anishshri-db commented on code in PR #45376:
URL: https://github.com/apache/spark/pull/45376#discussion_r1513592033
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sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala:
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@@ -676,6 +678,43 @@ class KeyValueGroupedDataset[K, V] private[sql](
)
}
+ private[sql] def transformWithState[U: Encoder](
+ statefulProcessor: StatefulProcessor[K, V, U],
+ timeoutMode: TimeoutMode,
+ 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,
+ timeoutMode,
+ outputMode,
+ child = logicalPlan
+ )
+
+ val twsDS = Dataset[U](
+ sparkSession,
+ transformWithState
+ )
+
+ val delay = existingWatermarkDelay.head
+
+ Dataset[U](sparkSession, EliminateEventTimeWatermark(
+ UpdateEventTimeWatermarkColumn(
Review Comment:
How does this modify the plan exactly ? if we have 2 or more
`TransformWithState` operators chained together, how would this work exactly ?
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