Github user yucai commented on a diff in the pull request: https://github.com/apache/spark/pull/21156#discussion_r200937190 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala --- @@ -76,8 +76,36 @@ case class SortMergeJoinExec( s"${getClass.getSimpleName} should not take $x as the JoinType") } - override def requiredChildDistribution: Seq[Distribution] = - HashClusteredDistribution(leftKeys) :: HashClusteredDistribution(rightKeys) :: Nil + private def avoidShuffleIfPossible( + joinKeys: Seq[Expression], + expressions: Seq[Expression]): Seq[Distribution] = { + val indices = expressions.map(x => joinKeys.indexWhere(_.semanticEquals(x))) + HashClusteredDistribution(indices.map(leftKeys(_))) :: + HashClusteredDistribution(indices.map(rightKeys(_))) :: Nil + } + + override def requiredChildDistribution: Seq[Distribution] = { + if (!conf.sortMergeJoinExecChildrenPartitioningDetection) { + return HashClusteredDistribution(leftKeys) :: HashClusteredDistribution(rightKeys) :: Nil + } + + val leftPartitioning = left.outputPartitioning + val rightPartitioning = right.outputPartitioning + leftPartitioning match { + case HashPartitioning(leftExpressions, _) + if leftPartitioning.satisfies(ClusteredDistribution(leftKeys)) => + avoidShuffleIfPossible(leftKeys, leftExpressions) + + case _ => rightPartitioning match { --- End diff -- Yes, you are right. The main purpose of this feature is for the bucketed table, so the `HashPartitioning` is enough. Actually, with the similar way, we can skip the shuffle for one side if it is `RangePartitioning` also, but I am not sure if it is really useful.
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