Spark 之 EnsureRequirements

ensureDistributionAndOrdering

  private def ensureDistributionAndOrdering(
      parent: Option[SparkPlan],
      originalChildren: Seq[SparkPlan],
      requiredChildDistributions: Seq[Distribution],
      requiredChildOrderings: Seq[Seq[SortOrder]],
      shuffleOrigin: ShuffleOrigin): Seq[SparkPlan] = {
    assert(requiredChildDistributions.length == originalChildren.length)
    assert(requiredChildOrderings.length == originalChildren.length)
    // Ensure that the operator's children satisfy their output distribution requirements.
    var children = originalChildren.zip(requiredChildDistributions).map {
      case (child, distribution) if child.outputPartitioning.satisfies(distribution) =>
        child
      case (child, BroadcastDistribution(mode)) =>
        BroadcastExchangeExec(mode, child)
      case (child, distribution) =>
        val numPartitions = distribution.requiredNumPartitions
          .getOrElse(conf.numShufflePartitions)
        ShuffleExchangeExec(distribution.createPartitioning(numPartitions), child, shuffleOrigin)
    }

    // Get the indexes of children which have specified distribution requirements and need to be
    // co-partitioned.
    val childrenIndexes = requiredChildDistributions.zipWithIndex.filter {
      case (_: ClusteredDistribution, _) => true
      case _ => false
    }.map(_._2)

    // Special case: if all sides of the join are single partition and it's physical size less than
    // or equal spark.sql.maxSinglePartitionBytes.
    val preferSinglePartition = childrenIndexes.forall { i =>
      children(i).outputPartitioning == SinglePartition &&
        children(i).logicalLink
          .forall(_.stats.sizeInBytes <= conf.getConf(SQLConf.MAX_SINGLE_PARTITION_BYTES))
    }

    // If there are more than one children, we'll need to check partitioning & distribution of them
    // and see if extra shuffles are necessary.
    if (childrenIndexes.length > 1 && !preferSinglePartition) {
      val specs = childrenIndexes.map(i => {
        val requiredDist = requiredChildDistributions(i)
        assert(requiredDist.isInstanceOf[ClusteredDistribution],
          s"Expected ClusteredDistribution but found ${requiredDist.getClass.getSimpleName}")
        i -> children(i).outputPartitioning.createShuffleSpec(
          requiredDist.asInstanceOf[ClusteredDistribution])
      }).toMap

      // Find out the shuffle spec that gives better parallelism. Currently this is done by
      // picking the spec with the largest number of partitions.
      //
      // NOTE: this is not optimal for the case when there are more than 2 children. Consider:
      //   (10, 10, 11)
      // where the number represent the number of partitions for each child, it's better to pick 10
      // here since we only need to shuffle one side - we'd need to shuffle two sides if we pick 11.
      //
      // However this should be sufficient for now since in Spark nodes with multiple children
      // always have exactly 2 children.

      // Whether we should consider `spark.sql.shuffle.partitions` and ensure enough parallelism
      // during shuffle. To achieve a good trade-off between parallelism and shuffle cost, we only
      // consider the minimum parallelism iff ALL children need to be re-shuffled.
      //
      // A child needs to be re-shuffled iff either one of below is true:
      //   1. It can't create partitioning by itself, i.e., `canCreatePartitioning` returns false
      //      (as for the case of `RangePartitioning`), therefore it needs to be re-shuffled
      //      according to other shuffle spec.
      //   2. It already has `ShuffleExchangeLike`, so we can re-use existing shuffle without
      //      introducing extra shuffle.
      //
      // On the other hand, in scenarios such as:
      //   HashPartitioning(5) <-> HashPartitioning(6)
      // while `spark.sql.shuffle.partitions` is 10, we'll only re-shuffle the left side and make it
      // HashPartitioning(6).
      val shouldConsiderMinParallelism = specs.forall(p =>
        !p._2.canCreatePartitioning || children(p._1).isInstanceOf[ShuffleExchangeLike]
      )
      // Choose all the specs that can be used to shuffle other children
      val candidateSpecs = specs
          .filter(_._2.canCreatePartitioning)
          .filter(p => !shouldConsiderMinParallelism ||
              children(p._1).outputPartitioning.numPartitions >= conf.defaultNumShufflePartitions)
      val bestSpecOpt = if (candidateSpecs.isEmpty) {
        None
      } else {
        // When choosing specs, we should consider those children with no `ShuffleExchangeLike` node
        // first. For instance, if we have:
        //   A: (No_Exchange, 100) <---> B: (Exchange, 120)
        // it's better to pick A and change B to (Exchange, 100) instead of picking B and insert a
        // new shuffle for A.
        val candidateSpecsWithoutShuffle = candidateSpecs.filter { case (k, _) =>
          !children(k).isInstanceOf[ShuffleExchangeLike]
        }
        val finalCandidateSpecs = if (candidateSpecsWithoutShuffle.nonEmpty) {
          candidateSpecsWithoutShuffle
        } else {
          candidateSpecs
        }
        // Pick the spec with the best parallelism
        Some(finalCandidateSpecs.values.maxBy(_.numPartitions))
      }

      // Check if the following conditions are satisfied:
      //   1. There are exactly two children (e.g., join). Note that Spark doesn't support
      //      multi-way join at the moment, so this check should be sufficient.
      //   2. All children are of `KeyGroupedPartitioning`, and they are compatible with each other
      // If both are true, skip shuffle.
      val isKeyGroupCompatible = parent.isDefined &&
          children.length == 2 && childrenIndexes.length == 2 && {
        val left = children.head
        val right = children(1)
        val newChildren = checkKeyGroupCompatible(
          parent.get, left, right, requiredChildDistributions)
        if (newChildren.isDefined) {
          children = newChildren.get
        }
        newChildren.isDefined
      }

      children = children.zip(requiredChildDistributions).zipWithIndex.map {
        case ((child, _), idx) if isKeyGroupCompatible || !childrenIndexes.contains(idx) =>
          child
        case ((child, dist), idx) =>
          if (bestSpecOpt.isDefined && bestSpecOpt.get.isCompatibleWith(specs(idx))) {
            child
          } else {
            val newPartitioning = bestSpecOpt.map { bestSpec =>
              // Use the best spec to create a new partitioning to re-shuffle this child
              val clustering = dist.asInstanceOf[ClusteredDistribution].clustering
              bestSpec.createPartitioning(clustering)
            }.getOrElse {
              // No best spec available, so we create default partitioning from the required
              // distribution
              val numPartitions = dist.requiredNumPartitions
                  .getOrElse(conf.numShufflePartitions)
              dist.createPartitioning(numPartitions)
            }

            child match {
              case ShuffleExchangeExec(_, c, so, ps) =>
                ShuffleExchangeExec(newPartitioning, c, so, ps)
              case _ => ShuffleExchangeExec(newPartitioning, child)
            }
          }
      }
    }

    // Now that we've performed any necessary shuffles, add sorts to guarantee output orderings:
    children = children.zip(requiredChildOrderings).map { case (child, requiredOrdering) =>
      // If child.outputOrdering already satisfies the requiredOrdering, we do not need to sort.
      if (SortOrder.orderingSatisfies(child.outputOrdering, requiredOrdering)) {
        child
      } else {
        SortExec(requiredOrdering, global = false, child = child)
      }
    }

    children
  }

ENSURE_REQUIREMENTS 表示自然需求， 不是认为的 reparation 等。

原文地址：https://blog.csdn.net/zhixingheyi_tian/article/details/143862734

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