Introduction

Welcome to the first tutorial on how to get started with Akka and Java. We assume that you already know what Akka and Java are 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 Maven project and running it from within Maven

Since they are so similar we will present them both.

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 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.

Here is the formula for the algorithm we will use:

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 the total result.

Tutorial source code

If you want don’t want to type in the code and/or set up a Maven project then you can check out the full tutorial from the Akka GitHub repository. It is in the akka-tutorials/akka-tutorial-first module. You can also browse it online here, with the actual source code here.

To check out the code using Git invoke the following command and you can then you can navigate down to the tutorial.

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Prerequisites

This tutorial assumes that you have Java 1.6 or later installed on you machine and java on your PATH. You 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 Java code.

You need to make sure that $JAVA_HOME environment variable is set to the root of the Java distribution. You also need to make sure that the $JAVA_HOME/bin is on your PATH.

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You can test your installation by invoking java:

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You can test your installation by invoking java:

Downloading and installing Akka

To build and run the tutorial sample from the command line, you have to download Akka. If you prefer to use SBT to build and run the sample then you can skip this section and jump to the next one.

Let’s get the akka-2.0.2.zip distribution of Akka from http://akka.io/downloads/ which includes everything we need for this tutorial. 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: set the AKKA_HOME environment variable to the root of the distribution. In my case I’m opening up a shell, navigating down to the distribution, and setting theAKKA_HOME variable.

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The distribution looks like this.

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• In the bin directory we have scripts for starting the Akka Microkernel.
• In the config directory we have the Akka conf files.
• In the deploy directory we can place applications to be run with the microkernel.
• In the doc directory we have the documentation, API, and doc JARs.
• In the lib directory we have the Scala and Akka JARs.
• In the src directory we have the source JARs for Akka.

The only JAR we will need for this tutorial (apart from the scala-library.jar JAR) is the akka-actor-2.0.2.jar JAR in the lib/akka directory. This is a self-contained JAR with zero dependencies and contains everything we need to write a system using Actors.

Akka is very modular and has many JARs for containing different features. The modules are:

• akka-actor – Actors
• akka-remote – Remote Actors
• akka-slf4j – SLF4J Event Handler Listener for logging with SLF4J
• akka-testkit – Toolkit for testing Actors
• akka-kernel – Akka microkernel for running a bare-bones mini application server
• akka-durable-mailboxes – Durable mailboxes: file-based, MongoDB, Redis, Beanstalk and Zookeeper

Downloading and installing Maven

Maven is an excellent build system that can be used to build both Java and Scala projects. If you want to use Maven for this tutorial then follow the following instructions, if not you can skip this section and the next.

First browse to http://maven.apache.org/download.html and download the 3.0.3 distribution.

To install Maven it is easiest to follow the instructions on http://maven.apache.org/download.html#Installation.

Creating an Akka Maven project

If you have not already done so, now is the time to create a Maven project for our tutorial. You do that by stepping into the directory you want to create your project in and invoking the mvn command.

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Now we have the basis for our Maven-based Akka project. Let’s step into the project directory.

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Here is the layout that Maven created:

As you can see we already have a Java source file called App.java, let’s now rename it to Pi.java.

We also need to edit the pom.xml build file. Let’s add the dependency we need as well as the Maven repository it should download it from. The Akka Maven repository can be found at http://akka.io/releases/ and Typesafe provides http://repo.typesafe.com/typesafe/releases/ that proxies several other repositories, including akka.io. It should now look something like this:

Start writing the code

Now it’s about time to start hacking.

We start by creating a Pi.java file and adding these import statements at the top of the file:

If you are using Maven in this tutorial then create the file in the src/main/java/akka/tutorial/first/javadirectory.

If you are using the command line tools then create the file wherever you want. We will create it in a directory called tutorial at the root of the Akka distribution, e.g. in$AKKA_HOME/tutorial/akka/tutorial/first/java/Pi.java.

Creating the messages

The design we are aiming for is to have one Master actor initiating the computation, creating a set of Workeractors. Then it splits up the work into discrete chunks, and sends these chunks to the different workers in a round-robin fashion. The master waits until all the workers have completed their work and sent back results for aggregation. When computation is completed the master sends the result to the Listener, which prints out the result.

