Spark：使用Scala在reduceByKey中的值的平均值而不是sum

Question

当调用reduceByKey时，它使用相同的键对所有值求和。有没有办法计算每个键的平均值？

// I calculate the sum like this and don't know how to calculate the avg
reduceByKey((x,y)=>(x+y)).collect


Array(((Type1,1),4.0), ((Type1,1),9.2), ((Type1,2),8), ((Type1,2),4.5), ((Type1,3),3.5), 
((Type1,3),5.0), ((Type2,1),4.6), ((Type2,1),4), ((Type2,1),10), ((Type2,1),4.3))

Answer 1

一种方法是使用mapValues和reduceByKey，这比aggregateByKey更容易。

.mapValues(value => (value, 1)) // map entry with a count of 1
.reduceByKey {
  case ((sumL, countL), (sumR, countR)) => 
    (sumL + sumR, countL + countR)
}
.mapValues { 
  case (sum , count) => sum / count 
}
.collect

https://www.safaribooksonline.com/library/view/learning-spark/9781449359034/ch04.html

Answer 2

有很多方法......但是一个简单的方法就是使用一个跟踪你的总数和计数并在最后计算平均值的类。这样的事情会起作用。

class AvgCollector(val tot: Double, val cnt: Int = 1) {
  def combine(that: AvgCollector) = new AvgCollector(tot + that.tot, cnt + that.cnt)
  def avg = tot / cnt 
}

val rdd2 = {
  rdd
  .map{ case (k,v) => (k, new AvgCollector(v)) }
  .reduceByKey(_ combine _)
  .map{ case (k,v) => (k, v.avg) }
}

...或者您可以使用aggregateByKey对类进行调整

class AvgCollector(val tot: Double, val cnt: Int = 1) {
  def ++(v: Double) = new AvgCollector(tot + v, cnt + 1)
  def combine(that: AvgCollector) = new AvgCollector(tot + that.tot, cnt + that.cnt)
  def avg = if (cnt > 0) tot / cnt else 0.0
}

rdd2 = {
  rdd
  .aggregateByKey( new AvgCollector(0.0,0) )(_ ++ _, _ combine _ )
  .map{ case (k,v) => (k, v.avg) }
}