perf: coalesce empty checkpoints in batch collector - Empty checkpoints (used by map/parallel branch resubmitters when resuming from timed waits) no longer count toward the 250-operation batch limit beyond the first. This prevents 300+ concurrent branch resumes from splitting across multiple API batches.

Author: AdityaAudi

src/aws_durable_execution_sdk_python/state.py

@@ -592,15 +592,21 @@ def checkpoint_batches_forever(self) -> None:
             batch: list[QueuedOperation] = self._collect_checkpoint_batch()
 
             if batch:
-                # Extract OperationUpdates from QueuedOperations for API call
-                updates: list[OperationUpdate] = [
-                    q.operation_update for q in batch if q.operation_update is not None
-                ]
+                # Extract OperationUpdates, excluding empty checkpoints from API call
+                updates: list[OperationUpdate] = []
+                empty_count = 0
+
+                for q in batch:
+                    if q.operation_update is not None:
+                        updates.append(q.operation_update)
+                    else:
+                        empty_count += 1
 
                 logger.debug(
-                    "Processing checkpoint batch with %d operations (%d non-empty)",
-                    len(batch),
+                    "Sending %d OperationUpdates out of %d operations, excluding %d empty checkpoints",
                     len(updates),
+                    len(batch),
+                    empty_count,
                 )
 
                 try:
@@ -687,26 +693,41 @@ def _collect_checkpoint_batch(self) -> list[QueuedOperation]:
         operation if queues are empty, then collects additional operations within the time
         window.
 
+        Empty checkpoints (operation_update=None) are coalesced: the first empty checkpoint
+        counts toward the batch operation limit, but subsequent empty checkpoints do not.
+        All empty checkpoints remain in the batch so their completion events are signaled.
+        This avoids unnecessary batches when many concurrent map/parallel branches resume
+        simultaneously and each queues an empty checkpoint.
+
         Returns:
             List of QueuedOperation objects ready for batch processing. Returns empty list
             if no operations are available.
         """
         batch: list[QueuedOperation] = []
+        has_empty_checkpoint = False
         total_size = 0
+        effective_operation_count = 0  # Operations that count toward batch limit
 
         # First, drain overflow queue (FIFO order preserved)
         try:
-            while len(batch) < self._batcher_config.max_batch_operations:
+            while effective_operation_count < self._batcher_config.max_batch_operations:
                 overflow_op = self._overflow_queue.get_nowait()
-                op_size = self._calculate_operation_size(overflow_op)
-
-                if total_size + op_size > self._batcher_config.max_batch_size_bytes:
-                    # Put back and stop
-                    self._overflow_queue.put(overflow_op)
-                    break
 
-                batch.append(overflow_op)
-                total_size += op_size
+                if overflow_op.operation_update is None:  # Empty checkpoint
+                    batch.append(overflow_op)
+                    if not has_empty_checkpoint:
+                        effective_operation_count += 1  # First empty counts toward limit
+                        has_empty_checkpoint = True
+                    # Subsequent empties don't count toward limit
+                else:
+                    op_size = self._calculate_operation_size(overflow_op)
+                    if total_size + op_size > self._batcher_config.max_batch_size_bytes:
+                        # Put back and stop
+                        self._overflow_queue.put(overflow_op)
+                        break
+                    batch.append(overflow_op)
+                    total_size += op_size
+                    effective_operation_count += 1
         except queue.Empty:
             pass
 
@@ -720,7 +741,13 @@ def _collect_checkpoint_batch(self) -> list[QueuedOperation]:
                     )  # Check stop signal every 100ms
                     self._checkpoint_queue.task_done()
                     batch.append(first_op)
-                    total_size += self._calculate_operation_size(first_op)
+
+                    if first_op.operation_update is None:
+                        has_empty_checkpoint = True
+                    else:
+                        total_size += self._calculate_operation_size(first_op)
+
+                    effective_operation_count = 1
                     break
                 except queue.Empty:
                     continue
@@ -735,7 +762,7 @@ def _collect_checkpoint_batch(self) -> list[QueuedOperation]:
         # Collect additional operations within the time window
         while (
             time.time() < batch_deadline
-            and len(batch) < self._batcher_config.max_batch_operations
+            and effective_operation_count < self._batcher_config.max_batch_operations
             and not self._checkpointing_stopped.is_set()
         ):
             remaining_time = min(
@@ -749,26 +776,37 @@ def _collect_checkpoint_batch(self) -> list[QueuedOperation]:
             try:
                 additional_op = self._checkpoint_queue.get(timeout=remaining_time)
                 self._checkpoint_queue.task_done()
-                op_size = self._calculate_operation_size(additional_op)
-
-                # Check if adding this operation would exceed size limit
-                if total_size + op_size > self._batcher_config.max_batch_size_bytes:
-                    # Put in overflow queue for next batch
-                    self._overflow_queue.put(additional_op)
-                    logger.debug(
-                        "Batch size limit reached, moving operation to overflow queue"
-                    )
-                    break
 
