[SPARK-57003][SQL][SS] Widen stateful operator output and state schema nullability

### What changes were proposed in this pull request?

Introduce a three-component fix for stateful-operator nullability drift, gated by `spark.sql.streaming.statefulOperator.alwaysNullableOutput.enabled` (pinned per-query via the offset log):

- (a) `WidenStatefulOpNullability.widenStateSchema`: every stateful physical exec widens its state key/value schema to fully nullable at construction. This covers `StateStoreSaveExec`, `BaseStreamingDeduplicateExec`, `StreamingSymmetricHashJoinExec`, `FlatMapGroupsWithStateExec`, `TransformWithStateExec` (including user-defined state variable col family schemas), `TransformWithStateInPySparkExec`, and `StreamingGlobalLimitExec`.
- (b) `WidenStatefulOpNullability.widenOutputForStatefulOp`: every stateful logical and physical operator widens its declared `output` to fully nullable.
- (c) `WidenStatefulOperatorAttributeNullability`: an optimizer rule that widens `AttributeReference`s inside stateful ops' internal expressions and propagates upward through ancestor expressions. The rule uses `resolveOperatorsUp` (bottom-up) and scopes the widening precisely: at a stateful operator, all children's output is included (for internal expression references like grouping keys); at non-stateful ancestors, only children whose subtrees contain a stateful operator are included, avoiding unnecessary widening of non-stateful siblings. The node's own `p.output` is excluded for non-stateful ancestors because the bottom-up traversal guarantees children are already transformed.

With the above fix, we aim to ensure the state schema to be "fully" nullable (top level column, nested column, and collection types) regardless of the input schema, and the output schema of the stateful operator to be also "fully" nullable as well. The change of output schema for stateful operator is necessary, because even if the input schema is non-nullable, state can produce the null value, hence the output can be nullable.

### Why are the changes needed?

This has been a long standing issue of streaming engine vs Query Optimizer.

By the nature of streaming query, the query is meant to be long-running, in many cases spans to multiple Spark versions. Also, the logical plan is not always the same across batches (e.g. there are multiple stream sources and one of the source does not have a new data at batch N). This puts the streaming query to be affected by analyzer and optimizer.

The state schema of stateful operator is mostly determined by the input schema of the stateful operator, and nullability isn't an exception. If the input schema has a nullable column, state schema would have a nullable column. Vice versa with non-nullable column.

For Query Optimizer, one of the optimizations is to flip the nullability, say, nullable to non-nullable if appropriate. This can be done directly or indirectly, and the most problematic case is when the optimization is applied "selectively".

The one of easy example is the elimination of Union: for the streaming query with multiple streams using Union, batch N could have one stream be non-empty while another stream to be empty. For that case,`PropagateEmptyRelation` can drop empty `Union` branches, causing a per-column nullability flip that propagates into a stateful operator's state schema across microbatches or restarts. This causes either `STATE_STORE_KEY_SCHEMA_NOT_COMPATIBLE` on restart or a codegen NPE when state-restored rows carry nulls in columns declared non-nullable.

### Does this PR introduce _any_ user-facing change?

No user-visible behavior change for new queries (all stateful operator outputs become nullable, which is semantically correct). Existing queries keep their original behavior via the offset log gate.

### How was this patch tested?

New `StreamingStatefulOperatorNullabilityDriftSuite` covering:
- New-query path: Union-branch-drop restart scenarios for aggregate, dropDuplicates, dropDuplicatesWithinWatermark, stream-stream join, flatMapGroupsWithState, and transformWithState.
- Codegen NPE regression with struct grouping keys.
- Existing-query path: widening forced off still triggers schema mismatch.
- State schema assertion validates all state stores and column families (both v2 file format and v3 directory format including `_stateSchema`).
- Rule-level: scope check (non-stateful subtrees skipped).
- Helper-level: `deepWidenAttribute` recursion into nested types.

