scala-async

eah dude, but try implementing the logic in debounce by yourself and see how readable that is. And that's exactly why Rx is problematic. On one hand because stream processing is fundamentally hard, no matter what model you choose. And on the other hand Rx comes with a lot of useful operators that do a lot for you, but then you have to learn about them. For the naysayers, I'll just leave this piece of code with a challenge for implementing it with core.async and compare in terms of readability and note this is a copy paste from actual production code ... commands .groupBy(w => (w.assetID, w.commandID)) .mergeMap { gr => gr.timeout(30.seconds, Observable.empty) .throttleLast(1.second) .distinctUntilChanged .echoRepeated(5.seconds) .whileBusyBuffer(DropOld(30)) } What it does is to split the signals for each asset and command, for each of these it's supposed to sample the signal by 1 second, but in case the same value is repeated over and over again or in case the channel goes silent, then the last value will end up signaled every 5 seconds (reducing the traffic to our OpenTSDB). Finally for each key we close the stream after 30 seconds of inactivity. And then for each such key it does buffering of at most 30 elements and in case the consumers are too slow, then these buffers start dropping older elements on overflow. And then we merge everything back. I would also show you how we are modeling state machines with the "scan" operator, state machines that are evolved from signals coming from multiple sources, but the sample would be too long. In any case "scan" allows you to use pure functions and data-structures, so you can test your business logic without interactions to third party services, mocks, stubs or whatever. I have to deal with such code all the time. And I've seen such code implemented in a classic fashion as well. You basically end up with Maps storing stuff and with ifs and whiles and with manual timers in an unholy dance of mutation so hard to understand and debug that it would make grown men cry. But then such solutions are not silver bullets. Rx, CSP, futures, actors are not silver bullets to be applied everywhere, with all of them having a sweet spot for which they excel. I'm actually using Rx, actors, futures, scala/sync in the same project and it's great. Also, one last note: Rx is not FRP ;-) [1] https://github.com/alexandru/monifu/ [2] https://github.com/scala/async

e things like monads and functors being modeled explicitly. It's more natural to do that in Scala because it's all a matter of describing functions with certain signatures. And you know, when working with combinations of flatMap and map, it's much easier if you have the compiler telling you where you've fucked up. Which is why many people do use things like Scalaz or Cats or Shapeless. On the other hand in Clojure it's much easier to build things like transducers, because there's no type-system to stay in your way. Clojure people work with lots of DSLs built over macros. You can see that in how Clojure exposes those channels in core.async and compare that to how asynchronous processing of events is done in Scala (e.g. Future, Iteratees, scalaz-stream, Rx, Akka actors, Reactive Streams, shameless plug: https://github.com/monifu/monifu). Of course, Scala has its own macros system, but like in other static languages, the macros in Scala are only being used by those that really know what they are doing. But because of that, in both languages you get features exposed as libraries instead of being special language features. For example here's Scala's equivalent for C#'s await/async exposed as a library: https://github.com/scala/async Which one is better is actually dependent on the problems you're trying to solve. The problems I'm working on have a very complex business logic and are a better mix for static typing. This is why people will never agree on which one is better, because it actually depends on context.

a constant dialog with the other side. Even in a simple producer/consumers relationship you need back-pressure. Pretending that the communication is somehow synchronous is not going to fix the non-deterministic nature of this process and the logic will end up looking mostly the same. This is because it's not the asynchronicity that bites you ;-) Quasar cannot make blocking I/O to not block threads, although it's clearly stated in the article, but that's just wrong. You can indeed patch connections to work with Quasar's fibers, working much like how you patch sockets in Python with gevent, so you can create your own JDBC data-source that plays nice, but that's not the same thing. This really means that your pieces of I/O logic are still separated into blocking and non-blocking (red versus blue) and I'd rather see which is which by seeing `Future[T]` or `Observable[T]` in my function signatures. Therefore I disagree that Quasar code is more readable, but at least I'm willing to admit that YMMV. Also, that Quasar actor given as an example has nothing to do with the actor model. This is because in the actor model the actors are untyped. If you type your actors, then you're not talking about the actor model anymore and you'd be better off with another computational model. At the moment it is completely futile to try to type state machines that work in a non-deterministic highly concurrent environment. And if you go through the effort of typing those actors, then you have to at least recognize that actors can communicate with multiple other actors at the same time and thus expose multiple interfaces. And then you can say that at the very least your typing says something more useful than Object or String. I could write more, but I got tired. [1] https://github.com/scala/async [2] http://doc.akka.io/docs/akka/snapshot/scala/actors.html#beco...