The Seductions of Scala, Part III - Concurrent Programming 225
This is my third and last blog entry on The Seductions of Scala, where we’ll look at concurrency using Actors and draw some final conclusions.
Writing Robust, Concurrent Programs with Scala
The most commonly used model of concurrency in imperative languages (and databases) uses shared, mutable state with access synchronization. (Recall that synchronization isn’t necessary for reading immutable objects.)
However, it’s widely known that this kind of concurrency programming is very difficult to do properly and few programmers are skilled enough to write such programs.
Because pure functional languages have no side effects and no shared, mutable state, there is nothing to synchronize. This is the main reason for the resurgent interest in function programming recently, as a potential solution to the so-called multicore problem.
Instead, most functional languages, in particular, Erlang and Scala, use the Actor model of concurrency, where autonomous “objects” run in separate processes or threads and they pass messages back and forth to communicate. The simplicity of the Actor model makes it far easier to create robust programs. Erlang processes are so lightweight that it is common for server-side applications to have thousands of communicating processes.
Actors in Scala
Let’s finish our survey of Scala with an example using Scala’s Actors library.
Here’s a simple Actor that just counts to 10, printing each number, one per second.
import scala.actors._
object CountingActor extends Actor {
def act() {
for (i <- 1 to 10) {
println("Number: "+i)
Thread.sleep(1000)
}
}
}
CountingActor.start()
The last line starts the actor, which implicitly invokes the act method. This actor does not respond to any messages from other actors.
Here is an actor that responds to messages, echoing the message it receives.
import scala.actors.Actor._
val echoActor = actor {
while (true) {
receive {
case msg => println("received: "+msg)
}
}
}
echoActor ! "hello"
echoActor ! "world!"
In this case, we do the equivalent of a Java “static import” of the methods on Actor, e.g., actor. Also, we don’t actually need a special class, we can just create an object with the desired behavior. This object has an infinite loop that effectively blocks while waiting for an incoming message. The receive method gets a block that is a match statement, which matches on anything received and prints it out.
Messages are sent using the target_actor ! message syntax.
As a final example, let’s do something non-trivial; a contrived network node monitor.
import scala.actors._
import scala.actors.Actor._
import java.net.InetAddress
import java.io.IOException
case class NodeStatusRequest(address: InetAddress, respondTo: Actor)
sealed abstract class NodeStatus
case class Available(address: InetAddress) extends NodeStatus
case class Unresponsive(address: InetAddress, reason: Option[String]) extends NodeStatus
object NetworkMonitor extends Actor {
def act() {
loop {
react { // Like receive, but uses thread polling for efficiency.
case NodeStatusRequest(address, actor) =>
actor ! checkNodeStatus(address)
case "EXIT" => exit()
}
}
}
val timeoutInMillis = 1000;
def checkNodeStatus(address: InetAddress) = {
try {
if (address.isReachable(timeoutInMillis))
Available(address)
else
Unresponsive(address, None)
} catch {
case ex: IOException =>
Unresponsive(address, Some("IOException thrown: "+ex.getMessage()))
}
}
}
// Try it out:
val allOnes = Array(1, 1, 1, 1).map(_.toByte)
NetworkMonitor.start()
NetworkMonitor ! NodeStatusRequest(InetAddress.getByName("www.scala-lang.org"), self)
NetworkMonitor ! NodeStatusRequest(InetAddress.getByAddress("localhost", allOnes), self)
NetworkMonitor ! NodeStatusRequest(InetAddress.getByName("www.objectmentor.com"), self)
NetworkMonitor ! "EXIT"
self ! "No one expects the Spanish Inquisition!!"
def handleNodeStatusResponse(response: NodeStatus) = response match {
// Sealed classes help here
case Available(address) =>
println("Node "+address+" is alive.")
case Unresponsive(address, None) =>
println("Node "+address+" is unavailable. Reason: <unknown>")
case Unresponsive(address, Some(reason)) =>
println("Node "+address+" is unavailable. Reason: "+reason)
}
for (i <- 1 to 4) self.receive { // Sealed classes don't help here
case (response: NodeStatus) => handleNodeStatusResponse(response)
case unexpected => println("Unexpected response: "+unexpected)
}
We begin by importing the Actor classes, the methods on Actor, like actor, and a few Java classes we need.
Next we define a sealed abstract base class. The sealed keyword tells the compiler that the only subclasses will be defined in this file. This is useful for the case statements that use them. The compiler will know that it doesn’t have to worry about potential cases that aren’t covered, if new NodeStatus subclasses are created. Otherwise, we would have to add a default case clause (e.g., case _ => ...) to prevent warnings (and possible errors!) about not matching an input. Sealed class hierarchies are a useful feature for robustness (but watch for potential Open/Closed Principle violations!).
