ulysses-you commented on a change in pull request #33188:
URL: https://github.com/apache/spark/pull/33188#discussion_r670066151
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File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala
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@@ -250,7 +250,12 @@ object EnsureRequirements extends Rule[SparkPlan] {
def apply(plan: SparkPlan): SparkPlan = plan.transformUp {
// TODO: remove this after we create a physical operator for
`RepartitionByExpression`.
- case operator @ ShuffleExchangeExec(upper: HashPartitioning, child, _) =>
+ // SPARK-35989: AQE will change the partition number so we should retain
the REPARTITION_BY_NUM
+ // shuffle which is specified by user. And also we can not remove
REBALANCE_PARTITIONS_BY_COL,
+ // it is a special shuffle used to rebalance partitions.
+ // So, here we only remove REPARTITION_BY_COL in AQE.
+ case operator @ ShuffleExchangeExec(upper: HashPartitioning, child,
shuffleOrigin)
+ if shuffleOrigin == REPARTITION_BY_COL ||
!conf.adaptiveExecutionEnabled =>
Review comment:
Sorry for going back to here. After some more thought, it would be
better to skip removing shuffle with all shuffle origin in AQE which include
the `REPARTITION_BY_COL`. Then the condition can be changed to `if
!conf.adaptiveExecutionEnabled`. Given this we can remove the final stage rules
in `AdaptiveSparkPlanExec` safely which seems a little hack.
For `REPARTITION_BY_COL`, I want to explain it's also unsafe to remove. We
may apply `OptimizeLocalShuffleReader` to change the output partitioning which
does not follow the semantics of `RepartitionByExpression`.
What do you think ? @HyukjinKwon @cloud-fan
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