anishshri-db commented on code in PR #45376:
URL: https://github.com/apache/spark/pull/45376#discussion_r1576698149
##########
sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala:
##########
@@ -739,6 +741,128 @@ class KeyValueGroupedDataset[K, V] private[sql](
)
}
+ /**
+ * (Scala-specific)
+ * Invokes methods defined in the stateful processor used in arbitrary state
API v2.
+ * We allow the user to act on per-group set of input rows along with keyed
state and the
+ * user can choose to output/return 0 or more rows.
+ * For a streaming dataframe, we will repeatedly invoke the interface
methods for new rows
+ * in each trigger and the user's state/state variables will be stored
persistently across
+ * invocations.
+ *
+ * @tparam U The type of the output objects. Must be encodable to Spark SQL
types.
+ * @param statefulProcessor Instance of statefulProcessor whose functions
will
+ * be invoked by the operator.
+ * @param timeMode The time mode semantics of the stateful
processor for timers and TTL.
+ * @param eventTimeColumnName eventTime column in the output dataset. Any
operations after
+ * transformWithState will use the new
eventTimeColumn. The user
+ * needs to ensure that the eventTime for emitted
output adheres to
+ * the watermark boundary, otherwise streaming
query will fail.
+ * @param outputMode The output mode of the stateful processor.
+ *
+ * See [[Encoder]] for more details on what types are encodable to Spark SQL.
+ */
+ private[sql] def transformWithState[U: Encoder](
+ statefulProcessor: StatefulProcessor[K, V, U],
+ timeMode: TimeMode,
+ eventTimeColumnName: String,
+ outputMode: OutputMode): Dataset[U] = {
+ val existingWatermarkDelay = logicalPlan.flatMap {
+ case EventTimeWatermark(_, delay, _) => Seq(delay)
+ case _ => Seq()
+ }
+
+ if (existingWatermarkDelay.isEmpty) {
+ throw QueryCompilationErrors.cannotAssignEventTimeColumn()
+ }
+
+ val transformWithState = TransformWithState[K, V, U](
+ groupingAttributes,
+ dataAttributes,
+ statefulProcessor,
+ timeMode,
+ outputMode,
+ child = logicalPlan
+ )
+
+ val twsDS = Dataset[U](
+ sparkSession,
+ transformWithState
+ )
+
+ val delay = existingWatermarkDelay.head
+
+ Dataset[U](sparkSession, EliminateEventTimeWatermark(
+ UpdateEventTimeWatermarkColumn(
+ UnresolvedAttribute(eventTimeColumnName),
+ delay,
+ twsDS.logicalPlan)))
+ }
+
+ /**
+ * (Scala-specific)
+ * Invokes methods defined in the stateful processor used in arbitrary state
API v2.
+ * Functions as the function above, but with additional initial state.
+ *
+ * @tparam U The type of the output objects. Must be encodable to Spark SQL
types.
+ * @tparam S The type of initial state objects. Must be encodable to Spark
SQL types.
+ * @param statefulProcessor Instance of statefulProcessor whose functions
will
+ * be invoked by the operator.
+ * @param timeMode The time mode semantics of the stateful
processor for
+ * timers and TTL.
+ * @param eventTimeColumnName eventTime column in the output dataset. Any
operations after
+ * transformWithState will use the new
eventTimeColumn. The user
+ * needs to ensure that the eventTime for emitted
output adheres to
+ * the watermark boundary, otherwise streaming
query will fail.
+ * @param outputMode The output mode of the stateful processor.
+ * @param initialState User provided initial state that will be used
to initiate state for
+ * the query in the first batch.
+ *
+ * See [[Encoder]] for more details on what types are encodable to Spark SQL.
+ */
+ private[sql] def transformWithState[U: Encoder, S: Encoder](
+ statefulProcessor: StatefulProcessorWithInitialState[K, V, U, S],
+ timeMode: TimeMode,
+ eventTimeColumnName: String,
+ outputMode: OutputMode,
+ initialState: KeyValueGroupedDataset[K, S]): Dataset[U] = {
+ val existingWatermarkDelay = logicalPlan.collect {
+ case EventTimeWatermark(_, delay, _) => delay
+ }
+
+ if (existingWatermarkDelay.isEmpty) {
Review Comment:
Could we consolidate this code with the one above using a private function ?
i guess the only diff here is that we are passing the `initalState` dataset ?
