shrirangmhalgi commented on code in PR #56243:
URL: https://github.com/apache/spark/pull/56243#discussion_r3354184289
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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:
Nit: `mapStats` is an `Option`, so stages where it's `None` contribute 0
bytes silently. If both sides of a join had `mapStats = None`, totalSize would
be 0 and the floor would be skipped entirely (`totalSize <= advisorySize` →
true → no floor applied). In practice this shouldn't happen because AQE
coalescing runs after stage completion, but a defensive assert or
early-return-if-empty guard would make the invariant explicit.
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