## 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。

```

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))

```

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