### Was this patch authored or co-authored using generative AI tooling?

Yes. Generated-by: Claude 4.7 Opus

Closes #56061 from HeartSaVioR/widen-stateful-op-nullability.

Authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
(cherry picked from commit 0fb04a4)
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>

Author: HeartSaVioR

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/WidenStatefulOperatorAttributeNullability.scala

@@ -0,0 +1,167 @@
+/*
+ * 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.spark.sql.catalyst.analysis
+
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, ExprId}
+import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, LogicalPlan}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.{DataType, StructType}
+
+/**
+ * Shared helpers for the stateful-operator nullability fix. The fix has three
+ * independent components, all gated by
+ * [[SQLConf.STATEFUL_OPERATOR_ALWAYS_NULLABLE_OUTPUT]] (pinned per-query via the
+ * offset log so existing queries keep their pre-fix behavior on restart):
+ *
+ *   - (a) `widenStateSchema`: explicit `asNullable` at every state-schema construction
+ *         site in each stateful physical exec.
+ *   - (b) `widenOutputForStatefulOp`: a per-op `output` override on every stateful logical
+ *         and physical operator, used by the operator's `output` definition.
+ *   - (c) [[WidenStatefulOperatorAttributeNullability]] (defined below in this file): a
+ *         custom optimizer rule that widens `AttributeReference`s inside stateful ops'
+ *         internal expressions and propagates upward to ancestor expressions.
+ */
+object WidenStatefulOpNullability {
+
+  def isEnabled: Boolean =
+    SQLConf.get.getConf(SQLConf.STATEFUL_OPERATOR_ALWAYS_NULLABLE_OUTPUT)
+
+  /**
+   * Recursively widens an attribute to be fully nullable: outer `nullable = true` plus
+   * every nested `StructField.nullable`, `ArrayType.containsNull`, and
+   * `MapType.valueContainsNull` flipped to `true` via
+   * [[org.apache.spark.sql.types.DataType#asNullable]].
+   */
+  def deepWidenAttribute(a: Attribute): Attribute = a match {
+    case ref: AttributeReference =>
+      AttributeReference(
+        ref.name, ref.dataType.asNullable, nullable = true, ref.metadata)(
+        ref.exprId, ref.qualifier)
+    case other => other.withNullability(true)
+  }
+
+  /**
+   * Component (a): widens a state schema to fully nullable. Stateful physical execs apply
+   * this at every `validateAndMaybeEvolveStateSchema(...)` call site and every
+   * `mapPartitionsWith*StateStore(...)` call site. When the conf is off, returns the
+   * schema unchanged.
+   */
+  def widenStateSchema(schema: StructType): StructType =
+    if (isEnabled) schema.asNullable else schema
+
+  /**
+   * Component (b): wraps a stateful operator's `output` to be fully nullable. The caller
+   * is responsible for only calling this from within an `output` definition on a stateful
+   * operator; gating is handled here via [[isEnabled]].
+   */
+  def widenOutputForStatefulOp(base: Seq[Attribute]): Seq[Attribute] =
+    if (isEnabled) base.map(deepWidenAttribute) else base
+
+  /**
+   * Recursively walks a schema and replaces any nested `StructType` that
+   * structurally matches `original` (by field names and base types, ignoring
+   * nullability) with `widened`. Used by TransformWithState execs to widen
+   * the grouping-key portion of col-family key schemas without touching
+   * user-defined key/value portions.
+   */
+  def widenGroupingKeyInSchema(
