spark編程模型（十）之RDD基礎轉換操作（Transformation Operation）——zip、zipPartitions

zip()

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

• zip函數用於將兩個RDD組合成Key/Value形式的RDD,這裏默認兩個RDD的partition數量以及元素數量都相同，否則會拋出異常

  scala> var rdd1 = sc.makeRDD(1 to 10,2)
  rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at makeRDD at :21
  
  scala> var rdd1 = sc.makeRDD(1 to 5,2)
  rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[1] at makeRDD at :21
  
  scala> var rdd2 = sc.makeRDD(Seq("A","B","C","D","E"),2)
  rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[2] at makeRDD at :21
  
  scala> rdd1.zip(rdd2).collect
  res0: Array[(Int, String)] = Array((1,A), (2,B), (3,C), (4,D), (5,E))           
  
  scala> rdd2.zip(rdd1).collect
  res1: Array[(String, Int)] = Array((A,1), (B,2), (C,3), (D,4), (E,5))
  
  scala> var rdd3 = sc.makeRDD(Seq("A","B","C","D","E"),3)
  rdd3: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[5] at makeRDD at :21
  
  scala> rdd1.zip(rdd3).collect
  java.lang.IllegalArgumentException: Can't zip RDDs with unequal numbers of partitions
  //如果兩個RDD分區數不同，則拋出異常
  

zipPartitions()

• zipPartitions函數將多個RDD按照partition組合成爲新的RDD，該函數需要組合的RDD具有相同的分區數，但對於每個分區內的元素數量沒有要求

• 參數是一個RDD

  • def zipPartitions[B, V](rdd2: RDD[B])(f: (Iterator[T], Iterator[B]) => Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[V]): RDD[V]

  • def zipPartitions[B, V](rdd2: RDD[B], preservesPartitioning: Boolean)(f: (Iterator[T], Iterator[B]) => Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[V]): RDD[V]

  • 這兩個區別就是參數preservesPartitioning，是否保留父RDD的partitioner分區信息

  • 映射方法f參數爲兩個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 :21
    
    scala> var rdd2 = sc.makeRDD(Seq("A","B","C","D","E"),2)
    rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[23] at makeRDD at :21
    
    //rdd1兩個分區中元素分佈：
    scala> rdd1.mapPartitionsWithIndex{
         |         (x,iter) => {
         |           var result = List[String]()
         |             while(iter.hasNext){
         |               result ::= ("part_" + x + "|" + iter.next())
         |             }
         |             result.iterator
         |            
         |         }
         |       }.collect
    res17: Array[String] = Array(part_0|2, part_0|1, part_1|5, part_1|4, part_1|3)
    
    //rdd2兩個分區中元素分佈
    scala> rdd2.mapPartitionsWithIndex{
         |         (x,iter) => {
         |           var result = List[String]()
         |             while(iter.hasNext){
         |               result ::= ("part_" + x + "|" + iter.next())
         |             }
         |             result.iterator
         |            
         |         }
         |       }.collect
    res18: Array[String] = Array(part_0|B, part_0|A, part_1|E, part_1|D, part_1|C)
    
    //rdd1和rdd2做zipPartition
    scala> rdd1.zipPartitions(rdd2){
         |       (rdd1Iter,rdd2Iter) => {
         |         var result = List[String]()
         |         while(rdd1Iter.hasNext && rdd2Iter.hasNext) {
         |           result::=(rdd1Iter.next() + "_" + rdd2Iter.next())
         |         }
         |         result.iterator
         |       }
         |     }.collect
    res19: Array[String] = Array(2_B, 1_A, 5_E, 4_D, 3_C)
    

• 參數是兩個RDD

  • def zipPartitions[B, C, V](rdd2: RDD[B], rdd3: RDD[C])(f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[V]): RDD[V]

  • def zipPartitions[B, C, V](rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean)(f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[V]): RDD[V]

  • 用法同上面，只不過該函數參數爲兩個RDD，映射方法f輸入參數爲兩個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 :21
    
    scala> var rdd2 = sc.makeRDD(Seq("A","B","C","D","E"),2)
    rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[28] at makeRDD at :21
    
    scala> 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中個分區元素分佈
    scala> rdd3.mapPartitionsWithIndex{
         |         (x,iter) => {
         |           var result = List[String]()
         |             while(iter.hasNext){
         |               result ::= ("part_" + x + "|" + iter.next())
         |             }
         |             result.iterator
         |            
         |         }
         |       }.collect
    res21: Array[String] = Array(part_0|b, part_0|a, part_1|e, part_1|d, part_1|c)
    
    //三個RDD做zipPartitions
    scala> 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 :27
    
    scala> rdd4.collect
    res23: Array[String] = Array(2_B_b, 1_A_a, 5_E_e, 4_D_d, 3_C_c)
    

• 參數是三個RDD

  • def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D])(f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[D], arg3: ClassTag[V]): RDD[V]

  • def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean)(f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[D], arg3: ClassTag[V]): RDD[V]

  • 用法同上面，只不過這裏又多了個一個RDD而已