akka---Getting Started Tutorial (Java): First Chapter
原文地址：http://doc.akka.io/docs/akka/2.0.2/intro/getting-started-first-java.html
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.
Note
Make sure that your?application.conf?is on the class path when you run the application. If running from inside Maven that should already be the case, otherwise you need to add the directory containing this file to the JVM’s?-classpath?option.
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.
轉(zhuǎn)載于:https://www.cnblogs.com/davidwang456/p/5087032.html
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