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Special case for parEvalMap with max concurrency of 1 #2577

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Sep 1, 2021
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Special case for parEvalMap with max concurrency of 1 #2577

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56 changes: 29 additions & 27 deletions core/shared/src/main/scala/fs2/Stream.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -2014,38 +2014,40 @@ final class Stream[+F[_], +O] private[fs2] (private[fs2] val underlying: Pull[F,
*/
def parEvalMap[F2[x] >: F[x], O2](
maxConcurrent: Int
)(f: O => F2[O2])(implicit F: Concurrent[F2]): Stream[F2, O2] = {
val fstream: F2[Stream[F2, O2]] = for {
chan <- Channel.bounded[F2, F2[Either[Throwable, O2]]](maxConcurrent)
chanReadDone <- F.deferred[Unit]
} yield {
def forkOnElem(o: O): F2[Stream[F2, Unit]] =
for {
value <- F.deferred[Either[Throwable, O2]]
send = chan.send(value.get).as {
Stream.eval(f(o).attempt.flatMap(value.complete(_).void))
)(f: O => F2[O2])(implicit F: Concurrent[F2]): Stream[F2, O2] =
if (maxConcurrent === 1) evalMap(f)
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@bastewart bastewart Aug 27, 2021 •
edited
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There is no direct assertion that maxConcurrent is > 0 in parEvalMap. There is an indirect on though as ultimately parEvalMap calls parJoin which does do this assertion.

else {
val fstream: F2[Stream[F2, O2]] = for {
chan <- Channel.bounded[F2, F2[Either[Throwable, O2]]](maxConcurrent)
chanReadDone <- F.deferred[Unit]
} yield {
def forkOnElem(o: O): F2[Stream[F2, Unit]] =
for {
value <- F.deferred[Either[Throwable, O2]]
send = chan.send(value.get).as {
Stream.eval(f(o).attempt.flatMap(value.complete(_).void))
}
eit <- chanReadDone.get.race(send)
} yield eit match {
case Left(()) => Stream.empty
case Right(stream) => stream
}
eit <- chanReadDone.get.race(send)
} yield eit match {
case Left(()) => Stream.empty
case Right(stream) => stream
}

val background = this
.evalMap(forkOnElem)
.parJoin(maxConcurrent)
.onFinalize(chanReadDone.get.race(chan.close).void)
val background = this
.evalMap(forkOnElem)
.parJoin(maxConcurrent)
.onFinalize(chanReadDone.get.race(chan.close).void)

val foreground =
chan.stream
.evalMap(identity)
.rethrow
.onFinalize(chanReadDone.complete(()).void)
val foreground =
chan.stream
.evalMap(identity)
.rethrow
.onFinalize(chanReadDone.complete(()).void)

foreground.concurrently(background)
foreground.concurrently(background)
}
Stream.eval(fstream).flatten
}
Stream.eval(fstream).flatten
}

/** Like [[Stream#evalMap]], but will evaluate effects in parallel, emitting the results
* downstream. The number of concurrent effects is limited by the `maxConcurrent` parameter.
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