Github user aokolnychyi commented on a diff in the pull request:
https://github.com/apache/spark/pull/18692#discussion_r145498671
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/joins.scala
---
@@ -152,3 +152,79 @@ object EliminateOuterJoin extends Rule[LogicalPlan]
with PredicateHelper {
if (j.joinType == newJoinType) f else Filter(condition,
j.copy(joinType = newJoinType))
}
}
+
+/**
+ * A rule that uses propagated constraints to infer join conditions. The
optimization is applicable
+ * only to CROSS joins. For other join types, adding inferred join
conditions would potentially
+ * shuffle children as child node's partitioning won't satisfy the JOIN
node's requirements
+ * which otherwise could have.
+ *
+ * For instance, if there is a CROSS join, where the left relation has 'a
= 1' and the right
+ * relation has 'b = 1', then the rule infers 'a = b' as a join predicate.
+ */
+object InferJoinConditionsFromConstraints extends Rule[LogicalPlan] with
PredicateHelper {
+
+ def apply(plan: LogicalPlan): LogicalPlan = {
+ if (SQLConf.get.constraintPropagationEnabled) {
+ inferJoinConditions(plan)
+ } else {
+ plan
+ }
+ }
+
+ private def inferJoinConditions(plan: LogicalPlan): LogicalPlan = plan
transform {
+ case join @ Join(left, right, Cross, conditionOpt) =>
+
+ val rightEqualToPredicates = join.constraints.collect {
--- End diff --
I thought about improving the time complexity here via a hash map with
semantic equals/hashcode. However, this idea will require a wrapper so I keep
it as it is.
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