Pondering Actor Design Trades

There's been a lot of discussion of the Scala actors library lately, much of it critical, and a recent flurry of alternate implementations.  The alternate implementations (except my languishing state-based one ;-) all have one thing in common:  They are several orders of magnitude simpler.  Writing a basic actor implementation is actually pretty trivial, especially given java.util.concurrent classes that provide a decent chunk of the functionality in Scala actors, all for free on JDK5+.  So this begs the question few questions:

1. Why is the standard Scala actor implementation so complex when others have done it in a such simpler fashion?
2. Is it better to have one, big actor library that supports a wide variety of use cases, or a bunch of smaller ones targeted at specific niches and programming styles?
3. If there are to be a bunch, should they just be conceptually similar (e.g. all based on the actor model), or should there be interoperability among them?

I'm not going to answer these questions now.  Instead, I'm going to try to start laying out some of what I believe to be the key characteristics of an actor implementation, and how they detract or enforce one another.  So here it goes:

1. Guarantees
2. Expressivity
3. Extensibility
4. Performance
5. Scalability

Guarantees

The purpose of a concurrency framework is to make concurrency easier.  Concurrency is hard largely because it is extremely difficult to reason about, and thus concurrent code tends to be hard to write, laden with bugs, and subject to various odd pitfalls.  By providing various guarantees, a concurrency framework makes it easier to reason about concurrent code.  Actors are intended to free the programmer from worrying about things like locks, semaphores, thread management, etc. by encapsulating all that complexity behind a simple interface, assuming the programmer follows some basic rules like "no shared mutable state among actors."

The problem with guarantees is that in they tend to break down in the presence of limited CPU and memory resources.

Expressivity

Expressivity is difficult to define.  For purposes here, I'm going to define it as the degree to which a concise, natural expression of the programmer's intent is supported, and illustrate it by comparing Scala Actor to Lift Actor.  Scala Actors allow you to execute logic independent of message processing (note: this a violation of the theoretical model for actors) by simply placing it in the act method.  Lift Actors, on the other hand, are only triggered when they receive of message (this is consistent with the theoretical model).  For example, this makes it so that Scala Actors can do things such as perform possibly costly setup operations in their own thread before they start listening for messages.  In order to accomplish this in the Lift model, the programmer must create the actor and then send it some sort of "init" message.  The same effect can be achieved with both implementations, but it is more naturally supported by Scala Actors.  Of course there is a tradeoff here, as deviating from the theoretical model potentially weakens any guarantees that the model may provide.  The Scala Actor way also implies that an Actor has an explicit lifecycle, which as we'll see later has other significant implications.

Another example is what I'll call the "nested react pattern."  It is relatively common to want an actor to take on a different behavior after processing a message, thus altering which messages are ignored and how the received messages are processed.

loop {
 react {
    case 'foo => { 
      // do some stuff...
      react {
        case 'bar => // do some other stuff... 
      } 
    } 
  } 
}

The code above alternates between processing 'foo messages and 'bar messages.  This can be done with Lift Actor as well, but the expression is a little less natural:

class MyActor extends LiftActor {
  private val fooMode: PartialFunction[Any, Unit] = {
    case 'foo => {
      // do some stuff
      mode = barMode
    }
  }
  private val barMode: PartialFunction[Any, Unit] = {
    case 'bar => {
      // do some other stuff...
      mode = fooMode
    }
  }
  private var mode = fooMode
  protected def messageHandler = mode
}

Finally, Lift Actors exclusively use an event-based model and have no support for blocking on a thread while waiting for a message, and thus looses the ability to express patterns such as the following:

loop {
  react {
    case 'converToNumber => {
      val i: Int = receive {
        case 'one => 1
        case 'two => 2
        case 'three => 3
      }
      reply(i)
    }
  }
}

Extensibility

For purposes here, I'm going to use "extensible" to mean that a piece of software is extensible if capabilities can be added without modifying the core or breaking its semantics in a amount of effort proportional to the size of the extension.  This is narrower than the traditional definition of extensibility, which also covers the ability of a system to evolve internally.  A good example of extensibility is the ability of both Scala Actors and Lift Actors to allow the user to specify a custom scheduler.  Other examples could include adding control structures, using a different data structure for a mailbox.

