Transformation處理的數據為Key-Value形式的算子大致可以分為：輸入分區與輸出分區一對一、聚集、連接操作。

輸入分區與輸出分區一對一

mapValues

mapValues：針對（Key，Value）型數據中的Value進行Map操作，而不對Key進行處理。

方框代表RDD分區。a=>a+2代表只對（ V1， 1）數據中的1進行加2操作，返回結果為3。

源碼：

  /**
   * Pass each value in the key-value pair RDD through a map function without changing the keys;
   * this also retains the original RDD's partitioning.
   */
  def mapValues[U](f: V => U): RDD[(K, U)] = {
    val cleanF = self.context.clean(f)
    new MapPartitionsRDD[(K, U), (K, V)](self,
      (context, pid, iter) => iter.map { case (k, v) => (k, cleanF(v)) },
      preservesPartitioning = true)
  }

單個RDD或兩個RDD聚集

（1）combineByKey

combineByKey是對單個Rdd的聚合。相當于將元素為（Int，Int）的RDD轉變為了（Int，Seq[Int]）類型元素的RDD。
定義combineByKey算子的說明如下：

• createCombiner： V => C， 在C不存在的情況下，如通過V創建seq C。

• mergeValue：(C, V) => C， 當C已經存在的情況下，需要merge，如把item V加到seq
  C中，或者疊加。
• mergeCombiners：(C,C) => C，合并兩個C。
• partitioner： Partitioner（分區器），Shuffle時需要通過Partitioner的分區策略進行分區。
• mapSideCombine： Boolean=true， 為了減小傳輸量，很多combine可以在map端先做。例如， 疊加可以先在一個partition中把所有相同的Key的Value疊加， 再shuffle。
• serializerClass：String=null，傳輸需要序列化，用戶可以自定義序列化類。

方框代表RDD分區。 通過combineByKey，將（V1，2）、 （V1，1）數據合并為（V1，Seq（2，1））。

源碼：

  /**
   * Generic function to combine the elements for each key using a custom set of aggregation
   * functions. Turns an RDD[(K, V)] into a result of type RDD[(K, C)], for a "combined type" C
   * Note that V and C can be different -- for example, one might group an RDD of type
   * (Int, Int) into an RDD of type (Int, Seq[Int]). Users provide three functions:
   *
   * - `createCombiner`, which turns a V into a C (e.g., creates a one-element list)
   * - `mergeValue`, to merge a V into a C (e.g., adds it to the end of a list)
   * - `mergeCombiners`, to combine two C's into a single one.
   *
   * In addition, users can control the partitioning of the output RDD, and whether to perform
   * map-side aggregation (if a mapper can produce multiple items with the same key).
   */
  def combineByKey[C](createCombiner: V => C,
      mergeValue: (C, V) => C,
      mergeCombiners: (C, C) => C,
      partitioner: Partitioner,
      mapSideCombine: Boolean = true,
      serializer: Serializer = null): RDD[(K, C)] = {
    require(mergeCombiners != null, "mergeCombiners must be defined") // required as of Spark 0.9.0
    if (keyClass.isArray) {
      if (mapSideCombine) {
        throw new SparkException("Cannot use map-side combining with array keys.")
      }
      if (partitioner.isInstanceOf[HashPartitioner]) {
        throw new SparkException("Default partitioner cannot partition array keys.")
      }
    }
    val aggregator = new Aggregator[K, V, C](
      self.context.clean(createCombiner),
      self.context.clean(mergeValue),
      self.context.clean(mergeCombiners))
    if (self.partitioner == Some(partitioner)) {
      self.mapPartitions(iter => {
        val context = TaskContext.get()
        new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context))
      }, preservesPartitioning = true)
    } else {
      new ShuffledRDD[K, V, C](self, partitioner)
        .setSerializer(serializer)
        .setAggregator(aggregator)
        .setMapSideCombine(mapSideCombine)
    }
  }

  /**
   * Simplified version of combineByKey that hash-partitions the output RDD.
   */
  def combineByKey[C](createCombiner: V => C,
      mergeValue: (C, V) => C,
      mergeCombiners: (C, C) => C,
      numPartitions: Int): RDD[(K, C)] = {
    combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numPartitions))
  }

（2）reduceByKey

reduceByKey是更簡單的一種情況，只是兩個值合并成一個值，所以createCombiner很簡單，就是直接返回v，而mergeValue和mergeCombiners的邏輯相同，沒有區別。

