spark编程模型（八）之RDD基础转换操作（Transformation Operation）——union、intersection、subtract

union()

• def union(other: RDD[T]): RDD[T]

• 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]

• 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(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.intersection(rdd2).collect
    res45: Array[Int] = Array(2)
     
    scala> var rdd3 = rdd1.intersection(rdd2)
    rdd3: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[59] at intersection at :25
     
    scala> rdd3.partitions.size
    res46: Int = 1
     
    scala> var rdd3 = rdd1.intersection(rdd2,2)
    rdd3: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[65] at intersection at :25
     
    scala> rdd3.partitions.size
    res47: Int = 2
  

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]

• 返回在RDD中出现，并且不在otherRDD中出现的元素，不去重

• 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[66] at makeRDD at :21
     
    scala> rdd1.collect
    res48: Array[Int] = Array(1, 2, 2, 3)
     
    scala> var rdd2 = sc.makeRDD(3 to 4)
    rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[67] at makeRDD at :21
     
    scala> rdd2.collect
    res49: Array[Int] = Array(3, 4)
     
    scala> rdd1.subtract(rdd2).collect
    res50: Array[Int] = Array(1, 2, 2)