RDD根据对父RDD的依赖关系，可分为窄依赖与宽依赖2种。
主要的区分之处在于父RDD的分区被多少个子RDD分区所依赖，如果一个就为窄依赖，多个则为宽依赖。更好的定义应该是：
窄依赖的定义是子RDD的每一个分区都依赖于父RDD的一个或者少量几个分区（不依赖于全部分区）
与依赖相关的以下5个类：
Dependency
<--NarrowDependency
<--OneToOneDependency
<--RangeDependency
<--ShuffleDependency
它们全部在同一个Scala文件中，Dependency是一个abstract class, NarrowDependency(abstract class)与ShuffleDependency直接继承与它，OneToOneDependency与RangeDependency继承自NarrowDependency，大致如上图所示。
因此，关于Dependency的真正实现有三个，2个窄依赖：OneToOneDependency与RangeDependency，一个宽依赖：ShuffleDependency。
（一）Dependency
Dependency是一个抽象类，所有的依赖相关的类都必须继承自它。Dependency只有一个成员变量，表示的是父RDD。
/**
* :: DeveloperApi ::
* Base class for dependencies.
*/
@DeveloperApi
abstract class Dependency[T] extends Serializable {
def rdd: RDD[T]
}
（一）窄依赖
1、NarrowDependency
看看代码中对NarrowDependency的说明：
Base class for dependencies where each partition of the child RDD depends on a small number of partitions of the parent RDD. Narrow dependencies allow for pipelined execution。
即窄依赖的定义应该是子RDD的每一个分区都依赖于父RDD的一个或者少量几个分区（不依赖于全部分区）。
/**
* :: DeveloperApi ::
* Base class for dependencies where each partition of the child RDD depends on a small number
* of partitions of the parent RDD. Narrow dependencies allow for pipelined execution.
*/
@DeveloperApi
abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {
/**
* 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]
override def rdd: RDD[T] = _rdd
}
getParents根据子RDD的分区ID返回父RDD的分区ID。
主构建函数中的rdd是父RDD，下同。
2、OneToOneDependency
一对一依赖，即每个子RDD的分区的与父RDD的分区一一对应。
/**
* :: DeveloperApi ::
* Represents a one-to-one dependency between partitions of the parent and child RDDs.
*/
@DeveloperApi
class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) {
override def getParents(partitionId: Int): List[Int] = List(partitionId)
}
重写了NarrowDependency的getParents方法，返回一个List，这个List只有一个元素，且与子RDD的分区ID相同。即子分区的ID与父分区的ID一一对应且相等。
3、RangeDependency
子RDD中的每个分区依赖于父RDD的几个分区，而父RDD的每个分区仅补一个子RDD分区所依赖，即多对一的关系。它仅仅被UnionRDD所使用。
/**
* :: 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
* @param outStart the start of the range in the child RDD
* @param length the length of the range
*/
@DeveloperApi
class RangeDependency[T](rdd: RDD[T], inStart: Int, outStart: Int, length: Int)
extends NarrowDependency[T](rdd) {
override def getParents(partitionId: Int): List[Int] = {
if (partitionId >= outStart && partitionId < outStart + length) {
List(partitionId - outStart + inStart)
} else {
Nil
}
}
}
（二）宽依赖
宽依赖只有一种：shuffleDependency，即子RDD依赖于父RDD的所有分区，父RDD的分每个区被所有子RDD的分区所依赖。
/**
* :: DeveloperApi ::
* Represents a dependency on the output of a shuffle stage. Note that in the case of shuffle,
* the RDD is transient since we don‘t need it on the executor side.
*
* @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 not set
* explicitly then the default serializer, as specified by `spark.serializer`
* config option, will be used.
* @param keyOrdering key ordering for RDD‘s shuffles
* @param aggregator map/reduce-side aggregator for RDD‘s shuffle
* @param mapSideCombine whether to perform partial aggregation (also known as map-side combine)
*/
@DeveloperApi
class ShuffleDependency[K: ClassTag, V: ClassTag, C: ClassTag](
@transient private val _rdd: RDD[_ <: Product2[K, V]],
val partitioner: Partitioner,
val serializer: Serializer = SparkEnv.get.serializer,
val keyOrdering: Option[Ordering[K]] = None,
val aggregator: Option[Aggregator[K, V, C]] = None,
val mapSideCombine: Boolean = false)
extends Dependency[Product2[K, V]] {
override def rdd: RDD[Product2[K, V]] = _rdd.asInstanceOf[RDD[Product2[K, V]]]
private[spark] val keyClassName: String = reflect.classTag[K].runtimeClass.getName
private[spark] val valueClassName: String = reflect.classTag[V].runtimeClass.getName
// Note: It‘s possible that the combiner class tag is null, if the combineByKey
// methods in PairRDDFunctions are used instead of combineByKeyWithClassTag.
private[spark] val combinerClassName: Option[String] =
Option(reflect.classTag[C]).map(_.runtimeClass.getName)
val shuffleId: Int = _rdd.context.newShuffleId()
val shuffleHandle: ShuffleHandle = _rdd.context.env.shuffleManager.registerShuffle(
shuffleId, _rdd.partitions.length, this)
_rdd.sparkContext.cleaner.foreach(_.registerShuffleForCleanup(this))
}
（三）stage的划分
DAG根据宽依赖来划分stage，每个宽依赖的处理均会是一个stage的划分点。同一个stage中的多个操作会在一个task中完成。因为子RDD的分区仅依赖于父RDD的一个分区，因此这些步骤可以串行执行。