Akka Streams Flow that distributes messages (according to a hashing function) across sub-flows. The idea is to have ordered processing per sub-flow but parallel processing across sub-flows.

distributionflow.scala

import akka.stream._
import akka.stream.scaladsl._
/***
  * Example based on numBuckets = 3
  *                                          --- bucket 1 flow --- ~mapAsync(parallelism)~ ---
  *                   |------------------| /                                                  \|---------------|
  * Open inlet[A] --- | Partition Fan Out|  --- bucket 2 flow --- ~mapAsync(parallelism)~ -----| Merge Fan In  | --- Open outlet[B]
  *                   |------------------| \                                                  /|---------------|
  *                                         --- bucket 3 flow --- ~mapAsync(parallelism)~ ---
  *
  *
  * @param numBuckets the number of sub-flows to create
  * @param parallelism the mapAsync (ordered) parallelism per sub flow
  * @param hash the hashing function used to decide
  * @param fn is the mapping function to be used for mapAsync
  * @tparam A is the input stream of elements of type A
  * @tparam B is the output streams of elements of type B
  * @return a Flow of elements from type A to type B
  */
def hashingDistribution[A, B](numBuckets: Int,
                              parallelism: Int,
                              hash: A => Int,
                              fn: A => Future[B]): Flow[A, B, NotUsed] =
  Flow.fromGraph(GraphDSL.create() { implicit builder =>
    import GraphDSL.Implicits._
    val numPorts = numBuckets
    val partitioner =
      builder.add(Partition[A](outputPorts = numPorts, partitioner = a => math.abs(hash(a)) % numPorts))
    val merger = builder.add(Merge[B](inputPorts = numPorts, eagerComplete = false))

    Range(0, numPorts).foreach { eachPort =>
      partitioner.out(eachPort) ~> Flow[A].mapAsync(parallelism)(fn) ~> merger.in(eachPort)
    }

    FlowShape(partitioner.in, merger.out)
  })

Credits to @ukayani for his help on designing this Flow

Example usage:

implicit val system: ActorSystem =  ActorSystem("streaming-system", ConfigFactory.load("akka {}"))
implicit val mat: ActorMaterializer = ActorMaterializer()
implicit val ec: ExecutionContext = system.dispatcher

def sampleAsyncCall(x: Int): Future[Int] = Future {
  Thread.sleep((x * 100L) % 10)
  x
}

Source
  .repeat(1 to 10000)
  .mapConcat(identity)
  .via(
    hashingDistribution[Int, Int](
      numBuckets = 100,
      parallelism = 2,
      hash = element => MurmurHash3.stringHash(element.toString),
      fn = sampleAsyncCall
    )
  )
  .to(Sink.foreach(println))
  .run()