yadavay-amzn commented on code in PR #56243:
URL: https://github.com/apache/spark/pull/56243#discussion_r3364200357
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sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CoalesceShufflePartitions.scala:
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@@ -90,9 +90,38 @@ case class CoalesceShufflePartitions(session: SparkSession)
extends AQEShuffleRe
}
}
+ // For groups that feed a partitioned join
(SortMergeJoin/ShuffledHashJoin), enforce a
+ // minimum partition count to avoid eliminating join parallelism.
+ // Design choice: we use a pre-coalesce floor (Option A) rather than
post-coalesce skew
+ // re-checking (Option B). Option A is simpler and avoids re-running skew
detection after
+ // coalescing. Option B would be more robust for edge cases but adds
significant complexity
+ // and can be explored as a follow-up.
+ val adjustedMinNumPartitionsByGroup =
coalesceGroups.zip(minNumPartitionsByGroup).map {
+ case (group, minNum) if group.feedsJoin =>
+ val totalSize = group.shuffleStages.flatMap(
Review Comment:
Added a comment explaining the invariant (mapStats is always Some after
stage materialization). The flatMap safely degrades to 0 if stats are
unexpectedly absent, which triggers the tiny-data path and skips the floor —
preserving correctness.
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