The sealed class hierarchy encapsulates all the possible node status values (somewhat contrived for the example). The node is either Available or Unresponsive. If Unresponsive, an optional reason message is returned.
Note that we only get the benefit of sealed classes here because we match on them in the handleNodeStatusResponse message, which requires a response argument of type NodeStatus. In contrast, the receive method effectively takes an Any argument, so sealed classes don’t help on the line with the comment “Sealed classes don’t help here”. In that case, we really need a default, the case unexpected => ... clause. (I added the message self ! "No one expects the Spanish Inquisition!!" to test this default handler.)
In the first draft of this blog post, I didn’t know these details about sealed classes. I used a simpler implementation that couldn’t benefit from sealed classes. Thanks to the first commenter, LaLit Pant, who corrected my mistake!
The NetworkMonitor loops, waiting for a NodeStatusRequest or the special string “EXIT”, which tells it to quit. Note that the actor sending the request passes itself, so the monitor can reply to it.
The checkNodeStatus attempts to contact the node, with a 1 second timeout. It returns an appropriate NodeStatus.
Then we try it out with three addresses. Note that we pass self as the requesting actor. This is an Actor wrapping the current thread, imported from Actor. It is analogous to Java’s Thread.currentThread().
Curiously enough, when I run this code, I get the following results.
Unexpected response: No one expects the Spanish Inquisition!!
Node www.scala-lang.org/128.178.154.102 is unavailable. Reason: <unknown>
Node localhost/1.1.1.1 is unavailable. Reason: <unknown>
Node www.objectmentor.com/206.191.6.12 is alive.
The message about the Spanish Inquisition was sent last, but processed first, probably because self sent it to itself.
I’m not sure why www.scala-lang.org couldn’t be reached. A longer timeout didn’t help. According to the Javadocs for InetAddress.isReachable), it uses ICMP ECHO REQUESTs if the privilege can be obtained, otherwise it tries to establish a TCP connection on port 7 (Echo) of the destination host. Perhaps neither is supported on the scala-lang.org site.
Conclusions
Here are some concluding observations about Scala vis-à-vis Java and other options.
A Better Java
Ignoring the functional programming aspects for a moment, I think Scala improves on Java in a number of very useful ways, including:
- A more succinct syntax. There’s far less boilerplate, like for fields and their accessors. Type inference and optional semicolons, curly braces, etc. also reduce “noise”.
- A true mixin model. The addition of traits solves the problem of not having a good DRY way to mix in additional functionality declared by Java interfaces.
- More flexible method names and invocation syntax. Java took away operator overloading; Scala gives it back, as well as other benefits of using non-alphanumeric characters in method names. (Ruby programmers enjoy writing
list.empty?, for example.) - Tuples. A personal favorite, I’ve always wanted the ability to return multiple values from a method, without having to create an ad hoc class to hold the values.
- Better separation of mutable vs. immutable objects. While Java provides some ability to make objects
final, Scala makes the distinction between mutability and immutability more explicit and encourages the latter as a more robust programming style. - First-class functions and closures. Okay, these last two points are really about FP, but they sure help in OO code, too!
- Better mechanisms for avoiding
null’s. TheOptiontype makes code more robust than allowingnullvalues. - Interoperability with Java libraries. Scala compiles to byte code so adding Scala code to existing Java applications is about as seamless as possible.
So, even if you don’t believe in FP, you will gain a lot just by using Scala as a better Java.
Functional Programming
But, you shouldn’t ignore the benefits of FP!
- Better robustness. Not only for concurrent programs, but using immutable objects (a.k.a. value objects) reduces the potential for bugs.
- A workable concurrency model. I use the term workable because so few developers can write robust concurrent code using the synchronization on shared state model. Even for those of you who can, why bother when Actors are so much easier??
- Reduced code complexity. Functional code tends to be very succinct. I can’t overestimate the importance of rooting out all accidental complexity in your code base. Excess complexity is one of the most pervasive detriments to productivity and morale that I see in my clients’ code bases!
- First-class functions and closures. Composition and succinct code are much easier with first-class functions.
- Pattern matching. FP-style pattern matching makes “routing” of messages and delegation much easier.
Of course, you can mimic some of these features in pure Java and I encourage you to do so if you aren’t using Scala.
Static vs. Dynamic Typing
The debate on the relative merits of static vs. dynamic typing is outside our scope, but I will make a few personal observations.
