关键字：Spark算子、Spark RDD分区、Spark RDD分区元素数量
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。
可以利用RDD的mapPartitionsWithIndex方法来统计每个分区中的元素及数量。
关于mapPartitionsWithIndex的介绍可以参考 mapPartitionsWithIndex的介绍：
http://lxw1234.com/archives/2015/07/348.htm
具体看例子：
//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
//–total-executor-cores 15
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
//统计rdd1每个分区中元素数量
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
//统计rdd1每个分区中有哪些元素
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
//从HDFS文件创建的RDD，包含65个分区，因为该文件由65个Block
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
//rdd2每个分区的元素数量
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...
更多关于Spark算子的介绍，可参考 Spark算子 ：
http://lxw1234.com/archives/tag/spark%E7%AE%97%E5%AD%90