viirya commented on a change in pull request #32594:
URL: https://github.com/apache/spark/pull/32594#discussion_r637693175
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ShufflePartitionsUtil.scala
##########
@@ -21,16 +21,160 @@ import scala.collection.mutable.ArrayBuffer
import org.apache.spark.MapOutputStatistics
import org.apache.spark.internal.Logging
-import org.apache.spark.sql.execution.{CoalescedPartitionSpec,
ShufflePartitionSpec}
+import org.apache.spark.sql.execution.{CoalescedPartitionSpec,
PartialReducerPartitionSpec, ShufflePartitionSpec}
object ShufflePartitionsUtil extends Logging {
final val SMALL_PARTITION_FACTOR = 0.2
final val MERGED_PARTITION_FACTOR = 1.2
/**
- * Coalesce the partitions from multiple shuffles. This method assumes that
all the shuffles
- * have the same number of partitions, and the partitions of same index will
be read together
- * by one task.
+ * Coalesce the partitions from multiple shuffles, either in their original
states, or applied
+ * with skew handling partition specs. If called on partitions containing
skew partition specs,
+ * this method will keep the skew partition specs intact and only coalesce
the partitions outside
+ * the skew sections.
+ *
+ * This method will return an empty result if the shuffles have been
coalesced already, or if
+ * they do not have the same number of partitions, or if the coalesced
result is the same as the
+ * input partition layout.
+ *
+ * @return A sequence of sequence of [[ShufflePartitionSpec]]s, which each
inner sequence as the
+ * new partition specs for its corresponding shuffle after
coalescing. If Nil is returned,
+ * then no coalescing is applied.
+ */
+ def coalescePartitions(
+ mapOutputStatistics: Seq[Option[MapOutputStatistics]],
+ inputPartitionSpecs: Seq[Option[Seq[ShufflePartitionSpec]]],
+ advisoryTargetSize: Long,
+ minNumPartitions: Int): Seq[Seq[ShufflePartitionSpec]] = {
+ assert(mapOutputStatistics.length == inputPartitionSpecs.length)
+
+ if (mapOutputStatistics.isEmpty) {
+ return Seq.empty
+ }
+
+ // If `minNumPartitions` is very large, it is possible that we need to use
a value less than
+ // `advisoryTargetSize` as the target size of a coalesced task.
+ val totalPostShuffleInputSize =
mapOutputStatistics.flatMap(_.map(_.bytesByPartitionId.sum)).sum
+ // The max at here is to make sure that when we have an empty table, we
only have a single
+ // coalesced partition.
+ // There is no particular reason that we pick 16. We just need a number to
prevent
+ // `maxTargetSize` from being set to 0.
+ val maxTargetSize = math.max(
+ math.ceil(totalPostShuffleInputSize / minNumPartitions.toDouble).toLong,
16)
+ val targetSize = math.min(maxTargetSize, advisoryTargetSize)
+
+ val shuffleIds =
mapOutputStatistics.flatMap(_.map(_.shuffleId)).mkString(", ")
+ logInfo(s"For shuffle($shuffleIds), advisory target size:
$advisoryTargetSize, " +
+ s"actual target size $targetSize.")
+
+ val numShuffles = mapOutputStatistics.length
+ // `ShuffleQueryStageExec#mapStats` returns None when the input RDD has 0
partitions,
+ // we should skip it when calculating the `partitionStartIndices`.
+ val validMetrics = mapOutputStatistics.flatten
+
+ if (inputPartitionSpecs.forall(_.isEmpty)) {
+ // If all input RDDs have 0 partition, we create an empty partition for
every shuffle reader.
+ if (validMetrics.isEmpty) {
+ return Seq.fill(numShuffles)(Seq(CoalescedPartitionSpec(0, 0)))
+ }
+
+ // We may have different pre-shuffle partition numbers, don't reduce
shuffle partition number
+ // 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).
+ if (validMetrics.map(_.bytesByPartitionId.length).distinct.length > 1) {
+ return Seq.empty
+ }
+
+ val numPartitions = validMetrics.head.bytesByPartitionId.length
+ val newPartitionSpecs = coalescePartitions(
+ 0, numPartitions, validMetrics, targetSize)
+ if (newPartitionSpecs.length < numPartitions) {
+ return Seq.fill(numShuffles)(newPartitionSpecs)
+ } else {
+ return Seq.empty
+ }
+ }
+
+ // Do not coalesce if any of the map output stats are missing or if not
all shuffles have
+ // partition specs, which should not happen in practice.
+ if (!mapOutputStatistics.forall(_.isDefined) ||
!inputPartitionSpecs.forall(_.isDefined)) {
+ logWarning("Could not apply partition coalescing because of missing
MapOutputStatistics " +
+ "or shuffle partition specs.")
+ return Seq.empty
+ }
+
+ // Extract the start indices of each partition spec. Give invalid index -1
to unexpected
+ // partition specs. When we reach here, it means skew join optimization
has been applied.
+ val partitionIndicesSeq = inputPartitionSpecs.map(_.get.map {
+ case CoalescedPartitionSpec(start, end) if start + 1 == end => start
+ case PartialReducerPartitionSpec(reducerId, _, _, _) => reducerId
+ case _ => -1 // invalid
+ })
+
+ // There should be no unexpected partition specs and the start indices
should be identical
+ // across all different shuffles.
+ assert(partitionIndicesSeq.distinct.length == 1 &&
partitionIndicesSeq.head.forall(_ >= 0),
+ s"Invalid shuffle partition specs: $inputPartitionSpecs")
+
+ // The indices may look like [0, 1, 2, 2, 2, 3, 4, 4, 5], and the repeated
`2` and `4` mean
+ // skewed partitions.
+ val partitionIndices = partitionIndicesSeq.head
+ val newPartitionSpecsSeq =
Seq.fill(numShuffles)(ArrayBuffer.empty[ShufflePartitionSpec])
+ val numPartitions = partitionIndices.length
+ var i = 0
+ var start = 0
+ while (i < numPartitions) {
+ if (i > 0 && partitionIndices(i - 1) == partitionIndices(i)) {
+ // a skew section detected, starting from partition(i - 1).
+ val repeatValue = partitionIndices(i)
+ // coalesce any partitions before partition(i - 1) and after the end
of latest skew section.
Review comment:
I think the following only does "coalesce any partitions before
partition(i - 1)" but not "coalesce any partitions before partition(i - 1)"?
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