Spark计算均值

作者：Syn良子 出处：http://www.cnblogs.com/cssdongl 转载请注明出处

用spark来快速计算分组的平均值，写法很便捷，话不多说上代码

object ColumnValueAvg extends App {
  /**
    * ID,Name,ADDRESS,AGE
    * 001,zhangsan,chaoyang,20
    * 002,zhangsa,chaoyang,27
    * 003,zhangjie,chaoyang,35
    * 004,lisi,haidian,24
    * 005,lier,haidian,40
    * 006,wangwu,chaoyang,90
    * 007,wangchao,haidian,80
    */
  val conf = new SparkConf().setAppName("test column value sum and avg").setMaster("local[1]")
  val sc = new SparkContext(conf)

  val textRdd = sc.textFile(args(0))

  //be careful the toInt here is necessary ,if no cast ,then it will be age string append
  val addressAgeMap = textRdd.map(x => (x.split(",")(2), x.split(",")(3).toInt))

  val sumAgeResult = addressAgeMap.reduceByKey(_ + _).collect().foreach(println)

  val avgAgeResult = addressAgeMap.combineByKey(
    (v) => (v, 1),
    (accu: (Int, Int), v) => (accu._1 + v, accu._2 + 1),
    (accu1: (Int, Int), accu2: (Int, Int)) => (accu1._1 + accu2._1, accu1._2 + accu2._2)
  ).mapValues(x => (x._1 / x._2).toDouble).collect().foreach(println)

  println("Sum and Avg calculate successfuly")

  sc.stop()

}

用textFile读取数据后，以address进行分组来求age的平均值,这里用combineByKey来计算，这是一个抽象层次很高的函数.稍微总结一下自己的理解

查看源代码会发现combineByKey定义如下

def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C)
    : RDD[(K, C)] = {
    combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self))
  }

combineByKey函数需要传递三个函数做为参数，分别为createCombiner、mergeValue、mergeCombiner,需要理解这三个函数的意义

结合数据来讲的话，combineByKey默认按照key来进行元素的combine,这里三个参数都是对value的一些操作

1>第一个参数createCombiner,如代码中定义的是 : (v) => (v, 1)

这里是创建了一个combiner,作用是当遍历rdd的分区时，遇到第一次出现的key值，那么生成一个(v,1)的combiner,比如这里key为address,当遇到第一个