spark编程模型（二十一）之RDD集合标量行为操作（Action Operation）——countByKey、foreach、foreachPartition、sortBy

countByKey

• def countByKey(): Map[K, Long]

• countByKey用于统计RDD[K,V]中每个K的数量

    scala> var rdd1 = sc.makeRDD(Array(("A",0),("A",2),("B",1),("B",2),("B",3)))
    rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[7] at makeRDD at :21
  
    scala> rdd1.countByKey
    res5: scala.collection.Map[String,Long] = Map(A -> 2, B -> 3)
  

foreach

• def foreach(f: (T) ⇒ Unit): Unit

• foreach用于遍历RDD,将函数f应用于每一个元素

• 但要注意，如果对RDD执行foreach，只会在Executor端有效，而并不是Driver端

• 比如：rdd.foreach(println)，只会在Executor的stdout中打印出来，Driver端是看不到的

• 使用accumulator共享变量与foreach结合，倒是个不错的选择

    scala> var cnt = sc.accumulator(0)
    cnt: org.apache.spark.Accumulator[Int] = 0
     
    scala> var rdd1 = sc.makeRDD(1 to 10,2)
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[5] at makeRDD at :21
     
    scala> rdd1.foreach(x => cnt += x)
     
    scala> cnt.value
    res51: Int = 55
     
    scala> rdd1.collect.foreach(println)
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
  

foreachPartition

• def foreachPartition(f: (Iterator[T]) ⇒ Unit): Unit

• foreachPartition和foreach类似，只不过是对每一个分区使用f

    scala> var rdd1 = sc.makeRDD(1 to 10,2)
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[5] at makeRDD at :21
     
    scala> var allsize = sc.accumulator(0)
    size: org.apache.spark.Accumulator[Int] = 0
     
    scala> var rdd1 = sc.makeRDD(1 to 10,2)
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[6] at makeRDD at :21
     
    scala>     rdd1.foreachPartition { x => {
         |       allsize += x.size
         |     }}
     
    scala> println(allsize.value)
    10
  

sortBy

• def sortBy[K](f: (T) ⇒ K, ascending: Boolean = true, numPartitions: Int = this.partitions.length)(implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T]

• sortBy根据给定的排序k函数将RDD中的元素进行排序

    scala> var rdd1 = sc.makeRDD(Seq(3,6,7,1,2,0),2)
  
    scala> rdd1.sortBy(x => x).collect
    res1: Array[Int] = Array(0, 1, 2, 3, 6, 7) //默认升序
     
    scala> rdd1.sortBy(x => x,false).collect
    res2: Array[Int] = Array(7, 6, 3, 2, 1, 0)  //降序
     
    //RDD[K,V]类型
    scala>var rdd1 = sc.makeRDD(Array(("A",2),("A",1),("B",6),("B",3),("B",7)))
     
    scala> rdd1.sortBy(x => x).collect
    res3: Array[(String, Int)] = Array((A,1), (A,2), (B,3), (B,6), (B,7))
     
    //按照V进行降序排序
    scala> rdd1.sortBy(x => x._2,false).collect
    res4: Array[(String, Int)] = Array((B,7), (B,6), (B,3), (A,2), (A,1))