1、Spark中採用依赖关系(Dependency)表示rdd之间的生成关系。Spark可利用Dependency计算出失效的RDD。在每一个RDD中都存在一个依赖关系的列表

private var dependencies_ : Seq[Dependency[_]] = null

用以记录各rdd中各partition的parent partition。

2、Spark中存在两类Dependency：

1）NarrowDependency表示的是一个父partition仅相应于一个子partition。这种依赖关系是不须要shuffle的。在这类依赖中。能够依据getParents方法获取某个partition的父partitions：

/** * :: DeveloperApi :: * Base class for dependencies where each partition of the parent RDD is used by at most one * partition of the child RDD.  Narrow dependencies allow for pipelined execution. */@DeveloperApiabstract class NarrowDependency[T](rdd: RDD[T]) extends Dependency(rdd) {  /**   * 唯一的接口。获得该partition的全部parent partition   * Get the parent partitions for a child partition.   * @param partitionId a partition of the child RDD   * @return the partitions of the parent RDD that the child partition depends upon   */  def getParents(partitionId: Int): Seq[Int]}

这类又可分为：

a、OneToOneDependency：表示一一相应的依赖关系，因为在这样的依赖中父partition与子partition Id是一致的，所以getParents直接原样返回。相应的转换操作有map和filter

class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) {  /**   * 事实上partitionId就是partition在RDD中的序号, 所以假设是一一相应, 那么parent和child中的partition的序号应该是一样的   */  override def getParents(partitionId: Int) = List(partitionId)//原样返回}

b、PruneDependency(org.apache.spark.rdd.PartitionPruningRDDPartition)：未详

/** * Represents a dependency between the PartitionPruningRDD and its parent. In this * case, the child RDD contains a subset of partitions of the parents'. */private[spark] class PruneDependency[T](rdd: RDD[T], @transient partitionFilterFunc: Int => Boolean)  extends NarrowDependency[T](rdd) {  @transient  val partitions: Array[Partition] = rdd.partitions    .filter(s => partitionFilterFunc(s.index)).zipWithIndex    .map { case(split, idx) => new PartitionPruningRDDPartition(idx, split) : Partition }  override def getParents(partitionId: Int) = {    List(partitions(partitionId).asInstanceOf[PartitionPruningRDDPartition].parentSplit.index)  }}

c、RangeDependency：这样的是父rdd的连续多个partitions相应子rdd中的连续多个partitions。相应的转换有union

/**Union * :: DeveloperApi :: * Represents a one-to-one dependency between ranges of partitions in the parent and child RDDs. * @param rdd the parent RDD * @param inStart the start of the range in the parent RDD parent RDD中区间的起始点 * @param outStart the start of the range in the child RDD child RDD中区间的起始点  * @param length the length of the range */@DeveloperApiclass RangeDependency[T](rdd: RDD[T], inStart: Int, outStart: Int, length: Int)  extends NarrowDependency[T](rdd) {  override def getParents(partitionId: Int) = {    if (partitionId >= outStart && partitionId < outStart + length) {//推断partitionId的合理性,必须在child RDD的合理partition范围      List(partitionId - outStart + inStart)//算出parent RDD中相应的partition id    } else {      Nil    }  }}

2）WideDependency：这样的依赖是指一个父partition能够相应子rdd中多个partitions。因为须要对父partition进行划分，故须要用到shuffle，而shuffle通常是採用键值对的。

这里为每一个shuffle分配了一个全局唯一的shuffleId。

为了进行shuffle。须要指定怎样进行shuffle，这相应于參数partitioner；因为shuffle是须要网络传输的。故须要进行序列化Serializer。在宽依赖中并无法获得partition相应的parent partitions？

/** * :: DeveloperApi :: * Represents a dependency on the output of a shuffle stage. * @param rdd the parent RDD * @param partitioner partitioner used to partition the shuffle output * @param serializer [[org.apache.spark.serializer.Serializer Serializer]] to use. If set to null, *                   the default serializer, as specified by `spark.serializer` config option, will *                   be used. */@DeveloperApiclass ShuffleDependency[K, V](    @transient rdd: RDD[_ <: Product2[K, V]],    val partitioner: Partitioner,//须要给出partitioner, 指示怎样完毕shuffle    val serializer: Serializer = null)//shuffle不象map能够在local进行, 往往须要网络传输或存储, 所以须要serializerClass  extends Dependency(rdd.asInstanceOf[RDD[Product2[K, V]]]) {  val shuffleId: Int = rdd.context.newShuffleId()//每一个shuffle须要分配一个全局的id, context.newShuffleId()的实现就是把全局id累加  rdd.sparkContext.cleaner.foreach(_.registerShuffleForCleanup(this))}