collectAsMap()
返回hashMap包含所有RDD中的分片，key如果重復，后邊的元素會覆蓋前面的元素。/**  * Return the key-value pairs in this RDD to the master as a Map.  *  * Warning: this doesn't return a multimap (so if you have multiple values to the same key, only  *          one value per key is PReserved in the map returned)  *  * @note this method should only be used if the resulting data is expected to be small, as  * all the data is loaded into the driver's memory.  */def collectAsMap(): Map[K, V] = self.withScope {  val data = self.collect()  val map = new mutable.HashMap[K, V]  map.sizeHint(data.length)  data.foreach { pair => map.put(pair._1, pair._2) }  map}
package com.dt.spark.main.RDDLearn.RDDCollectAsMapimport org.apache.spark.{SparkContext, SparkConf}/**  * Created by hjw on 17/2/4.  *//*collect (較常用)將RDD中的數(shù)據(jù)收集起來，變成一個Array，僅限數(shù)據(jù)量比較小的時候。collectAsMap()返回hashMap包含所有RDD中的分片，key如果重復，后邊的元素會覆蓋前面的元素。/**   * Return the key-value pairs in this RDD to the master as a Map.   *   * Warning: this doesn't return a multimap (so if you have multiple values to the same key, only   *          one value per key is preserved in the map returned)   *   * @note this method should only be used if the resulting data is expected to be small, as   * all the data is loaded into the driver's memory.   */  def collectAsMap(): Map[K, V] = self.withScope {    val data = self.collect()    val map = new mutable.HashMap[K, V]    map.sizeHint(data.length)    data.foreach { pair => map.put(pair._1, pair._2) }    map  } */object RDDCollectAsMapTest {  def main(args: Array[String]) {    val conf = new SparkConf()    conf.setAppName("test")    conf.setMaster("local")    val sc = new SparkContext(conf)    val rdd = sc.parallelize(List((1, "a"), (1, "b"), (1, "c"), (2, "d"), (2, "e"), (3, "g")) )    val rddMap = rdd.collectAsMap()    rddMap.foreach(println(_))//    (2,e)//    (1,c)//    (3,g)  }}