Spark算子：RDD基本转换transform操作–union、intersection、subtract

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Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_121)
Type in expressions to have them evaluated.
Type :help for more information.

union

def union(other: RDD[T]): RDD[T]

该函数比较简单，就是将两个RDD进行合并，不去重。

scala> var rdd1 = sc.makeRDD(1 to 2,1)
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[45] at makeRDD at :21
 
scala> rdd1.collect
res42: Array[Int] = Array(1, 2)
 
scala> var rdd2 = sc.makeRDD(2 to 3,1)
rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[46] at makeRDD at :21
 
scala> rdd2.collect
res43: Array[Int] = Array(2, 3)
 
scala> rdd1.union(rdd2).collect
res44: Array[Int] = Array(1, 2, 2, 3)

 

intersection

def intersection(other: RDD[T]): RDD[T]
def intersection(other: RDD[T], numPartitions: Int): RDD[T]
def intersection(other: RDD[T], partitioner: Partitioner)(implicit ord: Ordering[T] = null): RDD[T]

该函数返回两个RDD的交集，并且去重。
参数numPartitions指定返回的RDD的分区数。
参数partitioner用于指定分区函数

scala>  var rdd1 = sc.makeRDD(Seq(1,2,2,3))
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[2] at makeRDD at <console>:24

scala>  var rdd2 = sc.makeRDD(Seq(2,3,3,4,5))
rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[3] at makeRDD at <console>:24

scala> var rdd3 = rdd1.intersection(rdd2).collect;

rdd3: Array[Int] = Array(2, 3)

 

subtract

def subtract(other: RDD[T]): RDD[T]
def subtract(other: RDD[T], numPartitions: Int): RDD[T]
def subtract(other: RDD[T], partitioner: Partitioner)(implicit ord: Ordering[T] = null): RDD[T]

该函数类似于intersection，但返回在RDD中出现，并且不在otherRDD中出现的元素，不去重。
参数含义同intersection

scala> var rdd1 = sc.makeRDD(Seq(1,1,1,2,2,3,6,6,7))
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[16] at makeRDD at <console>:24

scala> var rdd3 = rdd1.subtract(rdd2).collect;

rdd3: Array[Int] = Array(1, 1, 1, 6, 6, 7)

参考http://lxw1234.com/archives/2015/07/345.htm