jingz-db commented on code in PR #48124:
URL: https://github.com/apache/spark/pull/48124#discussion_r1819740895
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sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/IncrementalExecution.scala:
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@@ -425,6 +425,29 @@ class IncrementalExecution(
eventTimeWatermarkForEviction = iwEviction)
))
+ case UpdateEventTimeColumnExec(eventTime, delay, None,
Review Comment:
The main difference is where we wrap the logical node. For Scala, we wrap
the event time column and output a dataset type
[here](https://github.com/apache/spark/pull/45376/files#diff-3437bb4bcaf2e18c305978985e474daab11e397dc5f4666c13c8e11da0d7180bR706).
For Python, we wrap the event time column and returns a Dataframe type
[here](https://github.com/apache/spark/pull/48124/files#diff-32b043dfe6b906fb2b240e8557f98b03a648ba792ce58d11b631744b34bcea71R481).
So that results in a slightly difference in the physical plan when the query
enters the `IncrementalExecution` case match here. I'll update this in the
comment as well.
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