+      schema: StructType,
+      original: StructType,
+      widened: StructType): StructType = {
+    if (!isEnabled) return schema
+    if (DataType.equalsIgnoreNullability(schema, original)) {
+      widened
+    } else {
+      StructType(schema.fields.map { field =>
+        field.dataType match {
+          case st: StructType
+              if DataType.equalsIgnoreNullability(st, original) =>
+            field.copy(dataType = widened)
+          case st: StructType =>
+            field.copy(dataType =
+              widenGroupingKeyInSchema(st, original, widened))
+          case _ => field
+        }
+      })
+    }
+  }
+}
+
+/**
+ * Component (c) of the stateful-operator nullability fix: a custom optimizer rule that
+ * widens `AttributeReference`s inside streaming-stateful operators' internal expressions
+ * and propagates the widening upward to ancestor operators' expressions.
+ *
+ * The rule does NOT introduce any new logical or physical node. It is purely an
+ * attribute-rewrite pass using `resolveOperatorsUp` (bottom-up): for every node whose
+ * subtree contains a stateful operator, collect `exprId`s from children's output, then
+ * deep-widen every `AttributeReference` in the node's expressions whose `exprId` is in
+ * that set via [[WidenStatefulOpNullability#deepWidenAttribute]].
+ *
+ * At a stateful operator itself, all children's output attributes are included because
+ * the operator's internal expressions (e.g. grouping keys) reference them directly.
+ * At non-stateful ancestor operators, only children whose subtrees contain a stateful
+ * operator are included, to avoid unnecessary widening of non-stateful siblings.
+ * The node's own `p.output` is not needed for non-stateful ancestors because the
+ * bottom-up traversal guarantees children are already transformed, so their output
+ * attributes are already nullable and the ancestor's expressions reference those
+ * children's `exprId`s.
+ *
+ * '''Scope.''' The walk only fires on nodes whose subtree contains a stateful operator.
+ *
+ * '''Ordering constraint.''' This rule must run AFTER every `UpdateAttributeNullability`
+ * invocation in both the main optimizer and AQE.
+ *
+ * '''Idempotence.''' [[WidenStatefulOpNullability#deepWidenAttribute]] is idempotent.
+ */
+object WidenStatefulOperatorAttributeNullability extends Rule[LogicalPlan] {
+
+  override def apply(plan: LogicalPlan): LogicalPlan = {
+    if (!conf.getConf(SQLConf.STATEFUL_OPERATOR_ALWAYS_NULLABLE_OUTPUT) ||
+        !plan.containsStatefulOperator) {
+      return plan
+    }
+    plan.resolveOperatorsUp {
+      case p if !p.resolved => p
+      case p: LeafNode => p
+      case p if !p.containsStatefulOperator => p
+      case p =>
+        val widenableAttrs = if (p.isStateful) {
+          p.output ++ p.children.flatMap(_.output)
+        } else {
+          p.children.filter(_.containsStatefulOperator).flatMap(_.output)
+        }
+        val widenableExprIds: Set[ExprId] = widenableAttrs
+          .iterator.collect { case ar: AttributeReference => ar.exprId }.toSet
+        if (widenableExprIds.isEmpty) {
+          p
+        } else {
+          p.transformExpressions {
+            case ar: AttributeReference if widenableExprIds.contains(ar.exprId) =>
+              val widened = WidenStatefulOpNullability.deepWidenAttribute(ar)
+              if (ar.dataType == widened.dataType && ar.nullable == widened.nullable) {
+                ar
+              } else {
+                widened
+              }
+          }
+        }
+    }
+  }
+}

