jchen5 commented on code in PR #45125:
URL: https://github.com/apache/spark/pull/45125#discussion_r1498343024
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala:
##########
@@ -328,6 +328,30 @@ abstract class Optimizer(catalogManager: CatalogManager)
// Do not optimize DPP subquery, as it was created from optimized plan
and we should not
// optimize it again, to save optimization time and avoid breaking
broadcast/subquery reuse.
case d: DynamicPruningSubquery => d
+ case s@ScalarSubquery(a@Aggregate(group, _, child), _, _, _, _,
mayHaveCountBug)
+ if mayHaveCountBug.nonEmpty && mayHaveCountBug.get =>
+ // This is a subquery with an aggregate that may suffer from a COUNT
bug.
+ // Detailed COUNT bug detection is done at a later stage (e.g. in
+ // RewriteCorrelatedScalarSubquery).
+ // Make sure that the output plan always has the same aggregate node
+ // (i.e., it is not being constant folded).
+ // Note that this does not limit optimization opportunities for the
subquery: after
+ // decorrelation is done, the subquery's body becomes part of the main
plan and all
+ // optimization rules are applied again.
+ val groupRefs = group.flatMap(x => x.references)
+ val projectOverSubqueryBody = Project(groupRefs ++ a.references.toSeq,
child)
Review Comment:
maybe projectOverAggregateChild would be clearer
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala:
##########
@@ -328,6 +328,30 @@ abstract class Optimizer(catalogManager: CatalogManager)
// Do not optimize DPP subquery, as it was created from optimized plan
and we should not
// optimize it again, to save optimization time and avoid breaking
broadcast/subquery reuse.
case d: DynamicPruningSubquery => d
+ case s@ScalarSubquery(a@Aggregate(group, _, child), _, _, _, _,
mayHaveCountBug)
+ if mayHaveCountBug.nonEmpty && mayHaveCountBug.get =>
+ // This is a subquery with an aggregate that may suffer from a COUNT
bug.
+ // Detailed COUNT bug detection is done at a later stage (e.g. in
+ // RewriteCorrelatedScalarSubquery).
+ // Make sure that the output plan always has the same aggregate node
+ // (i.e., it is not being constant folded).
+ // Note that this does not limit optimization opportunities for the
subquery: after
+ // decorrelation is done, the subquery's body becomes part of the main
plan and all
+ // optimization rules are applied again.
Review Comment:
There could potentially be changes in optimization due to different order in
which rules get applied, right? I think that would be quite edge case, but can
we confirm whether there are plan changes in benchmarks at least?
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