Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/10073#discussion_r46350732
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -133,6 +132,38 @@ object ExtractEquiJoinKeys extends Logging with
PredicateHelper {
}
/**
+ * A pattern that collects the filter and inner joins.
+ *
+ * Filter
+ * |
+ * inner Join
+ * / \ ----> (filters, Seq(plan1, plan2),
input)
+ * inner join plan2
+ * / \
+ * input plan1
+ */
+object FilterAndInnerJoins extends PredicateHelper {
+ def unapply(plan: LogicalPlan): Option[(LogicalPlan, Seq[LogicalPlan],
Seq[Expression])] =
+ plan match {
+ case f @ Filter(filterCondition, j @ Join(left, right, Inner, None))
=>
+
+ // flatten all inner joins, which are next to each other and has
no condition
+ def flattenJoin(plan: LogicalPlan): (LogicalPlan,
Seq[LogicalPlan]) = plan match {
--- End diff --
Building on what @nongli said, I think that you can have the top level
match handle both filters and joins, which should make this more powerful
(similar to what we do in `PhysicalOperation`
[here](https://github.com/apache/spark/pull/10073/files#diff-820e654df2a5133c0f86c17e2fc5512eR54)).
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