spark算子:partitionBy对数据进行分区
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def partitionBy(partitioner: Partitioner): RDD[(K, V)]
该函数根据partitioner函数生成新的ShuffleRDD,将原RDD重新分区。
scala> var rdd1 = sc.makeRDD(Array((1,"A"),(2,"B"),(3,"C"),(4,"D")),2) rdd1: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[23] at makeRDD at :21 scala> rdd1.partitions.size res20: Int = 2 //查看rdd1中每个分区的元素 scala> rdd1.mapPartitionsWithIndex{ | (partIdx,iter) => { | var part_map = scala.collection.mutable.Map[String,List[(Int,String)]]() | while(iter.hasNext){ | var part_name = "part_" + partIdx; | var elem = iter.next() | if(part_map.contains(part_name)) { | var elems = part_map(part_name) | elems ::= elem | part_map(part_name) = elems | } else { | part_map(part_name) = List[(Int,String)]{elem} | } | } | part_map.iterator | | } | }.collect res22: Array[(String, List[(Int, String)])] = Array((part_0,List((2,B), (1,A))), (part_1,List((4,D), (3,C)))) //(2,B),(1,A)在part_0中,(4,D),(3,C)在part_1中 //使用partitionBy重分区 scala> var rdd2 = rdd1.partitionBy(new org.apache.spark.HashPartitioner(2)) rdd2: org.apache.spark.rdd.RDD[(Int, String)] = ShuffledRDD[25] at partitionBy at :23 scala> rdd2.partitions.size res23: Int = 2 //查看rdd2中每个分区的元素 scala> rdd2.mapPartitionsWithIndex{ | (partIdx,iter) => { | var part_map = scala.collection.mutable.Map[String,List[(Int,String)]]() | while(iter.hasNext){ | var part_name = "part_" + partIdx; | var elem = iter.next() | if(part_map.contains(part_name)) { | var elems = part_map(part_name) | elems ::= elem | part_map(part_name) = elems | } else { | part_map(part_name) = List[(Int,String)]{elem} | } | } | part_map.iterator | } | }.collect res24: Array[(String, List[(Int, String)])] = Array((part_0,List((4,D), (2,B))), (part_1,List((3,C), (1,A)))) //(4,D),(2,B)在part_0中,(3,C),(1,A)在part_1中
参考:http://lxw1234.com/archives/2015/07/356.htm
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