cloud-fan commented on code in PR #57791:
URL: https://github.com/apache/spark/pull/57791#discussion_r3727695387
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeJoinCondition.scala:
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@@ -30,16 +30,24 @@ object OptimizeJoinCondition extends Rule[LogicalPlan] with
PredicateHelper {
override def apply(plan: LogicalPlan): LogicalPlan =
plan.transformWithPruning(
_.containsPattern(JOIN), ruleId) {
case j @ Join(_, _, _, condition, _) if condition.nonEmpty =>
- val newCondition =
condition.map(_.transformWithPruning(_.containsPattern(OR), ruleId) {
- case Or(EqualTo(l, r), And(IsNull(c1), IsNull(c2)))
- if (l.semanticEquals(c1) && r.semanticEquals(c2))
- || (l.semanticEquals(c2) && r.semanticEquals(c1)) =>
- EqualNullSafe(l, r)
- case Or(And(IsNull(c1), IsNull(c2)), EqualTo(l, r))
- if (l.semanticEquals(c1) && r.semanticEquals(c2))
- || (l.semanticEquals(c2) && r.semanticEquals(c1)) =>
- EqualNullSafe(l, r)
- })
+ val newCondition = condition.map(optimizeCondition)
j.copy(condition = newCondition)
}
+
+ // Rewriting the pattern to EqualNullSafe maps NULL to FALSE, so only
recurse through And/Or.
+ private def optimizeCondition(condition: Expression): Expression = condition
match {
+ case Or(EqualTo(l, r), And(IsNull(c1), IsNull(c2)))
+ if (l.semanticEquals(c1) && r.semanticEquals(c2))
+ || (l.semanticEquals(c2) && r.semanticEquals(c1)) =>
+ EqualNullSafe(l, r)
+ case Or(And(IsNull(c1), IsNull(c2)), EqualTo(l, r))
+ if (l.semanticEquals(c1) && r.semanticEquals(c2))
+ || (l.semanticEquals(c2) && r.semanticEquals(c1)) =>
+ EqualNullSafe(l, r)
+ case And(left, right) =>
+ And(optimizeCondition(left), optimizeCondition(right))
Review Comment:
Please keep the OR tree-pattern guard and return the original AND/OR node
when neither child changes. Without those checks, this rule now traverses and
allocates a fresh Boolean tree for every unrelated join condition that the
previous `transformWithPruning` skipped.
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