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala

@@ -18,7 +18,7 @@
 package org.apache.spark.sql.catalyst.plans.logical
 
 import org.apache.spark.sql.catalyst.{AliasIdentifier, InternalRow, SQLConfHelper}
-import org.apache.spark.sql.catalyst.analysis.{Analyzer, AnsiTypeCoercion, MultiInstanceRelation, Resolver, TypeCoercion, TypeCoercionBase, UnresolvedUnaryNode}
+import org.apache.spark.sql.catalyst.analysis.{Analyzer, AnsiTypeCoercion, MultiInstanceRelation, Resolver, TypeCoercion, TypeCoercionBase, UnresolvedUnaryNode, WidenStatefulOpNullability}
 import org.apache.spark.sql.catalyst.catalog.{CatalogStorageFormat, CatalogTable}
 import org.apache.spark.sql.catalyst.catalog.CatalogTable.VIEW_STORING_ANALYZED_PLAN
 import org.apache.spark.sql.catalyst.expressions._
@@ -746,7 +746,10 @@ case class Join(
     }
   }
 
-  override def output: Seq[Attribute] = Join.computeOutput(joinType, left.output, right.output)
+  override def output: Seq[Attribute] = {
+    val base = Join.computeOutput(joinType, left.output, right.output)
+    if (isStateful) WidenStatefulOpNullability.widenOutputForStatefulOp(base) else base
+  }
 
   override def metadataOutput: Seq[Attribute] = {
     joinType match {
@@ -1226,7 +1229,10 @@ case class Aggregate(
     expressions.forall(_.resolved) && childrenResolved && !hasWindowExpressions
   }
 
-  override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute)
+  override def output: Seq[Attribute] = {
+    val base = aggregateExpressions.map(_.toAttribute)
+    if (isStateful) WidenStatefulOpNullability.widenOutputForStatefulOp(base) else base
+  }
   override def metadataOutput: Seq[Attribute] = Nil
   override def maxRows: Option[Long] = {
     if (groupingExpressions.isEmpty) {
@@ -1750,7 +1756,10 @@ object Limit {
  * order.
  */
 case class GlobalLimit(limitExpr: Expression, child: LogicalPlan) extends UnaryNode {
-  override def output: Seq[Attribute] = child.output
+  override def output: Seq[Attribute] = {
+    val base = child.output
+    if (isStateful) WidenStatefulOpNullability.widenOutputForStatefulOp(base) else base
+  }
   override def maxRows: Option[Long] = {
     limitExpr match {
       case IntegerLiteral(limit) => Some(limit)
@@ -2005,7 +2014,10 @@ case class Sample(
  */
 case class Distinct(child: LogicalPlan) extends UnaryNode {
   override def maxRows: Option[Long] = child.maxRows
-  override def output: Seq[Attribute] = child.output
+  override def output: Seq[Attribute] = {
+    val base = child.output
+    if (isStateful) WidenStatefulOpNullability.widenOutputForStatefulOp(base) else base
+  }
   final override val nodePatterns: Seq[TreePattern] = Seq(DISTINCT_LIKE)
   override protected def withNewChildInternal(newChild: LogicalPlan): Distinct =
     copy(child = newChild)
@@ -2175,7 +2187,10 @@ case class Deduplicate(
     keys: Seq[Attribute],
     child: LogicalPlan) extends UnaryNode {
   override def maxRows: Option[Long] = child.maxRows
-  override def output: Seq[Attribute] = child.output
+  override def output: Seq[Attribute] = {
+    val base = child.output
+    if (isStateful) WidenStatefulOpNullability.widenOutputForStatefulOp(base) else base
+  }
   final override val nodePatterns: Seq[TreePattern] = Seq(DISTINCT_LIKE)
   override protected def withNewChildInternal(newChild: LogicalPlan): Deduplicate =
     copy(child = newChild)
@@ -2187,7 +2202,10 @@ case class DeduplicateWithinWatermark(keys: Seq[Attribute], child: LogicalPlan)
   override def references: AttributeSet = AttributeSet(keys) ++
     AttributeSet(child.output.filter(_.metadata.contains(EventTimeWatermark.delayKey)))
   override def maxRows: Option[Long] = child.maxRows
-  override def output: Seq[Attribute] = child.output
+  override def output: Seq[Attribute] = {
+    val base = child.output
+    if (isStateful) WidenStatefulOpNullability.widenOutputForStatefulOp(base) else base
+  }
   final override val nodePatterns: Seq[TreePattern] = Seq(DISTINCT_LIKE)
   override protected def withNewChildInternal(newChild: LogicalPlan): DeduplicateWithinWatermark =
     copy(child = newChild)

