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[SPARK-28356][SHUFFLE][FOLLOWUP] Fix case with different pre-shuffle partition numbers #25479

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[SPARK-28356][SHUFFLE][FOLLOWUP] Fix case with different pre-shuffle partition numbers #25479

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[SPARK-28356][FOLLOWUP] fix case when we have different pre-shuffle p…
peter-toth Aug 16, 2019
31436a8
add a example
peter-toth Aug 16, 2019
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7 changes: 6 additions & 1 deletion ...e/src/main/scala/org/apache/spark/sql/execution/adaptive/ReduceNumShufflePartitions.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -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
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@cloud-fan cloud-fan Aug 16, 2019

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let's also give an example about when we will have different pre-shuffle partition numbers.

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@peter-toth peter-toth Aug 16, 2019

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Ok, added. Please let me know if it should be more detailed.

// 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) {
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@viirya viirya Aug 16, 2019 •
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After we have this condition distinctNumPreShufflePartitions.length == 1, do we still need the assert at L136? Shall we remove the assert?

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@peter-toth peter-toth Aug 17, 2019 •
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Yes, we could remove it, but the assert has been there since the original version of ReduceNumShufflePartitions where the distinctNumPreShufflePartitions.length == 1 check was also included. I'm not sure what is the plan with ReduceNumShufflePartitions. @carsonwang, @maryannxue do you want to improve Union/SinglePartition handling in this rule? Shall we remove the assert?

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@carsonwang carsonwang Aug 19, 2019

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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.

val partitionStartIndices = estimatePartitionStartIndices(validMetrics.toArray)
// This transformation adds new nodes, so we must use `transformUp` here.
plan.transformUp {
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18 changes: 18 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/ReduceNumShufflePartitionsSuite.scala
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Expand Up @@ -587,4 +587,22 @@ class ReduceNumShufflePartitionsSuite extends SparkFunSuite with BeforeAndAfterA
}
withSparkSession(test, 200, None)
}

test("Union two datasets with different pre-shuffle partition number") {
val test: SparkSession => Unit = { spark: SparkSession =>
val df1 = spark.range(3).join(spark.range(3), "id").toDF()
val df2 = spark.range(3).groupBy().sum()

val resultDf = df1.union(df2)

checkAnswer(resultDf, Seq((0), (1), (2), (3)).map(i => Row(i)))
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@cloud-fan cloud-fan Aug 16, 2019

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does this fail without the fix?

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@peter-toth peter-toth Aug 16, 2019 •
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It does. The plan is:

AdaptiveSparkPlan(isFinalPlan=false)
+- Union
   :- Project [id#0L]
   :  +- SortMergeJoin [id#0L], [id#2L], Inner
   :     :- Sort [id#0L ASC NULLS FIRST], false, 0
   :     :  +- Exchange hashpartitioning(id#0L, 5), true
   :     :     +- Range (0, 3, step=1, splits=12)
   :     +- Sort [id#2L ASC NULLS FIRST], false, 0
   :        +- Exchange hashpartitioning(id#2L, 5), true
   :           +- Range (0, 3, step=1, splits=12)
   +- HashAggregate(keys=[], functions=[sum(id#6L)], output=[sum(id)#10L])
      +- Exchange SinglePartition, true
         +- HashAggregate(keys=[], functions=[partial_sum(id#6L)], output=[sum#14L])
            +- Range (0, 3, step=1, splits=12)

and the error comes from this assert: https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ReduceNumShufflePartitions.scala#L136

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@cloud-fan cloud-fan Aug 19, 2019

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Can you fill the Does this PR introduce any user-facing change section? Changing a query from failure to runnable is a user-facing change.

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@peter-toth peter-toth Aug 19, 2019

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Oh ok, sure, filled.


val finalPlan = resultDf.queryExecution.executedPlan
.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
// As the pre-shuffle partition number are different, we will skip reducing
// the shuffle partition numbers.
assert(finalPlan.collect { case p: CoalescedShuffleReaderExec => p }.length == 0)
}
withSparkSession(test, 100, None)
}
}