1. Beyond Mere Actors
Concurrent Functional
Programming with Scalaz
Rúnar Bjarnason

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

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

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

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

6. 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.

7. 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] =

8. 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.

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

10. 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.

11. 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.

12. 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.

13. 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.

14. 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

15. Actor Example

16. 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.

17. Problems with Actors

18. 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.

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

20. 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!

21. 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.

22. 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.

23. 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]

24. 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

25. 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.

26. Promises - Example

27. 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.

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

29. 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)))
}

30. 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)
}

31. 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.

32. Questions?