The challenge with extensibility is that in order to enable it, what could otherwise be treated as the internal bits of the library must instead have well defined interfaces for components along with appropriate hooks for inserting them.  For example, a while ago I did some work to make the MessageQueue used for the mailbox overrideable (it has temporarily been overcome-by-events due to other changes).  This is a small example, but it shows how extensibility requires a greater degree of forethought.

Extensibility also benefits substantially from simplicity.  Scala Actors are almost impossible to extend from outside the scala.actors package because of their heavy reliance on package-private methods and state (mostly fixed here, but I broke remote actors in the process so no patch yet).  Lift Actors, on the other hand, are very extensible, at least within the bounds of their design (purely event-based actors with no explicit lifecyle).  Many of the flow control mechanisms could be implemented on top of the baseline approach.

At this point we see that extensibility has an interesting relationship with expressivity.  I previously claimed that Scala Actors were more expressive because the wide variety of control structures they provide (and I didn't even touch on some of the DSL-like functionality that enables all sorts of interesting things).  However, given Lift Actors far simpler and more extensible foundation, there is much more opportunity to create custom control structures as extensions to Lift Actors without modifying the core.  Thus, if you are willing to do some lower-level programming, it could be argued that Lift Actors are in reality more expressive due to their extensibility.

Performance and Scalability

For purposes here, I'm going to treat performance as the rate a which an actor can receive and process messages at a relatively small, fixed number of simultaneous actors.   This means that improving performance in largely a matter of reducing the time it takes from when a message is initially sent to when user-code within the actor begins processing the message, including minimizing any pause between when an actor finishes processing one message and is available to start processing the next.  For moderate numbers of actors, performance is often maximized by having one thread per actor, and having the actor block while waiting for a message.  Given enough actors, the memory requirements of using a thread for each actor will eventually cause more slowdown than cost of scheduling a new reaction for each message.  This is illustrated in Philipp Haller's paper, "Actors that Unify Threads and Events" in the following graph:

Note that the above graph covers a microbenchmark running a simple, non-memory intensive task, and that the thread line is not a measurement of thread-bound actors, but rather of a simple threaded implementation.  However, my own benchmarking has shown that receive-based (ones that block on a thread) compare to event-based actors in almost the same way as threads to event-based actors in the above graph.  Also, remember that given a real application where heap space is needed for things besides the stacks of thousands of threads the point where the JVM throws an OutOfMemoryError will be much farther to the left.  There are also more subtle issues.  One of my first experiences with the Scala Actors library was creating a deadlock.  I created more thread-bound actors than the scheduler wanted to create threads, and thus actors were stuck blocking on threads waiting for messages from an actor that hadn't started yet because there were no available threads.  In other words, blocking can lead to situations such as deadlock, starvation, and simply extreme forms of unfairness with respect to how much CPU time is allocated each actor.  These all go against highly desirable guarantees that a actor library should provide outside of extreme circumstances.

Ultimately event-based actors make the better model.  For one, part of the reason why event-based Scala Actors are so expensive is that they suspend by throwing an exception to return control from user code to the library.  While exceptions have been heavily optimized in the JVM, especially in recent versions, they are still substantially slower than normal return paths.  Scala Actors need to use exceptions to suspend is a consequence of their expressivity.  Basically, because the library as little or no knowledge of what an actor is doing within a reaction, it cannot rely on traditional returns without introducing special control structures (see reactWhile numbers in one of my previous blogs).  Lift Actors, on the other hand, have do not need to use exceptions for control flow because the message processing cycle is essentially fixed - user code cannot intersperse weird (or even not-so-weird) patterns within it, or mix in blocking receives with event-based ones.  Another potential optimization of event-based actors is to have them block if there are plenty of threads available, and then release it if the thread they are on is needed by the scheduler.  To my knowledge this optimization is not implemented anywhere, but I think it would be relatively straight forward.  The only problem is that the actor becomes more tightly bound to its scheduler.

Parting Thoughts

Ultimately, time and community willing, I'd like to evolve what is here, plus solid treatment of a lot of lower-level details, into a Scala Improvement Document (SID).  There are a lot of subtle trades involved, and I think producing a general-purpose actors library is at least an order-of-magnitude more difficult than producing a special-purpose one.  I also believe that if an actor implementation is part of the standard library, then it should provide the necessary extension points for when users need something special-purpose they can create it and still leverage components of the standard library and interoperate with other actors.  In order words, I think it should define both the interface portion of an API along with providing a solid implementation.  I don't think we'll even get their without a clear and common understanding of the various considerations involved.

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