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Sequencing Scala Futures with bounded parallelism (without messing around with ExecutorContexts)

I have example how to do it with scalaz-stream. It's quite a lot of code because it's required to convert scala Future to scalaz Task (abstraction for deferred computation). However it's required to add it to project once. Another option is to use Task for defining 'doWork'. I personally prefer task for building async programs.

  import scala.concurrent.{Future =>
  import scala.util.Random

  import scalaz.concurrent._

  val P =

  val rnd = new Random()

  def doWork(symbol: String): SFuture[Unit] =
SFuture {
    println(s"Symbol: $symbol. Thread:

  val symbols = Seq("AAPL", "MSFT", "GOOGL",
    flatMap(s =>
Seq.fill(5)(s) =>

  implicit class Transformer[+T](fut: =>
SFuture[T]) {
    def toTask(implicit ec:
scala.concurrent.ExecutionContext): Task[T] = {
      import scala.util.{Failure, Success}
      import scalaz.syntax.either._
      Task.async {
        register =>
          fut.onComplete {
            case Success(v) =>
            case Failure(ex) =>

  implicit class ConcurrentProcess[O](val process:
Process[Task, O]) {
    def concurrently[O2](concurrencyLevel: Int)(f:
Channel[Task, O, O2]): Process[Task, O2] = {
      val actions =
          zipWith(f)((data, f) => f(data))

      val nestedActions =


  val workChannel = String) =>

  val process =

When you'll have all this transformation in scope, basically all you need is:

  val workChannel =
String) => doWork(s).toTask)

  val process =

Quite short and self-decribing

Categories : Scala

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