Spark 源码分析 -- RDD

关于RDD, 详细可以参考Spark的论文, 下面看下源码
A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
Represents an immutable, partitioned collection of elements that can be operated on in parallel.

* Internally, each RDD is characterized by five main properties:
*  - A list of partitions
*  - A function for computing each split
*  - A list of dependencies on other RDDs
*  - Optionally, a Partitioner for key-value RDDs (e.g. to say that the RDD is hash-partitioned)
*  - Optionally, a list of preferred locations to compute each split on (e.g. block locations for an HDFS file)

RDD分为一下几类,

basic(org.apache.spark.rdd.RDD): This class contains the basic operations available on all RDDs, such as `map`, `filter`, and `persist`.

org.apache.spark.rdd.PairRDDFunctions: contains operations available only on RDDs of key-value pairs, such as `groupByKey` and `join`

org.apache.spark.rdd.DoubleRDDFunctions: contains operations available only on RDDs of Doubles

org.apache.spark.rdd.SequenceFileRDDFunctions: contains operations available on RDDs that can be saved as SequenceFiles

 

RDD首先是泛型类, T表示存放数据的类型, 在处理数据是都是基于Iterator[T]
以SparkContext和依赖关系Seq deps为初始化参数
从RDD提供的这些接口大致就可以知道, 什么是RDD
1. RDD是一块数据, 可能比较大的数据, 所以不能保证可以放在一个机器的memory中, 所以需要分成partitions, 分布在集群的机器的memory
所以自然需要getPartitions, partitioner如果分区, getPreferredLocations分区如何考虑locality

Partition的定义很简单, 只有id, 不包含data

trait Partition extends Serializable {
  /**
   * Get the split's index within its parent RDD
   */
  def index: Int
  // A better default implementation of HashCode
  override def hashCode(): Int = index
}

2. RDD之间是有关联的, 一个RDD可以通过compute逻辑把父RDD的数据转化成当前RDD的数据, 所以RDD之间有因果关系

并且通过getDependencies, 可以取到所有的dependencies

3. RDD是可以被persisit的, 常用的是cache, 即StorageLevel.MEMORY_ONLY

4. RDD是可以被checkpoint的, 以提高failover的效率, 当有很长的RDD链时, 单纯的依赖replay会比较低效

5. RDD.iterator可以产生用于迭代真正数据的Iterator[T]

6. 在RDD上可以做各种transforms和actions

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 StorageLevel
    if (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 SparkContext
    sc.persistentRdds(id) = this
    this
  }
 
  /** 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 checkpointed
  private 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 = 0L
      while (iter.hasNext) {
        result += 1L
        iter.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]]
  }

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

 

这里先只讨论一些basic的RDD, pairRDD会单独讨论

FilteredRDD

One-to-one Dependency, FilteredRDD

使用FilteredRDD, 将当前RDD作为第一个参数, f函数作为第二个参数, 返回值是filter过后的RDD

  /**
   * 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))

在compute中, 对parent RDD的Iterator[T]进行filter操作

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 RDD
    f: 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].partitions
 
  override val partitioner = prev.partitioner    // Since filter cannot change a partition's keys
 
  override def compute(split: Partition, context: TaskContext) =
    firstParent[T].iterator(split, context).filter(f) //compute就是真正产生RDD的逻辑
}

 

UnionRDD

Range Dependency, 仍然是narrow的

先看看如果使用union的, 第二个参数是, 两个RDD的array, 返回值就是把这两个RDD union后产生的新的RDD

  /**
   * 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))

 

先定义UnionPartition, Union操作的特点是, 只是把多个RDD的partition合并到一个RDD中, 而partition本身没有变化, 所以可以直接重用parent partition

3个参数

idx, partition id, 在当前UnionRDD中的序号

rdd, parent RDD

splitIndex, parent partition的id

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
}

定义UnionRDD

class UnionRDD[T: ClassManifest](
    sc: SparkContext,
    @transient var rdds: Seq[RDD[T]])  //parent RDD Seq
  extends RDD[T](sc, Nil) {  // Nil since we implement getDependencies
 
  override 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 = 0
    for (rdd <- rdds; split <- rdd.partitions) {
      array(pos) = new UnionPartition(pos, rdd, split.index) //创建所有的UnionPartition
      pos += 1
    }
    array
  }
 
  override def getDependencies: Seq[Dependency[_]] = {
    val deps = new ArrayBuffer[Dependency[_]]
    var pos = 0
    for (rdd <- rdds) {
      deps += new RangeDependency(rdd, 0, pos, rdd.partitions.size)//创建RangeDependency
      pos += 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()
}