Welcome to the first tutorial on how to get started with Akka and Scala. We assume that you already know what Akka and Scala is and will now focus on the steps necessary to start your first project.
There are two variations of this first tutorial:
- creating a standalone project and run it from the command line
- creating a SBT (Simple Build Tool) project and running it from within SBT
Since they are so similar we will present them both in this tutorial.
The sample application that we will create is using actors to calculate the value of Pi. Calculating Pi is a CPU intensive operation and we will utilize Akka Actors to write a concurrent solution that scales out to multi-core processors. This sample will be extended in future tutorials to use Akka Remote Actors to scale out on multiple machines in a cluster.
We will be using an algorithm that is what is called "embarrassingly parallel" which just means that each job is completely isolated and not coupled with any other job. Since this algorithm is so parallelizable it suits the actor model very well.
In this particular algorithm the master splits the series into chunks which are sent out to each worker actor to be processed, when each worker has processed its chunk it sends a result back to the master which aggregates to total result.
This tutorial assumes that you have Jave 1.6 or later installed on you machine and ``java`` on your ``PATH``. I also need to know how to run commands in a shell (ZSH, Bash, DOS etc.) and a decent text editor or IDE to type in the Scala code in.
The first thing we need to do is to download Akka. Let's get the 1.1 distribution from `http://akka.io/downloads <http://akka.io/downloads/>`_. Once you have downloaded the distribution unzip it in the folder you would like to have Akka installed in, in my case I choose to install it in ``/Users/jboner/tools/``, simply by unzipping it to this directory.
You need to do one more thing in order to install Akka properly and that is to set the ``AKKA_HOME`` environment variable to the root of the distribution. In my case I'm opening up a shell and navigating down to the distribution and setting the ``AKKA_HOME`` variable::
$ cd /Users/jboner/tools/akka-1.1
$ export AKKA_HOME=`pwd`
$ echo $AKKA_HOME
/Users/jboner/tools/akka-1.1
If we now take a look at what we have in this distribution, looks like this::
The only JAR we will need for this tutorial (apart from the ``scala-library.jar`` JAR) is the ``akka-actor-1.1.jar`` JAR in the ``dist`` directory. This is a self-contained JAR with zero dependencies and contains the everything we need to write a system using Actors.
Akka is very modular and has many JARs for containing different features. The core distribution has seven modules:
-``akka-actor-1.1.jar`` -- Standard Actors
-``akka-typed-actor-1.1.jar`` -- Typed Actors
-``akka-remote-1.1.jar`` -- Remote Actors
-``akka-stm-1.1.jar`` -- STM (Software Transactional Memory) and transactional datastructures
-``akka-http-1.1.jar`` -- Akka Mist for continuation-based asynchronous HTTP and also Jersey integration
-``akka-testkit-1.1.jar`` -- Toolkit for testing Actors
We also have Akka Modules containing add-on modules for the core of Akka. You can download the Akka Modules distribution from TODO. It contains Akka core as well. We will not be needing any modules there today but for your information the module JARs are these:
-``akka-kernel-1.1.jar`` -- Akka microkernel for running a bare-bones mini application server (embeds Jetty etc.)
-``akka-amqp-1.1.jar`` -- AMQP integration
-``akka-camel-1.1.jar`` -- Apache Camel Actors integration (it's the best way to have your Akka application communicate with the rest of the world)
If you want to be able to build and run the tutorial sample from the command line then you have to install the Scala distribution. If you prefer to use SBT to build and run the sample then you need can skip this section and jump to the next one.
Scala can be downloaded from `http://www.scala-lang.org/downloads <http://www.scala-lang.org/downloads>`_. Browse there and download the Scala 2.9.0 final release. If you pick the ``tgz`` or ``zip`` distributions then just unzip it where you want it installed. If you pick the IzPack Installer then double click on it and follow the instructions.