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/object.scala

@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.api.java.function.FilterFunction
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.sql.{catalyst, Encoder, Row}
-import org.apache.spark.sql.catalyst.analysis.{Resolver, UnresolvedDeserializer}
+import org.apache.spark.sql.catalyst.analysis.{Resolver, UnresolvedDeserializer, WidenStatefulOpNullability}
 import org.apache.spark.sql.catalyst.encoders._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.objects.Invoke
@@ -568,6 +568,11 @@ case class FlatMapGroupsWithState(
       newLeft: LogicalPlan, newRight: LogicalPlan): FlatMapGroupsWithState =
     copy(child = newLeft, initialState = newRight)
   override def isStateful: Boolean = child.isStreaming
+
+  override def output: Seq[Attribute] = {
+    val base = super.output
+    if (isStateful) WidenStatefulOpNullability.widenOutputForStatefulOp(base) else base
+  }
 }
 
 object TransformWithState {
@@ -657,6 +662,11 @@ case class TransformWithState(
       newLeft: LogicalPlan, newRight: LogicalPlan): TransformWithState =
     copy(child = newLeft, initialState = newRight)
   override def isStateful: Boolean = child.isStreaming
+
+  override def output: Seq[Attribute] = {
+    val base = super.output
+    if (isStateful) WidenStatefulOpNullability.widenOutputForStatefulOp(base) else base
+  }
 }
 
 /** Factory for constructing new `FlatMapGroupsInR` nodes. */

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/pythonLogicalOperators.scala

@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.plans.logical
 
 import org.apache.spark.resource.ResourceProfile
 import org.apache.spark.sql.catalyst.SQLConfHelper
-import org.apache.spark.sql.catalyst.analysis.{FunctionRegistryBase, MultiInstanceRelation, UnresolvedAttribute, UnresolvedStar}
+import org.apache.spark.sql.catalyst.analysis.{FunctionRegistryBase, MultiInstanceRelation, UnresolvedAttribute, UnresolvedStar, WidenStatefulOpNullability}
 import org.apache.spark.sql.catalyst.analysis.TableFunctionRegistry.TableFunctionBuilder
 import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute, AttributeSet, Expression, ExpressionDescription, ExpressionInfo, JsonToStructs, PythonUDF, PythonUDTF}
 import org.apache.spark.sql.catalyst.trees.TreePattern._
@@ -159,7 +159,9 @@ case class FlatMapGroupsInPandasWithState(
     timeout: GroupStateTimeout,
     child: LogicalPlan) extends UnaryNode {
 
-  override def output: Seq[Attribute] = outputAttrs
+  override def output: Seq[Attribute] =
+    if (isStateful) WidenStatefulOpNullability.widenOutputForStatefulOp(outputAttrs)
+    else outputAttrs
 
   override def producedAttributes: AttributeSet = AttributeSet(outputAttrs)
 
@@ -206,7 +208,9 @@ case class TransformWithStateInPySpark(
 
   override def right: LogicalPlan = initialState
 
-  override def output: Seq[Attribute] = outputAttrs
+  override def output: Seq[Attribute] =
+    if (isStateful) WidenStatefulOpNullability.widenOutputForStatefulOp(outputAttrs)
+    else outputAttrs
 
   override def producedAttributes: AttributeSet = AttributeSet(outputAttrs)
 

sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala

@@ -3444,6 +3444,24 @@ object SQLConf {
       .booleanConf
       .createWithDefault(true)
 
