[GitHub] [spark] sunchao commented on a diff in pull request #39633: [SPARK-42038][SQL] SPJ: Support partially clustered distribution

["sunchao (via GitHub)" <gi...@apache.org>]

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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