AngersZhuuuu commented on code in PR #46263:
URL: https://github.com/apache/spark/pull/46263#discussion_r1590728816
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/InjectRuntimeFilter.scala:
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@@ -129,17 +136,36 @@ object InjectRuntimeFilter extends Rule[LogicalPlan] with
PredicateHelper with J
if (left.output.exists(_.semanticEquals(targetKey))) {
extract(left, AttributeSet.empty, hasHitFilter = false,
hasHitSelectiveFilter = false,
currentPlan = left, targetKey = targetKey).orElse {
- // We can also extract from the right side if the join keys are
transitive.
- lkeys.zip(rkeys).find(_._1.semanticEquals(targetKey)).map(_._2)
- .flatMap { newTargetKey =>
- extract(right, AttributeSet.empty,
- hasHitFilter = false, hasHitSelectiveFilter = false,
currentPlan = right,
- targetKey = newTargetKey)
- }
+ // For the exact join key match, like the left table here, it's
always OK to generate
+ // the runtime filter using this left table, no matter what the
join type is.
+ // This is because left table always produce a superset of output
of the join output
+ // regarding the left keys.
+ // For transitive join key match, it's different. The right table
here does
+ // not always generate a superset output regarding left keys.
+ // Let's look at an example
+ // left table: 1, 2, 3
+ // right table, 3, 4
+ // left outer join output: (1, null), (2, null), (3, 3)
+ // left keys: 1, 2, 3
+ // So we can't use right table to generate runtime filter.
+ if (canExtractRight(joinType)) {
+ lkeys.zip(rkeys).find(_._1.semanticEquals(targetKey)).map(_._2)
+ .flatMap { newTargetKey =>
+ extract(right, AttributeSet.empty,
+ hasHitFilter = false, hasHitSelectiveFilter = false,
currentPlan = right,
+ targetKey = newTargetKey)
+ }
+ } else {
+ None
+ }
}
} else if (right.output.exists(_.semanticEquals(targetKey))) {
- extract(right, AttributeSet.empty, hasHitFilter = false,
hasHitSelectiveFilter = false,
- currentPlan = right, targetKey = targetKey).orElse {
+ if (canExtractRight(joinType)) {
Review Comment:
Like current ?
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