如何使用Spark/Scala压缩集合？

Question

在Scala中,我可以使用以下方法展平集合:

val array = Array(List("1,2,3").iterator,List("1,4,5").iterator)
                                                  //> array  : Array[Iterator[String]] = Array(non-empty iterator, non-empty itera
                                                  //| tor)


    array.toList.flatten                      //> res0: List[String] = List(1,2,3, 1,4,5)

但是我如何在Spark中执行类似的操作？

阅读API文档http://spark.apache.org/docs/0.7.3/api/core/index.html#spark.RDD似乎没有提供此功能的方法？

Answer 1

使用flatMap和identity Predef,这比使用更具可读性x => x,例如

myRdd.flatMap(identity)

Answer 2

尝试使用身份地图函数(y => y)的flatMap :

scala> val x = sc.parallelize(List(List("a"), List("b"), List("c", "d")))
x: org.apache.spark.rdd.RDD[List[String]] = ParallelCollectionRDD[1] at parallelize at <console>:12

scala> x.collect()
res0: Array[List[String]] = Array(List(a), List(b), List(c, d))

scala> x.flatMap(y => y)
res3: org.apache.spark.rdd.RDD[String] = FlatMappedRDD[3] at flatMap at <console>:15

scala> x.flatMap(y => y).collect()
res4: Array[String] = Array(a, b, c, d)