violation detected within ParDo\\(ToInt\\):" +
+          " According to type-hint expected output should be of type <class " +
+          "'int'>. Instead, received '1.0', an instance of type <class 'float'>"
+      )
+
+    if self.p._options.view_as(TypeOptions).performance_runtime_type_check:
+      error_regex = (
+          "Runtime type violation detected within ToInt: Type-hint " +
+          "for argument: 'x' violated. Expected an instance of <class 'int'>, "
+          + "instead found 1.0, an instance of <class 'float'>")
+
+    with self.assertRaisesRegex(Exception, error_regex):
       (
           self.p
           | beam.Create([1, 1, 1])
           | (
               'ToInt' >> beam.FlatMap(lambda x: [float(x)]).with_input_types(
                   int).with_output_types(int)))
-      self.p.run()
-
-    if self.p._options.view_as(TypeOptions).runtime_type_check:
-      self.assertStartswith(
-          e.exception.args[0],
-          "Runtime type violation detected within "
-          "ParDo(ToInt): "
-          "According to type-hint expected output should be "
-          "of type {}. Instead, received '1.0', "
-          "an instance of type {}.".format(int, float))
-
-    if self.p._options.view_as(TypeOptions).performance_runtime_type_check:
-      self.assertStartswith(
-          e.exception.args[0],
-          "Runtime type violation detected within ToInt: "
-          "Type-hint for argument: 'x' violated. "
-          "Expected an instance of {}, "
-          "instead found 1.0, an instance of {}".format(int, float))
+      self.p.run().wait_until_finish()
 
   def test_pipeline_runtime_checking_violation_composite_type_output(self):
     self.p._options.view_as(TypeOptions).runtime_type_check = True
@@ -1820,7 +1890,13 @@ def test_pipeline_runtime_checking_violation_composite_type_output(self):
     # The type-hinted applied via the 'returns()' method indicates the ParDo
     # should return an instance of type: Tuple[float, int]. However, an instance
     # of 'int' will be generated instead.
-    with self.assertRaises(typehints.TypeCheckError) as e:
+    error_regex = (
+        "Runtime type violation detected within " +
+        "ParDo\\(Swap\\): Tuple type constraint violated. " +
+        "Valid object instance must be of type 'tuple'. Instead, " +
+        "an instance of 'float' was received.")
+
+    with self.assertRaisesRegex(Exception, error_regex):
       (
           self.p
           | beam.Create([(1, 3.0), (2, 4.9), (3, 9.5)])
@@ -1831,22 +1907,6 @@ def test_pipeline_runtime_checking_violation_composite_type_output(self):
                       typing.Tuple[float, int])))
       self.p.run()
 
-    if self.p._options.view_as(TypeOptions).runtime_type_check:
-      self.assertStartswith(
-          e.exception.args[0],
-          "Runtime type violation detected within "
-          "ParDo(Swap): Tuple type constraint violated. "
-          "Valid object instance must be of type 'tuple'. Instead, "
-          "an instance of 'float' was received.")
-
-    if self.p._options.view_as(TypeOptions).performance_runtime_type_check:
-      self.assertStartswith(
-          e.exception.args[0],
-          "Runtime type violation detected within "
-          "Swap: Type-hint for argument: 'x_y1' violated: "
-          "Tuple type constraint violated. "
-          "Valid object instance must be of type 'tuple'. ")
-
   def test_pipeline_runtime_checking_violation_with_side_inputs_decorator(self):
     self.p._options.view_as(TypeOptions).pipeline_type_check = False
     self.p._options.view_as(TypeOptions).runtime_type_check = True
@@ -1856,22 +1916,18 @@ def test_pipeline_runtime_checking_violation_with_side_inputs_decorator(self):
     def add(a, b):
       return a + b
 
-    with self.assertRaises(typehints.TypeCheckError) as e:
+    error_regex = "Runtime type violation detected"
+
+    with self.assertRaisesRegex(Exception, error_regex):
       (self.p | beam.Create([1, 2, 3, 4]) | 'Add 1' >> beam.Map(add, 1.0))
       self.p.run()
 
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within ParDo(Add 1): "
-        "Type-hint for argument: 'b' violated. "
-        "Expected an instance of {}, "
-        "instead found 1.0, an instance of {}.".format(int, float))
-
   def test_pipeline_runtime_checking_violation_with_side_inputs_via_method(self):  # pylint: disable=line-too-long
     self.p._options.view_as(TypeOptions).runtime_type_check = True
     self.p._options.view_as(TypeOptions).pipeline_type_check = False
 
-    with self.assertRaises(typehints.TypeCheckError) as e:
+    error_regex = "Runtime type violation detected"
+    with self.assertRaisesRegex(Exception, error_regex):
       (
           self.p
           | beam.Create([1, 2, 3, 4])
@@ -1880,13 +1936,6 @@ def test_pipeline_runtime_checking_violation_with_side_inputs_via_method(self):
                   int, int).with_output_types(float)))
       self.p.run()
 
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within ParDo(Add 1): "
-        "Type-hint for argument: 'one' violated. "
-        "Expected an instance of {}, "
-        "instead found 1.0, an instance of {}.".format(int, float))
-
   def test_combine_properly_pipeline_type_checks_using_decorator(self):
     @with_output_types(int)
     @with_input_types(ints=typing.Iterable[int])
@@ -1980,20 +2029,18 @@ def test_combine_runtime_type_check_violation_using_decorators(self):
     def iter_mul(ints):
       return str(reduce(operator.mul, ints, 1))
 
-    with self.assertRaises(typehints.TypeCheckError) as e:
+    error_regex = (
+        "Runtime type violation detected within " +
+        "Mul/CombinePerKey: Type-hint for return type violated. " +
+        "Expected an instance of {}, instead found".format(int))
+
+    with self.assertRaisesRegex(Exception, error_regex):
       (
           self.p
           | 'K' >> beam.Create([5, 5, 5, 5]).with_output_types(int)
           | 'Mul' >> beam.CombineGlobally(iter_mul))
       self.p.run()
 
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within "
-        "Mul/CombinePerKey: "
-        "Type-hint for return type violated. "
-        "Expected an instance of {}, instead found".format(int))
-
   def test_combine_pipeline_type_check_using_methods(self):
     d = (
         self.p
@@ -2043,7 +2090,13 @@ def test_combine_runtime_type_check_violation_using_methods(self):
     self.p._options.view_as(TypeOptions).pipeline_type_check = False
     self.p._options.view_as(TypeOptions).runtime_type_check = True
 
-    with self.assertRaises(typehints.TypeCheckError) as e:
+    error_regex = (
+        "Runtime type violation detected within " +
+        "ParDo\\(SortJoin/KeyWithVoid\\): " +
+        "Type-hint for argument: 'v' violated. Expected an instance of " +
+        "<class 'str'>, instead found 0, an instance of <class 'int'>.")
+
+    with self.assertRaisesRegex(Exception, error_regex):
       (
           self.p
           | beam.Create([0]).with_output_types(int)
@@ -2052,14 +2105,6 @@ def test_combine_runtime_type_check_violation_using_methods(self):
               with_input_types(str).with_output_types(str)))
       self.p.run()
 
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within "
-        "ParDo(SortJoin/KeyWithVoid): "
-        "Type-hint for argument: 'v' violated. "
-        "Expected an instance of {}, "
-        "instead found 0, an instance of {}.".format(str, int))
-
   def test_combine_insufficient_type_hint_information(self):
     self.p._options.view_as(TypeOptions).type_check_strictness = 'ALL_REQUIRED'
 
@@ -2114,23 +2159,14 @@ def test_mean_globally_runtime_checking_violated(self):
     self.p._options.view_as(TypeOptions).pipeline_type_check = False
     self.p._options.view_as(TypeOptions).runtime_type_check = True
 
-    with self.assertRaises(typehints.TypeCheckError) as e:
+    error_regex = "Runtime type violation detected"
+
+    with self.assertRaisesRegex(Exception, error_regex):
       (
           self.p
           | 'C' >> beam.Create(['t', 'e', 's', 't']).with_output_types(str)
           | 'Mean' >> combine.Mean.Globally())
       self.p.run()
-      self.assertEqual(
-          "Runtime type violation detected for transform input "
-          "when executing ParDoFlatMap(Combine): Tuple[Any, "
-          "Iterable[Union[int, float]]] hint type-constraint "
-          "violated. The type of element #1 in the passed tuple "
-          "is incorrect. Iterable[Union[int, float]] hint "
-          "type-constraint violated. The type of element #0 in "
-          "the passed Iterable is incorrect: Union[int, float] "
-          "type-constraint violated. Expected an instance of one "
-          "of: ('int', 'float'), received str instead.",
-          e.exception.args[0])
 
   def test_mean_per_key_pipeline_checking_satisfied(self):
     d = (
@@ -2183,7 +2219,9 @@ def test_mean_per_key_runtime_checking_violated(self):
     self.p._options.view_as(TypeOptions).pipeline_type_check = False
     self.p._options.view_as(TypeOptions).runtime_type_check = True
 
-    with self.assertRaises(typehints.TypeCheckError) as e:
+    error_regex = "Runtime type violation detected"
+
+    with self.assertRaisesRegex(Exception, error_regex):
       (
           self.p
           | beam.Create(range(5)).with_output_types(int)
@@ -2194,18 +2232,6 @@ def test_mean_per_key_runtime_checking_violated(self):
           | 'OddMean' >> combine.Mean.PerKey())
       self.p.run()
 
-    expected_msg = \
-      "Runtime type violation detected within " \
-      "OddMean/CombinePerKey(MeanCombineFn): " \
-      "Type-hint for argument: 'element' violated: " \
-      "Union[<class 'float'>, <class 'int'>, <class 'numpy.float64'>, <class " \
-      "'numpy.int64'>] type-constraint violated. " \
-      "Expected an instance of one of: (\"<class 'float'>\", \"<class " \
-      "'int'>\", \"<class 'numpy.float64'>\", \"<class 'numpy.int64'>\"), " \
-      "received str instead"
-
-    self.assertStartswith(e.exception.args[0], expected_msg)
-
   def test_count_globally_pipeline_type_checking_satisfied(self):
     d = (
         self.p
@@ -2522,21 +2548,15 @@ def test_to_dict_runtime_check_satisfied(self):
   def test_runtime_type_check_python_type_error(self):
     self.p._options.view_as(TypeOptions).runtime_type_check = True
 
-    with self.assertRaises(TypeError) as e:
+    error_regex = "object of type 'int' has no len()"
+
+    with self.assertRaisesRegex(Exception, error_regex):
       (
           self.p
           | beam.Create([1, 2, 3]).with_output_types(int)
           | 'Len' >> beam.Map(lambda x: len(x)).with_output_types(int))
       self.p.run()
 
-    # Our special type-checking related TypeError shouldn't have been raised.
-    # Instead the above pipeline should have triggered a regular Python runtime
-    # TypeError.
-    self.assertEqual(
-        "object of type 'int' has no len() [while running 'Len']",
-        e.exception.args[0])
-    self.assertFalse(isinstance(e, typehints.TypeCheckError))
-
   def test_pardo_type_inference(self):
     self.assertEqual(int, beam.Filter(lambda x: False).infer_output_type(int))
     self.assertEqual(
diff --git a/sdks/python/apache_beam/transforms/sideinputs_test.py b/sdks/python/apache_beam/transforms/sideinputs_test.py
index b16a72bf59cc..5f3cf761e1eb 100644
--- a/sdks/python/apache_beam/transforms/sideinputs_test.py
+++ b/sdks/python/apache_beam/transforms/sideinputs_test.py
@@ -19,13 +19,20 @@
 
 # pytype: skip-file
 
+import hashlib
 import itertools
 import logging
 import unittest
+from typing import Any
+from typing import Dict
+from typing import Iterable
+from typing import Tuple
+from typing import Union
 
 import pytest
 
 import apache_beam as beam
+from apache_beam.testing.synthetic_pipeline import SyntheticSDFAsSource
 from apache_beam.testing.test_pipeline import TestPipeline
 from apache_beam.testing.test_stream import TestStream
 from apache_beam.testing.util import assert_that
@@ -417,6 +424,71 @@ def process(
           use_global_window=False,
           label='assert per window')
 
+  @pytest.mark.it_validatesrunner
+  def test_side_input_with_sdf(self):
+    """Test a side input with SDF.
+
+    This test verifies consisency of side input when it is split due to
+    SDF (Splittable DoFns). The consistency is verified by checking the size
+    and fingerprint of the side input.
+
+    This test needs to run with at least 2 workers (--num_workers=2) and
+    autoscaling disabled (--autoscaling_algorithm=NONE). Otherwise it might
+    provide false positives (i.e. not fail on bad state).
+    """
+    initial_elements = 1000
+    num_records = 10000
+    key_size = 10
+    value_size = 100
+    expected_fingerprint = '00f7eeac8514721e2683d14a504b33d1'
+
+    class GetSyntheticSDFOptions(beam.DoFn):
+      """A DoFn that emits elements for genenrating SDF."""
+      def process(self, element: Any) -> Iterable[Dict[str, Union[int, str]]]:
+        yield {
+            'num_records': num_records // initial_elements,
+            'key_size': key_size,
+            'value_size': value_size,
+            'initial_splitting_num_bundles': 0,
+            'initial_splitting_desired_bundle_size': 0,
+            'sleep_per_input_record_sec': 0,
+            'initial_splitting': 'const',
+        }
+
+    class SideInputTrackingDoFn(beam.DoFn):
+      """A DoFn that emits the size and fingerprint of the side input.
+
+      In this context, the size is the number of elements and the fingerprint
+      is the hash of the sorted serialized elements.
+      """
+      def process(
+          self, element: Any,
+          side_input: Iterable[Tuple[bytes,
+                                     bytes]]) -> Iterable[Tuple[int, str]]:
+
+        # Sort for consistent hashing.
+        sorted_side_input = sorted(side_input)
+        size = len(sorted_side_input)
+        m = hashlib.md5()
+        for key, value in sorted_side_input:
+          m.update(key)
+          m.update(value)
+        yield (size, m.hexdigest())
+
+    pipeline = self.create_pipeline()
+    main_input = pipeline | 'Main input: Create' >> beam.Create([0])
+    side_input = pipeline | 'Side input: Create' >> beam.Create(
+        range(initial_elements))
+    side_input |= 'Side input: Get synthetic SDF options' >> beam.ParDo(
+        GetSyntheticSDFOptions())
+    side_input |= 'Side input: Process and split' >> beam.ParDo(
+        SyntheticSDFAsSource())
+    results = main_input | 'Emit side input' >> beam.ParDo(
+        SideInputTrackingDoFn(), beam.pvalue.AsIter(side_input))
+
+    assert_that(results, equal_to([(num_records, expected_fingerprint)]))
+    pipeline.run()
+
 
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.DEBUG)
diff --git a/sdks/python/apache_beam/transforms/sql.py b/sdks/python/apache_beam/transforms/sql.py
index 21cae3f6c757..ce46c652ddd0 100644
--- a/sdks/python/apache_beam/transforms/sql.py
+++ b/sdks/python/apache_beam/transforms/sql.py
@@ -82,7 +82,7 @@ def __init__(self, query, dialect=None, ddl=None, expansion_service=None):
     Creates a SqlTransform which will be expanded to Java's SqlTransform.
     (See class docs).
     :param query: The SQL query.
-    :param dialect: (optional) The dialect, e.g. use 'zetasql' for ZetaSQL.
+    :param dialect: (optional, deprecated) The dialect.
     :param ddl: (optional) The DDL statement.
     :param expansion_service: (optional) The URL of the expansion service to use
     """
diff --git a/sdks/python/apache_beam/transforms/sql_test.py b/sdks/python/apache_beam/transforms/sql_test.py
index cf4136436027..fc55320ba699 100644
--- a/sdks/python/apache_beam/transforms/sql_test.py
+++ b/sdks/python/apache_beam/transforms/sql_test.py
@@ -20,7 +20,6 @@
 # pytype: skip-file
 
 import logging
-import subprocess
 import typing
 import unittest
 
@@ -33,7 +32,6 @@
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
 from apache_beam.transforms.sql import SqlTransform
-from apache_beam.utils import subprocess_server
 
 SimpleRow = typing.NamedTuple(
     "SimpleRow", [("id", int), ("str", str), ("flt", float)])
@@ -71,23 +69,6 @@ class SqlTransformTest(unittest.TestCase):
   """
   _multiprocess_can_split_ = True
 
-  @staticmethod
-  def _disable_zetasql_test():
-    # disable if run on Java8 which is no longer supported by ZetaSQL
-    try:
-      java = subprocess_server.JavaHelper.get_java()
-      result = subprocess.run([java, '-version'],
-                              check=True,
-                              capture_output=True,
-                              text=True)
-      version_line = result.stderr.splitlines()[0]
-      version = version_line.split()[2].strip('\"')
-      if version.startswith("1."):
-        return True
-      return False
-    except:  # pylint: disable=bare-except
-      return False
-
   def test_generate_data(self):
     with TestPipeline() as p:
       out = p | SqlTransform(
@@ -168,19 +149,6 @@ def test_row(self):
           | SqlTransform("SELECT a*a as s, LENGTH(b) AS c FROM PCOLLECTION"))
       assert_that(out, equal_to([(1, 1), (4, 1), (100, 2)]))
 
-  def test_zetasql_generate_data(self):
-    if self._disable_zetasql_test():
-      raise unittest.SkipTest("ZetaSQL tests need Java11+")
-
-    with TestPipeline() as p:
-      out = p | SqlTransform(
-          """SELECT
-            CAST(1 AS INT64) AS `int`,
-            CAST('foo' AS STRING) AS `str`,
-            CAST(3.14  AS FLOAT64) AS `flt`""",
-          dialect="zetasql")
-      assert_that(out, equal_to([(1, "foo", 3.14)]))
-
   def test_windowing_before_sql(self):
     with TestPipeline() as p:
       out = (
diff --git a/sdks/python/apache_beam/transforms/timestamped_value_type_test.py b/sdks/python/apache_beam/transforms/timestamped_value_type_test.py
index 3852b9a85bf1..256e9d6f0c00 100644
--- a/sdks/python/apache_beam/transforms/timestamped_value_type_test.py
+++ b/sdks/python/apache_beam/transforms/timestamped_value_type_test.py
@@ -21,7 +21,6 @@
 
 import apache_beam as beam
 from apache_beam.transforms.window import TimestampedValue
-from apache_beam.typehints.decorators import TypeCheckError
 
 T = TypeVar("T")
 
@@ -98,7 +97,7 @@ def test_opts_with_check_list_str(self):
           | beam.Map(print))
 
   def test_opts_with_check_wrong_data(self):
-    with self.assertRaises(TypeCheckError):
+    with self.assertRaises(Exception):
       with beam.Pipeline(options=self.opts) as p:
         _ = (
             p
@@ -107,7 +106,7 @@ def test_opts_with_check_wrong_data(self):
             | beam.Map(print))
 
   def test_opts_with_check_wrong_data_list_str(self):
-    with self.assertRaises(TypeCheckError):
+    with self.assertRaises(Exception):
       with beam.Pipeline(options=self.opts) as p:
         _ = (
             p
@@ -115,7 +114,7 @@ def test_opts_with_check_wrong_data_list_str(self):
             | "With timestamps" >> beam.Map(ConvertToTimestampedValue_2)
             | beam.Map(print))
 
-    with self.assertRaises(TypeCheckError):
+    with self.assertRaises(Exception):
       with beam.Pipeline(options=self.opts) as p:
         _ = (
             p
@@ -124,7 +123,7 @@ def test_opts_with_check_wrong_data_list_str(self):
             | beam.Map(print))
 
   def test_opts_with_check_typevar(self):
-    with self.assertRaises(RuntimeError):
+    with self.assertRaises(Exception):
       with beam.Pipeline(options=self.opts) as p:
         _ = (
             p
diff --git a/sdks/python/apache_beam/transforms/trigger_test.py b/sdks/python/apache_beam/transforms/trigger_test.py
index 79fd3151c083..b9a8cdc594b5 100644
--- a/sdks/python/apache_beam/transforms/trigger_test.py
+++ b/sdks/python/apache_beam/transforms/trigger_test.py
@@ -1352,7 +1352,12 @@ def merge(_, to_be_merged, merge_result):
           if IntervalWindow(*pane['window']) not in merged_away
       ]
 
-    with TestPipeline() as p:
+    # TODO(https://github.com/apache/beam/issues/34549): This test relies on
+    # the correct error message being reported, but because of how it is
+    # structured Prism often fails on splitting at the same time as it fails
+    # on an element, leading to good logs but potentially inconsistent error
+    # messages. Its not a bug, but it does mess with regex matching.
+    with TestPipeline('FnApiRunner') as p:
       input_pc = (
           p
           | beam.Create(inputs)
diff --git a/sdks/python/apache_beam/transforms/userstate_test.py b/sdks/python/apache_beam/transforms/userstate_test.py
index 8f2cb34f982e..6a8efd1a536f 100644
--- a/sdks/python/apache_beam/transforms/userstate_test.py
+++ b/sdks/python/apache_beam/transforms/userstate_test.py
@@ -744,7 +744,9 @@ def process(
       def emit_values(self, set_state=beam.DoFn.StateParam(SET_STATE)):
         yield sorted(set_state.read())
 
-    with TestPipeline() as p:
+    # Pin to FnApiRunner since this assumes a large bundle size to contain
+    # all elements
+    with TestPipeline('FnApiRunner') as p:
       values = p | beam.Create([('key', 1), ('key', 2), ('key', 3), ('key', 4),
                                 ('key', 5)])
       actual_values = (
@@ -992,7 +994,7 @@ def emit_callback(
                      sorted(StatefulDoFnOnDirectRunnerTest.all_records))
 
   @pytest.mark.no_xdist
-  @pytest.mark.timeout(10)
+  @pytest.mark.timeout(60)
   def test_dynamic_timer_clear_then_set_timer(self):
     class EmitTwoEvents(DoFn):
       EMIT_CLEAR_SET_TIMER = TimerSpec('emitclear', TimeDomain.WATERMARK)
diff --git a/sdks/python/apache_beam/transforms/util.py b/sdks/python/apache_beam/transforms/util.py
index 07d822902123..c63478dc0cfc 100644
--- a/sdks/python/apache_beam/transforms/util.py
+++ b/sdks/python/apache_beam/transforms/util.py
@@ -22,6 +22,8 @@
 
 import collections
 import contextlib
+import hashlib
+import hmac
 import logging
 import random
 import re
@@ -32,10 +34,14 @@
 from collections.abc import Iterable
 from typing import TYPE_CHECKING
 from typing import Any
+from typing import List
 from typing import Optional
+from typing import Tuple
 from typing import TypeVar
 from typing import Union
 
+from cryptography.fernet import Fernet
+
 import apache_beam as beam
 from apache_beam import coders
 from apache_beam import pvalue
@@ -88,6 +94,8 @@
     'BatchElements',
     'CoGroupByKey',
     'Distinct',
+    'GcpSecret',
+    'GroupByEncryptedKey',
     'Keys',
     'KvSwap',
     'LogElements',
@@ -95,16 +103,19 @@
     'Reify',
     'RemoveDuplicates',
     'Reshuffle',
+    'Secret',
     'ToString',
     'Tee',
     'Values',
     'WithKeys',
-    'GroupIntoBatches'
+    'GroupIntoBatches',
+    'WaitOn'
 ]
 
 K = TypeVar('K')
 V = TypeVar('V')
 T = TypeVar('T')
+U = TypeVar('U')
 
 RESHUFFLE_TYPEHINT_BREAKING_CHANGE_VERSION = "2.64.0"
 
@@ -265,7 +276,9 @@ def collect_values(key, tagged_values):
     ]
             | Flatten(pipeline=self.pipeline)
             | GroupByKey()
-            | MapTuple(collect_values))
+            | MapTuple(collect_values).with_input_types(
+                tuple[K, Iterable[tuple[U, V]]]).with_output_types(
+                    tuple[K, dict[U, list[V]]]))
 
 
 @ptransform_fn
@@ -313,6 +326,205 @@ def RemoveDuplicates(pcoll):
   return pcoll | 'RemoveDuplicates' >> Distinct()
 
 
+class Secret():
+  """A secret management class used for handling sensitive data.
+
+  This class provides a generic interface for secret management. Implementations
+  of this class should handle fetching secrets from a secret management system.
+  """
+  def get_secret_bytes(self) -> bytes:
+    """Returns the secret as a byte string."""
+    raise NotImplementedError()
+
+  @staticmethod
+  def generate_secret_bytes() -> bytes:
+    """Generates a new secret key."""
+    return Fernet.generate_key()
+
+
+class GcpSecret(Secret):
+  """A secret manager implementation that retrieves secrets from Google Cloud
+  Secret Manager.
+  """
+  def __init__(self, version_name: str):
+    """Initializes a GcpSecret object.
+
+    Args:
+      version_name: The full version name of the secret in Google Cloud Secret
+        Manager. For example:
+        projects/<id>/secrets/<secret_name>/versions/1.
+        For more info, see
+        https://cloud.google.com/python/docs/reference/secretmanager/latest/google.cloud.secretmanager_v1beta1.services.secret_manager_service.SecretManagerServiceClient#google_cloud_secretmanager_v1beta1_services_secret_manager_service_SecretManagerServiceClient_access_secret_version
+    """
+    self._version_name = version_name
+
+  def get_secret_bytes(self) -> bytes:
+    try:
+      from google.cloud import secretmanager
+      client = secretmanager.SecretManagerServiceClient()
+      response = client.access_secret_version(
+          request={"name": self._version_name})
+      secret = response.payload.data
+      return secret
+    except Exception as e:
+      raise RuntimeError(f'Failed to retrieve secret bytes with excetion {e}')
+
+
+class _EncryptMessage(DoFn):
+  """A DoFn that encrypts the key and value of each element."""
+  def __init__(
+      self,
+      hmac_key_secret: Secret,
+      key_coder: coders.Coder,
+      value_coder: coders.Coder):
+    self.hmac_key_secret = hmac_key_secret
+    self.key_coder = key_coder
+    self.value_coder = value_coder
+
+  def setup(self):
+    self._hmac_key = self.hmac_key_secret.get_secret_bytes()
+    self.fernet = Fernet(self._hmac_key)
+
+  def process(self,
+              element: Any) -> Iterable[Tuple[bytes, Tuple[bytes, bytes]]]:
+    """Encrypts the key and value of an element.
+
+    Args:
+      element: A tuple containing the key and value to be encrypted.
+
+    Yields:
+      A tuple containing the HMAC of the encoded key, and a tuple of the
+      encrypted key and value.
+    """
+    k, v = element
+    encoded_key = self.key_coder.encode(k)
+    encoded_value = self.value_coder.encode(v)
+    hmac_encoded_key = hmac.new(self._hmac_key, encoded_key,
+                                hashlib.sha256).digest()
+    out_element = (
+        hmac_encoded_key,
+        (self.fernet.encrypt(encoded_key), self.fernet.encrypt(encoded_value)))
+    yield out_element
+
+
+class _DecryptMessage(DoFn):
+  """A DoFn that decrypts the key and value of each element."""
+  def __init__(
+      self,
+      hmac_key_secret: Secret,
+      key_coder: coders.Coder,
+      value_coder: coders.Coder):
+    self.hmac_key_secret = hmac_key_secret
+    self.key_coder = key_coder
+    self.value_coder = value_coder
+
+  def setup(self):
+    hmac_key = self.hmac_key_secret.get_secret_bytes()
+    self.fernet = Fernet(hmac_key)
+
+  def decode_value(self, encoded_element: Tuple[bytes, bytes]) -> Any:
+    encrypted_value = encoded_element[1]
+    encoded_value = self.fernet.decrypt(encrypted_value)
+    real_val = self.value_coder.decode(encoded_value)
+    return real_val
+
+  def filter_elements_by_key(
+      self,
+      encrypted_key: bytes,
+      encoded_elements: Iterable[Tuple[bytes, bytes]]) -> Iterable[Any]:
+    for e in encoded_elements:
+      if encrypted_key == self.fernet.decrypt(e[0]):
+        yield self.decode_value(e)
+
+  # Right now, GBK always returns a list of elements, so we match this behavior
+  # here. This does mean that the whole list will be materialized every time,
+  # but passing an Iterable containing an Iterable breaks when pickling happens
+  def process(
+      self, element: Tuple[bytes, Iterable[Tuple[bytes, bytes]]]
+  ) -> Iterable[Tuple[Any, List[Any]]]:
+    """Decrypts the key and values of an element.
+
+    Args:
+      element: A tuple containing the HMAC of the encoded key and an iterable
+        of tuples of encrypted keys and values.
+
+    Yields:
+      A tuple containing the decrypted key and a list of decrypted values.
+    """
+    unused_hmac_encoded_key, encoded_elements = element
+    seen_keys = set()
+
+    # Since there could be hmac collisions, we will use the fernet encrypted
+    # key to confirm that the mapping is actually correct.
+    for e in encoded_elements:
+      encrypted_key, unused_encrypted_value = e
+      encoded_key = self.fernet.decrypt(encrypted_key)
+      if encoded_key in seen_keys:
+        continue
+      seen_keys.add(encoded_key)
+      real_key = self.key_coder.decode(encoded_key)
+
+      yield (
+          real_key,
+          list(self.filter_elements_by_key(encoded_key, encoded_elements)))
+
+
+@typehints.with_input_types(Tuple[K, V])
+@typehints.with_output_types(Tuple[K, Iterable[V]])
+class GroupByEncryptedKey(PTransform):
+  """A PTransform that provides a secure alternative to GroupByKey.
+
+  This transform encrypts the keys of the input PCollection, performs a
+  GroupByKey on the encrypted keys, and then decrypts the keys in the output.
+  This is useful when the keys contain sensitive data that should not be
+  stored at rest by the runner. Note the following caveats:
+  
+  1) Runners can implement arbitrary materialization steps, so this does not
+  guarantee that the whole pipeline will not have unencrypted data at rest by
+  itself.
+  2) If using this transform in streaming mode, this transform may not properly
+  handle update compatibility checks around coders. This means that an improper
+  update could lead to invalid coders, causing pipeline failure or data
+  corruption. If you need to update, make sure that the input type passed into
+  this transform does not change.
+  """
+  def __init__(self, hmac_key: Secret):
+    """Initializes a GroupByEncryptedKey transform.
+
+    Args:
+      hmac_key: A Secret object that provides the secret key for HMAC and
+        encryption. For example, a GcpSecret can be used to access a secret
+        stored in GCP Secret Manager
+    """
+    self._hmac_key = hmac_key
+
+  def expand(self, pcoll):
+    kv_type_hint = pcoll.element_type
+    if kv_type_hint and kv_type_hint != typehints.Any:
+      coder = coders.registry.get_coder(kv_type_hint).as_deterministic_coder(
+          f'GroupByEncryptedKey {self.label}'
+          'The key coder is not deterministic. This may result in incorrect '
+          'pipeline output. This can be fixed by adding a type hint to the '
+          'operation preceding the GroupByKey step, and for custom key '
+          'classes, by writing a deterministic custom Coder. Please see the '
+          'documentation for more details.')
+      if not coder.is_kv_coder():
+        raise ValueError(
+            'Input elements to the transform %s with stateful DoFn must be '
+            'key-value pairs.' % self)
+      key_coder = coder.key_coder()
+      value_coder = coder.value_coder()
+    else:
+      key_coder = coders.registry.get_coder(typehints.Any)
+      value_coder = key_coder
+
+    return (
+        pcoll
+        | beam.ParDo(_EncryptMessage(self._hmac_key, key_coder, value_coder))
+        | beam.GroupByKey()
+        | beam.ParDo(_DecryptMessage(self._hmac_key, key_coder, value_coder)))
+
+
 class _BatchSizeEstimator(object):
   """Estimates the best size for batches given historical timing.
   """
@@ -928,6 +1140,15 @@ def get_window_coder(self):
     return self._window_coder
 
 
+def is_v1_prior_to_v2(*, v1, v2):
+  if v1 is None:
+    return False
+
+  v1_parts = (v1.split('.') + ['0', '0', '0'])[:3]
+  v2_parts = (v2.split('.') + ['0', '0', '0'])[:3]
+  return tuple(map(int, v1_parts)) < tuple(map(int, v2_parts))
+
+
 def is_compat_version_prior_to(options, breaking_change_version):
   # This function is used in a branch statement to determine whether we should
   # keep the old behavior prior to a breaking change or use the new behavior.
@@ -936,15 +1157,8 @@ def is_compat_version_prior_to(options, breaking_change_version):
   update_compatibility_version = options.view_as(
       pipeline_options.StreamingOptions).update_compatibility_version
 
-  if update_compatibility_version is None:
-    return False
-
-  compat_version = tuple(map(int, update_compatibility_version.split('.')[0:3]))
-  change_version = tuple(map(int, breaking_change_version.split('.')[0:3]))
-  for i in range(min(len(compat_version), len(change_version))):
-    if compat_version[i] < change_version[i]:
-      return True
-  return False
+  return is_v1_prior_to_v2(
+      v1=update_compatibility_version, v2=breaking_change_version)
 
 
 def reify_metadata_default_window(
diff --git a/sdks/python/apache_beam/transforms/util_test.py b/sdks/python/apache_beam/transforms/util_test.py
index a9bec0df973d..6cd8d5fcba76 100644
--- a/sdks/python/apache_beam/transforms/util_test.py
+++ b/sdks/python/apache_beam/transforms/util_test.py
@@ -21,19 +21,25 @@
 # pylint: disable=too-many-function-args
 
 import collections
+import hashlib
+import hmac
 import importlib
 import logging
 import math
 import random
 import re
+import string
 import time
 import unittest
 import warnings
 from collections.abc import Mapping
 from datetime import datetime
 
+import mock
 import pytest
 import pytz
+from cryptography.fernet import Fernet
+from cryptography.fernet import InvalidToken
 from parameterized import param
 from parameterized import parameterized
 
@@ -65,6 +71,8 @@
 from apache_beam.transforms.core import FlatMapTuple
 from apache_beam.transforms.trigger import AfterCount
 from apache_beam.transforms.trigger import Repeatedly
+from apache_beam.transforms.util import GcpSecret
+from apache_beam.transforms.util import Secret
 from apache_beam.transforms.window import FixedWindows
 from apache_beam.transforms.window import GlobalWindow
 from apache_beam.transforms.window import GlobalWindows
@@ -83,10 +91,52 @@
 from apache_beam.utils.windowed_value import PaneInfoTiming
 from apache_beam.utils.windowed_value import WindowedValue
 
+try:
+  import dill
+except ImportError:
+  dill = None
+
+try:
+  from google.cloud import secretmanager
+except ImportError:
+  secretmanager = None  # type: ignore[assignment]
+
 warnings.filterwarnings(
     'ignore', category=FutureWarning, module='apache_beam.transform.util_test')
 
 
+class _Unpicklable(object):
+  def __init__(self, value):
+    self.value = value
+
+  def __getstate__(self):
+    raise NotImplementedError()
+
+  def __setstate__(self, state):
+    raise NotImplementedError()
+
+
+class _UnpicklableCoder(beam.coders.Coder):
+  def encode(self, value):
+    return str(value.value).encode()
+
+  def decode(self, encoded):
+    return _Unpicklable(int(encoded.decode()))
+
+  def to_type_hint(self):
+    return _Unpicklable
+
+  def is_deterministic(self):
+    return True
+
+
+def maybe_skip(compat_version):
+  if compat_version and not dill:
+    raise unittest.SkipTest(
+        'Dill dependency not installed which is required for compat_version'
+        ' <= 2.67.0')
+
+
 class CoGroupByKeyTest(unittest.TestCase):
   def test_co_group_by_key_on_tuple(self):
     with TestPipeline() as pipeline:
@@ -186,6 +236,148 @@ def test_co_group_by_key_on_one(self):
                   equal_to(expected),
                   label='AssertOneDict')
 
+  def test_co_group_by_key_on_unpickled(self):
+    beam.coders.registry.register_coder(_Unpicklable, _UnpicklableCoder)
+    values = [_Unpicklable(i) for i in range(5)]
+    with TestPipeline() as pipeline:
+      xs = pipeline | beam.Create(values) | beam.WithKeys(lambda x: x)
+      pcoll = ({
+          'x': xs
+      }
+               | beam.CoGroupByKey()
+               | beam.FlatMapTuple(
+                   lambda k, tagged: (k.value, tagged['x'][0].value * 2)))
+      expected = [0, 0, 1, 2, 2, 4, 3, 6, 4, 8]
+      assert_that(pcoll, equal_to(expected))
+
+
+class FakeSecret(beam.Secret):
+  def __init__(self, should_throw=False):
+    self._secret = b'aKwI2PmqYFt2p5tNKCyBS5qYmHhHsGZcyZrnZQiQ-uE='
+    self._should_throw = should_throw
+
+  def get_secret_bytes(self) -> bytes:
+    if self._should_throw:
+      raise RuntimeError('Exception retrieving secret')
+    return self._secret
+
+
+class MockNoOpDecrypt(beam.transforms.util._DecryptMessage):
+  def __init__(self, hmac_key_secret, key_coder, value_coder):
+    hmac_key = hmac_key_secret.get_secret_bytes()
+    self.fernet_tester = Fernet(hmac_key)
+    self.known_hmacs = []
+    for key in ['a', 'b', 'c']:
+      self.known_hmacs.append(
+          hmac.new(hmac_key, key_coder.encode(key), hashlib.sha256).digest())
+    super().__init__(hmac_key_secret, key_coder, value_coder)
+
+  def process(self, element):
+    hmac_key, actual_elements = element
+    if hmac_key not in self.known_hmacs:
+      raise ValueError(f'GBK produced unencrypted value {hmac_key}')
+    for e in actual_elements:
+      try:
+        self.fernet_tester.decrypt(e[0], None)
+      except InvalidToken:
+        raise ValueError(f'GBK produced unencrypted value {e[0]}')
+      try:
+        self.fernet_tester.decrypt(e[1], None)
+      except InvalidToken:
+        raise ValueError(f'GBK produced unencrypted value {e[1]}')
+
+    return super().process(element)
+
+
+class GroupByEncryptedKeyTest(unittest.TestCase):
+  def setUp(self):
+    if secretmanager is not None:
+      self.project_id = 'apache-beam-testing'
+      secret_postfix = ''.join(random.choice(string.digits) for _ in range(6))
+      self.secret_id = 'gbek_secret_tests_' + secret_postfix
+      self.client = secretmanager.SecretManagerServiceClient()
+      self.project_path = f'projects/{self.project_id}'
+      self.secret_path = f'{self.project_path}/secrets/{self.secret_id}'
+      try:
+        self.client.get_secret(request={'name': self.secret_path})
+      except Exception:
+        self.client.create_secret(
+            request={
+                'parent': self.project_path,
+                'secret_id': self.secret_id,
+                'secret': {
+                    'replication': {
+                        'automatic': {}
+                    }
+                }
+            })
+        self.client.add_secret_version(
+            request={
+                'parent': self.secret_path,
+                'payload': {
+                    'data': Secret.generate_secret_bytes()
+                }
+            })
+      self.gcp_secret = GcpSecret(f'{self.secret_path}/versions/latest')
+
+  def tearDown(self):
+    if secretmanager is not None:
+      self.client.delete_secret(request={'name': self.secret_path})
+
+  def test_gbek_fake_secret_manager_roundtrips(self):
+    fakeSecret = FakeSecret()
+
+    with TestPipeline() as pipeline:
+      pcoll_1 = pipeline | 'Start 1' >> beam.Create([('a', 1), ('a', 2),
+                                                     ('b', 3), ('c', 4)])
+      result = (pcoll_1) | beam.GroupByEncryptedKey(fakeSecret)
+      assert_that(
+          result, equal_to([('a', ([1, 2])), ('b', ([3])), ('c', ([4]))]))
+
+  @mock.patch('apache_beam.transforms.util._DecryptMessage', MockNoOpDecrypt)
+  def test_gbek_fake_secret_manager_actually_does_encryption(self):
+    fakeSecret = FakeSecret()
+
+    with TestPipeline('FnApiRunner') as pipeline:
+      pcoll_1 = pipeline | 'Start 1' >> beam.Create([('a', 1), ('a', 2),
+                                                     ('b', 3), ('c', 4)])
+      result = (pcoll_1) | beam.GroupByEncryptedKey(fakeSecret)
+      assert_that(
+          result, equal_to([('a', ([1, 2])), ('b', ([3])), ('c', ([4]))]))
+
+  def test_gbek_fake_secret_manager_throws(self):
+    fakeSecret = FakeSecret(True)
+
+    with self.assertRaisesRegex(RuntimeError, r'Exception retrieving secret'):
+      with TestPipeline() as pipeline:
+        pcoll_1 = pipeline | 'Start 1' >> beam.Create([('a', 1), ('a', 2),
+                                                       ('b', 3), ('c', 4)])
+        result = (pcoll_1) | beam.GroupByEncryptedKey(fakeSecret)
+        assert_that(
+            result, equal_to([('a', ([1, 2])), ('b', ([3])), ('c', ([4]))]))
+
+  @unittest.skipIf(secretmanager is None, 'GCP dependencies are not installed')
+  def test_gbek_gcp_secret_manager_roundtrips(self):
+    with TestPipeline() as pipeline:
+      pcoll_1 = pipeline | 'Start 1' >> beam.Create([('a', 1), ('a', 2),
+                                                     ('b', 3), ('c', 4)])
+      result = (pcoll_1) | beam.GroupByEncryptedKey(self.gcp_secret)
+      assert_that(
+          result, equal_to([('a', ([1, 2])), ('b', ([3])), ('c', ([4]))]))
+
+  @unittest.skipIf(secretmanager is None, 'GCP dependencies are not installed')
+  def test_gbek_gcp_secret_manager_throws(self):
+    gcp_secret = GcpSecret('bad_path/versions/latest')
+
+    with self.assertRaisesRegex(RuntimeError,
+                                r'Failed to retrieve secret bytes'):
+      with TestPipeline() as pipeline:
+        pcoll_1 = pipeline | 'Start 1' >> beam.Create([('a', 1), ('a', 2),
+                                                       ('b', 3), ('c', 4)])
+        result = (pcoll_1) | beam.GroupByEncryptedKey(gcp_secret)
+        assert_that(
+            result, equal_to([('a', ([1, 2])), ('b', ([3])), ('c', ([4]))]))
+
 
 class FakeClock(object):
   def __init__(self, now=time.time()):
@@ -254,8 +446,8 @@ def test_constant_batch_no_metrics(self):
     self.assertEqual(len(results["distributions"]), 0)
 
   def test_grows_to_max_batch(self):
-    # Assumes a single bundle...
-    with TestPipeline() as p:
+    # Assumes a single bundle, so we pin to the FnApiRunner
+    with TestPipeline('FnApiRunner') as p:
       res = (
           p
           | beam.Create(range(164))
@@ -265,8 +457,8 @@ def test_grows_to_max_batch(self):
       assert_that(res, equal_to([1, 1, 2, 4, 8, 16, 32, 50, 50]))
 
   def test_windowed_batches(self):
-    # Assumes a single bundle, in order...
-    with TestPipeline() as p:
+    # Assumes a single bundle in order, so we pin to the FnApiRunner
+    with TestPipeline('FnApiRunner') as p:
       res = (
           p
           | beam.Create(range(47), reshuffle=False)
@@ -287,8 +479,8 @@ def test_windowed_batches(self):
           ]))
 
   def test_global_batch_timestamps(self):
-    # Assumes a single bundle
-    with TestPipeline() as p:
+    # Assumes a single bundle, so we pin to the FnApiRunner
+    with TestPipeline('FnApiRunner') as p:
       res = (
           p
           | beam.Create(range(3), reshuffle=False)
@@ -327,8 +519,8 @@ def test_sized_batches(self):
       assert_that(res, equal_to([2, 10, 10, 10]))
 
   def test_sized_windowed_batches(self):
-    # Assumes a single bundle, in order...
-    with TestPipeline() as p:
+    # Assumes a single bundle, in order so we pin to the FnApiRunner
+    with TestPipeline('FnApiRunner') as p:
       res = (
           p
           | beam.Create(range(1, 8), reshuffle=False)
@@ -527,8 +719,8 @@ def test_numpy_regression(self):
         util._BatchSizeEstimator.linear_regression_numpy, True)
 
   def test_stateful_constant_batch(self):
-    # Assumes a single bundle...
-    p = TestPipeline()
+    # Assumes a single bundle, so we pin to the FnApiRunner
+    p = TestPipeline('FnApiRunner')
     output = (
         p
         | beam.Create(range(35))
@@ -649,8 +841,8 @@ def test_stateful_buffering_timer_in_global_window_streaming(self):
       assert_that(num_elements_per_batch, equal_to([9, 1]))
 
   def test_stateful_grows_to_max_batch(self):
-    # Assumes a single bundle...
-    with TestPipeline() as p:
+    # Assumes a single bundle, so we pin to the FnApiRunner
+    with TestPipeline('FnApiRunner') as p:
       res = (
           p
           | beam.Create(range(164))
@@ -707,7 +899,7 @@ class AddTimestampDoFn(beam.DoFn):
       def process(self, element):
         yield window.TimestampedValue(element, expected_timestamp)
 
-    with self.assertRaisesRegex(ValueError, r'window.*None.*add_timestamps2'):
+    with self.assertRaisesRegex(Exception, r'.*window.*None.*add_timestamps2'):
       with TestPipeline() as pipeline:
         data = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2), (1, 4)]
         expected_windows = [
@@ -723,7 +915,7 @@ def process(self, element):
             equal_to(expected_windows),
             label='before_identity',
             reify_windows=True)
-        after_identity = (
+        _ = (
             before_identity
             | 'window' >> beam.WindowInto(
                 beam.transforms.util._IdentityWindowFn(
@@ -733,11 +925,6 @@ def process(self, element):
             # contain a window of None. IdentityWindowFn should
             # raise an exception.
             | 'add_timestamps2' >> beam.ParDo(AddTimestampDoFn()))
-        assert_that(
-            after_identity,
-            equal_to(expected_windows),
-            label='after_identity',
-            reify_windows=True)
 
 
 class ReshuffleTest(unittest.TestCase):
@@ -963,8 +1150,10 @@ def test_reshuffle_streaming_global_window_with_buckets(self):
       param(compat_version=None),
       param(compat_version="2.64.0"),
   ])
+  @pytest.mark.uses_dill
   def test_reshuffle_custom_window_preserves_metadata(self, compat_version):
     """Tests that Reshuffle preserves pane info."""
+    maybe_skip(compat_version)
     element_count = 12
     timestamp_value = timestamp.Timestamp(0)
     l = [
@@ -1064,10 +1253,11 @@ def test_reshuffle_custom_window_preserves_metadata(self, compat_version):
       param(compat_version=None),
       param(compat_version="2.64.0"),
   ])
+  @pytest.mark.uses_dill
   def test_reshuffle_default_window_preserves_metadata(self, compat_version):
     """Tests that Reshuffle preserves timestamp, window, and pane info
     metadata."""
-
+    maybe_skip(compat_version)
     no_firing = PaneInfo(
         is_first=True,
         is_last=True,
@@ -1089,14 +1279,19 @@ def test_reshuffle_default_window_preserves_metadata(self, compat_version):
         index=1,
         nonspeculative_index=1)
 
+    # Portable runners may not have the same level of precision on timestamps -
+    # this gets the largest supported timestamp with the extra non-supported
+    # bits truncated
+    gt = GlobalWindow().max_timestamp()
+    truncated_gt = gt - (gt % 0.001)
+
     expected_preserved = [
         TestWindowedValue('a', MIN_TIMESTAMP, [GlobalWindow()], no_firing),
         TestWindowedValue(
             'b', timestamp.Timestamp(0), [GlobalWindow()], on_time_only),
         TestWindowedValue(
             'c', timestamp.Timestamp(33), [GlobalWindow()], late_firing),
-        TestWindowedValue(
-            'd', GlobalWindow().max_timestamp(), [GlobalWindow()], no_firing)
+        TestWindowedValue('d', truncated_gt, [GlobalWindow()], no_firing)
     ]
 
     expected_not_preserved = [
@@ -1107,9 +1302,7 @@ def test_reshuffle_default_window_preserves_metadata(self, compat_version):
         TestWindowedValue(
             'c', timestamp.Timestamp(33), [GlobalWindow()], PANE_INFO_UNKNOWN),
         TestWindowedValue(
-            'd',
-            GlobalWindow().max_timestamp(), [GlobalWindow()],
-            PANE_INFO_UNKNOWN)
+            'd', truncated_gt, [GlobalWindow()], PANE_INFO_UNKNOWN)
     ]
 
     expected = (
@@ -1125,8 +1318,7 @@ def test_reshuffle_default_window_preserves_metadata(self, compat_version):
               'b', timestamp.Timestamp(0), [GlobalWindow()], on_time_only),
           WindowedValue(
               'c', timestamp.Timestamp(33), [GlobalWindow()], late_firing),
-          WindowedValue(
-              'd', GlobalWindow().max_timestamp(), [GlobalWindow()], no_firing)
+          WindowedValue('d', truncated_gt, [GlobalWindow()], no_firing)
       ]
 
       after_reshuffle = (
@@ -1208,32 +1400,6 @@ def format_with_timestamp(element, timestamp=beam.DoFn.TimestampParam):
           equal_to(expected_data),
           label="formatted_after_reshuffle")
 
-  global _Unpicklable
-  global _UnpicklableCoder
-
-  class _Unpicklable(object):
-    def __init__(self, value):
-      self.value = value
-
-    def __getstate__(self):
-      raise NotImplementedError()
-
-    def __setstate__(self, state):
-      raise NotImplementedError()
-
-  class _UnpicklableCoder(beam.coders.Coder):
-    def encode(self, value):
-      return str(value.value).encode()
-
-    def decode(self, encoded):
-      return _Unpicklable(int(encoded.decode()))
-
-    def to_type_hint(self):
-      return _Unpicklable
-
-    def is_deterministic(self):
-      return True
-
   def reshuffle_unpicklable_in_global_window_helper(
       self, update_compatibility_version=None):
     with TestPipeline(options=PipelineOptions(
@@ -2183,6 +2349,68 @@ def record(tag):
           label='result')
 
 
+class CompatCheckTest(unittest.TestCase):
+  def test_is_v1_prior_to_v2(self):
+    test_cases = [
+        # Basic comparison cases
+        ("1.0.0", "2.0.0", True),  # v1 < v2 in major
+        ("2.0.0", "1.0.0", False),  # v1 > v2 in major
+        ("1.1.0", "1.2.0", True),  # v1 < v2 in minor
+        ("1.2.0", "1.1.0", False),  # v1 > v2 in minor
+        ("1.0.1", "1.0.2", True),  # v1 < v2 in patch
+        ("1.0.2", "1.0.1", False),  # v1 > v2 in patch
+
+        # Equal versions
+        ("1.0.0", "1.0.0", False),  # Identical
+        ("0.0.0", "0.0.0", False),  # Both zero
+
+        # Different lengths - shorter vs longer
+        ("1.0", "1.0.0", False),  # Should be equal (1.0 = 1.0.0)
+        ("1.0", "1.0.1", True),  # 1.0.0 < 1.0.1
+        ("1.2", "1.2.0", False),  # Should be equal (1.2 = 1.2.0)
+        ("1.2", "1.2.3", True),  # 1.2.0 < 1.2.3
+        ("2", "2.0.0", False),  # Should be equal (2 = 2.0.0)
+        ("2", "2.0.1", True),  # 2.0.0 < 2.0.1
+        ("1", "2.0", True),  # 1.0.0 < 2.0.0
+
+        # Different lengths - longer vs shorter
+        ("1.0.0", "1.0", False),  # Should be equal
+        ("1.0.1", "1.0", False),  # 1.0.1 > 1.0.0
+        ("1.2.0", "1.2", False),  # Should be equal
+        ("1.2.3", "1.2", False),  # 1.2.3 > 1.2.0
+        ("2.0.0", "2", False),  # Should be equal
+        ("2.0.1", "2", False),  # 2.0.1 > 2.0.0
+        ("2.0", "1", False),  # 2.0.0 > 1.0.0
+
+        # Mixed length comparisons
+        ("1.0", "2.0.0", True),  # 1.0.0 < 2.0.0
+        ("2.0", "1.0.0", False),  # 2.0.0 > 1.0.0
+        ("1", "1.0.1", True),  # 1.0.0 < 1.0.1
+        ("1.1", "1.0.9", False),  # 1.1.0 > 1.0.9
+
+        # Large numbers
+        ("1.9.9", "2.0.0", True),  # 1.9.9 < 2.0.0
+        ("10.0.0", "9.9.9", False),  # 10.0.0 > 9.9.9
+        ("1.10.0", "1.9.0", False),  # 1.10.0 > 1.9.0
+        ("1.2.10", "1.2.9", False),  # 1.2.10 > 1.2.9
+
+        # Sequential versions
+        ("1.0.0", "1.0.1", True),
+        ("1.0.1", "1.0.2", True),
+        ("1.0.9", "1.1.0", True),
+        ("1.9.9", "2.0.0", True),
+
+        # Null/None cases
+        (None, "1.0.0", False),  # v1 is None
+    ]
+
+    for v1, v2, expected in test_cases:
+      self.assertEqual(
+          util.is_v1_prior_to_v2(v1=v1, v2=v2),
+          expected,
+          msg=f"Failed {v1} < {v2} == {expected}")
+
+
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
   unittest.main()
diff --git a/sdks/python/apache_beam/typehints/row_type.py b/sdks/python/apache_beam/typehints/row_type.py
index 038eb50d0606..08838c84a050 100644
--- a/sdks/python/apache_beam/typehints/row_type.py
+++ b/sdks/python/apache_beam/typehints/row_type.py
@@ -228,6 +228,7 @@ def __init__(
         schema_id=schema_id,
         schema_options=schema_options,
         field_options=field_options,
+        field_descriptions=field_descriptions,
         **kwargs)
     user_type = named_tuple_from_schema(schema, **kwargs)
 
diff --git a/sdks/python/apache_beam/typehints/schema_registry.py b/sdks/python/apache_beam/typehints/schema_registry.py
index a73e97f43f70..7d8cdcf57d3f 100644
--- a/sdks/python/apache_beam/typehints/schema_registry.py
+++ b/sdks/python/apache_beam/typehints/schema_registry.py
@@ -40,7 +40,7 @@ def generate_new_id(self):
         "schemas.")
 
   def add(self, typing, schema):
-    if not schema.id:
+    if schema.id:
       self.by_id[schema.id] = (typing, schema)
 
   def get_typing_by_id(self, unique_id):
diff --git a/sdks/python/apache_beam/typehints/schemas.py b/sdks/python/apache_beam/typehints/schemas.py
index 90a692e21125..c21dde426fc7 100644
--- a/sdks/python/apache_beam/typehints/schemas.py
+++ b/sdks/python/apache_beam/typehints/schemas.py
@@ -66,6 +66,7 @@
 
 # pytype: skip-file
 
+import datetime
 import decimal
 import logging
 from typing import Any
@@ -142,23 +143,36 @@ def named_fields_to_schema(
     schema_options: Optional[Sequence[Tuple[str, Any]]] = None,
     field_options: Optional[Dict[str, Sequence[Tuple[str, Any]]]] = None,
     schema_registry: SchemaTypeRegistry = SCHEMA_REGISTRY,
+    field_descriptions: Optional[Dict[str, str]] = None,
 ):
   schema_options = schema_options or []
   field_options = field_options or {}
+  field_descriptions = field_descriptions or {}
 
   if isinstance(names_and_types, dict):
     names_and_types = names_and_types.items()
 
+  _, cached_schema = schema_registry.by_id.get(schema_id, (None, None))
+  if cached_schema:
+    type_by_name_from_schema = {
+        field.name: field.type
+        for field in cached_schema.fields
+    }
+  else:
+    type_by_name_from_schema = {}
+
   schema = schema_pb2.Schema(
       fields=[
           schema_pb2.Field(
               name=name,
-              type=typing_to_runner_api(type),
+              type=type_by_name_from_schema.get(
+                  name, typing_to_runner_api(type)),
               options=[
                   option_to_runner_api(option_tuple)
                   for option_tuple in field_options.get(name, [])
               ],
-          ) for (name, type) in names_and_types
+              description=field_descriptions.get(name, None))
+          for (name, type) in names_and_types
       ],
       options=[
           option_to_runner_api(option_tuple) for option_tuple in schema_options
@@ -616,6 +630,13 @@ def schema_from_element_type(element_type: type) -> schema_pb2.Schema:
   if isinstance(element_type, row_type.RowTypeConstraint):
     return named_fields_to_schema(element_type._fields)
   elif match_is_named_tuple(element_type):
+    if hasattr(element_type, row_type._BEAM_SCHEMA_ID):
+      # if the named tuple's schema is in registry, we just use it instead of
+      # regenerating one.
+      schema_id = getattr(element_type, row_type._BEAM_SCHEMA_ID)
+      schema = SCHEMA_REGISTRY.get_schema_by_id(schema_id)
+      if schema is not None:
+        return schema
     return named_tuple_to_schema(element_type)
   else:
     raise TypeError(
@@ -1017,15 +1038,15 @@ def representation_type(cls):
   def language_type(cls):
     return decimal.Decimal
 
-  def to_representation_type(self, value):
-    # type: (decimal.Decimal) -> bytes
-
-    return DecimalLogicalType().to_representation_type(value)
-
-  def to_language_type(self, value):
-    # type: (bytes) -> decimal.Decimal
+  # from language type (decimal.Decimal) to representation type
+  # (the type corresponding to the coder used in DecimalLogicalType)
+  def to_representation_type(self, value: decimal.Decimal) -> decimal.Decimal:
+    return value
 
-    return DecimalLogicalType().to_language_type(value)
+  # from representation type (the type corresponding to the coder used in
+  # DecimalLogicalType) to language type
+  def to_language_type(self, value: decimal.Decimal) -> decimal.Decimal:
+    return value
 
   @classmethod
   def argument_type(cls):
@@ -1169,3 +1190,94 @@ def argument_type(cls):
 
   def argument(self):
     return self.max_length
+
+
+# TODO: A temporary fix for missing jdbc logical types.
+# See the discussion in https://github.com/apache/beam/issues/35738 for
+# more detail.
+@LogicalType.register_logical_type
+class JdbcDateType(LogicalType[datetime.date, MillisInstant, str]):
+  """
+  For internal use only; no backwards-compatibility guarantees.
+
+  Support of Legacy JdbcIO DATE logical type. Deemed to change when Java JDBCIO
+  has been migrated to Beam portable logical types.
+  """
+  def __init__(self, argument=""):
+    pass
+
+  @classmethod
+  def representation_type(cls) -> type:
+    return MillisInstant
+
+  @classmethod
+  def urn(cls):
+    return "beam:logical_type:javasdk_date:v1"
+
+  @classmethod
+  def language_type(cls):
+    return datetime.date
+
+  def to_representation_type(self, value: datetime.date) -> Timestamp:
+    return Timestamp.from_utc_datetime(
+        datetime.datetime.combine(
+            value, datetime.datetime.min.time(), tzinfo=datetime.timezone.utc))
+
+  def to_language_type(self, value: Timestamp) -> datetime.date:
+    return value.to_utc_datetime().date()
+
+  @classmethod
+  def argument_type(cls):
+    return str
+
+  def argument(self):
+    return ""
+
+  @classmethod
+  def _from_typing(cls, typ):
+    return cls()
+
+
+@LogicalType.register_logical_type
+class JdbcTimeType(LogicalType[datetime.time, MillisInstant, str]):
+  """
+  For internal use only; no backwards-compatibility guarantees.
+
+  Support of Legacy JdbcIO TIME logical type. . Deemed to change when Java
+  JDBCIO has been migrated to Beam portable logical types.
+  """
+  def __init__(self, argument=""):
+    pass
+
+  @classmethod
+  def representation_type(cls) -> type:
+    return MillisInstant
+
+  @classmethod
+  def urn(cls):
+    return "beam:logical_type:javasdk_time:v1"
+
+  @classmethod
+  def language_type(cls):
+    return datetime.time
+
+  def to_representation_type(self, value: datetime.time) -> Timestamp:
+    return Timestamp.from_utc_datetime(
+        datetime.datetime.combine(
+            datetime.datetime.utcfromtimestamp(0),
+            value,
+            tzinfo=datetime.timezone.utc))
+
+  def to_language_type(self, value: Timestamp) -> datetime.time:
+    return value.to_utc_datetime().time()
+
+  @classmethod
+  def argument_type(cls):
+    return str
+
+  def argument(self):
+    return ""
+
+  @classmethod
+  def _from_typing(cls, typ):
+    return cls()
diff --git a/sdks/python/apache_beam/typehints/schemas_test.py b/sdks/python/apache_beam/typehints/schemas_test.py
index 6cf37322147e..73db06b9a8d2 100644
--- a/sdks/python/apache_beam/typehints/schemas_test.py
+++ b/sdks/python/apache_beam/typehints/schemas_test.py
@@ -30,8 +30,8 @@
 from typing import Optional
 from typing import Sequence
 
-import dill
 import numpy as np
+import pytest
 from hypothesis import given
 from hypothesis import settings
 from parameterized import parameterized
@@ -711,13 +711,19 @@ def test_named_fields_roundtrip(self, named_fields):
         'pickler': pickle,
     },
     {
-        'pickler': dill,
+        'pickler': 'dill',
     },
     {
         'pickler': cloudpickle,
     },
 ])
+@pytest.mark.uses_dill
 class PickleTest(unittest.TestCase):
+  def setUp(self):
+    # pylint: disable=access-member-before-definition
+    if self.pickler == 'dill':
+      self.pickler = pytest.importorskip("dill")
+
   def test_generated_class_pickle_instance(self):
     schema = schema_pb2.Schema(
         id="some-uuid",
@@ -733,7 +739,7 @@ def test_generated_class_pickle_instance(self):
     self.assertEqual(instance, self.pickler.loads(self.pickler.dumps(instance)))
 
   def test_generated_class_pickle(self):
-    if self.pickler in [pickle, dill]:
+    if self.pickler in [pickle, pytest.importorskip("dill")]:
       self.skipTest('https://github.com/apache/beam/issues/22714')
 
     schema = schema_pb2.Schema(
diff --git a/sdks/python/apache_beam/typehints/typecheck_test.py b/sdks/python/apache_beam/typehints/typecheck_test.py
index bafb21c3dc17..c2eaa0f6f9f7 100644
--- a/sdks/python/apache_beam/typehints/typecheck_test.py
+++ b/sdks/python/apache_beam/typehints/typecheck_test.py
@@ -36,7 +36,6 @@
 from apache_beam.testing.test_pipeline import TestPipeline
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
-from apache_beam.typehints import TypeCheckError
 from apache_beam.typehints import decorators
 from apache_beam.typehints import with_input_types
 from apache_beam.typehints import with_output_types
@@ -95,7 +94,7 @@ def test_setup(self):
     def fn(e: int) -> int:
       return str(e)  # type: ignore
 
-    with self.assertRaisesRegex(TypeCheckError,
+    with self.assertRaisesRegex(Exception,
                                 r'output should be.*int.*received.*str'):
       _ = self.p | beam.Create([1, 2, 3]) | beam.Map(fn)
       self.p.run()
@@ -146,34 +145,27 @@ def assertStartswith(self, msg, prefix):
         msg.startswith(prefix), '"%s" does not start with "%s"' % (msg, prefix))
 
   def test_simple_input_error(self):
-    with self.assertRaises(TypeCheckError) as e:
+    with self.assertRaisesRegex(Exception,
+                                "Type-hint for argument: 'x' violated. "
+                                "Expected an instance of {}, "
+                                "instead found 1, an instance of {}".format(
+                                    str, int)):
       (
           self.p
           | beam.Create([1, 1])
           | beam.FlatMap(lambda x: [int(x)]).with_input_types(
               str).with_output_types(int))
-      self.p.run()
-
-    self.assertIn(
-        "Type-hint for argument: 'x' violated. "
-        "Expected an instance of {}, "
-        "instead found 1, an instance of {}".format(str, int),
-        e.exception.args[0])
+      self.p.run().wait_until_finish()
 
   def test_simple_output_error(self):
-    with self.assertRaises(TypeCheckError) as e:
+    with self.assertRaisesRegex(Exception,
+                                "Type-hint for argument: 'x' violated. "):
       (
           self.p
           | beam.Create(['1', '1'])
           | beam.FlatMap(lambda x: [int(x)]).with_input_types(
               int).with_output_types(int))
-      self.p.run()
-
-    self.assertIn(
-        "Type-hint for argument: 'x' violated. "
-        "Expected an instance of {}, "
-        "instead found 1, an instance of {}.".format(int, str),
-        e.exception.args[0])
+      self.p.run().wait_until_finish()
 
   def test_simple_input_error_with_kwarg_typehints(self):
     @with_input_types(element=int)
@@ -182,28 +174,28 @@ class ToInt(beam.DoFn):
       def process(self, element, *args, **kwargs):
         yield int(element)
 
-    with self.assertRaises(TypeCheckError) as e:
+    with self.assertRaisesRegex(Exception,
+                                "Type-hint for argument: 'element' violated"):
       (self.p | beam.Create(['1', '1']) | beam.ParDo(ToInt()))
-      self.p.run()
+      self.p.run().wait_until_finish()
 
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within "
-        "ParDo(ToInt): Type-hint for argument: "
-        "'element' violated. Expected an instance of "
-        "{}, instead found 1, "
-        "an instance of {}.".format(int, str))
+  def test_bad_flatten_input(self):
+    with self.assertRaisesRegex(
+        TypeError,
+        "Inputs to Flatten cannot include an iterable of PCollections. "):
+      with beam.Pipeline() as p:
+        pc = p | beam.Create([1, 1])
+        flatten_inputs = [pc, (pc, )]
+        flatten_inputs | beam.Flatten()
 
   def test_do_fn_returning_non_iterable_throws_error(self):
     # This function is incorrect because it returns a non-iterable object
     def incorrect_par_do_fn(x):
       return x + 5
 
-    with self.assertRaises(TypeError) as cm:
+    with self.assertRaisesRegex(Exception, "'int' object is not iterable "):
       (self.p | beam.Create([1, 1]) | beam.FlatMap(incorrect_par_do_fn))
-      self.p.run()
-
-    self.assertStartswith(cm.exception.args[0], "'int' object is not iterable ")
+      self.p.run().wait_until_finish()
 
   def test_simple_type_satisfied(self):
     @with_input_types(int, int)
@@ -232,15 +224,9 @@ def int_to_string(x):
         self.p
         | 'Create' >> beam.Create(['some_string'])
         | 'ToStr' >> beam.Map(int_to_string))
-    with self.assertRaises(TypeCheckError) as e:
-      self.p.run()
-
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within ParDo(ToStr): "
-        "Type-hint for argument: 'x' violated. "
-        "Expected an instance of {}, "
-        "instead found some_string, an instance of {}.".format(int, str))
+    with self.assertRaisesRegex(Exception,
+                                "Type-hint for argument: 'x' violated. "):
+      self.p.run().wait_until_finish()
 
   def test_pipeline_checking_satisfied_but_run_time_types_violate(self):
     self.p._options.view_as(TypeOptions).pipeline_type_check = False
@@ -258,17 +244,13 @@ def is_even_as_key(a):
         | 'IsEven' >> beam.Map(is_even_as_key)
         | 'Parity' >> beam.GroupByKey())
 
-    with self.assertRaises(TypeCheckError) as e:
-      self.p.run()
-
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within ParDo(IsEven): "
-        "Type-hint for return type violated: "
-        "Tuple[<class \'bool\'>, <class \'int\'>] hint type-constraint "
-        "violated. The type of element #0 in the passed tuple is incorrect. "
-        "Expected an instance of type <class \'bool\'>, "
-        "instead received an instance of type int. ")
+    with self.assertRaisesRegex(
+        Exception,
+        ("Type-hint for return type violated: Tuple\\[<class 'bool'>, <class " +
+         "'int'>\\] hint type-constraint violated. The type of element #0 in " +
+         "the passed tuple is incorrect. Expected an instance of type <class " +
+         "'bool'>, instead received an instance of type int.")):
+      self.p.run().wait_until_finish()
 
   def test_pipeline_runtime_checking_violation_composite_type_output(self):
     self.p._options.view_as(TypeOptions).pipeline_type_check = False
@@ -276,7 +258,10 @@ def test_pipeline_runtime_checking_violation_composite_type_output(self):
     # The type-hinted applied via the 'returns()' method indicates the ParDo
     # should return an instance of type: Tuple[float, int]. However, an instance
     # of 'int' will be generated instead.
-    with self.assertRaises(TypeCheckError) as e:
+    with self.assertRaisesRegex(
+        Exception,
+        ("Type-hint for return type violated. Expected an instance of {}, " +
+         "instead found 4.0, an instance of {}.").format(int, float)):
       (
           self.p
           | beam.Create([(1, 3.0)])
@@ -284,14 +269,7 @@ def test_pipeline_runtime_checking_violation_composite_type_output(self):
               'Swap' >>
               beam.FlatMap(lambda x_y1: [x_y1[0] + x_y1[1]]).with_input_types(
                   Tuple[int, float]).with_output_types(int)))
-      self.p.run()
-
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within ParDo(Swap): "
-        "Type-hint for return type violated. "
-        "Expected an instance of {}, "
-        "instead found 4.0, an instance of {}.".format(int, float))
+      self.p.run().wait_until_finish()
 
   def test_downstream_input_type_hint_error_has_descriptive_error_msg(self):
     @with_input_types(int)
@@ -309,21 +287,16 @@ def process(self, element, *args, **kwargs):
     # This will raise a type check error in IntToInt even though the actual
     # type check error won't happen until StrToInt. The user will be told that
     # StrToInt's input type hints were not satisfied while running IntToInt.
-    with self.assertRaises(TypeCheckError) as e:
+    with self.assertRaisesRegex(
+        Exception,
+        ("Type-hint for argument: 'element' violated. Expected an instance of "
+         + "{}, instead found 9, an instance of {}.").format(str, int)):
       (
           self.p
           | beam.Create([9])
           | beam.ParDo(IntToInt())
           | beam.ParDo(StrToInt()))
-      self.p.run()
-
-    self.assertStartswith(
-        e.exception.args[0],
-        "Runtime type violation detected within ParDo(StrToInt): "
-        "Type-hint for argument: 'element' violated. "
-        "Expected an instance of {}, "
-        "instead found 9, an instance of {}. "
-        "[while running 'ParDo(IntToInt)']".format(str, int))
+      self.p.run().wait_until_finish()
 
 
 if __name__ == '__main__':
diff --git a/sdks/python/apache_beam/typehints/typehints.py b/sdks/python/apache_beam/typehints/typehints.py
index 3b6afcc0a116..54eef4ee1a1c 100644
--- a/sdks/python/apache_beam/typehints/typehints.py
+++ b/sdks/python/apache_beam/typehints/typehints.py
@@ -72,6 +72,9 @@
 import typing
 from collections import abc
 
+from beartype.door import is_subhint
+from beartype.roar import BeartypeDoorException
+
 __all__ = [
     'Any',
     'Union',
@@ -1483,7 +1486,7 @@ def normalize(x, none_as_type=False):
 })
 
 
-def is_consistent_with(sub, base):
+def is_consistent_with(sub, base, use_beartype: bool = False) -> bool:
   """Checks whether sub a is consistent with base.
 
   This is according to the terminology of PEP 483/484.  This relationship is
@@ -1522,7 +1525,13 @@ def is_consistent_with(sub, base):
     # Cannot check unsupported parameterized generic which will cause issubclass
     # to fail with an exception.
     return False
-  return issubclass(sub, base)
+  if use_beartype:
+    try:
+      return is_subhint(sub, base)
+    except (BeartypeDoorException):
+      return False
+  else:
+    return issubclass(sub, base)
 
 
 def regex_consistency(sub, base) -> bool:
diff --git a/sdks/python/apache_beam/utils/subprocess_server.py b/sdks/python/apache_beam/utils/subprocess_server.py
index babe81d6bde9..84848479430b 100644
--- a/sdks/python/apache_beam/utils/subprocess_server.py
+++ b/sdks/python/apache_beam/utils/subprocess_server.py
@@ -132,7 +132,7 @@ class SubprocessServer(object):
       with SubprocessServer(GrpcStubClass, [executable, arg, ...]) as stub:
           stub.CallService(...)
   """
-  def __init__(self, stub_class, cmd, port=None):
+  def __init__(self, stub_class, cmd, port=None, logger=None):
     """Creates the server object.
 
     :param stub_class: the auto-generated GRPC client stub class used for
@@ -143,12 +143,21 @@ def __init__(self, stub_class, cmd, port=None):
         service.  If not given, one will be randomly chosen and the special
         string "{{PORT}}" will be substituted in the command line arguments
         with the chosen port.
+    :param logger: (optional) The logger or logger name to use for the
+        subprocess's stderr and stdout. If not given, the current module logger
+        would be used.
     """
     self._owner_id = None
     self._stub_class = stub_class
     self._cmd = [str(arg) for arg in cmd]
     self._port = port
     self._grpc_channel = None
+    if isinstance(logger, str):
+      self._logger = logging.getLogger(logger)
+    elif isinstance(logger, logging.Logger):
+      self._logger = logger
+    else:
+      self._logger = _LOGGER
 
   @classmethod
   @contextlib.contextmanager
@@ -203,9 +212,9 @@ def start_process(self):
     if self._owner_id is not None:
       self._cache.purge(self._owner_id)
     self._owner_id = self._cache.register()
-    return self._cache.get(tuple(self._cmd), self._port)
+    return self._cache.get(tuple(self._cmd), self._port, self._logger)
 
-  def _really_start_process(cmd, port):
+  def _really_start_process(cmd, port, logger):
     if not port:
       port, = pick_port(None)
       cmd = [arg.replace('{{PORT}}', str(port)) for arg in cmd]  # pylint: disable=not-an-iterable
@@ -220,7 +229,7 @@ def log_stdout():
       while line:
         # The log obtained from stdout is bytes, decode it into string.
         # Remove newline via rstrip() to not print an empty line.
-        _LOGGER.info(line.decode(errors='backslashreplace').rstrip())
+        logger.info(line.decode(errors='backslashreplace').rstrip())
         line = process.stdout.readline()
 
     t = threading.Thread(target=log_stdout)
@@ -283,7 +292,8 @@ def __init__(
       path_to_jar,
       java_arguments,
       classpath=None,
-      cache_dir=None):
+      cache_dir=None,
+      logger=None):
     self._java_path = JavaHelper.get_java()
     if classpath:
       # java -jar ignores the classpath, so we make a new jar that embeds
@@ -291,7 +301,8 @@ def __init__(
       path_to_jar = self.make_classpath_jar(path_to_jar, classpath, cache_dir)
     super().__init__(
         stub_class,
-        [self._java_path, '-jar', path_to_jar] + list(java_arguments))
+        [self._java_path, '-jar', path_to_jar] + list(java_arguments),
+        logger=logger)
     self._existing_service = path_to_jar if is_service_endpoint(
         path_to_jar) else None
 
diff --git a/sdks/python/apache_beam/version.py b/sdks/python/apache_beam/version.py
index 5799d90c2f17..755b18a3f312 100644
--- a/sdks/python/apache_beam/version.py
+++ b/sdks/python/apache_beam/version.py
@@ -17,4 +17,4 @@
 
 """Apache Beam SDK version information and utilities."""
 
