Spark算子：RDD分区中的元素和数量统计

1、分区逻辑

Spark RDD是被分区的，在生成RDD时候，一般可以指定分区的数量，如果不指定分区数量，当RDD从集合创建时候，则默认为该程序所分配到的资源的CPU核数，如果是从HDFS文件创建，默认为文件的Block数。

2、分区元素统计

可以利用RDD的mapPartitionsWithIndex方法来统计每个分区中的元素及数量。

示例1：

假如创建一个RDD，默认分区15个，因为我的spark-shell指定了一共使用15个CPU资源。

（1）分区数

scala> var rdd1 = sc.makeRDD(1 to 50)
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[17] at makeRDD at :21
 
scala> rdd1.partitions.size
res15: Int = 15

（2）分区中元素数量统计

rdd1.mapPartitionsWithIndex{
    (partIdx,iter) => {
      var part_map = scala.collection.mutable.Map[String,Int]()
        while(iter.hasNext){
          var part_name = "part_" + partIdx;
          if(part_map.contains(part_name)) {
            var ele_cnt = part_map(part_name)
            part_map(part_name) = ele_cnt + 1
          } else {
            part_map(part_name) = 1
          }
          iter.next()
        }
        part_map.iterator
       
    }
  }.collect
 
res16: Array[(String, Int)] = Array((part_0,3), (part_1,3), (part_2,4), (part_3,3), (part_4,3), (part_5,4), (part_6,3), 
(part_7,3), (part_8,4), (part_9,3), (part_10,3), (part_11,4), (part_12,3), (part_13,3), (part_14,4))
//从part_0到part_14，每个分区中的元素数量

（3）分区中元素展示

rdd1.mapPartitionsWithIndex{
  (partIdx,iter) => {
    var part_map = scala.collection.mutable.Map[String,List[Int]]()
      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]{elem}
        }
      }
      part_map.iterator
     
  }
}.collect
 
res17: Array[(String, List[Int])] = Array((part_0,List(3, 2, 1)), (part_1,List(6, 5, 4)), (part_2,List(10, 9, 8, 7)), (part_3,List(13, 12, 11)), 
(part_4,List(16, 15, 14)), (part_5,List(20, 19, 18, 17)), (part_6,List(23, 22, 21)), (part_7,List(26, 25, 24)), (part_8,List(30, 29, 28, 27)), 
(part_9,List(33, 32, 31)), (part_10,List(36, 35, 34)), (part_11,List(40, 39, 38, 37)), (part_12,List(43, 42, 41)), (part_13,List(46, 45, 44)), 
(part_14,List(50, 49, 48, 47)))
//从part_0到part14，每个分区中包含的元素

示例2：

从HDFS文件创建一个RDD，包含65个分区，因为该文件由65个Block。

（1）分区数

scala> var rdd2 = sc.textFile("/logs/2015-07-05/lxw1234.com.log")
rdd2: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[21] at textFile at :21
 
scala> rdd2.partitions.size
res18: Int = 65

（2）分区中元素数量统计

scala> rdd2.mapPartitionsWithIndex{
     |         (partIdx,iter) => {
     |           var part_map = scala.collection.mutable.Map[String,Int]()
     |             while(iter.hasNext){
     |               var part_name = "part_" + partIdx;
     |               if(part_map.contains(part_name)) {
     |                 var ele_cnt = part_map(part_name)
     |                 part_map(part_name) = ele_cnt + 1
     |               } else {
     |                 part_map(part_name) = 1
     |               }
     |               iter.next()
     |             }
     |             part_map.iterator
     |            
     |         }
     |       }.collect
 
 
res19: Array[(String, Int)] = Array((part_0,202496), (part_1,225503), (part_2,214375), (part_3,215909), 
(part_4,208941), (part_5,205379), (part_6,207894), (part_7,209496), (part_8,213806), (part_9,216962), 
(part_10,216091), (part_11,215820), (part_12,217043), (part_13,216556), (part_14,218702), (part_15,218625), 
(part_16,218519), (part_17,221056), (part_18,221250), (part_19,222092), (part_20,222339), (part_21,222779), 
(part_22,223578), (part_23,222869), (part_24,221543), (part_25,219671), (part_26,222871), (part_27,223200), 
(part_28,223282), (part_29,228212), (part_30,223978), (part_31,223024), (part_32,222889), (part_33,222106), 
(part_34,221563), (part_35,219208), (part_36,216928), (part_37,216733), (part_38,217214), (part_39,219978), 
(part_40,218155), (part_41,219880), (part_42,215833...