-                batch.append(additional_op)
-                total_size += op_size
+                if additional_op.operation_update is None:  # Empty checkpoint
+                    batch.append(additional_op)
+                    if not has_empty_checkpoint:
+                        effective_operation_count += 1  # First empty counts toward limit
+                        has_empty_checkpoint = True
+                    # Subsequent empties don't count toward limit
+                else:
+                    op_size = self._calculate_operation_size(additional_op)
+                    # Check if adding this operation would exceed size limit
+                    if total_size + op_size > self._batcher_config.max_batch_size_bytes:
+                        # Put in overflow queue for next batch
+                        self._overflow_queue.put(additional_op)
+                        logger.debug(
+                            "Batch size limit reached, moving operation to overflow queue"
+                        )
+                        break
+                    batch.append(additional_op)
+                    total_size += op_size
+                    effective_operation_count += 1
 
             except queue.Empty:
                 break
 
+        empty_count = sum(1 for q in batch if q.operation_update is None)
         logger.debug(
-            "Collected batch of %d operations, total size: %d bytes",
+            "Collected batch of %d operations (%d effective, %d non-empty, %d empty), total size: %d bytes",
             len(batch),
+            effective_operation_count,
+            len(batch) - empty_count,
+            empty_count,
             total_size,
         )
         return batch