方框代表RDD分區。 通過用戶自定義函數（A，B）=>（A+B），將相同Key的數據（V1，2）、（V1，1）的value相加，結果為（V1，3）。

源碼：

  /**
   * Merge the values for each key using an associative reduce function. This will also perform
   * the merging locally on each mapper before sending results to a reducer, similarly to a
   * "combiner" in MapReduce.
   */
  def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = {
    combineByKey[V]((v: V) => v, func, func, partitioner)
  }

  /**
   * Merge the values for each key using an associative reduce function. This will also perform
   * the merging locally on each mapper before sending results to a reducer, similarly to a
   * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.
   */
  def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = {
    reduceByKey(new HashPartitioner(numPartitions), func)
  }

  /**
   * Merge the values for each key using an associative reduce function. This will also perform
   * the merging locally on each mapper before sending results to a reducer, similarly to a
   * "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/
   * parallelism level.
   */
  def reduceByKey(func: (V, V) => V): RDD[(K, V)] = {
    reduceByKey(defaultPartitioner(self), func)
  }

（3）partitionBy

partitionBy函數對RDD進行分區操作。
如果原有RDD的分區器和現有分區器（partitioner）一致，則不重分區，如果不一致，則相當于根據分區器生成一個新的ShuffledRDD。

方框代表RDD分區。 通過新的分區策略將原來在不同分區的V1、 V2數據都合并到了一個分區。

源碼：

  /**
   * Return a copy of the RDD partitioned using the specified partitioner.
   */
  def partitionBy(partitioner: Partitioner): RDD[(K, V)] = {
    if (keyClass.isArray && partitioner.isInstanceOf[HashPartitioner]) {
      throw new SparkException("Default partitioner cannot partition array keys.")
    }
    if (self.partitioner == Some(partitioner)) {
      self
    } else {
      new ShuffledRDD[K, V, V](self, partitioner)
    }
  }

（4）cogroup

cogroup函數將兩個RDD進行協同劃分。對在兩個RDD中的Key-Value類型的元素，每個RDD相同Key的元素分別聚合為一個集合，并且返回兩個RDD中對應Key的元素集合的迭代器(K, (Iterable[V], Iterable[w]))。其中，Key和Value，Value是兩個RDD下相同Key的兩個數據集合的迭代器所構成的元組。

源碼：

  /**
   * For each key k in `this` or `other1` or `other2` or `other3`,
   * return a resulting RDD that contains a tuple with the list of values
   * for that key in `this`, `other1`, `other2` and `other3`.
   */
  def cogroup[W1, W2, W3](other1: RDD[(K, W1)],
      other2: RDD[(K, W2)],
      other3: RDD[(K, W3)],
      partitioner: Partitioner)
      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = {
    if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {
      throw new SparkException("Default partitioner cannot partition array keys.")
    }
    val cg = new CoGroupedRDD[K](Seq(self, other1, other2, other3), partitioner)
    cg.mapValues { case Array(vs, w1s, w2s, w3s) =>
       (vs.asInstanceOf[Iterable[V]],
         w1s.asInstanceOf[Iterable[W1]],
         w2s.asInstanceOf[Iterable[W2]],
         w3s.asInstanceOf[Iterable[W3]])
    }
  }

  /**
   * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
   * list of values for that key in `this` as well as `other`.
   */
  def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner)
      : RDD[(K, (Iterable[V], Iterable[W]))]  = {
    if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {
      throw new SparkException("Default partitioner cannot partition array keys.")
    }
    val cg = new CoGroupedRDD[K](Seq(self, other), partitioner)
    cg.mapValues { case Array(vs, w1s) =>
      (vs.asInstanceOf[Iterable[V]], w1s.asInstanceOf[Iterable[W]])
    }
  }

  /**
   * For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a
   * tuple with the list of values for that key in `this`, `other1` and `other2`.
   */
  def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], partitioner: Partitioner)
      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = {
    if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {
      throw new SparkException("Default partitioner cannot partition array keys.")
    }
    val cg = new CoGroupedRDD[K](Seq(self, other1, other2), partitioner)
    cg.mapValues { case Array(vs, w1s, w2s) =>
      (vs.asInstanceOf[Iterable[V]],
        w1s.asInstanceOf[Iterable[W1]],
        w2s.asInstanceOf[Iterable[W2]])
    }
  }

  /**
   * For each key k in `this` or `other1` or `other2` or `other3`,
   * return a resulting RDD that contains a tuple with the list of values
   * for that key in `this`, `other1`, `other2` and `other3`.
   */
  def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)])
      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = {
    cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3))
  }

