abstract class RDD[T: ClassManifest](@transient private var sc: SparkContext, //@transient, 不需要序列化@transient private var deps: Seq[Dependency[_]]) extends Serializable with Logging {

  /**辅助构造函数, 专门用于初始化1对1依赖关系的RDD,这种还是很多的, filter, map... 

     Construct an RDD with just a one-to-one dependency on one parent */def this(@transient oneParent: RDD[_]) = this(oneParent.context , List(new OneToOneDependency(oneParent)))  // 不同于一般的RDD, 这种情况因为只有一个parent, 所以直接传入parent RDD对象即可

  // =======================================================================// Methods that should be implemented by subclasses of RDD// =======================================================================/** Implemented by subclasses to compute a given partition. */def compute(split: Partition, context: TaskContext): Iterator[T]/*** Implemented by subclasses to return the set of partitions in this RDD. This method will only* be called once, so it is safe to implement a time-consuming computation in it.*/protected def getPartitions: Array[Partition]/*** Implemented by subclasses to return how this RDD depends on parent RDDs. This method will only* be called once, so it is safe to implement a time-consuming computation in it.*/protected def getDependencies: Seq[Dependency[_]] = deps/** Optionally overridden by subclasses to specify placement preferences. */protected def getPreferredLocations(split: Partition): Seq[String] = Nil/** Optionally overridden by subclasses to specify how they are partitioned. */val partitioner: Option[Partitioner] = None// =======================================================================// Methods and fields available on all RDDs// =======================================================================/** The SparkContext that created this RDD. */def sparkContext: SparkContext = sc/** A unique ID for this RDD (within its SparkContext). */val id: Int = sc.newRddId()/** A friendly name for this RDD */var name: String = null/*** Set this RDD's storage level to persist its values across operations after the first time* it is computed. This can only be used to assign a new storage level if the RDD does not* have a storage level set yet..*/def persist(newLevel: StorageLevel): RDD[T] = {// TODO: Handle changes of StorageLevelif (storageLevel != StorageLevel.NONE && newLevel != storageLevel) {throw new UnsupportedOperationException("Cannot change storage level of an RDD after it was already assigned a level")}storageLevel = newLevel// Register the RDD with the SparkContextsc.persistentRdds(id) = thisthis}/** Persist this RDD with the default storage level (`MEMORY_ONLY`). */def persist(): RDD[T] = persist(StorageLevel.MEMORY_ONLY)/** Persist this RDD with the default storage level (`MEMORY_ONLY`). */def cache(): RDD[T] = persist()

  /** Get the RDD's current storage level, or StorageLevel.NONE if none is set. */def getStorageLevel = storageLevel// Our dependencies and partitions will be gotten by calling subclass's methods below, and will// be overwritten when we're checkpointedprivate var dependencies_ : Seq[Dependency[_]] = null  @transient private var partitions_ : Array[Partition] = null /** An Option holding our checkpoint RDD, if we are checkpointed     * checkpoint就是把RDD存到磁盘文件中, 以提高failover的效率, 虽然也可以选择replay    * 并且在RDD的实现中, 如果存在checkpointRDD, 则可以直接从中读到RDD数据, 而不需要compute */private def checkpointRDD: Option[RDD[T]] = checkpointData.flatMap(_.checkpointRDD)

  /*** Internal method to this RDD; will read from cache if applicable, or otherwise compute it.* This should ''not'' be called by users directly, but is available for implementors of custom* subclasses of RDD.*/

  /** 这是RDD访问数据的核心, 在RDD中的Partition中只包含id而没有真正数据    * 那么如果获取RDD的数据? 参考storage模块     * 在cacheManager.getOrCompute中, 会将RDD和Partition id对应到相应的block, 并从中读出数据*/ final def iterator(split: Partition, context: TaskContext): Iterator[T] = {if (storageLevel != StorageLevel.NONE) {//StorageLevel不为None,说明这个RDD persist过, 可以直接读出来SparkEnv.get.cacheManager.getOrCompute(this, split, context, storageLevel)} else {computeOrReadCheckpoint(split, context) //如果没有persisit过, 只有从新计算出, 或从checkpoint中读出}}

  // Transformations (return a new RDD)
  //...... 各种transformations的接口,map, union...