With this in mind, let’s now create the messages that we want to have flowing in the system. We need four different messages:

• Calculate – sent to the Master actor to start the calculation
• Work – sent from the Master actor to the Worker actors containing the work assignment
• Result – sent from the Worker actors to the Master actor containing the result from the worker’s calculation
• PiApproximation – sent from the Master actor to the Listener actor containing the the final pi result and how long time the calculation took

Messages sent to actors should always be immutable to avoid sharing mutable state. So let’s start by creating three messages as immutable POJOs. We also create a wrapper Pi class to hold our implementation:

Creating the worker

Now we can create the worker actor. This is done by extending in the UntypedActor base class and defining the onReceive method. The onReceive 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:

As you can see we have now created an UntypedActor with a onReceive method as a handler for the Workmessage. In this handler we invoke the calculatePiFor(..) method, wrap the result in a Result message and send it back to the original sender using getContext().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 for future use.

The only thing missing in our Worker actor is the implementation on the calculatePiFor(..) method:

Creating the master

The master actor is a little bit more involved. In its constructor we create a round-robin router to make it easier to spread out the work evenly between the workers. Let’s do that first:

Now we have a router that is representing all our workers in a single abstraction. So now let’s create the master actor. We pass it three integer variables:

• nrOfWorkers – defining how many workers we should start up
• nrOfMessages – defining how many number chunks to send out to the workers
• nrOfElements – defining how big the number chunks sent to each worker should be

Here is the master actor:

A couple of things are worth explaining further.

Note that we are passing in a ActorRef to the Master actor. This is used to report the the final result to the outside world.

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 via its router.

The Result handler gets the value from the Result message and aggregates it to our pi member variable. We also keep track of how many results we have received back, and if that matches the number of tasks sent out, the Master actor considers itself done and sends the final result to the listener. When done it also invokes the getContext().stop(getSelf()) method to stop itself and all its supervised actors. In this case it has one supervised actor, the router, and this in turn has nrOfWorkers supervised actors. All of them will be stopped automatically as the invocation of any supervisor’s stop method will propagate down to all its supervised ‘children’.

Let’s capture this in code:

Creating the result listener

The listener is straightforward. When it receives the PiApproximation from the Master it prints the result and shuts down the ActorSystem.

Please note that shutting down the actor system should be done by that part of the application which can safely determine that everything has been said and done. In this case, it is the Listener actor, but in other scenarios it might be the main thread or some other external service. It is by no means required to callsystem.shutdown() from within that system.

Bootstrap the calculation

Now the only thing that is left to implement is the runner that should bootstrap and run the calculation for us. We do that by adding a main method to the enclosing Pi class in which we create a new instance of Pi and invoke method calculate in which we start up the Master actor and wait for it to finish:

As you can see the calculate method above it creates an ActorSystem and this is the Akka container which will contain all actors created in that “context”. An example of how to create actors in the container is the‘system.actorOf(...)’ line in the calculate method. In this case we create two top level actors. If you instead where in an actor context, i.e. inside an actor creating other actors, you should use getContext().actorOf(...). This is illustrated in the Master code above.

That’s it. Now we are done.

Before we package it up and run it, let’s take a look at the full code now, with package declaration, imports and all:

Run it as a command line application

If you have not typed in (or copied) the code for the tutorial as$AKKA_HOME/tutorial/akka/tutorial/first/java/Pi.java then now is the time. When that’s 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 Java’s compiler javac. Our application depends on the akka-actor-2.0.2.jar and the scala-library.jar JAR files, so let’s add them to the compiler classpath when we compile the source.

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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-2.0.2.jar and the scala-library.jar JAR files to the classpath as well as the classes we compiled ourselves.

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Yippee! It is working.

Run it inside Maven

If you used Maven, then you can run the application directly inside Maven. First you need to compile the project.

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When this in done we can run our application directly inside Maven.

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Yippee! It is working.

Overriding Configuration Externally (Optional)

The sample project includes an application.conf file in the resources directory:

If you uncomment the two lines, you should see a change in performance, hopefully for the better (you might want to increase the number of messages in the code to prolong the time the application runs). It should be noted that overriding only works if a router type is given, so just uncommenting nr-of-instances does not work; see Routing (Java) for more details.

Conclusion

We have learned how to create our first Akka project using 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 to compile and run an Akka project using either the tools on the command line or the SBT build system.

If you have a multi-core machine then I encourage you to try out different number of workers (number of working actors) by tweaking the nrOfWorkers variable to for example; 2, 4, 6, 8 etc. to see performance improvement by scaling up.

Happy hakking.