FS2 parallel evaluation of items with different discriminator

user_1335879 0
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In the following example, items with different discriminators ("a", "b" and "c") are evaluated (printed) in parallel:

package org.example

import cats.effect.std.Random
import cats.effect.{ExitCode, IO, IOApp, Temporal}
import cats.syntax.all._
import cats.{Applicative, Monad}
import fs2._

import scala.concurrent.duration._

object GitterQuestion extends IOApp {

  override def run(args: List[String]): IO[ExitCode] =
    Random.scalaUtilRandom[IO].flatMap { implicit random =>
      val flat = Stream(
        ("a", 1),
        ("a", 2),
        ("a", 3),

        ("b", 1),
        ("b", 2),
        ("b", 3),

        ("c", 1),
        ("c", 2),
        ("c", 3)
      ).covary[IO]

      val a = flat.filter(_._1 === "a").through(rndDelay)
      val b = flat.filter(_._1 === "b").through(rndDelay)
      val c = flat.filter(_._1 === "c").through(rndDelay)

      val nested = Stream(a, b, c)

      nested.parJoin(100).printlns.compile.drain.as(ExitCode.Success)
    }

  def rndDelay[F[_]: Monad: Random: Temporal, A]: Pipe[F, A, A] =
    in =>
      in.evalMap { v =>
        (Random[F].nextDouble.map(_.seconds) >>= Temporal[F].sleep) >> Applicative[F].pure(v)
      }
}

The result of running this program will look similar to this:

(c,1)
(a,1)
(c,2)
(a,2)
(c,3)
(b,1)
(a,3)
(b,2)
(b,3)

Note that there's no reordering between items with the same discriminator - they are processed sequentially. (a, 2) will never be printed before (a, 1).

In my real-world scenario, the discriminator values are not known ahead of time and there can be many of them, but I would like to have the same behavior, how can I do this?

fs2 cats-effect scala-cats scala
2 Answers

I believe that broadcastThrough does what you want.
(but make sure to check the Scaladoc carefully)

I am using IO directly for simplicity but it should be easy to adapt to abstract F[_]

def discriminateProcessing[A, B](stream: Stream[IO, A])(discriminators: List[A => Boolean])(pipe: Pipe[IO, A, B]): Stream[IO, B] = {
  val allPipes: List[Pipe[IO, A, B]] = discriminators.map { p =>
    s => s.filter(p).through(pipe)
  }

  stream.broadcastThrough(allPipes : _*)
}

Which would be used like this:

val result = discriminateProcessing(stream = flat)(discriminators = List(
  _._1 === "a",
  _._1 === "b",
  _._1 === "c"
)) { s =>
  s.evalMap { v =>
    random.nextDouble.map(_.seconds).flatMap(IO.sleep).as(v)
  }
}

You can see the code running here.

user_41114040

I think you need to roll your own groupBy function for this. I think you would have to create a Queue for every discriminator. Then for every Queue emit one inner Stream that pulls elements from that Queue.

Here's an untested and probably naive implementation of what I had in mind:

import cats.effect.std.Queue

val nested = 
  (flat.map(Some(_)) ++ Stream(None))
    .evalScan(Map.empty[String, Queue[IO, Option[(String, Int)]]] -> Option.empty[Stream[IO, (String, Int)]]){
      case ((map, _), t @ Some((key, value))) =>
        if (map.contains(key))
          map(key).offer(t).as(map -> None)
        else {
          for {
            q <- Queue.unbounded[IO, Option[(String, Int)]]
            _ <- q.offer(t)
            r = (map + (key -> q)) -> Some(Stream.fromQueueNoneTerminated(q))
          } yield r
        }
      case ((map, _), None) => 
      // None means the flat stream is finished
        map.values.toList.traverse(_.offer(None))
          .as(Map.empty -> None)
    }
    .map(_._2).unNone

val parallelism: Int = ???

nested
  .map(_.through(rndDelay))
  // produce and consume in parallel in order to 
  // avoid deadlocks in case of bounded parJoin
  .prefetchN(parallelism)
  .parJoin(parallelism)
  .printlns
  .compile
  .drain
  .as(ExitCode.Success)
user_13744610
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