maryannxue commented on a change in pull request #33188:
URL: https://github.com/apache/spark/pull/33188#discussion_r673227384
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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:
Changing sth. that would introduce regressions to the existing logic
just to get the potential benefit of sth. we have not implemented yet is NOT
acceptable.
A better solution here is to have a "root distribution (and potentially
ordering) requirement", which will be passed to EnsureRequirement and
ValidateRequirement, so that each custom-shuffle-reader rule (e.g.,
local-shuffle-reader, skew-join) can decide whether to apply just by calling
`ValidateRequirement` or add back necessary shuffle by calling
`EnsureRequirement`. There would be no need for final-stage hacks.
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