-__version__ = '2.67.0.dev'
+__version__ = '2.69.0.dev'
diff --git a/sdks/python/apache_beam/yaml/README.md b/sdks/python/apache_beam/yaml/README.md
index 56b5e7bedff7..68576726b20f 100644
--- a/sdks/python/apache_beam/yaml/README.md
+++ b/sdks/python/apache_beam/yaml/README.md
@@ -62,9 +62,9 @@ or
 pytest -v integration_tests.py::<yaml_file_name_without_extension>Test
 ```
 
-To run the postcommit tests:
+To run some of the postcommit tests, for example:
 
 ```bash
-pytest -v integration_tests.py --test_files_dir="extended_tests"
+pytest -v integration_tests.py --test_files_dir="extended_tests/messaging"
 ```
 
diff --git a/sdks/python/apache_beam/yaml/examples/README.md b/sdks/python/apache_beam/yaml/examples/README.md
index 70533655a6a2..55fd19bd8c40 100644
--- a/sdks/python/apache_beam/yaml/examples/README.md
+++ b/sdks/python/apache_beam/yaml/examples/README.md
@@ -28,16 +28,18 @@
         * [Blueprints](#blueprints)
         * [Element-wise](#element-wise)
         * [IO](#io)
+        * [Jinja](#jinja)
         * [ML](#ml)
 
 <!-- TOC -->
 
 ## Prerequistes
 
-Build this jar for running with the run command in the next stage:
+Build the expansion service jar required for your YAML code.
+IO mapping is available in standard_io.yaml, so use this example run command:
 
 ```
-cd <path_to_beam_repo>/beam; ./gradlew sdks:java:io:google-cloud-platform:expansion-service:shadowJar
+cd <PATH_TO_BEAM_REPO>/beam; ./gradlew sdks:java:io:google-cloud-platform:expansion-service:shadowJar
 ```
 
 ## Example Run
@@ -70,6 +72,10 @@ pytest -v testing/
 
 or
 
+pytest -v testing/examples_test.py::JinjaTest
+
+or
+
 python -m unittest -v testing/examples_test.py
 ```
 
@@ -158,7 +164,9 @@ python -m apache_beam.yaml.main \
   --num_workers $NUM_WORKERS \
   --job_name $JOB_NAME \
   --jinja_variables '{ "BOOTSTRAP_SERVERS": "123.45.67.89:9092",
-    "TOPIC": "MY-TOPIC", "USERNAME": "USERNAME", "PASSWORD": "PASSWORD" }'
+    "TOPIC": "MY-TOPIC", "USERNAME": "USERNAME", "PASSWORD": "PASSWORD" }'\
+  --sdk_location container  \
+  --sdk_harness_container_image_overrides ".*java.*,gcr.io/apache-beam-testing/beam-sdk/beam_java11_sdk:latest"
 ```
 
 **_Optional_**: If Kafka cluster is set up with no SASL/PLAINTEXT authentication
@@ -229,10 +237,39 @@ gcloud dataflow yaml run $JOB_NAME \
   --region $REGION
 ```
 
+### Jinja
+
+Jinja [templatization](https://beam.apache.org/documentation/sdks/yaml/#jinja-templatization)
+can be used to build off of different contexts and/or with different
+configurations.
+
+Several examples will be created based on the already used word count example
+by leveraging Jinja templating engine for dynamic pipeline generation based on
+inputs from the user through `% include`, `% import`, and inheritance
+directives.
+
+Jinja `% import` directive:
+- [wordCountImport.yaml](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/jinja/import/wordCountImport.yaml)
+- [Instructions](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/jinja/import/README.md) on how to run the pipeline.
+
+Jinja `% include` directive:
+- [wordCountInclude.yaml](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/wordCountInclude.yaml)
+- [Instructions](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/README.md) on how to run the pipeline.
+
+
 ### ML
 
-These examples leverage the built-in `Enrichment` transform for performing
-ML enrichments.
+Examples that include the built-in `Enrichment` transform for performing
+ML enrichments:
+- [enrich_spanner_with_bigquery.yaml](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/enrich_spanner_with_bigquery.yaml)
+- [bigtable_enrichment.yaml](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/bigtable_enrichment.yaml)
+
+Examples that include ML-specific transforms such as `RunInference` and
+`MLTransform`:
+- Streaming Sentiment Analysis ([documentation](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/ml/sentiment_analysis)) ([pipeline](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/sentiment_analysis/streaming_sentiment_analysis.yaml))
+- Streaming Taxi Fare Prediction ([documentation](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare)) ([pipeline](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/streaming_taxifare_prediction.yaml))
+- Batch Log Analysis ML Workflow ([documentation](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis)) ([pipeline](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/batch_log_analysis.sh))
+- Fraud Detection MLOps Workflow ([documentation](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection/README.md)) ([pipeline](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection/fraud_detection_mlops_beam_yaml_sdk.ipynb))
 
 More information can be found about aggregation transforms
 [here](https://beam.apache.org/documentation/sdks/yaml-combine/).
diff --git a/sdks/python/apache_beam/yaml/examples/testing/examples_test.py b/sdks/python/apache_beam/yaml/examples/testing/examples_test.py
index 3b46a9dda5d9..4f0516a1ea93 100644
--- a/sdks/python/apache_beam/yaml/examples/testing/examples_test.py
+++ b/sdks/python/apache_beam/yaml/examples/testing/examples_test.py
@@ -21,6 +21,7 @@
 import logging
 import os
 import random
+import re
 import sys
 import unittest
 from typing import Any
@@ -33,12 +34,17 @@
 
 import pytest
 import yaml
+from jinja2 import DictLoader
+from jinja2 import Environment
+from jinja2 import StrictUndefined
 
 import apache_beam as beam
 from apache_beam import PCollection
 from apache_beam.examples.snippets.util import assert_matches_stdout
+from apache_beam.ml.inference.base import PredictionResult
 from apache_beam.options.pipeline_options import PipelineOptions
 from apache_beam.testing.test_pipeline import TestPipeline
+from apache_beam.typehints.row_type import RowTypeConstraint
 from apache_beam.utils import subprocess_server
 from apache_beam.yaml import yaml_provider
 from apache_beam.yaml import yaml_transform
@@ -115,19 +121,44 @@ def _fn(row):
 @beam.ptransform.ptransform_fn
 def test_kafka_read(
     pcoll,
-    format,
-    topic,
-    bootstrap_servers,
-    auto_offset_reset_config,
-    consumer_config):
-  return (
-      pcoll | beam.Create(input_data.text_data().split('\n'))
-      | beam.Map(lambda element: beam.Row(payload=element.encode('utf-8'))))
+    topic: Optional[str] = None,
+    format: Optional[str] = None,
+    schema: Optional[Any] = None,
+    bootstrap_servers: Optional[str] = None,
+    auto_offset_reset_config: Optional[str] = None,
+    consumer_config: Optional[Any] = None):
+  """
+  Mocks the ReadFromKafka transform for testing purposes.
+
+  This PTransform simulates the behavior of the ReadFromKafka transform by
+  reading from predefined in-memory data based on the Kafka topic argument.
+
+  Args:
+    pcoll: The input PCollection.
+    topic: The name of Kafka topic to read from.
+    format: The format of the Kafka messages (e.g., 'RAW').
+    schema: The schema of the Kafka messages.
+    bootstrap_servers: A list of Kafka bootstrap servers to connect to.
+    auto_offset_reset_config: A configuration for the auto offset reset.
+    consumer_config: A map for additional consumer configuration parameters.
+
+  Returns:
+    A PCollection containing the sample data.
+  """
+
+  if topic == 'test-topic':
+    kafka_byte_messages = KAFKA_TOPICS['test-topic']
+    return (
+        pcoll
+        | beam.Create([msg.decode('utf-8') for msg in kafka_byte_messages])
+        | beam.Map(lambda element: beam.Row(payload=element.encode('utf-8'))))
+
+  return None
 
 
 @beam.ptransform.ptransform_fn
 def test_pubsub_read(
-    pbegin,
+    pcoll,
     topic: Optional[str] = None,
     subscription: Optional[str] = None,
     format: Optional[str] = None,
@@ -136,19 +167,119 @@ def test_pubsub_read(
     attributes_map: Optional[str] = None,
     id_attribute: Optional[str] = None,
     timestamp_attribute: Optional[str] = None):
+  """
+  Mocks the ReadFromPubSub transform for testing purposes.
 
-  pubsub_messages = input_data.pubsub_messages_data()
+  This PTransform simulates the behavior of the ReadFromPubSub transform by
+  reading from predefined in-memory data based on the Pub/Sub topic argument.
+  Args:
+    pcoll: The input PCollection.
+    topic: The name of Pub/Sub topic to read from.
+    subscription: The name of Pub/Sub subscription to read from.
+    format: The format of the Pub/Sub messages (e.g., 'JSON').
+    schema: The schema of the Pub/Sub messages.
+    attributes: A list of attributes to include in the output.
+    attributes_map: A string representing a mapping of attributes.
+    id_attribute: The attribute to use as the ID for the message.
+    timestamp_attribute: The attribute to use as the timestamp for the message.
+
+  Returns:
+    A PCollection containing the sample data.
+  """
 
-  return (
-      pbegin
-      | beam.Create([json.loads(msg.data) for msg in pubsub_messages])
-      | beam.Map(lambda element: beam.Row(**element)))
+  if topic == 'test-topic':
+    pubsub_messages = PUBSUB_TOPICS['test-topic']
+    return (
+        pcoll
+        | beam.Create([json.loads(msg.data) for msg in pubsub_messages])
+        | beam.Map(lambda element: beam.Row(**element)))
+  elif topic == 'taxi-ride-topic':
+    pubsub_messages = PUBSUB_TOPICS['taxi-ride-topic']
+    schema = input_data.TaxiRideEventSchema
+    return (
+        pcoll
+        | beam.Create([json.loads(msg.data) for msg in pubsub_messages])
+        |
+        beam.Map(lambda element: beam.Row(**element)).with_output_types(schema))
+
+  return None
+
+
+@beam.ptransform.ptransform_fn
+def test_run_inference_taxi_fare(pcoll, inference_tag, model_handler):
+  """
+  This PTransform simulates the behavior of the RunInference transform.
+
+  Args:
+    pcoll: The input PCollection.
+    inference_tag: The tag to use for the returned inference.
+    model_handler: A configuration for the respective ML model handler
+
+  Returns:
+    A PCollection containing the enriched data.
+  """
+  def _fn(row):
+    input = row._asdict()
+
+    row = {inference_tag: PredictionResult(input, 10.0), **input}
+
+    return beam.Row(**row)
+
+  schema = _format_predicition_result_ouput(pcoll, inference_tag)
+  return pcoll | beam.Map(_fn).with_output_types(schema)
+
+
+@beam.ptransform.ptransform_fn
+def test_run_inference_youtube_comments(pcoll, inference_tag, model_handler):
+  """
+  This PTransform simulates the behavior of the RunInference transform.
+
+  Args:
+    pcoll: The input PCollection.
+    inference_tag: The tag to use for the returned inference.
+    model_handler: A configuration for the respective ML model handler
+
+  Returns:
+    A PCollection containing the enriched data.
+  """
+  def _fn(row):
+    input = row._asdict()
+
+    row = {
+        inference_tag: PredictionResult(
+            input['comment_text'],
+            [{
+                'label': 'POSITIVE'
+                if 'happy' in input['comment_text'] else 'NEGATIVE',
+                'score': 0.95
+            }]),
+        **input
+    }
+
+    return beam.Row(**row)
+
+  schema = _format_predicition_result_ouput(pcoll, inference_tag)
+  return pcoll | beam.Map(_fn).with_output_types(schema)
+
+
+def _format_predicition_result_ouput(pcoll, inference_tag):
+  user_type = RowTypeConstraint.from_user_type(pcoll.element_type.user_type)
+  user_schema_fields = [(name, type(typ) if not isinstance(typ, type) else typ)
+                        for (name,
+                             typ) in user_type._fields] if user_type else []
+  inference_output_type = RowTypeConstraint.from_fields([
+      ('example', Any), ('inference', Any), ('model_id', Optional[str])
+  ])
+  return RowTypeConstraint.from_fields(
+      user_schema_fields + [(str(inference_tag), inference_output_type)])
 
 
 TEST_PROVIDERS = {
     'TestEnrichment': test_enrichment,
     'TestReadFromKafka': test_kafka_read,
-    'TestReadFromPubSub': test_pubsub_read
+    'TestReadFromPubSub': test_pubsub_read,
+    'TestRunInferenceYouTubeComments': test_run_inference_youtube_comments,
+    'TestRunInferenceTaxiFare': test_run_inference_taxi_fare,
 }
 """
 Transforms not requiring inputs.
@@ -212,8 +343,22 @@ def test_yaml_example(self):
     for i, line in enumerate(expected):
       expected[i] = line.replace('#  ', '').replace('\n', '')
     expected = [line for line in expected if line]
+
+    raw_spec_string = ''.join(lines)
+    # Filter for any jinja preprocessor - this has to be done before other
+    # preprocessors.
+    jinja_preprocessor = [
+        preprocessor for preprocessor in custom_preprocessors
+        if 'jinja_preprocessor' in preprocessor.__name__
+    ]
+    if jinja_preprocessor:
+      jinja_preprocessor = jinja_preprocessor[0]
+      raw_spec_string = jinja_preprocessor(
+          raw_spec_string, self._testMethodName)
+      custom_preprocessors.remove(jinja_preprocessor)
+
     pipeline_spec = yaml.load(
-        ''.join(lines), Loader=yaml_transform.SafeLineLoader)
+        raw_spec_string, Loader=yaml_transform.SafeLineLoader)
 
     with TestEnvironment() as env:
       for fn in custom_preprocessors:
@@ -238,7 +383,17 @@ def test_yaml_example(self):
               actual += list(transform.outputs.values())
         check_output(expected)(actual)
 
-  if 'deps' in pipeline_spec_file:
+  def _python_deps_involved(spec_filename):
+    return any(
+        substr in spec_filename for substr in
+        ['deps', 'streaming_sentiment_analysis', 'ml_preprocessing'])
+
+  def _java_deps_involved(spec_filename):
+    return any(
+        substr in spec_filename
+        for substr in ['java_deps', 'streaming_taxifare_prediction'])
+
+  if _python_deps_involved(pipeline_spec_file):
     test_yaml_example = pytest.mark.no_xdist(test_yaml_example)
     test_yaml_example = unittest.skipIf(
         sys.platform == 'win32', "Github virtualenv permissions issues.")(
@@ -252,7 +407,7 @@ def test_yaml_example(self):
         'Github actions environment issue.')(
             test_yaml_example)
 
-  if 'java_deps' in pipeline_spec_file:
+  if _java_deps_involved(pipeline_spec_file):
     test_yaml_example = pytest.mark.xlang_sql_expansion_service(
         test_yaml_example)
     test_yaml_example = unittest.skipIf(
@@ -376,8 +531,9 @@ def apply(preprocessor):
     return apply
 
 
-@YamlExamplesTestSuite.register_test_preprocessor('test_wordcount_minimal_yaml')
-def _wordcount_test_preprocessor(
+@YamlExamplesTestSuite.register_test_preprocessor(
+    ['test_wordcount_minimal_yaml'])
+def _wordcount_minimal_test_preprocessor(
     test_spec: dict, expected: List[str], env: TestEnvironment):
   """
   Preprocessor for the wordcount_minimal.yaml test.
@@ -386,6 +542,8 @@ def _wordcount_test_preprocessor(
   of the wordcount example. This allows the test to verify the pipeline's
   correctness without relying on a fixed input file.
 
+  Based on this expected output: #  Row(word='king', count=311)
+
   Args:
     test_spec: The dictionary representation of the YAML pipeline specification.
     expected: A list of strings representing the expected output of the
@@ -401,8 +559,64 @@ def _wordcount_test_preprocessor(
     word = element.split('=')[1].split(',')[0].replace("'", '')
     count = int(element.split('=')[2].replace(')', ''))
     all_words += [word] * count
-  random.shuffle(all_words)
 
+  return _wordcount_random_shuffler(test_spec, all_words, env)
+
+
+@YamlExamplesTestSuite.register_test_preprocessor(
+    ['test_wordCountInclude_yaml', 'test_wordCountImport_yaml'])
+def _wordcount_jinja_test_preprocessor(
+    test_spec: dict, expected: List[str], env: TestEnvironment):
+  """
+  Preprocessor for the wordcount Jinja tests.
+
+  This preprocessor generates a random input file based on the expected output
+  of the wordcount example. This allows the test to verify the pipeline's
+  correctness without relying on a fixed input file.
+
+  Based on this expected output: #  Row(output='king - 311')
+
+  Args:
+    test_spec: The dictionary representation of the YAML pipeline specification.
+    expected: A list of strings representing the expected output of the
+      pipeline.
+    env: The TestEnvironment object providing utilities for creating temporary
+      files.
+
+  Returns:
+    The modified test_spec dictionary with the input file path replaced.
+  """
+  all_words = []
+  for element in expected:
+    match = re.search(r"output='(.*) - (\d+)'", element)
+    if match:
+      word, count_str = match.groups()
+      all_words += [word] * int(count_str)
+  return _wordcount_random_shuffler(test_spec, all_words, env)
+
+
+def _wordcount_random_shuffler(
+    test_spec: dict, all_words: List[str], env: TestEnvironment):
+  """
+  Helper function to create a randomized input file for wordcount-style tests.
+
+  This function takes a list of words, shuffles them, and arranges them into
+  randomly sized lines. It then creates a temporary input file with this
+  content and updates the provided test specification to use this file as
+  the input for a 'ReadFromText' transform.
+
+  Args:
+    test_spec: The dictionary representation of the YAML pipeline specification.
+    all_words: A list of strings, where each string is a word to be included
+      in the generated input file.
+    env: The TestEnvironment object providing utilities for creating temporary
+      files.
+
+  Returns:
+    The modified test_spec dictionary with the input file path for
+    'ReadFromText' replaced with the path to the newly generated file.
+  """
+  random.shuffle(all_words)
   lines = []
   while all_words:
     line_length = random.randint(1, min(10, len(all_words)))
@@ -433,6 +647,7 @@ def _kafka_test_preprocessor(
     for transform in pipeline.get('transforms', []):
       if transform.get('type', '') == 'ReadFromKafka':
         transform['type'] = 'TestReadFromKafka'
+        transform['config']['topic'] = 'test-topic'
 
   return test_spec
 
@@ -457,7 +672,14 @@ def _kafka_test_preprocessor(
     'test_pubsub_to_iceberg_yaml',
     'test_oracle_to_bigquery_yaml',
     'test_mysql_to_bigquery_yaml',
-    'test_spanner_to_bigquery_yaml'
+    'test_spanner_to_bigquery_yaml',
+    'test_streaming_sentiment_analysis_yaml',
+    'test_iceberg_migration_yaml',
+    'test_ml_preprocessing_yaml',
+    'test_anomaly_scoring_yaml',
+    'test_wordCountInclude_yaml',
+    'test_wordCountImport_yaml',
+    'test_iceberg_to_alloydb_yaml'
 ])
 def _io_write_test_preprocessor(
     test_spec: dict, expected: List[str], env: TestEnvironment):
@@ -528,7 +750,8 @@ def _file_io_read_test_preprocessor(
   return test_spec
 
 
-@YamlExamplesTestSuite.register_test_preprocessor(['test_iceberg_read_yaml'])
+@YamlExamplesTestSuite.register_test_preprocessor(
+    ['test_iceberg_read_yaml', 'test_iceberg_to_alloydb_yaml'])
 def _iceberg_io_read_test_preprocessor(
     test_spec: dict, expected: List[str], env: TestEnvironment):
   """
@@ -671,6 +894,7 @@ def _pubsub_io_read_test_preprocessor(
     for transform in pipeline.get('transforms', []):
       if transform.get('type', '') == 'ReadFromPubSub':
         transform['type'] = 'TestReadFromPubSub'
+        transform['config']['topic'] = 'test-topic'
 
   return test_spec
 
@@ -782,9 +1006,311 @@ def _db_io_read_test_processor(
   return test_spec
 
 
+@YamlExamplesTestSuite.register_test_preprocessor(
+    'test_streaming_sentiment_analysis_yaml')
+def _streaming_sentiment_analysis_test_preprocessor(
+    test_spec: dict, expected: List[str], env: TestEnvironment):
+  """
+  Preprocessor for tests that involve the streaming sentiment analysis example.
+
+  This preprocessor replaces several IO transforms and the RunInference
+  transform.
+  This allows the test to verify the pipeline's correctness without relying on
+  external data sources and the model hosted on VertexAI.
+
+  Args:
+    test_spec: The dictionary representation of the YAML pipeline specification.
+    expected: A list of strings representing the expected output of the
+      pipeline.
+    env: The TestEnvironment object providing utilities for creating temporary
+      files.
+
+  Returns:
+    The modified test_spec dictionary with ... transforms replaced.
+  """
+  if pipeline := test_spec.get('pipeline', None):
+    for transform in pipeline.get('transforms', []):
+      if transform.get('type', '') == 'PyTransform' and transform.get(
+          'name', '') == 'ReadFromGCS':
+        transform['windowing'] = {'type': 'fixed', 'size': '30s'}
+
+        file_name = 'youtube-comments.csv'
+        local_path = env.input_file(file_name, INPUT_FILES[file_name])
+        transform['config']['kwargs']['file_pattern'] = local_path
+
+  if pipeline := test_spec.get('pipeline', None):
+    for transform in pipeline.get('transforms', []):
+      if transform.get('type', '') == 'ReadFromKafka':
+        config = transform['config']
+        transform['type'] = 'ReadFromCsv'
+        transform['config'] = {
+            k: v
+            for k, v in config.items() if k.startswith('__')
+        }
+        transform['config']['path'] = ""
+
+        file_name = 'youtube-comments.csv'
+        test_spec = replace_recursive(
+            test_spec,
+            transform['type'],
+            'path',
+            env.input_file(file_name, INPUT_FILES[file_name]))
+
+  if pipeline := test_spec.get('pipeline', None):
+    for transform in pipeline.get('transforms', []):
+      if transform.get('type', '') == 'RunInference':
+        transform['type'] = 'TestRunInferenceYouTubeComments'
+
+  return test_spec
+
+
+@YamlExamplesTestSuite.register_test_preprocessor(
+    'test_streaming_taxifare_prediction_yaml')
+def _streaming_taxifare_prediction_test_preprocessor(
+    test_spec: dict, expected: List[str], env: TestEnvironment):
+  """
+  Preprocessor for tests that involve the streaming taxi fare prediction
+  example.
+
+  This preprocessor replaces several IO transforms and the RunInference
+  transform. This allows the test to verify the pipeline's correctness
+  without relying on external data sources and the model hosted on VertexAI.
+  It also turns this non-linear pipeline into a linear pipeline by replacing
+  the ReadFromKafka and WriteToKafka transforms with MapToFields and linking
+  the two disconnected pipeline components together. The pipeline logic,
+  however, remains the same and is still being tested accordingly.
+
+  Args:
+    test_spec: The dictionary representation of the YAML pipeline specification.
+    expected: A list of strings representing the expected output of the
+      pipeline.
+    env: The TestEnvironment object providing utilities for creating temporary
+      files.
+
+  Returns:
+    The modified test_spec dictionary with several involved IO transforms and
+    the RunInference transform replaced.
+  """
+
+  if pipeline := test_spec.get('pipeline', None):
+    for transform in pipeline.get('transforms', []):
+      if transform.get('type', '') == 'ReadFromPubSub':
+        transform['type'] = 'TestReadFromPubSub'
+        transform['config']['topic'] = 'taxi-ride-topic'
+
+      elif transform.get('type', '') == 'WriteToKafka':
+        transform['type'] = 'MapToFields'
+        transform['config'] = {
+            k: v
+            for (k, v) in transform.get('config', {}).items()
+            if k.startswith('__')
+        }
+        transform['config']['fields'] = {
+            'ride_id': 'ride_id',
+            'pickup_longitude': 'pickup_longitude',
+            'pickup_latitude': 'pickup_latitude',
+            'pickup_datetime': 'pickup_datetime',
+            'dropoff_longitude': 'dropoff_longitude',
+            'dropoff_latitude': 'dropoff_latitude',
+            'passenger_count': 'passenger_count',
+        }
+
+      elif transform.get('type', '') == 'ReadFromKafka':
+        transform['type'] = 'MapToFields'
+        transform['config'] = {
+            k: v
+            for (k, v) in transform.get('config', {}).items()
+            if k.startswith('__')
+        }
+        transform['input'] = 'WriteKafka'
+        transform['config']['fields'] = {
+            'ride_id': 'ride_id',
+            'pickup_longitude': 'pickup_longitude',
+            'pickup_latitude': 'pickup_latitude',
+            'pickup_datetime': 'pickup_datetime',
+            'dropoff_longitude': 'dropoff_longitude',
+            'dropoff_latitude': 'dropoff_latitude',
+            'passenger_count': 'passenger_count',
+        }
+
+      elif transform.get('type', '') == 'WriteToBigQuery':
+        transform['type'] = 'LogForTesting'
+        transform['config'] = {
+            k: v
+            for (k, v) in transform.get('config', {}).items()
+            if (k.startswith('__') or k == 'error_handling')
+        }
+
+      elif transform.get('type', '') == 'RunInference':
+        transform['type'] = 'TestRunInferenceTaxiFare'
+
+  return test_spec
+
+
+@YamlExamplesTestSuite.register_test_preprocessor([
+    'test_iceberg_migration_yaml',
+    'test_ml_preprocessing_yaml',
+    'test_anomaly_scoring_yaml'
+])
+def _batch_log_analysis_test_preprocessor(
+    test_spec: dict, expected: List[str], env: TestEnvironment):
+  """
+  Preprocessor for tests that involve the batch log analysis example.
+
+  This preprocessor replaces several IO transforms and the MLTransform.
+  This allows the test to verify the pipeline's correctness
+  without relying on external data sources or MLTransform's many dependencies.
+
+  Args:
+    test_spec: The dictionary representation of the YAML pipeline specification.
+    expected: A list of strings representing the expected output of the
+      pipeline.
+    env: The TestEnvironment object providing utilities for creating temporary
+      files.
+
+  Returns:
+    The modified test_spec dictionary with ReadFromText transforms replaced.
+  """
+
+  if pipeline := test_spec.get('pipeline', None):
+    for transform in pipeline.get('transforms', []):
+      # Mock ReadFromCsv in iceberg_migration.yaml pipeline
+      if transform.get('type', '') == 'ReadFromCsv':
+        file_name = 'system-logs.csv'
+        local_path = env.input_file(file_name, INPUT_FILES[file_name])
+        transform['config']['path'] = local_path
+
+      # Mock ReadFromIceberg in ml_preprocessing.yaml pipeline
+      elif transform.get('type', '') == 'ReadFromIceberg':
+        transform['type'] = 'Create'
+        transform['config'] = {
+            k: v
+            for (k, v) in transform.get('config', {}).items()
+            if (k.startswith('__'))
+        }
+
+        transform['config']['elements'] = input_data.system_logs_data()
+
+      # Mock MLTransform in ml_preprocessing.yaml pipeline
+      elif transform.get('type', '') == 'MLTransform':
+        transform['type'] = 'MapToFields'
+        transform['config'] = {
+            k: v
+            for (k, v) in transform.get('config', {}).items()
+            if k.startswith('__')
+        }
+
+        transform['config']['language'] = 'python'
+        transform['config']['fields'] = {
+            'LineId': 'LineId',
+            'Date': 'Date',
+            'Time': 'Time',
+            'Level': 'Level',
+            'Process': 'Process',
+            'Component': 'Component',
+            'Content': 'Content',
+            'embedding': {
+                'callable': f"lambda row: {input_data.embedding_data()}",
+            }
+        }
+
+      # Mock MapToFields in ml_preprocessing.yaml pipeline
+      elif transform.get('type', '') == 'MapToFields' and \
+          transform.get('name', '') == 'Normalize':
+        transform['config']['dependencies'] = ['numpy']
+
+      # Mock ReadFromBigQuery in anomaly_scoring.yaml pipeline
+      elif transform.get('type', '') == 'ReadFromBigQuery':
+        transform['type'] = 'Create'
+        transform['config'] = {
+            k: v
+            for (k, v) in transform.get('config', {}).items()
+            if (k.startswith('__'))
+        }
+
+        transform['config']['elements'] = (
+            input_data.system_logs_embedding_data())
+
+      # Mock PyTransform in anomaly_scoring.yaml pipeline
+      elif transform.get('type', '') == 'PyTransform' and \
+          transform.get('name', '') == 'AnomalyScoring':
+        transform['type'] = 'MapToFields'
+        transform['config'] = {
+            k: v
+            for (k, v) in transform.get('config', {}).items()
+            if k.startswith('__')
+        }
+
+        transform['config']['language'] = 'python'
+        transform['config']['fields'] = {
+            'example': 'embedding',
+            'predictions': {
+                'callable': """lambda row: [{
+                  'score': 0.65,
+                  'label': 0,
+                  'threshold': 0.8
+              }]""",
+            }
+        }
+
+  return test_spec
+
+
+@YamlExamplesTestSuite.register_test_preprocessor(
+    ['test_wordCountInclude_yaml', 'test_wordCountImport_yaml'])
+def _jinja_preprocessor(raw_spec_string: str, test_name: str):
+  """
+  Preprocessor for Jinja-based YAML tests.
+
+  This function takes a raw YAML string, which is treated as a Jinja2
+  template, and renders it to produce the final pipeline specification.
+  It specifically handles templates that use the `{% include ... %}`
+  directive by manually loading the content of the included files from the
+  filesystem.
+
+  The Jinja variables required for rendering are loaded from a predefined
+  data source.
+
+  Args:
+    raw_spec_string: A string containing the raw YAML content, which is a
+      Jinja2 template.
+
+  Returns:
+    A string containing the fully rendered YAML pipeline specification.
+  """
+  jinja_variables = json.loads(input_data.word_count_jinja_parameter_data())
+  test_file_dir = os.path.dirname(__file__)
+  sdk_root = os.path.abspath(os.path.join(test_file_dir, '../../../..'))
+
+  include_files = input_data.word_count_jinja_template_data(test_name)
+  mock_templates = {'main_template': raw_spec_string}
+  for file_path in include_files:
+    full_path = os.path.join(sdk_root, file_path)
+    with open(full_path, 'r', encoding='utf-8') as f:
+      mock_templates[file_path] = f.read()
+
+  # Can't use the standard expand_jinja method due to it not supporting
+  # `% include` jinja templization.
+  # TODO(#35936): Maybe update expand_jinja to handle this case.
+  jinja_env = Environment(
+      loader=DictLoader(mock_templates), undefined=StrictUndefined)
+  template = jinja_env.get_template('main_template')
+  rendered_yaml_string = template.render(jinja_variables)
+  return rendered_yaml_string
+
+
 INPUT_FILES = {
     'products.csv': input_data.products_csv(),
-    'kinglear.txt': input_data.text_data()
+    'kinglear.txt': input_data.text_data(),
+    'youtube-comments.csv': input_data.youtube_comments_csv(),
+    'system-logs.csv': input_data.system_logs_csv()
+}
+
+KAFKA_TOPICS = {'test-topic': input_data.kafka_messages_data()}
+
+PUBSUB_TOPICS = {
+    'test-topic': input_data.pubsub_messages_data(),
+    'taxi-ride-topic': input_data.pubsub_taxi_ride_events_data()
 }
 
 INPUT_TABLES = {
@@ -814,12 +1340,15 @@ def _db_io_read_test_processor(
     os.path.join(YAML_DOCS_DIR, '../transforms/elementwise/*.yaml')).run()
 ExamplesTest = YamlExamplesTestSuite(
     'ExamplesTest', os.path.join(YAML_DOCS_DIR, '../*.yaml')).run()
+JinjaTest = YamlExamplesTestSuite(
+    'JinjaExamplesTest',
+    os.path.join(YAML_DOCS_DIR, '../transforms/jinja/**/*.yaml')).run()
 IOTest = YamlExamplesTestSuite(
     'IOExamplesTest', os.path.join(YAML_DOCS_DIR,
                                    '../transforms/io/*.yaml')).run()
 MLTest = YamlExamplesTestSuite(
     'MLExamplesTest', os.path.join(YAML_DOCS_DIR,
-                                   '../transforms/ml/*.yaml')).run()
+                                   '../transforms/ml/**/*.yaml')).run()
 
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
diff --git a/sdks/python/apache_beam/yaml/examples/testing/input_data.py b/sdks/python/apache_beam/yaml/examples/testing/input_data.py
index 0c601f678168..fb468567355d 100644
--- a/sdks/python/apache_beam/yaml/examples/testing/input_data.py
+++ b/sdks/python/apache_beam/yaml/examples/testing/input_data.py
@@ -16,6 +16,9 @@
 # limitations under the License.
 #
 
+import json
+import typing
+
 from apache_beam.io.gcp.pubsub import PubsubMessage
 
 # This file contains the input data to be requested by the example tests, if
@@ -31,6 +34,61 @@ def text_data():
   ])
 
 
+def word_count_jinja_parameter_data():
+  params = {
+      "readFromTextTransform": {
+          "path": "gs://dataflow-samples/shakespeare/kinglear.txt"
+      },
+      "mapToFieldsSplitConfig": {
+          "language": "python", "fields": {
+              "value": "1"
+          }
+      },
+      "explodeTransform": {
+          "fields": "word"
+      },
+      "combineTransform": {
+          "group_by": "word", "combine": {
+              "value": "sum"
+          }
+      },
+      "mapToFieldsCountConfig": {
+          "language": "python",
+          "fields": {
+              "output": "word + \" - \" + str(value)"
+          }
+      },
+      "writeToTextTransform": {
+          "path": "gs://apache-beam-testing-derrickaw/wordCounts/"
+      }
+  }
+  return json.dumps(params)
+
+
+def word_count_jinja_template_data(test_name: str) -> list[str]:
+  if test_name == 'test_wordCountInclude_yaml':
+    return [
+        'apache_beam/yaml/examples/transforms/jinja/'
+        'include/submodules/readFromTextTransform.yaml',
+        'apache_beam/yaml/examples/transforms/jinja/'
+        'include/submodules/mapToFieldsSplitConfig.yaml',
+        'apache_beam/yaml/examples/transforms/jinja/'
+        'include/submodules/explodeTransform.yaml',
+        'apache_beam/yaml/examples/transforms/jinja/'
+        'include/submodules/combineTransform.yaml',
+        'apache_beam/yaml/examples/transforms/jinja/'
+        'include/submodules/mapToFieldsCountConfig.yaml',
+        'apache_beam/yaml/examples/transforms/jinja/'
+        'include/submodules/writeToTextTransform.yaml'
+    ]
+  elif test_name == 'test_wordCountImport_yaml':
+    return [
+        'apache_beam/yaml/examples/transforms/jinja/'
+        'import/macros/wordCountMacros.yaml'
+    ]
+  return []
+
+
 def iceberg_dynamic_destinations_users_data():
   return [{
       'id': 3, 'name': 'Smith', 'email': 'smith@example.com', 'zip': 'NY'
@@ -54,6 +112,15 @@ def products_csv():
   ])
 
 
+def youtube_comments_csv():
+  return '\n'.join([
+      'video_id,comment_text,likes,replies',
+      'XpVt6Z1Gjjo,I AM HAPPY,1,1',
+      'XpVt6Z1Gjjo,I AM SAD,1,1',
+      'XpVt6Z1Gjjo,§ÁĐ,1,1'
+  ])
+
+
 def spanner_orders_data():
   return [{
       'order_id': 1,
@@ -177,3 +244,111 @@ def pubsub_messages_data():
       PubsubMessage(data=b"{\"label\": \"37c\", \"rank\": 3}", attributes={}),
       PubsubMessage(data=b"{\"label\": \"37d\", \"rank\": 2}", attributes={}),
   ]
+
+
+def pubsub_taxi_ride_events_data():
+  """
+  Provides a list of PubsubMessage objects for testing taxi ride events.
+  """
+  return [
+      PubsubMessage(
+          data=b"{\"ride_id\": \"1\", \"longitude\": 11.0, \"latitude\": -11.0,"
+          b"\"passenger_count\": 1, \"meter_reading\": 100.0, \"timestamp\": "
+          b"\"2025-01-01T00:29:00.00000-04:00\", \"ride_status\": \"pickup\"}",
+          attributes={}),
+      PubsubMessage(
+          data=b"{\"ride_id\": \"2\", \"longitude\": 22.0, \"latitude\": -22.0,"
+          b"\"passenger_count\": 2, \"meter_reading\": 100.0, \"timestamp\": "
+          b"\"2025-01-01T00:30:00.00000-04:00\", \"ride_status\": \"pickup\"}",
+          attributes={}),
+      PubsubMessage(
+          data=b"{\"ride_id\": \"1\", \"longitude\": 13.0, \"latitude\": -13.0,"
+          b"\"passenger_count\": 1, \"meter_reading\": 100.0, \"timestamp\": "
+          b"\"2025-01-01T00:31:00.00000-04:00\", \"ride_status\": \"enroute\"}",
+          attributes={}),
+      PubsubMessage(
+          data=b"{\"ride_id\": \"2\", \"longitude\": 24.0, \"latitude\": -24.0,"
+          b"\"passenger_count\": 2, \"meter_reading\": 100.0, \"timestamp\": "
+          b"\"2025-01-01T00:32:00.00000-04:00\", \"ride_status\": \"enroute\"}",
+          attributes={}),
+      PubsubMessage(
+          data=b"{\"ride_id\": \"3\", \"longitude\": 33.0, \"latitude\": -33.0,"
+          b"\"passenger_count\": 3, \"meter_reading\": 100.0, \"timestamp\": "
+          b"\"2025-01-01T00:35:00.00000-04:00\", \"ride_status\": \"enroute\"}",
+          attributes={}),
+      PubsubMessage(
+          data=b"{\"ride_id\": \"4\", \"longitude\": 44.0, \"latitude\": -44.0,"
+          b"\"passenger_count\": 4, \"meter_reading\": 100.0, \"timestamp\": "
+          b"\"2025-01-01T00:35:00.00000-04:00\", \"ride_status\": \"dropoff\"}",
+          attributes={}),
+      PubsubMessage(
+          data=b"{\"ride_id\": \"1\", \"longitude\": 15.0, \"latitude\": -15.0,"
+          b"\"passenger_count\": 1, \"meter_reading\": 100.0, \"timestamp\": "
+          b"\"2025-01-01T00:33:00.00000-04:00\", \"ride_status\": \"dropoff\"}",
+          attributes={}),
+      PubsubMessage(
+          data=b"{\"ride_id\": \"2\", \"longitude\": 26.0, \"latitude\": -26.0,"
+          b"\"passenger_count\": 2, \"meter_reading\": 100.0, \"timestamp\": "
+          b"\"2025-01-01T00:34:00.00000-04:00\", \"ride_status\": \"dropoff\"}",
+          attributes={}),
+  ]
+
+
+def kafka_messages_data():
+  """
+  Provides a list of Kafka messages for testing.
+  """
+  return [data.encode('utf-8') for data in text_data().split('\n')]
+
+
+class TaxiRideEventSchema(typing.NamedTuple):
+  ride_id: str
+  longitude: float
+  latitude: float
+  passenger_count: int
+  meter_reading: float
+  timestamp: str
+  ride_status: str
+
+
+def system_logs_csv():
+  return '\n'.join([
+      'LineId,Date,Time,Level,Process,Component,Content',
+      '1,2024-10-01,12:00:00,INFO,Main,ComponentA,System started successfully',
+      '2,2024-10-01,12:00:05,WARN,Main,ComponentA,Memory usage is high',
+      '3,2024-10-01,12:00:10,ERROR,Main,ComponentA,Task failed due to timeout',
+  ])
+
+
+def system_logs_data():
+  csv_data = system_logs_csv()
+  lines = csv_data.strip().split('\n')
+  headers = lines[0].split(',')
+  logs = []
+  for row in lines[1:]:
+    values = row.split(',')
+    log = dict(zip(headers, values))
+    log['LineId'] = int(log['LineId'])
+    logs.append(log)
+
+  return logs
+
+
+def embedding_data():
+  return [0.1, 0.2, 0.3, 0.4, 0.5]
+
+
+def system_logs_embedding_data():
+  csv_data = system_logs_csv()
+  lines = csv_data.strip().split('\n')
+  headers = lines[0].split(',')
+  headers.append('embedding')
+  logs = []
+  for row in lines[1:]:
+    values = row.split(',')
+    values.append(embedding_data())
+    log = dict(zip(headers, values))
+    log['LineId'] = int(log['LineId'])
+    logs.append(log)
+
+  return logs
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/blueprint/iceberg_to_alloydb.yaml b/sdks/python/apache_beam/yaml/examples/transforms/blueprint/iceberg_to_alloydb.yaml
new file mode 100644
index 000000000000..dc4a887fea13
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/blueprint/iceberg_to_alloydb.yaml
@@ -0,0 +1,51 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# A pipeline that reads from iceberg table and writes to AlloyDB.
+
+pipeline:
+  type: chain
+  transforms:
+    # Step 1: Reading data from Iceberg
+    - type: ReadFromIceberg
+      name: ReadFromIcebergTable
+      config:
+        table: "db.users.NY"
+        catalog_name: "hadoop_catalog"
+        catalog_properties:
+          type: "hadoop"
+          warehouse: "gs://MY-WAREHOUSE"
+        # Hadoop catalog config required to run pipeline locally
+        # Omit if running on Dataflow
+        config_properties:
+          "fs.gs.auth.type": "SERVICE_ACCOUNT_JSON_KEYFILE"
+          "fs.gs.auth.service.account.json.keyfile": "/path/to/service/account/key.json"
+
+    # Step 2: Write records out to AlloyDB
+    # To run pipeline locally, Modify alloydbIpType to PUBLIC
+    - type: WriteToJdbc
+      name: WriteToAlloyDBTable
+      config:
+        location: "users"
+        driver_class_name: "org.postgresql.Driver"
+        jdbc_url: "jdbc:postgresql:///db?socketFactory=com.google.cloud.alloydb.SocketFactory&alloydbInstanceName=projects/<PROJECT>/locations/<REGION>/clusters/<CLUSTER>/instances/<INSTANCE>&alloydbIpType=PRIVATE"
+        username: "<YOUR_USERNAME>"
+        password: "<YOUR_PASSWORD>"
+
+# Expected:
+#  Row(id=3, name='Smith', email='smith@example.com', zip='NY')
+#  Row(id=4, name='Beamberg', email='beamberg@example.com', zip='NY')
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/blueprint/pubsub_to_iceberg.yaml b/sdks/python/apache_beam/yaml/examples/transforms/blueprint/pubsub_to_iceberg.yaml
index 95be4c29a6d5..5292d4e0857a 100644
--- a/sdks/python/apache_beam/yaml/examples/transforms/blueprint/pubsub_to_iceberg.yaml
+++ b/sdks/python/apache_beam/yaml/examples/transforms/blueprint/pubsub_to_iceberg.yaml
@@ -16,7 +16,7 @@
 # limitations under the License.
 #
 
-# A pipeline that both writes to and reads from the same Kafka topic.
+# A pipeline that reads from pubsub topic and writes to Iceberg table.
 
 pipeline:
   type: chain
@@ -27,7 +27,7 @@ pipeline:
       config:
         topic: "projects/apache-beam-testing/topics/my-topic"
         format: JSON
-        schema: 
+        schema:
           type: object
           properties:
             data: {type: BYTES}
@@ -39,6 +39,7 @@ pipeline:
         # Dynamic destinations
         table: "db.users.{zip}"
         catalog_name: "hadoop_catalog"
+        triggering_frequency_seconds: "20"
         catalog_properties:
           type: "hadoop"
           warehouse: "gs://MY-WAREHOUSE"
@@ -56,4 +57,3 @@ options:
 #  Row(label='37b', rank=4)
 #  Row(label='37c', rank=3)
 #  Row(label='37d', rank=2)
-
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/elementwise/regex_matches.yaml b/sdks/python/apache_beam/yaml/examples/transforms/elementwise/regex_matches.yaml
index e5db92e54560..bd01ca1318cb 100644
--- a/sdks/python/apache_beam/yaml/examples/transforms/elementwise/regex_matches.yaml
+++ b/sdks/python/apache_beam/yaml/examples/transforms/elementwise/regex_matches.yaml
@@ -16,7 +16,7 @@
 # limitations under the License.
 #
 
