spark编程模型（十九）之RDD集合标量行为操作（Action Operation）——take、top、takeOrdered

take

• def take(num: Int): Array[T]

• take用于获取RDD中从0到num-1下标的元素，不排序

    scala> var rdd1 = sc.makeRDD(Seq(10, 4, 2, 12, 3))
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[40] at makeRDD at :21
     
    scala> rdd1.take(1)
    res0: Array[Int] = Array(10)                                                    
     
    scala> rdd1.take(2)
    res1: Array[Int] = Array(10, 4)
  

top

• def top(num: Int)(implicit ord: Ordering[T]): Array[T]

• top函数用于从RDD中，按照默认（降序）或者指定的排序规则，返回前num个元素

    scala> var rdd1 = sc.makeRDD(Seq(10, 4, 2, 12, 3))
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[40] at makeRDD at :21
     
    scala> rdd1.top(1)
    res2: Array[Int] = Array(12)
     
    scala> rdd1.top(2)
    res3: Array[Int] = Array(12, 10)
     
    //指定排序规则
    scala> implicit val myOrd = implicitly[Ordering[Int]].reverse
    myOrd: scala.math.Ordering[Int] = scala.math.Ordering$$anon$4@767499ef
     
    scala> rdd1.top(1)
    res4: Array[Int] = Array(2)
     
    scala> rdd1.top(2)
    res5: Array[Int] = Array(2, 3)
  

takeOrdered

• def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T]

• takeOrdered和top类似，只不过以和top相反的顺序返回元素

    scala> var rdd1 = sc.makeRDD(Seq(10, 4, 2, 12, 3))
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[40] at makeRDD at :21
     
    scala> rdd1.top(1)
    res4: Array[Int] = Array(2)
     
    scala> rdd1.top(2)
    res5: Array[Int] = Array(2, 3)
     
    scala> rdd1.takeOrdered(1)
    res6: Array[Int] = Array(12)
     
    scala> rdd1.takeOrdered(2)
    res7: Array[Int] = Array(12, 10)