Spark算子:RDD行动Action操作(1)–first、count、reduce、collect
2016-09-26 14:01
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关键字:Spark算子、Spark RDD行动Action、first、count、reduce、collect
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
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
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)
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)
更多关于Spark算子的介绍,可参考 Spark算子系列文章 :
http://lxw1234.com/archives/2015/07/363.htm
转载请注明:lxw的大数据田地 » Spark算子:RDD行动Action操作(1)–first、count、reduce、collect
first
def first(): Tfirst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(): Longcount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)
更多关于Spark算子的介绍,可参考 Spark算子系列文章 :
http://lxw1234.com/archives/2015/07/363.htm
转载请注明:lxw的大数据田地 » Spark算子:RDD行动Action操作(1)–first、count、reduce、collect
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