##########
sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala:
##########
@@ -739,6 +741,128 @@ class KeyValueGroupedDataset[K, V] private[sql](
)
}
+ /**
+ * (Scala-specific)
+ * Invokes methods defined in the stateful processor used in arbitrary state
API v2.
+ * We allow the user to act on per-group set of input rows along with keyed
state and the
+ * user can choose to output/return 0 or more rows.
+ * For a streaming dataframe, we will repeatedly invoke the interface
methods for new rows
+ * in each trigger and the user's state/state variables will be stored
persistently across
+ * invocations.
+ *
+ * @tparam U The type of the output objects. Must be encodable to Spark SQL
types.
+ * @param statefulProcessor Instance of statefulProcessor whose functions
will
+ * be invoked by the operator.
+ * @param timeMode The time mode semantics of the stateful
processor for timers and TTL.
+ * @param eventTimeColumnName eventTime column in the output dataset. Any
operations after
+ * transformWithState will use the new
eventTimeColumn. The user
+ * needs to ensure that the eventTime for emitted
output adheres to
+ * the watermark boundary, otherwise streaming
query will fail.
+ * @param outputMode The output mode of the stateful processor.
+ *
+ * See [[Encoder]] for more details on what types are encodable to Spark SQL.
+ */
+ private[sql] def transformWithState[U: Encoder](
+ statefulProcessor: StatefulProcessor[K, V, U],
+ timeMode: TimeMode,
+ eventTimeColumnName: String,
+ outputMode: OutputMode): Dataset[U] = {
+ val existingWatermarkDelay = logicalPlan.flatMap {
+ case EventTimeWatermark(_, delay, _) => Seq(delay)
+ case _ => Seq()
+ }
+
+ if (existingWatermarkDelay.isEmpty) {
+ throw QueryCompilationErrors.cannotAssignEventTimeColumn()
+ }
+
+ val transformWithState = TransformWithState[K, V, U](
+ groupingAttributes,
+ dataAttributes,
+ statefulProcessor,
+ timeMode,
+ outputMode,
+ child = logicalPlan
+ )
+
+ val twsDS = Dataset[U](
+ sparkSession,
+ transformWithState
+ )
+
+ val delay = existingWatermarkDelay.head
+
+ Dataset[U](sparkSession, EliminateEventTimeWatermark(
+ UpdateEventTimeWatermarkColumn(
+ UnresolvedAttribute(eventTimeColumnName),
+ delay,
+ twsDS.logicalPlan)))
+ }
+
+ /**
+ * (Scala-specific)
+ * Invokes methods defined in the stateful processor used in arbitrary state
API v2.
+ * Functions as the function above, but with additional initial state.
+ *
+ * @tparam U The type of the output objects. Must be encodable to Spark SQL
types.
+ * @tparam S The type of initial state objects. Must be encodable to Spark
SQL types.
+ * @param statefulProcessor Instance of statefulProcessor whose functions
will
+ * be invoked by the operator.
+ * @param timeMode The time mode semantics of the stateful
processor for
+ * timers and TTL.
+ * @param eventTimeColumnName eventTime column in the output dataset. Any
operations after
+ * transformWithState will use the new
eventTimeColumn. The user
+ * needs to ensure that the eventTime for emitted
output adheres to
+ * the watermark boundary, otherwise streaming
query will fail.
+ * @param outputMode The output mode of the stateful processor.
+ * @param initialState User provided initial state that will be used
to initiate state for
+ * the query in the first batch.
+ *
+ * See [[Encoder]] for more details on what types are encodable to Spark SQL.
+ */
+ private[sql] def transformWithState[U: Encoder, S: Encoder](
+ statefulProcessor: StatefulProcessorWithInitialState[K, V, U, S],
+ timeMode: TimeMode,
+ eventTimeColumnName: String,
+ outputMode: OutputMode,
+ initialState: KeyValueGroupedDataset[K, S]): Dataset[U] = {
+ val existingWatermarkDelay = logicalPlan.collect {
+ case EventTimeWatermark(_, delay, _) => delay
+ }
+
+ if (existingWatermarkDelay.isEmpty) {
Review Comment:
Could we consolidate this code with the one above using a private function ?
i guess the only diff here is that we are passing the `initialState` dataset ?
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