Johan,
Thanks for your suggestion -- that worked well! This is purely an academic
question at this point, but is there some way to (essentially) implement a
resource pool using a flow like:
Source(Seq(resource1, resource2, resource3)).map(resource => computeWith(
resource)) //<== at this point split stream, sending a resource "back
around" to Source, and sending something else to an output Sink?
For the above to work, my Source would have to block when it doesn't have
any resources to dispense.
I suspect that my question seems crazy to a seasoned streams user, but I'd
figured I'd ask. Basically the motivating use case is:
I have a lot of cases in which I have a fairly involved workflow with
branching logic that must check out resources at the beginning and return
them in the end. It would be nice to represent the logic of those
workflows purely using streams. If every block that uses a resource pool
has to be enclosed in it's own future, that logic becomes opaque, or at the
very least shifted to a different level than the overall streams logic. (I
can't do for example: Source(resources).zip(Source(workItems)).map((r,i)
=> computation1(r,i)).buffer(...).map((r,i) => computation2(r,i)).bcast
(etc,etc)).
-John
On Wednesday, April 6, 2016 at 4:50:57 AM UTC-7, Akka Team wrote:
>
> Hi John,
>
> You could hide step 2-4 behind a method returning a future, that way you
> can run the blocking operations on a separate threadpool.
>
> Pseudocode:
> def doWorkWithResource(item: WorkItem): Future[ComputationResult] = {
> Future {
> // acquire resource
> // do work
> // return resource to pool
> }(aSpecialExecutionContextForBlocking)
>
>
> Source(largeButFiniteQueue).mapAsync(parallellism)(doWorkWithResource).to(Sink(someKindOfOutput))
>
>
> The parallellism mapAsync parameter should then probably be the same as
> the number of threads in the blocking threadpool as no more functions can
> run in parallell anyway.
>
> --
> Johan Andrén
> Akka Team, Lightbend Inc.
>
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