  /*** Return a new RDD by applying a function to all elements of this RDD.*/def map[U: ClassManifest](f: T => U): RDD[U] = new MappedRDD(this, sc.clean(f))

  // Actions (launch a job to return a value to the user program)
  //......各种actions的接口,count, collect...

  /*** Return the number of elements in the RDD.*/def count(): Long = {// 只有在action中才会真正调用runJob, 所以transform都是lazy的sc.runJob(this, (iter: Iterator[T]) => {var result = 0Lwhile (iter.hasNext) {result += 1Liter.next()}result}).sum}

  // =======================================================================// Other internal methods and fields// =======================================================================  

  /** Returns the first parent RDD       返回第一个parent RDD*/protected[spark] def firstParent[U: ClassManifest] = {dependencies.head.rdd.asInstanceOf[RDD[U]]}

  //................
}

  /*** Return a new RDD containing only the elements that satisfy a predicate.*/def filter(f: T => Boolean): RDD[T] = new FilteredRDD(this, sc.clean(f))

private[spark] class FilteredRDD[T: ClassManifest]( //filter是典型的one-to-one dependency, 使用辅助构造函数 prev: RDD[T],   //parent RDDf: T => Boolean) //f,过滤函数extends RDD[T](prev) {//firstParent会从deps中取出第一个RDD对象, 就是传入的prev RDD, 在One-to-one Dependency中,parent和child的partition信息相同override def getPartitions: Array[Partition] = firstParent[T].partitionsoverride val partitioner = prev.partitioner    // Since filter cannot change a partition's keysoverride def compute(split: Partition, context: TaskContext) =firstParent[T].iterator(split, context).filter(f) //compute就是真正产生RDD的逻辑
}

  /*** Return the union of this RDD and another one. Any identical elements will appear multiple* times (use `.distinct()` to eliminate them).*/def union(other: RDD[T]): RDD[T] = new UnionRDD(sc, Array(this, other))

private[spark] class UnionPartition[T: ClassManifest](idx: Int, rdd: RDD[T], splitIndex: Int)extends Partition {var split: Partition = rdd.partitions(splitIndex)//从parent RDD中取出相应的partition, 重用def iterator(context: TaskContext) = rdd.iterator(split, context)//Iterator也可以重用def preferredLocations() = rdd.preferredLocations(split)override val index: Int = idx//partition id是新的, 因为多个合并后, 序号肯定会发生变化
}

class UnionRDD[T: ClassManifest](sc: SparkContext,@transient var rdds: Seq[RDD[T]])  //parent RDD Seqextends RDD[T](sc, Nil) {  // Nil since we implement getDependenciesoverride def getPartitions: Array[Partition] = {val array = new Array[Partition](rdds.map(_.partitions.size).sum) //UnionRDD的partition数,是所有parent RDD中的partition数目的和var pos = 0for (rdd <- rdds; split <- rdd.partitions) {array(pos) = new UnionPartition(pos, rdd, split.index) //创建所有的UnionPartitionpos += 1}array}override def getDependencies: Seq[Dependency[_]] = {val deps = new ArrayBuffer[Dependency[_]]var pos = 0for (rdd <- rdds) { deps += new RangeDependency(rdd, 0, pos, rdd.partitions.size)//创建RangeDependencypos += rdd.partitions.size)//由于是RangeDependency, 所以pos的递增是加上整个区间size}deps}override def compute(s: Partition, context: TaskContext): Iterator[T] =s.asInstanceOf[UnionPartition[T]].iterator(context)//Union的compute非常简单,什么都不需要做override def getPreferredLocations(s: Partition): Seq[String] =s.asInstanceOf[UnionPartition[T]].preferredLocations()
}