[GitHub] [spark] carsonwang commented on a change in pull request #25479: [SPARK-28356][SHUFFLE][FOLLOWUP] Fix case with different pre-shuffle partition numbers

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carsonwang commented on a change in pull request #25479: [SPARK-28356][SHUFFLE][FOLLOWUP] Fix case with different pre-shuffle partition numbers
URL: https://github.com/apache/spark/pull/25479#discussion_r315077053
 
 

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 File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ReduceNumShufflePartitions.scala
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 @@ -82,7 +82,12 @@ case class ReduceNumShufflePartitions(conf: SQLConf) extends Rule[SparkPlan] {
       // `ShuffleQueryStageExec` gives null mapOutputStatistics when the input RDD has 0 partitions,
       // we should skip it when calculating the `partitionStartIndices`.
       val validMetrics = shuffleMetrics.filter(_ != null)
-      if (validMetrics.nonEmpty) {
+      // We may have different pre-shuffle partition numbers, don't reduce shuffle partition number
+      // in that case. For example when we union fully aggregated data (data is arranged to a single
+      // partition) and a result of a SortMergeJoin (multiple partitions).
+      val distinctNumPreShufflePartitions =
+        validMetrics.map(stats => stats.bytesByPartitionId.length).distinct
+      if (validMetrics.nonEmpty && distinctNumPreShufflePartitions.length == 1) {
 
 Review comment:
   I think it is fine to remove it. We can improve the handling of `Union/SinglePartition` in future and it probably needs more changes and a new function to estimate the partition start indices. 

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