cloud-fan commented on code in PR #44661:
URL: https://github.com/apache/spark/pull/44661#discussion_r1455626733
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sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CoalesceShufflePartitions.scala:
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@@ -146,13 +147,15 @@ case class CoalesceShufflePartitions(session:
SparkSession) extends AQEShuffleRe
Seq(collectShuffleStageInfos(r))
case unary: UnaryExecNode => collectCoalesceGroups(unary.child)
case union: UnionExec => union.children.flatMap(collectCoalesceGroups)
- // If not all leaf nodes are exchange query stages, it's not safe to
reduce the number of
- // shuffle partitions, because we may break the assumption that all
children of a spark plan
- // have same number of output partitions.
// Note that, `BroadcastQueryStageExec` is a valid case:
// If a join has been optimized from shuffled join to broadcast join, then
the one side is
// `BroadcastQueryStageExec` and other side is `ShuffleQueryStageExec`. It
can coalesce the
// shuffle side as we do not expect broadcast exchange has same partition
number.
+ case join: BroadcastHashJoinExec =>
join.children.flatMap(collectCoalesceGroups)
+ case join: BroadcastNestedLoopJoinExec =>
join.children.flatMap(collectCoalesceGroups)
+ // If not all leaf nodes are exchange query stages, it's not safe to
reduce the number of
+ // shuffle partitions, because we may break the assumption that all
children of a spark plan
+ // have same number of output partitions.
case p if p.collectLeaves().forall(_.isInstanceOf[ExchangeQueryStageExec])
=>
Review Comment:
> we filter the right side for broadcast join in collectShuffleStageInfos
I didn't see any code related to it, can you elaborate?
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