How to Define Custom partitioner for Spark RDDs of equally sized partition where each partition has equal number of elements?

Question

I am new to Spark. I have a large dataset of elements[RDD] and I want to divide it into two exactly equal sized partitions maintaining order of elements. I tried using RangePartitioner like

var data = partitionedFile.partitionBy(new RangePartitioner(2, partitionedFile)) 

This doesn't give a satisfactory result because it divides roughly but not exactly equal sized maintaining order of elements. For example if there are 64 elements, we use Rangepartitioner, then it divides into 31 elements and 33 elements.

I need a partitioner such that I get exactly first 32 elements in one half and other half contains second set of 32 elements. Could you please help me by suggesting how to use a customized partitioner such that I get equally sized two halves, maintaining the order of elements?

Answer 1

Partitioners work by assigning a key to a partition. You would need prior knowledge of the key distribution, or look at all keys, to make such a partitioner. This is why Spark does not provide you with one.

In general you do not need such a partitioner. In fact I cannot come up with a use case where I would need equal-size partitions. What if the number of elements is odd?

Anyway, let us say you have an RDD keyed by sequential Ints, and you know how many in total. Then you could write a custom Partitioner like this:

class ExactPartitioner[V](     partitions: Int,     elements: Int)   extends Partitioner {    def getPartition(key: Any): Int = {     val k = key.asInstanceOf[Int]     // `k` is assumed to go continuously from 0 to elements-1.     return k * partitions / elements   } }