sahnib commented on code in PR #45376:
URL: https://github.com/apache/spark/pull/45376#discussion_r1589380529
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sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/TransformWithStateExec.scala:
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@@ -78,15 +78,32 @@ case class TransformWithStateExec(
override def shortName: String = "transformWithStateExec"
override def shouldRunAnotherBatch(newInputWatermark: Long): Boolean = {
+ if (timeMode == ProcessingTime) {
+ // TODO: check if we can return true only if actual timers are
registered, or there is
+ // expired state
+ true
+ } else if (outputMode == OutputMode.Append || outputMode ==
OutputMode.Update) {
+ eventTimeWatermarkForEviction.isDefined &&
+ newInputWatermark > eventTimeWatermarkForEviction.get
+ } else {
+ false
+ }
+ }
+
+ /**
+ * Controls watermark propagation to downstream modes. If timeMode is
+ * ProcessingTime, the output rows cannot be interpreted in eventTime, hence
+ * this node will not propagate watermark in this timeMode.
+ *
+ * For timeMode EventTime, output watermark is same as input Watermark
because
+ * transformWithState node does not buffer any input rows between
micro-batches.
Review Comment:
I meant to say that TransformWithState operator itself passes all the
inputRows to the StatefulProcessor function, and does not buffer any input
data.
I can see that it can be confusing when we implement other functionality
(like SessionWindow) on top of TWS, updated the doc as suggested.
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