Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/10454#discussion_r48388339
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -858,6 +859,30 @@ object PushPredicateThroughJoin extends
Rule[LogicalPlan] with PredicateHelper {
}
/**
+ * Push [[Limit]] operators through [[Join]] operators, iff the join type
is outer joins.
+ * Adding extra [[Limit]] operators on top of the outer-side
child/children.
+ */
+object PushLimitThroughOuterJoin extends Rule[LogicalPlan] with
PredicateHelper {
--- End diff --
I am not sure what is the best way to prove it. If we can add extra `limit`
below `union all`, `left outer` and `right outer`, can we add extra `limit`
below `full outer`? In the traditional RDBMS, ```full outer = union all(left
outer, right outer)```. I am not sure if Spark SQL has the same semantics.
`(A full outer join B) limit 5`
= `(A left outer join B) limit 5` `union all` `(A right outer join B) limit
5`
= `((A limit 5) left outer join (B limit 5))` `union all` `((A limit 5)
right outer join (B limit 5))`
= `((A limit 5) full outer join (B limit 5))`
However, inner join has a big issue if we are trying to add extra `limit`.
I am not sure my answer is clear.
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