I’ve been a dedicated Rubyist for a while. It is hard to deny the way that dynamic typing simplifies code and as I said in the previous section, I take code complexity very seriously.
Scala’s type system and type inference go a long way towards providing the benefits of static typing with the cleaner syntax of dynamic typing, but Scala doesn’t eliminate the extra complexity of static typing.
Recall my Observer example from the first blog post, where I used traits to implement it.
trait Observer[S] {
def receiveUpdate(subject: S);
}
trait Subject[S] {
this: S =>
private var observers: List[Observer[S]] = Nil
def addObserver(observer: Observer[S]) = observers = observer :: observers
def notifyObservers() = observers.foreach(_.receiveUpdate(this))
}
In Ruby, we might implement it this way.
module Subject
def add_observer(observer)
@observers ||= []
@observers << observer # append, rather than replace with new array
end
def notify_observers
@observers.each {|o| o.receive_update(self)} if @observers
end
end
There is no need for an Observer module. As long as every observer responds to the receive_update “message”, we’re fine.
I commented the line where I append to the existing @observers array, rather than build a new one, which would be the FP and Scala way. Appending to the existing array would be more typical of Ruby code, but this implementation is not as thread safe as an FP-style approach.
The trailing if expression in notify_observers means that nothing is done if @observers is still nil, i.e., it was never initialized in add_observer.
So, which is better? The amount of code is not that different, but it took me significantly longer to write the Scala version. In part, this was due to my novice chops, but the reason it took me so long was because I had to solve a design issue resulting from the static typing. I had to learn about the typed self construct used in the first line of the Subject trait. This was the only way to allow the Observer.receiveUpdate method accept to an argument of type S, rather than of type Subject[S]. It was worth it to me to achieve the “cleaner” API.
Okay, perhaps I’ll know this next time and spend about the same amount of time implementing a Ruby vs. Scala version of something. However, I think it’s notable that sometimes static typing can get in the way of your intentions and goal of achieving clarity. (At other times, the types add useful documentation.) I know this isn’t the only valid argument you can make, one way or the other, but it’s one reason that dynamic languages are so appealing.
Poly-paradigm Languages vs. Mixing Several Languages
So, you’re convinced that you should use FP sometimes and OOP sometimes. Should you pick a poly-paradigm language, like Scala? Or, should you combine several languages, each of which implements one paradigm?
A potential downside of Scala is that supporting different modularity paradigms, like OOP and FP, increases the complexity in the language. I think Odersky and company have done a superb job combining FP and OOP in Scala, but if you compare Scala FP code to Haskell or Erlang FP code, the latter tend to be more succinct and often easier to understand (once you learn the syntax).
Indeed, Scala will not be easy for developers to master. It will be a powerful tool for professionals. As a consultant, I work with developers with a range of skills. I would not expect some of them to prosper with Scala. Should that rule out the language? NO. Rather it would be better to “liberate” the better developers with a more powerful tool.
So, if your application needs OOP and FP concepts interspersed, consider Scala. If your application needs discrete services, some of which are predominantly OOP and others of which are predominantly FP, then consider Scala or Java for the OOP parts and Erlang or another FP language for the FP parts.
Also, Erlang’s Actor model is more mature than Scala’s, so Erlang might have an edge for a highly-concurrent server application.
Of course, you should do your own analysis…
Final Thoughts
Java the language has had a great ride. It was a godsend to us beleaguered C++ programmers in the mid ‘90’s. However, compared to Scala, Java now feels obsolete. The JVM is another story. It is arguably the best VM available.
I hope Scala replaces Java as the main JVM language for projects that prefer statically-typed languages. Fans of dynamically-typed languages might prefer JRuby, Groovy, or Jython. It’s hard to argue with all the OOP and FP goodness that Scala provides. You will learn a lot about good language and application design by learning Scala. It will certainly be a prominent tool in my toolkit from now on.
Nice post, Dean.
Some comments/feedback:
- just fyi: if an actor uses ‘receive’, it hogs a thread. If it uses ‘react’, it runs in a thread pool.
- another (slightly shorter) way to create the byte Array: val allOnes = Array(1,1,1,1).map(_.toByte)
- try using a larger timeout to reach scala-lang.org
- the fact that NodeStatus is sealed does not really help in this scenario, because of the signature of receive (or react for that matter): def receive®(f: PartialFunction[Any, R]): R
So – in the following code:
receive { case Available(address) => println(“Node “address” is alive.”) case Unresponsive(address, None) => println(“Node “address” is unavailable. Reason: “) case Unresponsive(address, Some(reason)) => println(“Node “address” is unavailable. Reason: “+reason) }
the block passed to receive gets turned into a PartialFunction that takes ‘Any’ as a parameter. Based on this, the compiler is unable to make use of the sealed nature of NodeStatus.