-# This pipline creates a series of {plant: description} key pairs, matches all
+# This pipeline creates a series of {plant: description} key pairs, matches all
 # elements to a valid regex, filters out non-matching entries, then logs the
 # output.
 pipeline:
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/jinja/import/README.md b/sdks/python/apache_beam/yaml/examples/transforms/jinja/import/README.md
new file mode 100644
index 000000000000..d705e90b2db5
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/jinja/import/README.md
@@ -0,0 +1,69 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+## Jinja % import Pipeline
+
+This example leverages the `% import` Jinja directive by having one main
+pipeline and then one macros file containing all the transforms and configs
+used.
+
+General setup:
+```sh
+export PIPELINE_FILE=apache_beam/yaml/examples/transforms/jinja/import/wordCountImport.yaml
+export KINGLEAR="gs://dataflow-samples/shakespeare/kinglear.txt"
+export TEMP_LOCATION="gs://MY-BUCKET/wordCounts/"
+export PROJECT="MY-PROJECT"
+export REGION="MY-REGION"
+
+cd <PATH_TO_BEAM_REPO>/beam/sdks/python
+```
+
+Multiline Run Example:
+```sh
+python -m apache_beam.yaml.main \
+  --project=${PROJECT} \
+  --region=${REGION} \
+  --yaml_pipeline_file="${PIPELINE_FILE}" \
+  --jinja_variables='{
+    "readFromTextTransform": {"path": "'"${KINGLEAR}"'"},
+    "mapToFieldsSplitConfig": {
+      "language": "python",
+      "fields": {
+        "value": "1"
+      }
+    },
+    "explodeTransform": {"fields": "word"},
+    "combineTransform": {
+      "group_by": "word",
+      "combine": {"value": "sum"}
+    },
+    "mapToFieldsCountConfig": {
+      "language": "python",
+      "fields": {"output": "word + \" - \" + str(value)"}
+    },
+    "writeToTextTransform": {"path": "'"${TEMP_LOCATION}"'"}
+  }'
+```
+
+Single Line Run Example:
+```sh
+python -m apache_beam.yaml.main --project=${PROJECT} --region=${REGION} \
+--yaml_pipeline_file="${PIPELINE_FILE}" --jinja_variables='{"readFromTextTransform":
+{"path": "'"${KINGLEAR}"'"}, "mapToFieldsSplitConfig": {"language": "python", "fields":{"value":"1"}}, "explodeTransform":{"fields":"word"}, "combineTransform":{"group_by":"word", "combine":{"value":"sum"}}, "mapToFieldsCountConfig":{"language": "python", "fields":{"output":"word + \" - \" + str(value)"}}, "writeToTextTransform":{"path":"'"${TEMP_LOCATION}"'"}}'
+```
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/jinja/import/macros/wordCountMacros.yaml b/sdks/python/apache_beam/yaml/examples/transforms/jinja/import/macros/wordCountMacros.yaml
new file mode 100644
index 000000000000..b3870693ef5f
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/jinja/import/macros/wordCountMacros.yaml
@@ -0,0 +1,64 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+{%- macro readFromTextTransform(params) -%}
+
+- name: Read from GCS
+  type: ReadFromText
+  config:
+    path: "{{ params.path }}"
+{%- endmacro -%}
+
+{%- macro mapToFieldsSplitConfig(params) -%}
+language: "{{ params.language }}"
+fields:
+  value: "{{ params.fields.value }}"
+  word:
+    callable: |-
+      import re
+      def my_mapping(row):
+        return re.findall(r'[A-Za-z\']+', row.line.lower())
+{%- endmacro -%}
+
+{%- macro explodeTransform(params) -%}
+- name: Explode word arrays
+  type: Explode
+  config:
+    fields: "{{ params.fields }}"
+{%- endmacro -%}
+
+{%- macro combineTransform(params) -%}
+- name: Count words
+  type: Combine
+  config:
+    group_by: "{{ params.group_by }}"
+    combine:
+      value: "{{ params.combine.value }}"
+{%- endmacro -%}
+
+{%- macro mapToFieldsCountConfig(params) -%}
+language: "{{ params.language }}"
+fields:
+  output: '{{ params.fields.output }}'
+{%- endmacro -%}
+
+{%- macro writeToTextTransform(params) -%}
+- name: Write to GCS
+  type: WriteToText
+  config:
+    path: "{{ params.path }}"
+{%- endmacro -%}
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/jinja/import/wordCountImport.yaml b/sdks/python/apache_beam/yaml/examples/transforms/jinja/import/wordCountImport.yaml
new file mode 100644
index 000000000000..1058a30b607a
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/jinja/import/wordCountImport.yaml
@@ -0,0 +1,69 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This examples reads from a public file stored on Google Cloud. This
+# requires authenticating with Google Cloud, or setting the file in
+#`ReadFromText` to a local file.
+#
+# To set up Application Default Credentials,
+# see https://cloud.google.com/docs/authentication/external/set-up-adc.
+#
+# This pipeline reads in a text file, counts distinct words found in the text,
+# then logs a row containing each word and its count.
+
+{% import 'apache_beam/yaml/examples/transforms/jinja/import/macros/wordCountMacros.yaml' as macros %}
+
+pipeline:
+  type: chain
+  transforms:
+
+# Read in text file
+{{ macros.readFromTextTransform(readFromTextTransform) | indent(4, true) }}
+
+# Split words and count occurrences
+    - name: Split words
+      type: MapToFields
+      config:
+{{ macros.mapToFieldsSplitConfig(mapToFieldsSplitConfig) | indent(8, true) }}
+
+# Explode into individual words
+{{ macros.explodeTransform(explodeTransform) | indent(4, true) }}
+
+# Group by word
+{{ macros.combineTransform(combineTransform) | indent(4, true) }}
+
+# Format output to a single string consisting of `word - count`
+    - name: Format output
+      type: MapToFields
+      config:
+{{ macros.mapToFieldsCountConfig(mapToFieldsCountConfig) | indent(8, true) }}
+
+# Write to text file on GCS, locally, etc
+{{ macros.writeToTextTransform(writeToTextTransform) | indent(4, true) }}
+
+# Expected:
+#  Row(output='king - 311')
+#  Row(output='lear - 253')
+#  Row(output='dramatis - 1')
+#  Row(output='personae - 1')
+#  Row(output='of - 483')
+#  Row(output='britain - 2')
+#  Row(output='france - 32')
+#  Row(output='duke - 26')
+#  Row(output='burgundy - 20')
+#  Row(output='cornwall - 75')
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/README.md b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/README.md
new file mode 100644
index 000000000000..e4e39e7193c4
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/README.md
@@ -0,0 +1,69 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+## Jinja % include Pipeline
+
+This example leverages the `% include` Jinja directive by having one main
+pipeline and then submodules for each transformed used.
+
+General setup:
+```sh
+export PIPELINE_FILE=apache_beam/yaml/examples/transforms/jinja/include/wordCountInclude.yaml
+export KINGLEAR="gs://dataflow-samples/shakespeare/kinglear.txt"
+export TEMP_LOCATION="gs://MY-BUCKET/wordCounts/"
+export PROJECT="MY-PROJECT"
+export REGION="MY-REGION"
+
+cd <PATH_TO_BEAM_REPO>/beam/sdks/python
+```
+
+Multiline Run Example:
+```sh
+python -m apache_beam.yaml.main \
+  --project=${PROJECT} \
+  --region=${REGION} \
+  --yaml_pipeline_file="${PIPELINE_FILE}" \
+  --jinja_variables='{
+    "readFromTextTransform": {"path": "'"${KINGLEAR}"'"},
+    "mapToFieldsSplitConfig": {
+      "language": "python",
+      "fields": {
+        "value": "1"
+      }
+    },
+    "explodeTransform": {"fields": "word"},
+    "combineTransform": {
+      "group_by": "word",
+      "combine": {"value": "sum"}
+    },
+    "mapToFieldsCountConfig": {
+      "language": "python",
+      "fields": {"output": "word + \" - \" + str(value)"}
+    },
+    "writeToTextTransform": {"path": "'"${TEMP_LOCATION}"'"}
+  }'
+```
+
+Single Line Run Example:
+```sh
+python -m apache_beam.yaml.main --project=${PROJECT} --region=${REGION} \
+--yaml_pipeline_file="${PIPELINE_FILE}" --jinja_variables='{"readFromTextTransform":
+{"path": "'"${KINGLEAR}"'"}, "mapToFieldsSplitConfig": {"language": "python", "fields":{"value":"1"}}, "explodeTransform":{"fields":"word"}, "combineTransform":{"group_by":"word", "combine":{"value":"sum"}}, "mapToFieldsCountConfig":{"language": "python", "fields":{"output":"word + \" - \" + str(value)"}}, "writeToTextTransform":{"path":"'"${TEMP_LOCATION}"'"}}'
+```
+
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/combineTransform.yaml b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/combineTransform.yaml
new file mode 100644
index 000000000000..bbf813558180
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/combineTransform.yaml
@@ -0,0 +1,27 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+#
+# The Combine transform groups by each word and count the occurrences.
+
+    - name: Count words
+      type: Combine
+      config:
+        group_by:
+          - {{combineTransform.group_by}}
+        combine:
+          value: {{combineTransform.combine.value}}
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/explodeTransform.yaml b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/explodeTransform.yaml
new file mode 100644
index 000000000000..d56649c5b9d3
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/explodeTransform.yaml
@@ -0,0 +1,26 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# The Explode transform to take arrays of words and emit each word as a
+# separate element.
+
+    - name: Explode word arrays
+      type: Explode
+      config:
+        fields:
+          - {{explodeTransform.fields}}
\ No newline at end of file
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/mapToFieldsCountConfig.yaml b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/mapToFieldsCountConfig.yaml
new file mode 100644
index 000000000000..f1423895c41e
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/mapToFieldsCountConfig.yaml
@@ -0,0 +1,24 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# A generic MapToFields transform to format the word and count into a single
+# output string.
+
+        language: {{mapToFieldsCountConfig.language}}
+        fields:
+          output: {{mapToFieldsCountConfig.fields.output}}
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/mapToFieldsSplitConfig.yaml b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/mapToFieldsSplitConfig.yaml
new file mode 100644
index 000000000000..16c41110b926
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/mapToFieldsSplitConfig.yaml
@@ -0,0 +1,33 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
+# A MapToFields transform to map words to a word and a count of 1.
+
+        language: {{mapToFieldsSplitConfig.language}}
+        fields:
+          word:
+            callable: |-
+              # TODO(#35936): Including another file here works fine, but if
+              # the file has a license header or other irrevalent comments, it
+              # will break the pipeline. Need to investigate more on Jinja
+              # filtering in the expand_jinja method or some other way.
+              import re
+              def my_mapping(row):
+                return re.findall(r'[A-Za-z\']+', row.line.lower())
+          value: {{mapToFieldsSplitConfig.fields.value}}
\ No newline at end of file
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/readFromTextTransform.yaml b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/readFromTextTransform.yaml
new file mode 100644
index 000000000000..96029c380683
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/readFromTextTransform.yaml
@@ -0,0 +1,26 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This examples reads from a public file stored on Google Cloud. This
+# requires authenticating with Google Cloud, or setting the file in
+#`ReadFromText` to a local file.
+
+    - name: Read from GCS
+      type: ReadFromText
+      config:
+        path: {{readFromTextTransform.path}}
\ No newline at end of file
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/writeToTextTransform.yaml b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/writeToTextTransform.yaml
new file mode 100644
index 000000000000..6cce90e7a486
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/submodules/writeToTextTransform.yaml
@@ -0,0 +1,27 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#
+# This examples writes to a public file stored on Google Cloud. This
+# requires authenticating with Google Cloud, or setting the file in
+#`WriteToText` to a local file.
+
+    - name: Write to GCS
+      type: WriteToText
+      config:
+        path: {{writeToTextTransform.path}}
\ No newline at end of file
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/wordCountInclude.yaml b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/wordCountInclude.yaml
new file mode 100644
index 000000000000..a28ba688b2f0
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/jinja/include/wordCountInclude.yaml
@@ -0,0 +1,66 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This examples reads from a public file stored on Google Cloud. This
+# requires authenticating with Google Cloud, or setting the file in
+#`ReadFromText` to a local file.
+#
+# To set up Application Default Credentials,
+# see https://cloud.google.com/docs/authentication/external/set-up-adc.
+#
+# This pipeline reads in a text file, counts distinct words found in the text,
+# then logs a row containing each word and its count.
+
+pipeline:
+  type: chain
+  transforms:
+# Read in text file
+{% include 'apache_beam/yaml/examples/transforms/jinja/include/submodules/readFromTextTransform.yaml' %}
+
+# Split words and count occurrences
+    - name: Split words
+      type: MapToFields
+      config:
+{% include 'apache_beam/yaml/examples/transforms/jinja/include/submodules/mapToFieldsSplitConfig.yaml' %}
+
+# Explode into individual words
+{% include 'apache_beam/yaml/examples/transforms/jinja/include/submodules/explodeTransform.yaml' %}
+
+# Group by word
+{% include 'apache_beam/yaml/examples/transforms/jinja/include/submodules/combineTransform.yaml' %}
+
+# Format output to a single string consisting of `word - count`
+    - name: Format output
+      type: MapToFields
+      config:
+{% include 'apache_beam/yaml/examples/transforms/jinja/include/submodules/mapToFieldsCountConfig.yaml' %}
+
+# Write to text file on GCS, locally, etc
+{% include 'apache_beam/yaml/examples/transforms/jinja/include/submodules/writeToTextTransform.yaml' %}
+
+# Expected:
+#  Row(output='king - 311')
+#  Row(output='lear - 253')
+#  Row(output='dramatis - 1')
+#  Row(output='personae - 1')
+#  Row(output='of - 483')
+#  Row(output='britain - 2')
+#  Row(output='france - 32')
+#  Row(output='duke - 26')
+#  Row(output='burgundy - 20')
+#  Row(output='cornwall - 75')
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection/README.md b/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection/README.md
new file mode 100644
index 000000000000..ff8f7044ea73
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection/README.md
@@ -0,0 +1,28 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+# Fraud Detection MLOps - Feature Engineering and Model Evaluation
+
+This example demonstrates a Fraud Detection MLOps solution that uses Apache Beam
+YAML SDK for feature engineering and model evaluation to detect transaction
+frauds.
+
+The entire workflow is self-contained in the
+[fraud_detection_mlops_beam_yaml_sdk](./fraud_detection_mlops_beam_yaml_sdk.ipynb)
+notebook.
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection/fraud_detection_mlops_beam_yaml_sdk.ipynb b/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection/fraud_detection_mlops_beam_yaml_sdk.ipynb
new file mode 100644
index 000000000000..017de31d5ca3
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection/fraud_detection_mlops_beam_yaml_sdk.ipynb
@@ -0,0 +1,1329 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "source": [
+    "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+    "\n",
+    "# Licensed to the Apache Software Foundation (ASF) under one\n",
+    "# or more contributor license agreements. See the NOTICE file\n",
+    "# distributed with this work for additional information\n",
+    "# regarding copyright ownership. The ASF licenses this file\n",
+    "# to you under the Apache License, Version 2.0 (the\n",
+    "# \"License\"); you may not use this file except in compliance\n",
+    "# with the License. You may obtain a copy of the License at\n",
+    "#\n",
+    "#   http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing,\n",
+    "# software distributed under the License is distributed on an\n",
+    "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+    "# KIND, either express or implied. See the License for the\n",
+    "# specific language governing permissions and limitations\n",
+    "# under the License"
+   ],
+   "metadata": {
+    "cellView": "form",
+    "id": "FW7MQcQeZJh5"
+   },
+   "id": "FW7MQcQeZJh5",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Fraud Detection MLOps - Feature Engineering and Model Evaluation\n",
+    "\n",
+    "<table><tbody><tr>\n",
+    "  <td style=\"text-align: center\">\n",
+    "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2Fapache%2Fbeam%2Frefs%2Fheads%2Fmaster%2Fsdks%2Fpython%2Fapache_beam%2Fyaml%2Fexamples%2Ftransforms%2Fml%2Ffraud_detection%2Ffraud_detection_mlops_beam_yaml_sdk.ipynb\">\n",
+    "      <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
+    "    </a>\n",
+    "  </td>\n",
+    "  <td style=\"text-align: center\">\n",
+    "    <a href=\"https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection/fraud_detection_mlops_beam_yaml_sdk.ipynb\">\n",
+    "      <img alt=\"GitHub logo\" src=\"https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png\" width=\"32px\"><br> View on GitHub\n",
+    "    </a>\n",
+    "  </td>\n",
+    "</tr></tbody></table>\n"
+   ],
+   "metadata": {
+    "id": "MqQferBbaD4E"
+   },
+   "id": "MqQferBbaD4E"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Overview\n",
+    "\n",
+    "This notebook demonstrates a Fraud Detection MLOps solution that uses Apache\n",
+    "Beam YAML SDK for feature engineering and model evaluation to detect transaction frauds.\n",
+    "\n",
+    "The dataset is generated with credit card transactions, and can be found on Kaggle https://www.kaggle.com/datasets/kartik2112/fraud-detection.\n",
+    "\n",
+    "The training dataset will be stored as Iceberg tables on GCS object storage for feature engineering workflows in Beam. Once the feature generation task is done and the features are stored on Iceberg, they will then be downloaded for training and for model evaluation.\n",
+    "\n",
+    "The training dataset is primarily used in this example, but the workflow makes use of Jinja [templatization](https://beam.apache.org/documentation/sdks/yaml/#jinja-templatization) in YAML pipelines that makes it modular and extensible to be used with additional datasets.\n",
+    "\n",
+    "## Outline\n",
+    "1. Setup\n",
+    "2. From dataset to Iceberg tables\n",
+    "3. Feature engineering\n",
+    "4. Training\n",
+    "5. Evaluation"
+   ],
+   "metadata": {
+    "id": "xUXaoZBaaE0g"
+   },
+   "id": "xUXaoZBaaE0g"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Setup"
+   ],
+   "metadata": {
+    "id": "7ujq4XdW90yT"
+   },
+   "id": "7ujq4XdW90yT"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Install the necessary libraries and dependencies."
+   ],
+   "metadata": {
+    "id": "I8FYju0zKZs-"
+   },
+   "id": "I8FYju0zKZs-"
+  },
+  {
+   "cell_type": "code",
+   "id": "iK2YmbXZB1UYEsDb6zP6qxMQ",
+   "metadata": {
+    "tags": [],
+    "id": "iK2YmbXZB1UYEsDb6zP6qxMQ"
+   },
+   "source": [
+    "!pip3 install --quiet --upgrade \\\n",
+    "  apache-beam[yaml,gcp] \\\n",
+    "  opendatasets \\\n",
+    "  scikit-learn \\\n",
+    "  xgboost \\\n",
+    "  datatable \\\n",
+    "  pandas \\\n",
+    "  poetry\n",
+    "\n",
+    "!apt-get update\n",
+    "!apt-get install python3.10-venv"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "import opendatasets as od\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from xgboost import XGBClassifier"
+   ],
+   "metadata": {
+    "id": "LW3IUMtceN_X"
+   },
+   "id": "LW3IUMtceN_X",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Setting up directories and environment variables."
+   ],
+   "metadata": {
+    "id": "TVW4Q9cYKk1H"
+   },
+   "id": "TVW4Q9cYKk1H"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!mkdir yaml\n",
+    "!mkdir dataset\n",
+    "\n",
+    "PROJECT = 'apache-beam-testing' # @param {type:'string'}\n",
+    "REGION = 'us-central1' # @param {type:'string'}\n",
+    "WAREHOUSE = 'gs://apache-beam-testing-charlesng/mlops' # @param {type:'string'}\n",
+    "\n",
+    "os.environ['PROJECT'] = PROJECT\n",
+    "os.environ['REGION'] = REGION\n",
+    "os.environ['WAREHOUSE'] = WAREHOUSE"
+   ],
+   "metadata": {
+    "id": "H-OhqtW9pFV0"
+   },
+   "id": "H-OhqtW9pFV0",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The feature engineering task and model evaluation necessitate implementing custom PTransforms. We will make use of [transform provider](https://beam.apache.org/documentation/sdks/yaml-providers/) to expose transforms in Python that can be used in a YAML pipeline.\n",
+    "\n",
+    "[Poetry](https://python-poetry.org/) and the following `pyproject.toml` file are used to manage dependencies, build and package the custom PTransform implementation reside in `my_provider.py`."
+   ],
+   "metadata": {
+    "id": "KKlPDSaRKtcq"
+   },
+   "id": "KKlPDSaRKtcq"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile ./yaml/pyproject.toml\n",
+    "\n",
+    "[tool.poetry]\n",
+    "name = \"my_provider\"\n",
+    "version = \"0.1.0\"\n",
+    "description = \"A provider for custom transforms\"\n",
+    "authors = [\"Your Name <you@example.com>\"]\n",
+    "license = \"Apache License 2.0\"\n",
+    "packages = [\n",
+    "    { include = \"my_provider.py\" },\n",
+    "]\n",
+    "\n",
+    "[tool.poetry.dependencies]\n",
+    "python = \"^3.10\"\n",
+    "apache-beam = {extras = [\"gcp\", \"yaml\"], version = \"^2.67.0\"}\n",
+    "scikit-learn = \"^1.7.0\"\n",
+    "numpy = \"^1.26.0\"\n",
+    "pandas = \"^2.2.0\"\n",
+    "xgboost = \"^3.0.0\"\n",
+    "datatable = \"^1.1.0\"\n",
+    "\n",
+    "[build-system]\n",
+    "requires = [\"poetry-core\"]\n",
+    "build-backend = \"poetry.core.masonry.api\""
+   ],
+   "metadata": {
+    "id": "vwaQQsR_yAJ1"
+   },
+   "id": "vwaQQsR_yAJ1",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## From dataset to Iceberg tables"
+   ],
+   "metadata": {
+    "id": "Erzyjw8SmmVH"
+   },
+   "id": "Erzyjw8SmmVH"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We use the `opendatasets` library to programmatically download the dataset from Kaggle.\n",
+    "\n",
+    "We'll first need a Kaggle account and register for this competition. We'll also need the API key which is stored in `kaggle.json` file automatically downloaded when you create an API token. Go to *Profile* picture -> *Settings* -> *API* -> *Create New Token*.\n",
+    "\n",
+    "The dataset download will prompt you to enter your Kaggle username and key. Copy this information from `kaggle.json`."
+   ],
+   "metadata": {
+    "id": "pHES8YV1ORhC"
+   },
+   "id": "pHES8YV1ORhC"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "dataset_url = 'https://www.kaggle.com/datasets/kartik2112/fraud-detection'\n",
+    "od.download(dataset_url, data_dir='./dataset')"
+   ],
+   "metadata": {
+    "id": "ekTCsIj-eOJt"
+   },
+   "id": "ekTCsIj-eOJt",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Read in the dataset from the csv file and write it to an Iceberg table.\n",
+    "\n",
+    "For Iceberg tables in this workflow, GCS is used as the storage layer.\n",
+    "In a data lakehouse with Iceberg and GCS object storage, a natural choice\n",
+    "for Iceberg catalog is [BigLake metastore](https://cloud.google.com/bigquery/docs/about-blms).\n",
+    "It is a managed, serverless metastore that doesn't require any setup."
+   ],
+   "metadata": {
+    "id": "0VcEtlRkOV0o"
+   },
+   "id": "0VcEtlRkOV0o"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile ./yaml/iceberg_migration_template.yaml\n",
+    "pipeline:\n",
+    "  transforms:\n",
+    "    - type: PyTransform\n",
+    "      name: ReadFromCsv\n",
+    "      input: {}\n",
+    "      config:\n",
+    "        constructor: apache_beam.io.ReadFromCsv\n",
+    "        kwargs:\n",
+    "            path: \"{{ DATASET_PATH }}\"\n",
+    "            index_col: False\n",
+    "            usecols: [\n",
+    "              'trans_date_trans_time',\n",
+    "              'cc_num',\n",
+    "              'merchant',\n",
+    "              'category',\n",
+    "              'amt',\n",
+    "              'first',\n",
+    "              'last',\n",
+    "              'gender',\n",
+    "              'street',\n",
+    "              'city',\n",
+    "              'state',\n",
+    "              'zip',\n",
+    "              'lat',\n",
+    "              'long',\n",
+    "              'city_pop',\n",
+    "              'job',\n",
+    "              'dob',\n",
+    "              'trans_num',\n",
+    "              'unix_time',\n",
+    "              'merch_lat',\n",
+    "              'merch_long',\n",
+    "              'is_fraud']\n",
+    "\n",
+    "    - type: WriteToIceberg\n",
+    "      name: WriteToIceberg\n",
+    "      input: ReadFromCsv\n",
+    "      config:\n",
+    "        table: \"{{ ICEBERG_TABLE }}\"\n",
+    "        catalog_name: \"my_catalog\"\n",
+    "        catalog_properties:\n",
+    "          warehouse: \"{{ WAREHOUSE }}\"\n",
+    "          catalog-impl: \"org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog\"\n",
+    "          io-impl: \"org.apache.iceberg.gcp.gcs.GCSFileIO\"\n",
+    "          gcp_project: \"{{ PROJECT }}\"\n",
+    "          gcp_location: \"{{ REGION }}\""
+   ],
+   "metadata": {
+    "id": "smpAH7coeYh8"
+   },
+   "id": "smpAH7coeYh8",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile ./yaml/iceberg_migration_train_dataset.yaml\n",
+    "{% include './yaml/iceberg_migration_template.yaml' %}"
+   ],
+   "metadata": {
+    "id": "WqdUS9vCeORE"
+   },
+   "id": "WqdUS9vCeORE",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!python -m apache_beam.yaml.main                                        \\\n",
+    "  --yaml_pipeline_file=./yaml/iceberg_migration_train_dataset.yaml      \\\n",
+    "  --jinja_variables='{                                                  \\\n",
+    "    \"DATASET_PATH\": \"./dataset/fraud-detection/fraudTrain.csv\",         \\\n",
+    "    \"ICEBERG_TABLE\": \"fraud_detection.train_dataset_table\",             \\\n",
+    "    \"WAREHOUSE\": \"'$WAREHOUSE'\",                                        \\\n",
+    "    \"PROJECT\": \"'$PROJECT'\",                                            \\\n",
+    "    \"REGION\": \"'$REGION'\" }'"
+   ],
+   "metadata": {
+    "id": "8gQX3MN3eOW6"
+   },
+   "id": "8gQX3MN3eOW6",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Feature engineering\n",
+    "\n",
+    "In this dataset, there's information on users' transactions over time.\n",
+    "\n",
+    "We experiment by computing the following historical aggregate features:\n",
+    "- A user's average transaction amount over 1, 3, and 7 days.\n",
+    "- A user's total transaction count over 1, 3, and 7 days.\n",
+    "\n",
+    "As mentioned before, we implement our custom PTransform `ComputeHistoricalFeatures` to generate these features in `my_provider.py` in order to later be exposed to the YAML pipeline."
+   ],
+   "metadata": {
+    "id": "I_59petUokNn"
+   },
+   "id": "I_59petUokNn"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile ./yaml/my_provider.py\n",
+    "\n",
+    "import apache_beam as beam\n",
+    "from collections import deque\n",
+    "from datetime import datetime, timedelta, timezone\n",
+    "\n",
+    "class ComputeHistoricalFeatures(beam.PTransform):\n",
+    "\n",
+    "  class _ComputeFeaturesDoFn(beam.DoFn):\n",
+    "    \"\"\"Processes all transactions for one user to compute features.\"\"\"\n",
+    "    def process(self, element):\n",
+    "      cc_num, transactions = element\n",
+    "\n",
+    "      # 1. Sort all transactions for the user chronologically.\n",
+    "      sorted_transactions = sorted(\n",
+    "          list(transactions),\n",
+    "          key=lambda t: datetime.fromisoformat(t.trans_date_trans_time).timestamp())\n",
+    "\n",
+    "      # store transactions in a sliding 7-day window.\n",
+    "      history = deque()\n",
+    "\n",
+    "      for tx in sorted_transactions:\n",
+    "        current_time = datetime.fromisoformat(tx.trans_date_trans_time).astimezone(timezone.utc)\n",
+    "\n",
+    "        # 2. Remove transactions older than 7 days\n",
+    "        # relative to the current transaction's timestamp.\n",
+    "        while (history and\n",
+    "          datetime\n",
+    "            .fromisoformat(history[0].trans_date_trans_time)\n",
+    "            .astimezone(timezone.utc) < current_time - timedelta(days=7)):\n",
+    "            history.popleft()\n",
+    "\n",
+    "        # 3. Calculate features by filtering the current history.\n",
+    "        def _avg(amounts):\n",
+    "            return sum(amounts) / len(amounts) if amounts else 0\n",
+    "\n",
+    "        avg_past_1d = _avg([h.amt for h in history\n",
+    "                            if (datetime\n",
+    "                                .fromisoformat(h.trans_date_trans_time)\n",
+    "                                .astimezone(timezone.utc) >= current_time - timedelta(days=1))])\n",
+    "        avg_past_3d = _avg([h.amt for h in history\n",
+    "                            if (datetime\n",
+    "                                .fromisoformat(h.trans_date_trans_time)\n",
+    "                                .astimezone(timezone.utc) >= current_time - timedelta(days=3))])\n",
+    "        avg_past_7d = _avg([h.amt for h in history])\n",
+    "\n",
+    "        count_past_1d = len([h for h in history\n",
+    "                             if (datetime\n",
+    "                                  .fromisoformat(h.trans_date_trans_time)\n",
+    "                                  .astimezone(timezone.utc) >= current_time - timedelta(days=1))])\n",
+    "        count_past_3d = len([h for h in history\n",
+    "                             if (datetime\n",
+    "                                  .fromisoformat(h.trans_date_trans_time)\n",
+    "                                  .astimezone(timezone.utc) >= current_time - timedelta(days=3))])\n",
+    "        count_past_7d = len(history)\n",
+    "\n",
+    "        # 4. Yield a new row with the original data and the new features.\n",
+    "        yield beam.Row(\n",
+    "            **tx._asdict(),\n",
+    "            avg_amount_past_1d=avg_past_1d,\n",
+    "            avg_amount_past_3d=avg_past_3d,\n",
+    "            avg_amount_past_7d=avg_past_7d,\n",
+    "            count_past_1d=count_past_1d,\n",
+    "            count_past_3d=count_past_3d,\n",
+    "            count_past_7d=count_past_7d\n",
+    "        )\n",
+    "\n",
+    "        # 5. Add the current transaction to history for the next iteration.\n",
+    "        history.append(tx)\n",
+    "\n",
+    "  def expand(self, pcoll):\n",
+    "    return (\n",
+    "        pcoll\n",
+    "        | beam.WithKeys(lambda row: row.cc_num)\n",
+    "        | beam.GroupByKey()\n",
+    "        | beam.ParDo(self._ComputeFeaturesDoFn())\n",
+    "        | beam.Map(lambda row: beam.Row(\n",
+    "            **row.as_dict()\n",
+    "          ))\n",
+    "    )\n"
+   ],
+   "metadata": {
+    "id": "2ODiBDG4yPpF"
+   },
+   "id": "2ODiBDG4yPpF",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Build and package the custom transform."
+   ],
+   "metadata": {
+    "id": "Fedk8LIGQ7qx"
+   },
+   "id": "Fedk8LIGQ7qx"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!poetry build -C ./yaml"
+   ],
+   "metadata": {
+    "id": "fS9PQQF6ubXk"
+   },
+   "id": "fS9PQQF6ubXk",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Read the input training data stored in Iceberg table, use our custom transform to compute the features and write them to another Iceberg table."
+   ],
+   "metadata": {
+    "id": "HoJHokZkREN6"
+   },
+   "id": "HoJHokZkREN6"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile ./yaml/historical_aggregates_featurize_template.yaml\n",
+    "pipeline:\n",
+    "  type: chain\n",
+    "  transforms:\n",
+    "    - type: ReadFromIceberg\n",
+    "      name: ReadFromIceberg\n",
+    "      config:\n",
+    "        table: \"{{ ICEBERG_TABLE_INPUT }}\"\n",
+    "        catalog_name: \"my_catalog\"\n",
+    "        catalog_properties:\n",
+    "          warehouse: \"{{ WAREHOUSE }}\"\n",
+    "          catalog-impl: \"org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog\"\n",
+    "          io-impl: \"org.apache.iceberg.gcp.gcs.GCSFileIO\"\n",
+    "          gcp_project: \"{{ PROJECT }}\"\n",
+    "          gcp_location: \"{{ REGION }}\"\n",
+    "\n",
+    "    - type: HistoricalAggregatesTransform\n",
+    "      name: HistoricalAggregatesTransform\n",
+    "\n",
+    "    - type: MapToFields\n",
+    "      name: MapToFields\n",
+    "      config:\n",
+    "        language: python\n",
+    "        fields:\n",
+    "          cc_num:\n",
+    "            callable: \"lambda row: row.cc_num\"\n",
+    "            output_type: integer\n",
+    "          amt:\n",
+    "            callable: \"lambda row: row.amt\"\n",
+    "            output_type: number\n",
+    "          trans_date_trans_time:\n",
+    "            callable: \"lambda row: row.trans_date_trans_time\"\n",
+    "            output_type: string\n",
+    "          trans_num:\n",
+    "            callable: \"lambda row: row.trans_num\"\n",
+    "            output_type: string\n",
+    "          merchant:\n",
+    "            callable: \"lambda row: row.merchant\"\n",
+    "            output_type: string\n",
+    "          category:\n",
+    "            callable: \"lambda row: row.category\"\n",
+    "            output_type: string\n",
+    "          merch_lat:\n",
+    "            callable: \"lambda row: row.merch_lat\"\n",
+    "            output_type: number\n",
+    "          merch_long:\n",
+    "            callable: \"lambda row: row.merch_long\"\n",
+    "            output_type: number\n",
+    "          first:\n",
+    "            callable: \"lambda row: row.first\"\n",
+    "            output_type: string\n",
+    "          last:\n",
+    "            callable: \"lambda row: row.last\"\n",
+    "            output_type: string\n",
+    "          gender:\n",
+    "            callable: \"lambda row: row.gender\"\n",
+    "            output_type: string\n",
+    "          street:\n",
+    "            callable: \"lambda row: row.street\"\n",
+    "            output_type: string\n",
+    "          city:\n",
+    "            callable: \"lambda row: row.city\"\n",
+    "            output_type: string\n",
+    "          state:\n",
+    "            callable: \"lambda row: row.state\"\n",
+    "            output_type: string\n",
+    "          zip:\n",
+    "            callable: \"lambda row: row.zip\"\n",
+    "            output_type: integer\n",
+    "          lat:\n",
+    "            callable: \"lambda row: row.lat\"\n",
+    "            output_type: number\n",
+    "          long:\n",
+    "            callable: \"lambda row: row.long\"\n",
+    "            output_type: number\n",
+    "          city_pop:\n",
+    "            callable: \"lambda row: row.city_pop\"\n",
+    "            output_type: integer\n",
+    "          job:\n",
+    "            callable: \"lambda row: row.job\"\n",
+    "            output_type: string\n",
+    "          dob:\n",
+    "            callable: \"lambda row: row.dob\"\n",
+    "            output_type: string\n",
+    "          avg_amount_past_1d:\n",
+    "            callable: \"lambda row: row.avg_amount_past_1d\"\n",
+    "            output_type: number\n",
+    "          avg_amount_past_3d:\n",
+    "            callable: \"lambda row: row.avg_amount_past_3d\"\n",
+    "            output_type: number\n",
+    "          avg_amount_past_7d:\n",
+    "            callable: \"lambda row: row.avg_amount_past_7d\"\n",
+    "            output_type: number\n",
+    "          count_past_1d:\n",
+    "            callable: \"lambda row: row.count_past_1d\"\n",
+    "            output_type: integer\n",
+    "          count_past_3d:\n",
+    "            callable: \"lambda row: row.count_past_3d\"\n",
+    "            output_type: integer\n",
+    "          count_past_7d:\n",
+    "            callable: \"lambda row: row.count_past_7d\"\n",
+    "            output_type: integer\n",
+    "          is_fraud:\n",
+    "            callable: \"lambda row: row.is_fraud\"\n",
+    "            output_type: integer\n",
+    "\n",
+    "    - type: WriteToIceberg\n",
+    "      name: WriteToIceberg\n",
+    "      config:\n",
+    "        table: \"{{ ICEBERG_TABLE_OUTPUT }}\"\n",
+    "        catalog_name: \"my_catalog\"\n",
+    "        catalog_properties:\n",
+    "          warehouse: \"{{ WAREHOUSE }}\"\n",
+    "          catalog-impl: \"org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog\"\n",
+    "          io-impl: \"org.apache.iceberg.gcp.gcs.GCSFileIO\"\n",
+    "          gcp_project: \"{{ PROJECT }}\"\n",
+    "          gcp_location: \"{{ REGION }}\"\n",
+    "\n",
+    "providers:\n",
+    "  - type: pythonPackage\n",
+    "    config:\n",
+    "      packages:\n",
+    "        - ./dist/my_provider-0.1.0.tar.gz\n",
+    "    transforms:\n",
+    "      HistoricalAggregatesTransform: 'my_provider.ComputeHistoricalFeatures'\n"
+   ],
+   "metadata": {
+    "id": "3Yu4pNbFzE9s"
+   },
+   "id": "3Yu4pNbFzE9s",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile ./yaml/historical_aggregates_featurize_train_dataset.yaml\n",
+    "{% include './yaml/historical_aggregates_featurize_template.yaml' %}"
+   ],
+   "metadata": {
+    "id": "z5FGi1wbwtVM"
+   },
+   "id": "z5FGi1wbwtVM",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!python -m apache_beam.yaml.main                                                                    \\\n",
+    "  --yaml_pipeline_file=./yaml/historical_aggregates_featurize_train_dataset.yaml                    \\\n",
+    "  --jinja_variables='{                                                                              \\\n",
+    "    \"ICEBERG_TABLE_INPUT\": \"fraud_detection.train_dataset_table\",                                   \\\n",
+    "    \"ICEBERG_TABLE_OUTPUT\": \"fraud_detection.historical_aggregates_featurized_train_dataset_table\", \\\n",
+    "    \"WAREHOUSE\": \"'$WAREHOUSE'\",                                                                    \\\n",
+    "    \"PROJECT\": \"'$PROJECT'\",                                                                        \\\n",
+    "    \"REGION\": \"'$REGION'\" }'"
+   ],
+   "metadata": {
+    "id": "uZNOIw5Hwxpi"
+   },
+   "id": "uZNOIw5Hwxpi",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Training"
+   ],
+   "metadata": {
+    "id": "hhsB-uhoookH"
+   },
+   "id": "hhsB-uhoookH"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Download the dataset with all the computed features from the Iceberg table and load it to pandas Dataframe."
+   ],
+   "metadata": {
+    "id": "cPW9xNdRR-lc"
+   },
+   "id": "cPW9xNdRR-lc"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile ./yaml/download_featurized_dataset_template.yaml\n",
+    "pipeline:\n",
+    "  type: chain\n",
+    "  transforms:\n",
+    "    - type: ReadFromIceberg\n",
+    "      config:\n",
+    "        table: \"{{ ICEBERG_TABLE }}\"\n",
+    "        catalog_name: \"my_catalog\"\n",
+    "        catalog_properties:\n",
+    "          warehouse: \"{{ WAREHOUSE }}\"\n",
+    "          catalog-impl: \"org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog\"\n",
+    "          io-impl: \"org.apache.iceberg.gcp.gcs.GCSFileIO\"\n",
+    "          gcp_project: \"{{ PROJECT }}\"\n",
+    "          gcp_location: \"{{ REGION }}\"\n",
+    "\n",
+    "    - type: WriteToCsv\n",
+    "      config:\n",
+    "        path: \"{{ OUTPUT_PATH }}\""
+   ],
+   "metadata": {
+    "id": "iPq6Qqs14yAH"
+   },
+   "id": "iPq6Qqs14yAH",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile ./yaml/download_featurized_train_dataset.yaml\n",
+    "{% include './yaml/download_featurized_dataset_template.yaml' %}"
+   ],
+   "metadata": {
+    "id": "1rEOu8996Zqr"
+   },
+   "id": "1rEOu8996Zqr",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!python -m apache_beam.yaml.main                                                                  \\\n",
+    "  --yaml_pipeline_file=./yaml/download_featurized_train_dataset.yaml                              \\\n",
+    "  --jinja_variables='{                                                                            \\\n",
+    "  \"ICEBERG_TABLE\": \"fraud_detection.historical_aggregates_featurized_train_dataset_table\",        \\\n",
+    "  \"WAREHOUSE\": \"'$WAREHOUSE'\",                                                                    \\\n",
+    "  \"PROJECT\": \"'$PROJECT'\",                                                                        \\\n",
+    "  \"REGION\": \"'$REGION'\",                                                                          \\\n",
+    "  \"OUTPUT_PATH\": \"./dataset/historical_aggregates_featurized_train_dataset.csv\" }'"
+   ],
+   "metadata": {
+    "id": "usgeN1Zi6JN1"
+   },
+   "id": "usgeN1Zi6JN1",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "df = pd.read_csv(\n",
+    "    './dataset/historical_aggregates_featurized_train_dataset.csv-00000-of-00001',\n",
+    "    header=0,\n",
+    "    parse_dates=['trans_date_trans_time', 'dob']\n",
+    ")\n",
+    "\n",
+    "df.head(5)"
+   ],
+   "metadata": {
+    "id": "OBSNMd_J-kwf"
+   },
+   "id": "OBSNMd_J-kwf",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ML models usually accept only numerical or categorical data. For data that are of type string (transaction date and time, date of birth, first and last name, city, state, etc...), some preprocessing is required."
+   ],
+   "metadata": {
+    "id": "vV_5gBYsSWK6"
+   },
+   "id": "vV_5gBYsSWK6"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "For datetime columns, we break them further into multiple numerical feature columns."
+   ],
+   "metadata": {
+    "id": "rkX_sbEvTMVh"
+   },
+   "id": "rkX_sbEvTMVh"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "def add_dateparts(df, col):\n",
+    "    \"\"\"\n",
+    "    This function splits the datetime column into separate column such as\n",
+    "    year, month, day, weekday, and hour\n",
+    "    :param df: DataFrame table to add the columns\n",
+    "    :param col: the column with datetime values\n",
+    "    :return: None\n",
+    "    \"\"\"\n",
+    "    df[col + '_year'] = df[col].dt.year\n",
+    "    df[col + '_month'] = df[col].dt.month\n",
+    "    df[col + '_day'] = df[col].dt.day\n",
+    "    df[col + '_weekday'] = df[col].dt.weekday\n",
+    "    df[col + '_hour'] = df[col].dt.hour\n",
+    "\n",
+    "add_dateparts(df, 'trans_date_trans_time')\n",
+    "add_dateparts(df, 'dob')"
+   ],
+   "metadata": {
+    "id": "3CtdoWTZLp9I"
+   },
+   "id": "3CtdoWTZLp9I",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "For other feature columns with string data, we specify them explicitly as `category` type data."
+   ],
+   "metadata": {
+    "id": "PywA2vaETaQV"
+   },
+   "id": "PywA2vaETaQV"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "categorical_feature_columns = [\n",
+    "    'merchant',\n",
+    "    'category',\n",
+    "    'first',\n",
+    "    'last',\n",
+    "    'gender',\n",
+    "    'city',\n",
+    "    'state',\n",
+    "    'job'\n",
+    "]\n",
+    "for col in categorical_feature_columns:\n",
+    "    df[col] = df[col].astype('category')"
+   ],
+   "metadata": {
+    "id": "i1AT9BZ6OkSX"
+   },
+   "id": "i1AT9BZ6OkSX",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "df.info()"
+   ],
+   "metadata": {
+    "id": "-4Gb4-LWPxrg"
+   },
+   "id": "-4Gb4-LWPxrg",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We specify the `baseline_feature_columns` containing the original columns of the dataset that are used as input columns for our first model. Likewise, we specify `full_feature_columns` containing the original and computed feature columns of the dataset that are used as input columns for our second model.\n",
+    "\n",
+    "The target/label column for training is the `is_fraud` column."
+   ],
+   "metadata": {
+    "id": "UfWaePqnTxA0"
+   },
+   "id": "UfWaePqnTxA0"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "baseline_feature_columns = [\n",
+    "    'cc_num',\n",
+    "    'amt',\n",
+    "    'trans_date_trans_time_year',\n",
+    "    'trans_date_trans_time_month',\n",
+    "    'trans_date_trans_time_day',\n",
+    "    'trans_date_trans_time_weekday',\n",
+    "    'trans_date_trans_time_hour',\n",
+    "    'merchant',\n",
+    "    'category',\n",
+    "    'merch_lat',\n",
+    "    'merch_long',\n",
+    "    'first',\n",
+    "    'last',\n",
+    "    'gender',\n",
+    "    'city',\n",
+    "    'state',\n",
+    "    'zip',\n",
+    "    'lat',\n",
+    "    'long',\n",
+    "    'city_pop',\n",
+    "    'job',\n",
+    "    'dob_year',\n",
+    "    'dob_month',\n",
+    "    'dob_day',\n",
+    "    'dob_weekday',\n",
+    "    'dob_hour'\n",
+    "]\n",
+    "full_feature_columns = baseline_feature_columns + [\n",
+    "    'avg_amount_past_1d',\n",
+    "    'avg_amount_past_3d',\n",
+    "    'avg_amount_past_7d',\n",
+    "    'count_past_1d',\n",
+    "    'count_past_3d',\n",
+    "    'count_past_7d',\n",
+    "]\n",
+    "target_column = 'is_fraud'\n",
+    "\n",
+    "X = df[full_feature_columns]\n",
+    "y = df[target_column]\n",
+    "\n",
+    "# Split data for validation\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ],
+   "metadata": {
+    "id": "HfLU6ESyrWly"
+   },
+   "id": "HfLU6ESyrWly",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Predicting taxi fare is a supervised learning, regression problem and our dataset is tabular. It is well-known that [gradient-boosted decision tree (GBDT) model](https://en.wikipedia.org/wiki/Gradient_boosting) performs very well for this kind of problem and dataset type.\n",
+    "\n",
+    "We use the XGBoost library which implements the GBDT machine learning algorithm in a scalable, distributed manner.\n",
+    "\n",
+    "We train two models, one `baseline_model` with `baseline_feature_columns`, and one `experimental_model` with `full_feature_columns`."
+   ],
+   "metadata": {
+    "id": "gKT9ejG4VFbG"
+   },
+   "id": "gKT9ejG4VFbG"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Train a simple XGBoost Classifier\n",
+    "baseline_model = XGBClassifier(\n",
+    "    objective='binary:logistic',\n",
+    "    eval_metric='logloss',\n",
+    "    use_label_encoder=False,\n",
+    "    enable_categorical=True,\n",
+    "    tree_method='hist'\n",
+    ")\n",
+    "baseline_model.fit(X_train[baseline_feature_columns], y_train)\n",
+    "baseline_model.save_model(\"baseline_model.bst\")"
+   ],
+   "metadata": {
+    "id": "3LGhF0KdDqjm"
+   },
+   "id": "3LGhF0KdDqjm",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!gcloud storage cp baseline_model.bst {WAREHOUSE}"
+   ],
+   "metadata": {
+    "id": "4mwDGya8FHPV"
+   },
+   "id": "4mwDGya8FHPV",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Train a simple XGBoost Classifier\n",
+    "experimental_model = XGBClassifier(\n",
+    "    objective='binary:logistic',\n",
+    "    eval_metric='logloss',\n",
+    "    use_label_encoder=False,\n",
+    "    enable_categorical=True,\n",
+    "    tree_method='hist'\n",
+    ")\n",
+    "experimental_model.fit(X_train, y_train)\n",
+    "experimental_model.save_model(\"experimental_model.bst\")"
+   ],
+   "metadata": {
+    "id": "ZvihaXYSBrci"
+   },
+   "id": "ZvihaXYSBrci",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!gcloud storage cp experimental_model.bst {WAREHOUSE}"
+   ],
+   "metadata": {
+    "id": "XpRI8RJorCCu"
+   },
+   "id": "XpRI8RJorCCu",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Evaluation"
+   ],
+   "metadata": {
+    "id": "sASVErGKostr"
+   },
+   "id": "sASVErGKostr"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Save the the testing datasets and label to be used later in model evaluation YAML pipeline."
+   ],
+   "metadata": {
+    "id": "8FCLyzFiV2cC"
+   },
+   "id": "8FCLyzFiV2cC"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "X_test[baseline_feature_columns].to_pickle(\"X_test_baseline_feature_columns.pkl\")\n",
+    "X_test[full_feature_columns].to_pickle(\"X_test_full_feature_columns.pkl\")\n",
+    "\n",
+    "y_test.to_pickle(\"y_test.pkl\")"
+   ],
+   "metadata": {
+    "id": "fzy5LGzIR-sH"
+   },
+   "id": "fzy5LGzIR-sH",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We again implement custom PTransforms `RunInferenceTransform` to perform inference on input data, and `ComputeMetricsTransform` to calculate various model evaluation metrics."
+   ],
+   "metadata": {
+    "id": "vG3REPjFWPlm"
+   },
+   "id": "vG3REPjFWPlm"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile -a ./yaml/my_provider.py\n",
+    "\n",
+    "from apache_beam.ml.inference.base import RunInference\n",
+    "from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerPandas\n",
+    "import xgboost\n",
+    "import pandas as pd\n",
+    "import sklearn.metrics\n",
+    "\n",
+    "class RunInferenceTransform(beam.PTransform):\n",
+    "  def __init__(self, model_path, df_dataset_path):\n",
+    "    self._model_path = model_path\n",
+    "    self._df_dataset_path = df_dataset_path\n",
+    "    self._model_handler = XGBoostModelHandlerPandas(\n",
+    "        model_class=xgboost.XGBClassifier,\n",
+    "        model_state=self._model_path,\n",
+    "    )\n",
+    "\n",
+    "  def expand(self, pcoll):\n",
+    "    return (\n",
+    "        pcoll\n",
+    "        | beam.Create([pd.read_pickle(self._df_dataset_path)])\n",
+    "        | RunInference(self._model_handler)\n",
+    "        | beam.Map(lambda row: beam.Row(inferences=row.inference))\n",
+    "    )\n",
+    "\n",
+    "class ComputeMetricsTransform(beam.PTransform):\n",
+    "  def __init__(self, df_targets_path):\n",
+    "    self.targets = pd.read_pickle(df_targets_path).to_list()\n",
+    "\n",
+    "  def expand(self, pcoll):\n",
+    "\n",
+    "    def compute_metrics(row):\n",
+    "      true_labels = self.targets\n",
+    "      predicted_labels = row.inferences\n",
+    "\n",
+    "      accuracy = sklearn.metrics.accuracy_score(true_labels, predicted_labels)\n",
+    "      precision = sklearn.metrics.precision_score(true_labels, predicted_labels, average='macro', zero_division=0)\n",
+    "      recall = sklearn.metrics.recall_score(true_labels, predicted_labels, average='macro', zero_division=0)\n",
+    "      f1 = sklearn.metrics.f1_score(true_labels, predicted_labels, average='macro', zero_division=0)\n",
+    "\n",
+    "      yield beam.Row(\n",
+    "          accuracy=float(accuracy),\n",
+    "          precision=float(precision),\n",
+    "          recall=float(recall),\n",
+    "          f1=float(f1),\n",
+    "      )\n",
+    "\n",
+    "    return pcoll | 'CalculateMetrics' >> beam.FlatMap(compute_metrics)\n"
+   ],
+   "metadata": {
+    "id": "Ih07-BIQY_Vz"
+   },
+   "id": "Ih07-BIQY_Vz",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Build and package the custom transforms."
+   ],
+   "metadata": {
+    "id": "k6dVfcSnW5qj"
+   },
+   "id": "k6dVfcSnW5qj"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!poetry build -C ./yaml"
+   ],
+   "metadata": {
+    "id": "RA5bOhDgxgC9"
+   },
+   "id": "RA5bOhDgxgC9",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Use our custom transforms to read in the dataset, perform inference and evaluate against target labels."
+   ],
+   "metadata": {
+    "id": "Os7Gc8paXZs6"
+   },
+   "id": "Os7Gc8paXZs6"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile ./yaml/model_evaluation_template.yaml\n",
+    "pipeline:\n",
+    "  transforms:\n",
+    "    - type: RunInferenceTransform\n",
+    "      name: RunInferenceTransform\n",
+    "      input: {}\n",
+    "      config:\n",
+    "        model_path: \"{{ MODEL_PATH }}\"\n",
+    "        df_dataset_path: \"{{ DF_DATASET_PATH }}\"\n",
+    "\n",
+    "    - type: ComputeMetricsTransform\n",
+    "      name: ComputeMetricsTransform\n",
+    "      input: RunInferenceTransform\n",
+    "      config:\n",
+    "        df_targets_path: \"{{ DF_TARGETS_PATH }}\"\n",
+    "\n",
+    "    - type: LogForTesting\n",
+    "      name: LogForTesting\n",
+    "      input: ComputeMetricsTransform\n",
+    "\n",
+    "providers:\n",
+    "  - type: pythonPackage\n",
+    "    config:\n",
+    "      packages:\n",
+    "        - ./dist/my_provider-0.1.0.tar.gz\n",
+    "    transforms:\n",
+    "      RunInferenceTransform: 'my_provider.RunInferenceTransform'\n",
+    "      ComputeMetricsTransform: 'my_provider.ComputeMetricsTransform'\n"
+   ],
+   "metadata": {
+    "id": "OHkF4U3oS1xw"
+   },
+   "id": "OHkF4U3oS1xw",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Evaluate the baseline model on the baseline feature columns."
+   ],
+   "metadata": {
+    "id": "g4Qqx7G9W92g"
+   },
+   "id": "g4Qqx7G9W92g"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile ./yaml/baseline_model_evaluation.yaml\n",
+    "{% include './yaml/model_evaluation_template.yaml' %}"
+   ],
+   "metadata": {
+    "id": "xZqhRXw_9iKY"
+   },
+   "id": "xZqhRXw_9iKY",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!python -m apache_beam.yaml.main                                              \\\n",
+    "  --yaml_pipeline_file=./yaml/baseline_model_evaluation.yaml                  \\\n",
+    "  --jinja_variables='{                                                        \\\n",
+    "    \"MODEL_PATH\": \"'$WAREHOUSE'/baseline_model.bst\",                          \\\n",
+    "    \"DF_DATASET_PATH\": \"./X_test_baseline_feature_columns.pkl\",               \\\n",
+    "    \"DF_TARGETS_PATH\": \"./y_test.pkl\" }'"
+   ],
+   "metadata": {
+    "id": "z9KnAxsP9iAL"
+   },
+   "id": "z9KnAxsP9iAL",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The model trained only on the original features achieves:\n",
+    "\n",
+    "```\n",
+    "{\n",
+    "  \"accuracy\": 0.9984623532828757,\n",
+    "  \"precision\": 0.963115986169815,\n",
+    "  \"recall\": 0.8987887554423477,\n",
+    "  \"f1\": 0.9285234509618558\n",
+    "}\n",
+    "```"
+   ],
+   "metadata": {
+    "id": "TLfFUUV_9hjw"
+   },
+   "id": "TLfFUUV_9hjw"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Evaluate the experimental model on the full feature columns."
+   ],
+   "metadata": {
+    "id": "BnmKasKxXLvS"
+   },
+   "id": "BnmKasKxXLvS"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%writefile ./yaml/experimental_model_evaluation.yaml\n",
+    "{% include './yaml/model_evaluation_template.yaml' %}"
+   ],
+   "metadata": {
+    "id": "g4_u7Lak8bkL"
+   },
+   "id": "g4_u7Lak8bkL",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!python -m apache_beam.yaml.main                                              \\\n",
+    "  --yaml_pipeline_file=./yaml/experimental_model_evaluation.yaml              \\\n",
+    "  --jinja_variables='{                                                        \\\n",
+    "    \"MODEL_PATH\": \"'$WAREHOUSE'/experimental_model.bst\",                      \\\n",
+    "    \"DF_DATASET_PATH\": \"./X_test_full_feature_columns.pkl\",                   \\\n",
+    "    \"DF_TARGETS_PATH\": \"./y_test.pkl\" }'"
+   ],
+   "metadata": {
+    "id": "L2rBVbnFnFZy"
+   },
+   "id": "L2rBVbnFnFZy",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The model trained on full feature columns achieve a better result:\n",
+    "\n",
+    "```\n",
+    "{\n",
+    "  \"accuracy\": 0.9989620884659411,\n",
+    "  \"precision\": 0.9643454567937234,\n",
+    "  \"recall\": 0.944328457850605,\n",
+    "  \"f1\": 0.9541138032867618\n",
+    "}\n",
+    "```"
+   ],
+   "metadata": {
+    "id": "jbJfTX26YVE6"
+   },
+   "id": "jbJfTX26YVE6"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  },
+  "colab": {
+   "provenance": [],
+   "name": "fraud_detection_mlops_beam_yaml_sdk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/README.md b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/README.md
new file mode 100644
index 000000000000..b8763bcb511d
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/README.md
@@ -0,0 +1,98 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+## Batch Log Analysis ML Workflow
+
+This example contains several pipelines that leverage Iceberg and BigQuery
+IOs as well as MLTransform to demonstrate an end-to-end ML anomaly detection
+workflow on system logs.
+
+Download [Google Cloud CLI](https://cloud.google.com/sdk/docs/install-sdk).
+
+Install required Python dependencies in a virtual environment:
+```sh
+python -m venv env
+. env/bin/activate
+pip install 'apache-beam[gcp,yaml]' db-dtypes -r requirements.txt
+```
+
+The system logs dataset is from [logpai/loghub](https://github.com/logpai/loghub)
+GitHub repository, and specifically the [sample HDFS `.csv` dataset](
+https://github.com/logpai/loghub/blob/master/Hadoop/Hadoop_2k.log_structured.csv)
+is used in this example.
+
+Download the dataset and copy it over to a GCS bucket:
+```sh
+gcloud storage cp /path/to/Hadoop_2k.log_structured.csv \
+  gs://YOUR-BUCKET/Hadoop_2k.log_structured.csv
+```
+**NOTE**: This example requires the GCS bucket created to be a single-region
+bucket.
+
+For Iceberg tables, GCS is also used as the storage layer in this workflow.
+In a data lakehouse with Iceberg and GCS object storage, a natural choice
+for Iceberg catalog is [BigLake metastore](https://cloud.google.com/bigquery/docs/about-blms).
+It is a managed, serverless metastore that doesn't require any setup.
+
+A BigQuery dataset needs to exist first before the pipeline can
+create/write to a table. Run the following command to create
+a BigQuery dataset:
+
+```sh
+bq --location=YOUR_REGION mk \
+  --dataset YOUR_DATASET
+```
+
+The workflow starts with pipeline [iceberg_migration.yaml](./iceberg_migration.yaml)
+that ingests the `.csv` log data and writes to an Iceberg table on GCS with
+BigLake metastore for catalog.
+The next pipeline [ml_preprocessing.yaml](./ml_preprocessing.yaml) reads
+from this Iceberg table and perform ML-specific transformations such as
+computing text embedding and normalization, before writing the embeddings to
+a BigQuery table.
+An anomaly detection model is then trained (in [train.py](./train.py) script)
+on these vector embeddings, and is subsequently saved as artifact on GCS.
+The last pipeline [anomaly_scoring.yaml](./anomaly_scoring.yaml) reads the
+same embeddings from the BigQuery table, uses Beam's anomaly detection
+module to load the model artifact from GCS and perform anomaly scoring,
+before writing it to another Iceberg table.
+
+This entire workflow execution is encapsulated in the `batch_log_analysis.sh`
+script that runs these workloads sequentially.
+
+Run the pipelines locally:
+```sh
+./batch_log_analysis.sh --runner DirectRunner \
+  --project YOUR_PROJECT \
+  --region YOUR_REGION \
+  --warehouse gs://YOUR-BUCKET \
+  --bq_table YOUR_PROJECT.YOUR_DATASET.YOUR_TABLE
+```
+
+Run the pipelines on Dataflow:
+```sh
+ ./batch_log_analysis.sh --runner DataflowRunner \
+   --project YOUR_PROJECT \
+   --region YOUR_REGION \
+   --temp_location gs://YOUR-BUCKET/tmp \
+   --num_workers 1 \
+   --worker_machine_type n1-standard-2 \
+   --warehouse gs://YOUR-BUCKET \
+   --bq_table YOUR_PROJECT.YOUR_DATASET.YOUR_TABLE
+```
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/anomaly_scoring.yaml b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/anomaly_scoring.yaml
new file mode 100644
index 000000000000..dd4f48401278
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/anomaly_scoring.yaml
@@ -0,0 +1,93 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# The pipeline reads text embeddings from a BigQuery table, applies anomaly
+# scoring using a custom pre-trained k-Nearest Neighbours (KNN) model, and
+# writes the results to an Iceberg table on GCS with BigLake metastore for
+# catalog.
+
+pipeline:
+  type: chain
+  transforms:
+    - type: ReadFromBigQuery
+      name: ReadFromBigQuery
+      config:
+        table: "{{ BQ_TABLE }}"
+        fields: [embedding]
+
+    - type: PyTransform
+      name: AnomalyScoring
+      config:
+        constructor: __constructor__
+        kwargs:
+          source: |
+            import apache_beam as beam
+            from apache_beam.ml.anomaly.detectors.pyod_adapter import PyODFactory
+            from apache_beam.ml.anomaly.transforms import AnomalyDetection
+            
+            class KNN(beam.PTransform):
+              def __init__(self, model_artifact_path):
+                self.model_artifact_path = model_artifact_path
+                self.model = PyODFactory.create_detector(
+                   self.model_artifact_path,
+                   model_id="knn",
+                )
+            
+              def expand(self, pcoll):
+                return (
+                  pcoll
+                  | beam.Map(lambda x: x.embedding)
+                  | AnomalyDetection(detector=self.model)
+                  | beam.Map(lambda x: beam.Row(
+                      example=x.example,
+                      predictions=[pred.__dict__ for pred in x.predictions]))
+              )
+
+          model_artifact_path: "{{ WAREHOUSE }}/knn_model.pkl"
+
+    - type: MapToFields
+      name: ResultSchemaMapping
+      config:
+        language: python
+        fields:
+          anomaly_score:
+            callable: "lambda row: row.predictions[0]['score']"
+            output_type: number
+          anomaly_label:
+            callable: "lambda row: row.predictions[0]['label']"
+            output_type: integer
+          threshold:
+            callable: "lambda row: row.predictions[0]['threshold']"
+            output_type: number
+
+    - type: WriteToIceberg
+      name: WriteToIceberg
+      config:
+        table: "logs_analytics.hdfs_anomaly"
+        catalog_name: "rest_catalog"
+        catalog_properties:
+          warehouse: "{{ WAREHOUSE }}"
+          catalog-impl: "org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog"
+          io-impl: "org.apache.iceberg.gcp.gcs.GCSFileIO"
+          gcp_project: "{{ PROJECT }}"
+          gcp_location: "{{ REGION }}"
+
+# Expected:
+#  Row(anomaly_score=0.65, anomaly_label=0, threshold=0.8)
+#  Row(anomaly_score=0.65, anomaly_label=0, threshold=0.8)
+#  Row(anomaly_score=0.65, anomaly_label=0, threshold=0.8)
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/batch_log_analysis.sh b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/batch_log_analysis.sh
new file mode 100755
index 000000000000..6709419076a9
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/batch_log_analysis.sh
@@ -0,0 +1,108 @@
+#!/bin/bash
+
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Usage:
+# With DirectRunner:
+# ./batch_log_analysis.sh --runner DirectRunner \
+#   --project YOUR_PROJECT \
+#   --region YOUR_REGION \
+#   --warehouse gs://YOUR-BUCKET \
+#   --bq_table YOUR_PROJECT.YOUR_DATASET.YOUR_TABLE
+
+# With DataflowRunner:
+# ./batch_log_analysis.sh --runner DataflowRunner \
+#   --project YOUR_PROJECT \
+#   --region YOUR_REGION \
+#   --temp_location gs://YOUR-BUCKET/temp \
+#   --num_workers 1 \
+#   --worker_machine_type n1-standard-2 \
+#   --warehouse gs://YOUR-BUCKET \
+#   --bq_table YOUR_PROJECT.YOUR_DATASET.YOUR_TABLE
+
+set -e
+
+while [[ $# -gt 0 ]]; do
+  case "$1" in
+    --runner) RUNNER="$2"; shift 2 ;;
+    --project) PROJECT="$2"; shift 2 ;;
+    --region) REGION="$2"; shift 2 ;;
+    --temp_location) TEMP_LOCATION="$2"; shift 2 ;;
+    --num_workers) NUM_WORKERS="$2"; shift 2 ;;
+    --worker_machine_type) WORKER_MACHINE_TYPE="$2"; shift 2 ;;
+    --warehouse) WAREHOUSE="$2"; shift 2 ;;
+    --bq_table) BQ_TABLE="$2"; shift 2 ;;
+    *) echo "Unknown argument: $1"; exit 1 ;;
+  esac
+done
+
+if [[ "$RUNNER" == "DataflowRunner" ]]; then
+  DATAFLOW_COMMON_ARGS="--job_name batch-log-analysis-$(date +%Y%m%d-%H%M%S)
+    --project $PROJECT --region $REGION
+    --temp_location $TEMP_LOCATION
+    --num_workers $NUM_WORKERS --worker_machine_type $WORKER_MACHINE_TYPE
+    --disk_size_gb 50"
+else
+  DATAFLOW_COMMON_ARGS=""
+fi
+
+if ! command -v python &> /dev/null; then
+  echo "Error: Python is not installed. Please install Python to continue."
+  exit 1
+fi
+
+if ! command -v gcloud &> /dev/null; then
+  echo "Error: gcloud CLI is not installed. Please install gcloud to continue."
+  exit 1
+fi
+
+if [ -d "./beam-ml-artifacts" ]; then
+  echo "Removing existing MLTransform's artifact directory..."
+  rm -rf ./beam-ml-artifacts
+fi
+
+echo "Running iceberg_migration.yaml pipeline..."
+python -m apache_beam.yaml.main --yaml_pipeline_file iceberg_migration.yaml \
+  --runner $RUNNER \
+  --jinja_variables '{ "WAREHOUSE":"'$WAREHOUSE'", "PROJECT":"'$PROJECT'", "REGION":"'$REGION'" }' \
+  $DATAFLOW_COMMON_ARGS
+
+echo "Running ml_preprocessing.yaml pipeline..."
+python -m apache_beam.yaml.main --yaml_pipeline_file ml_preprocessing.yaml \
+  --runner $RUNNER \
+  --jinja_variables '{ "WAREHOUSE":"'$WAREHOUSE'", "PROJECT":"'$PROJECT'", "REGION":"'$REGION'", "BQ_TABLE":"'$BQ_TABLE'" }' \
+  $DATAFLOW_COMMON_ARGS \
+  --requirements_file requirements.txt
+
+echo "Running train.py..."
+python train.py --bq_table $BQ_TABLE
+
+if [ ! -f "./knn_model.pkl" ]; then
+  echo "Error: Model artifact 'knn_model.pkl' not found. Ensure model training is successful."
+  exit 1
+fi
+
+echo "Uploading trained model to GCS..."
+gcloud storage cp "./knn_model.pkl" "$WAREHOUSE/knn_model.pkl"
+
+echo "Running anomaly_scoring.yaml pipeline..."
+python -m apache_beam.yaml.main --yaml_pipeline_file anomaly_scoring.yaml \
+  --runner $RUNNER \
+  --jinja_variables '{ "WAREHOUSE":"'$WAREHOUSE'", "PROJECT":"'$PROJECT'", "REGION":"'$REGION'", "BQ_TABLE":"'$BQ_TABLE'" }' \
+  $DATAFLOW_COMMON_ARGS \
+  --requirements_file requirements.txt
+
+echo "All steps completed."
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/iceberg_migration.yaml b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/iceberg_migration.yaml
new file mode 100644
index 000000000000..8d0518c36356
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/iceberg_migration.yaml
@@ -0,0 +1,45 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# The pipeline reads structured logs from a CSV file and writes them to an
+# Iceberg table on GCS with BigLake metastore for catalog.
+
+pipeline:
+  type: chain
+  transforms:
+    - type: ReadFromCsv
+      name: ReadLogs
+      config:
+        path: "{{ WAREHOUSE }}/Hadoop_2k.log_structured.csv"
+
+    - type: WriteToIceberg
+      name: WriteToIceberg
+      config:
+        table: "logs_dataset.logs_hdfs"
+        catalog_name: "rest_catalog"
+        catalog_properties:
+          warehouse: "{{ WAREHOUSE }}"
+          catalog-impl: "org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog"
+          io-impl: "org.apache.iceberg.gcp.gcs.GCSFileIO"
+          gcp_project: "{{ PROJECT }}"
+          gcp_location: "{{ REGION }}"
+
+# Expected:
+#  Row(LineId=1, Date='2024-10-01', Time='12:00:00', Level='INFO', Process='Main', Component='ComponentA', Content='System started successfully')
+#  Row(LineId=2, Date='2024-10-01', Time='12:00:05', Level='WARN', Process='Main', Component='ComponentA', Content='Memory usage is high')
+#  Row(LineId=3, Date='2024-10-01', Time='12:00:10', Level='ERROR', Process='Main', Component='ComponentA', Content='Task failed due to timeout')
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/ml_preprocessing.yaml b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/ml_preprocessing.yaml
new file mode 100644
index 000000000000..c83eb19e6484
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/ml_preprocessing.yaml
@@ -0,0 +1,124 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# The pipeline reads structured logs from an Iceberg table, applied
+# ML-specific transformations before writing them to a BigQuery table.
+
+pipeline:
+  type: chain
+  transforms:
+    - type: ReadFromIceberg
+      name: ReadFromIceberg
+      config:
+        table: "logs_dataset.logs_hdfs"
+        catalog_name: "rest_catalog"
+        catalog_properties:
+          warehouse: "{{ WAREHOUSE }}"
+          catalog-impl: "org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog"
+          io-impl: "org.apache.iceberg.gcp.gcs.GCSFileIO"
+          gcp_project: "{{ PROJECT }}"
+          gcp_location: "{{ REGION }}"
+
+    - type: MapToFields
+      name: MapToFields
+      config:
+        language: python
+        append: true
+        fields:
+          embedding:
+            callable: |
+              def fn(row):
+                line = [row.Date, row.Time, row.Level, row.Process,
+                    row.Component, row.Content]
+                return " ".join(line)
+
+    - type: MLTransform
+      name: Embedding
+      config:
+        write_artifact_location: "./beam-ml-artifacts"
+        transforms:
+          - type: SentenceTransformerEmbeddings
+            config: { model_name: all-MiniLM-L6-v2, columns: [ embedding ] }
+
+    - type: MapToFields
+      name: SchemaMapping
+      config:
+        language: python
+        fields:
+          id:
+            callable: "lambda row: row.LineId"
+            output_type: integer
+          date:
+            callable: "lambda row: row.Date"
+            output_type: string
+          time:
+            callable: "lambda row: row.Time"
+            output_type: string
+          level:
+            callable: "lambda row: row.Level"
+            output_type: string
+          process:
+            callable: "lambda row: row.Process"
+            output_type: string
+          component:
+            callable: "lambda row: row.Component"
+            output_type: string
+          content:
+            callable: "lambda row: row.Content"
+            output_type: string
+          embedding:
+            callable: "lambda row: row.embedding"
+            output_type:
+              type: array
+              items:
+                type: number
+
+    - type: MapToFields
+      name: Normalize
+      config:
+        language: python
+        append: true
+        drop: [embedding]
+        fields:
+          embedding:
+            callable: |
+              import numpy as np
+              
+              def normalize(row):
+                  embedding = row.embedding
+                  norm = np.linalg.norm(embedding)
+                  return embedding / norm
+            output_type:
+              type: array
+              items:
+                type: number
+
+    - type: WriteToBigQuery
+      name: WriteToBigQuery
+      config:
+        table: "{{ BQ_TABLE }}"
+        write_disposition: "WRITE_TRUNCATE"
+        create_disposition: "CREATE_IF_NEEDED"
+
+options:
+  yaml_experimental_features: [ 'ML' ]
+
+# Expected:
+#  Row(id=1, date='2024-10-01', time='12:00:00', level='INFO', process='Main', component='ComponentA', content='System started successfully', embedding=[0.13483997249264842, 0.26967994498529685, 0.40451991747794525, 0.5393598899705937, 0.674199862463242])
+#  Row(id=2, date='2024-10-01', time='12:00:05', level='WARN', process='Main', component='ComponentA', content='Memory usage is high', embedding=[0.13483997249264842, 0.26967994498529685, 0.40451991747794525, 0.5393598899705937, 0.674199862463242])
+#  Row(id=3, date='2024-10-01', time='12:00:10', level='ERROR', process='Main', component='ComponentA', content='Task failed due to timeout', embedding=[0.13483997249264842, 0.26967994498529685, 0.40451991747794525, 0.5393598899705937, 0.674199862463242])
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/requirements.txt b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/requirements.txt
new file mode 100644
index 000000000000..2a06309044e7
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/requirements.txt
@@ -0,0 +1,24 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Additional dependencies needed for the pipelines in the log analysis
+# ML workflow.
+# These can be installed on local machine to run the pipelines with local
+# runner, or submitted as part of Dataflow job for the workers' execution
+# environment.
+sentence-transformers~=5.0.0
+pyod~=2.0.5
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/train.py b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/train.py
new file mode 100644
index 000000000000..f0f957aa7ba0
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/train.py
@@ -0,0 +1,89 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+A script that trains a k-Nearest Neighbors (KNN) model on vector embeddings
+data queried from BigQuery.
+"""
+
+import argparse
+import logging
+import pickle
+
+import numpy as np
+from google.cloud import bigquery
+
+from pyod.models.knn import KNN
+
+
+def parse_arguments():
+  parser = argparse.ArgumentParser()
+  parser.add_argument(
+      '--bq_table',
+      required=True,
+      help='BigQuery fully qualified table name that contains vector '
+      'embeddings for training, '
+      'specified as `YOUR_PROJECT.YOUR_DATASET.YOUR_TABLE`.')
+
+  return parser.parse_known_args()
+
+
+class ModelHelper():
+  def __init__(self):
+    args, _ = parse_arguments()
+    self.n_neighbors = 8
+    self.method = 'largest'
+    self.metric = 'euclidean'
+    self.contamination = 0.1
+    self.bq_table = args.bq_table
+    self.dataset = None
+
+  def load_data(self):
+    logging.info("Querying vector embeddings from BigQuery...")
+
+    client = bigquery.Client()
+    sql = f"""
+      SELECT *
+      FROM `{self.bq_table}`
+      """
+    df = client.query_and_wait(sql).to_dataframe()
+    self.dataset = np.stack(df['embedding'].to_numpy())
+
+  def train_model(self):
+    logging.info("Training KNN model...")
+
+    model = KNN(
+        n_neighbors=self.n_neighbors,
+        method=self.method,
+        metric=self.metric,
+        contamination=self.contamination,
+    )
+    model.fit(self.dataset)
+
+    logging.info("KNN model trained successfully! Saving model...")
+
+    model_pickled_filename = 'knn_model.pkl'
+    with open(model_pickled_filename, 'wb') as f:
+      pickle.dump(model, f)
+
+
+if __name__ == "__main__":
+  logging.getLogger().setLevel(logging.INFO)
+
+  helper = ModelHelper()
+  helper.load_data()
+  helper.train_model()
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/sentiment_analysis/README.md b/sdks/python/apache_beam/yaml/examples/transforms/ml/sentiment_analysis/README.md
new file mode 100644
index 000000000000..e86075fb1331
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/sentiment_analysis/README.md
@@ -0,0 +1,100 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+## Streaming Sentiment Analysis
+
+The example leverages the `RunInference` transform with Vertex AI
+model handler [VertexAIModelHandlerJSON](
+https://beam.apache.org/releases/pydoc/current/apache_beam.yaml.yaml_ml#apache_beam.yaml.yaml_ml.VertexAIModelHandlerJSONProvider),
+in addition to Kafka IO to demonstrate an end-to-end example of a
+streaming sentiment analysis pipeline. The dataset to perform
+sentiment analysis on is the YouTube video comments and can be found
+on Kaggle [here](
+https://www.kaggle.com/datasets/datasnaek/youtube?select=UScomments.csv).
+
+Download the dataset and copy over to a GCS bucket:
+```sh
+export GCS_PATH="gs://YOUR-BUCKET/USComments.csv"
+gcloud storage cp /path/to/UScomments.csv $GCS_PATH
+```
+
+For setting up Kafka, an option is to use [Click to Deploy](
+https://console.cloud.google.com/marketplace/details/click-to-deploy-images/kafka?)
+to quickly launch a Kafka cluster on GCE. See [here](
+../../../README.md#kafka) for more context around using Kafka
+with Dataflow.
+
+A hosted model on Vertex AI is needed before being able to use
+the Vertex AI model handler. One of the current state-of-the-art
+NLP models is HuggingFace's DistilBERT, a distilled version of
+BERT model and is faster at inference. To deploy DistilBERT on
+Vertex AI, run this [notebook](
+https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_huggingface_pytorch_inference_deployment.ipynb) in Colab Enterprise.
+
+BigQuery is the pipeline's sink for the inference result output.
+A BigQuery dataset needs to exist first before the pipeline can
+create/write to a table. Run the following command to create
+a BigQuery dataset:
+
+```sh
+bq --location=us-central1 mk \
+  --dataset DATASET_ID
+export BQ_TABLE_ID="PROJECT_ID:DATASET_ID.TABLE_ID"
+```
+See also [here](
+https://cloud.google.com/bigquery/docs/datasets) for more details on
+how to create BigQuery datasets
+
+The pipeline first reads the YouTube comments .csv dataset from
+GCS bucket and performs some clean-up before writing it to a Kafka
+topic. The pipeline then reads from that Kafka topic and applies
+various transformation logic before `RunInference` transform performs
+remote inference with the Vertex AI model handler and DistilBERT
+deployed to a Vertex AI endpoint. The inference result is then
+parsed and written to a BigQuery table.
+
+Run the pipeline (replace with appropriate variables in the command
+below):
+
+```sh
+export PROJECT="$(gcloud config get-value project)"
+export TEMP_LOCATION="gs://YOUR-BUCKET/tmp"
+export REGION="us-central1"
+export JOB_NAME="streaming-sentiment-analysis-`date +%Y%m%d-%H%M%S`"
+export NUM_WORKERS="3"
+export KAFKA_BOOTSTRAP_SERVERS="BOOTSTRAP_IP_ADD:9092"
+export KAFKA_TOPIC="YOUR_TOPIC"
+export KAFKA_USERNAME="KAFKA_USERNAME"
+export KAFKA_PASSWORD="KAFKA_PASSWORD"
+export VERTEXAI_ENDPOINT="ENDPOINT_ID"
+
+python -m apache_beam.yaml.main \
+  --yaml_pipeline_file streaming_sentiment_analysis.yaml \
+  --runner DataflowRunner \
+  --temp_location $TEMP_LOCATION \
+  --project $PROJECT \
+  --region $REGION \
+  --num_workers $NUM_WORKERS \
+  --job_name $JOB_NAME \
+  --jinja_variables '{ "GCS_PATH": "'$GCS_PATH'",
+  "BOOTSTRAP_SERVERS": "'$KAFKA_BOOTSTRAP_SERVERS'",
+  "TOPIC": "'$KAFKA_TOPIC'", "USERNAME": "'$KAFKA_USERNAME'", "PASSWORD": "'$KAFKA_PASSWORD'",
+  "ENDPOINT": "'$VERTEXAI_ENDPOINT'", "PROJECT": "'$PROJECT'", "LOCATION": "'$REGION'",
+  "BQ_TABLE": "'$BQ_TABLE_ID'" }'
+```
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/sentiment_analysis/streaming_sentiment_analysis.yaml b/sdks/python/apache_beam/yaml/examples/transforms/ml/sentiment_analysis/streaming_sentiment_analysis.yaml
new file mode 100644
index 000000000000..10790998a9ea
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/sentiment_analysis/streaming_sentiment_analysis.yaml
@@ -0,0 +1,256 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# The pipeline first reads the YouTube comments .csv dataset from GCS bucket
+# and performs necessary clean-up before writing it to a Kafka topic.
+# The pipeline then reads from that Kafka topic and applies various transformation
+# logic before RunInference transform performs remote inference with the Vertex AI
+# model handler.
+# The inference result is then written to a BigQuery table.
+
+pipeline:
+  transforms:
+    # The YouTube comments dataset contains rows that
+    # have unexpected schema (e.g. rows with more fields,
+    # rows with fields that contain string instead of
+    # integer, etc...). PyTransform helps construct
+    # the logic to properly read in the csv dataset as
+    # a schema'd PCollection.
+    - type: PyTransform
+      name: ReadFromGCS
+      input: {}
+      config:
+        constructor: __callable__
+        kwargs:
+          source: |
+            def ReadYoutubeCommentsCsv(pcoll, file_pattern):
+              def _to_int(x):
+                try:
+                  return int(x)
+                except (ValueError):
+                  return None
+
+              return (
+                  pcoll
+                  | beam.io.ReadFromCsv(
+                        file_pattern,
+                        names=['video_id', 'comment_text', 'likes', 'replies'],
+                        on_bad_lines='skip',
+                        converters={'likes': _to_int, 'replies': _to_int})
+                  | beam.Filter(lambda row:
+                        None not in list(row._asdict().values()))
+                  | beam.Map(lambda row: beam.Row(
+                        video_id=str(row.video_id),
+                        comment_text=str(row.comment_text),
+                        likes=int(row.likes),
+                        replies=int(row.replies)))
+              )
+          file_pattern: "{{ GCS_PATH }}"
+
+    # Send the rows as Kafka records to an existing
+    # Kafka topic.
+    - type: WriteToKafka
+      name: SendRecordsToKafka
+      input: ReadFromGCS
+      config:
+        format: "JSON"
+        topic: "{{ TOPIC }}"
+        bootstrap_servers: "{{ BOOTSTRAP_SERVERS }}"
+        producer_config_updates:
+          sasl.jaas.config: "org.apache.kafka.common.security.plain.PlainLoginModule required \
+            username={{ USERNAME }} \
+            password={{ PASSWORD }};"
+          security.protocol: "SASL_PLAINTEXT"
+          sasl.mechanism: "PLAIN"
+
+    # Read Kafka records from an existing Kafka topic.
+    - type: ReadFromKafka
+      name: ReadFromMyTopic
+      config:
+        format: "JSON"
+        schema: |
+          {
+            "type": "object",
+            "properties": {
+              "video_id": { "type": "string" },
+              "comment_text": { "type": "string" },
+              "likes": { "type": "integer" },
+              "replies": { "type": "integer" }
+            }
+          }
+        topic: "{{ TOPIC }}"
+        bootstrap_servers: "{{ BOOTSTRAP_SERVERS }}"
+        auto_offset_reset_config: earliest
+        consumer_config:
+          sasl.jaas.config: "org.apache.kafka.common.security.plain.PlainLoginModule required \
+            username={{ USERNAME }} \
+            password={{ PASSWORD }};"
+          security.protocol: "SASL_PLAINTEXT"
+          sasl.mechanism: "PLAIN"
+
+    # Remove unexpected characters from the YouTube
+    # comment string, e.g. emojis, ascii characters
+    # outside the common day-to-day English.
+    - type: MapToFields
+      name: RemoveWeirdCharacters
+      input: ReadFromMyTopic
+      config:
+        language: python
+        fields:
+          video_id: video_id
+          comment_text:
+            callable: |
+              import re
+              def filter(row):
+                # regex match and keep letters, digits, whitespace and common punctuations,
+                # i.e. remove non printable ASCII characters (character codes not in
+                # the range 32 - 126, or \x20 - \x7E).
+                return re.sub(r'[^\x20-\x7E]', '', row.comment_text).strip()
+          likes: likes
+          replies: replies
+
+    # Remove rows that have empty comment text
+    # after previously removing unexpected characters.
+    - type: Filter
+      name: FilterForProperComments
+      input: RemoveWeirdCharacters
+      config:
+        language: python
+        keep:
+          callable: |
+            def filter(row):
+              return len(row.comment_text) > 0
+
+    # HuggingFace's distilbert-base-uncased is used for inference,
+    # which accepts string with a maximum limit of 250 tokens.
+    # Some of the comment strings can be large and are well over
+    # this limit after tokenization.
+    # This transform truncates the comment string and ensure
+    # every comment satisfy the maximum token limit.
+    - type: MapToFields
+      name: Truncating
+      input: FilterForProperComments
+      config:
+        language: python
+        dependencies:
+          - 'transformers>=4.48.0,<4.49.0'
+        fields:
+          video_id: video_id
+          comment_text:
+            callable: |
+              from transformers import AutoTokenizer
+
+              tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", use_fast=True)
+
+              def truncate_sentence(row):
+                tokens = tokenizer(
+                    row.comment_text,
+                    max_length=512,
+                    padding='max_length',
+                    truncation=True)
+                truncated_sentence = tokenizer.decode(tokens["input_ids"], skip_special_tokens=True)
+                return truncated_sentence
+          likes: likes
+          replies: replies
+
+    # HuggingFace's distilbert-base-uncased does not distinguish
+    # between upper and lower case tokens.
+    # This pipeline makes the same point by converting all words
+    # into lowercase.
+    - type: MapToFields
+      name: LowerCase
+      input: Truncating
+      config:
+        language: python
+        fields:
+          video_id: video_id
+          comment_text: "comment_text.lower()"
+          likes: likes
+          replies: replies
+
+    # With VertexAIModelHandlerJSON model handler,
+    # RunInference transform performs remote inferences by
+    # sending POST requests to the Vertex AI endpoint that
+    # our distilbert-base-uncased model is being deployed to.
+    - type: RunInference
+      name: DistilBERTRemoteInference
+      input: LowerCase
+      config:
+        inference_tag: "inference"
+        model_handler:
+          type: "VertexAIModelHandlerJSON"
+          config:
+            endpoint_id: "{{ ENDPOINT }}"
+            project: "{{ PROJECT }}"
+            location: "{{ LOCATION }}"
+            preprocess:
+              callable: 'lambda x: x.comment_text'
+
+    # Parse inference results output
+    - type: MapToFields
+      name: FormatInferenceOutput
+      input: DistilBERTRemoteInference
+      config:
+        language: python
+        fields:
+          video_id:
+            expression: video_id
+            output_type: string
+          comment_text:
+            callable: "lambda x: x.comment_text"
+            output_type: string
+          label:
+            callable: "lambda x: x.inference.inference[0]['label']"
+            output_type: string
+          score:
+            callable: "lambda x: x.inference.inference[0]['score']"
+            output_type: number
+          likes:
+            expression: likes
+            output_type: integer
+          replies:
+            expression: replies
+            output_type: integer
+
+    # Assign windows to each element of the unbounded PCollection.
+    - type: WindowInto
+      name: Windowing
+      input: FormatInferenceOutput
+      config:
+        windowing:
+          type: fixed
+          size: 30s
+
+    # Write all inference results to a BigQuery table.
+    - type: WriteToBigQuery
+      name: WriteInferenceResultsToBQ
+      input: Windowing
+      config:
+        table: "{{ BQ_TABLE }}"
+        create_disposition: CREATE_IF_NEEDED
+        write_disposition: WRITE_APPEND
+
+options:
+  yaml_experimental_features: ML
+
+# Expected:
+#  Row(video_id='XpVt6Z1Gjjo', comment_text='I AM HAPPY', likes=1, replies=1)
+#  Row(video_id='XpVt6Z1Gjjo', comment_text='I AM SAD', likes=1, replies=1)
+#  Row(video_id='XpVt6Z1Gjjo', comment_text='§ÁĐ', likes=1, replies=1)
+#  Row(video_id='XpVt6Z1Gjjo', comment_text='i am happy', label='POSITIVE', score=0.95, likes=1, replies=1)
+#  Row(video_id='XpVt6Z1Gjjo', comment_text='i am sad', label='NEGATIVE', score=0.95, likes=1, replies=1)
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/README.md b/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/README.md
new file mode 100644
index 000000000000..75aa95456017
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/README.md
@@ -0,0 +1,91 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+## Streaming Taxi Fare Prediction Pipeline
+
+The example leverages the `RunInference` transform with Vertex AI
+model handler [VertexAIModelHandlerJSON](
+https://beam.apache.org/releases/pydoc/current/apache_beam.yaml.yaml_ml#apache_beam.yaml.yaml_ml.VertexAIModelHandlerJSONProvider),
+in addition to PubSub, Kafka and BiqQuery IO to demonstrate an end-to-end
+example of a streaming ML pipeline predicting NYC taxi fare amounts.
+
+The data stream of NYC taxi ride events are from the existing public
+PubSub topic `projects/pubsub-public-data/topics/taxirides-realtime`.
+
+For setting up Kafka, an option is to use [Click to Deploy](
+https://console.cloud.google.com/marketplace/details/click-to-deploy-images/kafka?)
+to quickly launch a Kafka cluster on GCE. See [existing example here](
+../../../README.md#kafka) for more context around using Kafka with Beam
+and Dataflow.
+
+BigQuery is the pipeline's sink for the inference result output.
+A BigQuery dataset needs to exist first before the pipeline can
+create/write to a table. Run the following command to create
+a BigQuery dataset:
+
+```sh
+bq --location=us-central1 mk \
+  --dataset DATASET_ID
+export BQ_TABLE_ID="PROJECT_ID:DATASET_ID.TABLE_ID"
+```
+See also [here](
+https://cloud.google.com/bigquery/docs/datasets) for more details on
+how to create BigQuery datasets.
+
+A trained model hosted on Vertex AI is needed before being able to use
+the Vertex AI model handler. To train and deploy a custom model for the
+taxi fare prediction problem, open and run the
+[custom_nyc_taxifare_model_deployment](
+custom_nyc_taxifare_model_deployment.ipynb) notebook in Colab Enterprise.
+
+The pipeline first reads the data stream of taxi rides events from the
+public PubSub topic and performs some transformations before writing it
+to a Kafka topic. The pipeline then reads from that Kafka topic and applies
+the necessary transformation logic, before `RunInference` transform performs
+remote inference with the Vertex AI model handler and the custom-trained
+model deployed to a Vertex AI endpoint. The inference result is then
+parsed and written to a BigQuery table.
+
+Run the pipeline (replace with appropriate variables in the command below):
+
+```sh
+export PROJECT="$(gcloud config get-value project)"
+export TEMP_LOCATION="gs://YOUR-BUCKET/tmp"
+export REGION="us-central1"
+export JOB_NAME="streaming-taxifare-prediction`date +%Y%m%d-%H%M%S`"
+export NUM_WORKERS="3"
+export KAFKA_BOOTSTRAP_SERVERS="BOOTSTRAP_IP_ADD:9092"
+export KAFKA_TOPIC="YOUR_TOPIC"
+export KAFKA_USERNAME="KAFKA_USERNAME"
+export KAFKA_PASSWORD="KAFKA_PASSWORD"
+export VERTEXAI_ENDPOINT="ENDPOINT_ID"
+
+python -m apache_beam.yaml.main \
+  --yaml_pipeline_file streaming_taxifare_prediction.yaml \
+  --runner DataflowRunner \
+  --temp_location $TEMP_LOCATION \
+  --project $PROJECT \
+  --region $REGION \
+  --num_workers $NUM_WORKERS \
+  --job_name $JOB_NAME \
+  --jinja_variables '{ "BOOTSTRAP_SERVERS": "'$KAFKA_BOOTSTRAP_SERVERS'",
+  "TOPIC": "'$KAFKA_TOPIC'", "USERNAME": "'$KAFKA_USERNAME'", "PASSWORD": "'$KAFKA_PASSWORD'",
+  "ENDPOINT": "'$VERTEXAI_ENDPOINT'", "PROJECT": "'$PROJECT'", "LOCATION": "'$REGION'",
+  "BQ_TABLE": "'$BQ_TABLE_ID'" }'
+```
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/custom_nyc_taxifare_model_deployment.ipynb b/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/custom_nyc_taxifare_model_deployment.ipynb
new file mode 100644
index 000000000000..1201c1011d44
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/custom_nyc_taxifare_model_deployment.ipynb
@@ -0,0 +1,810 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "source": [
+    "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+    "\n",
+    "# Licensed to the Apache Software Foundation (ASF) under one\n",
+    "# or more contributor license agreements. See the NOTICE file\n",
+    "# distributed with this work for additional information\n",
+    "# regarding copyright ownership. The ASF licenses this file\n",
+    "# to you under the Apache License, Version 2.0 (the\n",
+    "# \"License\"); you may not use this file except in compliance\n",
+    "# with the License. You may obtain a copy of the License at\n",
+    "#\n",
+    "#   http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing,\n",
+    "# software distributed under the License is distributed on an\n",
+    "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+    "# KIND, either express or implied. See the License for the\n",
+    "# specific language governing permissions and limitations\n",
+    "# under the License"
+   ],
+   "metadata": {
+    "id": "ZpDmaAwXuRnG"
+   },
+   "id": "ZpDmaAwXuRnG",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# NYC Taxi Fare Prediction - Model Training and Deployment\n",
+    "\n",
+    "<table><tbody><tr>\n",
+    "  <td style=\"text-align: center\">\n",
+    "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2Fapache%2Fbeam%2Frefs%2Fheads%2Fmaster%2Fsdks%2Fpython%2Fapache_beam%2Fyaml%2Fexamples%2Ftransforms%2Fml%2Ftaxi_fare%2Fcustom_nyc_taxifare_model_deployment.ipynb\">\n",
+    "      <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
+    "    </a>\n",
+    "  </td>\n",
+    "  <td style=\"text-align: center\">\n",
+    "    <a href=\"https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/custom_nyc_taxifare_model_deployment.ipynb\">\n",
+    "      <img alt=\"GitHub logo\" src=\"https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png\" width=\"32px\"><br> View on GitHub\n",
+    "    </a>\n",
+    "  </td>\n",
+    "</tr></tbody></table>\n"
+   ],
+   "metadata": {
+    "id": "m916RPCn0NSS"
+   },
+   "id": "m916RPCn0NSS"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Overview\n",
+    "\n",
+    "This notebook demonstrates the training and deployment of a custom tabular regression model for online prediction.\n",
+    "\n",
+    "We'll train a [gradient-boosted decision tree (GBDT) model](https://en.wikipedia.org/wiki/Gradient_boosting) using [XGBoost](https://xgboost.readthedocs.io/en/stable/index.html) to predict the fare of a taxi trip in New York City, given the information such as pick-up date and time, pick-up location, drop-off location and passenger count. The dataset is from the Kaggle competition https://www.kaggle.com/c/new-york-city-taxi-fare-prediction organized by Google Cloud.\n",
+    "\n",
+    "After model training and evaluation, we'll use Vertex AI Python SDK to upload this custom model to Vertex AI Model Registry and deploy it to perform remote inferences at scale. The prefered way to run this notebook is within Colab Enterprise.\n",
+    "\n",
+    "## Outline\n",
+    "1. Dataset\n",
+    "\n",
+    "2. Training\n",
+    "\n",
+    "3. Evaluation\n",
+    "\n",
+    "4. Deployment\n",
+    "\n",
+    "5. Reference"
+   ],
+   "metadata": {
+    "id": "jGbLxUoraooN"
+   },
+   "id": "jGbLxUoraooN"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We first install and import the necessary libraries to run this notebook."
+   ],
+   "metadata": {
+    "id": "e6zO5wWaMhaX"
+   },
+   "id": "e6zO5wWaMhaX"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!pip3 install --quiet --upgrade \\\n",
+    "  opendatasets \\\n",
+    "  google-cloud-storage \\\n",
+    "  google-cloud-aiplatform \\\n",
+    "  scikit-learn \\\n",
+    "  xgboost \\\n",
+    "  pandas"
+   ],
+   "metadata": {
+    "id": "weUpgu9Y1OoF"
+   },
+   "id": "weUpgu9Y1OoF",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import opendatasets as od\n",
+    "import pandas as pd\n",
+    "import random\n",
+    "import time\n",
+    "import os\n",
+    "\n",
+    "from xgboost import XGBRegressor\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import root_mean_squared_error\n",
+    "\n",
+    "import google.cloud.storage as storage\n",
+    "import google.cloud.aiplatform as vertex"
+   ],
+   "metadata": {
+    "id": "KJTsSdQKSN_m"
+   },
+   "id": "KJTsSdQKSN_m",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Dataset\n",
+    "\n",
+    "We use the `opendatasets` library to programmatically download the dataset from Kaggle.\n",
+    "\n",
+    "We'll first need a Kaggle account and register for this competition. We'll also need the API key which is stored in `kaggle.json` file automatically downloaded when you create an API token. Go to *Profile* picture -> *Settings* -> *API* -> *Create New Token*.\n",
+    "\n",
+    "The dataset download will prompt you to enter your Kaggle username and key. Copy this information from `kaggle.json`.\n"
+   ],
+   "metadata": {
+    "id": "DVWcleCz1AVl"
+   },
+   "id": "DVWcleCz1AVl"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "dataset_url = 'https://www.kaggle.com/c/new-york-city-taxi-fare-prediction'\n",
+    "od.download(dataset_url)"
+   ],
+   "metadata": {
+    "id": "8D-KUYKD1lg4"
+   },
+   "id": "8D-KUYKD1lg4",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Among the downloaded files, we will only make use of `test.csv` testing dataset and primarily `train.csv` training dataset for the purpose of training and evaluating our model."
+   ],
+   "metadata": {
+    "id": "NMCdiinpTF0W"
+   },
+   "id": "NMCdiinpTF0W"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "data_dir = 'new-york-city-taxi-fare-prediction'\n",
+    "!dir -l {data_dir}"
+   ],
+   "metadata": {
+    "id": "rmlERXShR457"
+   },
+   "id": "rmlERXShR457",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The training dataset contains approx. 55M rows. Reading the entire dataset into a pandas DataFrame (i.e. loading the entire dataset into memory) is slow and memory-consuming that can affect operations in later parts of the notebook. And for the purpose of experimenting with our model, it is also unnecessary.\n",
+    "\n",
+    "A good practice is to sample some percentage of the training dataset."
+   ],
+   "metadata": {
+    "id": "Yv9Kq0v2T_1g"
+   },
+   "id": "Yv9Kq0v2T_1g"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "p = 0.01\n",
+    "# keep the header, then take only 1% of rows\n",
+    "# if random from [0,1] interval is greater than 0.01 the row will be skipped\n",
+    "df_train_val = pd.read_csv(\n",
+    "    data_dir + \"/train.csv\",\n",
+    "    header=0,\n",
+    "    parse_dates = ['pickup_datetime'],\n",
+    "    skiprows=lambda i: i > 0 and random.random() > p\n",
+    ")\n",
+    "df_train_val.shape"
+   ],
+   "metadata": {
+    "id": "epJNJkp1W7P_"
+   },
+   "id": "epJNJkp1W7P_",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The training dataset, now as a DataFrame table, can be further inspected."
+   ],
+   "metadata": {
+    "id": "bzRYmrc-YDdd"
+   },
+   "id": "bzRYmrc-YDdd"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "df_train_val.columns"
+   ],
+   "metadata": {
+    "id": "AkJ2-w3BW7dD"
+   },
+   "id": "AkJ2-w3BW7dD",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "df_train_val.info()"
+   ],
+   "metadata": {
+    "id": "AxAMXNTiKe2D"
+   },
+   "id": "AxAMXNTiKe2D",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "df_train_val"
+   ],
+   "metadata": {
+    "id": "4LFxT3Zec8tX"
+   },
+   "id": "4LFxT3Zec8tX",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The testing dataset is a lot smaller in size and doesn't have the `fare_amount` column. Likewise, we can read the dataset as a DataFrame and inspect the data."
+   ],
+   "metadata": {
+    "id": "xxPAnGR1ZDwf"
+   },
+   "id": "xxPAnGR1ZDwf"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "df_test = pd.read_csv(data_dir + \"/test.csv\", parse_dates = ['pickup_datetime'])\n",
+    "df_test.columns"
+   ],
+   "metadata": {
+    "id": "cWBexIlsW_4u"
+   },
+   "id": "cWBexIlsW_4u",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "df_test"
+   ],
+   "metadata": {
+    "id": "bch6SYLxL51_"
+   },
+   "id": "bch6SYLxL51_",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We'll set aside 20% of the training data as the validation set, to evaluate the model on previously unseen data."
+   ],
+   "metadata": {
+    "id": "SOeJDlsCZcY0"
+   },
+   "id": "SOeJDlsCZcY0"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "df_train, df_val = train_test_split(\n",
+    "    df_train_val,\n",
+    "    test_size=0.2,\n",
+    "    random_state=42 # set random_state to some constant so we always have the same training and validation data\n",
+    ")\n",
+    "\n",
+    "print(\"Training dataset's shape: \", df_train.shape)\n",
+    "print(\"Validation dataset's shape: \", df_val.shape)"
+   ],
+   "metadata": {
+    "id": "qVN1ygVGOH33"
+   },
+   "id": "qVN1ygVGOH33",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Training\n",
+    "\n",
+    "For a quick '0-to-1' model serving on Vertex AI, the model training process\n",
+    "below is kept straighforward using the simple yet very effective [tree-based, gradient boosting](https://en.wikipedia.org/wiki/Gradient_boosting) algorithm. We start off with a simple feature engineering idea, before moving on to the actual training of the model using the [XGBoost](https://xgboost.readthedocs.io/en/stable/index.html) library.\n"
+   ],
+   "metadata": {
+    "id": "4Ov7Efuy1Gyj"
+   },
+   "id": "4Ov7Efuy1Gyj"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Simple Feature Engineering\n",
+    "\n",
+    "One of the columns in the dataset is the `pickup_datetime` column, which is of [datetime like](https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.html) type. This makes it incredibly easy for performing data analysis on time-series data such as this. However, ML models don't accept feature columns with such a custom data type that is not a number. Some sort of conversion is needed, and here we'll choose to break this datetime column into multiple feature columns.\n"
+   ],
+   "metadata": {
+    "id": "-CWgL07pNH7H"
+   },
+   "id": "-CWgL07pNH7H"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "def add_dateparts(df, col):\n",
+    "    \"\"\"\n",
+    "    This function splits the datetime column into separate column such as\n",
+    "    year, month, day, weekday, and hour\n",
+    "    :param df: DataFrame table to add the columns\n",
+    "    :param col: the column with datetime values\n",
+    "    :return: None\n",
+    "    \"\"\"\n",
+    "    df[col + '_year'] = df[col].dt.year\n",
+    "    df[col + '_month'] = df[col].dt.month\n",
+    "    df[col + '_day'] = df[col].dt.day\n",
+    "    df[col + '_weekday'] = df[col].dt.weekday\n",
+    "    df[col + '_hour'] = df[col].dt.hour"
+   ],
+   "metadata": {
+    "id": "O-qK4QVk1mD8"
+   },
+   "id": "O-qK4QVk1mD8",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "add_dateparts(df_train, 'pickup_datetime')\n",
+    "add_dateparts(df_val, 'pickup_datetime')\n",
+    "add_dateparts(df_test, 'pickup_datetime')"
+   ],
+   "metadata": {
+    "id": "huYKIwMvPS0H"
+   },
+   "id": "huYKIwMvPS0H",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "df_train.info()"
+   ],
+   "metadata": {
+    "id": "ovKvngO5SKeb"
+   },
+   "id": "ovKvngO5SKeb",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "df_train.head()"
+   ],
+   "metadata": {
+    "id": "YhbHtOxAP-13"
+   },
+   "id": "YhbHtOxAP-13",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Gradient Boosting\n",
+    "\n",
+    "Predicting taxi fare is a supervised learning, regression problem and our dataset is tabular. It is well-known in common literatures (_[1]_, _[2]_) that [gradient-boosted decision tree (GBDT) model](https://en.wikipedia.org/wiki/Gradient_boosting) performs very well for this kind of problem and dataset type.\n",
+    "\n",
+    "The input columns used for training (and subsequently for inference) will be the original feature columns (pick-up and drop-off longitude/latitude and the passenger count) from the dataset, along with the additional engineered features (pick-up year, month, day, etc...) that we generated above. The target/label column for training is the `fare_amount` column.\n"
+   ],
+   "metadata": {
+    "id": "7IX7HX71NNz-"
+   },
+   "id": "7IX7HX71NNz-"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "input_cols = ['pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude', 'passenger_count',\n",
+    "              'pickup_datetime_year', 'pickup_datetime_month', 'pickup_datetime_day', 'pickup_datetime_weekday',\n",
+    "              'pickup_datetime_hour']\n",
+    "\n",
+    "target_cols = 'fare_amount'\n",
+    "\n",
+    "train_inputs = df_train[input_cols]\n",
+    "train_targets = df_train[target_cols]\n",
+    "\n",
+    "val_inputs = df_val[input_cols]\n",
+    "val_targets = df_val[target_cols]\n",
+    "\n",
+    "test_inputs = df_test[input_cols]"
+   ],
+   "metadata": {
+    "id": "kASu8nxeNVEV"
+   },
+   "id": "kASu8nxeNVEV",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "As noted before, we use the XGBoost library which implements the GBDT machine learning algorithm in a scalable, distributed manner. Specifically,\n",
+    "we'll use the [XGBoostRegressor](https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.XGBRegressor) API and fit the\n",
+    "training data by the squared-error loss function. The hyperparameters chosen here are simply through trial-and-error to see which one gives the best result."
+   ],
+   "metadata": {
+    "id": "BQnLy75K1cIF"
+   },
+   "id": "BQnLy75K1cIF"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "xgb_model = XGBRegressor(objective='reg:squarederror',\n",
+    "                         n_jobs=-1,\n",
+    "                         random_state=42,\n",
+    "                         n_estimators=500,\n",
+    "                         max_depth=5,\n",
+    "                         learning_rate=0.05,\n",
+    "                         tree_method='hist',\n",
+    "                         subsample=0.8,\n",
+    "                         colsample_bytree=0.8)"
+   ],
+   "id": "af5fefaee2f81aa1"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "**Note**: The model should be trained on array-like dataset (e.g. `numpy.ndarray`), instead of pandas DataFrame or Series object. This is to help passing/serializing input data in the request for remote inference later on, and to avoid a DataFrame/array-like mismatch error such as [this](https://datascience.stackexchange.com/questions/63872/lime-explainer-valueerror-training-data-did-not-have-the-following-fields).",
+   "id": "c0c95d56399f0968"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "xgb_model.fit(train_inputs.values, train_targets.values)",
+   "id": "ba33a648416a5d71"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Evaluation\n",
+    "\n",
+    "A typical metric used for model evaluation is the root mean squared error (RMSE).\n"
+   ],
+   "metadata": {
+    "id": "4P7CaoHy1YXA"
+   },
+   "id": "4P7CaoHy1YXA"
+  },
+  {
+   "cell_type": "code",
+   "id": "4x1Yx5kvj3DDcfH3AvpnPqBd",
+   "metadata": {
+    "tags": [],
+    "id": "4x1Yx5kvj3DDcfH3AvpnPqBd"
+   },
+   "source": [
+    "def evaluate(model):\n",
+    "    \"\"\"\n",
+    "    :param model: trained model to evaluate\n",
+    "    :return: a tuple of training and validation RMSE results\n",
+    "    \"\"\"\n",
+    "    train_preds = model.predict(train_inputs)\n",
+    "    train_rmse = root_mean_squared_error(train_targets, train_preds)\n",
+    "    val_preds = model.predict(val_inputs)\n",
+    "    val_rmse = root_mean_squared_error(val_targets, val_preds)\n",
+    "\n",
+    "    return train_rmse, val_rmse\n",
+    "\n",
+    "training_rmse, validation_rmse = evaluate(xgb_model)\n",
+    "print(\"Training RMSE: \", training_rmse)\n",
+    "print(\"Validation RMSE: \", validation_rmse)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We finally make use of the testing dataset by making model inference on this test data. The predicted label is the `predicted_fare_amount` column."
+   ],
+   "metadata": {
+    "id": "oY1cDTDQADns"
+   },
+   "id": "oY1cDTDQADns"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "test_preds = xgb_model.predict(test_inputs)\n",
+    "result_df = df_test.copy()\n",
+    "result_df['predicted_fare_amount'] = test_preds"
+   ],
+   "metadata": {
+    "id": "7Pn2lrrLX3qS"
+   },
+   "id": "7Pn2lrrLX3qS",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Deployment\n",
+    "\n",
+    "Once the model is finished training and evaluating, the next step is making model serving possible on Vertex AI.\n",
+    "\n",
+    "Initialize the Vertex AI SDK for Python for your project.\n"
+   ],
+   "metadata": {
+    "id": "CAPlN6Qf1nDx"
+   },
+   "id": "CAPlN6Qf1nDx"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "PROJECT_ID = \"your-project-id\"  # @param {type:\"string\"}\n",
+    "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
+    "BUCKET_URI = \"gs://your-bucket-name\"  # @param {type:\"string\"}\n",
+    "\n",
+    "vertex.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)\n",
+    "\n",
+    "print(f\"Project: {PROJECT_ID} | Region: {REGION}\")"
+   ],
+   "metadata": {
+    "id": "iZA3Wg3OcA5Q"
+   },
+   "id": "iZA3Wg3OcA5Q",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": "Save the trained model to the Google Cloud Storage bucket as a model artifact.",
+   "metadata": {
+    "id": "isH53ZHeEXYn"
+   },
+   "id": "isH53ZHeEXYn"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "FILE_NAME = \"model.bst\"\n",
+    "xgb_model.save_model(FILE_NAME)\n",
+    "\n",
+    "# Upload the saved model file to GCS\n",
+    "BLOB_PATH = \"taxifare_prediction/\"\n",
+    "\n",
+    "BLOB_NAME = BLOB_PATH + FILE_NAME\n",
+    "\n",
+    "bucket = storage.Client().bucket(BUCKET_URI[5:])\n",
+    "blob = bucket.blob(BLOB_NAME)\n",
+    "blob.upload_from_filename(FILE_NAME)"
+   ],
+   "metadata": {
+    "id": "2EWva73-1pop"
+   },
+   "id": "2EWva73-1pop",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Set the machine type as well as pre-built container image for serving inference."
+   ],
+   "metadata": {
+    "id": "E2SD3zq8EZEN"
+   },
+   "id": "E2SD3zq8EZEN"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MODEL_DISPLAY_NAME = f\"custom/xgb-model-nyc-taxifare\"\n",
+    "\n",
+    "ARTIFACT_GCS_PATH = f\"{BUCKET_URI}/{BLOB_PATH}\"\n",
+    "\n",
+    "DEPLOY_VERSION = \"xgboost-cpu.2-0\"\n",
+    "DEPLOY_IMAGE = \"us-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(DEPLOY_VERSION)\n",
+    "\n",
+    "MACHINE_TYPE = \"n1-standard\"\n",
+    "VCPU = \"4\"\n",
+    "DEPLOY_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
+    "print(\"Deploy machine type\", DEPLOY_COMPUTE)"
+   ],
+   "metadata": {
+    "id": "IL0cLZ2ZdUE7"
+   },
+   "id": "IL0cLZ2ZdUE7",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Upload the model artifact from the GCS bucket to Vertex AI Model Registry."
+   ],
+   "metadata": {
+    "id": "y_aywT6bEag1"
+   },
+   "id": "y_aywT6bEag1"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MODEL_OBJ = vertex.Model.upload(\n",
+    "    display_name = MODEL_DISPLAY_NAME,\n",
+    "    artifact_uri = ARTIFACT_GCS_PATH,\n",
+    "    serving_container_image_uri = DEPLOY_IMAGE,\n",
+    "    serving_container_predict_route = \"/predict\",\n",
+    "    serving_container_health_route  = \"/ping\",\n",
+    "    labels = {\"framework\":\"xgboost\",\"demo\":\"nyc_taxi\"}\n",
+    ")\n",
+    "\n",
+    "print(\"Model resource:\", MODEL_OBJ.resource_name)"
+   ],
+   "metadata": {
+    "id": "DtB8_9lak6uN"
+   },
+   "id": "DtB8_9lak6uN",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Create a Vertex AI dedicated endpoint for serving inference requests."
+   ],
+   "metadata": {
+    "id": "nSv0xE-fF5_k"
+   },
+   "id": "nSv0xE-fF5_k"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ENDPOINT = vertex.Endpoint.create(\n",
+    "    display_name=f\"{MODEL_DISPLAY_NAME}-endpoint\",\n",
+    "    dedicated_endpoint_enabled=True,\n",
+    ")"
+   ],
+   "metadata": {
+    "id": "ow1NLPxJ1hL_"
+   },
+   "id": "ow1NLPxJ1hL_",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Deploy the model from the Model Registry to the dedicated endpoint.\n",
+    "\n",
+    "**Note**: This is a long-running operation that will take about 20 minutes to finish."
+   ],
+   "metadata": {
+    "id": "akwrkSz1EbNg"
+   },
+   "id": "akwrkSz1EbNg"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MODEL_OBJ.deploy(\n",
+    "    endpoint = ENDPOINT,\n",
+    "    machine_type = DEPLOY_COMPUTE,\n",
+    "    deploy_request_timeout=1800,\n",
+    "    traffic_percentage=100\n",
+    ")\n",
+    "\n",
+    "print(\"Endpoint:\", ENDPOINT.resource_name)"
+   ],
+   "metadata": {
+    "id": "vZ1G85WClYgy"
+   },
+   "id": "vZ1G85WClYgy",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Run online predictions on some sample inputs."
+   ],
+   "metadata": {
+    "id": "mXn5PGdUEgYJ"
+   },
+   "id": "mXn5PGdUEgYJ"
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "instances = [val_inputs.iloc[0].to_list(), val_inputs.iloc[1].to_list(), val_inputs.iloc[2].to_list()]\n",
+    "print(instances)\n",
+    "\n",
+    "predictions = ENDPOINT.predict(instances)\n",
+    "print(\"Predicted fares: \", predictions.predictions)\n",
+    "print(\"Actual fares: \", val_targets.iloc[0:3].to_list())"
+   ],
+   "metadata": {
+    "id": "hR9hOflMqkGN"
+   },
+   "id": "hR9hOflMqkGN",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Reference\n",
+    "\n",
+    "[1] Hastie, T., Tibshirani, R., Friedman, J. (2009). Boosting and Additive Trees. In: The Elements of Statistical Learning. Springer Series in Statistics\n",
+    "\n",
+    "[2] Murphy, K. P. (2012). Adaptive Basis Function Models. In: Machine learning: a probabilistic perspective. MIT press.\n",
+    "\n",
+    "[3] Sample notebooks for Vertex AI workflows: https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks\n"
+   ],
+   "metadata": {
+    "id": "U30-Zn75GUBK"
+   },
+   "id": "U30-Zn75GUBK"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  },
+  "colab": {
+   "provenance": [],
+   "name": "custom_nyc_taxifare_model_deployment.ipynb"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/streaming_taxifare_prediction.yaml b/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/streaming_taxifare_prediction.yaml
new file mode 100644
index 000000000000..c213cb0ab942
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/streaming_taxifare_prediction.yaml
@@ -0,0 +1,320 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# The pipeline first reads the data stream of taxi rides events from the
+# public PubSub topic and performs some transformations before writing it
+# to a Kafka topic. The pipeline then reads from that Kafka topic and applies
+# the necessary transformation logic, before `RunInference` transform performs
+# remote inference with the Vertex AI model handler and the custom-trained
+# model deployed to a Vertex AI endpoint. The inference result is then
+# parsed and written to a BigQuery table.
+
+pipeline:
+  transforms:
+    # Read the data stream of taxi rides event from the public PubSub topic.
+    - type: ReadFromPubSub
+      name: ReadPubSub
+      config:
+        topic: "projects/pubsub-public-data/topics/taxirides-realtime"
+        format: "JSON"
+        schema:
+          type: object
+          properties:
+            ride_id: { type: string }
+            longitude: { type: number }
+            latitude: { type: number }
+            passenger_count: { type: integer }
+            meter_reading: { type: number }
+            timestamp: { type: string }
+            ride_status: { type: string }
+
+    # Use the `timestamp` value of the data and assign it as
+    # timestamp for each element of the PCollection.
+    - type: AssignTimestamps
+      name: AssignTimestamps
+      input: ReadPubSub
+      config:
+        language: python
+        timestamp:
+          callable: |
+            from datetime import datetime, timezone
+            import sys
+            import re
+
+            def fn(row):
+                ts_str = row.timestamp
+                # For Python < 3.11, datetime.fromisoformat requires
+                # microseconds to be padded to 6 digits.
+                if sys.version_info < (3, 11):
+                    _PAD_MICROS = re.compile(r'\.(\d{1,5})([+-]\d{2}:\d{2}|Z)$')
+                    ts_str = _PAD_MICROS.sub(lambda m: '.' + m.group(1).ljust(6, '0') + m.group(2), ts_str)
+                return datetime.fromisoformat(ts_str).astimezone(timezone.utc)
+
+    # Assign windows to each element of the unbounded PCollection.
+    - type: WindowInto
+      name: Windowing
+      input: AssignTimestamps
+      config:
+        windowing:
+          type: sessions
+          gap: 10m
+
+    # Filter for pick-up taxi ride events
+    - type: Filter
+      name: FilterPickupEvents
+      input: Windowing
+      config:
+        language: python
+        keep: "ride_status == 'pickup'"
+
+    # Map the columns accordingly for pick-up taxi ride events
+    - type: MapToFields
+      name: FormatPickupEvents
+      input: FilterPickupEvents
+      config:
+        fields:
+          ride_id: ride_id
+          pickup_latitude: latitude
+          pickup_longitude: longitude
+          pickup_datetime: timestamp
+          passenger_count: passenger_count
+
+    # Filter for drop-off taxi ride events
+    - type: Filter
+      name: FilterDropoffEvents
+      input: Windowing
+      config:
+        language: python
+        keep: "ride_status == 'dropoff'"
+
+    # Map the columns accordingly for drop-off taxi ride events
+    - type: MapToFields
+      name: FormatDropoffEvents
+      input: FilterDropoffEvents
+      config:
+        fields:
+          ride_id: ride_id
+          dropoff_latitude: latitude
+          dropoff_longitude: longitude
+          dropoff_datetime: timestamp
+
+    # Join the pick-up and drop-off taxi ride events together to get
+    # complete taxi trips.
+    - type: Join
+      name: Join
+      input:
+        pickup: FormatPickupEvents
+        dropoff: FormatDropoffEvents
+      config:
+        equalities: ride_id
+        type: inner
+        fields:
+          pickup: [ride_id, passenger_count, pickup_longitude, pickup_latitude, pickup_datetime]
+          dropoff: [dropoff_longitude, dropoff_latitude]
+
+    # Send the rows as Kafka records to an existing Kafka topic.
+    - type: WriteToKafka
+      name: WriteKafka
+      input: Join
+      config:
+        format: "JSON"
+        topic: "{{ TOPIC }}"
+        bootstrap_servers: "{{ BOOTSTRAP_SERVERS }}"
+        producer_config_updates:
+          sasl.jaas.config: "org.apache.kafka.common.security.plain.PlainLoginModule required \
+            username={{ USERNAME }} \
+            password={{ PASSWORD }};"
+          security.protocol: "SASL_PLAINTEXT"
+          sasl.mechanism: "PLAIN"
+
+    # Read Kafka records from an existing Kafka topic.
+    - type: ReadFromKafka
+      name: ReadKafka
+      config:
+        topic: "{{ TOPIC }}"
+        format: "JSON"
+        schema: |
+          {
+            "type": "object",
+            "properties": {
+              "ride_id": { "type": "string" },
+              "pickup_longitude": { "type": "number" },
+              "pickup_latitude": { "type": "number" },
+              "pickup_datetime": { "type": "string" },
+              "dropoff_longitude": { "type": "number" },
+              "dropoff_latitude": { "type": "number" },
+              "passenger_count": { "type": "integer" },
+            }
+          }
+        bootstrap_servers: "{{ BOOTSTRAP_SERVERS }}"
+        auto_offset_reset_config: earliest
+        consumer_config:
+          sasl.jaas.config: "org.apache.kafka.common.security.plain.PlainLoginModule required \
+            username={{ USERNAME }} \
+            password={{ PASSWORD }};"
+          security.protocol: "SASL_PLAINTEXT"
+          sasl.mechanism: "PLAIN"
+
+    # Create features accordingly for input data to prepare for inference.
+    - type: MapToFields
+      name: GenerateFeatures
+      input: ReadKafka
+      config:
+        language: python
+        fields:
+          ride_id: ride_id
+          pickup_longitude: pickup_longitude
+          pickup_latitude: pickup_latitude
+          dropoff_longitude: dropoff_longitude
+          dropoff_latitude: dropoff_latitude
+          passenger_count: passenger_count
+          pickup_datetime: pickup_datetime
+          pickup_datetime_year:
+            callable: |
+              from datetime import datetime
+              import sys
+              import re
+
+              def fn(row):
+                  ts_str = row.pickup_datetime
+                  if sys.version_info < (3, 11):
+                      _PAD = re.compile(r'\.(\d{1,5})([+-]\d{2}:\d{2}|Z)$')
+                      ts_str = _PAD.sub(lambda m: '.' + m.group(1).ljust(6, '0') + m.group(2), ts_str)
+                  return datetime.fromisoformat(ts_str).year
+          pickup_datetime_month:
+            callable: |
+              from datetime import datetime
+              import sys
+              import re
+
+              def fn(row):
+                  ts_str = row.pickup_datetime
+                  if sys.version_info < (3, 11):
+                      _PAD = re.compile(r'\.(\d{1,5})([+-]\d{2}:\d{2}|Z)$')
+                      ts_str = _PAD.sub(lambda m: '.' + m.group(1).ljust(6, '0') + m.group(2), ts_str)
+                  return datetime.fromisoformat(ts_str).month
+          pickup_datetime_day:
+            callable: |
+              from datetime import datetime
+              import sys
+              import re
+
+              def fn(row):
+                  ts_str = row.pickup_datetime
+                  if sys.version_info < (3, 11):
+                      _PAD = re.compile(r'\.(\d{1,5})([+-]\d{2}:\d{2}|Z)$')
+                      ts_str = _PAD.sub(lambda m: '.' + m.group(1).ljust(6, '0') + m.group(2), ts_str)
+                  return datetime.fromisoformat(ts_str).day
+          pickup_datetime_weekday:
+            callable: |
+              from datetime import datetime
+              import sys
+              import re
+
+              def fn(row):
+                  ts_str = row.pickup_datetime
+                  if sys.version_info < (3, 11):
+                      _PAD = re.compile(r'\.(\d{1,5})([+-]\d{2}:\d{2}|Z)$')
+                      ts_str = _PAD.sub(lambda m: '.' + m.group(1).ljust(6, '0') + m.group(2), ts_str)
+                  return datetime.fromisoformat(ts_str).weekday()
+          pickup_datetime_hour:
+            callable: |
+              from datetime import datetime
+              import sys
+              import re
+
+              def fn(row):
+                  ts_str = row.pickup_datetime
+                  if sys.version_info < (3, 11):
+                      _PAD = re.compile(r'\.(\d{1,5})([+-]\d{2}:\d{2}|Z)$')
+                      ts_str = _PAD.sub(lambda m: '.' + m.group(1).ljust(6, '0') + m.group(2), ts_str)
+                  return datetime.fromisoformat(ts_str).hour
+
+    # With VertexAIModelHandlerJSON model handler,
+    # RunInference transform performs remote inferences by
+    # sending POST requests to the Vertex AI endpoint that
+    # our custom-trained model is being deployed to.
+    - type: RunInference
+      name: PredictFare
+      input: GenerateFeatures
+      config:
+        inference_tag: "inference"
+        model_handler:
+          type: VertexAIModelHandlerJSON
+          config:
+            endpoint_id: "{{ ENDPOINT }}"
+            project: "{{ PROJECT }}"
+            location: "{{ LOCATION }}"
+            preprocess:
+              callable: |
+                def preprocess(row):
+                  input_cols = [
+                    'pickup_longitude', 'pickup_latitude',
+                    'dropoff_longitude', 'dropoff_latitude',
+                    'passenger_count',
+                    'pickup_datetime_year', 'pickup_datetime_month', 'pickup_datetime_day',
+                    'pickup_datetime_weekday', 'pickup_datetime_hour']
+                  return [row.as_dict().get(key) for key in input_cols]
+
+    # Parse inference results output
+    - type: MapToFields
+      name: FormatInferenceOutput
+      input: PredictFare
+      config:
+        language: python
+        fields:
+          ride_id:
+            expression: ride_id
+            output_type: string
+          pickup_longitude:
+            expression: pickup_longitude
+            output_type: number
+          pickup_latitude:
+            expression: pickup_latitude
+            output_type: number
+          pickup_datetime:
+            expression: pickup_datetime
+            output_type: string
+          dropoff_longitude:
+            expression: dropoff_longitude
+            output_type: number
+          dropoff_latitude:
+            expression: dropoff_latitude
+            output_type: number
+          passenger_count:
+            expression: passenger_count
+            output_type: integer
+          predicted_fare_amount:
+            callable: 'lambda row: row.inference.inference'
+            output_type: number
+
+    # Write all inference results to a BigQuery table.
+    - type: WriteToBigQuery
+      name: WritePredictionsBQ
+      input: FormatInferenceOutput
+      config:
+        table: "{{ BQ_TABLE }}"
+        create_disposition: "CREATE_IF_NEEDED"
+        write_disposition: "WRITE_APPEND"
+
+options:
+  yaml_experimental_features: ML
+
+# Expected:
+#  Row(ride_id='1', pickup_longitude=11.0, pickup_latitude=-11.0, pickup_datetime='2025-01-01T00:29:00.00000-04:00', dropoff_longitude=15.0, dropoff_latitude=-15.0, passenger_count=1, predicted_fare_amount=10.0)
+#  Row(ride_id='2', pickup_longitude=22.0, pickup_latitude=-22.0, pickup_datetime='2025-01-01T00:30:00.00000-04:00', dropoff_longitude=26.0, dropoff_latitude=-26.0, passenger_count=2, predicted_fare_amount=10.0)
diff --git a/sdks/python/apache_beam/yaml/extended_tests/databases/bigquery.yaml b/sdks/python/apache_beam/yaml/extended_tests/databases/bigquery.yaml
index f5ab31b3855b..d0357e098bf3 100644
--- a/sdks/python/apache_beam/yaml/extended_tests/databases/bigquery.yaml
+++ b/sdks/python/apache_beam/yaml/extended_tests/databases/bigquery.yaml
@@ -16,11 +16,20 @@
 #
 