+  val STATEFUL_OPERATOR_ALWAYS_NULLABLE_OUTPUT =
+    buildConf("spark.sql.streaming.statefulOperator.alwaysNullableOutput.enabled")
+      .internal()
+      .withBindingPolicy(ConfigBindingPolicy.SESSION)
+      .doc("When true, every streaming stateful operator reports its output schema with " +
+        "nullable=true on all columns (including nested struct fields, array elements, and " +
+        "map values), and the state schema is widened at every construction site, so the " +
+        "existing state schema " +
+        "compatibility check trivially passes regardless of input nullability. " +
+        "This prevents query-optimizer decisions (e.g., PropagateEmptyRelation dropping a " +
+        "Union branch) from flipping the state schema nullability across microbatches or " +
+        "restarts. The effective value is pinned per query via the offset log at batch 0, " +
+        "so pre-existing queries keep their original behavior; only newly started queries " +
+        "pick this up.")
+      .version("4.3.0")
+      .booleanConf
+      .createWithDefault(true)
+
   val FILESTREAM_SINK_METADATA_IGNORED =
     buildConf("spark.sql.streaming.fileStreamSink.ignoreMetadata")
       .internal()

sql/connect/client/jvm/src/test/scala/org/apache/spark/sql/connect/streaming/ClientStreamingQuerySuite.scala

@@ -86,7 +86,7 @@ class ClientStreamingQuerySuite extends QueryTest with RemoteSparkSession with L
         .count()
         .selectExpr("window.start as timestamp", "count as num_events")
 
-      assert(countsDF.schema.toDDL == "timestamp TIMESTAMP,num_events BIGINT NOT NULL")
+      assert(countsDF.schema.toDDL == "timestamp TIMESTAMP,num_events BIGINT")
 
       // Start the query
       val queryName = "sparkConnectStreamingQuery"

sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/AQEOptimizer.scala

@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution.adaptive
 
 import org.apache.spark.internal.LogKeys.{BATCH_NAME, RULE_NAME}
-import org.apache.spark.sql.catalyst.analysis.UpdateAttributeNullability
+import org.apache.spark.sql.catalyst.analysis.{UpdateAttributeNullability, WidenStatefulOperatorAttributeNullability}
 import org.apache.spark.sql.catalyst.optimizer.{ConvertToLocalRelation, EliminateLimits, OptimizeOneRowPlan}
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LogicalPlanIntegrity}
 import org.apache.spark.sql.catalyst.rules.{Rule, RuleExecutor}
@@ -44,7 +44,8 @@ class AQEOptimizer(conf: SQLConf, extendedRuntimeOptimizerRules: Seq[Rule[Logica
     Batch("Dynamic Join Selection", Once, DynamicJoinSelection),
     Batch("Eliminate Limits", fixedPoint, EliminateLimits),
     Batch("Optimize One Row Plan", fixedPoint, OptimizeOneRowPlan)) :+
-    Batch("User Provided Runtime Optimizers", fixedPoint, extendedRuntimeOptimizerRules: _*)
+    Batch("User Provided Runtime Optimizers", fixedPoint, extendedRuntimeOptimizerRules: _*) :+
+    Batch("Widen Stateful Op Nullability", Once, WidenStatefulOperatorAttributeNullability)
 
   final override protected def batches: Seq[Batch] = {
     val excludedRules = conf.getConf(SQLConf.ADAPTIVE_OPTIMIZER_EXCLUDED_RULES)

sql/core/src/main/scala/org/apache/spark/sql/execution/python/streaming/FlatMapGroupsInPandasWithStateExec.scala

@@ -20,6 +20,7 @@ import org.apache.spark.{JobArtifactSet, SparkException, SparkUnsupportedOperati
 import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.WidenStatefulOpNullability
 import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.{EventTimeTimeout, ProcessingTimeTimeout}
@@ -81,7 +82,8 @@ case class FlatMapGroupsInPandasWithStateExec(
   override protected val stateEncoder: ExpressionEncoder[Any] =
     ExpressionEncoder(stateType).resolveAndBind().asInstanceOf[ExpressionEncoder[Any]]
 
-  override def output: Seq[Attribute] = outAttributes
+  override def output: Seq[Attribute] =
+    WidenStatefulOpNullability.widenOutputForStatefulOp(outAttributes)
 
   private val sessionLocalTimeZone = conf.sessionLocalTimeZone
   private val pythonRunnerConf = ArrowPythonRunner.getPythonRunnerConfMap(conf)