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This is the ninth, and last, of several posts describing the evolution of scala.concurrent.Future in Scala 2.12.x. For the previous post, click here.

WARNING: ADVANCED TOPICS AHEAD

Feature: Improved Promise Linking (I promise!)

ACPS (no, not an abbreviation of ALLCAPS)

A really useful technique when working with Future is what I call Async Continuation Passing Style (ACPS), which was introduced to me by Marius Eriksen (@marius) years ago, and while we’re on the topic—Marius is awesome.

My casual definition of ACPS is the use of Future.flatMap/recoverWith to «Divide and Conquer»™ problems into what looks like ordinary recursive invocations. For Scala 2.12 and onwards, we also have transformWith.

Let’s say we want to create a method which will take a List of Future of Ints, and returns a Future of List of Try of Int. (Now that’s quite a mouthful isn’t it?!)

(The astute reader will react in horror at such a specific method signature, and will immediately yell at me for not calling it by its widely accepted name of sequence. BUT THIS IS AN EXAMPLE, PEOPLE! ^^)

import scala.concurrent._
import scala.util.{Try, Success}

//Define type aliases to cut down on the bracket-madness
//for this blog post format
type ListOfFutures = List[Future[Int]]
type FutureListOfTrys = Future[List[Try[Int]]]

def results(l: ListOfFutures)(implicit ec: ExecutionContext): FutureListOfTrys =
{
  // An inner method which allows us to recursively decompose
  // the problem and build up the solution
  // `f` is our current result, and `remaining` is what we have left to do
  def acps(f: FutureListOfTrys, remaining: ListOfFutures): FutureListOfTrys =
    remaining match {
      case Nil => f // When we hit the end of the list, we're done
      case r :: tail =>
        f.flatMap(list =>
          // as `f` completes, use the current result `list`
          // and when `r` completes, add its result to `list`
          // and carry forward `tail` as the `remaining` for the next `acps`
          acps(r.transform(result => Success(result :: list)), tail)
        )
    }
  // Our initial result is `Nil`, and initially `l` is what is `remaining`
  // and we need to `reverse` the result once it is done since
  // we are building up the answer in reverse order (List)
  acps(Future.successful(Nil), l).map(_.reverse)
}

Alright, I don’t know about you, but for me the first time I saw something like this it was some pretty mind-blowing stuff so if this is your first experience with it, take a couple of minutes to let it sink in, then have a look at the example below.

scala> val example = {
     |   import ExecutionContext.Implicits._
     |   List(Future(5),
     |        Future[Int](throw new Exception("foo")),
     |        Future("pigdog".hashCode))
     | }
example: List[scala.concurrent.Future[Int]] =
  List(Future(Success(5)), Future(Failure(java.lang.Exception: foo)), Future(Success(-988364562)))

scala> results(example)(ExecutionContext.global)
res0: FutureListOfTrys = Future(<not completed>)

scala> res0 // Or do `res0 foreach println`
res1: FutureListOfTrys =
  Future(Success(List(Success(5), Failure(java.lang.Exception: foo), Success(-988364562))))

Hopefully I have been able to illustrate how one can use ACPS to decompose problems recursively, without building up a callstack, and being more in line with trampolining.

Promise Linking

Now, if an implementation of Future is not careful, it is easy to create a space-leak with ACPS code.

The following example is an adaptation of the regression test for SI-7336

import scala.concurrent._
import scala.concurrent.duration._
import scala.concurrent.ExecutionContext.Implicits._

// Pick a large number
val arraySize = 1000000
// Make sure that we will hit OOME if Promise Linking doesn't work
val tooManyArrays = (Runtime.getRuntime().totalMemory() / arraySize).toInt * 100

// An example of possible space leak using ACPS by virtue of `recoverWith`:
def loopRW(i: Int, arraySize: Int): Future[Unit] = {
  val array = new Array[Byte](arraySize)
  Future(throw new Exception).recoverWith { case _ =>
    if (i == 0) {
      Future(())
    } else {
      array.size // Force closure to refer to array
      loopRW(i - 1, arraySize)
    }
  }
}

The code above may warrant some explaining.

The array.size-line is an example of using array in the closure. This forces it to be retained by the generated closure which is passed to flatMap/recoverWith.

The shape of the solutions which use ACPS is a «Chain of Futures» where the returned Future is only completed once the whole chain is finished, which creates the risk of «space leaks». (No, this is not about interstellar plumbing)

These two interact to create a space leak that grows over the Chain. This is even more problematic for chains of unbounded length.

Promise Linking is a solution to this problem— since we know that a Future returned from flatMap/recoverWith/transformWith can only be completed by the Future returned by the function passed to it, we can implement something very similar to the concept of Tail Call Optimization but for ACPS.

This elides the intermediate Promises in this Chain of Futures and instead directly completes the Promise associated with the Future. This involves migrating registered callbacks to the «root» Promise—the one which started the chain.

loopRW(tooManyArrays, arraySize) onComplete println

Outcome:

  • Fails (OutOfMemoryError) on 2.10.6 and 2.11.8
  • Succeeds on 2.12.0-M3

The reason for failing on 2.10.6 and 2.11.8 is that Promise Linking is for those versions only supported for flatMap, not recoverWith. And transformWith does not exist in those versions. In 2.12.x Promise Linking is implemented for transformWith which both flatMap and recoverWith uses, so they get the feature for free.

Benefits

  1. ACPS is a very useful way of encoding solutions
  2. Promise Linking addresses a certain class of space leaks introduced by ACPS
  3. 2.12 has Promise Linking properly implemented across the board

PS. scala.collection.immutable.List is a Stack

I hope you’ve enjoyed this series of blog posts on Scala Futures for 2.12!

Cheers,