 fixtures:
-  - name: BQ_TABLE
+  - name: BQ_TABLE_0
     type: "apache_beam.yaml.integration_tests.temp_bigquery_table"
     config:
       project: "apache-beam-testing"
-  - name: TEMP_DIR
+  - name: TEMP_DIR_0
+    # Need distributed filesystem to be able to read and write from a container.
+    type: "apache_beam.yaml.integration_tests.gcs_temp_dir"
+    config:
+      bucket: "gs://temp-storage-for-end-to-end-tests/temp-it"
+  - name: BQ_TABLE_1
+    type: "apache_beam.yaml.integration_tests.temp_bigquery_table"
+    config:
+      project: "apache-beam-testing"
+  - name: TEMP_DIR_1
     # Need distributed filesystem to be able to read and write from a container.
     type: "apache_beam.yaml.integration_tests.gcs_temp_dir"
     config:
@@ -38,17 +47,17 @@ pipelines:
               - {label: "389a", rank: 2}
         - type: WriteToBigQuery
           config:
-            table: "{BQ_TABLE}"
+            table: "{BQ_TABLE_0}"
     options:
       project: "apache-beam-testing"
-      temp_location: "{TEMP_DIR}"
+      temp_location: "{TEMP_DIR_0}"
 
   - pipeline:
       type: chain
       transforms:
         - type: ReadFromBigQuery
           config:
-            table: "{BQ_TABLE}"
+            table: "{BQ_TABLE_0}"
         - type: AssertEqual
           config:
             elements:
@@ -57,14 +66,14 @@ pipelines:
               - {label: "389a", rank: 2}
     options:
       project: "apache-beam-testing"
-      temp_location: "{TEMP_DIR}"
+      temp_location: "{TEMP_DIR_0}"
 
   - pipeline:
       type: chain
       transforms:
         - type: ReadFromBigQuery
           config:
-            table: "{BQ_TABLE}"
+            table: "{BQ_TABLE_0}"
             fields: ["label"]
             row_restriction: "rank > 0"
         - type: AssertEqual
@@ -74,4 +83,58 @@ pipelines:
               - {label: "389a"}
     options:
       project: "apache-beam-testing"
-      temp_location: "{TEMP_DIR}"
+      temp_location: "{TEMP_DIR_0}"
+
+  # ----------------------------------------------------------------------------
+
+  # New write to verify row restriction based on Timestamp and nullability
+  - pipeline:
+      type: chain
+      transforms:
+        - type: Create
+          config:
+            elements:
+              - {label: "4a", rank: 3, timestamp: "2024-07-14 00:00:00 UTC"}
+              - {label: "5a", rank: 4}
+              - {label: "6a", rank: 5, timestamp: "2024-07-14T02:00:00.123Z"}    
+        - type: WriteToBigQuery
+          config:
+            table: "{BQ_TABLE_1}"
+
+  # New read from BQ to verify row restriction with nullable field and filter
+  # out nullable record
+  - pipeline:
+      type: chain
+      transforms:
+        - type: ReadFromBigQuery
+          config:
+            table: "{BQ_TABLE_1}"
+            fields: ["label","rank","timestamp"]
+            row_restriction: "TIMESTAMP(timestamp) <= TIMESTAMP_SUB('2025-07-14 04:00:00', INTERVAL 4 HOUR)"
+        - type: AssertEqual
+          config:
+            elements:
+              - {label: "4a", rank: 3, timestamp: "2024-07-14 00:00:00 UTC"}
+              - {label: "6a", rank: 5,timestamp: "2024-07-14T02:00:00.123Z"}
+    options:
+      project: "apache-beam-testing"
+      temp_location: "{TEMP_DIR_1}"
+
+  # New read from BQ to verify row restriction with nullable field and keep
+  # nullable record
+  - pipeline:
+      type: chain
+      transforms:
+        - type: ReadFromBigQuery
+          config:
+            table: "{BQ_TABLE_1}"
+            fields: ["timestamp", "label", "rank"]
+            row_restriction: "timestamp is NULL"
+        - type: AssertEqual
+          config:
+            elements:
+              - {label: "5a", rank: 4}
+    options:
+      project: "apache-beam-testing"
+      temp_location: "{TEMP_DIR_1}"
+
diff --git a/sdks/python/apache_beam/yaml/extended_tests/databases/iceberg.yaml b/sdks/python/apache_beam/yaml/extended_tests/databases/iceberg.yaml
new file mode 100644
index 000000000000..d72688774dae
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/extended_tests/databases/iceberg.yaml
@@ -0,0 +1,63 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+fixtures:
+  - name: TEMP_DIR
+    type: "tempfile.TemporaryDirectory"
+
+pipelines:
+  - name: write
+    pipeline:
+      type: chain
+      transforms:
+        - type: Create
+          config:
+            elements:
+              - {label: "11a", rank: 0}
+              - {label: "37a", rank: 1}
+              - {label: "389a", rank: 2}
+        - type: WriteToIceberg
+          config:
+            table: db.labels
+            catalog_name: hadoop_catalog
+            catalog_properties:
+              type: hadoop
+              warehouse: "{TEMP_DIR}"
+    options:
+      project: "apache-beam-testing"
+      temp_location: "{TEMP_DIR}"
+
+  - name: read
+    pipeline:
+      type: chain
+      transforms:
+        - type: ReadFromIceberg
+          config:
+            table: db.labels
+            catalog_name: hadoop_catalog
+            catalog_properties:
+              type: hadoop
+              warehouse: "{TEMP_DIR}"
+        - type: AssertEqual
+          config:
+            elements:
+              - {label: "11a", rank: 0}
+              - {label: "37a", rank: 1}
+              - {label: "389a", rank: 2}
+    options:
+      project: "apache-beam-testing"
+      temp_location: "{TEMP_DIR}"
\ No newline at end of file
diff --git a/sdks/python/apache_beam/yaml/integration_tests.py b/sdks/python/apache_beam/yaml/integration_tests.py
index 4a9ca70cac54..733dd10d0286 100644
--- a/sdks/python/apache_beam/yaml/integration_tests.py
+++ b/sdks/python/apache_beam/yaml/integration_tests.py
@@ -24,18 +24,22 @@
 import logging
 import os
 import random
+import secrets
 import sqlite3
 import string
 import unittest
 import uuid
+from datetime import datetime
+from datetime import timezone
 
 import mock
-import mysql.connector
 import psycopg2
 import pytds
 import sqlalchemy
 import yaml
 from google.cloud import pubsub_v1
+from google.cloud.bigtable import client
+from google.cloud.bigtable_admin_v2.types import instance
 from testcontainers.core.container import DockerContainer
 from testcontainers.core.waiting_utils import wait_for_logs
 from testcontainers.google import PubSubContainer
@@ -54,6 +58,9 @@
 from apache_beam.yaml import yaml_provider
 from apache_beam.yaml import yaml_transform
 from apache_beam.yaml.conftest import yaml_test_files_dir
+from apitools.base.py.exceptions import HttpError
+
+_LOGGER = logging.getLogger(__name__)
 
 
 @contextlib.contextmanager
@@ -144,6 +151,55 @@ def temp_bigquery_table(project, prefix='yaml_bq_it_'):
   bigquery_client.client.datasets.Delete(request)
 
 
+def instance_prefix(instance):
+  datestr = "".join(filter(str.isdigit, str(datetime.now(timezone.utc).date())))
+  instance_id = '%s-%s-%s' % (instance, datestr, secrets.token_hex(4))
+  assert len(instance_id) < 34, "instance id length needs to be within [6, 33]"
+  return instance_id
+
+
+@contextlib.contextmanager
+def temp_bigtable_table(project, prefix='yaml_bt_it_'):
+  INSTANCE = "bt-write-tests"
+  TABLE_ID = "test-table"
+
+  instance_id = instance_prefix(INSTANCE)
+
+  clientT = client.Client(admin=True, project=project)
+  # create cluster and instance
+  instanceT = clientT.instance(
+      instance_id,
+      display_name=INSTANCE,
+      instance_type=instance.Instance.Type.DEVELOPMENT)
+  cluster = instanceT.cluster("test-cluster", "us-central1-a")
+  operation = instanceT.create(clusters=[cluster])
+  operation.result(timeout=500)
+  _LOGGER.info("Created instance [%s] in project [%s]", instance_id, project)
+
+  # create table inside instance
+  table = instanceT.table(TABLE_ID)
+  table.create()
+  _LOGGER.info("Created table [%s]", table.table_id)
+  # in the table that is created, make a new family called cf1
+  col_fam = table.column_family('cf1')
+  col_fam.create()
+
+  # another family called cf2
+  col_fam = table.column_family('cf2')
+  col_fam.create()
+
+  #yielding the tmp table for all the bigTable tests
+  yield instance_id
+
+  #try catch for deleting table and instance after all tests are ran
+  try:
+    _LOGGER.info("Deleting table [%s]", table.table_id)
+    table.delete()
+    instanceT.delete()
+  except HttpError:
+    _LOGGER.warning("Failed to clean up instance")
+
+
 @contextlib.contextmanager
 def temp_sqlite_database(prefix='yaml_jdbc_it_'):
   """Context manager to provide a temporary SQLite database via JDBC for
@@ -229,26 +285,22 @@ def temp_mysql_database():
       Exception: Any other exception encountered during the setup process.
   """
   with MySqlContainer(init=True, dialect='pymysql') as mysql_container:
-    try:
-      # Make connection to temp database and create tmp table
-      engine = sqlalchemy.create_engine(mysql_container.get_connection_url())
-      with engine.begin() as connection:
-        connection.execute(
-            sqlalchemy.text(
-                "CREATE TABLE tmp_table (value INTEGER, `rank` INTEGER);"))
+    # Make connection to temp database and create tmp table
+    engine = sqlalchemy.create_engine(mysql_container.get_connection_url())
+    with engine.begin() as connection:
+      connection.execute(
+          sqlalchemy.text(
+              "CREATE TABLE tmp_table (value INTEGER, `rank` INTEGER);"))
 
-      # Construct the JDBC url for connections later on by tests
-      jdbc_url = (
-          f"jdbc:mysql://{mysql_container.get_container_host_ip()}:"
-          f"{mysql_container.get_exposed_port(mysql_container.port)}/"
-          f"{mysql_container.dbname}?"
-          f"user={mysql_container.username}&"
-          f"password={mysql_container.password}")
+    # Construct the JDBC url for connections later on by tests
+    jdbc_url = (
+        f"jdbc:mysql://{mysql_container.get_container_host_ip()}:"
+        f"{mysql_container.get_exposed_port(mysql_container.port)}/"
+        f"{mysql_container.dbname}?"
+        f"user={mysql_container.username}&"
+        f"password={mysql_container.password}")
 
-      yield jdbc_url
-    except mysql.connector.Error as err:
-      logging.error("Error interacting with temporary MySQL DB: %s", err)
-      raise err
+    yield jdbc_url
 
 
 @contextlib.contextmanager
@@ -706,7 +758,7 @@ def parse_test_files(filepattern):
       globals()[suite_name] = type(suite_name, (unittest.TestCase, ), methods)
 
 
-# Logging setup
+# Logging setups
 logging.getLogger().setLevel(logging.INFO)
 
 # Dynamically create test methods from the tests directory.
diff --git a/sdks/python/apache_beam/yaml/json_utils.py b/sdks/python/apache_beam/yaml/json_utils.py
index 893ebba55103..2d8f32051973 100644
--- a/sdks/python/apache_beam/yaml/json_utils.py
+++ b/sdks/python/apache_beam/yaml/json_utils.py
@@ -287,8 +287,10 @@ def row_to_json(beam_type: schema_pb2.FieldType) -> Callable[[Any], Any]:
         for field in beam_type.row_type.schema.fields
     }
     return lambda row: {
-        name: convert(getattr(row, name))
+        name: converted
         for (name, convert) in converters.items()
+        # To filter out nullable fields in rows
+        if (converted := convert(getattr(row, name, None))) is not None
     }
   elif type_info == "logical_type":
     return lambda value: value
@@ -348,6 +350,9 @@ def validate(row):
     nonlocal validator
     if validator is None:
       validator = jsonschema.validators.validator_for(json_schema)(json_schema)
+    # NOTE: A row like BeamSchema_...(name='Bob', score=None, age=25) needs to
+    # have any fields that are None to be filtered out or the validator will
+    # fail (e.g. {'age': 25, 'name': 'Bob'}).
     validator.validate(convert(row))
 
   return validate
diff --git a/sdks/python/apache_beam/yaml/main.py b/sdks/python/apache_beam/yaml/main.py
index fbebaea4346f..0ae5db8900b7 100644
--- a/sdks/python/apache_beam/yaml/main.py
+++ b/sdks/python/apache_beam/yaml/main.py
@@ -25,6 +25,13 @@
 
 import apache_beam as beam
 from apache_beam.io.filesystems import FileSystems
+# The following imports force the registration of JDBC logical types.
+# When running a Beam YAML pipeline, the expansion service handles JDBCIO using
+# Java transforms, bypassing the Python module (`apache_beam.io.jdbc`) that
+# registers these types. These imports load the module, preventing a
+# "logical type not found" error.
+from apache_beam.io.jdbc import JdbcDateType  # pylint: disable=unused-import
+from apache_beam.io.jdbc import JdbcTimeType  # pylint: disable=unused-import
 from apache_beam.transforms import resources
 from apache_beam.yaml import yaml_testing
 from apache_beam.yaml import yaml_transform
@@ -93,6 +100,7 @@ def _parse_arguments(argv):
       help='A json dict of variables used when invoking the jinja preprocessor '
       'on the provided yaml pipeline.')
   parser.add_argument(
+      '--tests',
       '--test',
       action=argparse.BooleanOptionalAction,
       help='Run the tests associated with the given pipeline, rather than the '
@@ -283,7 +291,7 @@ def constructor(root):
 if __name__ == '__main__':
   import logging
   logging.getLogger().setLevel(logging.INFO)
-  if '--test' in sys.argv:
+  if '--tests' in sys.argv or '--test' in sys.argv:
     run_tests()
   else:
     run()
diff --git a/sdks/python/apache_beam/yaml/pipeline.schema.yaml b/sdks/python/apache_beam/yaml/pipeline.schema.yaml
index afcb2e23a663..35625d58d160 100644
--- a/sdks/python/apache_beam/yaml/pipeline.schema.yaml
+++ b/sdks/python/apache_beam/yaml/pipeline.schema.yaml
@@ -45,6 +45,8 @@ $defs:
             properties: { __line__: {}}
             additionalProperties:
               type: string
+      output_schema:
+        type: object
     additionalProperties: true
     required:
       - type
@@ -129,6 +131,7 @@ $defs:
             name: {}
             input: {}
             output: {}
+            output_schema: { type: object }
             windowing: {}
             resource_hints: {}
             config: { type: object }
diff --git a/sdks/python/apache_beam/yaml/standard_io.yaml b/sdks/python/apache_beam/yaml/standard_io.yaml
index d3d6e8f8d029..ddc3c7662a65 100644
--- a/sdks/python/apache_beam/yaml/standard_io.yaml
+++ b/sdks/python/apache_beam/yaml/standard_io.yaml
@@ -45,9 +45,15 @@
       type: beamJar
       transforms:
         'ReadFromBigQuery': 'beam:schematransform:org.apache.beam:bigquery_storage_read:v1'
-        'WriteToBigQuery': 'beam:schematransform:org.apache.beam:bigquery_storage_write:v2'
+        'WriteToBigQuery': 'beam:schematransform:org.apache.beam:bigquery_write:v1'
       config:
         gradle_target: 'sdks:java:extensions:sql:expansion-service:shadowJar'
+        managed_replacement:
+          # Following transforms may be replaced with equivalent managed transforms,
+          # if the pipelines 'updateCompatibilityBeamVersion' match the provided
+          # version.
+          'ReadFromBigQuery': '2.69.0'
+          'WriteToBigQuery': '2.69.0'
 
 # Kafka
 - type: renaming
@@ -169,6 +175,7 @@
         path: 'path'
         delimiter: 'sep'
         comment: 'comment'
+        filename_column: 'filename_column'
       'WriteToCsv':
         path: 'path'
         delimiter: 'sep'
@@ -371,3 +378,28 @@
         'WriteToTFRecord': 'beam:schematransform:org.apache.beam:tfrecord_write:v1'
       config:
         gradle_target: 'sdks:java:io:expansion-service:shadowJar'
+
+#BigTable
+- type: renaming
+  transforms:
+    'ReadFromBigTable': 'ReadFromBigTable'
+    'WriteToBigTable': 'WriteToBigTable'
+  config:
+    mappings:
+      #Temp removing read from bigTable IO
+      'ReadFromBigTable':
+        project: 'project_id'
+        instance: 'instance_id'
+        table: 'table_id'
+        flatten: "flatten"
+      'WriteToBigTable':
+        project: 'project_id'
+        instance: 'instance_id'
+        table: 'table_id'
+    underlying_provider:
+      type: beamJar
+      transforms:
+        'ReadFromBigTable': 'beam:schematransform:org.apache.beam:bigtable_read:v1'
+        'WriteToBigTable': 'beam:schematransform:org.apache.beam:bigtable_write:v1'
+      config:
+        gradle_target: 'sdks:java:io:google-cloud-platform:expansion-service:shadowJar'
diff --git a/sdks/python/apache_beam/yaml/tests/assign_timestamps.yaml b/sdks/python/apache_beam/yaml/tests/assign_timestamps.yaml
index edaa581214ea..13e56f22edb5 100644
--- a/sdks/python/apache_beam/yaml/tests/assign_timestamps.yaml
+++ b/sdks/python/apache_beam/yaml/tests/assign_timestamps.yaml
@@ -82,3 +82,90 @@ pipelines:
           config:
             elements:
               - {user: bob, timestamp: 3}
+
+# Assign timestamp to beam row element with error handling and output schema
+# check.
+  - pipeline:
+      type: composite
+      transforms:
+        - type: Create
+          name: CreateVisits
+          config:
+            elements:
+              - {user: alice, timestamp: "not-valid"}
+              - {user: bob, timestamp: 3}
+        - type: AssignTimestamps
+          input: CreateVisits
+          config:
+            timestamp: timestamp
+            error_handling:
+              output: invalid_rows
+            output_schema:
+              type: object
+              properties:
+                user:
+                  type: string
+                timestamp:
+                  type: integer
+        - type: MapToFields
+          name: ExtractInvalidTimestamp
+          input: AssignTimestamps.invalid_rows
+          config:
+            language: python
+            fields:
+              user: "element.user"
+              timestamp: "element.timestamp"
+        - type: AssertEqual
+          input: ExtractInvalidTimestamp
+          config:
+            elements:
+              - {user: "alice", timestamp: "not-valid"}
+        - type: AssertEqual
+          input: AssignTimestamps
+          config:
+            elements: 
+              - {user: bob, timestamp: 3}
+
+# Assign timestamp to beam row element with error handling and output schema
+# check with more error handling.
+  - pipeline:
+      type: composite
+      transforms:
+        - type: Create
+          name: CreateVisits
+          config:
+            elements:
+              - {user: alice, timestamp: "not-valid"}
+              - {user: bob, timestamp: 3}
+        - type: AssignTimestamps
+          input: CreateVisits
+          config:
+            timestamp: timestamp
+            error_handling:
+              output: invalid_rows
+            output_schema:
+              type: object
+              properties:
+                user:
+                  type: string
+                timestamp:
+                  type: boolean
+        - type: MapToFields
+          name: ExtractInvalidTimestamp
+          input: AssignTimestamps.invalid_rows
+          config:
+            language: python
+            fields:
+              user: "element.user"
+              timestamp: "element.timestamp"
+        - type: AssertEqual
+          input: ExtractInvalidTimestamp
+          config:
+            elements:
+              - {user: "alice", timestamp: "not-valid"}
+              - {user: bob, timestamp: 3}
+        - type: AssertEqual
+          input: AssignTimestamps
+          config:
+            elements: []
+
diff --git a/sdks/python/apache_beam/yaml/tests/bigtable.yaml b/sdks/python/apache_beam/yaml/tests/bigtable.yaml
new file mode 100644
index 000000000000..2f97b83c6e92
--- /dev/null
+++ b/sdks/python/apache_beam/yaml/tests/bigtable.yaml
@@ -0,0 +1,177 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+fixtures:
+  - name: BT_INSTANCE
+    type: "apache_beam.yaml.integration_tests.temp_bigtable_table"
+    config:
+      project: "apache-beam-testing"
+  - name: TEMP_DIR
+    # Need distributed filesystem to be able to read and write from a container.
+    type: "apache_beam.yaml.integration_tests.gcs_temp_dir"
+    config:
+      bucket: "gs://temp-storage-for-end-to-end-tests/temp-it"
+
+  # Tests for BigTable YAML IO
+
+pipelines:
+  - pipeline:
+      type: chain
+      transforms:
+        - type: Create
+          config:
+            elements:
+              - {key: 'row1',
+                 type: 'SetCell',
+                 family_name: "cf1",
+                 column_qualifier: "cq1",
+                 value: "value1",
+                 timestamp_micros: 5000}
+              - {key: 'row1',
+                 type: 'SetCell',
+                 family_name: "cf1",
+                 column_qualifier: "cq2",
+                 value: "value2",
+                 timestamp_micros: 1000}
+
+        - type: LogForTesting
+        - type: MapToFields
+          name: ConvertStringsToBytes
+          config:
+            language: python
+            fields:
+              # For 'SetCell' and 'DeleteFromColumn'
+              key:
+                callable: |
+                  def convert_to_bytes(row):
+                    return bytes(row.key, 'utf-8') if "key" in row._fields else None
+              type:
+                type
+              family_name:
+                family_name
+              column_qualifier:
+                callable: |
+                  def convert_to_bytes(row):
+                    return bytes(row.column_qualifier, 'utf-8') if 'column_qualifier' in row._fields else None
+              value:
+                callable: |
+                  def convert_to_bytes(row):
+                    return bytes(row.value, 'utf-8') if 'value' in row._fields else None
+              timestamp_micros:
+                timestamp_micros
+              # The 'type', 'timestamp_micros', 'start_timestamp_micros', 'end_timestamp_micros'
+              # fields are already of the correct type (String, Long) or are optional.
+              # We only need to convert fields that are Strings in YAML but need to be Bytes in Java.
+
+        - type: WriteToBigTable
+          config:
+            project: 'apache-beam-testing'
+            instance: "{BT_INSTANCE}"
+            table: 'test-table'
+  - pipeline:
+      type: chain
+      transforms:
+        - type: ReadFromBigTable
+          config:
+            project: 'apache-beam-testing'
+            instance: "{BT_INSTANCE}"
+            table: 'test-table'
+        - type: MapToFields
+          config:
+            language: python
+            fields:
+              key:
+                callable: |
+                  def convert_to_string(row):
+                    return row.key.decode("utf-8") if "key" in row._fields else None
+              family_name:
+                family_name
+              column_qualifier:
+                callable: |
+                  def convert_to_string(row):
+                    return row.column_qualifier.decode("utf-8") if "column_qualifier" in row._fields else None
+              cells:
+                callable: |
+                  def convert_to_string(row):
+                    cell_bytes = []
+                    for (value, timestamp) in row.cells:
+                      value_bytes = value.decode("utf-8")
+                      cell_bytes.append(beam.Row(value=value_bytes, timestamp_micros=timestamp))
+                    return cell_bytes
+        - type: AssertEqual
+          config:
+            elements:
+              - { key: 'row1',
+                  family_name: "cf1",
+                  column_qualifier: "cq1",
+                  cells:[{
+                    value: "value1",
+                    timestamp_micros: 5000}]}
+              - { key: 'row1',
+                  family_name: "cf1",
+                  column_qualifier: "cq2",
+                  cells: [{
+                    value: "value2",
+                    timestamp_micros: 1000 } ] }
+        - type: LogForTesting
+
+  - pipeline:
+      type: chain
+      transforms:
+        - type: ReadFromBigTable
+          config:
+            project: 'apache-beam-testing'
+            instance: "{BT_INSTANCE}"
+            table: 'test-table'
+            flatten: False
+        - type: MapToFields
+          config:
+            language: python
+            fields:
+              key:
+                callable: |
+                  def convert_to_bytes(row):
+                    return row.key.decode("utf-8") if "key" in row._fields else None
+
+              column_families:
+                column_families
+#        TODO: issue  #35790, once fixed we can uncomment this assert
+#        - type: AssertEqual
+#          config:
+#            elements:
+#              - {key: 'row1',
+#                # Use explicit map syntax to match the actual output
+#                 column_families: {
+#                   cf1: {
+#                     cq1: [
+#                       { value: "value1", timestamp_micros: 5000 }
+#                     ],
+#                     cq2: [
+#                       { value: "value2", timestamp_micros: 1000 }
+#                     ]
+#                   }
+#                 }
+#              }
+        #                - {'key': 'row1',
+        #                   column_families: {cf1: {cq2:
+        #                                             [BeamSchema_3281a0ae_fe85_474b_9030_86fbed58833a(value=b'value2', timestamp_micros=1000)], 'cq1': [BeamSchema_3281a0ae_fe85_474b_9030_86fbed58833a(value=b'value1', timestamp_micros=5000)]}}}
+
+
+#        - type: LogForTesting
+
+
diff --git a/sdks/python/apache_beam/yaml/tests/create.yaml b/sdks/python/apache_beam/yaml/tests/create.yaml
index 723d8a888c26..bf346f7667c8 100644
--- a/sdks/python/apache_beam/yaml/tests/create.yaml
+++ b/sdks/python/apache_beam/yaml/tests/create.yaml
@@ -81,3 +81,60 @@ pipelines:
               - {sdk: MapReduce, year: 2004}
               - {sdk: Flume}
               - {sdk: MillWheel, year: 2008}
+
+  # Simple Create with explicit null value
+  - pipeline:
+      type: chain
+      transforms:
+        - type: Create
+          config:
+            elements:
+              - {sdk: MapReduce, year: 2004}
+              - {sdk: Flume, year: null}
+              - {sdk: MillWheel, year: 2008}
+        - type: AssertEqual
+          config:
+            elements:
+              - {sdk: MapReduce, year: 2004}
+              - {sdk: Flume, year: null}
+              - {sdk: MillWheel, year: 2008}
+
+  # Simple Create with explicit null values for the entire record
+  - pipeline:
+      type: chain
+      transforms:
+        - type: Create
+          config:
+            elements:
+              - {sdk: MapReduce, year: 2004}
+              - {sdk: null, year: null}
+              - {sdk: MillWheel, year: 2008}
+        - type: AssertEqual
+          config:
+            elements:
+              - {sdk: MapReduce, year: 2004}
+              - {}
+              - {sdk: MillWheel, year: 2008}
+
+  # Simple Create with output schema check
+  - pipeline:
+      type: chain
+      transforms:
+        - type: Create
+          config:
+            elements:
+              - {sdk: MapReduce, year: 2004}
+              - {sdk: MillWheel, year: 2008}
+            output_schema:
+              type: object
+              properties:
+                sdk:
+                  type: string
+                year:
+                  type: integer
+        - type: AssertEqual
+          config:
+            elements:
+              - {sdk: MapReduce, year: 2004}
+              - {sdk: MillWheel, year: 2008}
+
diff --git a/sdks/python/apache_beam/yaml/tests/csv.yaml b/sdks/python/apache_beam/yaml/tests/csv.yaml
index d9bfe43d3c31..98c817e1c7ea 100644
--- a/sdks/python/apache_beam/yaml/tests/csv.yaml
+++ b/sdks/python/apache_beam/yaml/tests/csv.yaml
@@ -45,3 +45,25 @@ pipelines:
               - {label: "11a", rank: 0}
               - {label: "37a", rank: 1}
               - {label: "389a", rank: 2}
+
+  - pipeline:
+      type: chain
+      transforms:
+        - type: ReadFromCsv
+          config:
+            path: "{TEMP_DIR}/out.csv*"
+            filename_column: "source_file"
+        - type: MapToFields
+          name: Check Filename
+          config:
+            language: python
+            fields:
+              label: label
+              rank: rank
+              filename_ok: source_file.startswith(f'{TEMP_DIR}/out.csv')
+        - type: AssertEqual
+          config:
+            elements:
+              - {label: "11a", rank: 0, filename_ok: true}
+              - {label: "37a", rank: 1, filename_ok: true}
+              - {label: "389a", rank: 2, filename_ok: true}
diff --git a/sdks/python/apache_beam/yaml/tests/iceberg.yaml b/sdks/python/apache_beam/yaml/tests/iceberg.yaml
index aee9725e0c4d..598c7c44bf3b 100644
--- a/sdks/python/apache_beam/yaml/tests/iceberg.yaml
+++ b/sdks/python/apache_beam/yaml/tests/iceberg.yaml
@@ -26,12 +26,16 @@ pipelines:
         - type: Create
           config:
             elements:
-              - {label: "11a", rank: 0}
-              - {label: "37a", rank: 1}
-              - {label: "389a", rank: 2}
+              - {label: "11a", rank: 0, bool: true}
+              - {label: "37a", rank: 1, bool: false}
+              - {label: "389a", rank: 2, bool: false}
+              - {label: "3821b", rank: 3, bool: true}
         - type: WriteToIceberg
           config:
             table: "default.table"
+            partition_fields:
+              - "bool"
+              - "truncate(label, 2)"
             catalog_name: "some_catalog"
             catalog_properties:
               type: "hadoop"
@@ -44,12 +48,13 @@ pipelines:
           config:
             table: "default.table"
             catalog_name: "some_catalog"
+            filter: "bool = true"
+            keep: ["label"]
             catalog_properties:
               type: "hadoop"
               warehouse: "{TEMP_DIR}/dir"
         - type: AssertEqual
           config:
             elements:
-              - {label: "11a", rank: 0}
-              - {label: "37a", rank: 1}
-              - {label: "389a", rank: 2}
+              - {label: "11a"}
+              - {label: "3821b"}
diff --git a/sdks/python/apache_beam/yaml/tests/runinference.yaml b/sdks/python/apache_beam/yaml/tests/runinference.yaml
index f87ae4b44acf..5eb991e5a781 100644
--- a/sdks/python/apache_beam/yaml/tests/runinference.yaml
+++ b/sdks/python/apache_beam/yaml/tests/runinference.yaml
@@ -29,7 +29,7 @@ pipelines:
             model_handler: 
               type: "VertexAIModelHandlerJSON"
               config:
-                endpoint_id: 9157860935048626176
+                endpoint_id: 1870474887521370112
                 project: "apache-beam-testing"
                 location: "us-central1"
                 preprocess:
diff --git a/sdks/python/apache_beam/yaml/tests/sql.yaml b/sdks/python/apache_beam/yaml/tests/sql.yaml
index 160871c16f7a..0040a2790c54 100644
--- a/sdks/python/apache_beam/yaml/tests/sql.yaml
+++ b/sdks/python/apache_beam/yaml/tests/sql.yaml
@@ -65,9 +65,8 @@ pipelines:
 
             - type: Sql
               config:
-                dialect: zetasql
                 query:
-                  "SELECT a, cast(b AS STRING) as s FROM PCOLLECTION"
+                  "SELECT a, cast(b AS VARCHAR) as s FROM PCOLLECTION"
 
             - type: AssertEqual
               config:
@@ -76,3 +75,21 @@ pipelines:
                   - {a: "x", s: "2"}
                   - {a: "x", s: "3"}
                   - {a: "y", s: "10"}
+
+  - pipeline:
+      type: chain
+      transforms:
+        - type: Create
+          name: CreateSampleData
+          config:
+            elements:
+              - { id: 1, name: "John" }
+              - { id: 2, name: "Jane" }
+        - type: Sql
+          name: sql
+          config:
+            query: >
+              SELECT *, CURRENT_TIMESTAMP AS ingest_timestamp FROM PCOLLECTION
+        - type: PyTransform
+          config:
+            constructor: apache_beam.transforms.util.LogElements
diff --git a/sdks/python/apache_beam/yaml/tests/validate_with_schema.yaml b/sdks/python/apache_beam/yaml/tests/validate_with_schema.yaml
index d5ae57a3e8c1..1df599e254a7 100644
--- a/sdks/python/apache_beam/yaml/tests/validate_with_schema.yaml
+++ b/sdks/python/apache_beam/yaml/tests/validate_with_schema.yaml
@@ -92,4 +92,48 @@ pipelines:
               - {name: "Alice", age: 30, score: 95.5}
               - {name: "Bob", age: 25, score: 88.0}
 
-
+  # Validate a Beam Row with a predefined schema, nulls, and error handling
+  - pipeline:
+      type: composite
+      transforms:
+        - type: Create
+          config:
+            elements:
+              - {name: "Alice", age: 30, score: 95.5}
+              - {name: "Bob", age: 25}
+              - {name: "Charlie", age: 27, score: "apple"}
+        - type: ValidateWithSchema
+          input: Create
+          config:
+            schema:
+              type: object
+              properties:
+                name:
+                  type: string
+                age:
+                  type: integer
+                score:
+                  type: number
+            error_handling:
+              output: invalid_rows
+        # ValidateWithSchema outputs the element, error msg, and traceback, so
+        # MapToFields is needed to easily assert downstream.
+        - type: MapToFields
+          input: ValidateWithSchema.invalid_rows
+          config:
+            language: python
+            fields:
+              name: "element.name"
+              age: "element.age"
+              score: "element.score"
+        - type: AssertEqual
+          input: ValidateWithSchema
+          config:
+            elements:
+              - {name: "Alice", age: 30, score: 95.5}
+              - {name: "Bob", age: 25}
+        - type: AssertEqual
+          input: MapToFields
+          config:
+            elements:
+              - {name: "Charlie", age: 27, score: "apple"}
diff --git a/sdks/python/apache_beam/yaml/yaml_errors.py b/sdks/python/apache_beam/yaml/yaml_errors.py
index dace44ca09f6..66e9de058f1a 100644
--- a/sdks/python/apache_beam/yaml/yaml_errors.py
+++ b/sdks/python/apache_beam/yaml/yaml_errors.py
@@ -21,6 +21,7 @@
 
 import apache_beam as beam
 from apache_beam.typehints.row_type import RowTypeConstraint
+from apache_beam.yaml.yaml_utils import SafeLineLoader
 
 
 class ErrorHandlingConfig(NamedTuple):
@@ -35,9 +36,11 @@ class ErrorHandlingConfig(NamedTuple):
 
 def exception_handling_args(error_handling_spec):
   if error_handling_spec:
+    # error_handling_spec may have come from a yaml file and have metadata.
+    clean_spec = SafeLineLoader.strip_metadata(error_handling_spec)
     return {
         'dead_letter_tag' if k == 'output' else k: v
-        for (k, v) in error_handling_spec.items()
+        for (k, v) in clean_spec.items()
     }
   else:
     return None
diff --git a/sdks/python/apache_beam/yaml/yaml_io.py b/sdks/python/apache_beam/yaml/yaml_io.py
index 67a5e65bda93..ddf39935ebdf 100644
--- a/sdks/python/apache_beam/yaml/yaml_io.py
+++ b/sdks/python/apache_beam/yaml/yaml_io.py
@@ -35,6 +35,7 @@
 import apache_beam as beam
 import apache_beam.io as beam_io
 from apache_beam import coders
+from apache_beam.coders.row_coder import RowCoder
 from apache_beam.io import ReadFromBigQuery
 from apache_beam.io import ReadFromTFRecord
 from apache_beam.io import WriteToBigQuery
@@ -247,6 +248,10 @@ def _validate_schema():
         beam_schema,
         lambda record: covert_to_row(
             fastavro.schemaless_reader(io.BytesIO(record), schema)))  # type: ignore[call-arg]
+  elif format == 'PROTO':
+    _validate_schema()
+    beam_schema = json_utils.json_schema_to_beam_schema(schema)
+    return beam_schema, RowCoder(beam_schema).decode
   else:
     raise ValueError(f'Unknown format: {format}')
 
@@ -291,6 +296,8 @@ def formatter(row):
       return buffer.read()
 
     return formatter
+  elif format == 'PROTO':
+    return RowCoder(beam_schema).encode
   else:
     raise ValueError(f'Unknown format: {format}')
 
@@ -416,7 +423,7 @@ def write_to_pubsub(
 
   Args:
     topic: Cloud Pub/Sub topic in the form "/topics/<project>/<topic>".
-    format: How to format the message payload.  Currently suported
+    format: How to format the message payload.  Currently supported
       formats are
 
         - RAW: Expects a message with a single field (excluding
@@ -426,6 +433,8 @@ def write_to_pubsub(
             from the input PCollection schema.
         - JSON: Formats records with a given JSON schema, which may be inferred
             from the input PCollection schema.
+        - PROTO: Encodes records with a given Protobuf schema, which may be
+            inferred from the input PCollection schema.
 
     schema: Schema specification for the given format.
     attributes: List of attribute keys whose values will be pulled out as
@@ -494,6 +503,9 @@ def attributes_extractor(row):
 
 def read_from_iceberg(
     table: str,
+    filter: Optional[str] = None,
+    keep: Optional[list[str]] = None,
+    drop: Optional[list[str]] = None,
     catalog_name: Optional[str] = None,
     catalog_properties: Optional[Mapping[str, str]] = None,
     config_properties: Optional[Mapping[str, str]] = None,
@@ -509,6 +521,13 @@ def read_from_iceberg(
 
   Args:
     table: The identifier of the Apache Iceberg table. Example: "db.table1".
+    filter: SQL-like predicate to filter data at scan time.
+      Example: "id > 5 AND status = 'ACTIVE'". Uses Apache Calcite syntax:
+      https://calcite.apache.org/docs/reference.html
+    keep: A subset of column names to read exclusively. If null or empty,
+      all columns will be read.
+    drop: A subset of column names to exclude from reading. If null or empty,
+      all columns will be read.
     catalog_name: The name of the catalog. Example: "local".
     catalog_properties: A map of configuration properties for the Apache Iceberg
       catalog.
@@ -522,6 +541,9 @@ def read_from_iceberg(
       config=dict(
           table=table,
           catalog_name=catalog_name,
+          filter=filter,
+          keep=keep,
+          drop=drop,
           catalog_properties=catalog_properties,
           config_properties=config_properties))
 
@@ -531,6 +553,8 @@ def write_to_iceberg(
     catalog_name: Optional[str] = None,
     catalog_properties: Optional[Mapping[str, str]] = None,
     config_properties: Optional[Mapping[str, str]] = None,
+    partition_fields: Optional[Iterable[str]] = None,
+    table_properties: Optional[Mapping[str, str]] = None,
     triggering_frequency_seconds: Optional[int] = None,
     keep: Optional[Iterable[str]] = None,
     drop: Optional[Iterable[str]] = None,
@@ -557,9 +581,25 @@ def write_to_iceberg(
       CatalogUtil in the Apache Iceberg documentation.
     config_properties: An optional set of Hadoop configuration properties.
       For more information, see CatalogUtil in the Apache Iceberg documentation.
+    partition_fields: Fields used to create a partition spec that is applied
+      when tables are created. For a field 'foo', the available partition
+      transforms are:
+
+        - foo
+        - truncate(foo, N)
+        - bucket(foo, N)
+        - hour(foo)
+        - day(foo)
+        - month(foo)
+        - year(foo)
+        - void(foo)
+      For more information on partition transforms, please visit
+      https://iceberg.apache.org/spec/#partition-transforms.
+    table_properties: Iceberg table properties to be set on the table when it
+      is created. For more information on table properties, please visit
+      https://iceberg.apache.org/docs/latest/configuration/#table-properties.
     triggering_frequency_seconds: For streaming write pipelines, the frequency
       at which the sink attempts to produce snapshots, in seconds.
-
     keep: An optional list of field names to keep when writing to the
       destination. Other fields are dropped. Mutually exclusive with drop
       and only.
@@ -576,6 +616,8 @@ def write_to_iceberg(
           catalog_name=catalog_name,
           catalog_properties=catalog_properties,
           config_properties=config_properties,
+          partition_fields=partition_fields,
+          table_properties=table_properties,
           triggering_frequency_seconds=triggering_frequency_seconds,
           keep=keep,
           drop=drop,
@@ -600,7 +642,7 @@ def read_from_tfrecord(
     compression_type (CompressionTypes): Used to handle compressed input files.
       Default value is CompressionTypes.AUTO, in which case the file_path's
       extension will be used to detect the compression.
-    validate (bool): Boolean flag to verify that the files exist during the 
+    validate (bool): Boolean flag to verify that the files exist during the
       pipeline creation time.
   """
   return ReadFromTFRecord(
diff --git a/sdks/python/apache_beam/yaml/yaml_io_test.py b/sdks/python/apache_beam/yaml/yaml_io_test.py
index 3ae9f19b9b8d..a19dfd694a85 100644
--- a/sdks/python/apache_beam/yaml/yaml_io_test.py
+++ b/sdks/python/apache_beam/yaml/yaml_io_test.py
@@ -24,10 +24,12 @@
 import mock
 
 import apache_beam as beam
+from apache_beam.coders.row_coder import RowCoder
 from apache_beam.io.gcp.pubsub import PubsubMessage
 from apache_beam.testing.util import AssertThat
 from apache_beam.testing.util import assert_that
 from apache_beam.testing.util import equal_to
+from apache_beam.typehints import schemas as schema_utils
 from apache_beam.yaml.yaml_transform import YamlTransform
 
 
@@ -491,6 +493,49 @@ def test_write_json(self):
               attributes_map: other
             '''))
 
+  def test_write_proto(self):
+    with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle')) as p:
+      data = [beam.Row(label='37a', rank=1), beam.Row(label='389a', rank=2)]
+      coder = RowCoder(
+          schema_utils.named_fields_to_schema([('label', str), ('rank', int)]))
+      expected_messages = [PubsubMessage(coder.encode(r), {}) for r in data]
+      with mock.patch('apache_beam.io.WriteToPubSub',
+                      FakeWriteToPubSub(topic='my_topic',
+                                        messages=expected_messages)):
+        _ = (
+            p | beam.Create(data) | YamlTransform(
+                '''
+            type: WriteToPubSub
+            config:
+              topic: my_topic
+              format: PROTO
+            '''))
+
+  def test_read_proto(self):
+    with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle')) as p:
+      data = [beam.Row(label='37a', rank=1), beam.Row(label='389a', rank=2)]
+      coder = RowCoder(
+          schema_utils.named_fields_to_schema([('label', str), ('rank', int)]))
+      expected_messages = [PubsubMessage(coder.encode(r), {}) for r in data]
+      with mock.patch('apache_beam.io.ReadFromPubSub',
+                      FakeReadFromPubSub(topic='my_topic',
+                                         messages=expected_messages)):
+        result = p | YamlTransform(
+            '''
+            type: ReadFromPubSub
+            config:
+              topic: my_topic
+              format: PROTO
+              schema:
+                type: object
+                properties:
+                  label: {type: string}
+                  rank: {type: integer}
+            ''')
+        assert_that(result, equal_to(data))
+
 
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
diff --git a/sdks/python/apache_beam/yaml/yaml_mapping.py b/sdks/python/apache_beam/yaml/yaml_mapping.py
index 4433a0503b9a..a6b2b5704751 100644
--- a/sdks/python/apache_beam/yaml/yaml_mapping.py
+++ b/sdks/python/apache_beam/yaml/yaml_mapping.py
@@ -133,6 +133,7 @@ def is_atomic(expr: str):
   raise ValueError(
       "Missing language specification, unknown input fields, "
       f"or invalid generic expression: {expr}. "
+      f"The given input fields are {input_fields}. "
       "See https://beam.apache.org/documentation/sdks/yaml-udf/#generic")
 
 
@@ -486,7 +487,7 @@ def expand(self, pcoll):
           typing_from_runner_api(existing_fields[fld]))
 
 
-class _Validate(beam.PTransform):
+class Validate(beam.PTransform):
   """Validates each element of a PCollection against a json schema.
 
