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

最新推荐文章于 2025-04-29 08:58:34 发布

dehou1984

最新推荐文章于 2025-04-29 08:58:34 发布

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原文链接：http://www.cnblogs.com/oldsix666/articles/9458199.html

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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)

转载于:https://www.cnblogs.com/oldsix666/articles/9458199.html