Spark算子[17]：zip、zipPartitions、zipWithIndex、zipWithUniqueId 实例详解

zip

def zip[U](other: RDD[U])(implicit arg0: ClassTag[U]): RDD[(T, U)]

zip函数用于将两个RDD组合成Key/Value形式的RDD，这里默认两个RDD的partition数量以及元素数量都相同，否则会抛出异常。

scala> var rdd1 = sc.makeRDD(1 to 5,2)scala> var rdd2 = sc.makeRDD(Seq("A","B","C","D","E"),2)scala> var rdd3 = sc.makeRDD(Seq("A","B","C","D","E"),3)scala> rdd1.zip(rdd2).collectres0: Array[(Int, String)] = Array((1,A), (2,B), (3,C), (4,D), (5,E))           scala> rdd2.zip(rdd1).collectres1: Array[(String, Int)] = Array((A,1), (B,2), (C,3), (D,4), (E,5))scala> rdd1.zip(rdd3).collectjava.lang.IllegalArgumentException: Can't zip RDDs with unequal numbers of partitions//如果两个RDD分区数不同，则抛出异常

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zipPartitions

zipPartitions函数将多个RDD按照partition组合成为新的RDD，该函数需要组合的RDD具有相同的分区数，但对于每个分区内的元素数量没有要求。

该函数有好几种实现，可分为三类：

• 参数是一个RDD

  def zipPartitions[B: ClassTag, V: ClassTag]      (rdd2: RDD[B], preservesPartitioning: Boolean)      (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope {    new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2, preservesPartitioning)  }  def zipPartitions[B: ClassTag, V: ClassTag]      (rdd2: RDD[B])      (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope {    zipPartitions(rdd2, preservesPartitioning = false)(f)  }

这两个区别就是参数preservesPartitioning，是否保留父RDD的partitioner分区信息
映射方法f参数为两个RDD的迭代器。

scala> var rdd1 = sc.makeRDD(1 to 5,2)rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[22] at makeRDD at :21scala> var rdd2 = sc.makeRDD(Seq("A","B","C","D","E"),2)rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[23] at makeRDD at :21//rdd1两个分区中元素分布：scala> rdd1.mapPartitionsWithIndex{     |         (x,iter) => {     |           var result = List[String]()     |             while(iter.hasNext){     |               result ::= ("part_" + x + "|" + iter.next())     |             }     |             result.iterator     |                 |         }     |       }.collectres17: Array[String] = Array(part_0|2, part_0|1, part_1|5, part_1|4, part_1|3)//rdd2两个分区中元素分布scala> rdd2.mapPartitionsWithIndex{     |         (x,iter) => {     |           var result = List[String]()     |             while(iter.hasNext){     |               result ::= ("part_" + x + "|" + iter.next())     |             }     |             result.iterator     |                 |         }     |       }.collectres18: Array[String] = Array(part_0|B, part_0|A, part_1|E, part_1|D, part_1|C)//rdd1和rdd2做zipPartitionscala> rdd1.zipPartitions(rdd2){     |       (rdd1Iter,rdd2Iter) => {     |         var result = List[String]()     |         while(rdd1Iter.hasNext && rdd2Iter.hasNext) {     |           result::=(rdd1Iter.next() + "_" + rdd2Iter.next())     |         }     |         result.iterator     |       }     |     }.collectres19: Array[String] = Array(2_B, 1_A, 5_E, 4_D, 3_C)

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• 参数是两个RDD

  def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag]      (rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean)      (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope {    new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, preservesPartitioning)  }  def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag]      (rdd2: RDD[B], rdd3: RDD[C])      (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope {    zipPartitions(rdd2, rdd3, preservesPartitioning = false)(f)  }

用法同上面，只不过该函数参数为两个RDD，映射方法f输入参数为两个RDD的迭代器。

scala> var rdd1 = sc.makeRDD(1 to 5,2)rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[27] at makeRDD at :21scala> var rdd2 = sc.makeRDD(Seq("A","B","C","D","E"),2)rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[28] at makeRDD at :21scala> var rdd3 = sc.makeRDD(Seq("a","b","c","d","e"),2)rdd3: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[29] at makeRDD at :21//rdd3中个分区元素分布scala> rdd3.mapPartitionsWithIndex{     |         (x,iter) => {     |           var result = List[String]()     |             while(iter.hasNext){     |               result ::= ("part_" + x + "|" + iter.next())     |             }     |             result.iterator     |                 |         }     |       }.collectres21: Array[String] = Array(part_0|b, part_0|a, part_1|e, part_1|d, part_1|c)//三个RDD做zipPartitionsscala> var rdd4 = rdd1.zipPartitions(rdd2,rdd3){     |       (rdd1Iter,rdd2Iter,rdd3Iter) => {     |         var result = List[String]()     |         while(rdd1Iter.hasNext && rdd2Iter.hasNext && rdd3Iter.hasNext) {     |           result::=(rdd1Iter.next() + "_" + rdd2Iter.next() + "_" + rdd3Iter.next())     |         }     |         result.iterator     |       }     |     }rdd4: org.apache.spark.rdd.RDD[String] = ZippedPartitionsRDD3[33] at zipPartitions at :27scala> rdd4.collectres23: Array[String] = Array(2_B_b, 1_A_a, 5_E_e, 4_D_d, 3_C_c)

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• 参数是三个RDD
  用法同上面，只不过这里又多了个一个RDD而已。

  def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag]      (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean)      (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope {    new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, preservesPartitioning)  }  def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag]      (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D])      (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope {    zipPartitions(rdd2, rdd3, rdd4, preservesPartitioning = false)(f)  }

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zipWithIndex

def zipWithIndex(): RDD[(T, Long)]

该函数将RDD中的元素和这个元素在RDD中的ID（索引号）组合成键/值对。

scala> var rdd2 = sc.makeRDD(Seq("A","B","R","D","F"),2)rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[34] at makeRDD at :21scala> rdd2.zipWithIndex().collectres27: Array[(String, Long)] = Array((A,0), (B,1), (R,2), (D,3), (F,4))

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zipWithUniqueId

def zipWithUniqueId(): RDD[(T, Long)]

该函数将RDD中元素和一个唯一ID组合成键/值对，该唯一ID生成算法如下：
每个分区中第一个元素的唯一ID值为：该分区索引号，
每个分区中第N个元素的唯一ID值为：(前一个元素的唯一ID值) + (该RDD总的分区数)
看下面的例子：

scala> var rdd1 = sc.makeRDD(Seq("A","B","C","D","E","F"),2)rdd1: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[44] at makeRDD at :21//rdd1有两个分区，scala> rdd1.zipWithUniqueId().collectres32: Array[(String, Long)] = Array((A,0), (B,2), (C,4), (D,1), (E,3), (F,5))//总分区数为2//第一个分区第一个元素ID为0，第二个分区第一个元素ID为1//第一个分区第二个元素ID为0+2=2，第一个分区第三个元素ID为2+2=4//第二个分区第二个元素ID为1+2=3，第二个分区第三个元素ID为3+2=5

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转自：
http://lxw1234.com/archives/2015/07/350.htm
http://lxw1234.com/archives/2015/07/352.htm