Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/17491#discussion_r109190009
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/subquery.scala
---
@@ -498,3 +498,31 @@ object RewriteCorrelatedScalarSubquery extends
Rule[LogicalPlan] {
}
}
}
+
+/**
+ * This rule rewrites a EXISTS predicate sub-queries into an Aggregate
with count.
+ * So it doesn't be converted to a JOIN later.
+ */
+object RewriteEmptyExists extends Rule[LogicalPlan] with PredicateHelper {
+ private def containsAgg(plan: LogicalPlan): Boolean = {
+ plan.collect {
+ case a: Aggregate => a
+ }.nonEmpty
+ }
+
+ def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+ case Filter(condition, child) =>
+ val (withSubquery, withoutSubquery) =
+
splitConjunctivePredicates(condition).partition(SubqueryExpression.hasInOrExistsSubquery)
+ val newWithSubquery = withSubquery.map(_.transform {
+ case e @ Exists(sub, conditions, exprId) if conditions.isEmpty &&
!containsAgg(sub) =>
--- End diff --
For a simple count aggregation, I think it is cheap because it should prune
columns and no data from the table will be shuffled.
As the Exists has no correlated reference, so the join doesn't have keys
and so shuffling required, I think, but as there is no meaningful condition, it
will be recognized as cartesian product by optimizer.
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