ApacheSpark中的requiredChildDistribution和outputPartitioning有什么区别？

requiredChildDistribution和outputPartitioning都是Spark中的重要概念，但在功能和用法上有所区别。

requiredChildDistribution用于指定子节点之间的数据分布方式，用法如下：

case class MyOperator(
  left: SparkPlan,
  right: SparkPlan,
  **requiredChildDistribution: Seq[Distribution]**
) extends BinaryNode {

  override def output: Seq[Attribute] = left.output ++ right.output

  override def leftChild: SparkPlan = left

  override def rightChild: SparkPlan = right

  override def requiredChildDistribution: Seq[Distribution] = Seq(UnspecifiedDistribution, UnspecifiedDistribution)

  override def doExecute(): RDD[InternalRow] = {
    ...
  }
}

其中，requiredChildDistribution是一个Seq类型的参数，表示子节点之间的数据分布方式，默认值为UnspecifiedDistribution。若想指定子节点之间的数据分布方式，也可以使用其他的分布方式，例如：

case class MyOperator(
  left: SparkPlan,
  right: SparkPlan,
  **requiredChildDistribution: Seq[Distribution]**
) extends BinaryNode {

  override def output: Seq[Attribute] = left.output ++ right.output

  override def leftChild: SparkPlan = left

  override def rightChild: SparkPlan = right

  override def requiredChildDistribution: Seq[Distribution] = Seq(ClusteredDistribution(Seq("key")), ClusteredDistribution(Seq("key")))

  override def doExecute(): RDD[InternalRow] = {
    ...
  }
}

outputPartitioning用于指定输出RDD的分区方式，用法如下：

case class MyOperator(
  child: SparkPlan,
  **outputPartitioning: Partitioning**
) extends UnaryNode {

  override def output: Seq[Attribute] = child.output

  override def child: SparkPlan = child

  override def outputPartitioning: Partitioning = HashPartitioning(Seq("key"), 10)

  override def doExecute(): RDD[InternalRow] = {
    ...
  }
}

其中，outputPartitioning是一个Partitioning类型的参数，表示输出