tests/e2e/map_with_concurrent_waits_int_test.py

@@ -0,0 +1,198 @@
+"""Integration test: empty checkpoint coalescing with concurrent map + wait.
+
+Python equivalent of the Java MapWithConditionAndCallbackExample referenced in
+issue #325. Verifies that when many concurrent map branches resume from timed
+wait operations simultaneously, the empty checkpoints produced by the
+resubmitter (executor.py) are coalesced into minimal API calls instead of
+being split across multiple batches.
+
+Background
+----------
+When a map branch suspends via TimedSuspendExecution and later resumes, the
+ConcurrentExecutor resubmitter calls::
+
+    execution_state.create_checkpoint()  # empty checkpoint
+
+before resubmitting the branch. In high-concurrency scenarios (300+ branches)
+all resuming at the same time, 300+ empty checkpoints flood the checkpoint
+queue.
+
+Without the coalescing optimization (issue #325), the 250-operation batch limit
+causes these to be split across multiple batches → multiple API calls.
+With the optimization, all subsequent empty checkpoints beyond the first do
+NOT count toward the batch limit, so they are coalesced into a single batch
+and a single API call.
+
+These tests directly simulate that concurrent-checkpoint pattern by launching
+many threads that each call ``create_checkpoint()`` simultaneously, mirroring
+what the map resubmitter does when all branches resume at once.
+"""
+
+from __future__ import annotations
+
+import threading
+from concurrent.futures import ThreadPoolExecutor
+
+import pytest
+
+from aws_durable_execution_sdk_python.lambda_service import (
+    CheckpointOutput,
+    CheckpointUpdatedExecutionState,
+    LambdaClient,
+    OperationAction,
+    OperationUpdate,
+    OperationType,
+)
+from aws_durable_execution_sdk_python.state import (
+    CheckpointBatcherConfig,
+    ExecutionState,
+    QueuedOperation,
+)
+from aws_durable_execution_sdk_python.threading import CompletionEvent
+
+from unittest.mock import Mock
+
+
+def _make_state(
+    mock_client: Mock,
+    batch_time: float = 5.0,
+    max_ops: int = 250,
+) -> ExecutionState:
+    config = CheckpointBatcherConfig(
+        max_batch_size_bytes=10 * 1024 * 1024,
+        max_batch_time_seconds=batch_time,
+        max_batch_operations=max_ops,
+    )
+    return ExecutionState(
+        durable_execution_arn="test-arn",
+        initial_checkpoint_token="token-0",  # noqa: S106
+        operations={},
+        service_client=mock_client,
+        batcher_config=config,
+    )
+
+
+def _make_tracking_client() -> tuple[Mock, list]:
+    """Return a (mock LambdaClient, checkpoint_calls list) pair."""
+    calls: list[list] = []
+    mock_client = Mock(spec=LambdaClient)
+
+    def _checkpoint(durable_execution_arn, checkpoint_token, updates, client_token=None):
+        calls.append(list(updates))
+        return CheckpointOutput(
+            checkpoint_token=f"token_{len(calls)}",
+            new_execution_state=CheckpointUpdatedExecutionState(),
+        )
+
+    mock_client.checkpoint = _checkpoint
+    return mock_client, calls
+
+
+def test_map_with_concurrent_waits_coalesces_empty_checkpoints():
+    """300 concurrent branches all create empty checkpoints simultaneously.
+
+    Simulates the Java MapWithConditionAndCallbackExample scenario: 300 map
+    branches all resuming from a wait operation at the same time, each calling
+    the resubmitter which enqueues an empty checkpoint.
+
+    Without the coalescing optimization, the 250-op batch limit splits 300
+    empty checkpoints into 2 batches (250 + 50) → 2 API calls.
+    With the optimization (effective_operation_count stays 1 for empties),
+    all 300 are collected in a single batch → 1 API call.
+    """
+    mock_client, calls = _make_tracking_client()
+    state = _make_state(mock_client, batch_time=5.0, max_ops=250)
+
+    batcher = ThreadPoolExecutor(max_workers=1)
+    batcher.submit(state.checkpoint_batches_forever)
+
+    # 300 branches all call create_checkpoint() concurrently, each blocking
+    # until the batch is processed — mirrors the resubmitter pattern.
+    branch_count = 300
+    start_barrier = threading.Barrier(branch_count)
+    errors: list[Exception] = []
+
+    def branch_work():
+        try:
+            start_barrier.wait()          # all start simultaneously
+            state.create_checkpoint()     # empty checkpoint, synchronous
+        except Exception as e:            # noqa: BLE001
+            errors.append(e)
+
+    threads = [threading.Thread(target=branch_work) for _ in range(branch_count)]
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join(timeout=30)
+
+    try:
+        assert not errors, f"Branch errors: {errors}"
+
+        # All 300 empty checkpoints should be batched into 1 API call.
+        # Without the fix, 300 > 250 limit would produce 2 calls.
+        assert len(calls) == 1, (
+            f"Expected 1 coalesced API call for {branch_count} concurrent empty "
+            f"checkpoints, got {len(calls)}. The 250-op limit must not split empties."
+        )
+        assert calls[0] == [], "Empty checkpoints should produce an empty updates list"
+    finally:
+        state.stop_checkpointing()
+        batcher.shutdown(wait=True)
+
+
+def test_map_with_concurrent_waits_api_call_count_scales_with_real_ops_not_empties():
+    """400 empty checkpoints + 10 real ops → 1 API call with limit=11.
+
+    Demonstrates that the effective batch count is driven by real operations
+    (and only the *first* empty), not the total number of empties.
+
+    With limit=11: the first empty counts as effective_op 1, and each of the
+    10 real ops increments the count (effective_ops 2–11). The limit is hit
+    exactly when the last real op is collected. All 399 remaining empties are
+    coalesced in without incrementing the count.
+
+    Result: 1 batch (410 operations, 10 real) → 1 API call.
+    """
+    mock_client, calls = _make_tracking_client()
+    # limit = 1 (first empty) + 10 (real ops) = 11, so all fit in one batch
+    state = _make_state(mock_client, batch_time=5.0, max_ops=11)
+
+    batcher = ThreadPoolExecutor(max_workers=1)
+    batcher.submit(state.checkpoint_batches_forever)
+
+    completion_events: list[CompletionEvent] = []
+
+    try:
+        # 400 empty checkpoints (simulating concurrent branch resumes)
+        for _ in range(400):
+            ev = CompletionEvent()
+            completion_events.append(ev)
+            state._checkpoint_queue.put(QueuedOperation(None, ev))  # noqa: SLF001
+
+        # 10 real operations alongside the empties
+        for i in range(10):
+            op = OperationUpdate(
+                operation_id=f"op_{i}",
+                operation_type=OperationType.STEP,
+                action=OperationAction.START,
+            )
+            ev = CompletionEvent()
+            completion_events.append(ev)
+            state._checkpoint_queue.put(QueuedOperation(op, ev))  # noqa: SLF001
+
+        # Wait for all 410 to be processed
+        for ev in completion_events:
+            ev.wait()
+
+        # 1 empty (effective=1) + 10 real ops (effective=11) exhaust the batch
+        # limit exactly. The 399 remaining empties coalesce in → still 1 API call.
+        assert len(calls) == 1, (
+            f"Expected 1 API call with 400 empty + 10 real ops (limit=11), "
+            f"got {len(calls)}."
+        )
+        # Only the 10 real ops appear in the updates list; empties are excluded.
+        real_op_ids = {u.operation_id for batch in calls for u in batch}
+        assert real_op_ids == {f"op_{i}" for i in range(10)}
+    finally:
+        state.stop_checkpointing()
+        batcher.shutdown(wait=True)