[GitHub] [spark] cloud-fan commented on a diff in pull request #38557: [SPARK-38959][SQL][FOLLOWUP] Optimizer batch `PartitionPruning` should optimize subqueries

[GitBox <gi...@apache.org>]

cloud-fan commented on code in PR #38557:
URL: https://github.com/apache/spark/pull/38557#discussion_r1017300121


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sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/RowLevelOperationRuntimeGroupFiltering.scala:
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@@ -89,10 +88,8 @@ case class RowLevelOperationRuntimeGroupFiltering(optimizeSubqueries: Rule[Logic
       buildKeys: Seq[Attribute],
       pruningKeys: Seq[Attribute]): Expression = {
 
-    val buildQuery = Project(buildKeys, matchingRowsPlan)
-    val dynamicPruningSubqueries = pruningKeys.zipWithIndex.map { case (key, index) =>
-      DynamicPruningSubquery(key, buildQuery, buildKeys, index, onlyInBroadcast = false)
-    }
-    dynamicPruningSubqueries.reduce(And)
+    val buildQuery = Aggregate(buildKeys, buildKeys, matchingRowsPlan)

Review Comment:
   I don't see any downside. We can only reuse broadcast if the DPP filter is derived from a join, which doesn't apply here. What's better, now this is a normal subquery and we can trigger subquery reuse which is not possible for DPP subqueries.



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