Spark算子：RDD行动Action操作(1)–first、count、reduce、collect

Spark算子：RDD行动Action操作(1)–first、count、reduce、collect

 

关键字：Spark算子、Spark RDD行动Action、first、count、reduce、collect
first

def first(): T

first返回RDD中的第一个元素，不排序。

    scala> var rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
    rdd1: org.apache.spark.rdd.RDD[(String, String)] = ParallelCollectionRDD[33] at makeRDD at :21
     
    scala> rdd1.first
    res14: (String, String) = (A,1)
     
    scala> var rdd1 = sc.makeRDD(Seq(10, 4, 2, 12, 3))
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at makeRDD at :21
     
    scala> rdd1.first
    res8: Int = 10
     

count

def count(): Long

count返回RDD中的元素数量。

    scala> var rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
    rdd1: org.apache.spark.rdd.RDD[(String, String)] = ParallelCollectionRDD[34] at makeRDD at :21
     
    scala> rdd1.count
    res15: Long = 3
     

reduce

def reduce(f: (T, T) ⇒ T): T

根据映射函数f，对RDD中的元素进行二元计算，返回计算结果。

    scala> var rdd1 = sc.makeRDD(1 to 10,2)
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[36] at makeRDD at :21
     
    scala> rdd1.reduce(_ + _)
    res18: Int = 55
     
    scala> var rdd2 = sc.makeRDD(Array(("A",0),("A",2),("B",1),("B",2),("C",1)))
    rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[38] at makeRDD at :21
     
    scala> rdd2.reduce((x,y) => {
         |       (x._1 + y._1,x._2 + y._2)
         |     })
    res21: (String, Int) = (CBBAA,6)
     

collect

def collect(): Array[T]

collect用于将一个RDD转换成数组。

    scala> var rdd1 = sc.makeRDD(1 to 10,2)
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[36] at makeRDD at :21
     
    scala> rdd1.collect
    res23: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)