You also need to make sure that the ``scala-2.9.0/bin`` (if that is the directory where you installed Scala) is on your ``PATH``::
$ export PATH=$PATH:scala-2.9.0/bin
Now you can test you installation by invoking and see the printout::
$ scala -version
Scala code runner version 2.9.0.final -- Copyright 2002-2011, LAMP/EPFL
Looks like we are all good. Finally let's create a source file ``Pi.scala`` for the tutorial and put it in the root of the Akka distribution in the ``tutorial`` directory (you have to create it first).
SBT, short for 'Simple Build Tool' is an excellent build system written in Scala. You are using Scala to write the build scripts which gives you a lot of power. It has a plugin architecture with many plugins available, something that we will take advantage of soon. SBT is the preferred way of building software in Scala. If you want to use SBT for this tutorial then follow the following instructions, if not you can skip this section.
To install SBT and create a project for this tutorial it is easiest to follow the instructions on `this page <http://code.google.com/p/simple-build-tool/wiki/Setup>`_. The preferred SBT version to install is ``0.7.6``.
If you have created an SBT project then step into the newly created SBT project, create a source file ``Pi.scala`` for the tutorial sample and put it in the ``src/main/scala/`` directory.
So far we only have a standard Scala project but now we need to make our project an Akka project. You could add the dependencies manually to the build script, but the easiest way is to use Akka's SBT Plugin.
First we need to create the messages is that we want to have flowing in the system. Let's create three different messages:
-``Calculate`` -- starts the calculation
-``Work`` -- contains the work assignment
-``Result`` -- contains the result from the worker's calculation
Messages sent to actors should always be immutable to avoid sharing mutable state. In scala we have 'case classes' which make excellent messages. So let's start by creating three messages as case classes. We also create a common base trait for our messages (that we define as being ``sealed`` in order to prevent creating messages outside our control)::
sealed trait PiMessage
case object Calculate extends PiMessage
case class Work(arg: Int, nrOfElements: Int) extends PiMessage
case class Result(value: Double) extends PiMessage
Creating the worker
-------------------
Now we can create the worker actor. This is done by mixing in the ``Actor`` trait and defining the ``receive`` method. The ``receive`` method defines our message handler. We expect it to be able to handle the ``Work`` message so we need to add a handler for this message::
class Worker extends Actor {
def receive = {
case Work(arg, nrOfElements) =>
self reply Result(calculatePiFor(arg, nrOfElements)) // perform the work
}
}
As you can see we have now created an ``Actor`` with a ``receive`` method that as a handler for the ``Work`` message. In this handler we invoke the ``calculatePiFor(..)`` method, wraps the result in a ``Result`` message and sends it back to the original sender using ``self.reply``. In Akka the sender reference is implicitly passed along with the message so that the receiver can always reply or store away the sender reference use.
The only thing missing in our ``Worker`` actor is the implementation on the ``calculatePiFor(..)`` method. There are many ways we can implement this algorithm in Scala, now let's try to balance functional programming with efficiency and use a tail recursive function::
Here we use the classic trick with a local nested method to make sure that the compiler can perform a tail call optimization. We can ensure that the compiler will be able to do that by annotate tail recursive function with ``@tailrec``, with this annotation the compiler will emit an error if it can optimize it. With this implementation the calculation is really fast.
Creating the master
-------------------
The master actor is a little bit more involved. In its constructor we need to create the workers (the ``Worker`` actors) and start them. We will also wrap them in a load-balancing router to make it easier to spread out the work evenly between the workers. Let's do that first::
// create the workers
val workers = Vector.fill(nrOfWorkers)(actorOf[Worker].start)
// wrap them with a load-balancing router
val router = Routing.loadBalancerActor(CyclicIterator(workers)).start
As you can see we are using the ``actorOf`` factory method to create actors, this method returns as an ``ActorRef`` which is a reference to our newly created actor. This method is available in the ``Actor`` object but is usually imported::
import akka.actor.Actor._
Now we have a routes are representing all our workers in a single abstraction. If you paid attention to the code above to see that we were using the ``nrOfWorkers`` variable. This variable and others we have to pass to the ``Master`` actor in its constructor. So now let's create the master actor. We had to pass in three integer variables needed:
-``nrOfWorkers`` -- defining how many workers we should start up
First, we are passing in a ``java.util.concurrent.CountDownLatch`` to the ``Master`` actor. This latch is only used for plumbing, to have a simple way letting the outside world knowing when the master can deliver the result and shut down. In more idiomatic Akka code, as we will see in part two of this tutorial series, we would not use a latch.