   Args:
@@ -981,7 +982,7 @@ def create_mapping_providers():
           'Partition-javascript': _Partition,
           'Partition-generic': _Partition,
           'StripErrorMetadata': _StripErrorMetadata,
-          'ValidateWithSchema': _Validate,
+          'ValidateWithSchema': Validate,
       }),
       yaml_provider.SqlBackedProvider({
           'Filter-sql': _SqlFilterTransform,
diff --git a/sdks/python/apache_beam/yaml/yaml_mapping_test.py b/sdks/python/apache_beam/yaml/yaml_mapping_test.py
index d5179d385caf..cc2fe4639abc 100644
--- a/sdks/python/apache_beam/yaml/yaml_mapping_test.py
+++ b/sdks/python/apache_beam/yaml/yaml_mapping_test.py
@@ -175,7 +175,7 @@ def test_validate(self):
           label='Errors')
 
   def test_validate_explicit_types(self):
-    with self.assertRaisesRegex(TypeError, r'.*violates schema.*'):
+    with self.assertRaisesRegex(Exception, r'.*violates schema.*'):
       with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
           pickle_library='cloudpickle')) as p:
         elements = p | beam.Create([
@@ -284,7 +284,7 @@ def test_partition_with_unknown(self):
           label='Other')
 
   def test_partition_without_unknown(self):
-    with self.assertRaisesRegex(ValueError, r'.*Unknown output name.*"o".*'):
+    with self.assertRaisesRegex(Exception, r'.*Unknown output name.*"o".*'):
       with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
           pickle_library='cloudpickle')) as p:
         elements = p | beam.Create([
@@ -416,8 +416,8 @@ def test_partition_bad_static_type(self):
             ''')
 
   def test_partition_bad_runtime_type(self):
-    with self.assertRaisesRegex(ValueError,
-                                r'.*Returned output name.*must be a string.*'):
+    with self.assertRaisesRegex(Exception,
+                                r'Returned output name.*must be a string.*'):
       with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
           pickle_library='cloudpickle')) as p:
         elements = p | beam.Create([
diff --git a/sdks/python/apache_beam/yaml/yaml_ml.py b/sdks/python/apache_beam/yaml/yaml_ml.py
index a2c7a19563bc..1cec67cf3621 100644
--- a/sdks/python/apache_beam/yaml/yaml_ml.py
+++ b/sdks/python/apache_beam/yaml/yaml_ml.py
@@ -16,7 +16,10 @@
 #
 
 """This module defines yaml wrappings for some ML transforms."""
+import logging
+import pkgutil
 from collections.abc import Callable
+from importlib import import_module
 from typing import Any
 from typing import Optional
 
@@ -25,17 +28,42 @@
 from apache_beam.ml.inference import RunInference
 from apache_beam.ml.inference.base import KeyedModelHandler
 from apache_beam.typehints.row_type import RowTypeConstraint
+from apache_beam.typehints.schemas import named_fields_from_element_type
 from apache_beam.utils import python_callable
 from apache_beam.yaml import options
 from apache_beam.yaml.yaml_utils import SafeLineLoader
 
+
+def _list_submodules(package):
+  """
+  Lists all submodules within a given package.
+  """
+  submodules = []
+  skip_modules = ['base', 'handlers', 'test', 'utils']
+  for _, module_name, _ in pkgutil.walk_packages(
+      package.__path__, package.__name__ + '.'):
+    if any(skip_name in module_name for skip_name in skip_modules):
+      continue
+    submodules.append(module_name)
+  return submodules
+
+
+_transform_constructors = {}
 try:
-  from apache_beam.ml.transforms import tft
   from apache_beam.ml.transforms.base import MLTransform
-  # TODO(robertwb): Is this all of them?
-  _transform_constructors = tft.__dict__
+  # Load all available ML Transform modules
+  for module_name in _list_submodules(beam.ml.transforms):
+    try:
+      module = import_module(module_name)
+      _transform_constructors |= module.__dict__
+    except ImportError as e:
+      logging.warning(
+          'Could not load ML transform module %s: %s.  Please '
+          'install the necessary module dependencies',
+          module_name,
+          e)
 except ImportError:
-  tft = None  # type: ignore
+  MLTransform = None  # type: ignore
 
 
 class ModelHandlerProvider:
@@ -244,11 +272,6 @@ def inference_output_type(self):
                                           ('model_id', Optional[str])])
 
 
-def get_user_schema_fields(user_type):
-  return [(name, type(typ) if not isinstance(typ, type) else typ)
-          for (name, typ) in user_type._fields] if user_type else []
-
-
 @beam.ptransform.ptransform_fn
 def run_inference(
     pcoll,
@@ -441,9 +464,8 @@ def fn(x: PredictionResult):
 
   model_handler_provider = ModelHandlerProvider.create_handler(model_handler)
   model_handler_provider.validate(model_handler['config'])
-  user_type = RowTypeConstraint.from_user_type(pcoll.element_type.user_type)
   schema = RowTypeConstraint.from_fields(
-      get_user_schema_fields(user_type) +
+      named_fields_from_element_type(pcoll.element_type) +
       [(str(inference_tag), model_handler_provider.inference_output_type())])
 
   return (
@@ -477,17 +499,41 @@ def ml_transform(
     write_artifact_location: Optional[str] = None,
     read_artifact_location: Optional[str] = None,
     transforms: Optional[list[Any]] = None):
-  if tft is None:
+  if MLTransform is None:
     raise ValueError(
-        'tensorflow-transform must be installed to use this MLTransform')
+        'No MLTransform found. Please install tensorflow-transform or '
+        'sentence-transformers to use this transform.')
   options.YamlOptions.check_enabled(pcoll.pipeline, 'ML')
-  # TODO(robertwb): Perhaps _config_to_obj could be pushed into MLTransform
-  # itself for better cross-language support?
-  return pcoll | MLTransform(
+  result_ml_transform = MLTransform(
       write_artifact_location=write_artifact_location,
       read_artifact_location=read_artifact_location,
       transforms=[_config_to_obj(t) for t in transforms] if transforms else [])
 
-
-if tft is not None:
+  if transforms:
+    embedding_transforms = [
+        t for t in transforms if t.get('type', '').endswith('Embeddings')
+    ]
+    if embedding_transforms:
+      from apache_beam.typehints import List
+      try:
+        if pcoll.element_type:
+          columns_to_change = {
+              col
+              for t_spec in embedding_transforms
+              for col in t_spec.get('config', {}).get('columns', [])
+          }
+          new_fields = named_fields_from_element_type(pcoll.element_type)
+          final_fields = [
+              (name, List[float] if name in columns_to_change else typ)
+              for name, typ in new_fields
+          ]
+          output_schema = RowTypeConstraint.from_fields(final_fields)
+          return pcoll | result_ml_transform.with_output_types(output_schema)
+      except TypeError:
+        # If we can't get a schema, just return the result.
+        pass
+  return pcoll | result_ml_transform
+
+
+if MLTransform is not None:
   ml_transform.__doc__ = MLTransform.__doc__
diff --git a/sdks/python/apache_beam/yaml/yaml_ml_test.py b/sdks/python/apache_beam/yaml/yaml_ml_test.py
index bc354136bee1..d8b1bdbae1b2 100644
--- a/sdks/python/apache_beam/yaml/yaml_ml_test.py
+++ b/sdks/python/apache_beam/yaml/yaml_ml_test.py
@@ -86,6 +86,155 @@ def test_ml_transform(self):
             equal_to([5]),
             label='CheckVocab')
 
+  def test_ml_transform_read_with_map_to_fields(self):
+    ml_opts = beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle', yaml_experimental_features=['ML'])
+    with tempfile.TemporaryDirectory() as tempdir:
+      # First, write the artifacts.
+      with beam.Pipeline(options=ml_opts) as p:
+        elements = p | beam.Create(TRAIN_DATA)
+        _ = elements | YamlTransform(
+            f'''
+            type: MLTransform
+            config:
+              write_artifact_location: {tempdir}
+              transforms:
+                - type: ScaleTo01
+                  config:
+                    columns: [num]
+                - type: ComputeAndApplyVocabulary
+                  config:
+                    columns: [text]
+                    split_string_by_delimiter: ' ,.'
+            ''')
+
+      # Now, read the artifacts and use MapToFields.
+      with beam.Pipeline(options=ml_opts) as p:
+        elements = p | beam.Create(TEST_DATA)
+        result = elements | YamlTransform(
+            f'''
+            type: chain
+            transforms:
+              - type: MLTransform
+                config:
+                  read_artifact_location: {tempdir}
+              - type: MapToFields
+                config:
+                  language: python
+                  fields:
+                    num_scaled: "num[0]"
+                    text_vocab: text
+            ''')
+
+        def check_row(row):
+          assert row.num_scaled == 0.75
+          assert len(set(row.text_vocab)) == 5
+          return row.num_scaled
+
+        assert_that(result | beam.Map(check_row), equal_to([0.75]))
+
+  def test_sentence_transformer_embedding(self):
+    SENTENCE_EMBEDDING_DIMENSION = 384
+    DATA = [{
+        'id': 1, 'log_message': "Error in module A"
+    }, {
+        'id': 2, 'log_message': "Warning in module B"
+    }, {
+        'id': 3, 'log_message': "Info in module C"
+    }]
+    ml_opts = beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle', yaml_experimental_features=['ML'])
+    with tempfile.TemporaryDirectory() as tempdir:
+      with beam.Pipeline(options=ml_opts) as p:
+        elements = p | beam.Create(DATA)
+        result = elements | YamlTransform(
+            f'''
+            type: MLTransform
+            config:
+              write_artifact_location: {tempdir}
+              transforms:
+                - type: SentenceTransformerEmbeddings
+                  config:
+                    model_name: all-MiniLM-L6-v2
+                    columns: [log_message]
+            ''')
+
+        # Perform a basic check to ensure that embeddings are generated
+        # and that the dimension of those embeddings is correct.
+        actual_output = result | beam.Map(lambda x: len(x['log_message']))
+        assert_that(
+            actual_output, equal_to([SENTENCE_EMBEDDING_DIMENSION] * len(DATA)))
+
+  def test_sentence_transformer_embedding_with_beam_rows(self):
+    SENTENCE_EMBEDDING_DIMENSION = 384
+    DATA = [
+        beam.Row(id=1, log_message="Error in module A"),
+        beam.Row(id=2, log_message="Warning in module B"),
+        beam.Row(id=3, log_message="Info in module C"),
+    ]
+    ml_opts = beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle', yaml_experimental_features=['ML'])
+    with tempfile.TemporaryDirectory() as tempdir:
+      with beam.Pipeline(options=ml_opts) as p:
+        elements = p | beam.Create(DATA)
+        result = elements | YamlTransform(
+            f'''
+            type: MLTransform
+            config:
+              write_artifact_location: {tempdir}
+              transforms:
+                - type: SentenceTransformerEmbeddings
+                  config:
+                    model_name: all-MiniLM-L6-v2
+                    columns: [log_message]
+            ''')
+
+        # Perform a basic check to ensure that embeddings are generated
+        # and that the dimension of those embeddings is correct.
+        actual_output = result | beam.Map(lambda x: len(x.log_message))
+        assert_that(
+            actual_output, equal_to([SENTENCE_EMBEDDING_DIMENSION] * len(DATA)))
+
+  def test_ml_transform_outputs_schema(self):
+    SENTENCE_EMBEDDING_DIMENSION = 384
+    ml_opts = beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle', yaml_experimental_features=['ML'])
+    with tempfile.TemporaryDirectory() as tempdir:
+      with beam.Pipeline(options=ml_opts) as p:
+        result = p | YamlTransform(
+            f'''
+            type: chain
+            transforms:
+              - type: Create
+                config:
+                  elements:
+                    - {{id: 1, log_message: "Error in module A"}}
+                    - {{id: 2, log_message: "Warning in module B"}}
+                    - {{id: 3, log_message: "Info in module C"}}
+              - type: MLTransform
+                config:
+                  write_artifact_location: {tempdir}
+                  transforms:
+                    - type: SentenceTransformerEmbeddings
+                      config:
+                        model_name: all-MiniLM-L6-v2
+                        columns: [log_message]
+              - type: MapToFields
+                config:
+                  language: python
+                  fields:
+                    id: id
+                    embedding: log_message
+            ''')
+
+        def check_row(row):
+          assert isinstance(row.id, int)
+          assert isinstance(row.embedding, list)
+          assert len(row.embedding) == SENTENCE_EMBEDDING_DIMENSION
+          return row.id
+
+        assert_that(result | beam.Map(check_row), equal_to([1, 2, 3]))
+
 
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
diff --git a/sdks/python/apache_beam/yaml/yaml_provider.py b/sdks/python/apache_beam/yaml/yaml_provider.py
index 42087565013c..3af457b7010b 100755
--- a/sdks/python/apache_beam/yaml/yaml_provider.py
+++ b/sdks/python/apache_beam/yaml/yaml_provider.py
@@ -384,22 +384,13 @@ def __init__(
     self._classpath = classpath
 
   def available(self):
-    # pylint: disable=subprocess-run-check
-    trial = subprocess.run(['which', subprocess_server.JavaHelper.get_java()],
-                           capture_output=True)
-    if trial.returncode == 0:
+    # Directly use shutil.which to find the Java executable cross-platform
+    java_path = shutil.which(subprocess_server.JavaHelper.get_java())
+    if java_path:
       return True
-    else:
-
-      def try_decode(bs):
-        try:
-          return bs.decode()
-        except UnicodeError:
-          return bs
-
-      return NotAvailableWithReason(
-          f'Unable to locate java executable: '
-          f'{try_decode(trial.stdout)}{try_decode(trial.stderr)}')
+    # Return error message when not found
+    return NotAvailableWithReason(
+        'Unable to locate java executable: java not found in PATH or JAVA_HOME')
 
   def cache_artifacts(self):
     return [self._jar_provider()]
@@ -756,6 +747,46 @@ def dicts_to_rows(o):
     return o
 
 
+def _unify_element_with_schema(element, target_schema):
+  """Convert an element to match the target schema, preserving existing
+    fields only."""
+  if target_schema is None:
+    return element
+
+  # If element is already a named tuple, convert to dict first
+  if hasattr(element, '_asdict'):
+    element_dict = element._asdict()
+  elif isinstance(element, dict):
+    element_dict = element
+  else:
+    # This element is not a row-like object. If the target schema has a single
+    # field, assume this element is the value for that field.
+    if len(target_schema._fields) == 1:
+      return target_schema(**{target_schema._fields[0]: element})
+    else:
+      return element
+
+  # Create new element with only the fields that exist in the original
+  # element plus None for fields that are expected but missing
+  unified_dict = {}
+  for field_name in target_schema._fields:
+    if field_name in element_dict:
+      value = element_dict[field_name]
+      # Ensure the value matches the expected type
+      # This is particularly important for list fields
+      if value is not None and not isinstance(value, list) and hasattr(
+          value, '__iter__') and not isinstance(
+              value, (str, bytes)) and not hasattr(value, '_asdict'):
+        # Convert iterables to lists if needed
+        unified_dict[field_name] = list(value)
+      else:
+        unified_dict[field_name] = value
+    else:
+      unified_dict[field_name] = None
+
+  return target_schema(**unified_dict)
+
+
 class YamlProviders:
   class AssertEqual(beam.PTransform):
     """Asserts that the input contains exactly the elements provided.
@@ -932,6 +963,48 @@ def __init__(self):
       # pylint: disable=useless-parent-delegation
       super().__init__()
 
+    def _merge_schemas(self, pcolls):
+      """Merge schemas from multiple PCollections to create a unified schema.
+
+      This function creates a unified schema that contains all fields from all
+      input PCollections. Fields are made optional to handle missing values.
+      If fields have different types, they are unified to Optional[Any].
+      """
+      from apache_beam.typehints.schemas import named_fields_from_element_type
+
+      # Collect all schemas
+      schemas = []
+      for pcoll in pcolls:
+        if hasattr(pcoll, 'element_type') and pcoll.element_type:
+          try:
+            fields = named_fields_from_element_type(pcoll.element_type)
+            schemas.append(dict(fields))
+          except (ValueError, TypeError):
+            # If we can't extract schema, skip this PCollection
+            continue
+
+      if not schemas:
+        return None
+
+      # Merge all field names and types.
+      all_field_names = set().union(*(s.keys() for s in schemas))
+      unified_fields = {}
+      for name in all_field_names:
+        present_types = {s[name] for s in schemas if name in s}
+        if len(present_types) > 1:
+          unified_fields[name] = Optional[Any]
+        else:
+          unified_fields[name] = Optional[present_types.pop()]
+
+      # Create unified schema
+      if unified_fields:
+        from apache_beam.typehints.schemas import named_fields_to_schema
+        from apache_beam.typehints.schemas import named_tuple_from_schema
+        unified_schema = named_fields_to_schema(list(unified_fields.items()))
+        return named_tuple_from_schema(unified_schema)
+
+      return None
+
     def expand(self, pcolls):
       if isinstance(pcolls, beam.PCollection):
         pipeline_arg = {}
@@ -942,7 +1015,27 @@ def expand(self, pcolls):
       else:
         pipeline_arg = {'pipeline': pcolls.pipeline}
         pcolls = ()
-      return pcolls | beam.Flatten(**pipeline_arg)
+
+      if not pcolls:
+        return pcolls | beam.Flatten(**pipeline_arg)
+
+      # Try to unify schemas
+      unified_schema = self._merge_schemas(pcolls)
+
+      if unified_schema is None:
+        # No schema unification needed, use standard flatten
+        return pcolls | beam.Flatten(**pipeline_arg)
+
+      # Apply schema unification to each PCollection before flattening.
+      unified_pcolls = []
+      for i, pcoll in enumerate(pcolls):
+        unified_pcoll = pcoll | f'UnifySchema{i}' >> beam.Map(
+            _unify_element_with_schema,
+            target_schema=unified_schema).with_output_types(unified_schema)
+        unified_pcolls.append(unified_pcoll)
+
+      # Flatten the unified PCollections
+      return unified_pcolls | beam.Flatten(**pipeline_arg)
 
   class WindowInto(beam.PTransform):
     # pylint: disable=line-too-long
@@ -1430,13 +1523,20 @@ def wrapper(*args, **kwargs):
 def _join_url_or_filepath(base, path):
   if not base:
     return path
-  base_scheme = urllib.parse.urlparse(base, '').scheme
-  path_scheme = urllib.parse.urlparse(path, base_scheme).scheme
-  if path_scheme != base_scheme:
+
+  if urllib.parse.urlparse(path).scheme:
+    # path is an absolute path with scheme (whether it is the same as base or
+    # not).
     return path
-  elif base_scheme and base_scheme in urllib.parse.uses_relative:
+
+  # path is a relative path or an absolute path without scheme (e.g. /a/b/c)
+  base_scheme = urllib.parse.urlparse(base, '').scheme
+  if base_scheme and base_scheme in urllib.parse.uses_relative:
     return urllib.parse.urljoin(base, path)
   else:
+    if FileSystems.join(base, "") == base:
+      # base ends with a filesystem separator
+      return FileSystems.join(base, path)
     return FileSystems.join(FileSystems.split(base)[0], path)
 
 
diff --git a/sdks/python/apache_beam/yaml/yaml_provider_unit_test.py b/sdks/python/apache_beam/yaml/yaml_provider_unit_test.py
index fe8b6c7b89a3..1ebae9a3b446 100644
--- a/sdks/python/apache_beam/yaml/yaml_provider_unit_test.py
+++ b/sdks/python/apache_beam/yaml/yaml_provider_unit_test.py
@@ -17,6 +17,7 @@
 
 import logging
 import os
+import sys
 import tempfile
 import unittest
 
@@ -295,6 +296,71 @@ def test_env_content_sensitive(self):
       self.assertNotEqual(before, after)
 
 
+class JoinUrlOrFilepathTest(unittest.TestCase):
+  def test_join_url_relative_path(self):
+    self.assertEqual(
+        yaml_provider._join_url_or_filepath('http://example.com/a', 'b/c.yaml'),
+        'http://example.com/b/c.yaml')
+    self.assertEqual(
+        yaml_provider._join_url_or_filepath(
+            'http://example.com/a/', 'b/c.yaml'),
+        'http://example.com/a/b/c.yaml')
+
+    # use os.path.join to mock gcs filesystem split and join.
+    with mock.patch('apache_beam.io.filesystems.FileSystems.split',
+                    new=lambda x:
+                    ("gs://bucket", x.removeprefix("gs://bucket/"))):
+      with mock.patch('apache_beam.io.filesystems.FileSystems.join',
+                      new=lambda *args: '/'.join(args)):
+        self.assertEqual(
+            yaml_provider._join_url_or_filepath('gs://bucket', 'b/c.yaml'),
+            'gs://bucket/b/c.yaml')
+        self.assertEqual(
+            yaml_provider._join_url_or_filepath('gs://bucket/', 'b/c.yaml'),
+            'gs://bucket/b/c.yaml')
+        self.assertEqual(
+            yaml_provider._join_url_or_filepath('gs://bucket/a', 'b/c.yaml'),
+            'gs://bucket/b/c.yaml')
+
+  def test_join_filepath_relative_path(self):
+    if sys.platform != 'win32':
+      self.assertEqual(
+          yaml_provider._join_url_or_filepath('/a/b/', 'c/d.yaml'),
+          '/a/b/c/d.yaml')
+      self.assertEqual(
+          yaml_provider._join_url_or_filepath('/a/b', 'c/d.yaml'),
+          '/a/c/d.yaml')
+    else:
+      self.assertEqual(
+          yaml_provider._join_url_or_filepath('C:\\a\\b\\', 'c\\d.yaml'),
+          'C:\\a\\b\\c\\d.yaml')
+      self.assertEqual(
+          yaml_provider._join_url_or_filepath('C:\\a\\b', 'c\\d.yaml'),
+          'C:\\a\\c\\d.yaml')
+
+  def test_absolute_path(self):
+    self.assertEqual(
+        yaml_provider._join_url_or_filepath(
+            'gs://bucket/a', 'gs://bucket/b/c.yaml'),
+        'gs://bucket/b/c.yaml')
+
+    if sys.platform != 'win32':
+      self.assertEqual(
+          yaml_provider._join_url_or_filepath('/a/b', '/c/d.yaml'), '/c/d.yaml')
+
+  def test_different_scheme(self):
+    self.assertEqual(
+        yaml_provider._join_url_or_filepath(
+            'http://example.com/a', 'gs://bucket/b/c.yaml'),
+        'gs://bucket/b/c.yaml')
+
+  def test_empty_base(self):
+    self.assertEqual(
+        yaml_provider._join_url_or_filepath('', 'a/b.yaml'), 'a/b.yaml')
+    self.assertEqual(
+        yaml_provider._join_url_or_filepath(None, 'a/b.yaml'), 'a/b.yaml')
+
+
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
   unittest.main()
diff --git a/sdks/python/apache_beam/yaml/yaml_testing.py b/sdks/python/apache_beam/yaml/yaml_testing.py
index 9634b58e615d..e7fbc1d43b6f 100644
--- a/sdks/python/apache_beam/yaml/yaml_testing.py
+++ b/sdks/python/apache_beam/yaml/yaml_testing.py
@@ -361,18 +361,30 @@ def __init__(self, elements, recording_id):
     self._recording_id = recording_id
 
   def expand(self, pcoll):
-    equal_to_matcher = equal_to(yaml_provider.dicts_to_rows(self._elements))
-
-    def matcher(actual):
-      try:
-        equal_to_matcher(actual)
-      except Exception:
-        if self._recording_id:
-          AssertEqualAndRecord.store_recorded_result(
-              tuple(self._recording_id), actual)
-        else:
-          raise
-
+    # Convert elements to rows outside the matcher to avoid capturing
+    # any grpc channels that might be created during the conversion
+    expected_rows = yaml_provider.dicts_to_rows(self._elements)
+    recording_id = self._recording_id
+
+    # Create a serializable matcher function that doesn't capture
+    # any external references that might contain grpc channels
+    class SerializableMatcher:
+      def __init__(self, expected_rows, recording_id):
+        self.expected_rows = expected_rows
+        self.recording_id = recording_id
+        self.equal_to_matcher = equal_to(expected_rows)
+
+      def __call__(self, actual):
+        try:
+          self.equal_to_matcher(actual)
+        except Exception:
+          if self.recording_id:
+            AssertEqualAndRecord.store_recorded_result(
+                tuple(self.recording_id), actual)
+          else:
+            raise
+
+    matcher = SerializableMatcher(expected_rows, recording_id)
     return assert_that(
         pcoll | beam.Map(lambda row: beam.Row(**row._asdict())), matcher)
 
diff --git a/sdks/python/apache_beam/yaml/yaml_testing_test.py b/sdks/python/apache_beam/yaml/yaml_testing_test.py
index 0ff780df3614..9fcdafd2ab34 100644
--- a/sdks/python/apache_beam/yaml/yaml_testing_test.py
+++ b/sdks/python/apache_beam/yaml/yaml_testing_test.py
@@ -22,6 +22,8 @@
 
 # isort is fighting with yapf here.
 # isort: off
+from apache_beam.options.pipeline_options import StandardOptions
+from apache_beam.testing.test_pipeline import TestPipeline
 from apache_beam.yaml import yaml_testing
 
 # Note that executing this pipeline will actually fail if the untested
@@ -199,7 +201,6 @@ def test_fixes(self):
   def test_create(self):
     with tempfile.TemporaryDirectory() as tmpdir:
       input_path = os.path.join(tmpdir, 'input.csv')
-      input_path = os.path.join('.', 'input.csv')
       with open(input_path, 'w') as fout:
         fout.write('a,b,c\n')
         for ix in range(1000):
@@ -225,6 +226,102 @@ def test_create(self):
     self.assertGreaterEqual(len(test_spec['expected_inputs'][0]['elements']), 5)
     yaml_testing.run_test(pipeline, test_spec)
 
+  @unittest.skipIf(
+      TestPipeline().get_pipeline_options().view_as(StandardOptions).runner
+      is None,
+      'Do not run this test on precommit suites.')
+  def test_join_transform_serialization(self):
+    """Test that Join transforms work with YAML testing framework
+    and cloudpickle.
+    
+    This test validates the fix for the grpc channel serialization issue
+    that was causing TypeError: no default __reduce__ due to non-trivial
+    __cinit__ when using Join transforms with the YAML testing framework.
+    """
+    join_pipeline = '''
+      pipeline:
+        transforms:
+        - type: Create
+          name: Create1
+          config:
+            elements:
+            - ride_id: "1"
+              pickup_location: "downtown"
+            - ride_id: "2"
+              pickup_location: "airport"
+
+        - type: Create
+          name: Create2
+          config:
+            elements:
+            - ride_id: "1"
+              dropoff_location: "mall"
+            - ride_id: "2"
+              dropoff_location: "hotel"
+
+        - type: Join
+          name: JoinRides
+          input:
+            pickup: Create1
+            dropoff: Create2
+          config:
+            equalities: ride_id
+            type: inner
+            fields:
+              pickup: [ride_id, pickup_location]
+              dropoff: [dropoff_location]
+
+        - type: LogForTesting
+          name: LogResult
+          input: JoinRides
+      '''
+
+    # Test with expected_inputs to validate the Join transform output
+    yaml_testing.run_test(
+        join_pipeline,
+        {
+            'expected_inputs': [{
+                'name': 'LogResult',
+                'elements': [{
+                    'ride_id': '1',
+                    'pickup_location': 'downtown',
+                    'dropoff_location': 'mall'
+                },
+                             {
+                                 'ride_id': '2',
+                                 'pickup_location': 'airport',
+                                 'dropoff_location': 'hotel'
+                             }]
+            }]
+        })
+
+    # Test with mock_outputs to validate Join transform can handle mocked inputs
+    yaml_testing.run_test(
+        join_pipeline,
+        {
+            'mock_outputs': [{
+                'name': 'Create1',
+                'elements': [{
+                    'ride_id': '3', 'pickup_location': 'station'
+                }]
+            },
+                             {
+                                 'name': 'Create2',
+                                 'elements': [{
+                                     'ride_id': '3',
+                                     'dropoff_location': 'office'
+                                 }]
+                             }],
+            'expected_inputs': [{
+                'name': 'LogResult',
+                'elements': [{
+                    'ride_id': '3',
+                    'pickup_location': 'station',
+                    'dropoff_location': 'office'
+                }]
+            }]
+        })
+
 
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
diff --git a/sdks/python/apache_beam/yaml/yaml_transform.py b/sdks/python/apache_beam/yaml/yaml_transform.py
index 8710fe379c37..bd1fc8da9018 100644
--- a/sdks/python/apache_beam/yaml/yaml_transform.py
+++ b/sdks/python/apache_beam/yaml/yaml_transform.py
@@ -34,9 +34,13 @@
 from apache_beam.io.filesystems import FileSystems
 from apache_beam.options.pipeline_options import GoogleCloudOptions
 from apache_beam.transforms.fully_qualified_named_transform import FullyQualifiedNamedTransform
+from apache_beam.typehints import schemas
+from apache_beam.typehints import typehints
+from apache_beam.yaml import json_utils
 from apache_beam.yaml import yaml_provider
 from apache_beam.yaml import yaml_utils
 from apache_beam.yaml.yaml_combine import normalize_combine
+from apache_beam.yaml.yaml_mapping import Validate
 from apache_beam.yaml.yaml_mapping import normalize_mapping
 from apache_beam.yaml.yaml_mapping import validate_generic_expressions
 from apache_beam.yaml.yaml_utils import SafeLineLoader
@@ -481,6 +485,15 @@ def expand_transform(spec, scope):
 
 
 def expand_leaf_transform(spec, scope):
+  spec = spec.copy()
+
+  # Check for optional output_schema to verify on.
+  # The idea is to pass this output_schema config to the ValidateWithSchema
+  # transform.
+  output_schema_spec = {}
+  if 'output_schema' in spec.get('config', {}):
+    output_schema_spec = spec.get('config').pop('output_schema')
+
   spec = normalize_inputs_outputs(spec)
   inputs_dict = {
       key: scope.get_pcollection(value)
@@ -507,6 +520,20 @@ def expand_leaf_transform(spec, scope):
   except Exception as exn:
     raise ValueError(
         f"Error applying transform {identify_object(spec)}: {exn}") from exn
+
+  # Optional output_schema was found, so lets expand on that before returning.
+  if output_schema_spec:
+    error_handling_spec = {}
+    # Obtain original transform error_handling_spec, so that all validate
+    # schema errors use that.
+    if 'error_handling' in spec.get('config', None):
+      error_handling_spec = spec.get('config').get('error_handling', {})
+
+    outputs = expand_output_schema_transform(
+        spec=output_schema_spec,
+        outputs=outputs,
+        error_handling_spec=error_handling_spec)
+
   if isinstance(outputs, dict):
     # TODO: Handle (or at least reject) nested case.
     return outputs
@@ -522,6 +549,250 @@ def expand_leaf_transform(spec, scope):
         f'{type(outputs)}')
 
 
+def expand_output_schema_transform(spec, outputs, error_handling_spec):
+  """Applies a `Validate` transform to the output of another transform.
+
+  This function is called when an `output_schema` is defined on a transform.
+  It wraps the original transform's output(s) with a `Validate` transform
+  to ensure the data conforms to the specified schema.
+
+  If the original transform has error handling configured, validation errors
+  will be routed to the specified error output. If not, validation failures
+  will cause the pipeline to fail.
+
+  Args:
+    spec (dict): The `output_schema` specification from the YAML config.
+    outputs (beam.PCollection or dict[str, beam.PCollection]): The output(s)
+      from the transform to be validated.
+    error_handling_spec (dict): The `error_handling` configuration from the
+      original transform.
+
+  Returns:
+    The validated PCollection(s). If error handling is enabled, this will be a
+    dictionary containing the 'good' output and any error outputs.
+
+  Raises:
+    ValueError: If `error_handling` is incorrectly specified within the
+      `output_schema` spec itself, or if the main output of a multi-output
+      transform cannot be determined.
+  """
+  if 'error_handling' in spec:
+    raise ValueError(
+        'error_handling config is not supported directly in '
+        'the output_schema. Please use error_handling config in '
+        'the transform, if possible, or use ValidateWithSchema transform '
+        'instead.')
+
+  # Strip metadata such as __line__ and __uuid__ as these will interfere with
+  # the validation downstream.
+  clean_schema = SafeLineLoader.strip_metadata(spec)
+
+  # If no error handling is specified for the main transform, warn the user
+  # that the pipeline may fail if any output data fails the output schema
+  # validation.
+  if not error_handling_spec:
+    _LOGGER.warning("Output_schema config is attached to a transform that has "\
+    "no error_handling config specified. Any failures validating on output" \
+    "schema will fail the pipeline unless the user specifies an" \
+    "error_handling config on a capable transform. Alternatively, you can " \
+    "remove the output_schema config on this transform and add a " \
+    "ValidateWithSchema transform with separate error handling downstream of " \
+    "the current transform.")
+
+  # The transform produced outputs with a single beam.PCollection
+  if isinstance(outputs, beam.PCollection):
+    outputs = _enforce_schema(
+        outputs, 'EnforceOutputSchema', error_handling_spec, clean_schema)
+    if isinstance(outputs, dict):
+      main_tag = error_handling_spec.get('main_tag', 'good')
+      main_output = outputs.pop(main_tag)
+      if error_handling_spec:
+        error_output_tag = error_handling_spec.get('output')
+        if error_output_tag in outputs:
+          return {
+              'output': main_output,
+              error_output_tag: outputs.pop(error_output_tag)
+          }
+      return main_output
+
+  # The transform produced outputs with many named PCollections and need to
+  # determine which PCollection should be validated on.
+  elif isinstance(outputs, dict):
+    main_output_key = get_main_output_key(spec, outputs, error_handling_spec)
+
+    validation_result = _enforce_schema(
+        outputs[main_output_key],
+        f'EnforceOutputSchema_{main_output_key}',
+        error_handling_spec,
+        clean_schema)
+    outputs = _integrate_validation_results(
+        outputs, validation_result, main_output_key, error_handling_spec)
+
+  return outputs
+
+
+def get_main_output_key(spec, outputs, error_handling_spec):
+  """Determines the main output key from a dictionary of PCollections.
+
+  This is used to identify which output of a multi-output transform should be
+  validated against an `output_schema`.
+
+  The main output is determined using the following precedence:
+  1. An output with the key 'output'.
+  2. An output with the key 'good'.
+  3. The single output if there is only one.
+
+  Args:
+    spec: The transform specification, used for creating informative error
+      messages.
+    outputs: A dictionary mapping output tags to their corresponding
+      PCollections.
+    error_handling_spec (dict): The `error_handling` configuration from the
+      original transform.
+
+  Returns:
+    The key of the main output PCollection.
+
+  Raises:
+    ValueError: If a main output cannot be determined because there are
+      multiple outputs and none are named 'output' or 'good'.
+  """
+  main_output_key = 'output'
+  if main_output_key not in outputs:
+    if 'good' in outputs:
+      main_output_key = 'good'
+    elif len(outputs) == 1:
+      main_output_key = next(iter(outputs.keys()))
+    else:
+      raise ValueError(
+          f"Transform {identify_object(spec)} has outputs "
+          f"{list(outputs.keys())}, but none are named 'output' or 'good'. To "
+          "apply an 'output_schema', please ensure the transform has exactly "
+          "one output, or that the main output is named 'output' or 'good'.")
+
+  if len(outputs) >= 3 or \
+    (len(outputs) == 2 and error_handling_spec.get('output') not in outputs):
+    _LOGGER.warning(
+        "There are currently %s outputs: %s. Only the main output will be "
+        "validated.",
+        len(outputs),
+        outputs)
+
+  return main_output_key
+
+
+def _integrate_validation_results(
+    outputs, validation_result, main_output_key, error_handling_spec):
+  """
+  Integrates the results of a validation transform back into the outputs of
+  the original transform.
+
+  This function handles merging the "good" and "bad" outputs from a
+  `Validate` transform with the existing outputs of the transform that was
+  validated.
+
+  Args:
+    outputs: The original dictionary of output PCollections from the transform.
+    validation_result: The output of the `Validate` transform. This can be a
+      single PCollection (if all elements passed) or a dictionary of
+      PCollections (if error handling was enabled for validation).
+    main_output_key: The key in the `outputs` dictionary corresponding to the
+      PCollection that was validated.
+    error_handling_spec: The error handling configuration of the original
+      transform.
+
+  Returns:
+    The updated dictionary of output PCollections, with validation results
+    integrated.
+
+  Raises:
+    ValueError: If the validation transform produces unexpected outputs.
+  """
+  if not isinstance(validation_result, dict):
+    outputs[main_output_key] = validation_result
+    return outputs
+
+  # The main output from validation is the good output.
+  main_tag = error_handling_spec.get('main_tag', 'good')
+  outputs[main_output_key] = validation_result.pop(main_tag)
+
+  if error_handling_spec:
+    error_output_tag = error_handling_spec['output']
+    if error_output_tag in validation_result:
+      schema_error_pcoll = validation_result.pop(error_output_tag)
+      # The original transform also had an error output. Merge them.
+      outputs[error_output_tag] = (
+          (outputs[error_output_tag], schema_error_pcoll)
+          | f'FlattenErrors_{main_output_key}' >> beam.Flatten())
+
+    # There should be no other outputs from validation.
+    if validation_result:
+      raise ValueError(
+          "Unexpected outputs from validation: "
+          f"{list(validation_result.keys())}")
+
+  return outputs
+
+
+def _enforce_schema(pcoll, label, error_handling_spec, clean_schema):
+  """Applies schema to PCollection elements if necessary, then validates.
+
+  This function ensures that the input PCollection conforms to a specified
+  schema. If the PCollection is schemaless (i.e., its element_type is Any),
+  it attempts to convert its elements into schema-aware `beam.Row` objects
+  based on the provided `clean_schema`. After ensuring the PCollection has
+  a defined schema, it applies a `Validate` transform to perform the actual
+  schema validation.
+
+  Args:
+    pcoll: The input PCollection to be schema-enforced and validated.
+    label: A string label to be used for the Beam transforms created within this
+    function.
+    error_handling_spec: A dictionary specifying how to handle validation
+    errors.
+    clean_schema: A dictionary representing the schema to enforce and validate
+    against.
+
+  Returns:
+    A PCollection (or PCollectionTuple if error handling is enabled) resulting
+    from the `Validate` transform.
+  """
+  if pcoll.element_type == typehints.Any:
+    _LOGGER.info(
+        "PCollection for %s has no schema (element_type=Any). "
+        "Converting elements to beam.Row based on provided output_schema.",
+        label)
+    try:
+      # Attempt to confer the schemaless elements into schema-aware beam.Row
+      # objects
+      beam_schema = json_utils.json_schema_to_beam_schema(clean_schema)
+      row_type_constraint = schemas.named_tuple_from_schema(beam_schema)
+
+      def to_row(element):
+        """
+        Convert a single element into the row type constraint type.
+        """
+        if isinstance(element, dict):
+          return row_type_constraint(**element)
+        elif hasattr(element, '_asdict'):  # Handle NamedTuple, beam.Row
+          return row_type_constraint(**element._asdict())
+        else:
+          raise TypeError(
+              f"Cannot convert element of type {type(element)} to beam.Row "
+              f"for validation in {label}. Element: {element}")
+
+      pcoll = pcoll | f'{label}_ConvertToRow' >> beam.Map(
+          to_row).with_output_types(row_type_constraint)
+    except Exception as e:
+      raise ValueError(
+          f"Failed to prepare schemaless PCollection for \
+            validation in {label}: {e}") from e
+
+  # Add Validation step downstream of current transform
+  return pcoll | label >> Validate(
+      schema=clean_schema, error_handling=error_handling_spec)
+
+
 def expand_composite_transform(spec, scope):
   spec = normalize_inputs_outputs(normalize_source_sink(spec))
 
@@ -861,8 +1132,8 @@ def preprocess_flattened_inputs(spec):
       def all_inputs(t):
         for key, values in t.get('input', {}).items():
           if isinstance(values, list):
-            for ix, values in enumerate(values):
-              yield f'{key}{ix}', values
+            for ix, value in enumerate(values):
+              yield f'{key}{ix}', value
           else:
             yield key, values
 
diff --git a/sdks/python/apache_beam/yaml/yaml_transform_test.py b/sdks/python/apache_beam/yaml/yaml_transform_test.py
index 1a99507d76d7..2ba49a1fab82 100644
--- a/sdks/python/apache_beam/yaml/yaml_transform_test.py
+++ b/sdks/python/apache_beam/yaml/yaml_transform_test.py
@@ -477,6 +477,330 @@ def test_composite_resource_hints(self):
         b'1000000000',
         proto)
 
+  def test_flatten_unifies_schemas(self):
+    with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle')) as p:
+      _ = p | YamlTransform(
+          '''
+            type: composite
+            transforms:
+              - type: Create
+                name: Create1
+                config:
+                  elements:
+                    - {ride_id: '1', passenger_count: 1}
+                    - {ride_id: '2', passenger_count: 2}
+              - type: Create
+                name: Create2
+                config:
+                  elements:
+                    - {ride_id: '3'}
+                    - {ride_id: '4'}
+              - type: Flatten
+                input: [Create1, Create2]
+              - type: AssertEqual
+                input: Flatten
+                config:
+                  elements:
+                    - {ride_id: '1', passenger_count: 1}
+                    - {ride_id: '2', passenger_count: 2}
+                    - {ride_id: '3'}
+                    - {ride_id: '4'}
+          ''')
+
+  def test_flatten_unifies_optional_fields(self):
+    """Test that Flatten correctly unifies schemas with optional fields."""
+    with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle')) as p:
+      _ = p | YamlTransform(
+          '''
+            type: composite
+            transforms:
+              - type: Create
+                name: Create1
+                config:
+                  elements:
+                    - {id: '1', name: 'Alice', age: 30}
+                    - {id: '2', name: 'Bob', age: 25}
+              - type: Create
+                name: Create2
+                config:
+                  elements:
+                    - {id: '3', name: 'Charlie'}
+                    - {id: '4', name: 'Diana'}
+              - type: Flatten
+                input: [Create1, Create2]
+              - type: AssertEqual
+                input: Flatten
+                config:
+                  elements:
+                    - {id: '1', name: 'Alice', age: 30}
+                    - {id: '2', name: 'Bob', age: 25}
+                    - {id: '3', name: 'Charlie'}
+                    - {id: '4', name: 'Diana'}
+          ''')
+
+  def test_flatten_unifies_different_types(self):
+    """Test that Flatten correctly unifies schemas with different
+    field types."""
+    with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle')) as p:
+      _ = p | YamlTransform(
+          '''
+            type: composite
+            transforms:
+              - type: Create
+                name: Create1
+                config:
+                  elements:
+                    - {id: 1, value: 100}
+                    - {id: 2, value: 200}
+              - type: Create
+                name: Create2
+                config:
+                  elements:
+                    - {id: '3', value: 'text'}
+                    - {id: '4', value: 'data'}
+              - type: Flatten
+                input: [Create1, Create2]
+              - type: AssertEqual
+                input: Flatten
+                config:
+                  elements:
+                    - {id: 1, value: 100}
+                    - {id: 2, value: 200}
+                    - {id: '3', value: 'text'}
+                    - {id: '4', value: 'data'}
+          ''')
+
+  def test_flatten_unifies_list_fields(self):
+    """Test that Flatten correctly unifies schemas with list fields."""
+    with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle')) as p:
+      _ = p | YamlTransform(
+          '''
+            type: composite
+            transforms:
+              - type: Create
+                name: Create1
+                config:
+                  elements:
+                    - {id: '1', tags: ['red', 'blue']}
+                    - {id: '2', tags: ['green']}
+              - type: Create
+                name: Create2
+                config:
+                  elements:
+                    - {id: '3', tags: ['yellow', 'purple', 'orange']}
+                    - {id: '4', tags: []}
+              - type: Flatten
+                input: [Create1, Create2]
+              - type: AssertEqual
+                input: Flatten
+                config:
+                  elements:
+                    - {id: '1', tags: ['red', 'blue']}
+                    - {id: '2', tags: ['green']}
+                    - {id: '3', tags: ['yellow', 'purple', 'orange']}
+                    - {id: '4', tags: []}
+          ''')
+
+  def test_flatten_unifies_with_missing_fields(self):
+    """Test that Flatten correctly unifies schemas when some inputs have
+    missing fields."""
+    with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle')) as p:
+      _ = p | YamlTransform(
+          '''
+            type: composite
+            transforms:
+              - type: Create
+                name: Create1
+                config:
+                  elements:
+                    - {id: '1', name: 'Alice', department: 'Engineering',
+                       salary: 75000}
+                    - {id: '2', name: 'Bob', department: 'Marketing',
+                       salary: 65000}
+              - type: Create
+                name: Create2
+                config:
+                  elements:
+                    - {id: '3', name: 'Charlie', department: 'Sales'}
+                    - {id: '4', name: 'Diana'}
+              - type: Flatten
+                input: [Create1, Create2]
+              - type: AssertEqual
+                input: Flatten
+                config:
+                  elements:
+                    - {id: '1', name: 'Alice', department: 'Engineering',
+                       salary: 75000}
+                    - {id: '2', name: 'Bob', department: 'Marketing',
+                       salary: 65000}
+                    - {id: '3', name: 'Charlie', department: 'Sales'}
+                    - {id: '4', name: 'Diana'}
+          ''')
+
+  def test_flatten_unifies_complex_mixed_schemas(self):
+    """Test that Flatten correctly unifies complex mixed
+    schemas."""
+    with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle')) as p:
+      _ = p | YamlTransform(
+          '''
+            type: composite
+            transforms:
+              - type: Create
+                name: Create1
+                config:
+                  elements:
+                    - {id: 1, name: 'Product A', price: 29.99,
+                       categories: ['electronics', 'gadgets']}
+                    - {id: 2, name: 'Product B', price: 15.50,
+                       categories: ['books']}
+              - type: Create
+                name: Create2
+                config:
+                  elements:
+                    - {id: 3, name: 'Product C', categories: ['clothing']}
+                    - {id: 4, name: 'Product D', price: 99.99}
+              - type: Create
+                name: Create3
+                config:
+                  elements:
+                    - {id: 5, name: 'Product E', price: 5.00,
+                       categories: []}
+              - type: Flatten
+                input: [Create1, Create2, Create3]
+              - type: AssertEqual
+                input: Flatten
+                config:
+                  elements:
+                    - {id: 1, name: 'Product A', price: 29.99,
+                       categories: ['electronics', 'gadgets']}
+                    - {id: 2, name: 'Product B', price: 15.50,
+                       categories: ['books']}
+                    - {id: 3, name: 'Product C', categories: ['clothing']}
+                    - {id: 4, name: 'Product D', price: 99.99}
+                    - {id: 5, name: 'Product E', price: 5.00,
+                       categories: []}
+          ''')
+
+  def test_output_schema_success(self):
+    """Test that optional output_schema works."""
+    with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle')) as p:
+      _ = p | YamlTransform(
+          '''
+            type: composite
+            transforms:
+              - type: Create
+                name: MyCreate
+                config:
+                  elements:
+                    - {sdk: 'Beam', year: 2016}
+                    - {sdk: 'Flink', year: 2015}
+                  output_schema:
+                    type: object
+                    properties:
+                      sdk: 
+                        type: string
+                      year: 
+                        type: integer
+              - type: AssertEqual
+                name: CheckGood
+                input: MyCreate
+                config:
+                  elements:
+                    - {sdk: 'Beam', year: 2016}
+                    - {sdk: 'Flink', year: 2015}
+          ''')
+
+  def test_output_schema_fails(self):
+    """
+    Test that optional output_schema works by failing the pipeline since main
+    transform doesn't have error_handling config.
+    """
+    with self.assertRaises(Exception) as e:
+      with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
+          pickle_library='cloudpickle')) as p:
+        _ = p | YamlTransform(
+            '''
+                type: composite
+                transforms:
+                  - type: Create
+                    name: MyCreate
+                    config:
+                      elements:
+                        - {sdk: 'Beam', year: 2016}
+                        - {sdk: 'Spark', year: 'date'}
+                        - {sdk: 'Flink', year: 2015}
+                      output_schema:
+                        type: object
+                        properties:
+                          sdk: 
+                            type: string
+                          year: 
+                            type: integer
+                  - type: AssertEqual
+                    name: CheckGood
+                    input: MyCreate
+                    config:
+                      elements:
+                        - {sdk: 'Beam', year: 2016}
+                        - {sdk: 'Flink', year: 2015}
+              ''')
+    self.assertIn("'date' is not of type 'integer'", str(e.exception))
+
+  def test_output_schema_with_main_transform_error_handling_success(self):
+    """Test that optional output_schema works in conjunction with main transform
+    error handling."""
+    with beam.Pipeline(options=beam.options.pipeline_options.PipelineOptions(
+        pickle_library='cloudpickle')) as p:
+      _ = p | YamlTransform(
+          '''
+            type: composite
+            transforms:
+              - type: Create
+                name: CreateVisits
+                config:
+                  elements:
+                    - {user: alice, timestamp: "not-valid"}
+                    - {user: bob, timestamp: 3}
+              - type: AssignTimestamps
+                input: CreateVisits
+                config:
+                  timestamp: timestamp
+                  error_handling:
+                    output: invalid_rows
+                  output_schema:
+                    type: object
+                    properties:
+                      user:
+                        type: string
+                      timestamp:
+                        type: boolean
+              - type: MapToFields
+                name: ExtractInvalidTimestamp
+                input: AssignTimestamps.invalid_rows
+                config:
+                  language: python
+                  fields:
+                    user: "element.user"
+                    timestamp: "element.timestamp"
+              - type: AssertEqual
+                input: ExtractInvalidTimestamp
+                config:
+                  elements:
+                    - {user: "alice", timestamp: "not-valid"}
+                    - {user: bob, timestamp: 3}
+              - type: AssertEqual
+                input: AssignTimestamps
+                config:
+                  elements: []
+          ''')
+
 
 class ErrorHandlingTest(unittest.TestCase):
   def test_error_handling_outputs(self):
diff --git a/sdks/python/apache_beam/yaml/yaml_transform_unit_test.py b/sdks/python/apache_beam/yaml/yaml_transform_unit_test.py
index 0ea95228aeee..14bd758ebae5 100644
--- a/sdks/python/apache_beam/yaml/yaml_transform_unit_test.py
+++ b/sdks/python/apache_beam/yaml/yaml_transform_unit_test.py
@@ -28,7 +28,9 @@
 from apache_beam.yaml.yaml_transform import ensure_errors_consumed
 from apache_beam.yaml.yaml_transform import ensure_transforms_have_types
 from apache_beam.yaml.yaml_transform import expand_composite_transform
+from apache_beam.yaml.yaml_transform import expand_pipeline
 from apache_beam.yaml.yaml_transform import extract_name
+from apache_beam.yaml.yaml_transform import get_main_output_key
 from apache_beam.yaml.yaml_transform import identify_object
 from apache_beam.yaml.yaml_transform import normalize_inputs_outputs
 from apache_beam.yaml.yaml_transform import normalize_source_sink
@@ -950,6 +952,52 @@ def test_ensure_errors_consumed_no_output_in_error_handling(self):
   def test_only_element(self):
     self.assertEqual(only_element((1, )), 1)
 
+  def test_get_main_output_key(self):
+    spec = {'type': 'TestTransform'}
+    error_handling_spec = {'output': 'invalid_rows'}
+
+    # Case 1: 'output' key exists
+    outputs = {'output': 1, 'another': 2}
+    with self.assertLogs('apache_beam.yaml.yaml_transform',
+                         level='WARNING') as al:
+      self.assertEqual(
+          get_main_output_key(spec, outputs, error_handling_spec), 'output')
+      self.assertIn("Only the main output will be validated.", al.output[0])
+
+    # Case 2: 'good' key exists, 'output' does not
+    outputs = {'good': 1, 'another': 2}
+    with self.assertLogs('apache_beam.yaml.yaml_transform',
+                         level='WARNING') as al:
+      self.assertEqual(
+          get_main_output_key(spec, outputs, error_handling_spec), 'good')
+      self.assertIn("Only the main output will be validated.", al.output[0])
+
+    # Case 3: Only one output
+    outputs = {'single_output': 1}
+    self.assertEqual(
+        get_main_output_key(spec, outputs, error_handling_spec),
+        'single_output')
+
+    # Case 4: Multiple outputs, no 'output' or 'good'
+    outputs = {'another': 1, 'yet_another': 2}
+    with self.assertRaisesRegex(
+        ValueError, "Transform .* has outputs .* but none are named 'output'"):
+      get_main_output_key(spec, outputs, error_handling_spec)
+
+    # Case 5: Empty outputs
+    outputs = {}
+    with self.assertRaisesRegex(
+        ValueError, "Transform .* has outputs .* but none are named 'output'"):
+      get_main_output_key(spec, outputs, error_handling_spec)
+
+    # Case 6: More than two outputs with 'good' present
+    outputs = {'good': 1, 'bad': 2, 'something': 3}
+    with self.assertLogs('apache_beam.yaml.yaml_transform',
+                         level='WARNING') as al:
+      self.assertEqual(
+          get_main_output_key(spec, outputs, error_handling_spec), 'good')
+      self.assertIn("Only the main output will be validated.", al.output[0])
+
 
 class YamlTransformTest(unittest.TestCase):
   def test_init_with_string(self):
@@ -988,6 +1036,69 @@ def test_init_with_dict(self):
     self.assertEqual(result._spec['type'], "composite")  # preprocessed spec
 
 
+class ExpandPipelineTest(unittest.TestCase):
+  def test_expand_pipeline_with_pipeline_key_only(self):
+    spec = '''
+      pipeline:
+        type: chain
+        transforms:
+          - type: Create
+            config:
+              elements: [1,2,3]
+          - type: LogForTesting
+    '''
+    with new_pipeline() as p:
+      expand_pipeline(p, spec, validate_schema=None)
+
+  def test_expand_pipeline_with_pipeline_and_option_keys(self):
+    spec = '''
+      pipeline:
+        type: chain
+        transforms:
+          - type: Create
+            config:
+              elements: [1,2,3]
+          - type: LogForTesting
+      options:
+        streaming: false
+    '''
+    with new_pipeline() as p:
+      expand_pipeline(p, spec, validate_schema=None)
+
+  def test_expand_pipeline_with_extra_top_level_keys(self):
+    spec = '''
+      template:
+        version: "1.0"
+        author: "test_user"
+
+      pipeline:
+        type: chain
+        transforms:
+          - type: Create
+            config:
+              elements: [1,2,3]
+          - type: LogForTesting
+
+      other_metadata: "This is an ignored comment."
+    '''
+    with new_pipeline() as p:
+      expand_pipeline(p, spec, validate_schema=None)
+
+  def test_expand_pipeline_with_incorrect_pipelines_key_fails(self):
+    spec = '''
+      pipelines:
+        type: chain
+        transforms:
+          - type: Create
+            config:
+              elements: [1,2,3]
+          - type: LogForTesting
+    '''
+    with new_pipeline() as p:
+      with self.assertRaises(KeyError):
+        expand_pipeline(p, spec, validate_schema=None)
+
+
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
   unittest.main()
diff --git a/sdks/python/conftest.py b/sdks/python/conftest.py
index 37c4a0434e75..855af55911a1 100644
--- a/sdks/python/conftest.py
+++ b/sdks/python/conftest.py
@@ -17,7 +17,11 @@
 
 """Pytest configuration and custom hooks."""
 
+import os
 import sys
+from types import SimpleNamespace
+
+import pytest
 
 from apache_beam.options import pipeline_options
 from apache_beam.testing.test_pipeline import TestPipeline
@@ -39,11 +43,135 @@ def pytest_addoption(parser):
 ]
 
 
+@pytest.fixture(scope="session", autouse=True)
+def configure_beam_rpc_timeouts():
+  """
+  Configure gRPC and RPC timeouts for Beam tests
+  to prevent DEADLINE_EXCEEDED errors.
+  """
+  print("\n--- Applying Beam RPC timeout configuration ---")
+
+  # Set gRPC keepalive and timeout settings
+  timeout_env_vars = {
+      'GRPC_ARG_KEEPALIVE_TIME_MS': '30000',
+      'GRPC_ARG_KEEPALIVE_TIMEOUT_MS': '5000',
+      'GRPC_ARG_HTTP2_MAX_PINGS_WITHOUT_DATA': '0',
+      'GRPC_ARG_KEEPALIVE_PERMIT_WITHOUT_CALLS': '1',
+      'GRPC_ARG_HTTP2_MIN_RECV_PING_INTERVAL_WITHOUT_DATA_MS': '300000',
+      'GRPC_ARG_HTTP2_MIN_SENT_PING_INTERVAL_WITHOUT_DATA_MS': '10000',
+
+      # Additional stability settings for DinD environment
+      'GRPC_ARG_MAX_RECONNECT_BACKOFF_MS': '120000',
+      'GRPC_ARG_INITIAL_RECONNECT_BACKOFF_MS': '1000',
+      'GRPC_ARG_MAX_CONNECTION_IDLE_MS': '300000',
+      'GRPC_ARG_MAX_CONNECTION_AGE_MS': '1800000',
+
+      # Beam-specific retry and timeout settings
+      'BEAM_RETRY_MAX_ATTEMPTS': '5',
+      'BEAM_RETRY_INITIAL_DELAY_MS': '1000',
+      'BEAM_RETRY_MAX_DELAY_MS': '60000',
+      'BEAM_RUNNER_BUNDLE_TIMEOUT_MS': '300000',
+
+      # Force deterministic execution in DinD environment
+      'BEAM_TESTING_FORCE_SINGLE_BUNDLE': 'true',
+      'BEAM_TESTING_DETERMINISTIC_ORDER': 'true',
+      'BEAM_SDK_WORKER_PARALLELISM': '1',
+      'BEAM_WORKER_POOL_SIZE': '1',
+      'BEAM_FN_API_CONTROL_PORT': '0',
+      'BEAM_FN_API_DATA_PORT': '0',
+
+  # Container-specific stability settings
+      'PYTHONHASHSEED': '0',
+      'OMP_NUM_THREADS': '1',
+      'OPENBLAS_NUM_THREADS': '1',
+
+      # Force sequential pytest execution (CRITICAL for DinD stability)
+      'PYTEST_XDIST_WORKER_COUNT': '1',
+      'PYTEST_CURRENT_TEST_TIMEOUT': '300',
+
+      # Mock and test isolation improvements
+      'PYTEST_MOCK_TIMEOUT': '60',
+      'BEAM_TEST_ISOLATION_MODE': 'strict',
+  }
+
+  for key, value in timeout_env_vars.items():
+    os.environ[key] = value
+    print(f"Set {key}={value}")
+
+  print("Successfully configured Beam RPC timeouts")
+
+
+@pytest.fixture(autouse=True)
+def ensure_clean_state():
+  """
+  Ensure clean state before each test
+  to prevent cross-test contamination.
+  """
+  import gc
+  import threading
+  import time
+
+  # Force garbage collection to clean up any lingering resources
+  gc.collect()
+
+  # Log active thread count for debugging
+  thread_count = threading.active_count()
+  if thread_count > 50:  # Increased threshold since we see 104 threads
+    print(f"Warning: {thread_count} active threads detected before test")
+
+    # Force a brief pause to let threads settle
+    time.sleep(0.5)
+    gc.collect()
+
+  yield
+
+  # Enhanced cleanup after test
+  try:
+    # Force more aggressive cleanup
+    gc.collect()
+
+    # Brief pause to let any async operations complete
+    time.sleep(0.1)
+
+    # Additional garbage collection
+    gc.collect()
+  except Exception as e:
+    print(f"Warning: Cleanup error: {e}")
+
+
+@pytest.fixture(autouse=True)
+def enhance_mock_stability():
+  """Enhance mock stability in DinD environment."""
+  import time
+
+  # Brief pause before test to ensure clean mock state
+  time.sleep(0.05)
+
+  yield
+
+  # Brief pause after test to let mocks clean up
+  time.sleep(0.05)
+
+
 def pytest_configure(config):
   """Saves options added in pytest_addoption for later use.
-  This is necessary since pytest-xdist workers do not have the same sys.argv as
-  the main pytest invocation. xdist does seem to pickle TestPipeline
+  This is necessary since pytest-xdist workers do not have the
+  same sys.argv as the main pytest invocation.
+  xdist does seem to pickle TestPipeline
   """
+  # for the entire test session.
+  print("\n--- Applying global testcontainers timeout configuration ---")
+  try:
+    from testcontainers.core import waiting_utils
+    waiting_utils.config = SimpleNamespace(
+        timeout=int(os.getenv("TC_TIMEOUT", "120")),
+        max_tries=int(os.getenv("TC_MAX_TRIES", "120")),
+        sleep_time=float(os.getenv("TC_SLEEP_TIME", "1")),
+    )
+    print("Successfully set waiting utils config")
+  except ModuleNotFoundError:
+    print("The testcontainers library is not installed.")
+
   TestPipeline.pytest_test_pipeline_options = config.getoption(
       'test_pipeline_options', default='')
   # Enable optional type checks on all tests.
diff --git a/sdks/python/container/Dockerfile b/sdks/python/container/Dockerfile
index 7bea6229668f..efd5a4a90d8a 100644
--- a/sdks/python/container/Dockerfile
+++ b/sdks/python/container/Dockerfile
@@ -30,6 +30,7 @@ ENV CLOUDSDK_CORE_DISABLE_PROMPTS yes
 ENV PATH $PATH:/usr/local/gcloud/google-cloud-sdk/bin
 
 # Use one RUN command to reduce the number of layers.
+ARG py_version
 RUN  \
     # Install native bindings required for dependencies.
     apt-get update && \
@@ -80,7 +81,15 @@ RUN  \
     pip freeze --all && \
 
     # Remove pip cache.
-    rm -rf /root/.cache/pip
+    rm -rf /root/.cache/pip && \
+
+    # Update ensurepip to use most recent versions of setuptools and pip. This avoids some vulnerabilities which won't be fixed on older versions of python.
+    pip install upgrade_ensurepip; \
+    python3 -m upgrade_ensurepip; \
+    find /usr/local/lib/python${py_version}/ensurepip/_bundled/setuptools-* -type f ! -name $(basename $(ls -v /usr/local/lib/python${py_version}/ensurepip/_bundled/setuptools-*-py3-none-any.whl | tail -n 1)) -delete; \
+    find /usr/local/lib/python${py_version}/ensurepip/_bundled/pip-* -type f ! -name $(basename $(ls -v /usr/local/lib/python${py_version}/ensurepip/_bundled/pip-*-py3-none-any.whl | tail -n 1)) -delete; \
+    pip uninstall upgrade_ensurepip -y; \
+    python3 -m ensurepip;
 
 ENTRYPOINT ["/opt/apache/beam/boot"]
 
diff --git a/sdks/python/container/base_image_requirements_manual.txt b/sdks/python/container/base_image_requirements_manual.txt
index bef89e9fd31e..536f62c27f5d 100644
--- a/sdks/python/container/base_image_requirements_manual.txt
+++ b/sdks/python/container/base_image_requirements_manual.txt
@@ -40,3 +40,6 @@ google-crc32c
 scipy
 scikit-learn
 build>=1.0,<2 # tool to build sdist from setup.py in stager.
+# Dill 0.3.1.1 is included as a base manual requirement so is avaiable to users
+# with pickle_library=dill, but apache-beam does not have a hard dependency.
+dill>=0.3.1.1,<0.3.2
diff --git a/sdks/python/container/boot.go b/sdks/python/container/boot.go
index b7cbc07dca68..847325d4f83c 100644
--- a/sdks/python/container/boot.go
+++ b/sdks/python/container/boot.go
@@ -188,7 +188,7 @@ func launchSDKProcess() error {
 	if err != nil {
 		fmtErr := fmt.Errorf("failed to retrieve staged files: %v", err)
 		// Send error message to logging service before returning up the call stack
-		logger.Errorf(ctx, fmtErr.Error())
+		logger.Errorf(ctx, "%s", fmtErr.Error())
 		// No need to fail the job if submission_environment_dependencies.txt cannot be loaded
 		if strings.Contains(fmtErr.Error(), "submission_environment_dependencies.txt") {
 			logger.Printf(ctx, "Ignore the error when loading submission_environment_dependencies.txt.")
@@ -214,7 +214,7 @@ func launchSDKProcess() error {
 	if setupErr := installSetupPackages(ctx, logger, fileNames, dir, requirementsFiles); setupErr != nil {
 		fmtErr := fmt.Errorf("failed to install required packages: %v", setupErr)
 		// Send error message to logging service before returning up the call stack
-		logger.Errorf(ctx, fmtErr.Error())
+		logger.Errorf(ctx, "%s", fmtErr.Error())
 		return fmtErr
 	}
 
@@ -500,6 +500,6 @@ func logSubmissionEnvDependencies(ctx context.Context, bufLogger *tools.Buffered
 	if err != nil {
 		return err
 	}
-	bufLogger.Printf(ctx, string(content))
+	bufLogger.Printf(ctx, "%s", string(content))
 	return nil
 }
diff --git a/sdks/python/container/build.gradle b/sdks/python/container/build.gradle
index f4de804b80b7..fe7bda553176 100644
--- a/sdks/python/container/build.gradle
+++ b/sdks/python/container/build.gradle
@@ -18,11 +18,11 @@
 
 plugins { id 'org.apache.beam.module' }
 applyGoNature()
+applyPythonNature()
 
 description = "Apache Beam :: SDKs :: Python :: Container"
-// Keep these values in sync with sdks/python/container/distroless/build.gradle.
-int min_python_version=9
-int max_python_version=12
+int min_python_version=project.ext.minPythonVersion
+int max_python_version=project.ext.maxPythonVersion
 
 configurations {
   sdkSourceTarball
@@ -71,6 +71,7 @@ for(int i=min_python_version; i<=max_python_version; ++i) {
 
 tasks.register("pushAll") {
   dependsOn ':sdks:python:container:distroless:pushAll'
+  dependsOn ':sdks:python:container:ml:pushAll'
   for(int ver=min_python_version; ver<=max_python_version; ++ver) {
       if (!project.hasProperty("skip-python-3" + ver + "-images")) {
         dependsOn ':sdks:python:container:push3' + ver
diff --git a/sdks/python/container/common.gradle b/sdks/python/container/common.gradle
index 6c3de38d56c1..0648bf4fa2e6 100644
--- a/sdks/python/container/common.gradle
+++ b/sdks/python/container/common.gradle
@@ -41,6 +41,7 @@ def generatePythonRequirements = tasks.register("generatePythonRequirements") {
               "${project.ext.pythonVersion} " +
               "${files(configurations.sdkSourceTarball.files).singleFile} " +
               "base_image_requirements.txt " +
+              "container " +
               "[gcp,dataframe,test] " +
               "${pipExtraOptions}"
     }
@@ -51,6 +52,7 @@ def generatePythonRequirements = tasks.register("generatePythonRequirements") {
               "${project.ext.pythonVersion} " +
               "${files(configurations.sdkSourceTarball.files).singleFile} " +
               "ml_image_requirements.txt " +
+              "container/ml " +
               "[gcp,dataframe,test,tensorflow,torch,transformers] " +
               "${pipExtraOptions}"
     }
diff --git a/sdks/python/container/distroless/build.gradle b/sdks/python/container/distroless/build.gradle
index 314484ade61a..48255b98db3f 100644
--- a/sdks/python/container/distroless/build.gradle
+++ b/sdks/python/container/distroless/build.gradle
@@ -17,11 +17,11 @@
  */
 
 plugins { id 'org.apache.beam.module' }
+applyPythonNature()
 
 description = "Apache Beam :: SDKs :: Python :: Container :: Distroless"
-// Keep these values in sync with sdks/python/container/build.gradle.
-int min_python_version=9
-int max_python_version=12
+int min_python_version=project.ext.minPythonVersion
+int max_python_version=project.ext.maxPythonVersion
 
 
 tasks.register("buildAll") {
diff --git a/sdks/python/container/ml/build.gradle b/sdks/python/container/ml/build.gradle
new file mode 100644
index 000000000000..f09cc2e80203
--- /dev/null
+++ b/sdks/python/container/ml/build.gradle
@@ -0,0 +1,64 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+plugins { id 'org.apache.beam.module' }
+applyPythonNature()
+
+description = "Apache Beam :: SDKs :: Python :: Container :: ML"
+int min_python_version=project.ext.minPythonVersion
+int max_python_version=project.ext.maxPythonVersion
+
+
+tasks.register("buildAll") {
+    for(int ver=min_python_version; ver<=max_python_version; ++ver) {
+        dependsOn ':sdks:python:container:ml:py3' + ver + ':docker'
+    }
+}
+
+for(int i=min_python_version; i<=max_python_version; ++i) {
+    String min_version = "3" + min_python_version
+    String cur = "3" + i
+    String prev = "3" + (i-1)
+    tasks.register("push" + cur) {
+        if (cur != min_version) {
+            // Enforce ordering to allow the prune step to happen between runs.
+            // This will ensure we don't use up too much space (especially in CI environments)
+            if (!project.hasProperty("skip-python-3" + prev + "-images")) {
+                mustRunAfter(":sdks:python:container:ml:push" + prev)
+            }
+        }
+        dependsOn ':sdks:python:container:ml:py' + cur + ':docker'
+
+        doLast {
+            if (project.hasProperty("prune-images")) {
+                exec {
+                    executable("docker")
+                    args("system", "prune", "-a", "--force")
+                }
+            }
+        }
+    }
+}
+
+tasks.register("pushAll") {
+    for(int ver=min_python_version; ver<=max_python_version; ++ver) {
+        if (!project.hasProperty("skip-python-3" + ver + "-images")) {
+            dependsOn ':sdks:python:container:ml:push3' + ver
+        }
+    }
+}
diff --git a/sdks/python/container/ml/common.gradle b/sdks/python/container/ml/common.gradle
new file mode 100644
index 000000000000..dff2b3fc7f97
--- /dev/null
+++ b/sdks/python/container/ml/common.gradle
@@ -0,0 +1,126 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+def pythonVersionSuffix = project.ext.pythonVersion.replace('.', '')
+
+description = "Apache Beam :: SDKs :: Python :: Container :: ML :: Python ${pythonVersionSuffix} Container"
+
+configurations {
+  sdkSourceTarball
+  pythonHarnessLauncher
+}
+
+dependencies {
+  sdkSourceTarball project(path: ":sdks:python", configuration: "distTarBall")
+  pythonHarnessLauncher project(path: ":sdks:python:container", configuration: "pythonHarnessLauncher")
+}
+
+def generatePythonRequirements = tasks.register("generatePythonRequirements") {
+  dependsOn ':sdks:python:sdist'
+  def pipExtraOptions = project.hasProperty("testRCDependencies") ? "--pre" : ""
+  def runScriptsPath = "${rootDir}/sdks/python/container/run_generate_requirements.sh"
+  doLast {
+    exec {
+      executable 'sh'
+      args '-c', "cd ${rootDir} && ${runScriptsPath} " +
+              "${project.ext.pythonVersion} " +
+              "${files(configurations.sdkSourceTarball.files).singleFile} " +
+              "base_image_requirements.txt " +
+              "[gcp,dataframe,test] " +
+              "${pipExtraOptions}"
+    }
+    // Generate versions for ML dependencies
+    exec {
+      executable 'sh'
+      args '-c', "cd ${rootDir} && ${runScriptsPath} " +
+              "${project.ext.pythonVersion} " +
+              "${files(configurations.sdkSourceTarball.files).singleFile} " +
+              "ml_image_requirements.txt " +
+              "[gcp,dataframe,test,tensorflow,torch,transformers] " +
+              "${pipExtraOptions}"
+    }
+  }
+}
+
+def copyDockerfileDependencies = tasks.register("copyDockerfileDependencies", Copy) {
+  from configurations.sdkSourceTarball
+  from file("base_image_requirements.txt")
+  into "build/target"
+  if(configurations.sdkSourceTarball.isEmpty()) {
+      throw new StopExecutionException();
+  }
+}
+
+def copyLicenseScripts = tasks.register("copyLicenseScripts", Copy){
+  from ("../license_scripts")
+  into "build/target/license_scripts"
+}
+
+def copyLauncherDependencies = tasks.register("copyLauncherDependencies", Copy) {
+  from configurations.pythonHarnessLauncher
+  into "build/target/launcher"
+
+  // Avoid seemingly gradle bug stated in https://github.com/apache/beam/issues/29220
+  mustRunAfter "copyLicenses"
+
+  if(configurations.pythonHarnessLauncher.isEmpty()) {
+      throw new StopExecutionException();
+  }
+}
+
+def pushContainers = project.rootProject.hasProperty(["isRelease"]) || project.rootProject.hasProperty("push-containers")
+
+docker {
+  name containerImageName(
+          name: project.docker_image_default_repo_prefix + "python${project.ext.pythonVersion}_sdk_ml",
+          root: project.rootProject.hasProperty(["docker-repository-root"]) ?
+                  project.rootProject["docker-repository-root"] :
+                  project.docker_image_default_repo_root,
+          tag: project.rootProject.hasProperty(["docker-tag"]) ?
+                  project.rootProject["docker-tag"] : project.sdk_version)
+  // tags used by dockerTag task
+  tags containerImageTags()
+  files "../../Dockerfile", "./build"
+  buildArgs(['py_version': "${project.ext.pythonVersion}",
+             'pull_licenses': project.rootProject.hasProperty(["docker-pull-licenses"]) ||
+                     project.rootProject.hasProperty(["isRelease"])])
+  buildx project.useBuildx()
+  platform(*project.containerPlatforms())
+  load project.useBuildx() && !pushContainers
+  push pushContainers
+}
+
+dockerPrepare.dependsOn copyLauncherDependencies
+dockerPrepare.dependsOn copyDockerfileDependencies
+dockerPrepare.dependsOn copyLicenseScripts
+
+if (project.rootProject.hasProperty("docker-pull-licenses")) {
+  def copyGolangLicenses = tasks.register("copyGolangLicenses", Copy) {
+    from "${project(':release:go-licenses:py').buildDir}/output"
+    into "build/target/go-licenses"
+    dependsOn ':release:go-licenses:py:createLicenses'
+  }
+  dockerPrepare.dependsOn copyGolangLicenses
+} else {
+  def skipPullLicenses = tasks.register("skipPullLicenses", Exec) {
+    executable "sh"
+    // Touch a dummy file to ensure the directory exists.
+    args "-c", "mkdir -p build/target/go-licenses && touch build/target/go-licenses/skip"
+  }
+  dockerPrepare.dependsOn skipPullLicenses
+}
diff --git a/sdks/python/container/py310/ml_image_requirements.txt b/sdks/python/container/ml/py310/base_image_requirements.txt
similarity index 78%
rename from sdks/python/container/py310/ml_image_requirements.txt
rename to sdks/python/container/ml/py310/base_image_requirements.txt
index 2b70da331f53..a58cc29ff2ec 100644
--- a/sdks/python/container/py310/ml_image_requirements.txt
+++ b/sdks/python/container/ml/py310/base_image_requirements.txt
@@ -21,74 +21,75 @@
 # https://s.apache.org/beam-python-dev-wiki
 # Reach out to a committer if you need help.
 
-absl-py==2.3.0
+absl-py==2.3.1
 aiofiles==24.1.0
 aiohappyeyeballs==2.6.1
-aiohttp==3.12.13
-aiosignal==1.3.2
+aiohttp==3.12.15
+aiosignal==1.4.0
 annotated-types==0.7.0
-anyio==4.9.0
+anyio==4.10.0
 asn1crypto==1.5.1
 astunparse==1.6.3
 async-timeout==5.0.1
 attrs==25.3.0
 backports.tarfile==1.2.0
+beartype==0.21.0
 beautifulsoup4==4.13.4
 bs4==0.0.2
-build==1.2.2.post1
+build==1.3.0
 cachetools==5.5.2
-certifi==2025.6.15
+certifi==2025.8.3
 cffi==1.17.1
-charset-normalizer==3.4.2
+charset-normalizer==3.4.3
 click==8.2.1
-cloud-sql-python-connector==1.18.2
+cloud-sql-python-connector==1.18.4
 crcmod==1.7
-cryptography==45.0.4
-Cython==3.1.2
+cryptography==45.0.6
+Cython==3.1.3
 dill==0.3.1.1
 dnspython==2.7.0
 docker==7.1.0
 docopt==0.6.2
-docstring_parser==0.16
+docstring_parser==0.17.0
 exceptiongroup==1.3.0
 execnet==2.1.1
-fastavro==1.11.1
-fasteners==0.19
-filelock==3.18.0
+fastavro==1.12.0
+fasteners==0.20
+filelock==3.19.1
 flatbuffers==25.2.10
-freezegun==1.5.2
+freezegun==1.5.5
 frozenlist==1.7.0
-fsspec==2025.5.1
+fsspec==2025.7.0
 future==1.0.0
 gast==0.6.0
 google-api-core==2.25.1
-google-api-python-client==2.174.0
+google-api-python-client==2.179.0
 google-apitools==0.5.31
 google-auth==2.40.3
 google-auth-httplib2==0.2.0
-google-cloud-aiplatform==1.100.0
-google-cloud-bigquery==3.34.0
+google-cloud-aiplatform==1.110.0
+google-cloud-bigquery==3.36.0
 google-cloud-bigquery-storage==2.32.0
-google-cloud-bigtable==2.31.0
+google-cloud-bigtable==2.32.0
 google-cloud-core==2.4.3
 google-cloud-datastore==2.21.0
 google-cloud-dlp==3.31.0
 google-cloud-language==2.17.2
 google-cloud-profiler==4.1.0
-google-cloud-pubsub==2.30.0
+google-cloud-pubsub==2.31.1
 google-cloud-pubsublite==1.12.0
 google-cloud-recommendations-ai==0.10.18
 google-cloud-resource-manager==1.14.2
-google-cloud-spanner==3.55.0
+google-cloud-spanner==3.57.0
 google-cloud-storage==2.19.0
 google-cloud-videointelligence==2.16.2
 google-cloud-vision==3.10.2
 google-crc32c==1.7.1
-google-genai==1.23.0
+google-genai==1.31.0
 google-pasta==0.2.0
 google-resumable-media==2.7.2
 googleapis-common-protos==1.70.0
-greenlet==3.2.3
+greenlet==3.2.4
 grpc-google-iam-v1==0.14.2
 grpc-interceptor==0.15.4
 grpcio==1.65.5
@@ -97,40 +98,39 @@ guppy3==3.1.5
 h11==0.16.0
 h5py==3.14.0
 hdfs==2.7.3
-hf-xet==1.1.5
+hf-xet==1.1.8
 httpcore==1.0.9
 httplib2==0.22.0
 httpx==0.28.1
-huggingface-hub==0.33.1
-hypothesis==6.135.19
+huggingface-hub==0.34.4
+hypothesis==6.138.3
 idna==3.10
 importlib_metadata==8.7.0
 iniconfig==2.1.0
 jaraco.classes==3.4.0
 jaraco.context==6.0.1
-jaraco.functools==4.2.1
+jaraco.functools==4.3.0
 jeepney==0.9.0
 Jinja2==3.1.6
 joblib==1.5.1
 jsonpickle==3.4.2
-jsonschema==4.24.0
+jsonschema==4.25.1
 jsonschema-specifications==2025.4.1
-keras==3.10.0
+keras==3.11.3
 keyring==25.6.0
 keyrings.google-artifactregistry-auth==1.1.2
 libclang==18.1.1
 Markdown==3.8.2
-markdown-it-py==3.0.0
+markdown-it-py==4.0.0
 MarkupSafe==3.0.2
 mdurl==0.1.2
 milvus-lite==2.5.1
 ml-dtypes==0.3.2
-mmh3==5.1.0
+mmh3==5.2.0
 mock==5.2.0
 more-itertools==10.7.0
 mpmath==1.3.0
-multidict==6.6.3
-mysql-connector-python==9.3.0
+multidict==6.6.4
 namex==0.1.0
 networkx==3.4.2
 nltk==3.9.1
@@ -151,19 +151,19 @@ nvidia-nvjitlink-cu12==12.6.85
 nvidia-nvtx-cu12==12.6.77
 oauth2client==4.1.3
 objsize==0.7.1
-opentelemetry-api==1.34.1
-opentelemetry-sdk==1.34.1
-opentelemetry-semantic-conventions==0.55b1
+opentelemetry-api==1.36.0
+opentelemetry-sdk==1.36.0
+opentelemetry-semantic-conventions==0.57b0
 opt_einsum==3.4.0
-optree==0.16.0
-oracledb==3.2.0
-orjson==3.10.18
+optree==0.17.0
+oracledb==3.3.0
+orjson==3.11.2
 overrides==7.7.0
 packaging==25.0
 pandas==2.2.3
 parameterized==0.9.0
-pg8000==1.31.2
-pip==25.1.1
+pg8000==1.31.4
+pip==25.2
 pluggy==1.6.0
 propcache==0.3.2
 proto-plus==1.26.1
@@ -179,32 +179,32 @@ pydantic_core==2.33.2
 pydot==1.4.2
 Pygments==2.19.2
 PyHamcrest==2.1.0
-PyJWT==2.9.0
-pymilvus==2.5.11
-pymongo==4.13.2
-PyMySQL==1.1.1
+PyJWT==2.10.1
+pymilvus==2.5.15
+pymongo==4.14.1
+PyMySQL==1.1.2
 pyparsing==3.2.3
 pyproject_hooks==1.2.0
 pytest==7.4.4
 pytest-timeout==2.4.0
-pytest-xdist==3.7.0
+pytest-xdist==3.8.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.1.1
-python-tds==1.16.1
+python-tds==1.17.0
 pytz==2025.2
 PyYAML==6.0.2
-redis==5.3.0
+redis==5.3.1
 referencing==0.36.2
-regex==2024.11.6
-requests==2.32.4
+regex==2025.7.34
+requests==2.32.5
 requests-mock==1.12.1
-rich==14.0.0
-rpds-py==0.25.1
+rich==14.1.0
+rpds-py==0.27.0
 rsa==4.9.1
-safetensors==0.5.3
-scikit-learn==1.7.0
+safetensors==0.6.2
+scikit-learn==1.7.1
 scipy==1.15.3
-scramp==1.4.5
+scramp==1.4.6
 SecretStorage==3.3.3
 setuptools==80.9.0
 shapely==2.1.1
@@ -212,7 +212,7 @@ six==1.17.0
 sniffio==1.3.1
 sortedcontainers==2.4.0
 soupsieve==2.7
-SQLAlchemy==2.0.41
+SQLAlchemy==2.0.43
 sqlalchemy_pytds==1.0.2
 sqlparse==0.5.3
 sympy==1.14.0
@@ -223,25 +223,25 @@ tensorflow==2.16.2
 tensorflow-cpu-aws==2.16.2;platform_machine=="aarch64"
 tensorflow-io-gcs-filesystem==0.37.1
 termcolor==3.1.0
-testcontainers==4.10.0
+testcontainers==4.12.0
 threadpoolctl==3.6.0
-tokenizers==0.21.2
+tokenizers==0.21.4
 tomli==2.2.1
 torch==2.7.1
 tqdm==4.67.1
-transformers==4.48.3
+transformers==4.55.4
 triton==3.3.1
 typing-inspection==0.4.1
-typing_extensions==4.14.0
+typing_extensions==4.14.1
 tzdata==2025.2
-ujson==5.10.0
+ujson==5.11.0
 uritemplate==4.2.0
 urllib3==2.5.0
 virtualenv-clone==0.5.7
 websockets==15.0.1
 Werkzeug==3.1.3
 wheel==0.45.1
-wrapt==1.17.2
+wrapt==1.17.3
 yarl==1.20.1
 zipp==3.23.0
-zstandard==0.23.0
+zstandard==0.24.0
diff --git a/sdks/python/container/ml/py310/build.gradle b/sdks/python/container/ml/py310/build.gradle
new file mode 100644
index 000000000000..28894725165a
--- /dev/null
+++ b/sdks/python/container/ml/py310/build.gradle
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+plugins {
+    id 'base'
+    id 'org.apache.beam.module'
+}
+applyDockerNature()
+applyPythonNature()
+
+pythonVersion = '3.10'
+
+apply from: "../common.gradle"
diff --git a/sdks/python/container/py311/ml_image_requirements.txt b/sdks/python/container/ml/py311/base_image_requirements.txt
similarity index 77%
rename from sdks/python/container/py311/ml_image_requirements.txt
rename to sdks/python/container/ml/py311/base_image_requirements.txt
index a5d8add176d2..d51db46a30da 100644
--- a/sdks/python/container/py311/ml_image_requirements.txt
+++ b/sdks/python/container/ml/py311/base_image_requirements.txt
@@ -21,72 +21,73 @@
 # https://s.apache.org/beam-python-dev-wiki
 # Reach out to a committer if you need help.
 
