Github user hvanhovell commented on a diff in the pull request:
https://github.com/apache/spark/pull/12954#discussion_r62391407
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -1645,3 +1646,30 @@ object RewriteCorrelatedScalarSubquery extends
Rule[LogicalPlan] {
}
}
}
+
+/**
+ * Rewrite [[Filter]] plans that contain correlated [[ScalarSubquery]]
expressions. When these
+ * correlated [[ScalarSubquery]] expressions are wrapped in a some
Predicate expression, we rewrite
+ * them into [[PredicateSubquery]] expressions.
+ */
+object RewriteScalarSubqueriesInFilter extends Rule[LogicalPlan] {
--- End diff --
The advantage of Semi Join is that a subquery can actually return multiple
results for one row without causing correctness problems. My initial approach
was to relax the rules for scalar subquery (allow disjunctive predicates) and
prevent possible duplicates by using left semis. This didn't work because I was
also pulling non-correlated predicates through the aggregate (which makes its
results invalid).
I am not sure which predicates you want to push through the left semi.
Since all predicates that should be pushed down the left hand side are already
in the predicate condition. But I might be missing something here.
I have remove this in my last commit. I do feel that this might be a small
improvement over the current situation. Let's revisit this after Spark 2.0.
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