yadavay-amzn commented on code in PR #56243:
URL: https://github.com/apache/spark/pull/56243#discussion_r3364222351
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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(
+ _.shuffleStage.mapStats.map(_.bytesByPartitionId.sum)).sum
+ val advisorySize = advisoryPartitionSize(group)
+ if (totalSize <= advisorySize) {
+ // Tiny data: all join data fits in one advisory-sized partition, so
coalescing
+ // to 1 is fine -- no parallelism benefit from multiple partitions.
+ minNum
+ } else {
+ // When COALESCE_PARTITIONS_MIN_PARTITION_NUM is explicitly set, use
it as the floor.
+ // Otherwise, compute a data-aware floor: the number of partitions
needed to keep each
+ // partition at or below the advisory target size. The max(2, ...)
ensures we never
+ // collapse to a single reducer for join data -- for totalSize
between advisorySize and
+ // 2*advisorySize, ceil gives 1 or 2, so the floor of 2 prevents
single-partition joins.
+ val joinFloor =
conf.getConf(SQLConf.COALESCE_PARTITIONS_MIN_PARTITION_NUM)
Review Comment:
Good point — restructured. When the config is explicitly set, we now
early-return `minNum` (which already incorporates the config). The data-aware
heuristic only applies in the else branch (config not set).
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