-absl-py==2.3.0
+absl-py==2.3.1
 aiofiles==24.1.0
 aiohappyeyeballs==2.6.1
-aiohttp==3.12.13
-aiosignal==1.3.2
+aiohttp==3.12.15
+aiosignal==1.4.0
 annotated-types==0.7.0
-anyio==4.9.0
+anyio==4.10.0
 asn1crypto==1.5.1
 astunparse==1.6.3
 attrs==25.3.0
 backports.tarfile==1.2.0
+beartype==0.21.0
 beautifulsoup4==4.13.4
 bs4==0.0.2
-build==1.2.2.post1
+build==1.3.0
 cachetools==5.5.2
-certifi==2025.6.15
+certifi==2025.8.3
 cffi==1.17.1
-charset-normalizer==3.4.2
+charset-normalizer==3.4.3
 click==8.2.1
-cloud-sql-python-connector==1.18.2
+cloud-sql-python-connector==1.18.4
 crcmod==1.7
-cryptography==45.0.4
-Cython==3.1.2
+cryptography==45.0.6
+Cython==3.1.3
 dill==0.3.1.1
 dnspython==2.7.0
 docker==7.1.0
 docopt==0.6.2
-docstring_parser==0.16
+docstring_parser==0.17.0
 execnet==2.1.1
-fastavro==1.11.1
-fasteners==0.19
-filelock==3.18.0
+fastavro==1.12.0
+fasteners==0.20
+filelock==3.19.1
 flatbuffers==25.2.10
-freezegun==1.5.2
+freezegun==1.5.5
 frozenlist==1.7.0
-fsspec==2025.5.1
+fsspec==2025.7.0
 future==1.0.0
 gast==0.6.0
 google-api-core==2.25.1
-google-api-python-client==2.174.0
+google-api-python-client==2.179.0
 google-apitools==0.5.31
 google-auth==2.40.3
 google-auth-httplib2==0.2.0
-google-cloud-aiplatform==1.100.0
-google-cloud-bigquery==3.34.0
+google-cloud-aiplatform==1.110.0
+google-cloud-bigquery==3.36.0
 google-cloud-bigquery-storage==2.32.0
-google-cloud-bigtable==2.31.0
+google-cloud-bigtable==2.32.0
 google-cloud-core==2.4.3
 google-cloud-datastore==2.21.0
 google-cloud-dlp==3.31.0
 google-cloud-language==2.17.2
 google-cloud-profiler==4.1.0
-google-cloud-pubsub==2.30.0
+google-cloud-pubsub==2.31.1
 google-cloud-pubsublite==1.12.0
 google-cloud-recommendations-ai==0.10.18
 google-cloud-resource-manager==1.14.2
-google-cloud-spanner==3.55.0
+google-cloud-spanner==3.57.0
 google-cloud-storage==2.19.0
 google-cloud-videointelligence==2.16.2
 google-cloud-vision==3.10.2
 google-crc32c==1.7.1
-google-genai==1.23.0
+google-genai==1.31.0
 google-pasta==0.2.0
 google-resumable-media==2.7.2
 googleapis-common-protos==1.70.0
-greenlet==3.2.3
+greenlet==3.2.4
 grpc-google-iam-v1==0.14.2
 grpc-interceptor==0.15.4
 grpcio==1.65.5
@@ -95,40 +96,39 @@ guppy3==3.1.5
 h11==0.16.0
 h5py==3.14.0
 hdfs==2.7.3
-hf-xet==1.1.5
+hf-xet==1.1.8
 httpcore==1.0.9
 httplib2==0.22.0
 httpx==0.28.1
-huggingface-hub==0.33.1
-hypothesis==6.135.19
+huggingface-hub==0.34.4
+hypothesis==6.138.3
 idna==3.10
 importlib_metadata==8.7.0
 iniconfig==2.1.0
 jaraco.classes==3.4.0
 jaraco.context==6.0.1
-jaraco.functools==4.2.1
+jaraco.functools==4.3.0
 jeepney==0.9.0
 Jinja2==3.1.6
 joblib==1.5.1
 jsonpickle==3.4.2
-jsonschema==4.24.0
+jsonschema==4.25.1
 jsonschema-specifications==2025.4.1
-keras==3.10.0
+keras==3.11.3
 keyring==25.6.0
 keyrings.google-artifactregistry-auth==1.1.2
 libclang==18.1.1
 Markdown==3.8.2
-markdown-it-py==3.0.0
+markdown-it-py==4.0.0
 MarkupSafe==3.0.2
 mdurl==0.1.2
 milvus-lite==2.5.1
 ml-dtypes==0.3.2
-mmh3==5.1.0
+mmh3==5.2.0
 mock==5.2.0
 more-itertools==10.7.0
 mpmath==1.3.0
-multidict==6.6.3
-mysql-connector-python==9.3.0
+multidict==6.6.4
 namex==0.1.0
 networkx==3.5
 nltk==3.9.1
@@ -149,19 +149,19 @@ nvidia-nvjitlink-cu12==12.6.85
 nvidia-nvtx-cu12==12.6.77
 oauth2client==4.1.3
 objsize==0.7.1
-opentelemetry-api==1.34.1
-opentelemetry-sdk==1.34.1
-opentelemetry-semantic-conventions==0.55b1
+opentelemetry-api==1.36.0
+opentelemetry-sdk==1.36.0
+opentelemetry-semantic-conventions==0.57b0
 opt_einsum==3.4.0
-optree==0.16.0
-oracledb==3.2.0
-orjson==3.10.18
+optree==0.17.0
+oracledb==3.3.0
+orjson==3.11.2
 overrides==7.7.0
 packaging==25.0
 pandas==2.2.3
 parameterized==0.9.0
-pg8000==1.31.2
-pip==25.1.1
+pg8000==1.31.4
+pip==25.2
 pluggy==1.6.0
 propcache==0.3.2
 proto-plus==1.26.1
@@ -177,32 +177,32 @@ pydantic_core==2.33.2
 pydot==1.4.2
 Pygments==2.19.2
 PyHamcrest==2.1.0
-PyJWT==2.9.0
-pymilvus==2.5.11
-pymongo==4.13.2
-PyMySQL==1.1.1
+PyJWT==2.10.1
+pymilvus==2.5.15
+pymongo==4.14.1
+PyMySQL==1.1.2
 pyparsing==3.2.3
 pyproject_hooks==1.2.0
 pytest==7.4.4
 pytest-timeout==2.4.0
-pytest-xdist==3.7.0
+pytest-xdist==3.8.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.1.1
-python-tds==1.16.1
+python-tds==1.17.0
 pytz==2025.2
 PyYAML==6.0.2
-redis==5.3.0
+redis==5.3.1
 referencing==0.36.2
-regex==2024.11.6
-requests==2.32.4
+regex==2025.7.34
+requests==2.32.5
 requests-mock==1.12.1
-rich==14.0.0
-rpds-py==0.25.1
+rich==14.1.0
+rpds-py==0.27.0
 rsa==4.9.1
-safetensors==0.5.3
-scikit-learn==1.7.0
-scipy==1.16.0
-scramp==1.4.5
+safetensors==0.6.2
+scikit-learn==1.7.1
+scipy==1.16.1
+scramp==1.4.6
 SecretStorage==3.3.3
 setuptools==80.9.0
 shapely==2.1.1
@@ -210,7 +210,7 @@ six==1.17.0
 sniffio==1.3.1
 sortedcontainers==2.4.0
 soupsieve==2.7
-SQLAlchemy==2.0.41
+SQLAlchemy==2.0.43
 sqlalchemy_pytds==1.0.2
 sqlparse==0.5.3
 sympy==1.14.0
@@ -221,24 +221,24 @@ tensorflow==2.16.2
 tensorflow-cpu-aws==2.16.2;platform_machine=="aarch64"
 tensorflow-io-gcs-filesystem==0.37.1
 termcolor==3.1.0
-testcontainers==4.10.0
+testcontainers==4.12.0
 threadpoolctl==3.6.0
-tokenizers==0.21.2
+tokenizers==0.21.4
 torch==2.7.1
 tqdm==4.67.1
-transformers==4.48.3
+transformers==4.55.4
 triton==3.3.1
 typing-inspection==0.4.1
-typing_extensions==4.14.0
+typing_extensions==4.14.1
 tzdata==2025.2
-ujson==5.10.0
+ujson==5.11.0
 uritemplate==4.2.0
 urllib3==2.5.0
 virtualenv-clone==0.5.7
 websockets==15.0.1
 Werkzeug==3.1.3
 wheel==0.45.1
-wrapt==1.17.2
+wrapt==1.17.3
 yarl==1.20.1
 zipp==3.23.0
-zstandard==0.23.0
+zstandard==0.24.0
diff --git a/sdks/python/container/ml/py311/build.gradle b/sdks/python/container/ml/py311/build.gradle
new file mode 100644
index 000000000000..24ccc85c6b25
--- /dev/null
+++ b/sdks/python/container/ml/py311/build.gradle
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+plugins {
+    id 'base'
+    id 'org.apache.beam.module'
+}
+applyDockerNature()
+applyPythonNature()
+
+pythonVersion = '3.11'
+
+apply from: "../common.gradle"
diff --git a/sdks/python/container/py312/ml_image_requirements.txt b/sdks/python/container/ml/py312/base_image_requirements.txt
similarity index 77%
rename from sdks/python/container/py312/ml_image_requirements.txt
rename to sdks/python/container/ml/py312/base_image_requirements.txt
index e6e9a2930d26..f24d50a9a8ae 100644
--- a/sdks/python/container/py312/ml_image_requirements.txt
+++ b/sdks/python/container/ml/py312/base_image_requirements.txt
@@ -21,71 +21,72 @@
 # https://s.apache.org/beam-python-dev-wiki
 # Reach out to a committer if you need help.
 
-absl-py==2.3.0
+absl-py==2.3.1
 aiofiles==24.1.0
 aiohappyeyeballs==2.6.1
-aiohttp==3.12.13
-aiosignal==1.3.2
+aiohttp==3.12.15
+aiosignal==1.4.0
 annotated-types==0.7.0
-anyio==4.9.0
+anyio==4.10.0
 asn1crypto==1.5.1
 astunparse==1.6.3
 attrs==25.3.0
+beartype==0.21.0
 beautifulsoup4==4.13.4
 bs4==0.0.2
-build==1.2.2.post1
+build==1.3.0
 cachetools==5.5.2
-certifi==2025.6.15
+certifi==2025.8.3
 cffi==1.17.1
-charset-normalizer==3.4.2
+charset-normalizer==3.4.3
 click==8.2.1
-cloud-sql-python-connector==1.18.2
+cloud-sql-python-connector==1.18.4
 crcmod==1.7
-cryptography==45.0.4
-Cython==3.1.2
+cryptography==45.0.6
+Cython==3.1.3
 dill==0.3.1.1
 dnspython==2.7.0
 docker==7.1.0
 docopt==0.6.2
-docstring_parser==0.16
+docstring_parser==0.17.0
 execnet==2.1.1
-fastavro==1.11.1
-fasteners==0.19
-filelock==3.18.0
+fastavro==1.12.0
+fasteners==0.20
+filelock==3.19.1
 flatbuffers==25.2.10
-freezegun==1.5.2
+freezegun==1.5.5
 frozenlist==1.7.0
-fsspec==2025.5.1
+fsspec==2025.7.0
 future==1.0.0
 gast==0.6.0
 google-api-core==2.25.1
-google-api-python-client==2.174.0
+google-api-python-client==2.179.0
 google-apitools==0.5.31
 google-auth==2.40.3
 google-auth-httplib2==0.2.0
-google-cloud-aiplatform==1.100.0
-google-cloud-bigquery==3.34.0
+google-cloud-aiplatform==1.110.0
+google-cloud-bigquery==3.36.0
 google-cloud-bigquery-storage==2.32.0
-google-cloud-bigtable==2.31.0
+google-cloud-bigtable==2.32.0
 google-cloud-core==2.4.3
 google-cloud-datastore==2.21.0
 google-cloud-dlp==3.31.0
 google-cloud-language==2.17.2
 google-cloud-profiler==4.1.0
-google-cloud-pubsub==2.30.0
+google-cloud-pubsub==2.31.1
 google-cloud-pubsublite==1.12.0
 google-cloud-recommendations-ai==0.10.18
 google-cloud-resource-manager==1.14.2
-google-cloud-spanner==3.55.0
+google-cloud-spanner==3.57.0
 google-cloud-storage==2.19.0
 google-cloud-videointelligence==2.16.2
 google-cloud-vision==3.10.2
 google-crc32c==1.7.1
-google-genai==1.23.0
+google-genai==1.31.0
 google-pasta==0.2.0
 google-resumable-media==2.7.2
 googleapis-common-protos==1.70.0
-greenlet==3.2.3
+greenlet==3.2.4
 grpc-google-iam-v1==0.14.2
 grpc-interceptor==0.15.4
 grpcio==1.65.5
@@ -94,40 +95,39 @@ guppy3==3.1.5
 h11==0.16.0
 h5py==3.14.0
 hdfs==2.7.3
-hf-xet==1.1.5
+hf-xet==1.1.8
 httpcore==1.0.9
 httplib2==0.22.0
 httpx==0.28.1
-huggingface-hub==0.33.1
-hypothesis==6.135.20
+huggingface-hub==0.34.4
+hypothesis==6.138.3
 idna==3.10
 importlib_metadata==8.7.0
 iniconfig==2.1.0
 jaraco.classes==3.4.0
 jaraco.context==6.0.1
-jaraco.functools==4.2.1
+jaraco.functools==4.3.0
 jeepney==0.9.0
 Jinja2==3.1.6
 joblib==1.5.1
 jsonpickle==3.4.2
-jsonschema==4.24.0
+jsonschema==4.25.1
 jsonschema-specifications==2025.4.1
-keras==3.10.0
+keras==3.11.3
 keyring==25.6.0
 keyrings.google-artifactregistry-auth==1.1.2
 libclang==18.1.1
 Markdown==3.8.2
-markdown-it-py==3.0.0
+markdown-it-py==4.0.0
 MarkupSafe==3.0.2
 mdurl==0.1.2
 milvus-lite==2.5.1
 ml-dtypes==0.3.2
-mmh3==5.1.0
+mmh3==5.2.0
 mock==5.2.0
 more-itertools==10.7.0
 mpmath==1.3.0
-multidict==6.6.3
-mysql-connector-python==9.3.0
+multidict==6.6.4
 namex==0.1.0
 networkx==3.5
 nltk==3.9.1
@@ -148,19 +148,19 @@ nvidia-nvjitlink-cu12==12.6.85
 nvidia-nvtx-cu12==12.6.77
 oauth2client==4.1.3
 objsize==0.7.1
-opentelemetry-api==1.34.1
-opentelemetry-sdk==1.34.1
-opentelemetry-semantic-conventions==0.55b1
+opentelemetry-api==1.36.0
+opentelemetry-sdk==1.36.0
+opentelemetry-semantic-conventions==0.57b0
 opt_einsum==3.4.0
-optree==0.16.0
-oracledb==3.2.0
-orjson==3.10.18
+optree==0.17.0
+oracledb==3.3.0
+orjson==3.11.2
 overrides==7.7.0
 packaging==25.0
 pandas==2.2.3
 parameterized==0.9.0
-pg8000==1.31.2
-pip==25.1.1
+pg8000==1.31.4
+pip==25.2
 pluggy==1.6.0
 propcache==0.3.2
 proto-plus==1.26.1
@@ -176,32 +176,32 @@ pydantic_core==2.33.2
 pydot==1.4.2
 Pygments==2.19.2
 PyHamcrest==2.1.0
-PyJWT==2.9.0
-pymilvus==2.5.11
-pymongo==4.13.2
-PyMySQL==1.1.1
+PyJWT==2.10.1
+pymilvus==2.5.15
+pymongo==4.14.1
+PyMySQL==1.1.2
 pyparsing==3.2.3
 pyproject_hooks==1.2.0
 pytest==7.4.4
 pytest-timeout==2.4.0
-pytest-xdist==3.7.0
+pytest-xdist==3.8.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.1.1
-python-tds==1.16.1
+python-tds==1.17.0
 pytz==2025.2
 PyYAML==6.0.2
-redis==5.3.0
+redis==5.3.1
 referencing==0.36.2
-regex==2024.11.6
-requests==2.32.4
+regex==2025.7.34
+requests==2.32.5
 requests-mock==1.12.1
-rich==14.0.0
-rpds-py==0.25.1
+rich==14.1.0
+rpds-py==0.27.0
 rsa==4.9.1
-safetensors==0.5.3
-scikit-learn==1.7.0
-scipy==1.16.0
-scramp==1.4.5
+safetensors==0.6.2
+scikit-learn==1.7.1
+scipy==1.16.1
+scramp==1.4.6
 SecretStorage==3.3.3
 setuptools==80.9.0
 shapely==2.1.1
@@ -209,7 +209,7 @@ six==1.17.0
 sniffio==1.3.1
 sortedcontainers==2.4.0
 soupsieve==2.7
-SQLAlchemy==2.0.41
+SQLAlchemy==2.0.43
 sqlalchemy_pytds==1.0.2
 sqlparse==0.5.3
 sympy==1.14.0
@@ -219,24 +219,24 @@ tensorboard-data-server==0.7.2
 tensorflow==2.16.2
 tensorflow-cpu-aws==2.16.2;platform_machine=="aarch64"
 termcolor==3.1.0
-testcontainers==4.10.0
+testcontainers==4.12.0
 threadpoolctl==3.6.0
-tokenizers==0.21.2
+tokenizers==0.21.4
 torch==2.7.1
 tqdm==4.67.1
-transformers==4.48.3
+transformers==4.55.4
 triton==3.3.1
 typing-inspection==0.4.1
-typing_extensions==4.14.0
+typing_extensions==4.14.1
 tzdata==2025.2
-ujson==5.10.0
+ujson==5.11.0
 uritemplate==4.2.0
 urllib3==2.5.0
 virtualenv-clone==0.5.7
 websockets==15.0.1
 Werkzeug==3.1.3
 wheel==0.45.1
-wrapt==1.17.2
+wrapt==1.17.3
 yarl==1.20.1
 zipp==3.23.0
-zstandard==0.23.0
+zstandard==0.24.0
diff --git a/sdks/python/container/ml/py312/build.gradle b/sdks/python/container/ml/py312/build.gradle
new file mode 100644
index 000000000000..d4009f12a489
--- /dev/null
+++ b/sdks/python/container/ml/py312/build.gradle
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+plugins {
+    id 'base'
+    id 'org.apache.beam.module'
+}
+applyDockerNature()
+applyPythonNature()
+
+pythonVersion = '3.12'
+
+apply from: "../common.gradle"
diff --git a/sdks/python/container/ml/py313/build.gradle b/sdks/python/container/ml/py313/build.gradle
new file mode 100644
index 000000000000..4552ba0ef62d
--- /dev/null
+++ b/sdks/python/container/ml/py313/build.gradle
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+plugins {
+    id 'base'
+    id 'org.apache.beam.module'
+}
+applyDockerNature()
+applyPythonNature()
+
+pythonVersion = '3.13'
+
+apply from: "../common.gradle"
diff --git a/sdks/python/container/py39/ml_image_requirements.txt b/sdks/python/container/ml/py39/base_image_requirements.txt
similarity index 79%
rename from sdks/python/container/py39/ml_image_requirements.txt
rename to sdks/python/container/ml/py39/base_image_requirements.txt
index 3dab7e35b6d1..7b55eb7a8e7b 100644
--- a/sdks/python/container/py39/ml_image_requirements.txt
+++ b/sdks/python/container/ml/py39/base_image_requirements.txt
@@ -21,74 +21,75 @@
 # https://s.apache.org/beam-python-dev-wiki
 # Reach out to a committer if you need help.
 
-absl-py==2.3.0
+absl-py==2.3.1
 aiofiles==24.1.0
 aiohappyeyeballs==2.6.1
-aiohttp==3.12.13
-aiosignal==1.3.2
+aiohttp==3.12.15
+aiosignal==1.4.0
 annotated-types==0.7.0
-anyio==4.9.0
+anyio==4.10.0
 asn1crypto==1.5.1
 astunparse==1.6.3
 async-timeout==5.0.1
 attrs==25.3.0
 backports.tarfile==1.2.0
+beartype==0.21.0
 beautifulsoup4==4.13.4
 bs4==0.0.2
-build==1.2.2.post1
+build==1.3.0
 cachetools==5.5.2
-certifi==2025.6.15
+certifi==2025.8.3
 cffi==1.17.1
-charset-normalizer==3.4.2
+charset-normalizer==3.4.3
 click==8.1.8
-cloud-sql-python-connector==1.18.2
+cloud-sql-python-connector==1.18.4
 crcmod==1.7
-cryptography==45.0.4
-Cython==3.1.2
+cryptography==45.0.6
+Cython==3.1.3
 dill==0.3.1.1
 dnspython==2.7.0
 docker==7.1.0
 docopt==0.6.2
-docstring_parser==0.16
+docstring_parser==0.17.0
 exceptiongroup==1.3.0
 execnet==2.1.1
-fastavro==1.11.1
-fasteners==0.19
-filelock==3.18.0
+fastavro==1.12.0
+fasteners==0.20
+filelock==3.19.1
 flatbuffers==25.2.10
-freezegun==1.5.2
+freezegun==1.5.5
 frozenlist==1.7.0
-fsspec==2025.5.1
+fsspec==2025.7.0
 future==1.0.0
 gast==0.6.0
 google-api-core==2.25.1
-google-api-python-client==2.174.0
+google-api-python-client==2.179.0
 google-apitools==0.5.31
 google-auth==2.40.3
 google-auth-httplib2==0.2.0
-google-cloud-aiplatform==1.100.0
-google-cloud-bigquery==3.34.0
+google-cloud-aiplatform==1.110.0
+google-cloud-bigquery==3.36.0
 google-cloud-bigquery-storage==2.32.0
-google-cloud-bigtable==2.31.0
+google-cloud-bigtable==2.32.0
 google-cloud-core==2.4.3
 google-cloud-datastore==2.21.0
 google-cloud-dlp==3.31.0
 google-cloud-language==2.17.2
 google-cloud-profiler==4.1.0
-google-cloud-pubsub==2.30.0
+google-cloud-pubsub==2.31.1
 google-cloud-pubsublite==1.12.0
 google-cloud-recommendations-ai==0.10.18
 google-cloud-resource-manager==1.14.2
-google-cloud-spanner==3.55.0
+google-cloud-spanner==3.57.0
 google-cloud-storage==2.19.0
 google-cloud-videointelligence==2.16.2
 google-cloud-vision==3.10.2
 google-crc32c==1.7.1
-google-genai==1.23.0
+google-genai==1.31.0
 google-pasta==0.2.0
 google-resumable-media==2.7.2
 googleapis-common-protos==1.70.0
-greenlet==3.2.3
+greenlet==3.2.4
 grpc-google-iam-v1==0.14.2
 grpc-interceptor==0.15.4
 grpcio==1.65.5
@@ -97,23 +98,23 @@ guppy3==3.1.5
 h11==0.16.0
 h5py==3.14.0
 hdfs==2.7.3
-hf-xet==1.1.5
+hf-xet==1.1.8
 httpcore==1.0.9
 httplib2==0.22.0
 httpx==0.28.1
-huggingface-hub==0.33.1
-hypothesis==6.135.20
+huggingface-hub==0.34.4
+hypothesis==6.138.3
 idna==3.10
 importlib_metadata==8.7.0
 iniconfig==2.1.0
 jaraco.classes==3.4.0
 jaraco.context==6.0.1
-jaraco.functools==4.2.1
+jaraco.functools==4.3.0
 jeepney==0.9.0
 Jinja2==3.1.6
 joblib==1.5.1
 jsonpickle==3.4.2
-jsonschema==4.24.0
+jsonschema==4.25.1
 jsonschema-specifications==2025.4.1
 keras==3.10.0
 keyring==25.6.0
@@ -125,12 +126,11 @@ MarkupSafe==3.0.2
 mdurl==0.1.2
 milvus-lite==2.5.1
 ml-dtypes==0.3.2
-mmh3==5.1.0
+mmh3==5.2.0
 mock==5.2.0
 more-itertools==10.7.0
 mpmath==1.3.0
-multidict==6.6.3
-mysql-connector-python==9.3.0
+multidict==6.6.4
 namex==0.1.0
 networkx==3.2.1
 nltk==3.9.1
@@ -151,19 +151,19 @@ nvidia-nvjitlink-cu12==12.6.85
 nvidia-nvtx-cu12==12.6.77
 oauth2client==4.1.3
 objsize==0.7.1
-opentelemetry-api==1.34.1
-opentelemetry-sdk==1.34.1
-opentelemetry-semantic-conventions==0.55b1
+opentelemetry-api==1.36.0
+opentelemetry-sdk==1.36.0
+opentelemetry-semantic-conventions==0.57b0
 opt_einsum==3.4.0
-optree==0.16.0
-oracledb==3.2.0
-orjson==3.10.18
+optree==0.17.0
+oracledb==3.3.0
+orjson==3.11.2
 overrides==7.7.0
 packaging==25.0
 pandas==2.2.3
 parameterized==0.9.0
-pg8000==1.31.2
-pip==25.1.1
+pg8000==1.31.4
+pip==25.2
 pluggy==1.6.0
 propcache==0.3.2
 proto-plus==1.26.1
@@ -179,32 +179,32 @@ pydantic_core==2.33.2
 pydot==1.4.2
 Pygments==2.19.2
 PyHamcrest==2.1.0
-PyJWT==2.9.0
-pymilvus==2.5.11
-pymongo==4.13.2
-PyMySQL==1.1.1
+PyJWT==2.10.1
+pymilvus==2.5.15
+pymongo==4.14.1
+PyMySQL==1.1.2
 pyparsing==3.2.3
 pyproject_hooks==1.2.0
 pytest==7.4.4
 pytest-timeout==2.4.0
-pytest-xdist==3.7.0
+pytest-xdist==3.8.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.1.1
-python-tds==1.16.1
+python-tds==1.17.0
 pytz==2025.2
 PyYAML==6.0.2
-redis==5.3.0
+redis==5.3.1
 referencing==0.36.2
-regex==2024.11.6
-requests==2.32.4
+regex==2025.7.34
+requests==2.32.5
 requests-mock==1.12.1
-rich==14.0.0
-rpds-py==0.25.1
+rich==14.1.0
+rpds-py==0.27.0
 rsa==4.9.1
-safetensors==0.5.3
+safetensors==0.6.2
 scikit-learn==1.6.1
 scipy==1.13.1
-scramp==1.4.5
+scramp==1.4.6
 SecretStorage==3.3.3
 setuptools==80.9.0
 shapely==2.0.7
@@ -212,7 +212,7 @@ six==1.17.0
 sniffio==1.3.1
 sortedcontainers==2.4.0
 soupsieve==2.7
-SQLAlchemy==2.0.41
+SQLAlchemy==2.0.43
 sqlalchemy_pytds==1.0.2
 sqlparse==0.5.3
 sympy==1.14.0
@@ -223,25 +223,25 @@ tensorflow==2.16.2
 tensorflow-cpu-aws==2.16.2;platform_machine=="aarch64"
 tensorflow-io-gcs-filesystem==0.37.1
 termcolor==3.1.0
-testcontainers==4.10.0
+testcontainers==4.12.0
 threadpoolctl==3.6.0
-tokenizers==0.21.2
+tokenizers==0.21.4
 tomli==2.2.1
 torch==2.7.1
 tqdm==4.67.1
-transformers==4.48.3
+transformers==4.55.4
 triton==3.3.1
 typing-inspection==0.4.1
-typing_extensions==4.14.0
+typing_extensions==4.14.1
 tzdata==2025.2
-ujson==5.10.0
+ujson==5.11.0
 uritemplate==4.2.0
 urllib3==2.5.0
 virtualenv-clone==0.5.7
 websockets==15.0.1
 Werkzeug==3.1.3
 wheel==0.45.1
-wrapt==1.17.2
+wrapt==1.17.3
 yarl==1.20.1
 zipp==3.23.0
-zstandard==0.23.0
+zstandard==0.24.0
diff --git a/sdks/python/container/ml/py39/build.gradle b/sdks/python/container/ml/py39/build.gradle
new file mode 100644
index 000000000000..c5f55ae53af7
--- /dev/null
+++ b/sdks/python/container/ml/py39/build.gradle
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * License); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+plugins {
+    id 'base'
+    id 'org.apache.beam.module'
+}
+applyDockerNature()
+applyPythonNature()
+
+pythonVersion = '3.9'
+
+apply from: "../common.gradle"
diff --git a/sdks/python/container/py310/base_image_requirements.txt b/sdks/python/container/py310/base_image_requirements.txt
index 81834540267c..63d947772c2b 100644
--- a/sdks/python/container/py310/base_image_requirements.txt
+++ b/sdks/python/container/py310/base_image_requirements.txt
@@ -23,65 +23,66 @@
 
 aiofiles==24.1.0
 aiohappyeyeballs==2.6.1
-aiohttp==3.12.13
-aiosignal==1.3.2
+aiohttp==3.12.15
+aiosignal==1.4.0
 annotated-types==0.7.0
-anyio==4.9.0
+anyio==4.10.0
 asn1crypto==1.5.1
 async-timeout==5.0.1
 attrs==25.3.0
 backports.tarfile==1.2.0
+beartype==0.21.0
 beautifulsoup4==4.13.4
 bs4==0.0.2
-build==1.2.2.post1
+build==1.3.0
 cachetools==5.5.2
-certifi==2025.6.15
+certifi==2025.8.3
 cffi==1.17.1
-charset-normalizer==3.4.2
+charset-normalizer==3.4.3
 click==8.2.1
-cloud-sql-python-connector==1.18.2
+cloud-sql-python-connector==1.18.4
 crcmod==1.7
-cryptography==45.0.4
-Cython==3.1.2
+cryptography==45.0.6
+Cython==3.1.3
 dill==0.3.1.1
 dnspython==2.7.0
 docker==7.1.0
 docopt==0.6.2
-docstring_parser==0.16
+docstring_parser==0.17.0
 exceptiongroup==1.3.0
 execnet==2.1.1
-fastavro==1.11.1
-fasteners==0.19
-freezegun==1.5.2
+fastavro==1.12.0
+fasteners==0.20
+freezegun==1.5.5
 frozenlist==1.7.0
 future==1.0.0
 google-api-core==2.25.1
-google-api-python-client==2.174.0
+google-api-python-client==2.179.0
 google-apitools==0.5.31
 google-auth==2.40.3
 google-auth-httplib2==0.2.0
-google-cloud-aiplatform==1.100.0
-google-cloud-bigquery==3.34.0
+google-cloud-aiplatform==1.110.0
+google-cloud-bigquery==3.36.0
 google-cloud-bigquery-storage==2.32.0
-google-cloud-bigtable==2.31.0
+google-cloud-bigtable==2.32.0
 google-cloud-core==2.4.3
 google-cloud-datastore==2.21.0
 google-cloud-dlp==3.31.0
 google-cloud-language==2.17.2
 google-cloud-profiler==4.1.0
-google-cloud-pubsub==2.30.0
+google-cloud-pubsub==2.31.1
 google-cloud-pubsublite==1.12.0
 google-cloud-recommendations-ai==0.10.18
 google-cloud-resource-manager==1.14.2
-google-cloud-spanner==3.55.0
+google-cloud-spanner==3.57.0
 google-cloud-storage==2.19.0
 google-cloud-videointelligence==2.16.2
 google-cloud-vision==3.10.2
 google-crc32c==1.7.1
-google-genai==1.23.0
+google-genai==1.31.0
 google-resumable-media==2.7.2
 googleapis-common-protos==1.70.0
-greenlet==3.2.3
+greenlet==3.2.4
 grpc-google-iam-v1==0.14.2
 grpc-interceptor==0.15.4
 grpcio==1.65.5
@@ -92,43 +93,42 @@ hdfs==2.7.3
 httpcore==1.0.9
 httplib2==0.22.0
 httpx==0.28.1
-hypothesis==6.135.19
+hypothesis==6.138.3
 idna==3.10
 importlib_metadata==8.7.0
 iniconfig==2.1.0
 jaraco.classes==3.4.0
 jaraco.context==6.0.1
-jaraco.functools==4.2.1
+jaraco.functools==4.3.0
 jeepney==0.9.0
 Jinja2==3.1.6
 joblib==1.5.1
 jsonpickle==3.4.2
-jsonschema==4.24.0
+jsonschema==4.25.1
 jsonschema-specifications==2025.4.1
 keyring==25.6.0
 keyrings.google-artifactregistry-auth==1.1.2
 MarkupSafe==3.0.2
 milvus-lite==2.5.1
-mmh3==5.1.0
+mmh3==5.2.0
 mock==5.2.0
 more-itertools==10.7.0
-multidict==6.6.3
-mysql-connector-python==9.3.0
+multidict==6.6.4
 nltk==3.9.1
 numpy==2.2.6
 oauth2client==4.1.3
 objsize==0.7.1
-opentelemetry-api==1.34.1
-opentelemetry-sdk==1.34.1
-opentelemetry-semantic-conventions==0.55b1
-oracledb==3.2.0
-orjson==3.10.18
+opentelemetry-api==1.36.0
+opentelemetry-sdk==1.36.0
+opentelemetry-semantic-conventions==0.57b0
+oracledb==3.3.0
+orjson==3.11.2
 overrides==7.7.0
 packaging==25.0
 pandas==2.2.3
 parameterized==0.9.0
-pg8000==1.31.2
-pip==25.1.1
+pg8000==1.31.4
+pip==25.2
 pluggy==1.6.0
 propcache==0.3.2
 proto-plus==1.26.1
@@ -143,30 +143,30 @@ pydantic==2.11.7
 pydantic_core==2.33.2
 pydot==1.4.2
 PyHamcrest==2.1.0
-PyJWT==2.9.0
-pymilvus==2.5.11
-pymongo==4.13.2
-PyMySQL==1.1.1
+PyJWT==2.10.1
+pymilvus==2.5.15
+pymongo==4.14.1
+PyMySQL==1.1.2
 pyparsing==3.2.3
 pyproject_hooks==1.2.0
 pytest==7.4.4
 pytest-timeout==2.4.0
-pytest-xdist==3.7.0
+pytest-xdist==3.8.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.1.1
-python-tds==1.16.1
+python-tds==1.17.0
 pytz==2025.2
 PyYAML==6.0.2
-redis==5.3.0
+redis==5.3.1
 referencing==0.36.2
-regex==2024.11.6
-requests==2.32.4
+regex==2025.7.34
+requests==2.32.5
 requests-mock==1.12.1
-rpds-py==0.25.1
+rpds-py==0.27.0
 rsa==4.9.1
-scikit-learn==1.7.0
+scikit-learn==1.7.1
 scipy==1.15.3
-scramp==1.4.5
+scramp==1.4.6
 SecretStorage==3.3.3
 setuptools==80.9.0
 shapely==2.1.1
@@ -174,24 +174,24 @@ six==1.17.0
 sniffio==1.3.1
 sortedcontainers==2.4.0
 soupsieve==2.7
-SQLAlchemy==2.0.41
+SQLAlchemy==2.0.43
 sqlalchemy_pytds==1.0.2
 sqlparse==0.5.3
 tenacity==8.5.0
-testcontainers==4.10.0
+testcontainers==4.12.0
 threadpoolctl==3.6.0
 tomli==2.2.1
 tqdm==4.67.1
 typing-inspection==0.4.1
-typing_extensions==4.14.0
+typing_extensions==4.14.1
 tzdata==2025.2
-ujson==5.10.0
+ujson==5.11.0
 uritemplate==4.2.0
 urllib3==2.5.0
 virtualenv-clone==0.5.7
 websockets==15.0.1
 wheel==0.45.1
-wrapt==1.17.2
+wrapt==1.17.3
 yarl==1.20.1
 zipp==3.23.0
-zstandard==0.23.0
+zstandard==0.24.0
diff --git a/sdks/python/container/py311/base_image_requirements.txt b/sdks/python/container/py311/base_image_requirements.txt
index 2f81ea5e79d1..6ba596eeed3d 100644
--- a/sdks/python/container/py311/base_image_requirements.txt
+++ b/sdks/python/container/py311/base_image_requirements.txt
@@ -23,63 +23,64 @@
 
 aiofiles==24.1.0
 aiohappyeyeballs==2.6.1
-aiohttp==3.12.13
-aiosignal==1.3.2
+aiohttp==3.12.15
+aiosignal==1.4.0
 annotated-types==0.7.0
-anyio==4.9.0
+anyio==4.10.0
 asn1crypto==1.5.1
 attrs==25.3.0
 backports.tarfile==1.2.0
+beartype==0.21.0
 beautifulsoup4==4.13.4
 bs4==0.0.2
-build==1.2.2.post1
+build==1.3.0
 cachetools==5.5.2
-certifi==2025.6.15
+certifi==2025.8.3
 cffi==1.17.1
-charset-normalizer==3.4.2
+charset-normalizer==3.4.3
 click==8.2.1
-cloud-sql-python-connector==1.18.2
+cloud-sql-python-connector==1.18.4
 crcmod==1.7
-cryptography==45.0.4
-Cython==3.1.2
+cryptography==45.0.6
+Cython==3.1.3
 dill==0.3.1.1
 dnspython==2.7.0
 docker==7.1.0
 docopt==0.6.2
-docstring_parser==0.16
+docstring_parser==0.17.0
 execnet==2.1.1
-fastavro==1.11.1
-fasteners==0.19
-freezegun==1.5.2
+fastavro==1.12.0
+fasteners==0.20
+freezegun==1.5.5
 frozenlist==1.7.0
 future==1.0.0
 google-api-core==2.25.1
-google-api-python-client==2.174.0
+google-api-python-client==2.179.0
 google-apitools==0.5.31
 google-auth==2.40.3
 google-auth-httplib2==0.2.0
-google-cloud-aiplatform==1.100.0
-google-cloud-bigquery==3.34.0
+google-cloud-aiplatform==1.110.0
+google-cloud-bigquery==3.36.0
 google-cloud-bigquery-storage==2.32.0
-google-cloud-bigtable==2.31.0
+google-cloud-bigtable==2.32.0
 google-cloud-core==2.4.3
 google-cloud-datastore==2.21.0
 google-cloud-dlp==3.31.0
 google-cloud-language==2.17.2
 google-cloud-profiler==4.1.0
-google-cloud-pubsub==2.30.0
+google-cloud-pubsub==2.31.1
 google-cloud-pubsublite==1.12.0
 google-cloud-recommendations-ai==0.10.18
 google-cloud-resource-manager==1.14.2
-google-cloud-spanner==3.55.0
+google-cloud-spanner==3.57.0
 google-cloud-storage==2.19.0
 google-cloud-videointelligence==2.16.2
 google-cloud-vision==3.10.2
 google-crc32c==1.7.1
-google-genai==1.23.0
+google-genai==1.31.0
 google-resumable-media==2.7.2
 googleapis-common-protos==1.70.0
-greenlet==3.2.3
+greenlet==3.2.4
 grpc-google-iam-v1==0.14.2
 grpc-interceptor==0.15.4
 grpcio==1.65.5
@@ -90,43 +91,42 @@ hdfs==2.7.3
 httpcore==1.0.9
 httplib2==0.22.0
 httpx==0.28.1
-hypothesis==6.135.19
+hypothesis==6.138.3
 idna==3.10
 importlib_metadata==8.7.0
 iniconfig==2.1.0
 jaraco.classes==3.4.0
 jaraco.context==6.0.1
-jaraco.functools==4.2.1
+jaraco.functools==4.3.0
 jeepney==0.9.0
 Jinja2==3.1.6
 joblib==1.5.1
 jsonpickle==3.4.2
-jsonschema==4.24.0
+jsonschema==4.25.1
 jsonschema-specifications==2025.4.1
 keyring==25.6.0
 keyrings.google-artifactregistry-auth==1.1.2
 MarkupSafe==3.0.2
 milvus-lite==2.5.1
-mmh3==5.1.0
+mmh3==5.2.0
 mock==5.2.0
 more-itertools==10.7.0
-multidict==6.6.3
-mysql-connector-python==9.3.0
+multidict==6.6.4
 nltk==3.9.1
 numpy==2.2.6
 oauth2client==4.1.3
 objsize==0.7.1
-opentelemetry-api==1.34.1
-opentelemetry-sdk==1.34.1
-opentelemetry-semantic-conventions==0.55b1
-oracledb==3.2.0
-orjson==3.10.18
+opentelemetry-api==1.36.0
+opentelemetry-sdk==1.36.0
+opentelemetry-semantic-conventions==0.57b0
+oracledb==3.3.0
+orjson==3.11.2
 overrides==7.7.0
 packaging==25.0
 pandas==2.2.3
 parameterized==0.9.0
-pg8000==1.31.2
-pip==25.1.1
+pg8000==1.31.4
+pip==25.2
 pluggy==1.6.0
 propcache==0.3.2
 proto-plus==1.26.1
@@ -141,30 +141,30 @@ pydantic==2.11.7
 pydantic_core==2.33.2
 pydot==1.4.2
 PyHamcrest==2.1.0
-PyJWT==2.9.0
-pymilvus==2.5.11
-pymongo==4.13.2
-PyMySQL==1.1.1
+PyJWT==2.10.1
+pymilvus==2.5.15
+pymongo==4.14.1
+PyMySQL==1.1.2
 pyparsing==3.2.3
 pyproject_hooks==1.2.0
 pytest==7.4.4
 pytest-timeout==2.4.0
-pytest-xdist==3.7.0
+pytest-xdist==3.8.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.1.1
-python-tds==1.16.1
+python-tds==1.17.0
 pytz==2025.2
 PyYAML==6.0.2
-redis==5.3.0
+redis==5.3.1
 referencing==0.36.2
-regex==2024.11.6
-requests==2.32.4
+regex==2025.7.34
+requests==2.32.5
 requests-mock==1.12.1
-rpds-py==0.25.1
+rpds-py==0.27.0
 rsa==4.9.1
-scikit-learn==1.7.0
-scipy==1.16.0
-scramp==1.4.5
+scikit-learn==1.7.1
+scipy==1.16.1
+scramp==1.4.6
 SecretStorage==3.3.3
 setuptools==80.9.0
 shapely==2.1.1
@@ -172,23 +172,23 @@ six==1.17.0
 sniffio==1.3.1
 sortedcontainers==2.4.0
 soupsieve==2.7
-SQLAlchemy==2.0.41
+SQLAlchemy==2.0.43
 sqlalchemy_pytds==1.0.2
 sqlparse==0.5.3
 tenacity==8.5.0
-testcontainers==4.10.0
+testcontainers==4.12.0
 threadpoolctl==3.6.0
 tqdm==4.67.1
 typing-inspection==0.4.1
-typing_extensions==4.14.0
+typing_extensions==4.14.1
 tzdata==2025.2
-ujson==5.10.0
+ujson==5.11.0
 uritemplate==4.2.0
 urllib3==2.5.0
 virtualenv-clone==0.5.7
 websockets==15.0.1
 wheel==0.45.1
-wrapt==1.17.2
+wrapt==1.17.3
 yarl==1.20.1
 zipp==3.23.0
-zstandard==0.23.0
+zstandard==0.24.0
diff --git a/sdks/python/container/py312/base_image_requirements.txt b/sdks/python/container/py312/base_image_requirements.txt
index f39f7ab8f7df..c709b57164a8 100644
--- a/sdks/python/container/py312/base_image_requirements.txt
+++ b/sdks/python/container/py312/base_image_requirements.txt
@@ -23,62 +23,63 @@
 
 aiofiles==24.1.0
 aiohappyeyeballs==2.6.1
-aiohttp==3.12.13
-aiosignal==1.3.2
+aiohttp==3.12.15
+aiosignal==1.4.0
 annotated-types==0.7.0
-anyio==4.9.0
+anyio==4.10.0
 asn1crypto==1.5.1
 attrs==25.3.0
+beartype==0.21.0
 beautifulsoup4==4.13.4
 bs4==0.0.2
-build==1.2.2.post1
+build==1.3.0
 cachetools==5.5.2
-certifi==2025.6.15
+certifi==2025.8.3
 cffi==1.17.1
-charset-normalizer==3.4.2
+charset-normalizer==3.4.3
 click==8.2.1
-cloud-sql-python-connector==1.18.2
+cloud-sql-python-connector==1.18.4
 crcmod==1.7
-cryptography==45.0.4
-Cython==3.1.2
+cryptography==45.0.6
+Cython==3.1.3
 dill==0.3.1.1
 dnspython==2.7.0
 docker==7.1.0
 docopt==0.6.2
-docstring_parser==0.16
+docstring_parser==0.17.0
 execnet==2.1.1
-fastavro==1.11.1
-fasteners==0.19
-freezegun==1.5.2
+fastavro==1.12.0
+fasteners==0.20
+freezegun==1.5.5
 frozenlist==1.7.0
 future==1.0.0
 google-api-core==2.25.1
-google-api-python-client==2.174.0
+google-api-python-client==2.179.0
 google-apitools==0.5.31
 google-auth==2.40.3
 google-auth-httplib2==0.2.0
-google-cloud-aiplatform==1.100.0
-google-cloud-bigquery==3.34.0
+google-cloud-aiplatform==1.110.0
+google-cloud-bigquery==3.36.0
 google-cloud-bigquery-storage==2.32.0
-google-cloud-bigtable==2.31.0
+google-cloud-bigtable==2.32.0
 google-cloud-core==2.4.3
 google-cloud-datastore==2.21.0
 google-cloud-dlp==3.31.0
 google-cloud-language==2.17.2
 google-cloud-profiler==4.1.0
-google-cloud-pubsub==2.30.0
+google-cloud-pubsub==2.31.1
 google-cloud-pubsublite==1.12.0
 google-cloud-recommendations-ai==0.10.18
 google-cloud-resource-manager==1.14.2
-google-cloud-spanner==3.55.0
+google-cloud-spanner==3.57.0
 google-cloud-storage==2.19.0
 google-cloud-videointelligence==2.16.2
 google-cloud-vision==3.10.2
 google-crc32c==1.7.1
-google-genai==1.23.0
+google-genai==1.31.0
 google-resumable-media==2.7.2
 googleapis-common-protos==1.70.0
-greenlet==3.2.3
+greenlet==3.2.4
 grpc-google-iam-v1==0.14.2
 grpc-interceptor==0.15.4
 grpcio==1.65.5
@@ -89,43 +90,42 @@ hdfs==2.7.3
 httpcore==1.0.9
 httplib2==0.22.0
 httpx==0.28.1
-hypothesis==6.135.19
+hypothesis==6.138.3
 idna==3.10
 importlib_metadata==8.7.0
 iniconfig==2.1.0
 jaraco.classes==3.4.0
 jaraco.context==6.0.1
-jaraco.functools==4.2.1
+jaraco.functools==4.3.0
 jeepney==0.9.0
 Jinja2==3.1.6
 joblib==1.5.1
 jsonpickle==3.4.2
-jsonschema==4.24.0
+jsonschema==4.25.1
 jsonschema-specifications==2025.4.1
 keyring==25.6.0
 keyrings.google-artifactregistry-auth==1.1.2
 MarkupSafe==3.0.2
 milvus-lite==2.5.1
-mmh3==5.1.0
+mmh3==5.2.0
 mock==5.2.0
 more-itertools==10.7.0
-multidict==6.6.3
-mysql-connector-python==9.3.0
+multidict==6.6.4
 nltk==3.9.1
 numpy==2.2.6
 oauth2client==4.1.3
 objsize==0.7.1
-opentelemetry-api==1.34.1
-opentelemetry-sdk==1.34.1
-opentelemetry-semantic-conventions==0.55b1
-oracledb==3.2.0
-orjson==3.10.18
+opentelemetry-api==1.36.0
+opentelemetry-sdk==1.36.0
+opentelemetry-semantic-conventions==0.57b0
+oracledb==3.3.0
+orjson==3.11.2
 overrides==7.7.0
 packaging==25.0
 pandas==2.2.3
 parameterized==0.9.0
-pg8000==1.31.2
-pip==25.1.1
+pg8000==1.31.4
+pip==25.2
 pluggy==1.6.0
 propcache==0.3.2
 proto-plus==1.26.1
@@ -140,30 +140,30 @@ pydantic==2.11.7
 pydantic_core==2.33.2
 pydot==1.4.2
 PyHamcrest==2.1.0
-PyJWT==2.9.0
-pymilvus==2.5.11
-pymongo==4.13.2
-PyMySQL==1.1.1
+PyJWT==2.10.1
+pymilvus==2.5.15
+pymongo==4.14.1
+PyMySQL==1.1.2
 pyparsing==3.2.3
 pyproject_hooks==1.2.0
 pytest==7.4.4
 pytest-timeout==2.4.0
-pytest-xdist==3.7.0
+pytest-xdist==3.8.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.1.1
-python-tds==1.16.1
+python-tds==1.17.0
 pytz==2025.2
 PyYAML==6.0.2
-redis==5.3.0
+redis==5.3.1
 referencing==0.36.2
-regex==2024.11.6
-requests==2.32.4
+regex==2025.7.34
+requests==2.32.5
 requests-mock==1.12.1
-rpds-py==0.25.1
+rpds-py==0.27.0
 rsa==4.9.1
-scikit-learn==1.7.0
-scipy==1.16.0
-scramp==1.4.5
+scikit-learn==1.7.1
+scipy==1.16.1
+scramp==1.4.6
 SecretStorage==3.3.3
 setuptools==80.9.0
 shapely==2.1.1
@@ -171,23 +171,23 @@ six==1.17.0
 sniffio==1.3.1
 sortedcontainers==2.4.0
 soupsieve==2.7
-SQLAlchemy==2.0.41
+SQLAlchemy==2.0.43
 sqlalchemy_pytds==1.0.2
 sqlparse==0.5.3
 tenacity==8.5.0
-testcontainers==4.10.0
+testcontainers==4.12.0
 threadpoolctl==3.6.0
 tqdm==4.67.1
 typing-inspection==0.4.1
-typing_extensions==4.14.0
+typing_extensions==4.14.1
 tzdata==2025.2
-ujson==5.10.0
+ujson==5.11.0
 uritemplate==4.2.0
 urllib3==2.5.0
 virtualenv-clone==0.5.7
 websockets==15.0.1
 wheel==0.45.1
-wrapt==1.17.2
+wrapt==1.17.3
 yarl==1.20.1
 zipp==3.23.0
-zstandard==0.23.0
+zstandard==0.24.0
diff --git a/sdks/python/container/py313/base_image_requirements.txt b/sdks/python/container/py313/base_image_requirements.txt
index 3c93397afb1a..7d73bf53a928 100644
--- a/sdks/python/container/py313/base_image_requirements.txt
+++ b/sdks/python/container/py313/base_image_requirements.txt
@@ -21,151 +21,170 @@
 # https://s.apache.org/beam-python-dev-wiki
 # Reach out to a committer if you need help.
 
+aiofiles==24.1.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.12.15
+aiosignal==1.4.0
 annotated-types==0.7.0
-anyio==4.9.0
+anyio==4.10.0
+asn1crypto==1.5.1
 attrs==25.3.0
+beartype==0.21.0
 beautifulsoup4==4.13.4
 bs4==0.0.2
-build==1.2.2.post1
+build==1.3.0
 cachetools==5.5.2
-certifi==2025.4.26
+certifi==2025.8.3
 cffi==1.17.1
-charset-normalizer==3.4.2
-click==8.2.0
+charset-normalizer==3.4.3
+click==8.2.1
+cloud-sql-python-connector==1.18.4
 crcmod==1.7
-cryptography==45.0.2
-Cython==3.1.1
-Deprecated==1.2.18
-deprecation==2.1.0
+cryptography==45.0.6
+Cython==3.1.3
 dill==0.3.1.1
 dnspython==2.7.0
 docker==7.1.0
 docopt==0.6.2
-docstring_parser==0.16
+docstring_parser==0.17.0
 execnet==2.1.1
-fastavro==1.11.1
-fasteners==0.19
-freezegun==1.5.1
+fastavro==1.12.0
+fasteners==0.20
+freezegun==1.5.5
+frozenlist==1.7.0
 future==1.0.0
-google-api-core==2.24.2
+google-api-core==2.25.1
 google-apitools==0.5.32
-google-auth==2.40.1
+google-auth==2.40.3
 google-auth-httplib2==0.2.0
-google-cloud-aiplatform==1.93.1
-google-cloud-bigquery==3.33.0
-google-cloud-bigquery-storage==2.31.0
-google-cloud-bigtable==2.30.1
+google-cloud-aiplatform==1.110.0
+google-cloud-bigquery==3.36.0
+google-cloud-bigquery-storage==2.32.0
+google-cloud-bigtable==2.32.0
 google-cloud-core==2.4.3
 google-cloud-datastore==2.21.0
-google-cloud-dlp==3.29.0
-google-cloud-language==2.17.1
-google-cloud-pubsub==2.29.0
+google-cloud-dlp==3.31.0
+google-cloud-language==2.17.2
+google-cloud-pubsub==2.31.1
 google-cloud-pubsublite==1.12.0
-google-cloud-recommendations-ai==0.10.17
+google-cloud-recommendations-ai==0.10.18
 google-cloud-resource-manager==1.14.2
-google-cloud-spanner==3.54.0
+google-cloud-spanner==3.57.0
 google-cloud-storage==2.19.0
-google-cloud-videointelligence==2.16.1
-google-cloud-vision==3.10.1
+google-cloud-videointelligence==2.16.2
+google-cloud-vision==3.10.2
 google-crc32c==1.7.1
-google-genai==1.16.1
+google-genai==1.31.0
 google-resumable-media==2.7.2
 googleapis-common-protos==1.70.0
-greenlet==3.2.2
+greenlet==3.2.4
 grpc-google-iam-v1==0.14.2
 grpc-interceptor==0.15.4
-grpcio==1.71.0
-grpcio-status==1.71.0
+grpcio==1.74.0
+grpcio-status==1.71.2
 guppy3==3.1.5
 h11==0.16.0
 hdfs==2.7.3
 httpcore==1.0.9
 httplib2==0.22.0
 httpx==0.28.1
-hypothesis==6.131.20
+hypothesis==6.138.3
 idna==3.10
-importlib_metadata==8.6.1
+importlib_metadata==8.7.0
 iniconfig==2.1.0
 jaraco.classes==3.4.0
 jaraco.context==6.0.1
-jaraco.functools==4.1.0
+jaraco.functools==4.3.0
 jeepney==0.9.0
 Jinja2==3.1.6
-joblib==1.5.0
+joblib==1.5.1
 jsonpickle==3.4.2
-jsonschema==4.23.0
+jsonschema==4.25.1
 jsonschema-specifications==2025.4.1
 keyring==25.6.0
 keyrings.google-artifactregistry-auth==1.1.2
 MarkupSafe==3.0.2
-mmh3==5.1.0
+milvus-lite==2.5.1
+mmh3==5.2.0
 mock==5.2.0
 more-itertools==10.7.0
+multidict==6.6.4
 nltk==3.9.1
 numpy==2.2.6
 oauth2client==4.1.3
 objsize==0.7.1
-opentelemetry-api==1.33.1
-opentelemetry-sdk==1.33.1
-opentelemetry-semantic-conventions==0.54b1
-orjson==3.10.18
+opentelemetry-api==1.36.0
+opentelemetry-sdk==1.36.0
+opentelemetry-semantic-conventions==0.57b0
+oracledb==3.3.0
+orjson==3.11.2
 overrides==7.7.0
 packaging==25.0
 pandas==2.2.3
 parameterized==0.9.0
+pg8000==1.31.4
+pip==25.2
 pluggy==1.6.0
+propcache==0.3.2
 proto-plus==1.26.1
-protobuf==5.29.4
+protobuf==5.29.5
 psycopg2-binary==2.9.10
 pyarrow==18.1.0
 pyarrow-hotfix==0.7
 pyasn1==0.6.1
 pyasn1_modules==0.4.2
 pycparser==2.22
-pydantic==2.11.4
+pydantic==2.11.7
 pydantic_core==2.33.2
 pydot==1.4.2
 PyHamcrest==2.1.0
-PyJWT==2.9.0
-pymongo==4.13.0
-PyMySQL==1.1.1
+PyJWT==2.10.1
+pymilvus==2.6.0
+pymongo==4.14.1
+PyMySQL==1.1.2
 pyparsing==3.2.3
 pyproject_hooks==1.2.0
 pytest==7.4.4
 pytest-timeout==2.4.0
-pytest-xdist==3.6.1
+pytest-xdist==3.8.0
 python-dateutil==2.9.0.post0
+python-dotenv==1.1.1
+python-tds==1.17.0
 pytz==2025.2
 PyYAML==6.0.2
-redis==5.3.0
+redis==5.3.1
 referencing==0.36.2
-regex==2024.11.6
-requests==2.32.3
+regex==2025.7.34
+requests==2.32.5
 requests-mock==1.12.1
-rpds-py==0.25.0
+rpds-py==0.27.0
 rsa==4.9.1
-scikit-learn==1.6.1
-scipy==1.15.3
+scikit-learn==1.7.1
+scipy==1.16.1
+scramp==1.4.6
 SecretStorage==3.3.3
-setuptools==80.8.0
+setuptools==80.9.0
 shapely==2.1.1
 six==1.17.0
 sniffio==1.3.1
 sortedcontainers==2.4.0
 soupsieve==2.7
-SQLAlchemy==2.0.41
+SQLAlchemy==2.0.43
+sqlalchemy_pytds==1.0.2
 sqlparse==0.5.3
 tenacity==8.5.0
-testcontainers==3.7.1
+testcontainers==4.12.0
 threadpoolctl==3.6.0
 tqdm==4.67.1
-typing-inspection==0.4.0
-typing_extensions==4.13.2
+typing-inspection==0.4.1
+typing_extensions==4.14.1
 tzdata==2025.2
-urllib3==2.4.0
+ujson==5.11.0
+urllib3==2.5.0
 virtualenv-clone==0.5.7
 websockets==15.0.1
 wheel==0.45.1
-wrapt==1.17.2
-zipp==3.21.0
-zstandard==0.23.0
+wrapt==1.17.3
+yarl==1.20.1
+zipp==3.23.0
+zstandard==0.24.0
diff --git a/sdks/python/container/py39/base_image_requirements.txt b/sdks/python/container/py39/base_image_requirements.txt
index db7961186ba3..810dfcc2a6e5 100644
--- a/sdks/python/container/py39/base_image_requirements.txt
+++ b/sdks/python/container/py39/base_image_requirements.txt
@@ -23,65 +23,66 @@
 
 aiofiles==24.1.0
 aiohappyeyeballs==2.6.1
-aiohttp==3.12.13
-aiosignal==1.3.2
+aiohttp==3.12.15
+aiosignal==1.4.0
 annotated-types==0.7.0
-anyio==4.9.0
+anyio==4.10.0
 asn1crypto==1.5.1
 async-timeout==5.0.1
 attrs==25.3.0
 backports.tarfile==1.2.0
+beartype==0.21.0
 beautifulsoup4==4.13.4
 bs4==0.0.2
-build==1.2.2.post1
+build==1.3.0
 cachetools==5.5.2
-certifi==2025.6.15
+certifi==2025.8.3
 cffi==1.17.1
-charset-normalizer==3.4.2
+charset-normalizer==3.4.3
 click==8.1.8
-cloud-sql-python-connector==1.18.2
+cloud-sql-python-connector==1.18.4
 crcmod==1.7
-cryptography==45.0.4
-Cython==3.1.2
+cryptography==45.0.6
+Cython==3.1.3
 dill==0.3.1.1
 dnspython==2.7.0
 docker==7.1.0
 docopt==0.6.2
-docstring_parser==0.16
+docstring_parser==0.17.0
 exceptiongroup==1.3.0
 execnet==2.1.1
-fastavro==1.11.1
-fasteners==0.19
-freezegun==1.5.2
+fastavro==1.12.0
+fasteners==0.20
+freezegun==1.5.5
 frozenlist==1.7.0
 future==1.0.0
 google-api-core==2.25.1
-google-api-python-client==2.174.0
+google-api-python-client==2.179.0
 google-apitools==0.5.31
 google-auth==2.40.3
 google-auth-httplib2==0.2.0
-google-cloud-aiplatform==1.100.0
-google-cloud-bigquery==3.34.0
+google-cloud-aiplatform==1.110.0
+google-cloud-bigquery==3.36.0
 google-cloud-bigquery-storage==2.32.0
-google-cloud-bigtable==2.31.0
+google-cloud-bigtable==2.32.0
 google-cloud-core==2.4.3
 google-cloud-datastore==2.21.0
 google-cloud-dlp==3.31.0
 google-cloud-language==2.17.2
 google-cloud-profiler==4.1.0
-google-cloud-pubsub==2.30.0
+google-cloud-pubsub==2.31.1
 google-cloud-pubsublite==1.12.0
 google-cloud-recommendations-ai==0.10.18
 google-cloud-resource-manager==1.14.2
-google-cloud-spanner==3.55.0
+google-cloud-spanner==3.57.0
 google-cloud-storage==2.19.0
 google-cloud-videointelligence==2.16.2
 google-cloud-vision==3.10.2
 google-crc32c==1.7.1
-google-genai==1.23.0
+google-genai==1.31.0
 google-resumable-media==2.7.2
 googleapis-common-protos==1.70.0
-greenlet==3.2.3
+greenlet==3.2.4
 grpc-google-iam-v1==0.14.2
 grpc-interceptor==0.15.4
 grpcio==1.65.5
@@ -92,43 +93,42 @@ hdfs==2.7.3
 httpcore==1.0.9
 httplib2==0.22.0
 httpx==0.28.1
-hypothesis==6.135.20
+hypothesis==6.138.3
 idna==3.10
 importlib_metadata==8.7.0
 iniconfig==2.1.0
 jaraco.classes==3.4.0
 jaraco.context==6.0.1
-jaraco.functools==4.2.1
+jaraco.functools==4.3.0
 jeepney==0.9.0
 Jinja2==3.1.6
 joblib==1.5.1
 jsonpickle==3.4.2
-jsonschema==4.24.0
+jsonschema==4.25.1
 jsonschema-specifications==2025.4.1
 keyring==25.6.0
 keyrings.google-artifactregistry-auth==1.1.2
 MarkupSafe==3.0.2
 milvus-lite==2.5.1
-mmh3==5.1.0
+mmh3==5.2.0
 mock==5.2.0
 more-itertools==10.7.0
-multidict==6.6.3
-mysql-connector-python==9.3.0
+multidict==6.6.4
 nltk==3.9.1
 numpy==2.0.2
 oauth2client==4.1.3
 objsize==0.7.1
-opentelemetry-api==1.34.1
-opentelemetry-sdk==1.34.1
-opentelemetry-semantic-conventions==0.55b1
-oracledb==3.2.0
-orjson==3.10.18
+opentelemetry-api==1.36.0
+opentelemetry-sdk==1.36.0
+opentelemetry-semantic-conventions==0.57b0
+oracledb==3.3.0
+orjson==3.11.2
 overrides==7.7.0
 packaging==25.0
 pandas==2.2.3
 parameterized==0.9.0
-pg8000==1.31.2
-pip==25.1.1
+pg8000==1.31.4
+pip==25.2
 pluggy==1.6.0
 propcache==0.3.2
 proto-plus==1.26.1
@@ -143,30 +143,30 @@ pydantic==2.11.7
 pydantic_core==2.33.2
 pydot==1.4.2
 PyHamcrest==2.1.0
-PyJWT==2.9.0
-pymilvus==2.5.11
-pymongo==4.13.2
-PyMySQL==1.1.1
+PyJWT==2.10.1
+pymilvus==2.5.15
+pymongo==4.14.1
+PyMySQL==1.1.2
 pyparsing==3.2.3
 pyproject_hooks==1.2.0
 pytest==7.4.4
 pytest-timeout==2.4.0
-pytest-xdist==3.7.0
+pytest-xdist==3.8.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.1.1
-python-tds==1.16.1
+python-tds==1.17.0
 pytz==2025.2
 PyYAML==6.0.2
-redis==5.3.0
+redis==5.3.1
 referencing==0.36.2
-regex==2024.11.6
-requests==2.32.4
+regex==2025.7.34
+requests==2.32.5
 requests-mock==1.12.1
-rpds-py==0.25.1
+rpds-py==0.27.0
 rsa==4.9.1
 scikit-learn==1.6.1
 scipy==1.13.1
-scramp==1.4.5
+scramp==1.4.6
 SecretStorage==3.3.3
 setuptools==80.9.0
 shapely==2.0.7
@@ -174,24 +174,24 @@ six==1.17.0
 sniffio==1.3.1
 sortedcontainers==2.4.0
 soupsieve==2.7
-SQLAlchemy==2.0.41
+SQLAlchemy==2.0.43
 sqlalchemy_pytds==1.0.2
 sqlparse==0.5.3
 tenacity==8.5.0
-testcontainers==4.10.0
+testcontainers==4.12.0
 threadpoolctl==3.6.0
 tomli==2.2.1
 tqdm==4.67.1
 typing-inspection==0.4.1
-typing_extensions==4.14.0
+typing_extensions==4.14.1
 tzdata==2025.2
-ujson==5.10.0
+ujson==5.11.0
 uritemplate==4.2.0
 urllib3==2.5.0
 virtualenv-clone==0.5.7
 websockets==15.0.1
 wheel==0.45.1
-wrapt==1.17.2
+wrapt==1.17.3
 yarl==1.20.1
 zipp==3.23.0
-zstandard==0.23.0
+zstandard==0.24.0
diff --git a/sdks/python/container/run_generate_requirements.sh b/sdks/python/container/run_generate_requirements.sh
index 02ff9d2ccd6d..de14cbff2d50 100755
--- a/sdks/python/container/run_generate_requirements.sh
+++ b/sdks/python/container/run_generate_requirements.sh
@@ -39,11 +39,12 @@ fi
 PY_VERSION=$1
 SDK_TARBALL=$2
 REQUIREMENTS_FILE_NAME=$3
-EXTRAS=$4
+BASE_PATH=$4
+EXTRAS=$5
 # Use the PIP_EXTRA_OPTIONS environment variable to pass additional flags to the pip install command.
 # For example, you can include the --pre flag in $PIP_EXTRA_OPTIONS to download pre-release versions of packages.
 # Note that you can modify the behavior of the pip install command in this script by passing in your own $PIP_EXTRA_OPTIONS.
-PIP_EXTRA_OPTIONS=$5
+PIP_EXTRA_OPTIONS=$6
 
 if ! python"$PY_VERSION" --version > /dev/null 2>&1 ; then
   echo "Please install a python${PY_VERSION} interpreter. See s.apache.org/beam-python-dev-wiki for Python installation tips."
@@ -59,6 +60,10 @@ if [ -z "$REQUIREMENTS_FILE_NAME" ]; then
   REQUIREMENTS_FILE_NAME="base_image_requirements.txt"
 fi
 
+if [ -z "$BASE_PATH" ]; then
+  BASE_PATH="container"
+fi
+
 if [ -z "$EXTRAS" ]; then
   EXTRAS="[gcp,dataframe,test]"
 fi
@@ -85,7 +90,7 @@ echo "Installed dependencies:"
 pip freeze --all
 
 PY_IMAGE="py${PY_VERSION//.}"
-REQUIREMENTS_FILE=$PWD/sdks/python/container/$PY_IMAGE/$REQUIREMENTS_FILE_NAME
+REQUIREMENTS_FILE=$PWD/sdks/python/$BASE_PATH/$PY_IMAGE/$REQUIREMENTS_FILE_NAME
 cat <<EOT > "$REQUIREMENTS_FILE"
 #    Licensed to the Apache Software Foundation (ASF) under one or more
 #    contributor license agreements.  See the NOTICE file distributed with
diff --git a/sdks/python/gen_managed_doc.py b/sdks/python/gen_managed_doc.py
index 83f12c120c44..fa467d1ccf04 100644
--- a/sdks/python/gen_managed_doc.py
+++ b/sdks/python/gen_managed_doc.py
@@ -40,6 +40,9 @@
 aliases: [built-in]
 ---
 <!--
+DO NOT UPDATE THIS FILE. It is generated by .github/workflows/build_release_candidate.yml
+-->
+<!--
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
diff --git a/sdks/python/pytest.ini b/sdks/python/pytest.ini
index 2b53441927d9..3eee1a5c0e80 100644
--- a/sdks/python/pytest.ini
+++ b/sdks/python/pytest.ini
@@ -70,6 +70,8 @@ markers =
     uses_mock_api: tests that uses the mock API cluster.
     uses_feast: tests that uses feast in some way
     gemini_postcommit: gemini postcommits that need additional deps.
+    require_docker_in_docker: tests that require running Docker inside Docker (Docker-in-Docker), which is not supported on Beam’s self-hosted runners. Context: https://github.com/apache/beam/pull/35585
+    uses_dill: tests that require dill pickle library.
 
