sunchao commented on code in PR #39633:
URL: https://github.com/apache/spark/pull/39633#discussion_r1097903665
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/BatchScanExec.scala:
##########
@@ -132,12 +140,66 @@ case class BatchScanExec(
outputPartitioning match {
case p: KeyGroupedPartitioning =>
- val partitionMapping = finalPartitions.map(s =>
- s.head.asInstanceOf[HasPartitionKey].partitionKey() -> s).toMap
- finalPartitions = p.partitionValues.map { partValue =>
- // Use empty partition for those partition values that are not
present
- partitionMapping.getOrElse(partValue, Seq.empty)
+ // This means the input partitions are not grouped by partition
values. We'll need to
+ // check `groupByPartitionValues` and decide whether to group and
replicate splits within
+ // a partition.
Review Comment:
you're right - moved.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala:
##########
@@ -411,6 +336,235 @@ case class EnsureRequirements(
}
}
+ /**
+ * Checks whether two children, `left` and `right`, of a join operator have
compatible
+ * `KeyGroupedPartitioning`, and can benefit from storage-partitioned join.
+ *
+ * Returns the updated new children if the check is successful, otherwise
`None`.
+ */
+ private def checkKeyGroupCompatible(
+ parent: SparkPlan,
+ left: SparkPlan,
+ right: SparkPlan,
+ requiredChildDistribution: Seq[Distribution]): Option[Seq[SparkPlan]] = {
+ parent match {
+ case smj: SortMergeJoinExec =>
+ checkKeyGroupCompatible(left, right, smj.joinType,
requiredChildDistribution)
+ case sj: ShuffledHashJoinExec =>
+ checkKeyGroupCompatible(left, right, sj.joinType,
requiredChildDistribution)
+ case _ =>
+ None
+ }
+ }
+
+ private def checkKeyGroupCompatible(
+ left: SparkPlan,
+ right: SparkPlan,
+ joinType: JoinType,
+ requiredChildDistribution: Seq[Distribution]): Option[Seq[SparkPlan]] = {
+ assert(requiredChildDistribution.length == 2)
+
+ var newLeft = left
+ var newRight = right
+
+ val specs = Seq(left, right).zip(requiredChildDistribution).map { case (p,
d) =>
+ if (!d.isInstanceOf[ClusteredDistribution]) return None
+ val cd = d.asInstanceOf[ClusteredDistribution]
+ val specOpt = createKeyGroupedShuffleSpec(p.outputPartitioning, cd)
+ if (specOpt.isEmpty) return None
+ specOpt.get
+ }
+
+ val leftSpec = specs.head
+ val rightSpec = specs(1)
+
+ var isCompatible = false
+ if (!conf.v2BucketingPushPartValuesEnabled) {
+ isCompatible = leftSpec.isCompatibleWith(rightSpec)
+ } else {
+ logInfo("Pushing common partition values for storage-partitioned join")
+ isCompatible = leftSpec.areKeysCompatible(rightSpec)
+
+ // Partition expressions are compatible. Regardless of whether partition
values
+ // match from both sides of children, we can we can calculate a superset
of
+ // partition values and push-down to respective data sources so they can
adjust
+ // their output partitioning by filling missing partition keys with empty
+ // partitions. As result, we can still avoid shuffle.
+ //
+ // For instance, if two sides of a join have partition expressions
+ // `day(a)` and `day(b)` respectively
+ // (the join query could be `SELECT ... FROM t1 JOIN t2 on t1.a =
t2.b`), but
+ // with different partition values:
+ // `day(a)`: [0, 1]
+ // `day(b)`: [1, 2, 3]
+ // Following the case 2 above, we don't have to shuffle both sides, but
instead can
+ // just push the common set of partition values: `[0, 1, 2, 3]` down to
the two data
+ // sources.
+ if (isCompatible) {
+ val leftPartValues = leftSpec.partitioning.partitionValues
+ val rightPartValues = rightSpec.partitioning.partitionValues
+
+ logInfo(
+ s"""
+ |Left side # of partitions: ${leftPartValues.size}
+ |Right side # of partitions: ${rightPartValues.size}
+ |""".stripMargin)
+
+ var mergedPartValues = Utils.mergeOrdered(
+ Seq(leftPartValues, rightPartValues))(leftSpec.ordering)
+ .toSeq
+ .distinct
+ .map(v => (v, 1))
+
+ logInfo(s"After merging, there are ${mergedPartValues.size}
partitions")
+
+ var replicateLeftSide = false
+ var replicateRightSide = false
+ var applyPartialClustering = false
+
+ // This means we allow partitions that are not clustered on their
values,
+ // that is, multiple partitions with the same partition value. In the
+ // following, we calculate how many partitions that each distinct
partition
+ // value has, and pushdown the information to scans, so they can
adjust their
+ // final input partitions respectively.
+ if (conf.v2BucketingPartiallyClusteredDistributionEnabled) {
+ logInfo("Calculating partially clustered distribution for " +
+ "storage-partitioned join")
+
+ val canReplicateLeft = canReplicateLeftSide(joinType)
+ val canReplicateRight = canReplicateRightSide(joinType)
Review Comment:
+1. Added comments for this.
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