Beyond Mere Actors

Beyond Mere Actors Concurrent Functional Programming with Scalaz Rúnar Bjarnason

Traditional Java Concurrency Manual creation of threads Manual synchronisation One thread per process Processes communicate by shared mutable state

Traditional Java Concurrency Manual creation of threads Manual synchronisation One thread per process Processes communicate by shared mutable state Problem: Threads do not compose

java.util.concurrent Since JDK 5 Includes useful building blocks for making higher-level abstractions. Atomic references Countdown latches ExecutorService Callable Future

Futures Provided by java.util.concurrent.Future Future[A] represents a computation of type A, executing concurrently. Future.get: A

Futures ExecutorService is a means of turning Callable[A] into Future [A] implicit def toCallable[A](a: => A) = new Callable[A] { def call = a } implicit val s = Executors.newCachedThreadPool e1 and e2 are evaluated concurrently val x = s submit { e1 } val y = e2 Futures can participate in expressions: val z = f(x.get, y) No mention of threads in this code. No shared state.

Futures There's a serious problem. How would we implement this function? def fmap[A,B](fu: Future[A], f: A => B) (implicit s: ExecutorService):Future[B] =

Futures We have to call Future.get, blocking the thread. def fmap[A,B](fu: Future[A], f: A => B) (implicit s: ExecutorService):Future[B] = s submit f(fu.get) This is a barrier to composition. Futures cannot be composed without blocking a thread.

scalaz.concurrent Strategy - Abstracts over ways of evaluating expressions concurrently. Actor - Light-weight thread-like process, communicates by asynchronous messaging. Promise - Compose concurrent functions.

Parallel Strategies ExecutorService: Callable[A] => Future[A] Callable[A] ~= Function0[A] Future[A] ~= Function0[A] Also written: () => A Strategy[A] ~= Function0[A] => Function0[A] Also written: (() => A) => () => A Turns a lazy expression of type A into an expression of the same type.

scalaz.concurrent.Strategy Separates the concerns of parallelism and algorithm. Captures some threading pattern and isolates the rest of your code from threading. Executor - wraps the expression in a Callable, turns it into a Future via an implicit ExecutorService. Naive - Starts a new thread for each expression. Sequential - Evaluates each expression in the current thread (no concurrency). Identity - Performs no evaluation at all.

Parallel Strategies You can write your own Strategies. Some (crazy) ideas: Delegate to the fork/join scheduler. Run in the Swing thread. Call a remote machine. Ask a human, or produce a random result.

Actors Provide asynchronous communication among processes. Processes receive messages on a queue. Messages can be enqueued with no waiting. An actor is always either suspended (waiting for messages) or working (acting on a message). An actor processes messages in some (but any) order.

scalaz.concurrent.Actor These are not scala.actors. Differences: Simpler. Scalaz Actors are distilled to the essentials. Messages are typed. Actor is sealed, and instantiated by supplying: type A effect: A => Unit (implicit) strategy: Strategy[Unit] (Optional) Error Handler: Throwable => Unit Strategy + Effect = Actor

Actor Example

Actor: Contravariant Cofunctor An actor can be composed with a function: x.comap(f) Comap has this type: comap: (B => A) => Actor[A] => Actor[B] Returns a new actor that applies f to its messages and sends the result to actor x. x comap f is equivalent to x compose f, but results in an Actor, as opposed to a function.

Problems with Actors

Problems with Actors You have to think about state and process communication. An actor can (must?) expose its state. It's all about side-effects! Side-effects absolutely do not compose. You cannot compose Actors with each other. Actor[A] ~= (A => Unit) There's not a lot you can do with Unit.

scalaz.concurrent.Promise Similar to Future, but non-blocking. Implements map and flatMap without calling get.

scalaz.concurrent.Promise Constructed by giving an expression to promise: lazy val e:String = {Thread sleep 5000; "Hi."} val p: Promise[String] = promise(e) Takes an implicit Strategy[Unit]. The expression is evaluated concurrently by the Strategy. Think of this as forking a process. The result is available later by calling p.get. This blocks the current thread. But we never have to call it!

On Time-Travel Promised values are available in the future. What does it mean to get a value out of the future? Time-travel into the future is easy. Just wait. But we don't have to go into the future. We can give our future-selves instructions. Instead of getting values out of the future, we send computations into the future.

Lifting a function into the future Consider: promise(e).map(f) map has the following type: (A => B) => Promise[A] => Promise[B] We take an ordinary function and turn it into a function that operates on Promises. It's saying: Evaluate e concurrently, applying f to the result when it's ready. Returns a Promise of the final result.

Composing concurrent functions A concurrent function is of the type A => Promise[B] Syntax sugar to make any function a concurrent function: val g = f.promise promise(f: A => B) = (a:A) => promise(f(a)) We can bind the arguments of concurrent functions to promised values, using flatMap: promise(e).flatMap(g) flatMap has this type: (A => Promise[B]) => Promise[A] => Promise[B]

Composing concurrent functions We can compose concurrent functions with each other too. If f: A => Promise[B] and g: B => Promise[C] then (f >=> g): A => Promise[C] (f >=> g)(x) is equivalent to f(x) flatMap g

Joining Promises join[A]: Promise[Promise[A]] => Promise[A] (promise { promise { expression } }).join Join removes the "inner brackets". A process that forks other processes can join with them later, without synchronizing or blocking. A process whose result depends on child processes is still just a single Promise, and thus can run in a single thread. Therefore, pure promises cannot deadlock or starve.

Promises - Example

But wait, there's more! Parallel counterparts of map, flatMap, and zipWith: parMap, parFlatMap, and parZipWith x.parMap(f) Where x can be a List, Stream, Function, Option, Promise, etc. Scalaz provides parMap on any Functor. parZipWith for parallel zipping of any Applicative Functor. parFlatMap is provided for any Monad.

Advanced Topics If you understand Promise, then you understand monads.

Advanced Topics Functor is simply this interface: trait Functor[F[_]] { def fmap[A, B](r: F[A], f: A => B): F[B] } Functors are "mappable". Any implementation of this interface is a functor. Here's the Promise functor: new Functor[Promise] { def fmap[A, B](t: Promise[A], f: A => B) = t.flatMap(a => promise(f(a))) }

Advanced Topics Monad is simply this interface: trait Monad[M[_]] extends Functor[M] { fork[A](a: A): M[A] join[A](a: M[M[A]]): M[A] } Monads are fork/map/joinable. Any implementation of this interface is a monad. Here's the Promise monad: new Monad[Promise] { def fork[A](a: A) = promise(a) def join[A](a: Promise[Promise[A]]) = a.flatMap(Functions.identity) }

Welcome to Scalaz Scalaz is a general-purpose library for higher-order programming. There's a lot here. Go play around, and ask questions on the Scalaz Google Group. For more information: http://code.google.com/p/scalaz Documentation is lacking, but we're working on that. A release of Scalaz 5.0 will coincide with a release of Scala 2.8.

Questions?

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