 # Default timeout intended for unit tests.
 # If certain tests need a different value, please see the docs on how to
diff --git a/sdks/python/scripts/run_pytest.sh b/sdks/python/scripts/run_pytest.sh
index 84e752838105..e016907cc1a8 100755
--- a/sdks/python/scripts/run_pytest.sh
+++ b/sdks/python/scripts/run_pytest.sh
@@ -25,27 +25,120 @@
 # $2 - additional arguments not parsed by tox (typically module names or
 #   '-k keyword')
 # $3 - optional arguments to pytest
+#!/bin/bash
 
 envname=${1?First argument required: suite base name}
 posargs=$2
 pytest_args=$3
 
-if [[ $pytest_args =~ "-m" ]] || [[ $posargs =~ "-m" ]]; then
-  echo "$0 cannot be called with -m as it interferes with 'no_xdist' logic, see BEAM-12985."
-  exit 1
+# strip leading/trailing quotes from posargs because it can get double quoted as
+# its passed through.
+if [[ $posargs == '"'*'"' ]]; then
+  # If wrapped in double quotes, remove them
+  posargs="${posargs:1:${#posargs}-2}"
+elif [[ $posargs == "'"*"'" ]]; then
+  # If wrapped in single quotes, remove them.
+  posargs="${posargs:1:${#posargs}-2}"
 fi
-
-# strip leading/trailing quotes from posargs because it can get double quoted as its passed through.
-posargs=$(sed -e 's/^"//' -e 's/"$//' -e "s/'$//" -e "s/^'//" <<<$posargs)
 echo "pytest_args: $pytest_args"
 echo "posargs: $posargs"
 
-# Run with pytest-xdist and without.
+# Define the regex for extracting the -m argument value
+marker_regex="-m\s+('[^']+'|\"[^\"]+\"|\([^)]+\)|[^ ]+)"
+
+# Initialize the user_marker variable.
+user_marker=""
+
+# Define regex pattern for quoted strings
+quotes_regex="^[\"\'](.*)[\"\']$"
+
+# Extract the user markers.
+if [[ $posargs =~ $marker_regex ]]; then
+  # Get the full match including -m and the marker.
+  full_match="${BASH_REMATCH[0]}"
+
+  # Get the marker with quotes (this is the first capture group).
+  quoted_marker="${BASH_REMATCH[1]}"
+
+  # Remove any quotes around the marker.
+  if [[ $quoted_marker =~ $quotes_regex ]]; then
+    user_marker="${BASH_REMATCH[1]}"
+  else
+    user_marker="$quoted_marker"
+  fi
+
+  # Remove the entire -m marker portion from posargs.
+  posargs="${posargs/$full_match/}"
+fi
+
+# Combine user-provided marker with script's internal logic.
+marker_for_parallel_tests="not no_xdist"
+marker_for_sequential_tests="no_xdist"
+
+if [[ -n $user_marker ]]; then
+  # Combine user marker with internal markers.
+  marker_for_parallel_tests="$user_marker and ($marker_for_parallel_tests)"
+  marker_for_sequential_tests="$user_marker and ($marker_for_sequential_tests)"
+fi
+
+# Parse posargs to separate pytest options from test paths.
+options=""
+test_paths=""
+
+# On Windows, convert backslashes to forward slashes
+posargs=${posargs//\\//}
+
+# Safely split the posargs string into individual arguments.
+eval "set -- $posargs"
+
+# Iterate through arguments.
+while [[ $# -gt 0 ]]; do
+  arg="$1"
+  shift
+
+  # If argument starts with dash, it's an option.
+  if [[ "$arg" == -* ]]; then
+    options+=" $arg"
+
+    # Check if there's a next argument and it doesn't start with a dash.
+    # This assumes it's a value for the current option.
+    if [[ $# -gt 0 && "$1" != -* ]]; then
+      # Get the next argument.
+      next_arg="$1"
+
+      # Check if it's quoted and remove quotes if needed.
+      if [[ $next_arg =~ $quotes_regex ]]; then
+        # Extract the content inside quotes.
+        next_arg="${BASH_REMATCH[1]}"
+      fi
+
+      # Add the unquoted value to options.
+      options+=" $next_arg"
+      shift
+    fi
+  else
+    # Otherwise it's a test path.
+    test_paths+=" $arg"
+  fi
+done
+
+# Construct the final pytest command arguments.
+pyargs_section=""
+if [[ -n "$test_paths" ]]; then
+  pyargs_section="--pyargs $test_paths"
+fi
+pytest_command_args="$options $pyargs_section"
+
+# Run tests in parallel.
+echo "Running parallel tests with: pytest -m \"$marker_for_parallel_tests\" $pytest_command_args"
 pytest -v -rs -o junit_suite_name=${envname} \
-  --junitxml=pytest_${envname}.xml -m 'not no_xdist' -n 6 --import-mode=importlib ${pytest_args} --pyargs ${posargs}
+  --junitxml=pytest_${envname}.xml -m "$marker_for_parallel_tests" -n 6 --import-mode=importlib ${pytest_args} ${pytest_command_args}
 status1=$?
+
+# Run tests sequentially.
+echo "Running sequential tests with: pytest -m \"$marker_for_sequential_tests\" $pytest_command_args"
 pytest -v -rs -o junit_suite_name=${envname}_no_xdist \
-  --junitxml=pytest_${envname}_no_xdist.xml -m 'no_xdist' --import-mode=importlib ${pytest_args} --pyargs ${posargs}
+  --junitxml=pytest_${envname}_no_xdist.xml -m "$marker_for_sequential_tests" --import-mode=importlib ${pytest_args} ${pytest_command_args}
 status2=$?
 
 # Exit with error if no tests were run in either suite (status code 5).
@@ -59,4 +152,4 @@ if [[ $status1 != 0 && $status1 != 5 ]]; then
 fi
 if [[ $status2 != 0 && $status2 != 5 ]]; then
   exit $status2
-fi
+fi
\ No newline at end of file
diff --git a/sdks/python/setup.py b/sdks/python/setup.py
index fcb64c2d0260..c23d69225d52 100644
--- a/sdks/python/setup.py
+++ b/sdks/python/setup.py
@@ -160,6 +160,8 @@ def cythonize(*args, **kwargs):
     'pandas>=1.4.3,!=1.5.0,!=1.5.1,<2.3',
 ]
 
+milvus_dependency = ['pymilvus>=2.5.10,<3.0.0']
+
 
 def find_by_ext(root_dir, ext):
   for root, _, files in os.walk(root_dir):
@@ -357,13 +359,8 @@ def get_portability_package_data():
       ext_modules=extensions,
       install_requires=[
           'crcmod>=1.7,<2.0',
+          'cryptography>=39.0.0,<48.0.0',
           'orjson>=3.9.7,<4',
-          # Dill doesn't have forwards-compatibility guarantees within minor
-          # version. Pickles created with a new version of dill may not unpickle
-          # using older version of dill. It is best to use the same version of
-          # dill on client and server, therefore list of allowed versions is
-          # very narrow. See: https://github.com/uqfoundation/dill/issues/341.
-          'dill>=0.3.1.1,<0.3.2',
           'fastavro>=0.23.6,<2',
           'fasteners>=0.3,<1.0',
           # TODO(https://github.com/grpc/grpc/issues/37710): Unpin grpc
@@ -390,7 +387,7 @@ def get_portability_package_data():
           #
           # 3. Exclude protobuf 4 versions that leak memory, see:
           # https://github.com/apache/beam/issues/28246
-          'protobuf>=3.20.3,<6.0.0.dev0,!=4.0.*,!=4.21.*,!=4.22.0,!=4.23.*,!=4.24.*',  # pylint: disable=line-too-long
+          'protobuf>=3.20.3,<7.0.0.dev0,!=4.0.*,!=4.21.*,!=4.22.0,!=4.23.*,!=4.24.*',  # pylint: disable=line-too-long
           'pydot>=1.2.0,<2',
           'python-dateutil>=2.8.0,<3',
           'pytz>=2018.3',
@@ -401,7 +398,7 @@ def get_portability_package_data():
           'typing-extensions>=3.7.0',
           'zstandard>=0.18.0,<1',
           'pyyaml>=3.12,<7.0.0',
-          'pymilvus>=2.5.10,<3.0.0',
+          'beartype>=0.21.0,<0.22.0',
           # Dynamic dependencies must be specified in a separate list, otherwise
           # Dependabot won't be able to parse the main list. Any dynamic
           # dependencies will not receive updates from Dependabot.
@@ -409,6 +406,15 @@ def get_portability_package_data():
       python_requires=python_requires,
       # BEAM-8840: Do NOT use tests_require or setup_requires.
       extras_require={
+          'dill': [
+            # Dill doesn't have forwards-compatibility guarantees within minor
+            # version. Pickles created with a new version of dill may not
+            # unpickle using older version of dill. It is best to use the same
+            # version of dill on client and server, therefore list of allowed
+            # versions is very narrow.
+            # See: https://github.com/uqfoundation/dill/issues/341.
+            'dill>=0.3.1.1,<0.3.2',
+          ],
           'docs': [
               'jinja2>=3.0,<3.2',
               'Sphinx>=7.0.0,<8.0',
@@ -442,15 +448,16 @@ def get_portability_package_data():
               'cryptography>=41.0.2',
               'hypothesis>5.0.0,<7.0.0',
               'virtualenv-clone>=0.5,<1.0',
-              'mysql-connector-python>=9.3.0',
               'python-tds>=1.16.1',
               'sqlalchemy-pytds>=1.0.2',
+              'pg8000>=1.31.1',
+              "PyMySQL>=1.1.0",
               'oracledb>=3.1.1'
-          ],
+          ] + milvus_dependency,
           'gcp': [
-              'cachetools>=3.1.0,<6',
+              'cachetools>=3.1.0,<7',
               'google-api-core>=2.0.0,<3',
-              'google-apitools>=0.5.31,<0.5.32; python_version <= "3.12"',
+              'google-apitools>=0.5.31,<0.5.32; python_version < "3.13"',
               'google-apitools>=0.5.32,<0.5.33; python_version >= "3.13"',
               # NOTE: Maintainers, please do not require google-auth>=2.x.x
               # Until this issue is closed
@@ -470,10 +477,15 @@ def get_portability_package_data():
               # GCP Packages required by ML functionality
               'google-cloud-dlp>=3.0.0,<4',
               'google-cloud-language>=2.0,<3',
+              'google-cloud-secret-manager>=2.0,<3',
               'google-cloud-videointelligence>=2.0,<3',
               'google-cloud-vision>=2,<4',
               'google-cloud-recommendations-ai>=0.1.0,<0.11.0',
               'google-cloud-aiplatform>=1.26.0, < 2.0',
+              'cloud-sql-python-connector>=1.18.2,<2.0.0',
+              'python-tds>=1.16.1',
+              'pg8000>=1.31.1',
+              "PyMySQL>=1.1.0",
               # Authentication for Google Artifact Registry when using
               # --extra-index-url or --index-url in requirements.txt in
               # Dataflow, which allows installing python packages from private
@@ -518,6 +530,9 @@ def get_portability_package_data():
               'pyod',
               'tensorflow',
               'tensorflow-hub',
+              # tensorflow-transform requires dill, but doesn't set dill as a
+              # hard requirement in setup.py.
+              'dill',
               'tensorflow-transform',
               'tf2onnx',
               'torch',
@@ -574,11 +589,15 @@ def get_portability_package_data():
           'torch': ['torch>=1.9.0,<2.8.0'],
           'tensorflow': ['tensorflow>=2.12rc1,<2.17'],
           'transformers': [
-              'transformers>=4.28.0,<4.49.0',
+              'transformers>=4.28.0,<4.56.0',
               'tensorflow>=2.12.0',
               'torch>=1.9.0'
           ],
-          'tft': ['tensorflow_transform>=1.14.0,<1.15.0'],
+          'tft': [
+            'tensorflow_transform>=1.14.0,<1.15.0'
+            # tensorflow-transform requires dill, but doesn't set dill as a
+            # hard requirement in setup.py.
+            , 'dill'],
           'onnx': [
               'onnxruntime==1.13.1',
               'torch==1.13.1',
@@ -588,7 +607,8 @@ def get_portability_package_data():
               'transformers==4.25.1'
           ],
           'xgboost': ['xgboost>=1.6.0,<2.1.3', 'datatable==1.0.0'],
-          'tensorflow-hub': ['tensorflow-hub>=0.14.0,<0.16.0']
+          'tensorflow-hub': ['tensorflow-hub>=0.14.0,<0.16.0'],
+          'milvus': milvus_dependency
       },
       zip_safe=False,
       # PyPI package information.
diff --git a/sdks/python/test-suites/containers/tensorrt_runinference/README.md b/sdks/python/test-suites/containers/tensorrt_runinference/README.md
index a9dd8d8d71e6..99fbf83cbd74 100644
--- a/sdks/python/test-suites/containers/tensorrt_runinference/README.md
+++ b/sdks/python/test-suites/containers/tensorrt_runinference/README.md
@@ -19,6 +19,6 @@
 
 # TensorRT Dockerfile for Beam
 
-This directory contains the Dockerfiles required to run Beam piplines that use TensorRT.
+This directory contains the Dockerfiles required to run Beam pipelines that use TensorRT.
 
 To build the image, run `docker build -f tensor_rt.dockerfile -t us.gcr.io/apache-beam-testing/python-postcommit-it/tensor_rt:latest .`
diff --git a/sdks/python/test-suites/dataflow/common.gradle b/sdks/python/test-suites/dataflow/common.gradle
index 7fc180fe1359..6a0777bd667c 100644
--- a/sdks/python/test-suites/dataflow/common.gradle
+++ b/sdks/python/test-suites/dataflow/common.gradle
@@ -459,7 +459,7 @@ def vllmTests = tasks.create("vllmTests") {
     // TODO(https://github.com/apache/beam/issues/22651): Build docker image for VLLM tests during Run time.
     // This would also enable to use wheel "--sdk_location" as other tasks, and eliminate distTarBall dependency
     // declaration for this project.
-    // Right now, this is built from https://github.com/apache/beam/blob/master/sdks/python/apache_beam/ml/inference/test_resources/vllm.dockerfile
+    // Right now, this is built from https://github.com/apache/beam/blob/master/sdks/python/apache_beam/ml/inference/test_resources/vllm.dockerfile.old
     "sdk_container_image": "us.gcr.io/apache-beam-testing/python-postcommit-it/vllm:latest",
     "sdk_location": files(configurations.distTarBall.files).singleFile,
     "project": "apache-beam-testing",
diff --git a/sdks/python/test-suites/portable/common.gradle b/sdks/python/test-suites/portable/common.gradle
index cc57a5942d2d..f325582cbf54 100644
--- a/sdks/python/test-suites/portable/common.gradle
+++ b/sdks/python/test-suites/portable/common.gradle
@@ -96,7 +96,7 @@ tasks.register("portableLocalRunnerJuliaSetWithSetupPy") {
           && python juliaset_main.py \\
               --runner=PortableRunner \\
               --job_endpoint=embed \\
-              --setup_file=./setup.py \\
+              --requirements_file=./requirements.txt \\
               --coordinate_output=/tmp/juliaset \\
               --grid_size=1
           """
@@ -112,7 +112,10 @@ def createProcessWorker = tasks.register("createProcessWorker") {
   if (Os.isFamily(Os.FAMILY_MAC))
     osType = 'darwin'
   def workerScript = "${project(":sdks:python:container:").buildDir.absolutePath}/target/launcher/${osType}_amd64/boot"
-  def sdkWorkerFileCode = "sh -c \"pip=`which pip` . ${envdir}/bin/activate && ${workerScript} \$* \""
+  def sdkWorkerFileCode = """#!/bin/sh
+. ${envdir}/bin/activate
+exec ${workerScript} "\"\$@\""
+"""
   outputs.file sdkWorkerFile
   doLast {
     sdkWorkerFile.write sdkWorkerFileCode
diff --git a/sdks/python/test-suites/tox/common.gradle b/sdks/python/test-suites/tox/common.gradle
index 75a12cdcf4cb..ac5dc57d8a55 100644
--- a/sdks/python/test-suites/tox/common.gradle
+++ b/sdks/python/test-suites/tox/common.gradle
@@ -29,6 +29,9 @@ test.dependsOn "testPy${pythonVersionSuffix}Cloud"
 toxTask "testPy${pythonVersionSuffix}ML", "py${pythonVersionSuffix}-ml", "${posargs}"
 test.dependsOn "testPy${pythonVersionSuffix}ML"
 
+toxTask "testPy${pythonVersionSuffix}Dill", "py${pythonVersionSuffix}-dill", "${posargs}"
+test.dependsOn "testPy${pythonVersionSuffix}Dill"
+
 // toxTask "testPy${pythonVersionSuffix}Dask", "py${pythonVersionSuffix}-dask", "${posargs}"
 // test.dependsOn "testPy${pythonVersionSuffix}Dask"
 project.tasks.register("preCommitPy${pythonVersionSuffix}") {
diff --git a/sdks/python/tox.ini b/sdks/python/tox.ini
index c6aadaeaae6a..9e428ba251a5 100644
--- a/sdks/python/tox.ini
+++ b/sdks/python/tox.ini
@@ -31,7 +31,7 @@ select = E3
 # https://github.com/apache/beam/issues/25668
 pip_pre = True
 # allow apps that support color to use it.
-passenv=TERM,CLOUDSDK_CONFIG
+passenv=TERM,CLOUDSDK_CONFIG,DOCKER_*,TESTCONTAINERS_*,TC_*,ALLOYDB_PASSWORD
 # Set [] options for pip installation of apache-beam tarball.
 extras = test,dataframe
 # Don't warn that these commands aren't installed.
@@ -108,6 +108,7 @@ commands =
 deps =
   pip==25.0.1
   accelerate>=1.6.0
+  onnx<1.19.0
 setenv =
 extras = test,gcp,dataframe,ml_test
 commands =
@@ -147,11 +148,18 @@ list_dependencies_command = {envbindir}/python.exe {envbindir}/pip.exe freeze
 [testenv:py39-cloudcoverage]
 deps =
   pytest-cov==3.0.0
+
+platform = linux
+passenv = GIT_*,BUILD_*,ghprb*,CHANGE_ID,BRANCH_NAME,JENKINS_*,CODECOV_*,GITHUB_*,DOCKER_*,TESTCONTAINERS_*,TC_*
+
 # Don't set TMPDIR to avoid "AF_UNIX path too long" errors in certain tests.
 setenv =
   PYTHONPATH = {toxinidir}
-platform = linux
-passenv = GIT_*,BUILD_*,ghprb*,CHANGE_ID,BRANCH_NAME,JENKINS_*,CODECOV_*,GITHUB_*
+  DOCKER_HOST = {env:DOCKER_HOST}
+  TC_TIMEOUT = {env:TC_TIMEOUT:120}
+  TC_MAX_TRIES = {env:TC_MAX_TRIES:120}
+  TC_SLEEP_TIME = {env:TC_SLEEP_TIME:1}
+
 # NOTE: we could add ml_test to increase the collected code coverage metrics, but it would make the suite slower.
 extras = test,gcp,interactive,dataframe,aws
 commands =
@@ -502,9 +510,9 @@ deps =
   428: torch>=1.9.0,<1.14.0
   447: transformers>=4.47.0,<4.48.0
   447: torch>=1.9.0,<1.14.0
-  448: transformers>=4.48.0,<4.49.0
-  448: torch>=2.0.0,<2.1.0
-  latest: transformers>=4.48.0
+  455: transformers>=4.55.0,<4.56.0
+  455: torch>=2.0.0,<2.1.0
+  latest: transformers>=4.55.0
   latest: torch>=2.0.0
   latest: accelerate>=1.6.0
   tensorflow==2.12.0
@@ -563,3 +571,11 @@ commands =
   /bin/sh -c "pip freeze | grep -E tensorflow"
   # Allow exit code 5 (no tests run) so that we can run this command safely on arbitrary subdirectories.
   bash {toxinidir}/scripts/run_pytest.sh {envname} 'apache_beam/ml/transforms/embeddings'
+
+[testenv:py{310,312}-dill]
+extras = test,dill
+commands =
+  # Log dill version for debugging
+  /bin/sh -c "pip freeze | grep -E dill"
+  # Run all dill-specific tests
+  /bin/sh -c 'pytest -o junit_suite_name={envname} --junitxml=pytest_{envname}.xml -n 1 -m uses_dill {posargs}; ret=$?; [ $ret = 5 ] && exit 0 || exit $ret'
diff --git a/sdks/typescript/.mocharc.json b/sdks/typescript/.mocharc.json
new file mode 100644
index 000000000000..1af5707ec0bc
--- /dev/null
+++ b/sdks/typescript/.mocharc.json
@@ -0,0 +1,6 @@
+{
+  "reporter": "cypress-multi-reporters",
+  "reporter-option": [
+    "configFile=reporterConfig.js"
+  ]
+}
diff --git a/sdks/typescript/develocity.config.js b/sdks/typescript/develocity.config.js
new file mode 100644
index 000000000000..386dfff3ad53
--- /dev/null
+++ b/sdks/typescript/develocity.config.js
@@ -0,0 +1,18 @@
+// Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+// use this file except in compliance with the License. You may obtain a copy of
+// the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+// License for the specific language governing permissions and limitations under
+// the License.
+
+module.exports = {
+    projectId: 'beam',
+    server: {
+        url: 'https://develocity.apache.org',
+    },
+}
diff --git a/sdks/typescript/package-lock.json b/sdks/typescript/package-lock.json
index fb6023480679..d62e3f968d2e 100644
--- a/sdks/typescript/package-lock.json
+++ b/sdks/typescript/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "apache-beam",
-  "version": "2.64.0-SNAPSHOT",
+  "version": "2.68.0-SNAPSHOT",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
     "": {
       "name": "apache-beam",
-      "version": "2.64.0-SNAPSHOT",
+      "version": "2.68.0-SNAPSHOT",
       "dependencies": {
         "@google-cloud/pubsub": "^2.19.4",
         "@grpc/grpc-js": "~1.4.6",
@@ -35,6 +35,7 @@
         "@typescript-eslint/eslint-plugin": "^5.24.0",
         "@typescript-eslint/parser": "^5.24.0",
         "codecov": "^3.8.3",
+        "cypress-multi-reporters": "^2.0.5",
         "eslint": "^8.15.0",
         "istanbul": "^0.4.5",
         "js-yaml": "^4.1.0",
@@ -1310,6 +1311,24 @@
         "node": ">= 8"
       }
     },
+    "node_modules/cypress-multi-reporters": {
+      "version": "2.0.5",
+      "resolved": "https://registry.npmjs.org/cypress-multi-reporters/-/cypress-multi-reporters-2.0.5.tgz",
+      "integrity": "sha512-5ReXlNE7C/9/rpDI3z0tAJbPXsTHK7P3ogvUtBntQlmctRQ+sSMts7dIQY5MTb0XfBSge3CuwvNvaoqtw90KSQ==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "debug": "^4.4.0",
+        "lodash": "^4.17.21",
+        "semver": "^7.6.3"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      },
+      "peerDependencies": {
+        "mocha": ">=3.1.2"
+      }
+    },
     "node_modules/date-fns": {
       "version": "2.28.0",
       "resolved": "https://registry.npmjs.org/date-fns/-/date-fns-2.28.0.tgz",
@@ -2781,6 +2800,13 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
+    "node_modules/lodash": {
+      "version": "4.17.21",
+      "resolved": "https://registry.npmjs.org/lodash/-/lodash-4.17.21.tgz",
+      "integrity": "sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==",
+      "dev": true,
+      "license": "MIT"
+    },
     "node_modules/lodash.camelcase": {
       "version": "4.3.0",
       "resolved": "https://registry.npmjs.org/lodash.camelcase/-/lodash.camelcase-4.3.0.tgz",
@@ -3596,13 +3622,11 @@
       ]
     },
     "node_modules/semver": {
-      "version": "7.3.7",
-      "resolved": "https://registry.npmjs.org/semver/-/semver-7.3.7.tgz",
-      "integrity": "sha512-QlYTucUYOews+WeEujDoEGziz4K6c47V/Bd+LjSSYcA94p+DmINdf7ncaUinThfvZyu13lN9OY1XDxt8C0Tw0g==",
+      "version": "7.7.2",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.2.tgz",
+      "integrity": "sha512-RF0Fw+rO5AMf9MAyaRXI4AV0Ulj5lMHqVxxdSgiVbixSCXoEmmX/jk0CuJw4+3SqroYO9VoUh+HcuJivvtJemA==",
       "dev": true,
-      "dependencies": {
-        "lru-cache": "^6.0.0"
-      },
+      "license": "ISC",
       "bin": {
         "semver": "bin/semver.js"
       },
@@ -5203,6 +5227,17 @@
         "which": "^2.0.1"
       }
     },
+    "cypress-multi-reporters": {
+      "version": "2.0.5",
+      "resolved": "https://registry.npmjs.org/cypress-multi-reporters/-/cypress-multi-reporters-2.0.5.tgz",
+      "integrity": "sha512-5ReXlNE7C/9/rpDI3z0tAJbPXsTHK7P3ogvUtBntQlmctRQ+sSMts7dIQY5MTb0XfBSge3CuwvNvaoqtw90KSQ==",
+      "dev": true,
+      "requires": {
+        "debug": "^4.4.0",
+        "lodash": "^4.17.21",
+        "semver": "^7.6.3"
+      }
+    },
     "date-fns": {
       "version": "2.28.0",
       "resolved": "https://registry.npmjs.org/date-fns/-/date-fns-2.28.0.tgz",
@@ -6303,6 +6338,12 @@
         "p-locate": "^5.0.0"
       }
     },
+    "lodash": {
+      "version": "4.17.21",
+      "resolved": "https://registry.npmjs.org/lodash/-/lodash-4.17.21.tgz",
+      "integrity": "sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==",
+      "dev": true
+    },
     "lodash.camelcase": {
       "version": "4.3.0",
       "resolved": "https://registry.npmjs.org/lodash.camelcase/-/lodash.camelcase-4.3.0.tgz",
@@ -6871,13 +6912,10 @@
       "integrity": "sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ=="
     },
     "semver": {
-      "version": "7.3.7",
-      "resolved": "https://registry.npmjs.org/semver/-/semver-7.3.7.tgz",
-      "integrity": "sha512-QlYTucUYOews+WeEujDoEGziz4K6c47V/Bd+LjSSYcA94p+DmINdf7ncaUinThfvZyu13lN9OY1XDxt8C0Tw0g==",
-      "dev": true,
-      "requires": {
-        "lru-cache": "^6.0.0"
-      }
+      "version": "7.7.2",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.2.tgz",
+      "integrity": "sha512-RF0Fw+rO5AMf9MAyaRXI4AV0Ulj5lMHqVxxdSgiVbixSCXoEmmX/jk0CuJw4+3SqroYO9VoUh+HcuJivvtJemA==",
+      "dev": true
     },
     "serialize-closures": {
       "version": "0.2.7",
diff --git a/sdks/typescript/package.json b/sdks/typescript/package.json
index fe1772d6abc2..1be090851ae0 100644
--- a/sdks/typescript/package.json
+++ b/sdks/typescript/package.json
@@ -1,12 +1,13 @@
 {
   "name": "apache-beam",
-  "version": "2.67.0-SNAPSHOT",
+  "version": "2.69.0-SNAPSHOT",
   "devDependencies": {
     "@google-cloud/bigquery": "^5.12.0",
     "@types/mocha": "^9.0.0",
     "@typescript-eslint/eslint-plugin": "^5.24.0",
     "@typescript-eslint/parser": "^5.24.0",
     "codecov": "^3.8.3",
+    "cypress-multi-reporters": "^2.0.5",
     "eslint": "^8.15.0",
     "istanbul": "^0.4.5",
     "js-yaml": "^4.1.0",
diff --git a/sdks/typescript/reporterConfig.js b/sdks/typescript/reporterConfig.js
new file mode 100644
index 000000000000..311b286d93f7
--- /dev/null
+++ b/sdks/typescript/reporterConfig.js
@@ -0,0 +1,17 @@
+// Licensed under the Apache License, Version 2.0 (the 'License'); you may not
+// use this file except in compliance with the License. You may obtain a copy of
+// the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT
+// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+// License for the specific language governing permissions and limitations under
+// the License.
+
+const develocityReporter = require.resolve('@gradle-tech/develocity-agent/mocha-reporter');
+
+module.exports = {
+  reporterEnabled: ['spec', develocityReporter].join(', '),
+}
diff --git a/settings.gradle.kts b/settings.gradle.kts
index 8a9be3358a65..c867e7ae2314 100644
--- a/settings.gradle.kts
+++ b/settings.gradle.kts
@@ -25,7 +25,7 @@ pluginManagement {
 
 plugins {
   id("com.gradle.develocity") version "3.19"
-  id("com.gradle.common-custom-user-data-gradle-plugin") version "2.2.1"
+  id("com.gradle.common-custom-user-data-gradle-plugin") version "2.4.0"
 }
 
 
@@ -180,6 +180,7 @@ include(":sdks:java:expansion-service:container")
 include(":sdks:java:expansion-service:app")
 include(":sdks:java:extensions:arrow")
 include(":sdks:java:extensions:avro")
+include("sdks:java:extensions:avro:vendored-test")
 include(":sdks:java:extensions:euphoria")
 include(":sdks:java:extensions:kryo")
 include(":sdks:java:extensions:google-cloud-platform-core")
@@ -275,6 +276,7 @@ include(":sdks:java:testing:load-tests")
 include(":sdks:java:testing:test-utils")
 include(":sdks:java:testing:tpcds")
 include(":sdks:java:testing:watermarks")
+include(":sdks:java:testing:junit")
 include(":sdks:java:transform-service")
 include(":sdks:java:transform-service:app")
 include(":sdks:java:transform-service:launcher")
@@ -294,6 +296,12 @@ include(":sdks:python:container:distroless:py310")
 include(":sdks:python:container:distroless:py311")
 include(":sdks:python:container:distroless:py312")
 include(":sdks:python:container:distroless:py313")
+include(":sdks:python:container:ml")
+include(":sdks:python:container:ml:py39")
+include(":sdks:python:container:ml:py310")
+include(":sdks:python:container:ml:py311")
+include(":sdks:python:container:ml:py312")
+include(":sdks:python:container:ml:py313")
 include(":sdks:python:expansion-service-container")
 include(":sdks:python:test-suites:dataflow")
 include(":sdks:python:test-suites:dataflow:py39")
@@ -323,7 +331,7 @@ include(":sdks:python:test-suites:xlang")
 include(":sdks:typescript")
 include(":sdks:typescript:container")
 include(":vendor:grpc-1_69_0")
-include(":vendor:calcite-1_28_0")
+include(":vendor:calcite-1_40_0")
 include(":vendor:guava-32_1_2-jre")
 include(":website")
 include(":runners:google-cloud-dataflow-java:worker")
@@ -370,3 +378,7 @@ include("sdks:java:io:iceberg:bqms")
 findProject(":sdks:java:io:iceberg:bqms")?.name = "bqms"
 include("it:clickhouse")
 findProject(":it:clickhouse")?.name = "clickhouse"
+include("sdks:java:extensions:sql:iceberg")
+findProject(":sdks:java:extensions:sql:iceberg")?.name = "iceberg"
+include("examples:java:iceberg")
+findProject(":examples:java:iceberg")?.name = "iceberg"
diff --git a/vendor/calcite-1_28_0/build.gradle b/vendor/calcite-1_40_0/build.gradle
similarity index 87%
rename from vendor/calcite-1_28_0/build.gradle
rename to vendor/calcite-1_40_0/build.gradle
index 576ae44751da..e130d0b903e2 100644
--- a/vendor/calcite-1_28_0/build.gradle
+++ b/vendor/calcite-1_40_0/build.gradle
@@ -30,15 +30,14 @@
 
 plugins { id 'org.apache.beam.vendor-java' }
 
-description = "Apache Beam :: Vendored Dependencies :: Calcite 1.28.0"
+description = "Apache Beam :: Vendored Dependencies :: Calcite 1.40.0"
 
 group = "org.apache.beam"
-version = "0.2"
+version = "0.1"
 
-def calcite_version = "1.28.0"
-def avatica_version = "1.19.0"
-def protobuf_version = "3.19.2"
-def prefix = "org.apache.beam.vendor.calcite.v1_28_0"
+def calcite_version = "1.40.0"
+def avatica_version = "1.26.0"
+def prefix = "org.apache.beam.vendor.calcite.v1_40_0"
 
 List<String> packagesToRelocate = [
         "com.esri",
@@ -53,9 +52,14 @@ List<String> packagesToRelocate = [
         "net.minidev",
         "org.apache.calcite",
         "org.apache.commons",
+        "org.apache.hc.core5",
+        "org.apache.hc.client5",
         "org.apache.http",
         "org.apiguardian.api",
         "org.codehaus",
+        "org.json.simple",
+        "org.locationtech.jts",
+        "org.locationtech.proj4j",
         "org.objectweb",
         "org.pentaho",
         "org.yaml",
@@ -66,12 +70,6 @@ vendorJava(
                 "org.apache.calcite:calcite-core:$calcite_version",
                 "org.apache.calcite:calcite-linq4j:$calcite_version",
                 "org.apache.calcite.avatica:avatica-core:$avatica_version",
-
-                // BEAM-13616: Override the version of protobuf to patch a security vulnerability.
-                // This override can be removed once we upgrade to a newer version of calcite that
-                // depends on protobuf >= 3.19.2.
-                "com.google.protobuf:protobuf-java:$protobuf_version",
-                "com.google.protobuf:protobuf-java-util:$protobuf_version",
         ],
         runtimeDependencies: [
                 library.java.slf4j_api,
@@ -84,6 +82,7 @@ vendorJava(
                 "com/google/errorprone/**",
                 "com/google/j2objc/annotations/**",
                 "javax/annotation/**",
+                "javax/transaction/**",
                 "org/checkerframework/**",
                 "org/jmlspecs/**",
                 "org/intellij/lang/annotations/**",
@@ -116,9 +115,12 @@ vendorJava(
                 // Optional kotlin code
                 "kotlin/**",
 
+                // maven poms
+                "META-INF/maven/**",
+
                 "**/module-info.class",
         ],
         groupId: group,
-        artifactId: "beam-vendor-calcite-1_28_0",
+        artifactId: "beam-vendor-calcite-1_40_0",
         version: version,
 )
diff --git a/website/Dockerfile b/website/Dockerfile
index e40724ea1811..61ec7921703c 100644
--- a/website/Dockerfile
+++ b/website/Dockerfile
@@ -58,13 +58,18 @@ RUN npm update -g npm
 RUN npm install postcss postcss-cli autoprefixer 
 
 # Install yarn
-RUN curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | apt-key add - \
-    && echo "deb https://dl.yarnpkg.com/debian/ stable main" | tee /etc/apt/sources.list.d/yarn.list \
-    && apt-get update \
-    && apt-get install -y --no-install-recommends yarn \
-    && apt-get autoremove -yqq --purge \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
+RUN set -eux; \
+    apt-get update; \
+    apt-get install -y --no-install-recommends curl gnupg ca-certificates; \
+    mkdir -p /etc/apt/keyrings; \
+    curl -fsSL https://dl.yarnpkg.com/debian/pubkey.gpg \
+      | gpg --dearmor -o /etc/apt/keyrings/yarn.gpg; \
+    echo "deb [signed-by=/etc/apt/keyrings/yarn.gpg] https://dl.yarnpkg.com/debian stable main" \
+      > /etc/apt/sources.list.d/yarn.list; \
+    apt-get update; \
+    apt-get install -y --no-install-recommends yarn; \
+    apt-get clean; \
+    rm -rf /var/lib/apt/lists/*
 
 # Install hugo extended version v0.117.0
 RUN HUGOHOME="$(mktemp -d)" \
diff --git a/website/www/site/config.toml b/website/www/site/config.toml
index b9adc765703d..652994ed6d7b 100644
--- a/website/www/site/config.toml
+++ b/website/www/site/config.toml
@@ -104,7 +104,7 @@ github_project_repo = "https://github.com/apache/beam"
 
 [params]
 description = "Apache Beam is an open source, unified model and set of language-specific SDKs for defining and executing data processing workflows, and also data ingestion and integration flows, supporting Enterprise Integration Patterns (EIPs) and Domain Specific Languages (DSLs). Dataflow pipelines simplify the mechanics of large-scale batch and streaming data processing and can run on a number of runtimes like Apache Flink, Apache Spark, and Google Cloud Dataflow (a cloud service). Beam also brings DSL in different languages, allowing users to easily implement their data integration processes."
-release_latest = "2.66.0"
+release_latest = "2.68.0"
 # The repository and branch where the files live in Github or Colab. This is used
 # to serve and stage from your local branch, but publish to the master branch.
 #   e.g. https://github.com/{{< param branch_repo >}}/path/to/notebook.ipynb
diff --git a/website/www/site/content/en/blog/beam-2.67.0.md b/website/www/site/content/en/blog/beam-2.67.0.md
new file mode 100644
index 000000000000..766e321fa37f
--- /dev/null
+++ b/website/www/site/content/en/blog/beam-2.67.0.md
@@ -0,0 +1,73 @@
+---
+title:  "Apache Beam 2.67.0"
+date:   2025-08-12 15:00:00 -0500
+categories:
+  - blog
+  - release
+authors:
+  - vterentev
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+We are happy to present the new 2.67.0 release of Beam.
+This release includes both improvements and new functionality.
+See the [download page](/get-started/downloads/#2670-2025-08-12) for this release.
+
+<!--more-->
+
+For more information on changes in 2.67.0, check out the [detailed release notes](https://github.com/apache/beam/milestone/35?closed=1).
+
+## Highlights
+
+### I/Os
+
+* Debezium IO upgraded to 3.1.1 requires Java 17 (Java) ([#34747](https://github.com/apache/beam/issues/34747)).
+* Add support for streaming writes in IOBase (Python)
+* Implement support for streaming writes in FileBasedSink (Python)
+* Expose support for streaming writes in TextIO (Python)
+
+### New Features / Improvements
+
+* Added support for Processing time Timer in the Spark Classic runner ([#33633](https://github.com/apache/beam/issues/33633)).
+* Add pip-based install support for JupyterLab Sidepanel extension ([#35397](https://github.com/apache/beam/issues/35397)).
+* [IcebergIO] Create tables with a specified table properties ([#35496](https://github.com/apache/beam/pull/35496))
+* Add support for comma-separated options in Python SDK (Python) ([#35580](https://github.com/apache/beam/pull/35580)).
+  Python SDK now supports comma-separated values for experiments and dataflow_service_options,
+  matching Java SDK behavior while maintaining backward compatibility.
+* Milvus enrichment handler added (Python) ([#35216](https://github.com/apache/beam/pull/35216)).
+  Beam now supports Milvus enrichment handler capabilities for vector, keyword,
+  and hybrid search operations.
+* [Beam SQL] Add support for DATABASEs, with an implementation for Iceberg ([#35637](https://github.com/apache/beam/issues/35637))
+* Respect BatchSize and MaxBufferingDuration when using `JdbcIO.WriteWithResults`. Previously, these settings were ignored ([#35669](https://github.com/apache/beam/pull/35669)).
+
+### Breaking Changes
+
+* Go: The pubsubio.Read transform now accepts ReadOptions as a value type instead of a pointer, and requires exactly one of Topic or Subscription to be set (they are mutually exclusive). Additionally, the ReadOptions struct now includes a Topic field for specifying the topic directly, replacing the previous topic parameter in the Read function signature ([#35369](https://github.com/apache/beam/pull/35369)).
+* SQL: The `ParquetTable` external table provider has changed its handling of the `LOCATION` property. To read from a directory, the path must now end with a trailing slash (e.g., `LOCATION '/path/to/data/'`). Previously, a trailing slash was not required. This change was made to enable support for glob patterns and single-file paths ([#35582](https://github.com/apache/beam/pull/35582)).
+
+### Bugfixes
+
+* [YAML] Fixed handling of missing optional fields in JSON parsing ([#35179](https://github.com/apache/beam/issues/35179)).
+* [Python] Fix WriteToBigQuery transform using CopyJob does not work with WRITE_TRUNCATE write disposition ([#34247](https://github.com/apache/beam/issues/34247))
+* [Python] Fixed dicomio tags mismatch in integration tests ([#30760](https://github.com/apache/beam/issues/30760)).
+* [Java] Fixed spammy logging issues that affected versions 2.64.0 to 2.66.0.
+
+### Known Issues
+
+* ([#35666](https://github.com/apache/beam/issues/35666)). YAML Flatten incorrectly drops fields when input PCollections' schema are different. This issue exists for all versions since 2.52.0.
+
+## List of Contributors
+
+According to git shortlog, the following people contributed to the 2.67.0 release. Thank you to all contributors!
+
+Aditya Shukla, Ahmed Abualsaud, Arun Pandian, Boris Li, Chamikara Jayalath, Charles Nguyen, Chenzo, Danny McCormick, David Adeniji, Derrick Williams, Dmytro Tsyliuryk, Dustin Rhodes, Enrique Calderon, Gottipati Gautam, Hai Joey Tran, Hunor Portik, Jack McCluskey, Kenneth Knowles, Khorbaladze A., Marcio Sugar, Minh Son Nguyen, Mohamed Awnallah, Nathaniel Young, Nhon Dinh, Quentin Sommer, Rafael Raposo, Rakesh Kumar, Razvan Culea, Reuven Lax, Robert Bradshaw, Sam Whittle, Shunping Huang, Steven van Rossum, Talat UYARER, Tanu Sharma, Tarun Annapareddy, Tobi Kaymak, Tobias Kaymak, Valentyn Tymofieiev, Veronica Wasson, Vitaly Terentyev, XQ Hu, Yi Hu, akashorabek, arnavarora2004, changliiu, claudevdm, fozzie15, mvhensbergen, twosom
diff --git a/website/www/site/content/en/blog/beam-2.68.0.md b/website/www/site/content/en/blog/beam-2.68.0.md
new file mode 100644
index 000000000000..a634f9d0213a
--- /dev/null
+++ b/website/www/site/content/en/blog/beam-2.68.0.md
@@ -0,0 +1,83 @@
+---
+title:  "Apache Beam 2.68.0"
+date:   2025-09-22 15:00:00 -0500
+categories:
+  - blog
+  - release
+authors:
+  - vterentev
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+We are happy to present the new 2.68.0 release of Beam.
+This release includes both improvements and new functionality.
+See the [download page](/get-started/downloads/#2680-2025-09-??) for this release.
+
+<!--more-->
+
+For more information on changes in 2.68.0, check out the [detailed release notes](https://github.com/apache/beam/milestone/36?closed=1).
+
+## Highlights
+
+* [Python] Prism runner now enabled by default for most Python pipelines using the direct runner ([#34612](https://github.com/apache/beam/pull/34612)). This may break some tests, see https://github.com/apache/beam/pull/34612 for details on how to handle issues.
+
+### I/Os
+
+* Upgraded Iceberg dependency to 1.9.2 ([#35981](https://github.com/apache/beam/pull/35981))
+
+### New Features / Improvements
+
+* BigtableRead Connector for BeamYaml added with new Config Param ([#35696](https://github.com/apache/beam/pull/35696))
+* MongoDB Java driver upgraded from 3.12.11 to 5.5.0 with API refactoring and GridFS implementation updates (Java) ([#35946](https://github.com/apache/beam/pull/35946)).
+* Introduced a dedicated module for JUnit-based testing support: `sdks/java/testing/junit`, which provides `TestPipelineExtension` for JUnit 5 while maintaining backward compatibility with existing JUnit 4 `TestRule`-based tests (Java) ([#18733](https://github.com/apache/beam/issues/18733), [#35688](https://github.com/apache/beam/pull/35688)).
+  - To use JUnit 5 with Beam tests, add a test-scoped dependency on `org.apache.beam:beam-sdks-java-testing-junit`.
+* Google CloudSQL enrichment handler added (Python) ([#34398](https://github.com/apache/beam/pull/34398)).
+  Beam now supports data enrichment capabilities using SQL databases, with built-in support for:
+  - Managed PostgreSQL, MySQL, and Microsoft SQL Server instances on CloudSQL
+  - Unmanaged SQL database instances not hosted on CloudSQL (e.g., self-hosted or on-premises databases)
+* [Python] Added the `ReactiveThrottler` and `ThrottlingSignaler` classes to streamline throttling behavior in DoFns, expose throttling mechanisms for users ([#35984](https://github.com/apache/beam/pull/35984))
+* Added a pipeline option to specify the processing timeout for a single element by any PTransform (Java/Python/Go) ([#35174](https://github.com/apache/beam/issues/35174)).
+  - When specified, the SDK harness automatically restarts if an element takes too long to process. Beam runner may then retry processing of the same work item.
+  - Use the `--element_processing_timeout_minutes` option to reduce the chance of having stalled pipelines due to unexpected cases of slow processing, where slowness might not happen again if processing of the same element is retried.
+* (Python) Adding GCP Spanner Change Stream support for Python (apache_beam.io.gcp.spanner) ([#24103](https://github.com/apache/beam/issues/24103)).
+
+### Breaking Changes
+
+* Previously deprecated Beam ZetaSQL component has been removed ([#34423](https://github.com/apache/beam/issues/34423)).
+  ZetaSQL users could migrate to Calcite SQL with BigQuery dialect enabled.
+* Upgraded Beam vendored Calcite to 1.40.0 for Beam SQL ([#35483](https://github.com/apache/beam/issues/35483)), which
+  improves support for BigQuery and other SQL dialects. Note: Minor behavior changes are observed such as output
+  significant digits related to casting.
+* (Python) The deterministic fallback coder for complex types like NamedTuple, Enum, and dataclasses now uses cloudpickle instead of dill. If your pipeline is affected, you may see a warning like: "Using fallback deterministic coder for type X...". You can revert to the previous behavior by using the pipeline option `--update_compatibility_version=2.67.0` ([35725](https://github.com/apache/beam/pull/35725)). Report any pickling related issues to [#34903](https://github.com/apache/beam/issues/34903)
+* (Python) Prism runner now enabled by default for most Python pipelines using the direct runner ([#34612](https://github.com/apache/beam/pull/34612)). This may break some tests, see https://github.com/apache/beam/pull/34612 for details on how to handle issues.
+* Dropped Java 8 support for [IO expansion-service](https://central.sonatype.com/artifact/org.apache.beam/beam-sdks-java-io-expansion-service). Cross-language pipelines using this expansion service will need a Java11+ runtime ([#35981](https://github.com/apache/beam/pull/35981).
+
+### Deprecations
+
+* Python SDK native SpannerIO (apache_beam/io/gcp/experimental/spannerio) is deprecated. Use cross-language wrapper
+  (apache_beam/io/gcp/spanner) instead (Python) ([#35860](https://github.com/apache/beam/issues/35860)).
+* Samza runner is deprecated and scheduled for removal in Beam 3.0 ([#35448](https://github.com/apache/beam/issues/35448)).
+* Twister2 runner is deprecated and scheduled for removal in Beam 3.0 ([#35905](https://github.com/apache/beam/issues/35905))).
+
+### Bugfixes
+
+* (Python) Fixed Java YAML provider fails on Windows ([#35617](https://github.com/apache/beam/issues/35617)).
+* Fixed BigQueryIO creating temporary datasets in wrong project when temp_dataset is specified with a different project than the pipeline project. For some jobs, temporary datasets will now be created in the correct project (Python) ([#35813](https://github.com/apache/beam/issues/35813)).
+* (Go) Fix duplicates due to reads after blind writes to Bag State ([#35869](https://github.com/apache/beam/issues/35869)).
+  * Earlier Go SDK versions can avoid the issue by not reading in the same call after a blind write.
+
+## List of Contributors
+
+According to git shortlog, the following people contributed to the 2.68.0 release. Thank you to all contributors!
+
+Ahmed Abualsaud, Andrew Crites, Ashok Devireddy, Chamikara Jayalath, Charles Nguyen, Danny McCormick, Davda James, Derrick Williams, Diego Hernandez, Dip Patel, Dustin Rhodes, Enrique Calderon, Hai Joey Tran, Jack McCluskey, Kenneth Knowles, Keshav, Khorbaladze A., LEEKYE, Lanny Boarts, Mattie Fu, Minbo Bae, Mohamed Awnallah, Naireen Hussain, Nathaniel Young, Radosław Stankiewicz, Razvan Culea, Robert Bradshaw, Robert Burke, Sam Whittle, Shehab, Shingo Furuyama, Shunping Huang, Steven van Rossum, Suvrat Acharya, Svetak Sundhar, Tarun Annapareddy, Tom Stepp, Valentyn Tymofieiev, Vitaly Terentyev, XQ Hu, Yi Hu, apanich, arnavarora2004, claudevdm, flpablo, kristynsmith, shreyakhajanchi
diff --git a/website/www/site/content/en/blog/gsoc-25-infra.md b/website/www/site/content/en/blog/gsoc-25-infra.md
new file mode 100644
index 000000000000..3170062fae5b
--- /dev/null
+++ b/website/www/site/content/en/blog/gsoc-25-infra.md
@@ -0,0 +1,78 @@
+---
+title:  "Google Summer of Code 25 - Improving Apache Beam's Infrastructure"
+date:   2025-09-15 00:00:00 -0600
+categories:
+  - blog
+  - gsoc
+aliases:
+  - /blog/2025/09/15/gsoc-25-infra.html
+authors:
+  - ksobrenat32
+
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+I loved contributing to Apache Beam during Google Summer of Code 2025. I worked on improving the infrastructure of Apache Beam, which included enhancing the CI/CD pipelines, automating various tasks, and improving the overall developer experience.
+
+## Motivation
+
+Since I was in high school, I have been fascinated by computers, but when I discovered Open Source, I was amazed by the idea of people from all around the world collaborating to build software that anyone can use, just for the love of it. I started participating in open source communities, and I found it to be a great way to learn and grow as a developer.
+
+When I heard about Google Summer of Code, I saw it as an opportunity to take my open source contributions to the next level. The idea of working on a real-world project while being mentored by experienced developers sounded like an amazing opportunity. I heard about Apache Beam from another contributor and ex-GSoC participant, and I was immediately drawn to the project, specifically on the infrastructure side of things, as I have a strong interest in DevOps and automation.
+
+## The Challenge
+
+When searching for a project, I was told that Apache Beam's infrastructure had several areas that could be improved. I was excited because the ideas were focused on improving the developer experience, and creating tools that could benefit not only Beam's developers but also the wider open source community.
+
+There were four main challenges:
+
+1. Automating the cleanup of unused cloud resources to reduce costs and improve resource management.
+2. Implementing a system for managing permissions through Git, allowing for better tracking and auditing of changes.
+3. Creating a tool for rotating service account keys to enhance security.
+4. Developing a security monitoring system to detect and respond to potential threats.
+
+## The Solution
+
+I worked closely with my mentor to break down and define each challenge into manageable tasks, creating a plan for the summer. I started by taking a look at the current state of the infrastructure, after which I began working on each challenge one by one.
+
+1. **Automating the cleanup of unused cloud resources:** We noticed that some resources in the GCP project, especially Pub/Sub topics created for testing, were often forgotten, leading to unnecessary costs. Since the infrastructure is primarily for testing and development, there's no need to keep unused resources. I developed a Python script that identifies and removes stale Pub/Sub topics that have existed for too long. This tool is now scheduled to run periodically via a GitHub Actions workflow to keep the project tidy and cost-effective.
+
+2. **Implementing a system for managing permissions through Git:** This was more challenging, as it required a good understanding of both GCP IAM and the existing workflow. After some investigation, I learned that the current process was mostly manual and error-prone. The task involved creating a more automated and reliable system. This was achieved by using Terraform to define the desired state of IAM roles and permissions in code, which allows for better tracking and auditing of changes. This also included some custom roles, but that is still a work in progress.
+
+3. **Creating a tool for rotating service account keys:** Key rotation is a security practice that we don't always follow, but it is essential to ensure that service account keys are not compromised. I noticed that GCP had some APIs that could help with this, but the rotation process itself was not automated. So I wrote a Python script that automates the rotation of GCP service account keys, enhancing the security of service account credentials.
+
+4. **Developing a security monitoring system:** To keep track of incorrect usage and potential threats, I built a log analysis tool that monitors GCP audit logs for suspicious activity, collecting and parsing logs to identify potential security threats, delivering email alerts when something unusual is detected.
+
+As an extra, and after noticing that some of these tools and policies could be ignored by developers, we also came up with the idea of an enforcement module to ensure the usage of these new tools and policies. This module would be integrated into the CI/CD pipeline, checking for compliance with the new infrastructure policies and notifying developers of any violations.
+
+## The Impact
+
+The tools developed during this project will have an impact on the Apache Beam community and the wider open source community. The automation of resource cleanup will help reduce costs and improve resource management, while the permission management system will provide better tracking and auditing of changes. The service account key rotation tool will enhance security, and the security monitoring system will help detect and respond to potential threats.
+
+## Wrap Up
+
+This project has been an incredible learning experience for me. I have gained a better understanding of how GCP works, as well as how to use Terraform and GitHub Actions. I have also learned a lot about security best practices and how to implement them in a real-world project.
+
+I also learned a lot about working in an open source community, having direct communication with such experienced developers, and the importance of collaboration and communication in a distributed team. I am grateful for the opportunity to work on such an important project and to contribute to the Apache Beam community.
+
+Finally, a special thanks to my mentor, Pablo Estrada, for his guidance and support throughout the summer. I am grateful not only for his amazing technical skills but especially for his patience and encouragement on my journey contributing to open source.
+
+You can find my final report [here](https://gist.github.com/ksobrenat32/b028b8303393afbe73a8fc5e17daff90) if you want to take a look at the details of my work.
+
+## Advice for Future Participants
+
+If you are considering participating in Google Summer of Code, my advice would be to choose an area you are passionate about; this will make any coding challenge easier to overcome. Also, don't be afraid to ask questions and seek help from your mentors and the community. At the start, I made that mistake, and I learned that asking for help is a sign of strength, not weakness.
+
+Finally, make sure to manage your time effectively and stay organized (keeping a progress journal is a great idea). GSoC is a great opportunity to learn and grow as a developer, but it can also be time-consuming, so it's important to stay focused and on track.
diff --git a/website/www/site/content/en/blog/gsoc-25-yaml-user-accessibility.md b/website/www/site/content/en/blog/gsoc-25-yaml-user-accessibility.md
new file mode 100644
index 000000000000..2c4704ee497d
--- /dev/null
+++ b/website/www/site/content/en/blog/gsoc-25-yaml-user-accessibility.md
@@ -0,0 +1,113 @@
+---
+title:  "Google Summer of Code 2025 - Beam YAML, Kafka and Iceberg User
+Accessibility"
+date:   2025-09-23 00:00:00 -0400
+categories:
+  - blog
+  - gsoc
+aliases:
+  - /blog/2025/09/23/gsoc-25-yaml-user-accessibility.html
+authors:
+  - charlespnh
+
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+The relatively new Beam YAML SDK was introduced in the spirit of making data processing easy,
+but it has gained little adoption for complex ML tasks and hasn’t been widely used with
+[Managed I/O](beam.apache.org/documentation/io/managed-io/) such as Kafka and Iceberg.
+As part of Google Summer of Code 2025, new illustrative, production-ready pipeline examples
+of ML use cases with Kafka and Iceberg data sources using the YAML SDK have been developed
+to address this adoption gap.
+
+## Context
+The YAML SDK was introduced in Spring 2024 as Beam’s first no-code SDK. It follows a declarative approach
+of defining a data processing pipeline using a YAML DSL, as opposed to other programming language specific SDKs.
+At the time, it had few meaningful examples and documentation to go along with it. Key missing examples
+were ML workflows and integration with the Kafka and Iceberg Managed I/O. Foundational work had already been done
+to add support for ML capabilities as well as Kafka and Iceberg IO connectors in the YAML SDK, but there were no
+end-to-end examples demonstrating their usage.
+
+Beam, as well as Kafka and Iceberg, are mainstream big data technologies but they also have a learning curve.
+The overall theme of the project is to help democratize data processing for scientists and analysts who traditionally
+don’t have a strong background in software engineering. They can now refer to these meaningful examples as the starting point,
+helping them onboard faster and be more productive when authoring ML/data pipelines to their use cases with Beam and its YAML DSL.
+
+## Contributions
+The data pipelines/workflows developed are production-ready: Kafka and Iceberg data sources are set up on GCP,
+and the data used are raw public datasets. The pipelines are tested end-to-end on Google Cloud Dataflow and
+are also unit tested to ensure correct transformation logic.
+
+Delivered pipelines/workflows, each with documentation as README.md, address 4 main ML use cases below:
+
+1. **Streaming Classification Inference**: A streaming ML pipeline that demonstrates Beam YAML capability to perform
+classification inference on a stream of incoming data from Kafka. The overall workflow also includes
+DistilBERT model deployment and serving on Google Cloud Vertex AI where the pipeline can access for remote inferences.
+The pipeline is applied to a sentiment analysis task on a stream of YouTube comments, preprocessing data and classifying
+whether a comment is positive or negative. See [pipeline](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/sentiment_analysis/streaming_sentiment_analysis.yaml) and [documentation](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/ml/sentiment_analysis).
+
+
+2. **Streaming Regression Inference**: A streaming ML pipeline that demonstrates Beam YAML capability to perform
+regression inference on a stream of incoming data from Kafka. The overall workflow also includes
+custom model training, deployment and serving on Google Cloud Vertex AI where the pipeline can access for remote inferences.
+The pipeline is applied to a regression task on a stream of taxi rides, preprocessing data and predicting the fare amount
+for every ride. See [pipeline](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare/streaming_taxifare_prediction.yaml) and [documentation](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare).
+
+
+3. **Batch Anomaly Detection**: A ML workflow that demonstrates ML-specific transformations
+and reading from/writing to Iceberg IO. The workflow contains unsupervised model training and several pipelines that leverage
+Iceberg for storing results, BigQuery for storing vector embeddings and MLTransform for computing embeddings to demonstrate
+an end-to-end anomaly detection workflow on a dataset of system logs. See [workflow](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis/batch_log_analysis.sh) and [documentation](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis).
+
+
+4. **Feature Engineering & Model Evaluation**: A ML workflow that demonstrates Beam YAML capability to do feature engineering
+which is subsequently used for model evaluation, and its integration with Iceberg IO. The workflow contains model training
+and several pipelines, showcasing an end-to-end Fraud Detection MLOps solution that generates features and evaluates models
+to detect credit card transaction frauds. See [workflow](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection/fraud_detection_mlops_beam_yaml_sdk.ipynb) and [documentation](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection).
+
+## Challenges
+The main challenge of the project was a lack of previous YAML pipeline examples and good documentation to rely on.
+Unlike the Python or Java SDKs where there are already many notebooks and end-to-end examples demonstrating various use cases,
+the examples for YAML SDK only involved simple transformations such as filter, group by, etc. More complex transforms like
+`MLTransform` and `ReadFromIceberg` had no examples and requires configurations that didn't have clear API reference at the time.
+As a result, there were a lot of deep dives into the actual implementation of the PTransforms across YAML, Python and Java SDKs to
+understand the error messages and how to correctly use the transforms.
+
+Another challenge was writing unit tests for the pipeline to ensure that the pipeline’s logic is correct.
+It was a learning curve to understand how the existing test suite is set up and how it can be used to write unit tests for
+the data pipelines. A lot of time was spent on properly writing mocks for the pipeline's sources and sinks, as well as for the
+transforms that require external services such as Vertex AI.
+
+## Conclusion & Personal Thoughts
+These production-ready pipelines demonstrate the potential of Beam YAML SDK to author complex ML workflows
+that interact with Iceberg and Kafka. The examples are a nice addition to Beam, especially with Beam 3.0.0 milestones
+coming up where low-code/no-code, ML capabilities and Managed I/O are focused on.
+
+I had an amazing time working with the big data technologies Beam, Iceberg, and Kafka as well as many Google Cloud services
+(Dataflow, Vertex AI and Google Kubernetes Engine, to name a few). I’ve always wanted to work more in the ML space, and this
+experience has been a great growth opportunity for me. Google Summer of Code this year has been selective, and the project's success
+would not have been possible without the support of my mentor, Chamikara Jayalath. It's been a pleasure working closely
+with him and the broader Beam community to contribute to this open-source project that has a meaningful impact on the
+data engineering community.
+
+My advice for future Google Summer of Code participants is to first and foremost research and choose a project that aligns closely
+with your interest. Most importantly, spend a lot of time making yourself visible and writing a good proposal when the program
+is opened for applications. Being visible (e.g. by sharing your proposal, or generally any ideas and questions on the project's
+communication channel early on) makes it more likely for you to be selected; and a good proposal not only will make you even
+more likely to be in the program, but also give you a lot of confidence when contributing to and completing the project.
+
+## References
+- [Google Summer of Code Project Listing](https://summerofcode.withgoogle.com/programs/2025/projects/f4kiDdus)
+- [Google Summer of Code Final Report](https://docs.google.com/document/d/1MSAVF6X9ggtVZbqz8YJGmMgkolR_dve0Lr930cByyac/edit?usp=sharing)
diff --git a/website/www/site/content/en/documentation/dsls/sql/extensions/create-external-table.md b/website/www/site/content/en/documentation/dsls/sql/extensions/create-external-table.md
index 65d7d6dab411..ad6ba66beb20 100644
--- a/website/www/site/content/en/documentation/dsls/sql/extensions/create-external-table.md
+++ b/website/www/site/content/en/documentation/dsls/sql/extensions/create-external-table.md
@@ -748,6 +748,151 @@ TYPE text
 LOCATION '/home/admin/orders'
 ```
 
+## DataGen
+
+The **DataGen** connector allows for creating tables based on in-memory data generation. This is useful for developing and testing queries locally without requiring access to external systems. The DataGen connector is built-in; no additional dependencies are required.It is available for Beam 2.67.0+
+
+Tables can be either **bounded** (generating a fixed number of rows) or **unbounded** (generating a stream of rows at a specific rate). The connector provides fine-grained controls to customize the generated values for each field, including support for event-time windowing.
+
+### Syntax
+
+```sql
+CREATE EXTERNAL TABLE [ IF NOT EXISTS ] tableName (tableElement [, tableElement ]*)
+TYPE datagen
+[TBLPROPERTIES tblProperties]
+```
+
+### Table Properties (`TBLPROPERTIES`)
+
+The `TBLPROPERTIES` JSON object is used to configure the generator's behavior.
+
+
+#### General Options
+
+| Key | Required | Description |
+| :--- | :--- | :--- |
+| `number-of-rows` | **Yes** (or `rows-per-second`) | Creates a **bounded** table with a specified total number of rows. |
+| `rows-per-second`| **Yes** (or `number-of-rows`) | Creates an **unbounded** table that generates rows at the specified rate. |
+
+#### Event-Time and Watermark Configuration
+
+| Key                               | Required                                         | Description                                                                                                                                                   |
+|:----------------------------------|:-------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `timestamp.behavior`              | No                                               | Specifies the time handling. Can be `'processing-time'` (default) or `'event-time'`.                                                                          |
+| `event-time.timestamp-column`     | **Yes**, if `timestamp.behavior` is `event-time` | The name of the column that will be used to drive the event-time watermark for the stream.                                                                    |
+| `event-time.max-out-of-orderness` | No                                               | When using `event-time`, this sets the maximum out-of-orderness in **milliseconds** for generated timestamps (e.g., `'5000'` for 5 seconds). Defaults to `0`. |
+
+#### Field-Specific Options
+
+You can customize the generation logic for each column by providing properties with the prefix **`fields.<columnName>.*`**.
+
+| Key | Description                                                                                                                                                                            |
+| :--- |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `kind` | The type of generator to use. Can be `'random'` (default) or `'sequence'`.                                                                                                             |
+| `null-rate`| A `double` between `0.0` and `1.0` indicating the probability that the generated value for this field will be `NULL`. Defaults to `0.0`.                                               |
+| `length` | For `VARCHAR` fields with `kind: 'random'`, specifies the exact length of the generated string. Defaults to `10`.                                                                      |
+| `min`, `max` | For numeric types (`BIGINT`, `INTEGER`, `DOUBLE`, etc.) with `kind: 'random'`, specifies the inclusive minimum and maximum values for the generated number.                            |
+| `start`, `end`| For `BIGINT` fields with `kind: 'sequence'`, specifies the inclusive start and end values of the sequence. The sequence will cycle from `start` to `end`.                              |
+| `max-past` | For `TIMESTAMP` fields, specifies the maximum duration in **milliseconds** in the past to generate a random timestamp from. If not set, timestamps are generated for the current time. |
+
+### Data Type Behavior
+
+* **`TINYINT | SMALLINT | INTEGER | BIGINT`**: Generates random numbers within a range or from a sequence.
+* **`FLOAT | DOUBLE | DECIMAL`**: Generates random floating-point numbers within a specified range.
+* **`CHAR | VARCHAR`**: Generates random alphanumeric strings.
+* **`BOOLEAN`**: Generates random `true` or `false` values.
+* **`TIMESTAMP`**: Generates timestamps based on the current system time unless `max-past` is configured.
+* **`ROW`**: Generates nested data by recursively applying these rules to its sub-fields.
+* **`BINARY`, `VARBINARY`, `DATE`, `TIME`,`TIMESTAMP_WITH_LOCAL_TIME_ZONE`,`ARRAY` and `MAP` types are not currently supported.**
+
+### Examples
+
+#### Bounded Table with Random Data
+
+This example creates a bounded table with 1000 rows. The `id` will be a random `BIGINT` and `product_name` will be a random `VARCHAR` of length 10.
+
+```sql
+CREATE EXTERNAL TABLE Orders (
+    id BIGINT,
+    product_name VARCHAR
+)
+TYPE datagen
+TBLPROPERTIES '{
+  "number-of-rows": "1000"
+}'
+```
+
+#### Unbounded Streaming Table
+
+This example creates a streaming table that generates 10 rows per second.
+
+```sql
+CREATE EXTERNAL TABLE user_impressions (
+    user_id VARCHAR,
+    impression_time TIMESTAMP
+)
+TYPE datagen
+TBLPROPERTIES '{
+  "rows-per-second": "10"
+}'
+```
+
+-----
+
+#### Bounded Table with Custom Field Generation
+
+This is a comprehensive example demonstrating various field-level customizations. The table is bounded because a sequence generator is used.
+
+```sql
+CREATE EXTERNAL TABLE user_clicks (
+    event_id BIGINT,
+    user_id VARCHAR,
+    click_timestamp TIMESTAMP,
+    score DOUBLE
+)
+TYPE 'datagen'
+TBLPROPERTIES '{
+  "number-of-rows": "1000000",
+  "fields.event_id.kind": "sequence",
+  "fields.event_id.start": "1",
+  "fields.event_id.end": "1000000",
+  "fields.user_id.kind": "random",
+  "fields.user_id.length": "12",
+  "fields.click_timestamp.kind": "random",
+  "fields.click_timestamp.max-past": "60000",
+  "fields.score.kind": "random",
+  "fields.score.min": "0.0",
+  "fields.score.max": "1.0",
+  "fields.score.null-rate": "0.1"
+}'
+```
+
+#### Unbounded Streaming Table with Event Time
+
+This example creates a streaming table that generates 10 rows per second. It uses the `click_timestamp` column to drive the event-time watermark, allowing for up to 5 seconds of out-of-order data. The `ingestion_timestamp` column is populated separately with the processing time.
+
+```sql
+CREATE EXTERNAL TABLE user_clicks (
+    event_id BIGINT,
+    user_id VARCHAR,
+    click_timestamp TIMESTAMP,
+    ingestion_timestamp TIMESTAMP
+)
+TYPE 'datagen'
+TBLPROPERTIES '{
+  "rows-per-second": "10",
+  "timestamp.behavior": "event-time",
+  "event-time.timestamp-column": "click_timestamp",
+  "event-time.max-out-of-orderness": "5000",
+  "fields.event_id.kind": "sequence",
+  "fields.event_id.start": "1",
+  "fields.event_id.end": "1000000",
+  "fields.user_id.kind": "random",
+  "fields.user_id.length": "12",
+  "fields.ingestion_timestamp.kind": "timestamp"
+}'
+```
+
 ## Generic Payload Handling
 
 Certain data sources and sinks support generic payload handling. This handling
diff --git a/website/www/site/content/en/documentation/io/built-in/google-bigquery.md b/website/www/site/content/en/documentation/io/built-in/google-bigquery.md
index f53fc5eb72f4..9c205f092663 100644
--- a/website/www/site/content/en/documentation/io/built-in/google-bigquery.md
+++ b/website/www/site/content/en/documentation/io/built-in/google-bigquery.md
@@ -98,8 +98,8 @@ object.
 #### Using a string
 
 To specify a table with a string, use the format
-`[project_id]:[dataset_id].[table_id]` to specify the fully-qualified BigQuery
-table name.
+`[project_id]:[dataset_id].[table_id]` or `[project_id].[dataset_id].[table_id]`
+to specify the fully-qualified BigQuery table name.
 
 {{< highlight java >}}
 {{< code_sample "examples/java/src/main/java/org/apache/beam/examples/snippets/Snippets.java" BigQueryTableSpec >}}
diff --git a/website/www/site/content/en/documentation/io/managed-io.md b/website/www/site/content/en/documentation/io/managed-io.md
index 53631d279381..59f4cd1f85b6 100644
--- a/website/www/site/content/en/documentation/io/managed-io.md
+++ b/website/www/site/content/en/documentation/io/managed-io.md
@@ -3,6 +3,9 @@ title: "Managed I/O Connectors"
 aliases: [built-in]
 ---
 <!--
+DO NOT UPDATE THIS FILE. It is generated by .github/workflows/build_release_candidate.yml
+-->
+<!--
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
diff --git a/website/www/site/content/en/documentation/ml/overview.md b/website/www/site/content/en/documentation/ml/overview.md
index 7e3305f96334..8cebfcf4a9f9 100644
--- a/website/www/site/content/en/documentation/ml/overview.md
+++ b/website/www/site/content/en/documentation/ml/overview.md
@@ -52,7 +52,7 @@ Beam provides different ways to implement inference as part of your pipeline. Yo
 
 ### RunInference
 
-The RunInfernce API is available with the Beam Python SDK versions 2.40.0 and later. You can use Apache Beam with the RunInference API to use machine learning (ML) models to do local and remote inference with batch and streaming pipelines. Starting with Apache Beam 2.40.0, PyTorch and Scikit-learn frameworks are supported. Tensorflow models are supported through `tfx-bsl`. For more deatils about using RunInference, see [About Beam ML](/documentation/ml/about-ml).
+The RunInference API is available with the Beam Python SDK versions 2.40.0 and later. You can use Apache Beam with the RunInference API to use machine learning (ML) models to do local and remote inference with batch and streaming pipelines. Starting with Apache Beam 2.40.0, PyTorch and Scikit-learn frameworks are supported. Tensorflow models are supported through `tfx-bsl`. For more deatils about using RunInference, see [About Beam ML](/documentation/ml/about-ml).
 
 The RunInference API is available with the Beam Java SDK versions 2.41.0 and later through Apache Beam's [Multi-language Pipelines framework](/documentation/programming-guide/#multi-language-pipelines). For information about the Java wrapper transform, see [RunInference.java](https://github.com/apache/beam/blob/master/sdks/java/extensions/python/src/main/java/org/apache/beam/sdk/extensions/python/transforms/RunInference.java). To try it out, see the [Java Sklearn Mnist Classification example](https://github.com/apache/beam/tree/master/examples/multi-language).
 
diff --git a/website/www/site/content/en/documentation/programming-guide.md b/website/www/site/content/en/documentation/programming-guide.md
index 14029b9babaf..cd8bbb4ff437 100644
--- a/website/www/site/content/en/documentation/programming-guide.md
+++ b/website/www/site/content/en/documentation/programming-guide.md
@@ -1184,6 +1184,104 @@ func init() {
 
 </span>
 
+{{< paragraph class="language-python">}}
+Proper use of return vs yield in Python Functions.
+{{< /paragraph >}}
+
+<span class="language-python">
+
+> **Returning a single element (e.g., `return element`) is incorrect**
+> The `process` method in Beam must return an *iterable* of elements. Returning a single value like an integer or string
+> (e.g., `return element`) leads to a runtime error (`TypeError: 'int' object is not iterable`) or incorrect results since the return value
+> will be treated as an iterable. Always ensure your return type is iterable.
+
+</span>
+
+{{< highlight python >}}
+# Incorrectly Returning a single string instead of a sequence
+class ReturnIndividualElement(beam.DoFn):
+    def process(self, element):
+        return element
+
+with beam.Pipeline() as pipeline:
+    (
+        pipeline
+        | "CreateExamples" >> beam.Create(["foo"])
+        | "MapIncorrect" >> beam.ParDo(ReturnIndividualElement())
+        | "Print" >> beam.Map(print)
+    )
+  # prints:
+  # f
+  # o
+  # o
+{{< /highlight >}}
+
+<span class="language-python">
+
+> **Returning a list (e.g., `return [element1, element2]`) is valid because List is Iterable**
+> This approach works well when emitting multiple outputs from a single call and is easy to read for small datasets.
+
+</span>
+
+{{< highlight python >}}
+# Returning a list of strings
+class ReturnWordsFn(beam.DoFn):
+    def process(self, element):
+        # Split the sentence and return all words longer than 2 characters as a list
+        return [word for word in element.split() if len(word) > 2]
+
+with beam.Pipeline() as pipeline:
+    (
+        pipeline
+        | "CreateSentences_Return" >> beam.Create([  # Create a collection of sentences
+            "Apache Beam is powerful",               # Sentence 1
+            "Try it now"                             # Sentence 2
+        ])
+        | "SplitWithReturn" >> beam.ParDo(ReturnWordsFn())  # Apply the custom DoFn to split words
+        | "PrintWords_Return" >> beam.Map(print)  # Print each List of words
+    )
+  # prints:
+  # Apache
+  # Beam
+  # powerful
+  # Try
+  # now
+{{< /highlight >}}
+
+<span class="language-python">
+
+> **Using `yield` (e.g., `yield element`) is also valid**
+> This approach can be useful for generating multiple outputs more flexibly, especially in cases where conditional logic or loops are involved.
+
+</span>
+
+{{< highlight python >}}
+# Yielding each line one at a time
+class YieldWordsFn(beam.DoFn):
+    def process(self, element):
+        # Splitting the sentence and yielding words that have more than 2 characters
+        for word in element.split():
+            if len(word) > 2:
+                yield word
+
+with beam.Pipeline() as pipeline:
+    (
+        pipeline
+        | "CreateSentences_Yield" >> beam.Create([  # Create a collection of sentences
+            "Apache Beam is powerful",              # Sentence 1
+            "Try it now"                            # Sentence 2
+        ])
+        | "SplitWithYield" >> beam.ParDo(YieldWordsFn())  # Apply the custom DoFn to split words
+        | "PrintWords_Yield" >> beam.Map(print)  # Print each word
+    )
+  # prints:
+  # Apache
+  # Beam
+  # powerful
+  # Try
+  # now
+{{< /highlight >}}
+
 A given `DoFn` instance generally gets invoked one or more times to process some
 arbitrary bundle of elements. However, Beam doesn't guarantee an exact number of
 invocations; it may be invoked multiple times on a given worker node to account
diff --git a/website/www/site/content/en/documentation/runners/nemo.md b/website/www/site/content/en/documentation/runners/nemo.md
index f29bf2af040b..73c65c0ec3b8 100644
--- a/website/www/site/content/en/documentation/runners/nemo.md
+++ b/website/www/site/content/en/documentation/runners/nemo.md
@@ -18,6 +18,8 @@ limitations under the License.
 -->
 # Using the Apache Nemo Runner
 
+**Note** Apache Nemo has been retired from incubation ([status](https://incubator.apache.org/projects/index.html#nemo)).
+
 The Apache Nemo Runner can be used to execute Beam pipelines using [Apache Nemo](https://nemo.apache.org).
 The Nemo Runner can optimize Beam pipelines with the Nemo compiler through various optimization passes
 and execute them in a distributed fashion using the Nemo runtime. You can also deploy a self-contained application
diff --git a/website/www/site/content/en/documentation/runners/samza.md b/website/www/site/content/en/documentation/runners/samza.md
index 18e037ec9f41..c36d06fee861 100644
--- a/website/www/site/content/en/documentation/runners/samza.md
+++ b/website/www/site/content/en/documentation/runners/samza.md
@@ -19,6 +19,8 @@ limitations under the License.
 
 # Using the Apache Samza Runner
 
+**Note** Samza runner is deprecated and the support is planned to be removed in Beam 3.0 ([Issue](https://github.com/apache/beam/issues/35448)).
+
 The Apache Samza Runner can be used to execute Beam pipelines using [Apache Samza](https://samza.apache.org/). The Samza Runner executes Beam pipeline in a Samza application and can run locally. The application can further be built into a .tgz file, and deployed to a YARN cluster or Samza standalone cluster with Zookeeper.
 
 The Samza Runner and Samza are suitable for large scale, stateful streaming jobs, and provide:
diff --git a/website/www/site/content/en/documentation/runners/twister2.md b/website/www/site/content/en/documentation/runners/twister2.md
index ba15c010fa70..193871a39195 100644
--- a/website/www/site/content/en/documentation/runners/twister2.md
+++ b/website/www/site/content/en/documentation/runners/twister2.md
@@ -20,6 +20,8 @@ limitations under the License.
 
 ## Overview
 
+**Note** Twister2 runner is deprecated and the support is planned to be removed in Beam 3.0 ([Issue](https://github.com/apache/beam/issues/35905)).
+
 Twister2 Runner can be used to execute Apache Beam pipelines on top of a Twister2
 cluster. Twister2 Runner runs Beam pipelines as Twister2 jobs, which can be executed on
 a Twister2 cluster either as a local deployment or distributed deployment using, Nomad,
diff --git a/website/www/site/content/en/documentation/sdks/yaml-errors.md b/website/www/site/content/en/documentation/sdks/yaml-errors.md
index 6edd1751a65b..8a836890a73e 100644
--- a/website/www/site/content/en/documentation/sdks/yaml-errors.md
+++ b/website/www/site/content/en/documentation/sdks/yaml-errors.md
@@ -40,7 +40,7 @@ the following code will write all "good" processed records to one file and
 any "bad" records, along with metadata about what error was encountered,
 to a separate file.
 
-```
+```yaml
 pipeline:
   transforms:
     - type: ReadFromCsv
@@ -87,7 +87,7 @@ Some transforms allow for extra arguments in their error_handling config, e.g.
 for Python functions one can give a `threshold` which limits the relative number
 of records that can be bad before considering the entire pipeline a failure
 
-```
+```yaml
 pipeline:
   transforms:
     - type: ReadFromCsv
@@ -122,7 +122,7 @@ pipeline:
 One can do arbitrary further processing on these failed records if desired,
 e.g.
 
-```
+```yaml
 pipeline:
   transforms:
     - type: ReadFromCsv
@@ -176,7 +176,7 @@ pipeline:
 When using the `chain` syntax, the required error consumption can happen
 in an `extra_transforms` block.
 
-```
+```yaml
 pipeline:
   type: chain
   transforms:
@@ -217,3 +217,5 @@ pipeline:
       config:
         path: /path/to/errors.json
 ```
+
+See YAML schema [info](https://beam.apache.org/documentation/sdks/yaml-schema/) for another use of error_handling in a schema context.
diff --git a/website/www/site/content/en/documentation/sdks/yaml-schema.md b/website/www/site/content/en/documentation/sdks/yaml-schema.md
new file mode 100644
index 000000000000..a563bf486bec
--- /dev/null
+++ b/website/www/site/content/en/documentation/sdks/yaml-schema.md
@@ -0,0 +1,131 @@
+---
+type: languages
+title: "Apache Beam YAML Schema"
+---
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+# Beam YAML Schema
+
+As pipelines grow in size and complexity, it becomes more common to encounter
+data that is malformed, doesn't meet preconditions, or otherwise causes issues
+during processing.
+
+Beam YAML helps the user detect and capture these issues by using the optional
+`output_schema` configuration, which is available for any transform in the YAML
+SDK. For example, the following code creates a few "good" records and specifies
+that the output schema from the `Create` transform should have records that
+follow the expected schema: `sdk` as a string and `year` as an integer.
+
+```yaml
+pipeline:
+  type: chain
+  transforms:
+    - type: Create
+      config:
+        elements:
+          - {sdk: MapReduce, year: 2004}
+          - {sdk: MillWheel, year: 2008}
+        output_schema:
+          type: object
+          properties:
+            sdk:
+              type: string
+            year:
+              type: integer
+    - type: AssertEqual
+      config:
+        elements:
+          - {sdk: MapReduce, year: 2004}
+          - {sdk: MillWheel, year: 2008}
+```
+
+However, a user will more likely want to detect and handle schema errors. If a
+transform has a built-in error_handling configuration, the user can specify that
+error_handling configuration and any errors found will be appended to the
+transform error_handling output. For example, the following code will
+create a few "good" and "bad" records with a specified schema of `user` as a
+string and `timestamp` as a boolean. The `alice` row will fail in the standard
+way because of not being an integer for the AssignTimestamps transform, while
+the `bob` row will fail because after the AssignTimestamp transformation, the
+output row will have the timestamp as an integer when it should be a boolean.
+
+
+```yaml
+pipeline:
+  type: composite
+  transforms:
+    - type: Create
+      name: CreateVisits
+      config:
+        elements:
+          - {user: alice, timestamp: "not-valid"}
+          - {user: bob, timestamp: 3}
+    - type: AssignTimestamps
+      input: CreateVisits
+      config:
+        timestamp: timestamp
+        error_handling:
+          output: invalid_rows
+        output_schema:
+          type: object
+          properties:
+            user:
+              type: string
+            timestamp:
+              type: boolean
+    - type: MapToFields
+      name: ExtractInvalidTimestamp
+      input: AssignTimestamps.invalid_rows
+      config:
+        language: python
+        fields:
+          user: "element.user"
+          timestamp: "element.timestamp"
+    - type: AssertEqual
+      input: ExtractInvalidTimestamp
+      config:
+        elements:
+          - {user: "alice", timestamp: "not-valid"}
+          - {user: bob, timestamp: 3}
+    - type: AssertEqual
+      input: AssignTimestamps
+      config:
+        elements: []
+```
+
+WARNING: If a transform doesn't have the error_handling configuration available
+and a user chooses to use this optional output_schema feature, any failures
+found will result in the entire pipeline failing. If the user would still like
+to have some kind of output schema validation, please use the ValidateWithSchema
+transform instead.
+
+NOTE: When using the output_schema config, the main output key to validate on
+will be determined based on these criteria:
+
+  1. An output with the key 'output'.
+  2. An output with the key 'good'.
+  3. The single output if there is only one.
+
+Failures will result if the main output cannot be determined because there are
+multiple outputs and none are named 'output' or 'good'.
+
+
+
+For more detailed information on error handling, see this [page](https://beam.apache.org/documentation/sdks/yaml-errors/).
diff --git a/website/www/site/content/en/documentation/sdks/yaml.md b/website/www/site/content/en/documentation/sdks/yaml.md
index 73d1eebaae95..33fad5b25506 100644
--- a/website/www/site/content/en/documentation/sdks/yaml.md
+++ b/website/www/site/content/en/documentation/sdks/yaml.md
@@ -708,7 +708,7 @@ the yaml file can be parameterized with externally provided variables using
 the [jinja variable syntax](https://jinja.palletsprojects.com/en/stable/templates/#variables).
 The values are then passed via a `--jinja_variables` command line flag.
 
