Spark API 详解/大白话解释 之 map、mapPartitions、mapValues、mapWith、flatMap、flatMapWith、flatMapValues

重点看mapPartitions

本文转自http://blog.csdn.net/guotong1988/article/details/50555185，所有权力归原作者所有。

map(function) 
map是对RDD中的每个元素都执行一个指定的函数来产生一个新的RDD。任何原RDD中的元素在新RDD中都有且只有一个元素与之对应。

举例：

val a = sc.parallelize(1 to 9, 3)val b = a.map(x => x*2)//x => x*2是一个函数，x是传入参数即RDD的每个元素，x*2是返回值a.collect//结果Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9)b.collect//结果Array[Int] = Array(2, 4, 6, 8, 10, 12, 14, 16, 18)

当然map也可以把Key变成Key-Value对

val a = sc.parallelize(List("dog", "tiger", "lion", "cat", "panther", " eagle"), 2)val b = a.map(x => (x, 1))b.collect.foreach(println(_))/*(dog,1)(tiger,1)(lion,1)(cat,1)(panther,1)( eagle,1)*/

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mapPartitions(function) 
map()的输入函数是应用于RDD中每个元素，而mapPartitions()的输入函数是应用于每个分区

package testimport scala.Iteratorimport org.apache.spark.SparkConfimport org.apache.spark.SparkContextobject TestRdd {  def sumOfEveryPartition(input: Iterator[Int]): Int = {    var total = 0    input.foreach { elem =>      total += elem    }    total  }  def main(args: Array[String]) {    val conf = new SparkConf().setAppName("Spark Rdd Test")    val spark = new SparkContext(conf)    val input = spark.parallelize(List(1, 2, 3, 4, 5, 6), 2)//RDD有6个元素，分成2个partition    val result = input.mapPartitions(      partition => Iterator(sumOfEveryPartition(partition)))//partition是传入的参数，是个list，要求返回也是list，即Iterator(sumOfEveryPartition(partition))    result.collect().foreach {      println(_)//6 15    }    spark.stop()  }}

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mapValues(function) 
原RDD中的Key保持不变，与新的Value一起组成新的RDD中的元素。因此，该函数只适用于元素为KV对的RDD。

val a = sc.parallelize(List("dog", "tiger", "lion", "cat", "panther", " eagle"), 2)val b = a.map(x => (x.length, x))b.mapValues("x" + _ + "x").collect

//"x" + _ + "x"等同于everyInput =>"x" + everyInput + "x" 
//结果 
Array( 
(3,xdogx), 
(5,xtigerx), 
(4,xlionx), 
(3,xcatx), 
(7,xpantherx), 
(5,xeaglex) 
)

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mapWith和flatMapWith 
感觉用得不多，参考http://blog.csdn.net/jewes/article/details/39896301

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flatMap(function) 
与map类似，区别是原RDD中的元素经map处理后只能生成一个元素，而原RDD中的元素经flatmap处理后可生成多个元素

val a = sc.parallelize(1 to 4, 2)val b = a.flatMap(x => 1 to x)//每个元素扩展b.collect/*结果    Array[Int] = Array( 1,                            1, 2,                            1, 2, 3,                            1, 2, 3, 4)*/

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flatMapValues(function)

val a = sc.parallelize(List((1,2),(3,4),(5,6)))val b = a.flatMapValues(x=>1 to x)b.collect.foreach(println(_))/*(1,1)(1,2)(3,1)(3,2)(3,3)(3,4)(5,1)(5,2)(5,3)(5,4)(5,5)(5,6)*/