How to compute cumulative sum using Spark

Question

I have an rdd of (String,Int) which is sorted by key

val data = Array(("c1",6), ("c2",3),("c3",4))
val rdd = sc.parallelize(data).sortByKey

Now I want to start the value for the first key with zero and the subsequent keys as sum of the previous keys.

Eg: c1 = 0 , c2 = c1's value , c3 = (c1 value +c2 value) , c4 = (c1+..+c3 value) expected output:

(c1,0), (c2,6), (c3,9)...

Is it possible to achieve this ? I tried it with map but the sum is not preserved inside the map.

var sum = 0 ;
val t = keycount.map{ x => { val temp = sum; sum = sum + x._2 ; (x._1,temp); }}

Answer 1

1. Compute partial results for each partition:

   val partials = rdd.mapPartitionsWithIndex((i, iter) => {
     val (keys, values) = iter.toSeq.unzip
     val sums  = values.scanLeft(0)(_ + _)
     Iterator((keys.zip(sums.tail), sums.last))
   })
   

2. Collect partials sums

   val partialSums = partials.values.collect
   

3. Compute cumulative sum over partitions and broadcast it:

   val sumMap = sc.broadcast(
     (0 until rdd.partitions.size)
       .zip(partialSums.scanLeft(0)(_ + _))
       .toMap
   )
   

4. Compute final results:

   val result = partials.keys.mapPartitionsWithIndex((i, iter) => {
     val offset = sumMap.value(i)
     if (iter.isEmpty) Iterator()
     else iter.next.map{case (k, v) => (k, v + offset)}.toIterator
   })