tanelk commented on a change in pull request #30999:
URL: https://github.com/apache/spark/pull/30999#discussion_r554702109
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
##########
@@ -349,11 +349,19 @@ abstract class Optimizer(catalogManager: CatalogManager)
*/
object EliminateDistinct extends Rule[LogicalPlan] {
override def apply(plan: LogicalPlan): LogicalPlan = plan
transformExpressions {
- case ae: AggregateExpression if ae.isDistinct =>
- ae.aggregateFunction match {
- case _: Max | _: Min => ae.copy(isDistinct = false)
- case _ => ae
- }
+ case ae: AggregateExpression if ae.isDistinct &&
isDuplicateAgnostic(ae.aggregateFunction) =>
+ ae.copy(isDistinct = false)
+ }
+
+ private def isDuplicateAgnostic(af: AggregateFunction): Boolean = af match {
+ case _: Max => true
+ case _: Min => true
+ case _: BitAndAgg => true
+ case _: BitOrAgg => true
+ case _: First => true
Review comment:
In another PR I try to argue that they should be deterministic: #29810
TLDR: An analogous aggregator would be sum on float and double datatype -
its result does depend on the order of its inputs, but is deterministic.
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