[GitHub] [spark] cloud-fan commented on a change in pull request #25121: [SPARK-28356][SQL] Do not reduce the number of partitions for repartition in adaptive execution

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cloud-fan commented on a change in pull request #25121: [SPARK-28356][SQL] Do not reduce the number of partitions for repartition in adaptive execution
URL: https://github.com/apache/spark/pull/25121#discussion_r313691232
 
 

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 File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ReduceNumShufflePartitions.scala
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 @@ -76,12 +82,7 @@ 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)
-      // We may get different pre-shuffle partition number if user calls repartition manually.
-      // We don't reduce shuffle partition number in that case.
-      val distinctNumPreShufflePartitions =
-        validMetrics.map(stats => stats.bytesByPartitionId.length).distinct
-
-      if (validMetrics.nonEmpty && distinctNumPreShufflePartitions.length == 1) {
+      if (validMetrics.nonEmpty) {
 
 Review comment:
   ah `SinglePartition` is an exception. So it's still possible to hit `distinctNumPreShufflePartitions.length > 1` here. Let's add back this check @carsonwang @maryannxue 
   
   thanks for reporting it!

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