Github user hvanhovell commented on a diff in the pull request:
https://github.com/apache/spark/pull/11635#discussion_r60421291
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -958,6 +958,26 @@ object PushDownPredicate extends Rule[LogicalPlan]
with PredicateHelper {
project.copy(child = Filter(replaceAlias(condition, aliasMap),
grandChild))
+ // Push [[Filter]] operators through [[Window]] operators. Parts of
the predicate that can be
+ // pushed beneath must satisfy three conditions:
+ // 1. involving one and only one column that is part of window
partitioning key.
--- End diff --
So I am not sure I agree with you here.
Lets take your example. We filter the rows with the following predicate:
`key + value > 2`. We use both `key` and `value` in `PARTITION BY` clause, this
means they are constant during window function evaluation. The value of `key +
value > 1` will (as a result) also be constant during window evaluation. This
predicate would filter out entire partitions, so we can safely push it down.
I think we can safely push down any deterministic filter which only
references partitioning columns. Let me know what you think.
I am not sure why Hive does not push this down, but this could well be due
to the way Hive evaluates window functions (PTFs).
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