Second, we are adding a couple of life-cycle callback methods; ``preStart`` and ``postStop``. In the ``preStart`` callback we are recording the time when the actor is started and in the ``postStop`` callback we are printing out the result (the approximation of Pi) and the time it took to calculate it. In this call but we also invoke ``latch.countDown`` to tell the outside world that we are done.
But we are not done yet. We are missing the message handler for the ``Master`` actor. This message handler needs to be able to react to two different messages:
-``Calculate`` -- which should start the calculation
-``Result`` -- which should aggregate the different results
The ``Calculate`` handler is sending out work to all the ``Worker`` actors and after doing that also sends a ``Broadcast(PoisonPill)`` message to the router, this will make the route or send out the ``PoisonPill`` message to all the actors in this representing (in our case all the ``Worker`` actors). The ``PoisonPill`` is a special kind of message that tells the receiver to shut himself down using the normal shutdown; ``self.stop``. Then we also send a ``PoisonPill`` to the router itself (since it's also an actor that we want to shut down).
The ``Result`` handler is simpler, here we just get the value from the ``Result`` message and aggregate it to our ``pi`` member variable. We also keep track of how many results we have received back and if it matches the number of tasks sent out the ``Master`` actor considers itself done and shuts himself down.
Now, let's capture this in code::
// message handler
def receive = {
case Calculate =>
// schedule work
for (arg <- 0 until nrOfMessages) router ! Work(arg, nrOfElements)
// send a PoisonPill to all workers telling them to shut down themselves
router ! Broadcast(PoisonPill)
// send a PoisonPill to the router, telling him to shut himself down
router ! PoisonPill
case Result(value) =>
// handle result from the worker
pi += value
nrOfResults += 1
if (nrOfResults == nrOfMessages) self.stop
}
Bootstrap the calculation
-------------------------
Now the only thing that is left to implement his the runner that should bootstrap and run his calculation for us. We do that by creating an object that we call ``Pi``, here we can extend the ``App`` trait in Scala which means that we will be able to run this as an application directly from the command line. The ``Pi`` object is a perfect container module for our actors and messages, so let's put them all there. We also create a method ``calculate`` in which we start up the ``Master`` actor and waits for it to finish::
If you have not typed (or copied) in the code for the tutorial in the ``$AKKA_HOME/tutorial/Pi.scala`` then now is the time. When that is done open up a shell and step in to the Akka distribution (``cd $AKKA_HOME``).
First we need to compile the source file. That is done with Scala's compiler ``scalac``. Our application depends on the ``akka-actor-1.1.jar`` JAR file, so let's add that to the compiler classpath when we compile the source::
When we have compiled the source file we are ready to run the application. This is done with ``java`` but yet again we need to add the ``akka-actor-1.1.jar`` JAR file to the classpath, this time we also need to add the Scala runtime library ``scala-library.jar`` and the classes we compiled ourselves to the classpath::
Now we have learned how to create our first Akka project utilizing Akka's actors to speed up a computation intensive problem by scaling out on multi-core processors (also known as scaling up). We have also learned how to compile and run an Akka project utilizing either the tools on the command line or the SBT build system.
Now we are ready to take on more advanced problems. In the next tutorial we will build upon this one, refactor it into more idiomatic Akka and Scala code and introduce a few new concepts and abstractions. Whenever you feel ready, join my in the `Getting Started Tutorial: Second Chapter <TODO>`_.