As opposed to this, if you had a function that took a NodeStatus as a parameter, and if this function was implemented via a pattern match, then NodeStatus being sealed would help the compiler generate a ‘match is not exhaustive warning’ for missing case statements.
Great article. In your section regarding static vs. dynamic typing, you can utilize the many of the features of FP to make the code a little closer to the Ruby example. You mention that in the Ruby example there is no need for the Observer, here is an example of how to re-write the Subject trait such that you simple add arbitrary methods to be notified:
I would argue that this is a little nicer than the Ruby example (in terms of flexibility) as it put no constraints on the name of the method so you could very easily add on observer from a 3rd party library which doesn’t have to implement any contract.
Keep up the great work.
@LaLit, thanks for your detailed suggestions for improvements, especially for clarifying how sealed classes and receive/react work with them (i.e., NOT!). I incorporated all of your suggestions, hopefully without more mistakes ;)
@Chris, nice! That is a most excellent improvement and really shows the beauty of first-class functions. I really love the way it minimizes the observer “abstraction” to it’s bare essence. The arbitrary name I picked “receiveUpdate” is accidental complexity!
Replacing Observer with a structural type might be more Ruby-like without losing compile-time type safety. For reference: http://neilbartlett.name/blog/2007/09/13/statically-checked-duck-typing-in-scala/ http://debasishg.blogspot.com/2008/06/scala-to-java-smaller-inheritance.html
Structural typing lets you capture something very close to the Ruby code. While I’m at it, there are a couple of minor tweaks to help readability: the use of List[Observer]() to cut down on the typing and the use of += on the list.
trait Subject { type Observer = { def receiveUpdate(subject:Any) } private var observers = List[Observer]() def addObserver(observer:Observer) = observers += observer def notifyObservers = observers foreach (_.receiveUpdate(this)) }All that said, I like the self typed code under some circumstances. It expresses a different semantic requirement than the structurally typed Scala code or the duck typed Ruby code. There’s even a stronger way to use types so that the Observer and Subject have to “know” about each other – you can’t just send arbitrary Observers to a Subject. It is a bit more work to do – but it’s nice that I can use Scala to express this entire range of very loose to very tight typing.
Also, I like the list of functions technique. It generalizes the pattern to a very different thing – something like a mix of Subject/Observer and Command. Also, that approach should translate to Ruby just fine using blocks.
yet another way – this incurs no run-time hit and is quite readable:
val allOnes:Array[Byte] = Array(1, 1, 1, 1)
If your looking for an actor oriented solution in Java you should check out the DataRush engine. It allows for actors(called nodes) and messages(called tokens) to create automatically scaling java applications. We have gotten it to light up 16 cores to 84% cpu utilization. The website is pervasivedatarush dot com.
I’ve tried simplifying the Observer pattern out using the techniques described in the comments, and I believe that there’s something missing… calling ‘anonfunc(this)’ does not work because ‘this’ is a Subject and not an Account. Do we not still need to declare the Subject as a ‘trait Subject[S]’ and then either use the ‘this: S =>’ trick or call the function as ‘anonfunc(this.asInstanceOf[S])’? (anonfunc = _ – the formatting of the comment engine doesn’t like the _ so I renamed it to anonfunc)
Doing anything else (including using Any) means that the Observer must cast down on its own, no? It seems much more reasonable to do this in Subject where Subject truly knows what S is.
Great articles. I’ve very much enjoyed reading through them.
Excellent write-ups. The use of the JVM and ability to leverage the vast Java resources around seem very appealing.
You’ve certainly inspired me to dig deeper into the scala realm!
> // Like receive, but uses thread polling for efficiency. Shouldn’t it be “thread pooling” instead of “thread polling”? A typo?
I agree Greg. The vast amount of resources for Java make it hard to tackle. Given JVM’s capabilities it makes it very approachable.
@Chris, nice! That is a most excellent improvement and really shows the beauty of first-class functions. I really love the way it minimizes the observer “abstraction” to it’s bare essence. The arbitrary name I picked “receiveUpdate” is accidental complexity!
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I like the list of functions technique. It generalizes the pattern to a very different thing – something like a mix of Subject/Observer and Command. Also, that approach should translate to Ruby just fine using blocks.
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