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))