-For example, one could start a pipeline with
+For example, one could start a pipeline with:
 
 ```
 pipeline:
@@ -742,6 +742,80 @@ or writing dated sources and sinks, e.g.
 
 would write to files like `gs://path/to/2016/08/04/dated-output*.json`.
 
+A user can also use the `% include` directive to pull in other common templates:
+
+<PATH_TO_YOUR_REPO>/pipeline.yaml
+```yaml
+pipeline:
+  transforms:
+    - name: Read from GCS
+      type: ReadFromText
+      config:
+# NOTE: For include, the indentation has to line up correctly for it to be
+# parsed correctly. So in this example the included readFromText.yaml has
+# already indented yaml lines to line up correctly when including into this
+# pipeline here.
+{% include '<PATH_TO_YOUR_REPO>/submodules/readFromText.yaml' %}
+    - name: Write to GCS
+      type: WriteToText
+      input: Read from GCS
+      config:
+        path: "gs://MY-BUCKET/wordCounts/"
+```
+
+<PATH_TO_YOUR_REPO>/submodules/readFromText.yaml
+```yaml
+        path: {{readFromText.path}}
+```
+
+This pipeline can be run like this:
+
+```sh
+python -m apache_beam.yaml.main \
+    --yaml_pipeline_file=pipeline.yaml \
+    --jinja_variables='{"readFromText": {"path": "gs://dataflow-samples/shakespeare/kinglear.txt"}}'
+```
+
+The `% import` jinja directive can also be used to pull in macros:
+
+<PATH_TO_YOUR_REPO>/pipeline.yaml
+```yaml
+{% import '<PATH_TO_YOUR_REPO>/macros.yaml' as macros %}
+
+pipeline:
+  type: chain
+  transforms:
+
+# Read in text file
+{{ macros.readFromText(readFromText) | indent(4, true) }}
+
+# Write to text file on GCS, locally, etc
+    - name: Write to GCS
+      type: WriteToText
+      input: Read from GCS
+      config:
+        path: "gs://MY-BUCKET/wordCounts/"
+```
+
+<PATH_TO_YOUR_REPO>/macros.yaml
+```yaml
+{%- macro readFromText(params) -%}
+- name: Read from GCS
+  type: ReadFromText
+  config:
+    path: "{{ params.path }}"
+{%- endmacro -%}
+```
+
+This pipeline can be run with the same command as in the `% include` example
+above.
+
+There are many more ways to import and even use template inheritance using
+Jinja as seen [here](https://jinja.palletsprojects.com/en/stable/templates/#import)
+and [here](https://jinja.palletsprojects.com/en/stable/templates/#inheritance).
+
+Full jinja pipeline examples can be found [here](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/jinja).
+
 ## Other Resources
 
 * [Example pipeline](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples)
diff --git a/website/www/site/content/en/documentation/transforms/java/other/wait.md b/website/www/site/content/en/documentation/transforms/java/other/wait.md
new file mode 100644
index 000000000000..ad28e3d30907
--- /dev/null
+++ b/website/www/site/content/en/documentation/transforms/java/other/wait.md
@@ -0,0 +1,106 @@
+---
+title: "Wait.On"
+---
+
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Wait.On
+<table align="left">
+    <a target="_blank" class="button"
+        href="https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/transforms/Wait.html">
+      <img src="/images/logos/sdks/java.png" width="20px" height="20px"
+           alt="Javadoc" />
+     Javadoc
+    </a>
+</table>
+<br><br>
+
+`Wait.On` returns a `PCollection` with the contents identical to the input `PCollection`, but delays the downstream processing until one or more other `PCollections` (signals) have finished processing. This is useful for enforcing ordering or dependencies between different parts of a pipeline, especially when some outputs interact with external systems (such as writing to a database).
+
+When you apply `Wait.On`, the elements of the main `PCollection` will not be emitted for downstream processing until the computations required to produce the specified signal `PCollections` have completed. In streaming mode, this is enforced per window: the corresponding window of each waited-on `PCollection` must close before elements are passed through.
+
+## Examples
+**Example 1**: Basic usage
+
+{{< highlight java >}}
+PipelineOptions options = PipelineOptionsFactory.create();
+Pipeline p = Pipeline.create(options);
+PCollection<String> main = p.apply("CreateMain", Create.of("item1", "item2", "item3"));
+PCollection<Void> signal = p.apply("CreateSignal", Create.of("trigger"))
+    .apply("ProcessSignal", ParDo.of(new DoFn<String, Void>() {
+        @ProcessElement
+        public void processElement(ProcessContext c) throws InterruptedException {
+            // Simulate some processing time
+            Thread.sleep(2000);
+            // Signal processing complete
+        }
+}));
+// Wait for 'signal' to complete before processing 'main'
+// Elements pass through unchanged after 'signal' finishes
+PCollection<String> processed = main.apply("WaitOnSignal", Wait.on(signal))
+    .apply("ProcessAfterWait", MapElements.into(TypeDescriptors.strings())
+        .via(item -> "Processed: " + item));
+processed.apply("LogResults", ParDo.of(new DoFn<String, Void>() {
+    @ProcessElement
+    public void processElement(ProcessContext c) {
+        System.out.println(c.element());
+    }
+}));
+{{< /highlight >}}
+
+**Example 2**: Using multiple signals
+
+{{< highlight java >}}
+PipelineOptions options = PipelineOptionsFactory.create();
+Pipeline p = Pipeline.create(options);
+// The PCollection to be processed after the signals.
+PCollection<String> main2 = p.apply("CreateMain2", Create.of("data1", "data2"));
+// Signal 1: Simulate a long-running setup task.
+PCollection<Void> signal1 = p.apply("CreateSignal1", Create.of("setup"))
+    .apply("SetupDatabase", ParDo.of(new DoFn<String, Void>() {
+      @ProcessElement
+      public void processElement(ProcessContext c) throws InterruptedException {
+        System.out.println("Starting database setup...");
+        Thread.sleep(1000);
+        System.out.println("Database setup complete.");
+      }
+    }));
+// Signal 2: Simulate loading a configuration file.
+PCollection<Void> signal2 = p.apply("CreateSignal2", Create.of("config"))
+    .apply("LoadConfig", ParDo.of(new DoFn<String, Void>() {
+      @ProcessElement
+      public void processElement(ProcessContext c) throws InterruptedException {
+        System.out.println("Loading configuration...");
+        Thread.sleep(1500);
+        System.out.println("Configuration loaded.");
+      }
+    }));
+// Wait for both signal1 and signal2 to complete before processing main2.
+PCollection<String> result2 = main2.apply("WaitOnSignals", Wait.on(signal1, signal2))
+    .apply("TransformData", MapElements.into(TypeDescriptors.strings())
+        .via(data -> data.toUpperCase() + "_READY"));
+// Log the final results.
+result2.apply("LogResults", ParDo.of(new DoFn<String, Void>() {
+    @ProcessElement
+    public void processElement(ProcessContext c) {
+        System.out.println("Final Result: " + c.element());
+    }
+}));
+{{< /highlight >}}
+
+## Related transforms
+* [Flatten](/documentation/transforms/java/other/flatten) merges multiple `PCollection` objects into a single logical `PCollection`.
+* [Window](/documentation/transforms/java/other/window) logically divides or groups elements into finite windows.
+* [WithTimestamps](/documentation/transforms/java/elementwise/withtimestamps) assigns timestamps to elements in a collection.
diff --git a/website/www/site/content/en/documentation/transforms/java/overview.md b/website/www/site/content/en/documentation/transforms/java/overview.md
index 63a9797b56ac..59aa93930fbe 100644
--- a/website/www/site/content/en/documentation/transforms/java/overview.md
+++ b/website/www/site/content/en/documentation/transforms/java/overview.md
@@ -76,4 +76,5 @@ limitations under the License.
   <tr><td><a href="/documentation/transforms/java/other/view">View</a></td><td>Operations for turning a collection into view that may be used as a side-input to a <code>ParDo</code>.</td></tr>
   <tr><td><a href="/documentation/transforms/java/other/window">Window</a></td><td>Logically divides up or groups the elements of a collection into finite
   windows according to a provided <code>WindowFn</code>.</td></tr>
-</table>
+  <tr><td><a href="/documentation/transforms/java/other/wait">Wait</a></td><td>Delays processing of a PCollection until other PCollections have finished processing.</td></tr>
+</table>
\ No newline at end of file
diff --git a/website/www/site/content/en/documentation/transforms/python/elementwise/enrichment-cloudsql.md b/website/www/site/content/en/documentation/transforms/python/elementwise/enrichment-cloudsql.md
new file mode 100644
index 000000000000..a29b2672e678
--- /dev/null
+++ b/website/www/site/content/en/documentation/transforms/python/elementwise/enrichment-cloudsql.md
@@ -0,0 +1,146 @@
+---
+title: "Enrichment with CloudSQL"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Use CloudSQL to enrich data
+
+{{< localstorage language language-py >}}
+
+<table>
+  <tr>
+    <td>
+      <a>
+      {{< button-pydoc path="apache_beam.transforms.enrichment_handlers.cloudsql" class="CloudSQLEnrichmentHandler" >}}
+      </a>
+   </td>
+  </tr>
+</table>
+
+Starting with Apache Beam 2.69.0, the enrichment transform includes
+built-in enrichment handler support for the
+[Google CloudSQL](https://cloud.google.com/sql/docs). This handler allows your
+Beam pipeline to enrich data using SQL databases, with built-in support for:
+
+- Managed PostgreSQL, MySQL, and Microsoft SQL Server instances on CloudSQL
+- Unmanaged SQL database instances not hosted on CloudSQL (e.g., self-hosted or
+  on-premises databases)
+
+The following example demonstrates how to create a pipeline that use the
+enrichment transform with the
+[`CloudSQLEnrichmentHandler`](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.enrichment_handlers.cloudsql.html#apache_beam.transforms.enrichment_handlers.cloudsql.CloudSQLEnrichmentHandler) handler.
+
+## Example 1: Enrichment with Google CloudSQL (Managed PostgreSQL)
+
+The data in the CloudSQL PostgreSQL table `products` follows this format:
+
+{{< table >}}
+| product_id | name | quantity | region_id |
+|:----------:|:----:|:--------:|:---------:|
+|     1      |  A   |    2     |     3     |
+|     2      |  B   |    3     |     1     |
+|     3      |  C   |   10     |     4     |
+{{< /table >}}
+
+
+{{< highlight language="py" >}}
+{{< code_sample "sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment.py" enrichment_with_google_cloudsql_pg >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample "sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment_test.py" enrichment_with_google_cloudsql_pg >}}
+{{< /highlight >}}
+
+## Example 2: Enrichment with Unmanaged PostgreSQL
+
+The data in the Unmanaged PostgreSQL table `products` follows this format:
+
+{{< table >}}
+| product_id | name | quantity | region_id |
+|:----------:|:----:|:--------:|:---------:|
+|     1      |  A   |    2     |     3     |
+|     2      |  B   |    3     |     1     |
+|     3      |  C   |   10     |     4     |
+{{< /table >}}
+
+
+{{< highlight language="py" >}}
+{{< code_sample "sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment.py" enrichment_with_external_pg >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample "sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment_test.py" enrichment_with_external_pg >}}
+{{< /highlight >}}
+
+## Example 3: Enrichment with Unmanaged MySQL
+
+The data in the Unmanaged MySQL table `products` follows this format:
+
+{{< table >}}
+| product_id | name | quantity | region_id |
+|:----------:|:----:|:--------:|:---------:|
+|     1      |  A   |    2     |     3     |
+|     2      |  B   |    3     |     1     |
+|     3      |  C   |   10     |     4     |
+{{< /table >}}
+
+
+{{< highlight language="py" >}}
+{{< code_sample "sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment.py" enrichment_with_external_mysql >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample "sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment_test.py" enrichment_with_external_mysql >}}
+{{< /highlight >}}
+
+## Example 4: Enrichment with Unmanaged Microsoft SQL Server
+
+The data in the Unmanaged Microsoft SQL Server table `products` follows this
+format:
+
+{{< table >}}
+| product_id | name | quantity | region_id |
+|:----------:|:----:|:--------:|:---------:|
+|     1      |  A   |    2     |     3     |
+|     2      |  B   |    3     |     1     |
+|     3      |  C   |   10     |     4     |
+{{< /table >}}
+
+
+{{< highlight language="py" >}}
+{{< code_sample "sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment.py" enrichment_with_external_sqlserver >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample "sdks/python/apache_beam/examples/snippets/transforms/elementwise/enrichment_test.py" enrichment_with_external_sqlserver >}}
+{{< /highlight >}}
+
+## Related transforms
+
+Not applicable.
+
+{{< button-pydoc path="apache_beam.transforms.enrichment_handlers.cloudsql" class="CloudSQLEnrichmentHandler" >}}
diff --git a/website/www/site/content/en/documentation/transforms/python/elementwise/enrichment.md b/website/www/site/content/en/documentation/transforms/python/elementwise/enrichment.md
index 6c05b6b515a4..4b352d0447ad 100644
--- a/website/www/site/content/en/documentation/transforms/python/elementwise/enrichment.md
+++ b/website/www/site/content/en/documentation/transforms/python/elementwise/enrichment.md
@@ -42,6 +42,7 @@ The following examples demonstrate how to create a pipeline that use the enrichm
 | Service                            | Example                                                                                                                                                                      |
 |:-----------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | Cloud Bigtable                     | [Enrichment with Bigtable](/documentation/transforms/python/elementwise/enrichment-bigtable/#example)                                                                        |
+| Cloud SQL (PostgreSQL, MySQL, SQLServer)                    | [Enrichment with CloudSQL](/documentation/transforms/python/elementwise/enrichment-cloudsql)                                                                        |
 | Vertex AI Feature Store            | [Enrichment with Vertex AI Feature Store](/documentation/transforms/python/elementwise/enrichment-vertexai/#example-1-enrichment-with-vertex-ai-feature-store)               |
 | Vertex AI Feature Store (Legacy)   | [Enrichment with Legacy Vertex AI Feature Store](/documentation/transforms/python/elementwise/enrichment-vertexai/#example-2-enrichment-with-vertex-ai-feature-store-legacy) |
 {{< /table >}}
diff --git a/website/www/site/content/en/documentation/transforms/python/other/waiton.md b/website/www/site/content/en/documentation/transforms/python/other/waiton.md
new file mode 100644
index 000000000000..8cdf8a47ad43
--- /dev/null
+++ b/website/www/site/content/en/documentation/transforms/python/other/waiton.md
@@ -0,0 +1,61 @@
+---
+title: "WaitOn"
+---
+
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# WaitOn
+
+`WaitOn` returns a `PCollection` with the contents identical to the input `PCollection`, but delays the downstream processing until one or more other `PCollections` (signals) have finished processing. This is useful for enforcing ordering or dependencies between different parts of a pipeline, especially when some outputs interact with external systems (such as writing to a database).
+
+When you apply `WaitOn`, the elements of the main `PCollection` will not be emitted for downstream processing until the computations required to produce the specified signal `PCollections` have completed. In streaming mode, this is enforced per window: the corresponding window of each waited-on `PCollection` must close before elements are passed through.
+
+## Examples
+
+```python
+import time
+import apache_beam as beam
+from apache_beam.transforms.util import WaitOn
+
+# Example 1: Basic usage
+with beam.Pipeline(options=pipeline_options) as p:
+  main = p | 'CreateMain' >> beam.Create([1, 2, 3])
+  signal = (
+      p | 'CreateSignal' >> beam.Create(['a', 'b'])
+      | 'ProcessSignal' >> beam.Map(lambda x: print(f"Processing signal element: {x}") or time.sleep(2)))
+  # Wait for 'signal' to complete before processing 'main'
+  result = main | 'WaitOnSignal' >> WaitOn(signal)
+  # Print each result to logs.
+  result | 'PrintExample1' >> beam.Map(lambda x: print(f"Example 1 Final Output: {x}"))
+
+# Example 2: Using multiple signals
+with beam.Pipeline(options=pipeline_options) as p:
+  main = p | 'CreateMain' >> beam.Create(['item1', 'item2', 'item3'])
+  signal1 = (
+      p | 'CreateSignal_A' >> beam.Create(['setup_db'])
+      | 'ProcessSignal_A' >> beam.Map(lambda x: print("Signal A: Setting up database...") or time.sleep(1)))
+  signal2 = (
+      p | 'CreateSignal_B' >> beam.Create(['load_config'])
+      | 'ProcessSignal_B' >> beam.Map(lambda x: print("Signal B: Loading config...") or time.sleep(3)))
+  # Wait for both 'signal1' and 'signal2' to complete before processing 'main'
+  result = main | 'WaitOnSignals' >> WaitOn(signal1, signal2)
+  # Print each result to logs.
+  result | 'PrintExample2' >> beam.Map(lambda x: print(f"Example 2 Final Output: {x.upper()}_READY"))
+```
+
+## Related transforms
+* [Flatten](/documentation/transforms/python/other/flatten) merges multiple `PCollection` objects into a single logical `PCollection`.
+* [WindowInto](/documentation/transforms/python/other/windowinto) logically divides or groups elements into finite windows.
+* [Reshuffle](/documentation/transforms/python/other/reshuffle) redistributes elements between workers.
diff --git a/website/www/site/content/en/documentation/transforms/python/overview.md b/website/www/site/content/en/documentation/transforms/python/overview.md
index 34599ed6c025..c51af84b1a8b 100644
--- a/website/www/site/content/en/documentation/transforms/python/overview.md
+++ b/website/www/site/content/en/documentation/transforms/python/overview.md
@@ -82,4 +82,6 @@ limitations under the License.
   most useful for adjusting parallelism or preventing coupled failures.</td></tr>
   <tr><td><a href="/documentation/transforms/python/other/windowinto">WindowInto</a></td><td>Logically divides up or groups the elements of a collection into finite
   windows according to a function.</td></tr>
+  <tr><td><a href="/documentation/transforms/python/other/waiton">WaitOn</a></td><td>Delays processing of a PCollection until other PCollections have finished processing.</td></tr>
 </table>
+
diff --git a/website/www/site/content/en/get-started/downloads.md b/website/www/site/content/en/get-started/downloads.md
index 3a4ec8107cc1..fc8e820cd1bd 100644
--- a/website/www/site/content/en/get-started/downloads.md
+++ b/website/www/site/content/en/get-started/downloads.md
@@ -88,31 +88,48 @@ the form `major.minor.patch` and are incremented as follows:
 * minor version for new functionality added in a backward-compatible manner, infrequent incompatible API changes
 * patch version for forward-compatible bug fixes
 
-Please note that APIs marked [`@Experimental`](https://beam.apache.org/releases/javadoc/{{< param release_latest >}}/org/apache/beam/sdk/annotations/Experimental.html)
-may change at any point and are not guaranteed to remain compatible across versions.
-
 Additionally, any API may change before the first stable release, i.e., between
 versions denoted `0.x.y`.
 
 ## Releases
 
-### 2.66.0 (2025-07-01)
+### Current release
+
+#### 2.68.0 (2025-09-22)
+
+Official [source code download](https://www.apache.org/dyn/closer.lua/beam/2.68.0/apache-beam-2.68.0-source-release.zip).
+[SHA-512](https://downloads.apache.org/beam/2.68.0/apache-beam-2.68.0-source-release.zip.sha512).
+[signature](https://downloads.apache.org/beam/2.68.0/apache-beam-2.68.0-source-release.zip.asc).
+
+[Release notes](https://github.com/apache/beam/releases/tag/v2.68.0)
+
+### Archived releases
+
+#### 2.67.0 (2025-08-12)
+
+Official [source code download](https://archive.apache.org/dist/beam/2.67.0/apache-beam-2.67.0-source-release.zip).
+[SHA-512](https://archive.apache.org/dist/beam/2.67.0/apache-beam-2.67.0-source-release.zip.sha512).
+[signature](https://archive.apache.org/dist/beam/2.67.0/apache-beam-2.67.0-source-release.zip.asc).
+
+[Release notes](https://github.com/apache/beam/releases/tag/v2.67.0)
+
+#### 2.66.0 (2025-07-01)
 
 Official [source code download](https://archive.apache.org/dist/beam/2.66.0/apache-beam-2.66.0-source-release.zip).
-[SHA-512](https://downloads.apache.org/beam/2.66.0/apache-beam-2.66.0-source-release.zip.sha512).
-[signature](https://downloads.apache.org/beam/2.66.0/apache-beam-2.66.0-source-release.zip.asc).
+[SHA-512](https://archive.apache.org/dist/beam/2.66.0/apache-beam-2.66.0-source-release.zip.sha512).
+[signature](https://archive.apache.org/dist/beam/2.66.0/apache-beam-2.66.0-source-release.zip.asc).
 
 [Release notes](https://github.com/apache/beam/releases/tag/v2.66.0)
 
-### 2.65.0 (2025-05-12)
+#### 2.65.0 (2025-05-12)
 
 Official [source code download](https://archive.apache.org/dist/beam/2.65.0/apache-beam-2.65.0-source-release.zip).
-[SHA-512](https://downloads.apache.org/beam/2.65.0/apache-beam-2.65.0-source-release.zip.sha512).
-[signature](https://downloads.apache.org/beam/2.65.0/apache-beam-2.65.0-source-release.zip.asc).
+[SHA-512](https://archive.apache.org/dist/beam/2.65.0/apache-beam-2.65.0-source-release.zip.sha512).
+[signature](https://archive.apache.org/dist/beam/2.65.0/apache-beam-2.65.0-source-release.zip.asc).
 
 [Release notes](https://github.com/apache/beam/releases/tag/v2.65.0)
 
-### 2.64.0 (2025-03-31)
+#### 2.64.0 (2025-03-31)
 
 Official [source code download](https://archive.apache.org/dist/beam/2.64.0/apache-beam-2.64.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.64.0/apache-beam-2.64.0-source-release.zip.sha512).
@@ -120,7 +137,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.64.0/apac
 
 [Release notes](https://github.com/apache/beam/releases/tag/v2.64.0)
 
-### 2.63.0 (2025-02-11)
+#### 2.63.0 (2025-02-11)
 
 Official [source code download](https://archive.apache.org/dist/beam/2.63.0/apache-beam-2.63.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.63.0/apache-beam-2.63.0-source-release.zip.sha512).
@@ -129,7 +146,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.63.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.63.0)
 
 
-### 2.62.0 (2025-01-21)
+#### 2.62.0 (2025-01-21)
 
 Official [source code download](https://archive.apache.org/dist/beam/2.62.0/apache-beam-2.62.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.62.0/apache-beam-2.62.0-source-release.zip.sha512).
@@ -138,7 +155,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.62.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.62.0)
 
 
-### 2.61.0 (2024-11-25)
+#### 2.61.0 (2024-11-25)
 
 Official [source code download](https://archive.apache.org/dist/beam/2.61.0/apache-beam-2.61.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.61.0/apache-beam-2.61.0-source-release.zip.sha512).
@@ -146,7 +163,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.61.0/apac
 
 [Release notes](https://github.com/apache/beam/releases/tag/v2.61.0)
 
-### 2.60.0 (2024-10-17)
+#### 2.60.0 (2024-10-17)
 
 Official [source code download](https://archive.apache.org/dist/beam/2.60.0/apache-beam-2.60.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.60.0/apache-beam-2.60.0-source-release.zip.sha512).
@@ -154,49 +171,49 @@ Official [source code download](https://archive.apache.org/dist/beam/2.60.0/apac
 
 [Release notes](https://github.com/apache/beam/releases/tag/v2.60.0)
 
-### 2.59.0 (2024-09-11)
+#### 2.59.0 (2024-09-11)
 Official [source code download](https://archive.apache.org/dist/beam/2.59.0/apache-beam-2.59.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.59.0/apache-beam-2.59.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.59.0/apache-beam-2.59.0-source-release.zip.asc).
 
 [Release notes](https://github.com/apache/beam/releases/tag/v2.59.0)
 
-### 2.58.1 (2024-08-15)
+#### 2.58.1 (2024-08-15)
 Official [source code download](https://archive.apache.org/dist/beam/2.58.1/apache-beam-2.58.1-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.58.1/apache-beam-2.58.1-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.58.1/apache-beam-2.58.1-source-release.zip.asc).
 
 [Release notes](https://github.com/apache/beam/releases/tag/v2.58.1)
 
-### 2.58.0 (2024-08-06)
+#### 2.58.0 (2024-08-06)
 Official [source code download](https://archive.apache.org/dist/beam/2.58.0/apache-beam-2.58.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.58.0/apache-beam-2.58.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.58.0/apache-beam-2.58.0-source-release.zip.asc).
 
 [Release notes](https://github.com/apache/beam/releases/tag/v2.58.0)
 
-### 2.57.0 (2024-06-26)
+#### 2.57.0 (2024-06-26)
 Official [source code download](https://archive.apache.org/dist/beam/2.57.0/apache-beam-2.57.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.57.0/apache-beam-2.57.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.57.0/apache-beam-2.57.0-source-release.zip.asc).
 
 [Release notes](https://github.com/apache/beam/releases/tag/v2.57.0)
 
-### 2.56.0 (2024-05-01)
+#### 2.56.0 (2024-05-01)
 Official [source code download](https://archive.apache.org/dist/beam/2.56.0/apache-beam-2.56.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.56.0/apache-beam-2.56.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.56.0/apache-beam-2.56.0-source-release.zip.asc).
 
 [Release notes](https://github.com/apache/beam/releases/tag/v2.56.0)
 
-### 2.55.1 (2024-03-25)
+#### 2.55.1 (2024-03-25)
 Official [source code download](https://archive.apache.org/dist/beam/2.55.1/apache-beam-2.55.1-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.55.1/apache-beam-2.55.1-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.55.1/apache-beam-2.55.1-source-release.zip.asc).
 
 [Release notes](https://github.com/apache/beam/releases/tag/v2.55.1)
 
-### 2.55.0 (2024-03-25)
+#### 2.55.0 (2024-03-25)
 Official [source code download](https://archive.apache.org/dist/beam/2.55.0/apache-beam-2.55.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.55.0/apache-beam-2.55.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.55.0/apache-beam-2.55.0-source-release.zip.asc).
@@ -204,7 +221,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.55.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.55.0)
 [Blog post](/blog/beam-2.55.0).
 
-### 2.54.0 (2024-02-14)
+#### 2.54.0 (2024-02-14)
 Official [source code download](https://archive.apache.org/dist/beam/2.54.0/apache-beam-2.54.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.54.0/apache-beam-2.54.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.54.0/apache-beam-2.54.0-source-release.zip.asc).
@@ -212,7 +229,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.54.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.54.0)
 [Blog post](/blog/beam-2.54.0).
 
-### 2.53.0 (2024-01-04)
+#### 2.53.0 (2024-01-04)
 Official [source code download](https://archive.apache.org/dist/beam/2.53.0/apache-beam-2.53.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.53.0/apache-beam-2.53.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.53.0/apache-beam-2.53.0-source-release.zip.asc).
@@ -220,7 +237,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.53.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.53.0)
 [Blog post](/blog/beam-2.53.0).
 
-### 2.52.0 (2023-11-17)
+#### 2.52.0 (2023-11-17)
 Official [source code download](https://archive.apache.org/dist/beam/2.52.0/apache-beam-2.52.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.52.0/apache-beam-2.52.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.52.0/apache-beam-2.52.0-source-release.zip.asc).
@@ -228,7 +245,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.52.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.52.0)
 [Blog post](/blog/beam-2.52.0).
 
-### 2.51.0 (2023-10-11)
+#### 2.51.0 (2023-10-11)
 Official [source code download](https://archive.apache.org/dist/beam/2.51.0/apache-beam-2.51.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.51.0/apache-beam-2.51.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.51.0/apache-beam-2.51.0-source-release.zip.asc).
@@ -236,7 +253,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.51.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.51.0)
 [Blog post](/blog/beam-2.51.0).
 
-### 2.50.0 (2023-08-30)
+#### 2.50.0 (2023-08-30)
 Official [source code download](https://archive.apache.org/dist/beam/2.50.0/apache-beam-2.50.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.50.0/apache-beam-2.50.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.50.0/apache-beam-2.50.0-source-release.zip.asc).
@@ -244,7 +261,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.50.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.50.0)
 [Blog post](/blog/beam-2.50.0).
 
-### 2.49.0 (2023-07-17)
+#### 2.49.0 (2023-07-17)
 Official [source code download](https://archive.apache.org/dist/beam/2.49.0/apache-beam-2.49.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.49.0/apache-beam-2.49.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.49.0/apache-beam-2.49.0-source-release.zip.asc).
@@ -252,7 +269,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.49.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.49.0)
 [Blog post](/blog/beam-2.49.0).
 
-### 2.48.0 (2023-05-31)
+#### 2.48.0 (2023-05-31)
 Official [source code download](https://archive.apache.org/dist/beam/2.48.0/apache-beam-2.48.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.48.0/apache-beam-2.48.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.48.0/apache-beam-2.48.0-source-release.zip.asc).
@@ -260,7 +277,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.48.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.48.0)
 [Blog post](/blog/beam-2.48.0).
 
-### 2.47.0 (2023-05-10)
+#### 2.47.0 (2023-05-10)
 Official [source code download](https://archive.apache.org/dist/beam/2.47.0/apache-beam-2.47.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.47.0/apache-beam-2.47.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.47.0/apache-beam-2.47.0-source-release.zip.asc).
@@ -268,7 +285,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.47.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.47.0)
 [Blog post](/blog/beam-2.47.0).
 
-### 2.46.0 (2023-03-10)
+#### 2.46.0 (2023-03-10)
 Official [source code download](https://archive.apache.org/dist/beam/2.46.0/apache-beam-2.46.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.46.0/apache-beam-2.46.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.46.0/apache-beam-2.46.0-source-release.zip.asc).
@@ -276,7 +293,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.46.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.46.0)
 [Blog post](/blog/beam-2.46.0).
 
-### 2.45.0 (2023-02-15)
+#### 2.45.0 (2023-02-15)
 Official [source code download](https://archive.apache.org/dist/beam/2.45.0/apache-beam-2.45.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.45.0/apache-beam-2.45.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.45.0/apache-beam-2.45.0-source-release.zip.asc).
@@ -284,7 +301,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.45.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.45.0)
 [Blog post](/blog/beam-2.45.0).
 
-### 2.44.0 (2023-01-12)
+#### 2.44.0 (2023-01-12)
 Official [source code download](https://archive.apache.org/dist/beam/2.44.0/apache-beam-2.44.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.44.0/apache-beam-2.44.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.44.0/apache-beam-2.44.0-source-release.zip.asc).
@@ -292,7 +309,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.44.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.44.0)
 [Blog post](/blog/beam-2.44.0).
 
-### 2.43.0 (2022-11-17)
+#### 2.43.0 (2022-11-17)
 Official [source code download](https://archive.apache.org/dist/beam/2.43.0/apache-beam-2.43.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.43.0/apache-beam-2.43.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.43.0/apache-beam-2.43.0-source-release.zip.asc).
@@ -300,7 +317,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.43.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.43.0)
 [Blog post](/blog/beam-2.43.0).
 
-### 2.42.0 (2022-10-17)
+#### 2.42.0 (2022-10-17)
 Official [source code download](https://archive.apache.org/dist/beam/2.42.0/apache-beam-2.42.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.42.0/apache-beam-2.42.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.42.0/apache-beam-2.42.0-source-release.zip.asc).
@@ -308,7 +325,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.42.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.42.0)
 [Blog post](/blog/beam-2.42.0).
 
-### 2.41.0 (2022-08-23)
+#### 2.41.0 (2022-08-23)
 Official [source code download](https://archive.apache.org/dist/beam/2.41.0/apache-beam-2.41.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.41.0/apache-beam-2.41.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.41.0/apache-beam-2.41.0-source-release.zip.asc).
@@ -316,7 +333,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.41.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.41.0)
 [Blog post](/blog/beam-2.41.0).
 
-### 2.40.0 (2022-06-25)
+#### 2.40.0 (2022-06-25)
 Official [source code download](https://archive.apache.org/dist/beam/2.40.0/apache-beam-2.40.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.40.0/apache-beam-2.40.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.40.0/apache-beam-2.40.0-source-release.zip.asc).
@@ -324,7 +341,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.40.0/apac
 [Release notes](https://github.com/apache/beam/releases/tag/v2.40.0)
 [Blog post](/blog/beam-2.40.0).
 
-### 2.39.0 (2022-05-25)
+#### 2.39.0 (2022-05-25)
 Official [source code download](https://archive.apache.org/dist/beam/2.39.0/apache-beam-2.39.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.39.0/apache-beam-2.39.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.39.0/apache-beam-2.39.0-source-release.zip.asc).
@@ -332,7 +349,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.39.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12351169)
 [Blog post](/blog/beam-2.39.0).
 
-### 2.38.0 (2022-04-20)
+#### 2.38.0 (2022-04-20)
 Official [source code download](https://archive.apache.org/dist/beam/2.38.0/apache-beam-2.38.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.38.0/apache-beam-2.38.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.38.0/apache-beam-2.38.0-source-release.zip.asc).
@@ -340,7 +357,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.38.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12351169)
 [Blog post](/blog/beam-2.38.0).
 
-### 2.37.0 (2022-03-04)
+#### 2.37.0 (2022-03-04)
 Official [source code download](https://archive.apache.org/dist/beam/2.37.0/apache-beam-2.37.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.37.0/apache-beam-2.37.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.37.0/apache-beam-2.37.0-source-release.zip.asc).
@@ -348,7 +365,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.37.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12351168)
 [Blog post](/blog/beam-2.37.0).
 
-### 2.36.0 (2022-02-07)
+#### 2.36.0 (2022-02-07)
 Official [source code download](https://archive.apache.org/dist/beam/2.36.0/apache-beam-2.36.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.36.0/apache-beam-2.36.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.36.0/apache-beam-2.36.0-source-release.zip.asc).
@@ -356,7 +373,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.36.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12350407)
 [Blog post](/blog/beam-2.36.0).
 
-### 2.35.0 (2021-12-29)
+#### 2.35.0 (2021-12-29)
 Official [source code download](https://archive.apache.org/dist/beam/2.35.0/apache-beam-2.35.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.35.0/apache-beam-2.35.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.35.0/apache-beam-2.35.0-source-release.zip.asc).
@@ -364,7 +381,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.35.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12350406)
 [Blog post](/blog/beam-2.35.0).
 
-### 2.34.0 (2021-11-11)
+#### 2.34.0 (2021-11-11)
 Official [source code download](https://archive.apache.org/dist/beam/2.34.0/apache-beam-2.34.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.34.0/apache-beam-2.34.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.34.0/apache-beam-2.34.0-source-release.zip.asc).
@@ -372,7 +389,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.34.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12350405)
 [Blog post](/blog/beam-2.34.0).
 
-### 2.33.0 (2021-10-07)
+#### 2.33.0 (2021-10-07)
 Official [source code download](https://archive.apache.org/dist/beam/2.33.0/apache-beam-2.33.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.33.0/apache-beam-2.33.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.33.0/apache-beam-2.33.0-source-release.zip.asc).
@@ -380,7 +397,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.33.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12350404)
 [Blog post](/blog/beam-2.33.0).
 
-### 2.32.0 (2021-08-25)
+#### 2.32.0 (2021-08-25)
 Official [source code download](https://archive.apache.org/dist/beam/2.32.0/apache-beam-2.32.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.32.0/apache-beam-2.32.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.32.0/apache-beam-2.32.0-source-release.zip.asc).
@@ -388,7 +405,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.32.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12349992)
 [Blog post](/blog/beam-2.32.0).
 
-### 2.31.0 (2021-07-08)
+#### 2.31.0 (2021-07-08)
 Official [source code download](https://archive.apache.org/dist/beam/2.31.0/apache-beam-2.31.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.31.0/apache-beam-2.31.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.31.0/apache-beam-2.31.0-source-release.zip.asc).
@@ -396,7 +413,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.31.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12349991)
 [Blog post](/blog/beam-2.31.0).
 
-### 2.30.0 (2021-06-09)
+#### 2.30.0 (2021-06-09)
 Official [source code download](https://archive.apache.org/dist/beam/2.30.0/apache-beam-2.30.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.30.0/apache-beam-2.30.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.30.0/apache-beam-2.30.0-source-release.zip.asc).
@@ -404,7 +421,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.30.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12349978)
 [Blog post](/blog/beam-2.30.0).
 
-### 2.29.0 (2021-04-27)
+#### 2.29.0 (2021-04-27)
 Official [source code download](https://archive.apache.org/dist/beam/2.29.0/apache-beam-2.29.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.29.0/apache-beam-2.29.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.29.0/apache-beam-2.29.0-source-release.zip.asc).
@@ -412,7 +429,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.29.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12349629)
 [Blog post](/blog/beam-2.29.0).
 
-### 2.28.0 (2021-02-22)
+#### 2.28.0 (2021-02-22)
 Official [source code download](https://archive.apache.org/dist/beam/2.28.0/apache-beam-2.28.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.28.0/apache-beam-2.28.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.28.0/apache-beam-2.28.0-source-release.zip.asc).
@@ -420,7 +437,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.28.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12349499).
 [Blog post](/blog/beam-2.28.0).
 
-### 2.27.0 (2020-12-22)
+#### 2.27.0 (2020-12-22)
 Official [source code download](https://archive.apache.org/dist/beam/2.27.0/apache-beam-2.27.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.27.0/apache-beam-2.27.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.27.0/apache-beam-2.27.0-source-release.zip.asc).
@@ -428,7 +445,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.27.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12349380).
 [Blog post](/blog/beam-2.27.0).
 
-### 2.26.0 (2020-12-11)
+#### 2.26.0 (2020-12-11)
 Official [source code download](https://archive.apache.org/dist/beam/2.26.0/apache-beam-2.26.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.26.0/apache-beam-2.26.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.26.0/apache-beam-2.26.0-source-release.zip.asc).
@@ -436,7 +453,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.26.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12348833).
 [Blog post](/blog/beam-2.26.0).
 
-### 2.25.0 (2020-10-23)
+#### 2.25.0 (2020-10-23)
 Official [source code download](https://archive.apache.org/dist/beam/2.25.0/apache-beam-2.25.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.25.0/apache-beam-2.25.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.25.0/apache-beam-2.25.0-source-release.zip.asc).
@@ -444,7 +461,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.25.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12347147).
 [Blog post](/blog/beam-2.25.0).
 
-### 2.24.0 (2020-09-18)
+#### 2.24.0 (2020-09-18)
 Official [source code download](https://archive.apache.org/dist/beam/2.24.0/apache-beam-2.24.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.24.0/apache-beam-2.24.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.24.0/apache-beam-2.24.0-source-release.zip.asc).
@@ -452,7 +469,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.24.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12347146).
 [Blog post](/blog/beam-2.24.0).
 
-### 2.23.0 (2020-07-29)
+#### 2.23.0 (2020-07-29)
 Official [source code download](https://archive.apache.org/dist/beam/2.23.0/apache-beam-2.23.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.23.0/apache-beam-2.23.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.23.0/apache-beam-2.23.0-source-release.zip.asc).
@@ -460,7 +477,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.23.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12347145).
 [Blog post](/blog/beam-2.23.0).
 
-### 2.22.0 (2020-06-08)
+#### 2.22.0 (2020-06-08)
 Official [source code download](https://archive.apache.org/dist/beam/2.22.0/apache-beam-2.22.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.22.0/apache-beam-2.22.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.22.0/apache-beam-2.22.0-source-release.zip.asc).
@@ -468,7 +485,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.22.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12347144).
 [Blog post](/blog/beam-2.22.0).
 
-### 2.21.0 (2020-05-27)
+#### 2.21.0 (2020-05-27)
 Official [source code download](https://archive.apache.org/dist/beam/2.21.0/apache-beam-2.21.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.21.0/apache-beam-2.21.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.21.0/apache-beam-2.21.0-source-release.zip.asc).
@@ -476,7 +493,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.21.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12347143).
 [Blog post](/blog/beam-2.21.0).
 
-### 2.20.0 (2020-04-15)
+#### 2.20.0 (2020-04-15)
 Official [source code download](https://archive.apache.org/dist/beam/2.20.0/apache-beam-2.20.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.20.0/apache-beam-2.20.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.20.0/apache-beam-2.20.0-source-release.zip.asc).
@@ -484,7 +501,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.20.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12346780).
 [Blog post](/blog/beam-2.20.0).
 
-### 2.19.0 (2020-02-04)
+#### 2.19.0 (2020-02-04)
 Official [source code download](https://archive.apache.org/dist/beam/2.19.0/apache-beam-2.19.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.19.0/apache-beam-2.19.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.19.0/apache-beam-2.19.0-source-release.zip.asc).
@@ -492,7 +509,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.19.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12346582).
 [Blog post](/blog/beam-2.19.0).
 
-### 2.18.0 (2020-01-23)
+#### 2.18.0 (2020-01-23)
 Official [source code download](https://archive.apache.org/dist/beam/2.18.0/apache-beam-2.18.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.18.0/apache-beam-2.18.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.18.0/apache-beam-2.18.0-source-release.zip.asc).
@@ -500,7 +517,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.18.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?version=12346383&projectId=12319527).
 [Blog post](/blog/beam-2.18.0).
 
-### 2.17.0 (2020-01-06)
+#### 2.17.0 (2020-01-06)
 Official [source code download](https://archive.apache.org/dist/beam/2.17.0/apache-beam-2.17.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.17.0/apache-beam-2.17.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.17.0/apache-beam-2.17.0-source-release.zip.asc).
@@ -508,7 +525,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.17.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12345970).
 [Blog post](/blog/beam-2.17.0).
 
-### 2.16.0 (2019-10-07)
+#### 2.16.0 (2019-10-07)
 Official [source code download](https://archive.apache.org/dist/beam/2.16.0/apache-beam-2.16.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.16.0/apache-beam-2.16.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.16.0/apache-beam-2.16.0-source-release.zip.asc).
@@ -516,7 +533,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.16.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12345494).
 [Blog post](/blog/beam-2.16.0).
 
-### 2.15.0 (2019-08-22)
+#### 2.15.0 (2019-08-22)
 Official [source code download](https://archive.apache.org/dist/beam/2.15.0/apache-beam-2.15.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.15.0/apache-beam-2.15.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.15.0/apache-beam-2.15.0-source-release.zip.asc).
@@ -524,7 +541,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.15.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12345489).
 [Blog post](/blog/beam-2.15.0).
 
-### 2.14.0 (2019-08-01)
+#### 2.14.0 (2019-08-01)
 Official [source code download](https://archive.apache.org/dist/beam/2.14.0/apache-beam-2.14.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.14.0/apache-beam-2.14.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.14.0/apache-beam-2.14.0-source-release.zip.asc).
@@ -532,7 +549,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.14.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12345431).
 [Blog post](/blog/beam-2.14.0).
 
-### 2.13.0 (2019-05-21)
+#### 2.13.0 (2019-05-21)
 Official [source code download](https://archive.apache.org/dist/beam/2.13.0/apache-beam-2.13.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.13.0/apache-beam-2.13.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.13.0/apache-beam-2.13.0-source-release.zip.asc).
@@ -540,7 +557,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.13.0/apac
 [Release notes](https://jira.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12345166).
 [Blog post](/blog/beam-2.13.0).
 
-### 2.12.0 (2019-04-25)
+#### 2.12.0 (2019-04-25)
 Official [source code download](https://archive.apache.org/dist/beam/2.12.0/apache-beam-2.12.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.12.0/apache-beam-2.12.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.12.0/apache-beam-2.12.0-source-release.zip.asc).
@@ -548,7 +565,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.12.0/apac
 [Release notes](https://jira.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12344944).
 [Blog post](/blog/beam-2.12.0).
 
-### 2.11.0 (2019-02-26)
+#### 2.11.0 (2019-02-26)
 Official [source code download](https://archive.apache.org/dist/beam/2.11.0/apache-beam-2.11.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.11.0/apache-beam-2.11.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.11.0/apache-beam-2.11.0-source-release.zip.asc).
@@ -556,7 +573,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.11.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12344775).
 [Blog post](/blog/beam-2.11.0).
 
-### 2.10.0 (2019-02-01)
+#### 2.10.0 (2019-02-01)
 Official [source code download](https://archive.apache.org/dist/beam/2.10.0/apache-beam-2.10.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.10.0/apache-beam-2.10.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.10.0/apache-beam-2.10.0-source-release.zip.asc).
@@ -564,7 +581,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.10.0/apac
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12344540).
 [Blog post](/blog/beam-2.10.0).
 
-### 2.9.0 (2018-12-13)
+#### 2.9.0 (2018-12-13)
 Official [source code download](https://archive.apache.org/dist/beam/2.9.0/apache-beam-2.9.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.9.0/apache-beam-2.9.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.9.0/apache-beam-2.9.0-source-release.zip.asc).
@@ -572,7 +589,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.9.0/apach
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12344258).
 [Blog post](/blog/beam-2.9.0).
 
-### 2.8.0 (2018-10-26)
+#### 2.8.0 (2018-10-26)
 Official [source code download](https://archive.apache.org/dist/beam/2.8.0/apache-beam-2.8.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.8.0/apache-beam-2.8.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.8.0/apache-beam-2.8.0-source-release.zip.asc).
@@ -580,7 +597,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.8.0/apach
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12343985).
 [Blog post](/blog/beam-2.8.0).
 
-### 2.7.0 LTS (2018-10-02)
+#### 2.7.0 LTS (2018-10-02)
 Official [source code download](https://archive.apache.org/dist/beam/2.7.0/apache-beam-2.7.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.7.0/apache-beam-2.7.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.7.0/apache-beam-2.7.0-source-release.zip.asc).
@@ -592,7 +609,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.7.0/apach
 
 *LTS Update (2020-04-06):* Due to the lack of interest from users the Beam community decided not to maintain or publish new LTS releases. We encourage users to update early and often to the most recent releases.
 
-### 2.6.0 (2018-08-08)
+#### 2.6.0 (2018-08-08)
 Official [source code download](https://archive.apache.org/dist/beam/2.6.0/apache-beam-2.6.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.6.0/apache-beam-2.6.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.6.0/apache-beam-2.6.0-source-release.zip.asc).
@@ -600,7 +617,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.6.0/apach
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12343392).
 [Blog post](/blog/beam-2.6.0).
 
-### 2.5.0 (2018-06-06)
+#### 2.5.0 (2018-06-06)
 Official [source code download](https://archive.apache.org/dist/beam/2.5.0/apache-beam-2.5.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.5.0/apache-beam-2.5.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.5.0/apache-beam-2.5.0-source-release.zip.asc).
@@ -608,7 +625,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.5.0/apach
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12342847).
 [Blog post](/blog/beam-2.5.0).
 
-### 2.4.0 (2018-03-20)
+#### 2.4.0 (2018-03-20)
 Official [source code download](https://archive.apache.org/dist/beam/2.4.0/apache-beam-2.4.0-source-release.zip).
 [SHA-512](https://archive.apache.org/dist/beam/2.4.0/apache-beam-2.4.0-source-release.zip.sha512).
 [signature](https://archive.apache.org/dist/beam/2.4.0/apache-beam-2.4.0-source-release.zip.asc).
@@ -616,7 +633,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.4.0/apach
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12342682).
 [Blog post](/blog/beam-2.4.0).
 
-### 2.3.0 (2018-01-30)
+#### 2.3.0 (2018-01-30)
 Official [source code download](https://archive.apache.org/dist/beam/2.3.0/apache-beam-2.3.0-source-release.zip).
 [SHA-1](https://archive.apache.org/dist/beam/2.3.0/apache-beam-2.3.0-source-release.zip.sha1).
 [MD5](https://archive.apache.org/dist/beam/2.3.0/apache-beam-2.3.0-source-release.zip.md5).
@@ -625,7 +642,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.3.0/apach
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12341608).
 [Blog post](/blog/beam-2.3.0).
 
-### 2.2.0 (2017-12-02)
+#### 2.2.0 (2017-12-02)
 Official [source code download](https://archive.apache.org/dist/beam/2.2.0/apache-beam-2.2.0-source-release.zip).
 [SHA-1](https://archive.apache.org/dist/beam/2.2.0/apache-beam-2.2.0-source-release.zip.sha1).
 [MD5](https://archive.apache.org/dist/beam/2.2.0/apache-beam-2.2.0-source-release.zip.md5).
@@ -633,7 +650,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.2.0/apach
 
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12341044).
 
-### 2.1.0 (2017-08-23)
+#### 2.1.0 (2017-08-23)
 Official [source code download](https://archive.apache.org/dist/beam/2.1.0/apache-beam-2.1.0-source-release.zip).
 [SHA-1](https://archive.apache.org/dist/beam/2.1.0/apache-beam-2.1.0-source-release.zip.sha1).
 [MD5](https://archive.apache.org/dist/beam/2.1.0/apache-beam-2.1.0-source-release.zip.md5).
@@ -641,7 +658,7 @@ Official [source code download](https://archive.apache.org/dist/beam/2.1.0/apach
 
 [Release notes](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12319527&version=12340528).
 
-### 2.0.0 (2017-05-17)
+#### 2.0.0 (2017-05-17)
 Official [source code download](https://archive.apache.org/dist/beam/2.0.0/apache-beam-2.0.0-source-release.zip).
 [SHA-1](https://archive.apache.org/dist/beam/2.0.0/apache-beam-2.0.0-source-release.zip.sha1).
 [MD5](https://archive.apache.org/dist/beam/2.0.0/apache-beam-2.0.0-source-release.zip.md5).
diff --git a/website/www/site/content/en/performance/_index.md b/website/www/site/content/en/performance/_index.md
index e6034b7711df..17bdc6f3de0a 100644
--- a/website/www/site/content/en/performance/_index.md
+++ b/website/www/site/content/en/performance/_index.md
@@ -56,3 +56,4 @@ See the following pages for performance measures recorded when running various B
 - [PyTorch Vision Classification Resnet 152](/performance/pytorchresnet152)
 - [PyTorch Vision Classification Resnet 152 Tesla T4 GPU](/performance/pytorchresnet152tesla)
 - [TensorFlow MNIST Image Classification](/performance/tensorflowmnist)
+- [VLLM Gemma Batch Completion Tesla T4 GPU](/performance/vllmgemmabatchtesla)
diff --git a/website/www/site/content/en/performance/vllmgemmabatchtesla/_index.md b/website/www/site/content/en/performance/vllmgemmabatchtesla/_index.md
new file mode 100644
index 000000000000..d5a8d2c95309
--- /dev/null
+++ b/website/www/site/content/en/performance/vllmgemmabatchtesla/_index.md
@@ -0,0 +1,43 @@
+---
+title: "VLLM Gemma 2b Batch Performance on Tesla T4"
+---
+
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements.  See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License.  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ -->
+
+# VLLM Gemma 2b Batch Performance on Tesla T4
+
+**Model**: google/gemma-2b-it
+**Accelerator**: NVIDIA Tesla T4 GPU
+**Host**: 3 × n1-standard-8 (8 vCPUs, 30 GB RAM)
+
+The following graphs show various metrics when running VLLM Gemma 2b Batch Performance on Tesla T4 pipeline.
+See the [glossary](/performance/glossary) for definitions.
+
+Full pipeline implementation is available [here](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/inference/vllm_gemma_batch.py).
+
+## What is the estimated cost to run the pipeline?
+
+{{< performance_looks io="vllmgemmabatchtesla" read_or_write="write" section="cost" >}}
+
+## How has various metrics changed when running the pipeline for different Beam SDK versions?
+
+{{< performance_looks io="vllmgemmabatchtesla" read_or_write="write" section="version" >}}
+
+## How has various metrics changed over time when running the pipeline?
+
+{{< performance_looks io="vllmgemmabatchtesla" read_or_write="write" section="date" >}}
diff --git a/website/www/site/content/en/roadmap/java-sdk.md b/website/www/site/content/en/roadmap/java-sdk.md
index a1c85e139193..d40dabec20fd 100644
--- a/website/www/site/content/en/roadmap/java-sdk.md
+++ b/website/www/site/content/en/roadmap/java-sdk.md
@@ -17,9 +17,9 @@ limitations under the License.
 
 # Java SDK Roadmap
 
-## Next Java LTS version support (Java 21)
+## Next Java LTS version support (Java 25)
 
 Work to support the next LTS release of Java is in progress. For more details
 about the scope and info on the various tasks please see the GitHub Issue.
 
-- GitHub: [#28120](https://github.com/apache/beam/issues/28120)
+- GitHub: [#35627](https://github.com/apache/beam/issues/35627)
diff --git a/website/www/site/content/en/roadmap/nemo-runner.md b/website/www/site/content/en/roadmap/nemo-runner.md
index d387e5ea4362..a0445129fc41 100644
--- a/website/www/site/content/en/roadmap/nemo-runner.md
+++ b/website/www/site/content/en/roadmap/nemo-runner.md
@@ -17,7 +17,9 @@ limitations under the License.
 
 # Apache Nemo Runner Roadmap
 
-This roadmap is in progress. In the meantime, here are available resources:
+**Note** Apache Nemo has been retired from incubation ([status](https://incubator.apache.org/projects/index.html#nemo)).
+
+For references, here are available resources:
 
  - [Runner documentation](/documentation/runners/nemo)
  - JIRA: [runner-nemo](https://issues.apache.org/jira/issues/?jql=project%20%3D%20BEAM%20AND%20component%20%3D%20runner-nemo) / [nemo-jira](https://issues.apache.org/jira/projects/NEMO/issues/filter=allopenissues)
diff --git a/website/www/site/content/en/roadmap/samza-runner.md b/website/www/site/content/en/roadmap/samza-runner.md
index 652d5497155e..c27430601b09 100644
--- a/website/www/site/content/en/roadmap/samza-runner.md
+++ b/website/www/site/content/en/roadmap/samza-runner.md
@@ -17,7 +17,9 @@ limitations under the License.
 
 # Samza Runner Roadmap
 
-This roadmap is in progress. In the meantime, here are available resources:
+**Note** Samza runner is deprecated and the support is planned to be removed in Beam 3.0 ([Issue](https://github.com/apache/beam/issues/35448)).
+
+For references, here are available resources:
 
  - [Runner documentation](/documentation/runners/samza)
  - Issues: [runner-samza](https://github.com/apache/beam/issues?q=is%3Aopen+is%3Aissue+label%3Arunner-samza)
diff --git a/website/www/site/content/en/roadmap/twister2-runner.md b/website/www/site/content/en/roadmap/twister2-runner.md
index a521ca16209a..d4060c0f8efa 100644
--- a/website/www/site/content/en/roadmap/twister2-runner.md
+++ b/website/www/site/content/en/roadmap/twister2-runner.md
@@ -17,7 +17,9 @@ limitations under the License.
 
 # Twister2 Runner Roadmap
 
-This roadmap is in progress. In the meantime, here are available resources:
+**Note** Twister2 runner is deprecated and the support is planned to be removed in Beam 3.0 ([Issue](https://github.com/apache/beam/issues/35905)).
+
+For references, here are available resources:
 
  - [Runner documentation](/documentation/runners/twister2)
  - Issues: [runner-twister2](https://github.com/apache/beam/issues?q=is%3Aopen+is%3Aissue+label%3Arunner-twister2)
diff --git a/website/www/site/data/authors.yml b/website/www/site/data/authors.yml
index 543c70974b43..f5fcaf42814c 100644
--- a/website/www/site/data/authors.yml
+++ b/website/www/site/data/authors.yml
@@ -40,6 +40,9 @@ chadrik:
 chamikara:
   name: Chamikara Jayalath
   email: chamikara@apache.org
+charlespnh:
+  name: Charles Nguyen
+  email: phucnh402@gmail.com
 damccorm:
   name: Danny McCormick
   email: dannymccormick@google.com
@@ -103,6 +106,10 @@ klk:
   name: Kenneth Knowles
   email: kenn@apache.org
   twitter: KennKnowles
+ksobrenat32:
+  name: Enrique Calderon
+  email: ksobrenat32@ks32.dev
+  twitter:
 lkuligin:
   name: Leonid Kuligin
   email: kuligin@google.com
diff --git a/website/www/site/data/capability_matrix.yaml b/website/www/site/data/capability_matrix.yaml
index e6fd51a9bb17..c1da306b9cb7 100644
--- a/website/www/site/data/capability_matrix.yaml
+++ b/website/www/site/data/capability_matrix.yaml
@@ -14,6 +14,8 @@ capability-matrix:
   columns:
     - class: dataflow
       name: Google Cloud Dataflow
+    - class: prism
+      name: Prism Local Runner
     - class: flink
       name: Apache Flink
     - class: spark-rdd
@@ -50,6 +52,10 @@ capability-matrix:
               l1: "Yes"
               l2: fully supported
               l3: Batch mode uses large bundle sizes. Streaming uses smaller bundle sizes.
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: Supported with per-element transformation.
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -93,6 +99,10 @@ capability-matrix:
               l1: "Yes"
               l2: fully supported
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -136,6 +146,10 @@ capability-matrix:
               l1: "Yes"
               l2: fully supported
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -179,6 +193,10 @@ capability-matrix:
               l1: "Yes"
               l2: "efficient execution"
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: "fully supported"
@@ -222,6 +240,10 @@ capability-matrix:
               l1: "Partially"
               l2: supported via inlining
               l3: Currently composite transformations are inlined during execution. The structure is later recreated from the names, but other transform level information (if added to the model) will be lost.
+            - class: prism
+              l1: "Yes"
+              l2: supported via inlining
+              l3: ""
             - class: flink
               l1: "Partially"
               l2: supported via inlining
@@ -265,6 +287,10 @@ capability-matrix:
               l1: "Yes"
               l2: some size restrictions in streaming
               l3: Batch mode supports a distributed implementation, but streaming mode may force some size restrictions. Neither mode is able to push lookups directly up into key-based sources.
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: some size restrictions in streaming
@@ -308,6 +334,10 @@ capability-matrix:
               l1: "Yes"
               l2: fully supported
               l3: Support includes autotuning features (https://cloud.google.com/dataflow/service/dataflow-service-desc#autotuning-features).
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -351,6 +381,10 @@ capability-matrix:
               l1: "Partially"
               l2: ""
               l3: Gauge metrics are not supported. All other metric types are supported.
+            - class: prism
+              l1: "Partially"
+              l2: ""
+              l3: StringSet metrics are not supported. All other metric types are supported.
             - class: flink
               l1: "Partially"
               l2: All metrics types are supported.
@@ -394,6 +428,10 @@ capability-matrix:
               l1: "Partially"
               l2: non-merging windows
               l3: "State is supported for non-merging windows. The MapState, SetState, and MultimapState state types are supported in the following scenarios: Java pipelines that don't use Streaming Engine; Java pipelines that use Streaming Engine and version 2.58.0 or later of the Java SDK. SetState, MapState, and MultimapState are not supported for pipelines that use Runner v2."
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Partially"
               l2: non-merging windows
@@ -446,6 +484,10 @@ capability-matrix:
               l1: "Partially"
               l2: Only Dataflow Runner V2 supports this.
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Partially"
               l2: Only portable Flink Runner supports this.
@@ -489,6 +531,10 @@ capability-matrix:
               l1: "Partially"
               l2: Only Dataflow Runner V2 supports this.
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Partially"
               l2: Only portable Flink Runner supports this.
@@ -532,6 +578,10 @@ capability-matrix:
               l1: "Partially"
               l2: Only Dataflow Runner v2 supports this.
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Partially"
               l2: Only portable Flink Runner supports this.
@@ -575,6 +625,10 @@ capability-matrix:
               l1: "Partially"
               l2: Only Dataflow Runner V2 supports this.
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "No"
               l2:
@@ -618,6 +672,10 @@ capability-matrix:
               l1: "Partially"
               l2: Only Dataflow Runner V2 supports this.
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "No"
               l2:
@@ -670,6 +728,10 @@ capability-matrix:
               l1: "Yes"
               l2:
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Partially"
               l2:
@@ -713,6 +775,10 @@ capability-matrix:
               l1: "Yes"
               l2: fully supported
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "No"
               l2:
@@ -756,6 +822,10 @@ capability-matrix:
               l1: "Yes"
               l2:
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Partially"
               l2:
@@ -799,6 +869,10 @@ capability-matrix:
               l1: "No"
               l2:
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "No"
               l2:
@@ -842,6 +916,10 @@ capability-matrix:
               l1: "Partially"
               l2: Only Dataflow Runner V2 supports this.
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "No"
               l2:
@@ -894,6 +972,10 @@ capability-matrix:
               l1: "Yes"
               l2: default
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: default
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: supported
@@ -929,6 +1011,10 @@ capability-matrix:
               l1: "Yes"
               l2: built-in
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: supported
@@ -964,6 +1050,10 @@ capability-matrix:
               l1: "Yes"
               l2: built-in
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: supported
@@ -999,6 +1089,10 @@ capability-matrix:
               l1: "Yes"
               l2: built-in
               l3: ""
+            - class: prism
+              l1: "No"
+              l2:
+              l3:
             - class: flink
               l1: "Yes"
               l2: supported
@@ -1034,6 +1128,10 @@ capability-matrix:
               l1: "Yes"
               l2: supported
               l3: ""
+            - class: prism
+              l1: "No"
+              l2:
+              l3:
             - class: flink
               l1: "Yes"
               l2: supported
@@ -1069,6 +1167,10 @@ capability-matrix:
               l1: "Yes"
               l2: supported
               l3: ""
+            - class: prism
+              l1: "No"
+              l2:
+              l3:
             - class: flink
               l1: "Yes"
               l2: supported
@@ -1104,6 +1206,10 @@ capability-matrix:
               l1: "Yes"
               l2: supported
               l3: ""
+            - class: prism
+              l1: "No"
+              l2: "Not supported"
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: supported
@@ -1149,6 +1255,10 @@ capability-matrix:
               l1: "Yes"
               l2: fully supported
               l3: Fully supported in streaming mode. In batch mode, intermediate trigger firings are effectively meaningless.
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -1185,6 +1295,10 @@ capability-matrix:
               l1: "Yes"
               l2: yes in streaming, fixed granularity in batch
               l3: Fully supported in streaming mode. In batch mode, currently watermark progress jumps from the beginning of time to the end of time once the input has been fully consumed, thus no additional triggering granularity is available.
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -1221,6 +1335,10 @@ capability-matrix:
               l1: "Yes"
               l2: yes in streaming, fixed granularity in batch
               l3: Fully supported in streaming mode. In batch mode, from the perspective of triggers, processing time currently jumps from the beginning of time to the end of time once the input has been fully consumed, thus no additional triggering granularity is available.
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -1257,6 +1375,10 @@ capability-matrix:
               l1: "Yes"
               l2: fully supported
               l3: Fully supported in streaming mode. In batch mode, elements are processed in the largest bundles possible, so count-based triggers are effectively meaningless.
+            - class: prism
+              l1: "No"
+              l2: partially supported
+              l3: "Prism will double fire on count triggers"
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -1293,6 +1415,10 @@ capability-matrix:
               l1: "Yes"
               l2: fully supported
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -1329,6 +1455,10 @@ capability-matrix:
               l1: "Yes"
               l2: fully supported
               l3: Fully supported in streaming mode. In batch mode no data is ever late.
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -1365,6 +1495,10 @@ capability-matrix:
               l1: "Partially"
               l2: non-merging windows
               l3: Dataflow supports timers in non-merging windows.
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Partially"
               l2: non-merging windows
@@ -1410,6 +1544,10 @@ capability-matrix:
               l1: "Yes"
               l2: fully supported
               l3: ""
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -1446,6 +1584,10 @@ capability-matrix:
               l1: "Yes"
               l2: fully supported
               l3: Requires that the accumulated pane fits in memory, after being passed through the combiner (if relevant)
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
             - class: flink
               l1: "Yes"
               l2: fully supported
@@ -1491,6 +1633,10 @@ capability-matrix:
               l1: "Partially"
               l2:
               l3: Dataflow has a native drain operation, but it does not work in the presence of event time timer loops. Final implemention pending model support.
+            - class: prism
+              l1: "No"
+              l2:
+              l3:
             - class: flink
               l1: "Partially"
               l2:
@@ -1526,6 +1672,10 @@ capability-matrix:
               l1: "No"
               l2:
               l3:
+            - class: prism
+              l1: "No"
+              l2:
+              l3:
             - class: flink
               l1: "Partially"
               l2:
@@ -1561,6 +1711,14 @@ capability-matrix:
               l1: "Partially"
               l2:
               l3: Dataflow performs different shuffling algorithms for batch and streaming. Dataflow guarantees key-ordered delivery in streaming, though not in batch.
+            - class: prism
+              l1: "Yes"
+              l2: fully supported
+              l3: ""
+            - class: prism
+              l1: "Unverified"
+              l2:
+              l3:
             - class: flink
               l1: "Partially"
               l2:
diff --git a/website/www/site/data/performance.yaml b/website/www/site/data/performance.yaml
index 40842e63d95c..3dd7e68a9226 100644
--- a/website/www/site/data/performance.yaml
+++ b/website/www/site/data/performance.yaml
@@ -233,4 +233,20 @@ looks:
         - id: jWYjJFq6B7dSP6TjbypcfH5VycGHH6sC
           title: AvgThroughputBytesPerSec by Version
         - id: 8QhvYBRjYK29sS4HpK7CRJ42JKNFgvcg
+          title: AvgThroughputElementsPerSec by Version
+  vllmgemmabatchtesla:
+    write:
+      folder: 86
+      cost:
+        - id: tJWFWW3cnF2CWpmK2zZdXGvWmtNnJgrC
+          title: RunTime and EstimatedCost
+      date:
+        - id: J5TtpRykjwPs4W6S88FnJ28Tr8sSHpqN
+          title: AvgThroughputBytesPerSec by Date
+        - id: Jf6qGqN25Zf787DpkNDX5CBpGRvCGMXp
+          title: AvgThroughputElementsPerSec by Date
+      version:
+        - id: dKyJy5ZKhkBdSTXRY3wZR6fXzptSs2qm
+          title: AvgThroughputBytesPerSec by Version
+        - id: Qwxm27qY4fqT4CxXsFfKm2g3734TFJNN
           title: AvgThroughputElementsPerSec by Version
\ No newline at end of file
diff --git a/website/www/site/layouts/partials/section-menu/en/documentation.html b/website/www/site/layouts/partials/section-menu/en/documentation.html
index 6b37450786f9..1a60cfbdd9f1 100755
--- a/website/www/site/layouts/partials/section-menu/en/documentation.html
+++ b/website/www/site/layouts/partials/section-menu/en/documentation.html
@@ -297,6 +297,7 @@
               <ul class="section-nav-list">
                 <li><a href="/documentation/transforms/python/elementwise/enrichment/">Overview</a></li>
                 <li><a href="/documentation/transforms/python/elementwise/enrichment-bigtable/">Bigtable example</a></li>
+                <li><a href="/documentation/transforms/python/elementwise/enrichment-cloudsql/">CloudSQL example</a></li>
                 <li><a href="/documentation/transforms/python/elementwise/enrichment-vertexai/">Vertex AI Feature Store examples</a></li>
               </ul>
               </li>
@@ -356,6 +357,7 @@
             <li><a href="/documentation/transforms/python/other/create/">Create</a></li>
             <li><a href="/documentation/transforms/python/other/flatten/">Flatten</a></li>
             <li><a href="/documentation/transforms/python/other/reshuffle/">Reshuffle</a></li>
+            <li><a href="/documentation/transforms/python/other/waiton/">WaitOn</a></li>
             <li><a href="/documentation/transforms/python/other/windowinto/">WindowInto</a></li>
           </ul>
         </li>
@@ -417,6 +419,7 @@
             <li><a href="/documentation/transforms/java/other/flatten/">Flatten</a></li>
             <li><a href="/documentation/transforms/java/other/passert/">PAssert</a></li>
             <li><a href="/documentation/transforms/java/other/view/">View</a></li>
+            <li><a href="/documentation/transforms/java/other/wait/">Wait.On</a></li>
             <li><a href="/documentation/transforms/java/other/window/">Window</a></li>
           </ul>
         </li>