关键字：Spark算子、Spark RDD键值转换、partitionBy、mapValues、flatMapValues

partitionBy

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

mapValues

def mapValues[U](f: (V) => U): RDD[(K, U)]

同基本转换操作中的map，只不过mapValues是针对[K,V]中的V值进行map操作。

scala> var rdd1 = sc.makeRDD(Array((1,"A"),(2,"B"),(3,"C"),(4,"D")),2)
rdd1: org.apache.spark.rdd.RDD[(Int, String)] = ParallelCollectionRDD[27] at makeRDD at :21

scala> rdd1.mapValues(x => x + "_").collect
res26: Array[(Int, String)] = Array((1,A_), (2,B_), (3,C_), (4,D_))

flatMapValues

def flatMapValues[U](f: (V) => TraversableOnce[U]): RDD[(K, U)]

同基本转换操作中的flatMap，只不过flatMapValues是针对[K,V]中的V值进行flatMap操作。

scala> rdd1.flatMapValues(x => x + "_").collect
res36: Array[(Int, Char)] = Array((1,A), (1,_), (2,B), (2,_), (3,C), (3,_), (4,D), (4,_))

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

如果觉得本博客对您有帮助，请 赞助作者 。

转载请注明：lxw的大数据田地 » Spark算子：RDD键值转换操作(1)–partitionBy、mapValues、flatMapValues