  /**
   * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
   * list of values for that key in `this` as well as `other`.
   */
  def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = {
    cogroup(other, defaultPartitioner(self, other))
  }

  /**
   * For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a
   * tuple with the list of values for that key in `this`, `other1` and `other2`.
   */
  def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)])
      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = {
    cogroup(other1, other2, defaultPartitioner(self, other1, other2))
  }

  /**
   * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
   * list of values for that key in `this` as well as `other`.
   */
  def cogroup[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))] = {
    cogroup(other, new HashPartitioner(numPartitions))
  }

  /**
   * For each key k in `this` or `other1` or `other2`, return a resulting RDD that contains a
   * tuple with the list of values for that key in `this`, `other1` and `other2`.
   */
  def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], numPartitions: Int)
      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = {
    cogroup(other1, other2, new HashPartitioner(numPartitions))
  }

  /**
   * For each key k in `this` or `other1` or `other2` or `other3`,
   * return a resulting RDD that contains a tuple with the list of values
   * for that key in `this`, `other1`, `other2` and `other3`.
   */
  def cogroup[W1, W2, W3](other1: RDD[(K, W1)],
      other2: RDD[(K, W2)],
      other3: RDD[(K, W3)],
      numPartitions: Int)
      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = {
    cogroup(other1, other2, other3, new HashPartitioner(numPartitions))
  }

連接

（1）join

join對兩個需要連接的RDD進行cogroup函數操作。cogroup操作之后形成的新RDD，對每個key下的元素進行笛卡爾積操作，返回的結果再展平，對應Key下的所有元組形成一個集合，最后返回RDD[(K，(V，W))]。
join的本質是通過cogroup算子先進行協同劃分，再通過flatMapValues將合并的數據打散。

對兩個RDD的join操作示意圖。 大方框代表RDD，小方框代表RDD中的分區。函數對擁有相同Key的元素（例如V1）為Key，以做連接后的數據結果為（V1，（1，1））和（V1，（1，2））。

源碼：

  /**
   * Return an RDD containing all pairs of elements with matching keys in `this` and `other`. Each
   * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
   * (k, v2) is in `other`. Uses the given Partitioner to partition the output RDD.
   */
  def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = {
    this.cogroup(other, partitioner).flatMapValues( pair =>
      for (v <- pair._1.iterator; w <- pair._2.iterator) yield (v, w)
    )
  }

（2）leftOuterJoin和rightOuterJoin

LeftOuterJoin（左外連接）和RightOuterJoin（右外連接）相當于在join的基礎上先判斷一側的RDD元素是否為空，如果為空，則填充為空。 如果不為空，則將數據進行連接運算，并返回結果。

源碼：

  /**
   * Perform a left outer join of `this` and `other`. For each element (k, v) in `this`, the
   * resulting RDD will either contain all pairs (k, (v, Some(w))) for w in `other`, or the
   * pair (k, (v, None)) if no elements in `other` have key k. Uses the given Partitioner to
   * partition the output RDD.
   */
  def leftOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))] = {
    this.cogroup(other, partitioner).flatMapValues { pair =>
      if (pair._2.isEmpty) {
        pair._1.iterator.map(v => (v, None))
      } else {
        for (v <- pair._1.iterator; w <- pair._2.iterator) yield (v, Some(w))
      }
    }
  }

  /**
   * Perform a right outer join of `this` and `other`. For each element (k, w) in `other`, the
   * resulting RDD will either contain all pairs (k, (Some(v), w)) for v in `this`, or the
   * pair (k, (None, w)) if no elements in `this` have key k. Uses the given Partitioner to
   * partition the output RDD.
   */
  def rightOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner)
      : RDD[(K, (Option[V], W))] = {
    this.cogroup(other, partitioner).flatMapValues { pair =>
      if (pair._1.isEmpty) {
        pair._2.iterator.map(w => (None, w))
      } else {
        for (v <- pair._1.iterator; w <- pair._2.iterator) yield (Some(v), w)
      }
    }
  }

轉載請注明作者Jason Ding及其出處
GitCafe博客主頁(http://jasonding1354.gitcafe.io/)
Github博客主頁(http://jasonding1354.github.io/)
CSDN博客(http://blog.csdn.net/jasonding1354)
簡書主頁(http://www.lxweimin.com/users/2bd9b48f6ea8/latest_articles)
Google搜索jasonding1354進入我的博客主頁